IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
A fuzzy logic controllerfora two link functional manipulatorIJCNCJournal
This paper presents a new approach for designing a Fuzzy Logic Controller "FLC"for a dynamically multivariable nonlinear coupling system. The conventional controller with constant gains for different operating points may not be sufficient to guarantee satisfactory performance for Robot manipulator. The Fuzzy Logic Controller utilizes the error and the change of error as fuzzy linguistic inputs to regulate the system performance. The proposed controller have been developed to simulate the dynamic behavior of A
Two-Link Functional Manipulator. The new controller uses only the available information of the input-output for controlling the position and velocity of the robot axes of the motion of the end effectors
—This paper presents a new image based visual servoing (IBVS) control scheme for omnidirectional wheeled mobile robots with four swedish wheels. The contribution is the proposal of a scheme that consider the overall dynamic of the system; this means, we put together mechanical and electrical dynamics. The actuators are direct current (DC) motors, which imply that the system input signals are armature voltage applied to DC motors. In our control scheme the PD control law and eye-to-hand camera configuration are used to compute the armature voltages and to measure system states, respectively. Stability proof is performed via Lypunov direct method and LaSalle's invariance principle. Simulation and experimental results were performed in order to validate the theoretical proposal and to show the good performance of the posture errors. Keywords—IBVS, posture control, omnidirectional wheeled mobile robot, dynamic actuator, Lyapunov direct method.
Comparative Analysis for NN-Based Adaptive Back-stepping Controller and Back-...IJERA Editor
This work primarily addresses the design and implementation of a neural network based controller for the trajectory tracking of a differential drive mobile robot. The proposed control algorithm is an NN-based adaptive controller which tunes the gains of the back-stepping controller online according to the robot reference trajectory and its initial posture. In this method, a neural network is needed to learn the characteristics of the plant dynamics and make use of it to determine the future inputs that will minimize error performance index so as to compensate the back-stepping controller gains. The advantages and disadvantages of theproposed control algorithms will be discussed in each section with illustrations.Comprehensive system modeling including robot kinematics and dynamics modeling has been done. The dynamic modeling is done using Newtonian and Lagrangian methodologies for nonholonomic systems and the results are compared to verify the accuracy of each method. Simulation of the robot model and different controllers has been done using Matlab and Matlab Simulink.
A fuzzy logic controllerfora two link functional manipulatorIJCNCJournal
This paper presents a new approach for designing a Fuzzy Logic Controller "FLC"for a dynamically multivariable nonlinear coupling system. The conventional controller with constant gains for different operating points may not be sufficient to guarantee satisfactory performance for Robot manipulator. The Fuzzy Logic Controller utilizes the error and the change of error as fuzzy linguistic inputs to regulate the system performance. The proposed controller have been developed to simulate the dynamic behavior of A
Two-Link Functional Manipulator. The new controller uses only the available information of the input-output for controlling the position and velocity of the robot axes of the motion of the end effectors
—This paper presents a new image based visual servoing (IBVS) control scheme for omnidirectional wheeled mobile robots with four swedish wheels. The contribution is the proposal of a scheme that consider the overall dynamic of the system; this means, we put together mechanical and electrical dynamics. The actuators are direct current (DC) motors, which imply that the system input signals are armature voltage applied to DC motors. In our control scheme the PD control law and eye-to-hand camera configuration are used to compute the armature voltages and to measure system states, respectively. Stability proof is performed via Lypunov direct method and LaSalle's invariance principle. Simulation and experimental results were performed in order to validate the theoretical proposal and to show the good performance of the posture errors. Keywords—IBVS, posture control, omnidirectional wheeled mobile robot, dynamic actuator, Lyapunov direct method.
Comparative Analysis for NN-Based Adaptive Back-stepping Controller and Back-...IJERA Editor
This work primarily addresses the design and implementation of a neural network based controller for the trajectory tracking of a differential drive mobile robot. The proposed control algorithm is an NN-based adaptive controller which tunes the gains of the back-stepping controller online according to the robot reference trajectory and its initial posture. In this method, a neural network is needed to learn the characteristics of the plant dynamics and make use of it to determine the future inputs that will minimize error performance index so as to compensate the back-stepping controller gains. The advantages and disadvantages of theproposed control algorithms will be discussed in each section with illustrations.Comprehensive system modeling including robot kinematics and dynamics modeling has been done. The dynamic modeling is done using Newtonian and Lagrangian methodologies for nonholonomic systems and the results are compared to verify the accuracy of each method. Simulation of the robot model and different controllers has been done using Matlab and Matlab Simulink.
ROBOTICS-ROBOT KINEMATICS AND ROBOT PROGRAMMINGTAMILMECHKIT
Forward Kinematics, Inverse Kinematics and Difference; Forward Kinematics and Reverse Kinematics of manipulators with Two, Three Degrees of Freedom (in 2 Dimension), Four Degrees of freedom (in 3 Dimension) Jacobians, Velocity and Forces-Manipulator Dynamics, Trajectory Generator, Manipulator Mechanism Design-Derivations and problems. Lead through Programming, Robot programming Languages-VAL Programming-Motion Commands, Sensor Commands, End Effector commands and simple Programs
A COMPARATIVE ANALYSIS OF FUZZY BASED HYBRID ANFIS CONTROLLER FOR STABILIZATI...ijscmcjournal
This paper illustrates a Comparative study of highly non-linear, complex and multivariable Inverted
Pendulum (IP) system on Cart using different soft computing techniques. Firstly, a Fuzzy logic controller
was designed using triangular and trapezoidal shape Membership functions (MF's). The trapezoidal fuzzy
controller shows better results in comparison to triangular fuzzy controller. Secondly, an Adaptive neuro
fuzzy inference system (ANFIS) controller was used to optimize the results obtained from trapezoidal fuzzy
controller. Finally, the study illustrates the effect of variation in shape of MF's on Performance parameters
of the IP system. The results shows that ANFIS controller provides better results in comparison to both
fuzzy controller.
A Comparative Analysis of Fuzzy Based Hybrid Anfis Controller for Stabilizati...ijscmcj
This paper illustrates a Comparative study of highly non-linear, complex and multivariable Inverted Pendulum (IP) system on Cart using different soft computing techniques. Firstly, a Fuzzy logic controller was designed using triangular and trapezoidal shape Membership functions (MF's). The trapezoidal fuzzy controller shows better results in comparison to triangular fuzzy controller. Secondly, an Adaptive neuro fuzzy inference system (ANFIS) controller was used to optimize the results obtained from trapezoidal fuzzy controller. Finally, the study illustrates the effect of variation in shape of MF's on Performance parameters of the IP system. The results shows that ANFIS controller provides better results in comparison to both fuzzy controller.
PUMA 560 TRAJECTORY CONTROL USING NSGA-II TECHNIQUE WITH REAL VALUED OPERATORSijscmc
In the industry, Multi-objectives problems are a big defy and they are also hard to be conquered by conventional methods. For this reason, heuristic algorithms become an executable choice when facing this kind of problems. The main objective of this work is to investigate the use of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) technique using the real valued recombination and the real valued mutation in the tuning of the computed torque controller gains of a PUMA560 arm manipulator. The NSGA-II algorithm with real valued operators searches for the controller gains so that the six Integral of the Absolute Errors (IAE) in joint space are minimized. The implemented model under MATLAB allows an optimization of the Proportional-Derivative computed torque controller parameters while the cost functions and time are simultaneously minimized.. Moreover, experimental results also show that the real valued recombination and the real valued mutation operators can improve the performance of NSGA-II effectively.
CPREDICTION OF INVERSE KINEMATICS SOLUTION OF A REDUNDANT MANIPULATOR USING A...Ijripublishers Ijri
In this thesis, a method for forward and inverse kinematics analysis of a 5-DOF and a 7- DOF Redundant manipulator
is proposed. Obtaining the trajectory and computing the required joint angles for a higher DOF robot manipulator is one
of the important concerns in robot kinematics and control. The difficulties in solving the inverse kinematics equations
of these redundant robot manipulator arises due to the presence of uncertain, time varying and non-linear nature of
equations having transcendental functions. In this thesis, the ability of ANFIS is used to the generated data for solving
inverse kinematics problem. A single- output Sugeno-type FIS using grid partitioning has been modeled in this work.
The forward kinematics and inverse kinematics for a 5-DOF and 7-DOF manipulator are analyzed systemically. The Efficiency
of ANFIS can be concluded by observing the surface plot, residual plot and normal probability plot. This current
study in using different nonlinear models for the prediction of the IKs of a 5-DOF and 7-DOF Redundant manipulator
will give a valuable source of information for other modellers.
Keywords: 5-DOF and 7-DOF Redundant Robot Manipulator; Inverse kinematics; ANFIS; Denavit-Harbenterg (D-H)
notation.
Methodology of Mathematical error-Based Tuning Sliding Mode ControllerCSCJournals
Design a nonlinear controller for second order nonlinear uncertain dynamical systems is one of the most important challenging works. This paper focuses on the design of a chattering free mathematical error-based tuning sliding mode controller (MTSMC) for highly nonlinear dynamic robot manipulator, in presence of uncertainties. In order to provide high performance nonlinear methodology, sliding mode controller is selected. Pure sliding mode controller can be used to control of partly known nonlinear dynamic parameters of robot manipulator. Conversely, pure sliding mode controller is used in many applications; it has an important drawback namely; chattering phenomenon which it can causes some problems such as saturation and heat the mechanical parts of robot manipulators or drivers. In order to reduce the chattering this research is used the switching function in presence of mathematical error-based method instead of switching function method in pure sliding mode controller. The results demonstrate that the sliding mode controller with switching function is a model-based controllers which works well in certain and partly uncertain system. Pure sliding mode controller has difficulty in handling unstructured model uncertainties. To solve this problem applied mathematical model-free tuning method to sliding mode controller for adjusting the sliding surface gain (ë ). Since the sliding surface gain (ë) is adjusted by mathematical model free-based tuning method, it is nonlinear and continuous. In this research new ë is obtained by the previous ë multiple sliding surface slopes updating factor (á). Chattering free mathematical error-based tuning sliding mode controller is stable controller which eliminates the chattering phenomenon without to use the boundary layer saturation function. Lyapunov stability is proved in mathematical error-based tuning sliding mode controller with switching (sign) function. This controller has acceptable performance in presence of uncertainty (e.g., overshoot=0%, rise time=0.8 second, steady state error = 1e-9 and RMS error=1.8e-12).
Super-twisting sliding mode based nonlinear control for planar dual arm robotsjournalBEEI
In this paper, a super-twisting algorithm sliding mode controller is proposed for a planar dual arm robot. The control strategy for the manipulator system can effectively counteract chattering phenomenon happened with conventional sliding mode approach. The modeling is implemented in order to provide the capability of maneuvering object in translational and rotational motions. The control is developed for a 2n-link robot and subsequently simulations is carried out for a 4-link system. Comparative numerical study shows that the designed controller performance with good tracking ability and smaller chattering compared with basic sliding mode controller.
MODELLING AND SIMULATION OF INVERTED PENDULUM USING INTERNAL MODEL CONTROLJournal For Research
The internal model control (IMC) philosophy relies on the internal model principle, which states that control can be achieved only if the control system encapsulates, either implicitly or explicitly, some representation of the process to be controlled. In particular, if the control scheme is developed based on an exact model of the process, then perfect control is theoretically possible. Transfer function of Inverted Pendulum is selected as the base of design, which examines IMC controller. Matlab/simulink is used to simulate the procedures and validate the performance. The results shows robustness of the IMC and got graded responses when compared with PID. Furthermore, a comparison between the PID and IMC was shows that IMC gives better response specifications.
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
ROBOTICS-ROBOT KINEMATICS AND ROBOT PROGRAMMINGTAMILMECHKIT
Forward Kinematics, Inverse Kinematics and Difference; Forward Kinematics and Reverse Kinematics of manipulators with Two, Three Degrees of Freedom (in 2 Dimension), Four Degrees of freedom (in 3 Dimension) Jacobians, Velocity and Forces-Manipulator Dynamics, Trajectory Generator, Manipulator Mechanism Design-Derivations and problems. Lead through Programming, Robot programming Languages-VAL Programming-Motion Commands, Sensor Commands, End Effector commands and simple Programs
A COMPARATIVE ANALYSIS OF FUZZY BASED HYBRID ANFIS CONTROLLER FOR STABILIZATI...ijscmcjournal
This paper illustrates a Comparative study of highly non-linear, complex and multivariable Inverted
Pendulum (IP) system on Cart using different soft computing techniques. Firstly, a Fuzzy logic controller
was designed using triangular and trapezoidal shape Membership functions (MF's). The trapezoidal fuzzy
controller shows better results in comparison to triangular fuzzy controller. Secondly, an Adaptive neuro
fuzzy inference system (ANFIS) controller was used to optimize the results obtained from trapezoidal fuzzy
controller. Finally, the study illustrates the effect of variation in shape of MF's on Performance parameters
of the IP system. The results shows that ANFIS controller provides better results in comparison to both
fuzzy controller.
A Comparative Analysis of Fuzzy Based Hybrid Anfis Controller for Stabilizati...ijscmcj
This paper illustrates a Comparative study of highly non-linear, complex and multivariable Inverted Pendulum (IP) system on Cart using different soft computing techniques. Firstly, a Fuzzy logic controller was designed using triangular and trapezoidal shape Membership functions (MF's). The trapezoidal fuzzy controller shows better results in comparison to triangular fuzzy controller. Secondly, an Adaptive neuro fuzzy inference system (ANFIS) controller was used to optimize the results obtained from trapezoidal fuzzy controller. Finally, the study illustrates the effect of variation in shape of MF's on Performance parameters of the IP system. The results shows that ANFIS controller provides better results in comparison to both fuzzy controller.
PUMA 560 TRAJECTORY CONTROL USING NSGA-II TECHNIQUE WITH REAL VALUED OPERATORSijscmc
In the industry, Multi-objectives problems are a big defy and they are also hard to be conquered by conventional methods. For this reason, heuristic algorithms become an executable choice when facing this kind of problems. The main objective of this work is to investigate the use of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) technique using the real valued recombination and the real valued mutation in the tuning of the computed torque controller gains of a PUMA560 arm manipulator. The NSGA-II algorithm with real valued operators searches for the controller gains so that the six Integral of the Absolute Errors (IAE) in joint space are minimized. The implemented model under MATLAB allows an optimization of the Proportional-Derivative computed torque controller parameters while the cost functions and time are simultaneously minimized.. Moreover, experimental results also show that the real valued recombination and the real valued mutation operators can improve the performance of NSGA-II effectively.
CPREDICTION OF INVERSE KINEMATICS SOLUTION OF A REDUNDANT MANIPULATOR USING A...Ijripublishers Ijri
In this thesis, a method for forward and inverse kinematics analysis of a 5-DOF and a 7- DOF Redundant manipulator
is proposed. Obtaining the trajectory and computing the required joint angles for a higher DOF robot manipulator is one
of the important concerns in robot kinematics and control. The difficulties in solving the inverse kinematics equations
of these redundant robot manipulator arises due to the presence of uncertain, time varying and non-linear nature of
equations having transcendental functions. In this thesis, the ability of ANFIS is used to the generated data for solving
inverse kinematics problem. A single- output Sugeno-type FIS using grid partitioning has been modeled in this work.
The forward kinematics and inverse kinematics for a 5-DOF and 7-DOF manipulator are analyzed systemically. The Efficiency
of ANFIS can be concluded by observing the surface plot, residual plot and normal probability plot. This current
study in using different nonlinear models for the prediction of the IKs of a 5-DOF and 7-DOF Redundant manipulator
will give a valuable source of information for other modellers.
Keywords: 5-DOF and 7-DOF Redundant Robot Manipulator; Inverse kinematics; ANFIS; Denavit-Harbenterg (D-H)
notation.
Methodology of Mathematical error-Based Tuning Sliding Mode ControllerCSCJournals
Design a nonlinear controller for second order nonlinear uncertain dynamical systems is one of the most important challenging works. This paper focuses on the design of a chattering free mathematical error-based tuning sliding mode controller (MTSMC) for highly nonlinear dynamic robot manipulator, in presence of uncertainties. In order to provide high performance nonlinear methodology, sliding mode controller is selected. Pure sliding mode controller can be used to control of partly known nonlinear dynamic parameters of robot manipulator. Conversely, pure sliding mode controller is used in many applications; it has an important drawback namely; chattering phenomenon which it can causes some problems such as saturation and heat the mechanical parts of robot manipulators or drivers. In order to reduce the chattering this research is used the switching function in presence of mathematical error-based method instead of switching function method in pure sliding mode controller. The results demonstrate that the sliding mode controller with switching function is a model-based controllers which works well in certain and partly uncertain system. Pure sliding mode controller has difficulty in handling unstructured model uncertainties. To solve this problem applied mathematical model-free tuning method to sliding mode controller for adjusting the sliding surface gain (ë ). Since the sliding surface gain (ë) is adjusted by mathematical model free-based tuning method, it is nonlinear and continuous. In this research new ë is obtained by the previous ë multiple sliding surface slopes updating factor (á). Chattering free mathematical error-based tuning sliding mode controller is stable controller which eliminates the chattering phenomenon without to use the boundary layer saturation function. Lyapunov stability is proved in mathematical error-based tuning sliding mode controller with switching (sign) function. This controller has acceptable performance in presence of uncertainty (e.g., overshoot=0%, rise time=0.8 second, steady state error = 1e-9 and RMS error=1.8e-12).
Super-twisting sliding mode based nonlinear control for planar dual arm robotsjournalBEEI
In this paper, a super-twisting algorithm sliding mode controller is proposed for a planar dual arm robot. The control strategy for the manipulator system can effectively counteract chattering phenomenon happened with conventional sliding mode approach. The modeling is implemented in order to provide the capability of maneuvering object in translational and rotational motions. The control is developed for a 2n-link robot and subsequently simulations is carried out for a 4-link system. Comparative numerical study shows that the designed controller performance with good tracking ability and smaller chattering compared with basic sliding mode controller.
MODELLING AND SIMULATION OF INVERTED PENDULUM USING INTERNAL MODEL CONTROLJournal For Research
The internal model control (IMC) philosophy relies on the internal model principle, which states that control can be achieved only if the control system encapsulates, either implicitly or explicitly, some representation of the process to be controlled. In particular, if the control scheme is developed based on an exact model of the process, then perfect control is theoretically possible. Transfer function of Inverted Pendulum is selected as the base of design, which examines IMC controller. Matlab/simulink is used to simulate the procedures and validate the performance. The results shows robustness of the IMC and got graded responses when compared with PID. Furthermore, a comparison between the PID and IMC was shows that IMC gives better response specifications.
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
1355666- 3456Check list cuestionario_ auditoria123 EN MATERIA AMBIENTAL *.* Margoth CR
COMO REALIZAR UNA AUDITORIA EN UNA EMPRESA DEACUERDO A LA NORMA INTERNACIONAL ISO 14000 EN MATERIA AMBIENTAL, EN CUANTO A RESIDUOS SOLIDOS, MATERIA FORESTAL, EN AGUAS, EN SUELO, EN CUANTO A LA CONTAMINACION ATMOSFERICA
Conférence Fablabs, ateliers de fabrication numérique et d'innovation collabo...polenumerique33
Conférence "Fablabs, ateliers de fabrication numérique : l'innovation collaborative du prototype au produit fini" du Pôle Numérique de la CCI Bordeaux - Organisé le 30 Mars 2015 en partenariat avec Cap Sciences, IUT Bordeaux et IP Sphère. avec le témoignage de VENTEC iBMS et Agence ZEM.
Manifestation "CCI Innovation" labellisée "Semaine de l'industrie"
dans le cadre du programme "CyberSudoe Innov'" et le soutien de l'Union Européenne
Proxima Systems - Présentation Corporative [FR]ProximaSystems
Nous sommes un fabricant d'outils innovants pour la surveillance et la gestion à distance des machines, des équipements et immeubles qui favorisent une efficacité et une rentabilité maximales grâce à la mesure automatique et continue de tous les facteurs dont dépend le résultat.
Fractional order PID for tracking control of a parallel robotic manipulator t...ISA Interchange
This paper presents the tracking control for a robotic manipulator type delta employing fractional order PID controllers with computed torque control strategy. It is contrasted with an integer order PID controller with computed torque control strategy. The mechanical structure, kinematics and dynamic models of the delta robot are descripted. A SOLIDWORKS/MSC-ADAMS/MATLAB co-simulation model of the delta robot is built and employed for the stages of identification, design, and validation of control strategies. Identification of the dynamic model of the robot is performed using the least squares algorithm. A linearized model of the robotic system is obtained employing the computed torque control strategy resulting in a decoupled double integrating system. From the linearized model of the delta robot, fractional order PID and integer order PID controllers are designed, analyzing the dynamical behavior for many evaluation trajectories. Controllers robustness is evaluated against external disturbances employing performance indexes for the joint and spatial error, applied torque in the joints and trajectory tracking. Results show that fractional order PID with the computed torque control strategy has a robust performance and active disturbance rejection when it is applied to parallel robotic manipulators on tracking tasks.
Mathematical modeling and kinematic analysis of 5 degrees of freedom serial l...IJECEIAES
Modeling and kinematic analysis are crucial jobs in robotics that entail identifying the position of the robot’s joints in order to accomplish particular tasks. This article uses an algebraic approach to model the kinematics of a serial link, 5 degrees of freedom (DOF) manipulator. The analytical method is compared to an optimization strategy known as sequential least squares programming (SLSQP). Using an Intel RealSense 3D camera, the colored object is picked up and placed using vision-based technology, and the pixel location of the object is translated into robot coordinates. The LOBOT LX15D serial bus servo controller was used to transmit these coordinates to the robotic arm. Python3 programming language was used throughout the entire analysis. The findings demonstrated that both analytical and optimized inverse kinematic solutions correctly identified colored objects and positioned them in their appropriate goal points.
Balancing a Segway robot using LQR controller based on genetic and bacteria f...TELKOMNIKA JOURNAL
A two-wheeled single seat Segway robot is a special kind of wheeled mobile robot, using it as a human transporter system needs applying a robust control system to overcome its inherent unstable problem. The mathematical model of the system dynamics is derived and then state space formulation for the system is presented to enable design state feedback controller scheme. In this research, an optimal control system based on linear quadratic regulator (LQR) technique is proposed to stabilize the mobile robot. The LQR controller is designed to control the position and yaw rotation of the two-wheeled vehicle. The proposed balancing robot system is validated by simulating the LQR using Matlab software. Two tuning methods, genetic algorithm (GA) and bacteria foraging optimization algorithm (BFOA) are used to obtain optimal values for controller parameters. A comparison between the performance of both controllers GA-LQR and BFO-LQR is achieved based on the standard control criteria which includes rise time, maximum overshoot, settling time and control input of the system. Simulation results suggest that the BFOA-LQR controller can be adopted to balance the Segway robot with minimal overshoot and oscillation frequency.
Design Novel Nonlinear Controller Applied to Robot Manipulator: Design New Fe...Waqas Tariq
In this paper, fuzzy adaptive base tuning feedback linearization fuzzy methodology to adaption gain is introduced. The system performance in feedback linearization controller and feedback linearization fuzzy controller are sensitive to the main controller coefficient. Therefore, compute the optimum value of main controller coefficient for a system is the main important challenge work. This problem has solved by adjusting main fuzzy controller continuously in real-time. In this way, the overall system performance has improved with respect to the classical feedback linearization controller and feedback linearization fuzzy controller. Adaptive feedback linearization fuzzy controller solved external disturbance as well as mathematical nonlinear equivalent part by applied fuzzy supervisory method in feedback linearization fuzzy controller. The addition of an adaptive law to a feedback linearization fuzzy controller to online tune the parameters of the fuzzy rules in use will ensure a moderate computational load. Refer to this research; tuning methodology can online adjust coefficient parts of the fuzzy rules. Since this algorithm for is specifically applied to a robot manipulator.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Abstract Robots are expected to be new tools for the operations and observations in the extreme environments where humans have difficulties in direct access. One of the important matters to realize mobile robots for extreme environments is to establish systems in their structures which are strong enough to disturbances. Also, while considering surveillance in inaccessible remote areas, a need arises for the presence of a robot capable of intruding into small crevices as well as provides proper surveillance. This work aims at the implementation of a snake robot for surveillance operations in remote areas. A biologically inspired robot with various motion patterns is taken into consideration. An important problem in the control of locomotion of robots with multiple degrees of freedom is in adapting the locomotors patterns to the properties of the environment. This has been overcome by using control techniques capable of integrating the motion patterns of a snake. Here an attempt is taken to focus on the creeping locomotion of a living snake. In hybrid model, the optimal locomotion of the snake robot is tried to achieve by comparing it with that of a living snake. A wireless real time vision processing is also employed within the robot to improve its performance. The presence of Video acquisition along with processing will be an added advantage for implementation of the robot for highly precise and difficult surveillance applications. Real time processing of video enables proper and efficient control towards obstacle avoidance pattern of the robot. This ensures that the locomotion of the robot is in a bio-inspired highly efficient path towards the target. Keywords: Collision-free behavior, neural oscillator, snake locomotion, steering, real time vision processing
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Evaluation Performance of 2nd Order Nonlinear System: Baseline Control Tunabl...Waqas Tariq
Design a nonlinear controller for second order nonlinear uncertain dynamical systems (e.g., internal combustion engine) is one of the most important challenging works. This paper focuses on the comparative study between two important nonlinear controllers namely; computed torque controller (CTC) and sliding mode controller (SMC) and applied to internal combustion (IC) engine in presence of uncertainties. In order to provide high performance nonlinear methodology, sliding mode controller and computed torque controller are selected. Pure SMC and CTC can be used to control of partly known nonlinear dynamic parameters of IC engine. Pure sliding mode controller and computed torque controller have difficulty in handling unstructured model uncertainties. To solve this problem applied linear error-based tuning method to sliding mode controller and computed torque controller for adjusting the sliding surface gain (ë ) and linear inner loop gain (K). Since the sliding surface gain (ë) and linear inner loop gain (K) are adjusted by linear error-based tuning method. In this research new ë and new K are obtained by the previous ë and K multiple gains updating factor(á). The results demonstrate that the error-based linear SMC and CTC are model-based controllers which works well in certain and uncertain system. These controllers have acceptable performance in presence of uncertainty.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Intelligent swarm algorithms for optimizing nonlinear sliding mode controller...IJECEIAES
This work introduces an accurate and fast approach for optimizing the parameters of robot manipulator controller. The approach of sliding mode control (SMC) was proposed as it documented an effective tool for designing robust controllers for complex high-order linear and nonlinear dynamic systems operating under uncertain conditions. In this work Intelligent particle swarm optimization (PSO) and social spider optimization (SSO) were used for obtaining the best values for the parameters of sliding mode control (SMC) to achieve consistency, stability and robustness. Additional design of integral sliding mode control (ISMC) was implemented to the dynamic system to achieve the high control theory of sliding mode controller. For designing particle swarm optimizer (PSO) and social spider optimization (SSO) processes, mean square error performances index was considered. The effectiveness of the proposed system was tested with six degrees of freedom robot manipulator by using (PUMA) robot. The iteration of SSO and PSO algorithms with mean square error and objective function were obtained, with best fitness for (SSO =4.4876 푒 −6 and (PSO)=3.4948 푒 −4 .
Smart element aware gate controller for intelligent wheeled robot navigationIJECEIAES
The directing of a wheeled robot in an unknown moving environment with physical barriers is a difficult proposition. In particular, having an optimal or near-optimal path that avoids obstacles is a major challenge. In this paper, a modified neuro-controller mechanism is proposed for controlling the movement of an indoor mobile robot. The proposed mechanism is based on the design of a modified Elman neural network (MENN) with an effective element aware gate (MEEG) as the neuro-controller. This controller is updated to overcome the rigid and dynamic barriers in the indoor area. The proposed controller is implemented with a mobile robot known as Khepera IV in a practical manner. The practical results demonstrate that the proposed mechanism is very efficient in terms of providing shortest distance to reach the goal with maximum velocity as compared with the MENN. Specifically, the MEEG is better than MENN in minimizing the error rate by 58.33%.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Fractional order PID controller tuned by bat algorithm for robot trajectory c...nooriasukmaningtyas
This paper deals with implementing the tuning process of the gains of fractional order proportional integral derivative (FOPID) controller designed for trajectory tracking control for two-link robotic manipulators by using a Bat algorithm. Two objective functions with weight values assigned has been utilized for achieving the minimization operation of errors in joint positions and torque outputs values of robotic manipulators. To show the effectiveness of using a Bat algorithm in tuning FOPID parameters, a comparison has been made with particle swarm optimization algorithm (PSO). The validity of the proposed controllers has been examined in case of presence of disturbance and friction. The results of simulations have clearly explained the efficiency of FOPID controller tuned by Bat algorithm as compared with FOPID controller tuned by PSO algorithm.
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!SOFTTECHHUB
As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
GridMate - End to end testing is a critical piece to ensure quality and avoid...ThomasParaiso2
End to end testing is a critical piece to ensure quality and avoid regressions. In this session, we share our journey building an E2E testing pipeline for GridMate components (LWC and Aura) using Cypress, JSForce, FakerJS…
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
A tale of scale & speed: How the US Navy is enabling software delivery from l...
Hq2513761382
1. D Narendra Kumar, S LalithaKumari , Veeravasantarao D / International Journal of
Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 2, Issue 5, September- October 2012, pp.1376-1382
Dynamic Adaptive Control of Mobile Robot UsingRBF Networks
D Narendra Kumar#, S LalithaKumari #, Veeravasantarao D*
#
Department of Power and Industrial Drives, GMRIT, Rajam, Andra Pradesh, India
*
Indian Institute of Technology, Kanpur, India
Abstract
In this paper, an adaptive neuro-control The robot studied in this research is a kind of a
systemwith two levels is proposed for the motion simplenonholonomic mechanical system.
control of anonholonomic mobile robot. In the Nonholonomic property is seen inmany mechanical
first level, a PD controller is designed to generate and robotic systems, particularly thoseusing velocity
linear and angular velocities, necessary to tracka inputs. Smaller control space compared
reference trajectory. In the second level, a neural withconfiguration space (lesser control signals than
network converts the desired velocities, provided independentcontrolling variables) causes conditional
by the first level, intoa torque control. The controllability ofthese systems. So the feasible
advantage of the control approach is that, no trajectory is limited. Thismeans that a mobile robot
knowledge about the dynamic model is required, with parallel wheels can’t movelaterally.
and no synaptic weight changing is needed in Nonholonomic constraint is a differential equationon
presence of robot’s parameter’s variation (mass the base of state variables, it’s not integrable.
or inertia).By introducing appropriate Rolling butnot sliding is a source of this constraint.
Lyapunovfunctions asymptotic stability of state The control strategy proposed on this paper
variables and stability of system is guaranteed. addressesthe dynamic compensation of mobile
The tracking performance of neural controller robotsand only requires information about the robot
under disturbances is compared with PD localization.The problem statement is presented
controller. Sinusoidal trajectory and lamniscate onsection 2 and the kinematic and dynamic model
trajectories are considered for this comparison. ofthe considered robot, on section 3 and 4. Neural
controller design as well as the main control
Keywords— Direct Adaptive Control, RBF systemdesign is presented on section 5 and 6. Some
Networks, Trajectory tracking, Set point results and final considerations are also presented on
tracking, Lyapunov stability section 6.
I. INTRODUCTION II. PROBLEM STATEMENT
Navigation control of mobile robots has The dynamics of a mobile robot is time
been studied by many authors in the last decade, variant and changes with disturbances. The dynamic
since they are increasingly used in wide range of model is composed of two consecutive part;
applications. At the beginning, the research effort kinematic model and equations of linear and angular
was focused only on the kinematic model, assuming torques. By transforming dynamic error equationsof
that there is perfect velocity tracking [1]. Later on, kinematic model to mobile coordinates, the
the research has been conducted to design navigation trackingproblem changes to stabilization. In the
controllers, including also the dynamics of the robot trajectory tracking problem, the robot mustreach and
[2], [3]. Taking into account the specific robot follow a trajectory in the Cartesian spacestarting
dynamics is more realistic, because the assumption from a given initial configuration.The trajectory
“perfect velocity tracking” does not hold in practice. tracking problem is simpler than thestabilisation
Furthermore, during the robot motion, the robot problem because there is no need to controlthe robot
parameters may change due to surface friction, orientation: it is automatically compensatedas the
additional load, among others. Therefore, it is robot follows the trajectory, provided that the
desirable to develop a robust navigation control, specified trajectory respects the non-
which has the following capabilities: i) ability to holonomicconstraints of the robot. Controller is
successfully handle estimation errors and noise in designed in twoconsecutive parts: in the first part
sensor signals, ii) “perfect” velocity tracking, and iii) kinematic stabilization isdone using simple PD
adaptation ability, in presence of time varying control laws, in the second one, direct adaptive
parameters in the dynamical model. control using RBF Networks has been used for
Artificial neural networks are one of the exponentialstabilization of linear and angular
most popular intelligent techniques widely applied in velocities. Uncertainties inthe parameters of
engineering. Their ability to handle complex input- dynamic model (mass and inertia) havebeen
output mapping, without detailed analytical model, compensated using model reference adaptive
and robustness for noise environment make them an control.
ideal choice for real implementations.
1376 | P a g e
2. D Narendra Kumar, S LalithaKumari , Veeravasantarao D / International Journal of
Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 2, Issue 5, September- October 2012, pp.1376-1382
III. KINEMATIC CONTROL angular displacement,𝑠 and𝜃, so that 𝑠 = 𝑣and 𝜃 =
In this paper the mobile robot with 𝜔. One could easily design a control system basedon
differential driveis used(Fig. 1). The robot has two the block diagram on Fig. 2, if s and 𝜃 are
driving wheels mounted on thesame axis and a free measurableand 𝑠 𝑟𝑒𝑓 and 𝜃 𝑟𝑒𝑓 are defined. This
front wheel. The two driving wheels controllercan be based on any of the classic design
areindependently driven by two actuators to achieve techniquesfor linear systems where the controller
both thetransition and orientation. The position of receives the error signal and generates the input to
the mobile robot inthe global frame {X,O,Y} can be the plant (a PD,for example).
defined by the position of themass center of the
mobile robot system, denoted by C, oralternatively
by position A, which is the center of mobile
robotgear, and the angle between robot local frame
𝑥 𝑚 , 𝐶, 𝑦 𝑚 andglobal frame.
A. Kinematic model
Kinematic equations [9] of the two wheeled
mobile robot are: Fig. 2 Kinematic Control system block diagram
𝑥 cos
(𝜃) 0 As the design of such a controller is simple,
𝑦 = sin 𝑣
(𝜃) 0 , (1) thismodel has been used for the control system
𝜔 design, despite of two problems that still hold: the
𝜃 0 1
linear displacementsalong a trajectory is practically
𝑣 𝑟 𝑟 𝑣𝑅 unmeasurableand 𝑠 𝑟𝑒𝑓 is meaningless. However,
And = 𝑟 −
𝑟
𝑣𝐿 , (2)
𝜔 𝐷 𝐷 these problemscan be contoured, as will be shown
on the nextsection.
B. Kinematic controller design
The robot stabilisation problem can be
divided into two different control problems: robot
positioning control and robot orientating control.
The robot positioningcontrol must assure the
achievement of a desiredposition ( 𝑥 𝑟𝑒𝑓 ; 𝑦 𝑟𝑒𝑓 ),
regardless of the robot orientation.The robot
orientating control must assurethe achievement of
the desired position and orientation(𝑥 𝑟𝑒𝑓 ; 𝑦 𝑟𝑒𝑓 ; 𝜃 𝑟𝑒𝑓 ).
In this paper we only consider the positioning
control.
Fig. 3 illustrates the positioning problem,
where∆𝑙is the distance between the robot and the
desired reference(𝑥 𝑟𝑒𝑓 ;𝑦 𝑟𝑒𝑓 ), in the Cartesian space.
Fig.1 The representation of a nonholonomic mobile The robotpositioning control problem will be solved
robot if we assure ∆𝑙 → 0. This is not trivial since the
Where 𝑥 and 𝑦 are coordinates of the center 𝑙variable does not appear in the model of equation 1.
of mobile robotgear, 𝜃is the angle that represents the
orientation of thevehicle, 𝑣 and 𝜔 are linear and
angular velocities of thevehicle, 𝑣 𝑅 and 𝑣 𝐿 are
velocities of right and left wheels, 𝑟 is awheel
diameter and 𝐷is the mobile robot base length.Inputs
of kinematic model of mobile robot are velocities of
right and left wheels 𝑣 𝑅 and 𝑣 𝐿 .
The mainfeature of this model for wheeled
mobile robots isthe presence of nonholonomic
constraints, due to therolling without slipping
condition between the wheelsand the ground.The
nonholonomic constraints imposethat the system
generalized velocities cannot assume independent
values.
In order to reduce the model complexity [5], Fig. 3Robot positioning problem
one couldrewrite it in terms of the robot linear and
1377 | P a g e
3. D Narendra Kumar, S LalithaKumari , Veeravasantarao D / International Journal of
Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 2, Issue 5, September- October 2012, pp.1376-1382
To overcome this problem, we can define two
newvariables, ∆λ and ϕ . ∆λ ¸ is the distance to R,
thenearest point from the desired reference that lies
onthe robot orientation line; ϕis the angle of the
vectorthat binds the robot position to the desired
reference.We can also define ∆ϕ as the difference
between the𝜙 angle and the robot orientation:∆𝜙 =
𝜙 − 𝜃.We can now easily conclude that:
𝛥𝜆
∆𝑙 = (3)
𝑐𝑜𝑠 (𝛥𝜙) Fig. 4Robot positioning controller
So, if ∆𝜆 → 0 and ∆𝜙 → 0then∆𝑙 → 0. That
is,if we design a control system that assures the ¸ C. Set point tracking
and∆λ and ∆ϕ converges to zero, then the desired On Fig. 5 a simulation of the robot
reference, xref and yref is achieved. Thus, the robot stabilization control problem is shown, where the
positioningcontrol problem can be solved by initial position of robot is different and the desired
applying any controlstrategy that assures such position is fixed. A simple PD controller has been
convergence. implemented as positioning controller.
The block diagram in Fig. 2 suggests that Fig. 6 shows the linear and angular errors
the systemcan be controlled using linear and angular convergenceto zero, thus, assuring the achievement
references, sref and θref , respectively. We will ofthe control objective.
generatethese references in order to ensure the
converge of∆𝜆and𝛥𝜙to zero, as required by equation
3. In otherwords, we want 𝑒 𝑠 = ∆𝜆¸ and 𝑒 𝜃 = ∆𝜙.
Different initial positions
Thus, if thecontroller assures the errors convergence
to zero, therobot positioning control problem is
solved.To make 𝑒 𝜃 = ∆𝜙 ,we just need to
define 𝜃 𝑟𝑒𝑓 = 𝜙 ,so 𝑒 𝜃 = 𝜃 𝑟𝑒𝑓 − 𝜃 = 𝜙 − 𝜃 = ∆𝜙 .
For this, we make:
𝑦 𝑟𝑒𝑓 − 𝑦 Δ𝑦 𝑟𝑒𝑓
𝜃 𝑟𝑒𝑓 = 𝑡𝑎𝑛−1 = 𝑡𝑎𝑛−1 (4)
𝑥 𝑟𝑒𝑓 − 𝑥 Δ𝑥 𝑟𝑒𝑓
To calculate 𝑒 𝑠 is generally not very simple,
because 𝑠 output signal cannot be measured and
wecannot easily calculate a suitable value for 𝑠 𝑟𝑒𝑓 .
Butif we define the 𝑅 point in Fig. 3 as the
referencepoint for the 𝑠 controller, only in this case Target position
it is truethat 𝑒 𝑠 = 𝑠 𝑟𝑒𝑓 − 𝑠 = ∆𝜆. So:
Fig. 5 Robot stabilization for different initial
𝑒 𝑠 = Δ𝜆 = Δ𝑙 . cos Δ𝜙 = (5) conditions
2 2 Δ𝑦 𝑟𝑒𝑓
Δ𝑥 𝑟𝑒𝑓 + Δ𝑦 𝑟𝑒𝑓 . 𝑐𝑜𝑠 𝑡𝑎𝑛−1 − 𝜃
Δ𝑥 𝑟𝑒𝑓
The complete robot positioning controller,
based on the diagram of Fig. 2 and the equations 4
and 5, is presented on Fig. 4. It can be used as a
stand-alonerobot control system if the problem is
just to drive torobot to a given position (𝑥 𝑟𝑒𝑓 ;𝑦 𝑟𝑒𝑓 ),
regardless of the final robot orientation.
Controller
𝑣𝑑 𝑘 𝑠 𝑒 𝑠 + 𝑘 𝑠𝑑 𝑒 𝑠
𝑢= 𝜔 = (6)
𝑑 𝑘 𝜃 𝑒 𝜃 + 𝑘 𝜃𝑑 𝑒 𝜃
Fig. 6 Linear and angular errors
IV. DYNAMIC CONTROL
In this section, a dynamic model of
anonholonomic mobile robot with motor torques will
be derived first.
1378 | P a g e
4. D Narendra Kumar, S LalithaKumari , Veeravasantarao D / International Journal of
Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 2, Issue 5, September- October 2012, pp.1376-1382
A. Dynamic model nonlinearities are not known completely they can be
The dynamic equations of motion can be approximated either by neural networks or by fuzzy
expressed as [10] systems. The controller then uses these estimates to
𝐴𝜃 𝑅 + 𝐵𝜃 𝐿 = 𝜏 𝑅 − 𝐾1 𝜃 𝑅 (7) linearize the system. The parameters of the
𝐵𝜃 𝑅 + 𝐴𝜃 𝐿 = 𝜏 𝐿 − 𝐾1 𝜃 𝐿 (8) controller are updated such that the output tracking
Where error converges to zero with time while the closed
𝑀𝑟 2 𝐼 𝐴 + 𝑀𝑑2 𝑟 2 loop stability is maintained. The design technique is
𝐴= + + 𝐼0 popularly known as direct adaptive control technique.
4 4𝑅2
A. Function approximation
𝑀𝑟 2 𝐼 𝐴 + 𝑀𝑑2 𝑟 2 The control problem becomes difficult
𝐵= − 9
4 4𝑅 2 when 𝑔(𝑋) is unkown because the fact that the
Here M is the mass of the entire vehicle, 𝐼 𝐴 approximation of 𝑔(𝑋)can be zero at times which
is the moment of inertia of the entire vehicle makes controller unbounded. For simplicity, we
considering point A,𝐼0 is the moment of inertia of the have considered 𝑓(𝑋) as unknown function and
dθR dθL 𝑔 𝑋 as known function. Radial basis function
rotor/wheel and 𝑑𝑡 and 𝑑𝑡 are angular
velocities of the right and left network (RBFN) is used to approximate 𝑓(𝑋). Fig. 7
wheelrespectively. τR , τL are right and left wheel shows RBF network. The weight update law of the
K RBF network is derived such a way that the closed
motor torques. A1 = 0.5. loop system is Lyapunov stable and the output
tracking error converges to zero with time.
B. State space model In the equation (12), 𝑓(𝑋) can be
Substitute θR , θL as ωR , ωL respectively in approximated as 𝑓 𝑋 = 𝑊 𝑇 ∅(𝑋) using a radial
equations (7), (8) and convert these velocities into basis function network. Then the control law
linear and angular velocities using equation (2). 𝑈 = 𝑔 −1 (𝑋) −𝑓 (𝑋) + 𝑋 𝑑 + 𝐾𝑒 will stabilize the
Then the state space model will become system (equation 10) in the sense of Lyapunov
𝑣 𝑣 𝜏𝑅
= 𝐴𝑋 + 𝐵𝑈 𝜏 (10) provided 𝑊 is updated using the update
𝜔 𝜔 𝐿 2
𝑋 −𝐶
Where 𝐴 𝑋 , 𝐵 𝑈 are functions of parameters A and B. 𝑇. − .
law𝑊 = −𝐹∅𝑒 Where ∅(𝑋) = 𝑒 2𝜎
C. Feedback linearization Φ(X)
The above model (equation 10) is similar to X
a general state space model of nonlinear system as 𝑓(𝑋)
follows V W
V
𝑋 = 𝑓(𝑋) + 𝑔 𝑋 𝑈 (11) E
E
When the nonlinearities𝑓(𝑋) and 𝑔(𝑋) are C
C
completely known, feedback linearization can be T T
used to design controller for a system, where the O
O
controller may have a form [8]: R
R
𝑈 = 𝑔 −1 (𝑋) −𝑓 𝑋 + 𝑋 𝑑 + 𝐾 𝑒 (12)
Here, 𝑒 = 𝑋 𝑑 − 𝑋 where 𝑋 𝑑 represents Fig. 7 Multi input-output RBF network
desired state vector. The above mentioned control
law makes the closed loop error dynamics linear as B. Weight update law
well as stable thus the error converges to zero with Let us assume that there exists an ideal weight
time. 𝑊 such that the original function 𝑓(𝑋) can be
But these nonlinear parameters are represented as𝑓 𝑋 = 𝑊 𝑇 ∅(𝑋).
unknown in reality. So neural network models are Control 𝑈in the system (equation 11) we get,
used to estimate these functions and use it in control 𝑋 = 𝑓 𝑋 + 𝑔 𝑋 𝑔 −1 𝑋 [−𝑓 𝑋 + 𝑋 𝑑 + 𝐾𝑒]
structure. = 𝑊 𝑇 ∅ − 𝑊 𝑇 ∅ + 𝑋 𝑑 + 𝐾𝑒 (13)
Defining 𝑊 = 𝑊 − 𝑊 then equation 13 will be
V. NEURAL CONTROLLER
𝑋 = 𝑊 𝑇 ∅ + 𝑋 𝑑 + 𝐾𝑒(14)
Feedback linearization is a useful control
design technique in control systems literature where 𝑋 𝑑 − 𝑋 = 𝑒 = −𝑊 𝑇 ∅ − 𝐾𝑒 (15)
a large class of nonlinear systems can be made linear Consider a Lyapunov function candidate
1 1
by nonlinear state feedback. The controller can be 𝑉 = 𝑒 2 + 𝑊 𝑇 𝐹 −1 𝑊(16)
2 2
proposed in such a way that the closed loop error Where F is a positive definite matrix.
dynamics become linear as well as stable. The main Differentiating equation (16),
problem with this control scheme is that cancellation 𝑉 = 𝑒𝑒 + 𝑊 𝑇 𝐹 −1 𝑊 (17)
of the nonlinear dynamics depends upon the exact Substituting 𝑒 from equation (15) into equation
knowledge of system nonlinearities. When system (17)
1379 | P a g e
5. D Narendra Kumar, S LalithaKumari , Veeravasantarao D / International Journal of
Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 2, Issue 5, September- October 2012, pp.1376-1382
𝑉 = 𝑒 −𝑊 𝑇 ∅ − 𝐾𝑒 + 𝑊 𝑇 𝐹 −1 𝑊 (18) kinematic control are𝑘 𝑠 = 0.21, 𝑘 𝜃 = 0.6 𝑎𝑛𝑑 𝑘 𝑠𝑑 =
𝑘 𝜃𝑑 = 0.01. We used 6 hidden neurons and set the
Since W is constant, we can write𝑊 = 𝑊 − 𝑊 =
gain matrix as 𝐾 = 1.5 0; 0 1.5 . The initial
−𝑊. Thus, values of learning rate, weights, centers and sigma
𝑉 = −𝐾𝑒 2 − 𝑊 𝑇 ∅𝑒 − 𝑊 𝑇 𝐹 −1 𝑊 are tuned such a way that it provides good tracking
= −𝐾𝑒 2 − 𝑊 𝑇 ∅𝑒 + 𝐹 −1 𝑊 (19) performance.
Equating the second term of equation (19) to 0,
we get
∅𝑒 + 𝐹 −1 𝑊 = 0
𝑇
Or, 𝑊 = 𝐹∅𝑒 (20)
Using update law (equation 20), equation 19
becomes,
𝑉 = −𝐾𝑒 2 (21)
Since 𝑉 > 0and 𝑉 ≤ 0, this shows the stability in
the sense of Lyapunov so that 𝑒 and 𝑊(hence𝑊) are
bounded.
So the weight update law is
𝑊𝑛𝑒𝑤 = 𝑊 𝑜𝑙𝑑 + 𝐹∅𝑒 𝑇 (22)
VI. MAIN BLOCK DIAGRAM
The block diagram of overall controller [7] Fig. 9Tracking the lemniscate trajectory
structure is shown in Fig. 8. The errors determined The simulation results obtained by neural
between desired trajectory positions and robot actual networkcontroller are shown in Figs. 9-11. Results
positions are used to determine the desired velocities achieved in Figs. 9-10 demonstrate the good position
using kinematic control discussed in section III. tracking performance. Fig. 11 shows that the error in
These desired velocities are compared with actual velocities is almost zero whereas a slight error
wheel velocities and use the errors to generate left observed in displacement. It clearly shows that the
and right wheel torques for the two motors using the PD kinematic controller performance affects the
control law discussed in section V. Here the overall tracking performance.
𝑣 𝜏𝑅 The velocities generated from torque
state 𝑋 = , control input is 𝑈 = 𝜏 and the control are exactly matched with the values obtained
𝜔 𝐿
nonlinearities are𝑓 𝑋 = 𝐴 𝑋 𝑋 𝑎𝑛𝑑 𝑔 𝑋 = 𝐵 𝑈 . And from the kinematic control such that it tracks the
𝑣𝑑 − 𝑣 trajectory (Fig. 10). Theproposed neural controller
the error is 𝑒 = 𝜔 − 𝜔 .
𝑑 also ensures small values of the controlinput torques
Neural for obtaining the reference position trajectories (Fig.
Controller (U= 10). Our simulations proved that motor torque of
𝑔−1 (𝑋) −𝑓 (𝑋) 1Nm/sec is sufficient to drive the robot motion. This
+ 𝑋 𝑑 + 𝐾𝑒
mean that smaller power ofDC motors is requested.
PD Controller
𝑣 𝑑 = 𝑘 𝑠 𝑒 𝑠 + 𝑘 𝑠𝑑 𝑒 𝑠
𝜔 𝑑 = 𝑘 𝜃 𝑒 𝜃 + 𝑘 𝜃𝑑 𝑒 𝜃
Fig. 8 Main block diagram of mobile robot
A. Trajectory tracking
The effectiveness of the neural network
controller is demonstrated in the case of tracking of
a lamniscate curve. The trajectory tracking problem
for a mobile robot is based on a virtual reference
robot that has to be tracked. The overall system is
designed and implemented within Matlab
environment. The geometric parameters of mobile Fig. 10Inputs to the robot
robot are assumed as r = 0.08m, D = 0.4m, d = 0.1m.
M=5kg, Ia = 0.05, m0=0.1kg and I0=0.0005. The
initial position of robot is 𝑥0 𝑦0 𝜃0 =
1 3 300 and the initial robot velocities
are 𝑣, 𝜔 = 0.1, 1 . PD controller gains for
1380 | P a g e
6. D Narendra Kumar, S LalithaKumari , Veeravasantarao D / International Journal of
Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 2, Issue 5, September- October 2012, pp.1376-1382
Sudden forces
Fig. 11 displacement, angular and velocity errors Fig. 13Left and right wheel motor torques
B. Neural controller performance with C. Neural controller performance with
disturbances convergence
This test is performed to analyze control As the neural control structure is adaptive,
performance when any disturbance occurred on the the weights are automatically adjusted using update
robot. We have chosen sinusoidal trajectory for this law such that it tracks the trajectory though any
purpose as to prove neural controller performance changes happen to dynamics. So the velocity error
improves when time increases. We applied sudden keeps on reducing with the time and hence the
forces on robot at two different time instants and tracking performance improves. If the control
observed robot come back to the desired trajectory. structure uses previous saturated weights as initial
The neural controller performance is compared with weights for the next time reboot of robotmakes the
the classical PD controller. The dynamic PD error further decreases to lower values. Whereas this
is not possible in case of PD controller as the gains
controller 𝜏 𝑅 = 𝑘 𝑤𝑟 𝑒 𝑣 + 𝑘 𝑤𝑟𝑑 𝑒 𝑣 , 𝜏 𝐿 =
are fixed for a particular dynamics and external
𝑘𝑤𝑙 𝑒𝑤 + 𝑘𝑤𝑙𝑑𝑒𝑤gains which are used to generate environments. Fig. 14 shows that in case of neural
torques from the velocity errorsare𝑘 𝑤𝑟 = 0.8, 𝑘 𝑤𝑙 = controller, the RMS error in X, Y coordinates
0.2 𝑎𝑛𝑑 𝑘 𝑤𝑟𝑑 = 0.53, 𝑘 𝑤𝑙𝑑 = 0.01 . The total run decreases faster with time than a PD controller.
time is 150sec and two high forces (equal to 10 and
2 2
15 Nm/sec) are appliedat 75sec and 50sec. Fig. 12 𝑖 𝑖
𝑁 𝑒 𝑥 +𝑒 𝑦
shows that the neural controller is able to stabilize 𝑅𝑀𝑆 𝐸𝑟𝑟𝑜𝑟 = 𝑖=1 , where N is
𝑁
the robot quickly and makes the robot move in the
number of iterations.𝑒 𝑖𝑥 , 𝑒 𝑖𝑦 are𝑖 𝑡 iteration errors in
desired path smoothly compared to PD
controller.From Fig. 13, we can say that the neural 𝑥, 𝑦 coordinates.
controller generated torques is smooth and low.
Disturbances
Fig. 14 RMS error with number of iterations (or w.r.t
Fig. 12Tracking performance when sudden forces time)
applied
CONCLUSIONS
In this paper, we presented a simple method
of controlling velocities to achieve desired trajectory
by converting 𝑥, 𝑦, 𝜃 into linear displacement ( 𝑆)
1381 | P a g e
7. D Narendra Kumar, S LalithaKumari , Veeravasantarao D / International Journal of
Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 2, Issue 5, September- October 2012, pp.1376-1382
and 𝜃which takes care of nonholonomic constraints.
We also proposed direct adaptive control method
using RBF networks to generate motor torques such
that the velocities generated from kinematic control
are achieved. We observed that neural controller
performance is better than better than PD controller
when disturbances occurred. It also converges faster
than PD.
REFERENCES
[1] I. kolmanovsky and N. H. McClamroch,
“Development in Nonholonomic Control
Problems,” IEEE Control Systems, pp. 20–
36, December 1995.
[2] T. Fukao, H. Nakagawa, and N. Adachi,
“Adaptive Tracking Controlof a
Nonholonomic Mobile Robot,” IEEE
transactions on Roboticsand Automation,
vol. 16, pp. 609–615, October 2000.
[3] R. Fierro and F. L. Lewis, “Control of a
Nonholonomic MobileRobot Using Neural
Networks,” IEEE Transactions on neural
networks,vol. 9, pp. 589–600, July 1998.
[4] Luca, A., Oriolo, G., Samson, C., and
Laumond, J. P. (1998). Robot Motion
Planning and Control, chapter Feedback
Control of a Nonholomic Car-like Robot.
[5] Frederico C. VIEIRA, Adelardo A. D.
MEDEIROS, Pablo J. ALSINIA, Antonio P.
ARAUJO Jr. “Position And Orientation
Control of A Two Wheeled Differentailly
Driven Nonholonomic Mobile Robot”
[6] IndraniKar, LaxmidharBehera, “Direct
adaptive neural control for affine nonlinear
systems,” Applied Soft Computing., vol. 9,
pp. 756–764, Oct. 2008.
[7] Ali Gholipour, M.J. Yazdanpanah
“Dynamic Tracking Control of
Nonholonomic Mobile Robot with Model
Reference Adaption for Uncertain
Parameters”Control and Intelligent
Processing center for Excellence,
University of Tehran
[8] IndraniKar, “Intelligent Control Schemes
for Nonlinear Systems,” Ph.D. thesis,
Indian Institute ofTechnology, Kanpur,
India, Jan. 2008.
[9] JasminVelagic, Nedim Osmic, and
BakirLacevic, “Neural Network Controller
for Mobile Robot Motion Control,” World
Acadamy of Science, Engineering and
Technology, 47, 2008.
[10] JasminVelagic, BakirLacevic and Nedim
Osmic “Nonlinear Motion Control of
Mobile Robot Dynamic Model”University
of Sarajevo Bosnia and Herzegovina
1382 | P a g e