1) A PID controller was designed using ISE, IAE, ITAE and MSE error criteria for a stable linear time invariant continuous system. A bacterial foraging particle swarm optimization (BF-PSO) technique was used to tune the PID controller gains (Kp, Ki, Kd) to meet performance specifications.
2) The PID controller was applied to the system using each error criteria and the closed-loop responses were observed and compared. For the IAE criteria, the system had 0% overshoot and undershoot with a settling time of 7.2172e-006 seconds and rise time of 4.0551e-006 seconds.
3) The BF-PSO algorithm combines
In recent times, fractional order controllers are gaining more interest. There are several fractional order controllers are available in literature. Still, tuning of these controllers is one of the main issues which the control community is facing. In this paper, online tuning of five dierent fractional order controllers is discussed viz. tilted proportional-integral-derivative (T-PID) controller, fractional order proportional-integral (FO-PI) controller, fractional order proportional-derivative (FO-PD) controller, fractional order proportional-integral-derivative (FO-PID) controller. A reference tracking method is proposed for tuning of fractional order controllers. First order with dead time (FOWDT) system is used to check feasibility of the control strategy.
Welcome to International Journal of Engineering Research and Development (IJERD)IJERD Editor
Â
call for paper 2012, hard copy of journal, research paper publishing, where to publish research paper,
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
Design and optimization of pid controller using genetic algorithmeSAT Journals
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Abstract Natural evolution is mimicked by Genetic Algorithms (GAs) which is a stochastic global search method used for optimization. . In missile control systems Proportional Integral Derivative (PID) control is widely used, but due to empirically selected parameters Kp, Ki, Kd it is difficult to achieve parameter optimization. Genetic algorithm is a search algorithm that is based on natural selection and genetics principles.GA is a computational algorithm which deals with genetics of the human body. It evolves with the number of iterations. After ever iteration a better result is expected. These results are checked for the error. The fittest roots or solution are considered for the next generation based on the selection criterion. GA randomly generates the initial population of the PID control parameters according to the calculation of selection (Normalized Geometric Selection), crossover (Arithmetic Crossover) and mutation (Uniform Mutation), thus optimizing the control parameters. Mean Square Error (MSE) value is chosen as the performance assessment index. For a missile altitude control Proportional Integral Derivative (PID) controller using genetic algorithm is implemented & compared with the classical method Zeigler-Nichols (Z-N) in the paper. Z-N method is classical method which tunes the parameters of PID. The parameters of PID are difficult to tune. Tuned parameters give the optimum solution. Optimum solution generally converges to a solution having minimum error. Minimum error gives a response of the system in terms of maximum over shoot, Settling time, Rise time & Steady State Error. The designed PID with the Genetic Algorithm has much faster response than the classical method.
In this paper, the non-invasive
methodology for removing Fetal Electrocardiogram
(FECG) is gained by subtracting the balanced
variation of maternal electrocardiogram (MECG)
movement from the abdominal electrocardiogram
(AECG) banner. The banner assessed from the
mother's guts (AECG) is regularly overpowered by
maternal heartbeat. The maternal portion of the
AECG is the nonlinearly changed variation of
MECG. This paper uses an Adaptive Neuro-Fuzzy
Interference System (ANFIS) structure. It is used
for finding the non-direct change and the ensuing
banner are set up with people based request
figurings. This strategy involves some specific
issues which are a direct result of the low force of
the fetal ECG which is sullied by various
wellsprings of checks. It joins maternal ECG,
electromyogram (EMG) signals, power line
impedances and sporadic electronic upheavals.
Along these lines, we are proposing an improved
multimode PSO estimation for overcoming this
issue. In addition, methods like wavelet change,
flexible filtering, thresholding are moreover used.
We have furthermore empowered the thoracic and
stomach signals using MATLAB programming. It
uses only two signs recorded at the thoracic and
stomach regions of mother's skin. Similarly, our
test shows that the proposed count can expel FECG
banner immediately in pregnancy period and that is
one of the basic focal points of figuring.
In recent times, fractional order controllers are gaining more interest. There are several fractional order controllers are available in literature. Still, tuning of these controllers is one of the main issues which the control community is facing. In this paper, online tuning of five dierent fractional order controllers is discussed viz. tilted proportional-integral-derivative (T-PID) controller, fractional order proportional-integral (FO-PI) controller, fractional order proportional-derivative (FO-PD) controller, fractional order proportional-integral-derivative (FO-PID) controller. A reference tracking method is proposed for tuning of fractional order controllers. First order with dead time (FOWDT) system is used to check feasibility of the control strategy.
Welcome to International Journal of Engineering Research and Development (IJERD)IJERD Editor
Â
call for paper 2012, hard copy of journal, research paper publishing, where to publish research paper,
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
Design and optimization of pid controller using genetic algorithmeSAT Journals
Â
Abstract Natural evolution is mimicked by Genetic Algorithms (GAs) which is a stochastic global search method used for optimization. . In missile control systems Proportional Integral Derivative (PID) control is widely used, but due to empirically selected parameters Kp, Ki, Kd it is difficult to achieve parameter optimization. Genetic algorithm is a search algorithm that is based on natural selection and genetics principles.GA is a computational algorithm which deals with genetics of the human body. It evolves with the number of iterations. After ever iteration a better result is expected. These results are checked for the error. The fittest roots or solution are considered for the next generation based on the selection criterion. GA randomly generates the initial population of the PID control parameters according to the calculation of selection (Normalized Geometric Selection), crossover (Arithmetic Crossover) and mutation (Uniform Mutation), thus optimizing the control parameters. Mean Square Error (MSE) value is chosen as the performance assessment index. For a missile altitude control Proportional Integral Derivative (PID) controller using genetic algorithm is implemented & compared with the classical method Zeigler-Nichols (Z-N) in the paper. Z-N method is classical method which tunes the parameters of PID. The parameters of PID are difficult to tune. Tuned parameters give the optimum solution. Optimum solution generally converges to a solution having minimum error. Minimum error gives a response of the system in terms of maximum over shoot, Settling time, Rise time & Steady State Error. The designed PID with the Genetic Algorithm has much faster response than the classical method.
In this paper, the non-invasive
methodology for removing Fetal Electrocardiogram
(FECG) is gained by subtracting the balanced
variation of maternal electrocardiogram (MECG)
movement from the abdominal electrocardiogram
(AECG) banner. The banner assessed from the
mother's guts (AECG) is regularly overpowered by
maternal heartbeat. The maternal portion of the
AECG is the nonlinearly changed variation of
MECG. This paper uses an Adaptive Neuro-Fuzzy
Interference System (ANFIS) structure. It is used
for finding the non-direct change and the ensuing
banner are set up with people based request
figurings. This strategy involves some specific
issues which are a direct result of the low force of
the fetal ECG which is sullied by various
wellsprings of checks. It joins maternal ECG,
electromyogram (EMG) signals, power line
impedances and sporadic electronic upheavals.
Along these lines, we are proposing an improved
multimode PSO estimation for overcoming this
issue. In addition, methods like wavelet change,
flexible filtering, thresholding are moreover used.
We have furthermore empowered the thoracic and
stomach signals using MATLAB programming. It
uses only two signs recorded at the thoracic and
stomach regions of mother's skin. Similarly, our
test shows that the proposed count can expel FECG
banner immediately in pregnancy period and that is
one of the basic focal points of figuring.
Implementation of an Effective Biogeography Based Algorithm
(EBBO) for Economic Load Dispatch (ELD) problems in power system in order to obtain optimal
economic dispatch with minimum generation cost. Approach: A viable methodology has been
implemented for a 20 unit generator system to minimize the fuel cost function considering the
transmission loss and system operating limit constraints and is compared with other approaches such as
BBO, Lambda Iteration and Hopfield Model. Results: Proposed algorithm has been applied to ELD
problems for verifying its feasibility and the comparison of results are tabulated and pictorial
visualization for convergence of EBBO is represented. Conclusion: Comparing with the other existing
techniques, the EBBO gives better result by considering the quality of the solution obtained. This
method could be an alternative approach for solving the ELD problems in practical power system.
TUNING OF GAINS IN BASIS WEIGHT USING FUZZY CONTROLLERSpaperpublications3
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Abstract: new generation fuzzy logic controllers are based on the integration of conventional and fuzzy controllers. India being the fastest growing market for paper globally presents an exciting industry scenario. The main requirement concerns measured and controlled variable for paper including basis weight and moisture. This research is an effort to achieve better performance with nonlinear processes in servo applications. Three different structures on fuzzy-p, fuzzy-pd and fuzzy-pd+i are studied for variable inputusing simulink and fuzzy logic toolbox. Analysis of four scaling gainsge, gce, gie and gu is done to obtain most appropriate output.
Keywords:fuzzy controllers, fuzzy-p, fuzzy-pd, fuzzy-pd+i, basis weight, simulink.
Stability analysis and design of algorithmic tuned pid controller for bio rea...eSAT Journals
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Abstract The paper proposes a detailed analysis of various intelligent algorithms for the application of tuning PID controllers for time delayed model with a specific stability analysis on the unstable phase of a Bio- reactor system. The tuning process is focused mainly to search the optimal controller parameters (Kp, Ki, Kd) by minimizing the performance criterion and with the help of the objective function. Relative studies on various cost functions like integral of squared error (ISE), integral of absolute error (IAE), integrated time squared error (ITSE), integrated time absolute error (ITAE) have also been performed for the tuned system. The stability analysis for the algorithmic tuned PID controller by Nyquist plot is also performed for the various working phases of the considered bio reactor model. The simulation results show that the proposed tuning approach provides superior results such as smooth set point tracking, error minimization and increased stable response. Keywords: stability, PID, PSO, BFO, FF, bio reactor
Design and optimization of pid controller using genetic algorithmeSAT Publishing House
Â
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.
Development of Adaptive Neuro Fuzzy Inference System for Estimation of Evapot...ijsrd.com
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The accuracy of an adaptive neurofuzzy computing technique in estimation of reference evapotranspiration (ETo) is investigated in this paper. The model is based on Adaptive Neurofuzzy Inference System (ANFIS) and uses commonly available weather information such as the daily climatic data, Maximum and Minimum Air Temperature, Relative Humidity, Wind Speed and Sunshine hours from station, Karjan (Latitude - 22°03'10.95"N, Longitude - 73°07'24.65"E), in Vadodara (Gujarat), are used as inputs to the neurofuzzy model to estimate ETo obtained using the FAO-56 Penman.Monteith equation. The daily meteorological data of two years from 2009 and 2010 at Karjan Takuka, Vadodara, are used to train the model, and the data in 2011 is used to predict the ETo in that year and to validate the model. The ETo in training period (Train- ETo) and the predicted results (Test-ETo) are compared with the ETo computed by Penman-Monteith method (PM-ETo) using "gDailyET" Software. The results indicate that the PM-ETo values are closely and linearly correlated with Train- ETo and Test- ETo with Root Mean Squared Error (RMSE) and showed the higher significances of the Train- ETo and Test- ETo. The results indict the feasibility of using the convenient model to resolve the problems of agriculture irrigation with intelligent algorithm, and more accurate weather forecast, appropriate membership function and suitable fuzzy rules.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Multi-Robot Formation Control Based on Leader-Follower Optimized by the IGAIOSRjournaljce
Â
To improve the efficiency of multi-robot formation control, a new formation control algorithm based on leader-follower optimized by the immune genetic algorithm (IGA) is put forward in this paper. Firstly, the formation control is realized by leader-follower algorithm. Then, the proportion coefficients k1, k2 in leaderfollower is optimized by the immune genetic algorithm. Finally, the optimized proportion coefficients k1 and k2 is used in the leader-follower algorithm to finish the multi-robot formation control. Compared with other three formation control algorithms (i.e. GA, simple leader-follower algorithm, behavior algorithm), the experimental results of multi-robot formation control in two environments show that the formation control performance at time and step finishing formation of the proposed formation control algorithm is obviously improved, which verifies the validity of this algorithm.
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.
Decline of Real Power Loss and Preservation of Voltage Stability by using Chi...ijeei-iaes
Â
In this paper, a new Chirping Algorithm (CA) is developed based on the chirping behaviour of crickets for solving the reactive power optimization problem. It is based on the chirping behaviour of crickets (insect) found almost everywhere around the world. The proposed Chirping Algorithm (CA) has been tested in standard IEEE 30, 57,118 bus test systems and simulation results show clearly the better performance of the proposed algorithm in reducing the real power loss and control variables are well within the limits.
In this paper, a modified invasive weed optimization (IWO) algorithm is presented for
optimization of multiobjective flexible job shop scheduling problems (FJSSPs) with the criteria
to minimize the maximum completion time (makespan), the total workload of machines and the
workload of the critical machine. IWO is a bio-inspired metaheuristic that mimics the
ecological behaviour of weeds in colonizing and finding suitable place for growth and
reproduction. IWO is developed to solve continuous optimization problems thatâs why the
heuristic rule the Smallest Position Value (SPV) is used to convert the continuous position
values to the discrete job sequences. The computational experiments show that the proposed
algorithm is highly competitive to the state-of-the-art methods in the literature since it is able to
find the optimal and best-known solutions on the instances studied.
A new design of fuzzy logic controller optimized by PSO-SCSO applied to SFO-D...IJECEIAES
Â
In this article, a new strategy for the design of fuzzy logic controllers (FLC) is proposed. This strategy is based on the optimization of the FLC, by the hybridization between the particle swarm optimization algorithm (PSO) and the sine-cosine swarm optimization algorithm (SCSO), This new strategy is called FLC-PSCSO. The input-output gains and the geometric shapes of the triangular membership functions of the FLC are the objective functions to be optimized. The optimization of the latter is obtained by minimizing a cost function based on the combination of two criteria, the integral time absolute error (ITAE) and the integral absolute error (IAE). A comparison between the conventional FLC and the proposed FLC-PSCSO is made. The FLC optimized by PSCSO shows a remarkable improvement in the performance of the controlled induction motor.
Implementation of an Effective Biogeography Based Algorithm
(EBBO) for Economic Load Dispatch (ELD) problems in power system in order to obtain optimal
economic dispatch with minimum generation cost. Approach: A viable methodology has been
implemented for a 20 unit generator system to minimize the fuel cost function considering the
transmission loss and system operating limit constraints and is compared with other approaches such as
BBO, Lambda Iteration and Hopfield Model. Results: Proposed algorithm has been applied to ELD
problems for verifying its feasibility and the comparison of results are tabulated and pictorial
visualization for convergence of EBBO is represented. Conclusion: Comparing with the other existing
techniques, the EBBO gives better result by considering the quality of the solution obtained. This
method could be an alternative approach for solving the ELD problems in practical power system.
TUNING OF GAINS IN BASIS WEIGHT USING FUZZY CONTROLLERSpaperpublications3
Â
Abstract: new generation fuzzy logic controllers are based on the integration of conventional and fuzzy controllers. India being the fastest growing market for paper globally presents an exciting industry scenario. The main requirement concerns measured and controlled variable for paper including basis weight and moisture. This research is an effort to achieve better performance with nonlinear processes in servo applications. Three different structures on fuzzy-p, fuzzy-pd and fuzzy-pd+i are studied for variable inputusing simulink and fuzzy logic toolbox. Analysis of four scaling gainsge, gce, gie and gu is done to obtain most appropriate output.
Keywords:fuzzy controllers, fuzzy-p, fuzzy-pd, fuzzy-pd+i, basis weight, simulink.
Stability analysis and design of algorithmic tuned pid controller for bio rea...eSAT Journals
Â
Abstract The paper proposes a detailed analysis of various intelligent algorithms for the application of tuning PID controllers for time delayed model with a specific stability analysis on the unstable phase of a Bio- reactor system. The tuning process is focused mainly to search the optimal controller parameters (Kp, Ki, Kd) by minimizing the performance criterion and with the help of the objective function. Relative studies on various cost functions like integral of squared error (ISE), integral of absolute error (IAE), integrated time squared error (ITSE), integrated time absolute error (ITAE) have also been performed for the tuned system. The stability analysis for the algorithmic tuned PID controller by Nyquist plot is also performed for the various working phases of the considered bio reactor model. The simulation results show that the proposed tuning approach provides superior results such as smooth set point tracking, error minimization and increased stable response. Keywords: stability, PID, PSO, BFO, FF, bio reactor
Design and optimization of pid controller using genetic algorithmeSAT Publishing House
Â
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.
Development of Adaptive Neuro Fuzzy Inference System for Estimation of Evapot...ijsrd.com
Â
The accuracy of an adaptive neurofuzzy computing technique in estimation of reference evapotranspiration (ETo) is investigated in this paper. The model is based on Adaptive Neurofuzzy Inference System (ANFIS) and uses commonly available weather information such as the daily climatic data, Maximum and Minimum Air Temperature, Relative Humidity, Wind Speed and Sunshine hours from station, Karjan (Latitude - 22°03'10.95"N, Longitude - 73°07'24.65"E), in Vadodara (Gujarat), are used as inputs to the neurofuzzy model to estimate ETo obtained using the FAO-56 Penman.Monteith equation. The daily meteorological data of two years from 2009 and 2010 at Karjan Takuka, Vadodara, are used to train the model, and the data in 2011 is used to predict the ETo in that year and to validate the model. The ETo in training period (Train- ETo) and the predicted results (Test-ETo) are compared with the ETo computed by Penman-Monteith method (PM-ETo) using "gDailyET" Software. The results indicate that the PM-ETo values are closely and linearly correlated with Train- ETo and Test- ETo with Root Mean Squared Error (RMSE) and showed the higher significances of the Train- ETo and Test- ETo. The results indict the feasibility of using the convenient model to resolve the problems of agriculture irrigation with intelligent algorithm, and more accurate weather forecast, appropriate membership function and suitable fuzzy rules.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Multi-Robot Formation Control Based on Leader-Follower Optimized by the IGAIOSRjournaljce
Â
To improve the efficiency of multi-robot formation control, a new formation control algorithm based on leader-follower optimized by the immune genetic algorithm (IGA) is put forward in this paper. Firstly, the formation control is realized by leader-follower algorithm. Then, the proportion coefficients k1, k2 in leaderfollower is optimized by the immune genetic algorithm. Finally, the optimized proportion coefficients k1 and k2 is used in the leader-follower algorithm to finish the multi-robot formation control. Compared with other three formation control algorithms (i.e. GA, simple leader-follower algorithm, behavior algorithm), the experimental results of multi-robot formation control in two environments show that the formation control performance at time and step finishing formation of the proposed formation control algorithm is obviously improved, which verifies the validity of this algorithm.
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.
Decline of Real Power Loss and Preservation of Voltage Stability by using Chi...ijeei-iaes
Â
In this paper, a new Chirping Algorithm (CA) is developed based on the chirping behaviour of crickets for solving the reactive power optimization problem. It is based on the chirping behaviour of crickets (insect) found almost everywhere around the world. The proposed Chirping Algorithm (CA) has been tested in standard IEEE 30, 57,118 bus test systems and simulation results show clearly the better performance of the proposed algorithm in reducing the real power loss and control variables are well within the limits.
In this paper, a modified invasive weed optimization (IWO) algorithm is presented for
optimization of multiobjective flexible job shop scheduling problems (FJSSPs) with the criteria
to minimize the maximum completion time (makespan), the total workload of machines and the
workload of the critical machine. IWO is a bio-inspired metaheuristic that mimics the
ecological behaviour of weeds in colonizing and finding suitable place for growth and
reproduction. IWO is developed to solve continuous optimization problems thatâs why the
heuristic rule the Smallest Position Value (SPV) is used to convert the continuous position
values to the discrete job sequences. The computational experiments show that the proposed
algorithm is highly competitive to the state-of-the-art methods in the literature since it is able to
find the optimal and best-known solutions on the instances studied.
A new design of fuzzy logic controller optimized by PSO-SCSO applied to SFO-D...IJECEIAES
Â
In this article, a new strategy for the design of fuzzy logic controllers (FLC) is proposed. This strategy is based on the optimization of the FLC, by the hybridization between the particle swarm optimization algorithm (PSO) and the sine-cosine swarm optimization algorithm (SCSO), This new strategy is called FLC-PSCSO. The input-output gains and the geometric shapes of the triangular membership functions of the FLC are the objective functions to be optimized. The optimization of the latter is obtained by minimizing a cost function based on the combination of two criteria, the integral time absolute error (ITAE) and the integral absolute error (IAE). A comparison between the conventional FLC and the proposed FLC-PSCSO is made. The FLC optimized by PSCSO shows a remarkable improvement in the performance of the controlled induction motor.
A Unified PID Control Methodology to Meet Plant ObjectivesJim Cahill
Â
Presented at the AIChE 2013 Spring Meeting and 9th Global Congress on Process Safety meeting by Greg McMillan, CDI Process & Industrial and Hector Torres, Eastman Chemical
Embellished Particle Swarm Optimization Algorithm for Solving Reactive Power ...ijeei-iaes
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This paper proposes Embellished Particle Swarm Optimization (EPSO) algorithm for solving reactive power problem .The main concept of Embellished Particle Swarm Optimization is to extend the single population PSO to the interacting multi-swarm model. Through this multi-swarm cooperative approach, diversity in the whole swarm community can be upheld. Concurrently, the swarm-to-swarm mechanism drastically speeds up the swarm community to converge to the global near optimum. In order to evaluate the performance of the proposed algorithm, it has been tested in standard IEEE 57,118 bus systems and results show that Embellished Particle Swarm Optimization (EPSO) is more efficient in reducing the Real power losses when compared to other standard reported algorithms.
A hybrid bacterial foraging and modified particle swarm optimization for mode...IJECEIAES
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This paper study the model reduction procedures used for the reduction of large-scale dynamic models into a smaller one through some sort of differential and algebraic equations. A confirmed relevance between these two models exists, and it shows same characteristics under study. These reduction procedures are generally utilized for mitigating computational complexity, facilitating system analysis, and thence reducing time and costs. This paper comes out with a study showing the impact of the consolidation between the Bacterial-Foraging (BF) and Modified particle swarm optimization (MPSO) for the reduced order model (ROM). The proposed hybrid algorithm (BF-MPSO) is comprehensively compared with the BF and MPSO algorithms; a comparison is also made with selected existing techniques.
DriP PSO- A fast and inexpensive PSO for drifting problem spacesZubin Bhuyan
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Particle Swarm Optimization is a class of stochastic, population based optimization techniques which are mostly suitable for static problems. However, real world optimization problems are time variant, i.e., the problem space changes over time. Several researches have been done to address this dynamic optimization problem using Particle Swarms. In this paper we probe the issues of tracking and optimizing Particle Swarms in a dynamic system where the problem-space drifts in a particular direction. Our assumption is that the approximate amount of drift is known, but the direction of the drift is unknown. We propose a Drift Predictive PSO (DriP-PSO) model which does not incur high computation cost, and is very fast and accurate. The main idea behind this technique is to use a few stagnant particles to determine the approximate direction in which the problem-space is drifting so that the particle velocities may be adjusted accordingly in the subsequent iteration of the algorithm.
This paper analyses the optimal power system planning with DGs used as real and reactive power compensator. Recently planning of DG placement reactive power compensation are the major problems in distribution system. As the requirement in the power is more the DG placement becomes important. When planned to make the DG placement, cost analysis becomes as a major concern. And if the DGs operate as reactive power compensator it is most helpful in power quality maintenance. So, this paper deals with the optimal power system planning with renewable DGs which can be used as a reactive power compensators. The problem is formulated and solved using popular meta-heuristic techniques called cuckoo search algorithm (CSA) and particle swarm optimization (PSO). the comparative results are presented.
PSO APPLIED TO DESIGN OPTIMAL PD CONTROL FOR A UNICYCLE MOBILE ROBOTJaresJournal
Â
In this work, we propose a Particle Swarm Optimization (PSO) to design Proportional Derivative
controllers (PD) for the control of Unicycle Mobile Robot. To stabilize and drive the robot precisely with
the predefined trajectory, a decentralized control structure is adopted where four PD controllers are used.
Their parameters are given simultaneously by the proposed algorithm (PSO). The performance of the
system from its desired behavior is quantified by an objective function (SE). Simulation results are
presented to show the efficiency of the method. ).
The results are very conclusive and satisfactory in terms of stability and trajectory tracking of unicycle
mobile robot
Using particle swarm optimization to solve test functions problemsriyaniaes
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In this paper the benchmarking functions are used to evaluate and check the particle swarm optimization (PSO) algorithm. However, the functions utilized have two dimension but they selected with different difficulty and with different models. In order to prove capability of PSO, it is compared with genetic algorithm (GA). Hence, the two algorithms are compared in terms of objective functions and the standard deviation. Different runs have been taken to get convincing results and the parameters are chosen properly where the Matlab software is used. Where the suggested algorithm can solve different engineering problems with different dimension and outperform the others in term of accuracy and speed of convergence.
Dynamic Economic Dispatch Assessment Using Particle Swarm Optimization TechniquejournalBEEI
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This paper presents the application of particle swarm optimization (PSO) technique for solving the dynamic economic dispatch (DED) problem. The DED is one of the main functions in power system planning in order to obtain optimum power system operation and control. It determines the optimal operation of generating units at every predicted load demands over a certain period of time. The optimum operation of generating units is obtained by referring to the minimum total generation cost while the system is operating within its limits. The DED based PSO technique is tested on a 9-bus system containing of three generator bus, six load bus and twelve transmission lines.
COMPARING THE CUCKOO ALGORITHM WITH OTHER ALGORITHMS FOR ESTIMATING TWO GLSD ...csandit
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This study introduces and compares different methods for estimating the two parameters of
generalized logarithmic series distribution. These methods are the cuckoo search optimization,
maximum likelihood estimation, and method of moments algorithms. All the required
derivations and basic steps of each algorithm are explained. The applications for these
algorithms are implemented through simulations using different sample sizes (n = 15, 25, 50,
100). Results are compared using the statistical measure mean square error.
Optimal PID controller design using artificial bee colony algorithm for robot...nooriasukmaningtyas
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Proportional integral derivation (PID) controller is used in this paper for optimal design, and tuning by zeigler and nichol (ZN) with artificial bee colony algorithm. The best parameter were found using these algorithms for best performance of a robot arm. The advantage of using ABC were highlighted. The controller using the new algorithm was tested for valid control process. Different colony size has been performed for tuning process, settling time, from time domain performance, rise time, overshot, and steady state error with ABC tuning give better dynamic performance than controller using the (ZN).
Optimal control of load frequency control power system based on particle swar...theijes
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In this work, PSO is proposed to set the gains of PID controller for LFC in single power systems area. This work has very significant issue because of persistent and random change load through working of power system. The proposed algorithm offer fluent performance, stable, and fast convergence to target value. Simulation results using MATLAB R2015a demonstrate that the proposed controller has more efficient of dynamic performance, better convergence, fast response from the other methods depend on rise and settling time of frequency deviation.
This study introduces and compares different methods for estimating the two parameters of generalized logarithmic series distribution. These methods are the cuckoo search optimization, maximum likelihood estimation, and method of moments algorithms. All the required derivations and basic steps of each algorithm are explained. The applications for these algorithms are implemented through simulations using different sample sizes (n = 15, 25, 50, 100). Results are compared using the statistical measure mean square error.
Electrically small antennas: The art of miniaturizationEditor IJARCET
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We are living in the technological era, were we preferred to have the portable devices rather than unmovable devices. We are isolating our self rom the wires and we are becoming the habitual of wireless world what makes the device portable? I guess physical dimensions (mechanical) of that particular device, but along with this the electrical dimension is of the device is also of great importance. Reducing the physical dimension of the antenna would result in the small antenna but not electrically small antenna. We have different definition for the electrically small antenna but the one which is most appropriate is, where k is the wave number and is equal to and a is the radius of the imaginary sphere circumscribing the maximum dimension of the antenna. As the present day electronic devices progress to diminish in size, technocrats have become increasingly concentrated on electrically small antenna (ESA) designs to reduce the size of the antenna in the overall electronics system. Researchers in many fields, including RF and Microwave, biomedical technology and national intelligence, can benefit from electrically small antennas as long as the performance of the designed ESA meets the system requirement.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
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Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
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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
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Dev Dives: Train smarter, not harder â active learning and UiPath LLMs for do...UiPathCommunity
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đĽ Speed, accuracy, and scaling â discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Miningâ˘:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing â with little to no training required
Get an exclusive demo of the new family of UiPath LLMs â GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
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JMeter webinar - integration with InfluxDB and GrafanaRTTS
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Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overviewâ
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
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Are you looking to streamline your workflows and boost your projectsâ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, youâre in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part âEssentials of Automationâ series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Hereâs what youâll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
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Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
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Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
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Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
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Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
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- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
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DevOps and Testing slides at DASA ConnectKari Kakkonen
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My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
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1. ISSN: 2278 â 1323
International Journal of Advanced Research in Computer Engineering & Technology (IJARCET)
Volume 2, Issue 7, July 2013
2333
www.ijarcet.org
ď
Abstractâ A PID controller is designed using ISE, IAE, ITAE and
MSE error criteria for stable linear time invariant continuous
system. A BF-PSO PID controller is designed for the plant to meet
the desired performance specifications by using BF-PSO
optimization algorithm. PID controller gain parameters Kp,Ki,Kd
are designed and applied to the PID controller system .The PID
controller closed loop response is observed for ISE, IAE, IATE
and MSE error criteria. A comparison of system performance
observed for all four criteria.
KeywordsâPID Controller Tuning, BF-PSO, Optimization
technique, Close loop feedback.
1. PID controller system
PID controller consists of Proportional, Integral and
Derivative gains. The PID feedback control system is
illustrated in Fig. 1 where r, e, y are respectively the
reference, error and controlled variables. Where Kp is
proportional gain ,Ki is integral gain and Kd is derivative
gain.
Fig1.1: A common feedback PID control system
In the diagram of Fig.1, G(s) is the plant transfer function
and C(s) is the PID controller transfer function that is given
as:
Manuscript received July, 2013.
Yogendra Kumar Soni is PG (M.TECH) Student of UCE, Rajasthan
Technical University KOTA (Mob.No.9784398967)
Rajesh Bhatt is Assit. Prof. of UCE, Rajasthan Technical University KOTA
(Mob.No.9413222142;)
C(s) = Kp+ + Kd [1]
Where Kp ,Ki, Kd parameters of the PID controllers that are
going to be tuned using BF-PSO.
2 Performance evaluation criteria
Quantification of system performance is achieved through a
performance index. The performance selected depends on the
process under consideration and is chosen such that
emphasis is placed on specific aspects of system
performance. Furthermore, performance index is defined as a
quantitative measure to depict the system performance of the
designed PID controller. Using this technique an âoptimum
systemâ can often be designed and a set of PID parameters in
the system can be adjusted to meet the required specification.
For a PID- controlled system, there are often four indices to
depict the system performance ISE, IAE, IATE and MSE.
They are defined as follows:
ISE Index:
ISE = [2]
IAE Index:
IAE = [3]
IATE Index:
IATE = [4]
MSE Index:
MSE =1/t [5]
The following performance indexes are used to minimize the
overshoot, settling time, steady state error and reference
tracking error for PSO-PID controller system. Therefore, for
the SA-based PID tuning, these performance indexes are
used as the objective function. In other word, the objective in
the SA-based optimization is to seek a set of PID parameters
such that the feedback control system has minimum
performance index.
3. Overview of PSO Algorithm
PSO is optimization algorithm based on evolutionary
computation technique. The basic PSO is developed from
research on swarm such as fish schooling and bird flocking.
After it was firstly introduced in 1995, a modified PSO was
then introduced in 1998 to improve the performance of the
original PSO. A new parameter called inertia weight is
added. This is a commonly used PSO where inertia weight is
linearly decreasing during iteration in addition to another
common type of PSO which is reported by Clerc. The later is
BF-PSO optimized PID Controller design
using ISE, IAE, IATE and MSE error criteria
Yogendra Kumar Soni1
and Rajesh Bhatt(Assit. Prof.)2
2. ISSN: 2278 â 1323
International Journal of Advanced Research in Computer Engineering & Technology (IJARCET)
Volume 2, Issue 7, July 2013
www.ijarcet.org
2334
the one used in this paper. In PSO, instead of using genetic
operators, individuals called as particles are âevolvedâ by
cooperation and competition among themselves through
generations. A particle represents a potential solution to a
problem. Each particle adjusts its flying according to its own
flying experience and its companion flying experience. Each
particle is treated as a point in a D-dimensional space. The
ith particle is represented as XI=(xi1,xi2,âŚ,xiD). The best
previous position (giving the minimum fitness value) of any
particle is recorded and represented as PI=(pi1,pi2,âŚ,piD),
this is called pbest. The index of the best particle among all
particles in the population is represented by the symbol g,
called as gbest. The velocity for the particle i is represented
as VI= (vi1,vi2,âŚ,viD). The particles are updated according
to the following equations:
=W. + *rand()*( )
+ *rand()*( ) [6]
= + [7]
where c1 and c2 are two positive constant. As recommended
in Clercâs PSO, the constants are c1=1.2,c2=0.5. While
rand() is random function between 0 and 1, and m represents
iteration. Eq.7 is used to calculate particleâs new velocity
according to its previous velocity and the distances of its
current position from its own best experience (position) and
the groupâs best experience. Then the particle flies toward a
new position according to Eq.8. The performance of each
particle is measured according to a pre-defined fitness
function (performance index), which is related to the
problem to be solved. Inertia weight, w is brought into the
equation to balance between the global search and local
search capability. It can be a positive constant or even
positive linear or nonlinear function of time. A guaranteed
convergence of PSO proposed by Clerc set w=0.9. It has been
also shown that PSO with different number of particles
(swarm size) has reasonably similar performance. Swarm
size of 10-50 is usually selected. Here, we set 40.
4. BACTERIAL FORAGING OPTIMIZATION
Introduction Based on the research of foraging behaviour of
E.colli bacteria Kevin M.Passino and Liu exploited a variety
of bacterial foraging and swarming behaviour, discussing
how to connect social foraging process with distributed
non-gradient optimization.In the bacterial foraging
optimization process four motile behaviours are mimicked:-
1)CHEMOTAXIS:
A chemotactic step can be defined as a tumble followed by a
tumble or a tumble followed by a run lifetime.To represent a
tumble a unit length random direction, (j), is generated ; this
will be used to define the direction of movement after a
tumble. In particular
i(j+1,k,l) = i(j,k,l) + C(i)* (j) [8]
Where i(j,k,l) represents the ith bacterium at jth chemotactic,
kth reproductive and lth elimination and dispersal step.C(i)
is the size of the step taken in the random direction specified
by a tumble(run length unit).
2)SWARMING:
E.Colli cellscan cooperatively self organize into highly
structured colonies with elevated environmental adaptability
using an intricate communication mechanism.Overall, cells
provide an attraction signal to each other so they swarm
together.The mathematical representation for swarming can
be represented by
Jcc(θ,P(j,k,l)) =Jicc(θ,θi(j,k,l)) =Σ[ Dattract * exp(-Wattract
*ÎŁ( θm âθi m)2)] +ÎŁ[Hrepellant * exp(-Wrepellant *ÎŁ (θm
âθi m)2)] [9]
Where Jcc(θ,P(j,k,l)) is the cost function value to be added to
the actual cost function to be minimized to present a time
varying cost function,S is the total number of bacteria ,P is
the number of parameters to be optimized which are present
in each bacterium and Dattract ,Wattract ,hrepellant
,Wrepellant are different coefficients that should be properly
choosen.
3)REPRODUCTION:
The least healthier bacteria die and the other each healthier
bacteria split into two new bacteria each placed in the same
location.
4) ELIMINATION AND DISPERSAL:
It is possible that in the local environment, the lives of a
population of bacteria changes either gradually (eg, via
consumption of nutrients) or suddenly due to some other
influence. Events can occur that all the bacteria in a region
are killed or a group is dispersed into a new part of the
environment. They have the effect of possibly destroying the
chemotactic progress, but they have also the effect of
assisting the chemotactic process, since dispersal may place
bacteria near good food sources. From a board perspective,
elimination and disposal are parts of the population level
long distance motile behavior.
5.COMBINED PARTICLE SWARM OPTIMIZATION
(BF-PSO)
BF-PSO algorithm combines both BFO and PSO. The aim is
to make PSO ability to exchange social information and BF
abilityin finding new solution byelimination and dispersal, a
unit length direction of tumble behavior is randomly
generated. Random direction may lead to delay in reaching
the global solution. In "BF-PSO" algorithm the unit length
random direction of tumble behavior can be decided by the
global best position and the best position of each bacterium.
During the chemotaxis loop tumble direction is updated by:
ɸ(j+1) = Ď*ɸ(j) +c1*rand*(pbest â pcurrent) + c2 * rand*
(gbest - pcurrent) [10]
Where pbest is the best position of each bacterium and gbest
is the global best bacterium. The brief pseudo-code of
BF-PSO has been provided below. Algorithm to find optimal
parameters using BFO for the objective function ITAE is
described below
FLOWCHART OF THE BF-PSO algorithm
3. ISSN: 2278 â 1323
International Journal of Advanced Research in Computer Engineering & Technology (IJARCET)
Volume 2, Issue 7, July 2013
2335
www.ijarcet.org
Fig5.1 Flow chart of BF-PSO algorithm.
6. SIMULATION AND RESULTS
The transfer function (G) for the process is.
2.295 s^3 - 0.01173s^2 - 1.547e-005s + 6.115e-007
---------------------------------------------------------------------
s^4 - 0.01114 s^3 + 5.288e-005 s^2 + 9.232e-010 s +
2.06e-014 [11]
Fig 6.1 Step response of open loop plant
PID Controller tuning using BF-PSO algorithms [1]
Now we tune PID parameters Kp, Ki, Kd for above reduced
order system by using hybrid BF-PSO algorithm.. Fitness
Functions are used for designing PID controller ISE, IAE,
ITAE and ITSE .We set the following parameters
dimension of search space =3;
The number of bacteria =10;
Number of chemotactic steps =10;
Limits the length of a swim =4;
The number of reproduction steps =4;
The number of elimination-dispersal events =2;
The number of bacteria reproductions (splits) per generation
=s/2;
The probability that each bacteria will be
eliminated/dispersed =0.25;
c(:,1)=0.5*ones(s,1); the run length.
We use the following PSO parameters
C1=1.2;
C2= 0.5;
W=0.9;
5.1 BF-PSO-PID Controller tuning using ISE
We apply the BF-PSO algorithm to tune the PID parameters
Kp,Ki and Kd to meet the system performance criteria. We
use the Integral of Squired Error. We obtain the
kp = 362.0052,ki = -18.5505 and kd = 24.7977
Fig. 6.2 The step response of the BF-PSO-PID controller
system for ISE.
The step response of the BF-PSO-PID controller system is
shown in figure 6.2
5.2 BF-PSO-PID Controller tuning using IATE
We apply the BF-PSO to tune the PID parameters Kp,Ki and
Kd to meet the system performance criteria. We use the
Integral of absolute time error .We obtain the kp =
4.6879e+005,ki = 385.5821 and kd = 848.8285
The step response of the BF-PSO PID controller system is
shown in figure 6.3
Fig. 6.3 The step response of the BF-PSO PID controller
system for IATE.
4. ISSN: 2278 â 1323
International Journal of Advanced Research in Computer Engineering & Technology (IJARCET)
Volume 2, Issue 7, July 2013
www.ijarcet.org
2336
5.3 BF-PSO -PID Controller tuning using IAE
We apply the BFO-PSO to tune the PID parameters Kp,Ki
and Kd to meet the system performance criteria. We use the
Integral of absolute error. We obtain the
kp = 12.3353,ki = 0.3128 and kd = 2.3478e+005;
. The step response of the BF-PSO PID controller system
is shown in figure 6.7.
Fig. 6.7 The step response of the BF-PSO PID controller
system for IAE.
5.4 BF-PSO -PID Controller tuning using MSE
We apply the BF-PSO to tune the PID parameters Kp,Ki and
Kd to meet the system performance criteria. We use the Mean
Squire Error. We obtain the kp = -0.0938,ki = 1.5905 and kd
= 1.2114e+004
The step response of the BF-PSO PID controller system is
shown in figure 6.8.
Fig. 6.8 The step response of the BF-PSO PID controller
system for MSE.
Table1.1 Comparision of BF-PSO PID Controller system
for Different Error Critria
Error criteria
Parameters
ISE IATE IAE MSE
Kp 362.0052 4.6879e
+005
12.3353; -0.093
8
Ki -18.5505 385.582
1;
0.3128 1.5905
Kd 24.7977 848.828
5
2.3478e+
005
1.2114
e+004
RiseTime 0.0829 7.2113e
-004
4.0551e-
006
0.4605
SettlingTime 0.2081
0.0051 7.2172e-
006
0.5641
SettlingMin 1.0000
0.9487 0.9028 1.0000
SettlingMax 1.0876
1.1464 1.0000 1.0000
Overshoot 8.7613
14.6420
0
1.5343
e-005
Undershoot 0 0 0 0
Peak
1.0876 1.1464 1.0000 1.0000
PeakTime
0.1127 0.0019 1.9352e-
005
33.963
1
7.Conclusion
We can see from the results that open loop plant is highly
unstable system.Then we design the PID controller system
using BF-PSO algorithm.From the Table 1.1 we can see that
overshoot , rise time and settling time is minimun for IAE
criteria.For the above BF-PSO PID controller system with
IAE criteria undershoot 0 % ,overshoot 0 % and settling time
7.2172e-006 seconds and rise time 4.0551e-006 seconds.
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