This document outlines the key topics of a course on modern control systems. It compares modern and classical control theories, describing modern control theory as applicable to nonlinear and time-varying systems using time-domain and frequency-domain approaches, while classical control theory is only applicable to linear time-invariant single-input single-output systems using frequency-domain approaches. It also describes open-loop and closed-loop control systems, and mathematical modeling of control systems using state-space representations. Examples of modeling mechanical and electrical systems are provided.
Introduction to Hybrid Vehicle System Modeling and Control - 2013 - Liu - App...sravan66
This document discusses mathematical modeling of hybrid vehicle systems. It begins by explaining the two main approaches to building mathematical models: using physical principles or observed system behaviors. Dynamic systems can be modeled using differential equations, state space equations, or transfer functions. Models are classified as static/dynamic, time-varying/invariant, deterministic/stochastic, continuous/discrete, linear/nonlinear, and lumped-parameter/distributed-parameter. State space models represent systems using state vectors, input vectors, output vectors, and coefficient matrices to describe the relationships between these variables.
Stability and stabilization of discrete-time systems with time-delay via Lyap...IJERA Editor
The stability and stabilization problems for discrete systems with time-delay are discussed .The stability and
stabilization criterion are expressed in the form of linear matrix inequalities (LMI). An effective method
allowing us transforming a bilinear matrix Inequality (BMI) to a linear matrix Inequality (LMI) is developed.
Based on these conditions, a state feedback controller with gain is designed. An illustrative numerical example
is provided to show the effectiveness of the proposed method and the reliability of the results.
This document provides an introduction to system dynamics and mathematical modeling of dynamic systems. It defines key concepts such as:
- A system is made up of interacting components that work together to achieve an objective. It has inputs from the environment and outputs responses to those inputs.
- Dynamic systems have outputs that vary over time even if inputs are held constant, due to internal feedback loops within the system.
- Mathematical models of dynamic systems use equations, often differential equations, to describe the system's behavior based on physical laws. The accuracy of a model's predictions depends on how well it approximates the real system.
- Engineering systems like mechanical, electrical, thermal and fluid systems can all be modeled as dynamic systems using appropriate equations
This document discusses control systems and their analysis using state space models. It defines the key components of a control system and explains how state space representation models systems using state variables and matrices. The document also covers analyzing stability, controllability and observability of state space models.
This document provides an overview of a control systems engineering course. It outlines the course syllabus which covers classical and modern control techniques including modeling, analysis in the time and frequency domains, and controller design methods. The general content includes system modeling, analysis of open and closed loop systems, stability analysis, and compensation techniques. Recommended textbooks are provided and prerequisites of differential equations, linear algebra, and basic physics systems are listed. Finally, basic definitions of elements in a control system including controllers, actuators, sensors, and the design process are introduced.
Control system introduction for different applicationAnoopCadlord1
The document provides an overview of control systems design. It begins by describing the general process for designing a control system, which involves modeling interconnected system components to achieve a desired purpose. Examples of early control systems are discussed to illustrate fundamental feedback principles still used today. Modern applications of control engineering are then briefly mentioned. The document notes that a design gap exists between complex physical systems and their models. An iterative design process is used to effectively address this gap while meeting performance and cost objectives.
Comparison of backstepping, sliding mode and PID regulators for a voltage inv...IJECEIAES
In the present paper, an efficient and performant nonlinear regulator is designed for the control of the pulse width modulation (PWM) voltage inverter that can be used in a standalone photovoltaic microgrid. The main objective of our control is to produce a sinusoidal voltage output signal with amplitude and frequency that are fixed by the reference signal for different loads including linear or nonlinear types. A comparative performance study of controllers based on linear and non-linear techniques such as backstepping, sliding mode, and proportional integral derivative (PID) is developed to ensure the best choice among these three types of controllers. The performance of the system is investigated and compared under various operating conditions by simulations in the MATLAB/Simulink environment to demonstrate the effectiveness of the control methods. Our investigation shows that the backstepping controller can give better performance than the sliding mode and PID controllers. The accuracy and efficiency of the proposed backstepping controller are verified experimentally in terms of tracking objectives.
Introduction to Hybrid Vehicle System Modeling and Control - 2013 - Liu - App...sravan66
This document discusses mathematical modeling of hybrid vehicle systems. It begins by explaining the two main approaches to building mathematical models: using physical principles or observed system behaviors. Dynamic systems can be modeled using differential equations, state space equations, or transfer functions. Models are classified as static/dynamic, time-varying/invariant, deterministic/stochastic, continuous/discrete, linear/nonlinear, and lumped-parameter/distributed-parameter. State space models represent systems using state vectors, input vectors, output vectors, and coefficient matrices to describe the relationships between these variables.
Stability and stabilization of discrete-time systems with time-delay via Lyap...IJERA Editor
The stability and stabilization problems for discrete systems with time-delay are discussed .The stability and
stabilization criterion are expressed in the form of linear matrix inequalities (LMI). An effective method
allowing us transforming a bilinear matrix Inequality (BMI) to a linear matrix Inequality (LMI) is developed.
Based on these conditions, a state feedback controller with gain is designed. An illustrative numerical example
is provided to show the effectiveness of the proposed method and the reliability of the results.
This document provides an introduction to system dynamics and mathematical modeling of dynamic systems. It defines key concepts such as:
- A system is made up of interacting components that work together to achieve an objective. It has inputs from the environment and outputs responses to those inputs.
- Dynamic systems have outputs that vary over time even if inputs are held constant, due to internal feedback loops within the system.
- Mathematical models of dynamic systems use equations, often differential equations, to describe the system's behavior based on physical laws. The accuracy of a model's predictions depends on how well it approximates the real system.
- Engineering systems like mechanical, electrical, thermal and fluid systems can all be modeled as dynamic systems using appropriate equations
This document discusses control systems and their analysis using state space models. It defines the key components of a control system and explains how state space representation models systems using state variables and matrices. The document also covers analyzing stability, controllability and observability of state space models.
This document provides an overview of a control systems engineering course. It outlines the course syllabus which covers classical and modern control techniques including modeling, analysis in the time and frequency domains, and controller design methods. The general content includes system modeling, analysis of open and closed loop systems, stability analysis, and compensation techniques. Recommended textbooks are provided and prerequisites of differential equations, linear algebra, and basic physics systems are listed. Finally, basic definitions of elements in a control system including controllers, actuators, sensors, and the design process are introduced.
Control system introduction for different applicationAnoopCadlord1
The document provides an overview of control systems design. It begins by describing the general process for designing a control system, which involves modeling interconnected system components to achieve a desired purpose. Examples of early control systems are discussed to illustrate fundamental feedback principles still used today. Modern applications of control engineering are then briefly mentioned. The document notes that a design gap exists between complex physical systems and their models. An iterative design process is used to effectively address this gap while meeting performance and cost objectives.
Comparison of backstepping, sliding mode and PID regulators for a voltage inv...IJECEIAES
In the present paper, an efficient and performant nonlinear regulator is designed for the control of the pulse width modulation (PWM) voltage inverter that can be used in a standalone photovoltaic microgrid. The main objective of our control is to produce a sinusoidal voltage output signal with amplitude and frequency that are fixed by the reference signal for different loads including linear or nonlinear types. A comparative performance study of controllers based on linear and non-linear techniques such as backstepping, sliding mode, and proportional integral derivative (PID) is developed to ensure the best choice among these three types of controllers. The performance of the system is investigated and compared under various operating conditions by simulations in the MATLAB/Simulink environment to demonstrate the effectiveness of the control methods. Our investigation shows that the backstepping controller can give better performance than the sliding mode and PID controllers. The accuracy and efficiency of the proposed backstepping controller are verified experimentally in terms of tracking objectives.
In this paper, the tracking control scheme is presented using the framework of finite-time sliding mode control (SMC) law and high-gain observer for disturbed/uncertain multi-motor driving systems under the consideration multi-output systems. The convergence time of sliding mode control is estimated in connection with linear matrix inequalities (LMIs). The input state stability (ISS) of proposed controller was analyzed by Lyapunov stability theory. Finally, the extensive simulation results are given to validate the advantages of proposed control design.
This document discusses state space analysis and related concepts. It defines state as a group of variables that summarize a system's history to predict future outputs. The minimum number of state variables required is equal to the number of storage elements in the system. These state variables form a state vector. The document also covers state space representation, diagonalization, solving state equations, the state transition matrix, and concepts of controllability and observability.
Here are the key points about the mathematical model that will be used:
- The system under study is a closed loop, single-input single-output (SISO), linear, time-invariant (LTI), causal, dynamic control system.
- The mathematical model that typically characterizes this class of system is a linear ordinary differential equation (ODE) with constant coefficients.
- The model will be spatially homogeneous, meaning it does not consider variations in variables with space, only temporal variations. This assumes a lumped parameter approach.
- The model is deterministic and continuous-time, meaning the signal values can be predicted with certainty at any point in time.
- In summary, a spatially homogeneous
2-DOF Block Pole Placement Control Application To: Have-DASH-IIBITT MissileZac Darcy
In a multivariable servomechanism design, it is required that the output vector tracks a certain reference
vector while satisfying some desired transient specifications, for this purpose a 2DOF control law
consisting of state feedback gain and feedforward scaling gain is proposed. The control law is designed
using block pole placement technique by assigning a set of desired Block poles in different canonical forms.
The resulting control is simulated for linearized model of the HAVE DASH II BTT missile; numerical
results are analyzed and compared in terms of transient response, gain magnitude, performance
robustness, stability robustness and tracking. The suitable structure for this case study is then selected.
2-DOF Block Pole Placement Control Application To: Have-DASH-IIBITT MissileZac Darcy
In a multivariable servomechanism design, it is required that the output vector tracks a certain reference
vector while satisfying some desired transient specifications, for this purpose a 2DOF control law
consisting of state feedback gain and feedforward scaling gain is proposed. The control law is designed
using block pole placement technique by assigning a set of desired Block poles in different canonical forms.
The resulting control is simulated for linearized model of the HAVE DASH II BTT missile; numerical
results are analyzed and compared in terms of transient response, gain magnitude, performance
robustness, stability robustness and tracking. The suitable structure for this case study is then selected.
This document contains a lesson plan for Module 1 of a control systems course. The module covers topics such as introduction to control systems, types of control systems, effect of feedback systems, and differential equations of physical, mechanical, and electrical systems. Examples of various control systems like thermostats, traffic signals, and aircraft are provided. Terminology used in control systems like command input, reference input, and disturbance are defined. The types of control systems - open loop and closed loop - are described along with their characteristics. General considerations for designing control systems like stability, accuracy, and speed of response are also outlined.
TEST GENERATION FOR ANALOG AND MIXED-SIGNAL CIRCUITS USING HYBRID SYSTEM MODELSVLSICS Design
In this paper we propose an approach for testing time-domain properties of analog and mixed-signal circuits. The approach is based on an adaptation of a recently developed test generation technique for hybrid systems and a new concept of coverage for such systems. The approach is illustrated by its application to some benchmark circuits.
Test Generation for Analog and Mixed-Signal Circuits Using Hybrid System Mode...VLSICS Design
In this paper we propose an approach for testing time-domain properties of analog and mixed-signal circuits. The approach is based on an adaptation of a recently developed test generation technique for hybrid systems and a new concept of coverage for such systems. The approach is illustrated by its application to some benchmark circuits.
SLIDING MODE CONTROLLER DESIGN FOR GLOBAL CHAOS SYNCHRONIZATION OF COULLET SY...ijistjournal
This paper derives new results for the design of sliding mode controller for the global chaos synchronization of identical Coullet systems (1981). The synchronizer results derived in this paper for the complete chaos synchronization of identical hyperchaotic systems are established using sliding control theory and Lyapunov stability theory. Since the Lyapunov exponents are not required for these calculations, the sliding mode control method is very effective and convenient to achieve global chaos synchronization of the identical Coullet systems. Numerical simulations are shown to illustrate and validate the synchronization schemes derived in this paper for the identical Coullet systems.
SLIDING MODE CONTROLLER DESIGN FOR GLOBAL CHAOS SYNCHRONIZATION OF COULLET SY...ijistjournal
This paper derives new results for the design of sliding mode controller for the global chaos synchronization of identical Coullet systems (1981). The synchronizer results derived in this paper for the complete chaos synchronization of identical hyperchaotic systems are established using sliding control theory and Lyapunov stability theory. Since the Lyapunov exponents are not required for these calculations, the sliding mode control method is very effective and convenient to achieve global chaos synchronization of the identical Coullet systems. Numerical simulations are shown to illustrate and validate the synchronization schemes derived in this paper for the identical Coullet systems.
1) The document analyzes the boundedness and domain of attraction of a fractional-order wireless power transfer (WPT) system.
2) It establishes a fractional-order piecewise affine model of the WPT system and derives sufficient conditions for boundedness using Lyapunov functions and inequality techniques.
3) The results provide a way to estimate the domain of attraction of the fractional-order WPT system and systems with periodically intermittent control.
2-DOF BLOCK POLE PLACEMENT CONTROL APPLICATION TO:HAVE-DASH-IIBTT MISSILEZac Darcy
In a multivariable servomechanism design, it is required that the output vector tracks a certain reference
vector while satisfying some desired transient specifications, for this purpose a 2DOF control law
consisting of state feedback gain and feedforward scaling gain is proposed. The control law is designed
using block pole placement technique by assigning a set of desired Block poles in different canonical forms.
The resulting control is simulated for linearized model of the HAVE DASH II BTT missile; numerical
results are analyzed and compared in terms of transient response, gain magnitude, performance
robustness, stability robustness and tracking. The suitable structure for this case study is then selected.
1) Control theory deals with analyzing and designing closed-loop control systems to achieve desired output behaviors.
2) The document provides examples of modeling control systems using transfer functions and state-space representations. These include modeling an RF control system and passive and active filter circuits.
3) State-space representation involves expressing higher-order differential equations as a set of first-order equations and representing the system using matrix equations that can be analyzed and simulated on computers. This allows visualization and analysis of dynamic systems.
Here are the summaries for the 3 questions:
1. An open-loop control system operates without feedback of the output, while a closed-loop control system uses feedback of the output to the input. An example of open-loop is a basic speed control system, while cruise control in cars is closed-loop as it senses and adjusts speed based on feedback.
2. The main design objectives of any control system are: achieving the desired transient response, minimizing steady-state error, ensuring stability, and making the system robust to parameter variations.
3. The total system response C is found using superposition as C = CR + CU1 + CU2, where CR, CU1, and CU2 are the individual
The document discusses dynamic systems and modeling dynamic systems. It provides examples of dynamic systems from various domains like mechanical, electrical, biological, and economic systems. It defines dynamic systems as systems where the present output depends on both present and past inputs. The document discusses modeling dynamic systems using mathematical models and provides guidelines for formulating control-oriented models, including defining the objective, system boundaries, relevant stocks, conservation laws, and relations between flows and levels. It provides examples of modeling a room's thermal dynamics and a vehicle's motion and energy consumption.
The document discusses control systems and provides examples. It begins by describing the general process for designing a control system and examines examples throughout history. Modern control engineering includes strategies to improve manufacturing, energy efficiency, automobiles, and other applications. The document also discusses the gap between physical systems and their models in control system design and how an iterative process can effectively address this gap.
This document provides an overview of a Wikibook on control systems. It discusses both classical and modern control techniques, as well as analog and digital systems. The book covers topics such as system modeling, transforms, stability analysis, controllers, optimal control, and nonlinear systems. It is intended for undergraduate and graduate engineering students and requires knowledge of calculus, differential equations, and linear algebra. The document provides context on the history and applications of control systems, and how the book is organized.
This document describes the design and implementation of a controller for an inverted pendulum on a cart system. It provides the nonlinear and linearized models of the system and designs a PID controller using root locus analysis. Simulation results show the uncompensated system is unstable but the controlled system with PID controller and pre-compensator meets design specifications with less than 0.2 seconds settling time and 8% overshoot for a unit step input.
Event triggered control design of linear networked systems with quantizationsISA Interchange
This paper is concerned with the control design problem of event-triggered networked systems with both state and control input quantizations. Firstly, an innovative delay system model is proposed that describes the network conditions, state and control input quantizations, and event-triggering mechanism in a unified framework. Secondly, based on this model, the criteria for the asymptotical stability analysis and control synthesis of event-triggered networked control systems are established in terms of linear matrix inequalities (LMIs). Simulation results are given to illustrate the effectiveness of the proposed method.
Computer Applications in Power Systems 2023 SECOND.pdfhussenbelew
The document discusses real-time applications of computers in power systems. It describes how SCADA systems are used for monitoring, control, and management of electric power grids. Key functions of SCADA include data acquisition, remote control, supervision, historical data analysis, and various control applications specific to power generation, transmission, and distribution. Real-time monitoring and control allow for faster response to disturbances, optimized system operation, and more reliable power delivery.
The document discusses various aspects of power system reliability including adequacy, security, and stability. It defines adequacy as relating to having sufficient generation and transmission facilities to meet customer demand. Security pertains to how the system responds to disturbances like loss of generation or transmission. Stability refers to generators staying synchronized during disturbances. The document also discusses reliability assessment techniques like loss of load probability and expectation indices used to evaluate generation adequacy. Distribution reliability is assessed using indices that consider customer interruptions and outage times.
In this paper, the tracking control scheme is presented using the framework of finite-time sliding mode control (SMC) law and high-gain observer for disturbed/uncertain multi-motor driving systems under the consideration multi-output systems. The convergence time of sliding mode control is estimated in connection with linear matrix inequalities (LMIs). The input state stability (ISS) of proposed controller was analyzed by Lyapunov stability theory. Finally, the extensive simulation results are given to validate the advantages of proposed control design.
This document discusses state space analysis and related concepts. It defines state as a group of variables that summarize a system's history to predict future outputs. The minimum number of state variables required is equal to the number of storage elements in the system. These state variables form a state vector. The document also covers state space representation, diagonalization, solving state equations, the state transition matrix, and concepts of controllability and observability.
Here are the key points about the mathematical model that will be used:
- The system under study is a closed loop, single-input single-output (SISO), linear, time-invariant (LTI), causal, dynamic control system.
- The mathematical model that typically characterizes this class of system is a linear ordinary differential equation (ODE) with constant coefficients.
- The model will be spatially homogeneous, meaning it does not consider variations in variables with space, only temporal variations. This assumes a lumped parameter approach.
- The model is deterministic and continuous-time, meaning the signal values can be predicted with certainty at any point in time.
- In summary, a spatially homogeneous
2-DOF Block Pole Placement Control Application To: Have-DASH-IIBITT MissileZac Darcy
In a multivariable servomechanism design, it is required that the output vector tracks a certain reference
vector while satisfying some desired transient specifications, for this purpose a 2DOF control law
consisting of state feedback gain and feedforward scaling gain is proposed. The control law is designed
using block pole placement technique by assigning a set of desired Block poles in different canonical forms.
The resulting control is simulated for linearized model of the HAVE DASH II BTT missile; numerical
results are analyzed and compared in terms of transient response, gain magnitude, performance
robustness, stability robustness and tracking. The suitable structure for this case study is then selected.
2-DOF Block Pole Placement Control Application To: Have-DASH-IIBITT MissileZac Darcy
In a multivariable servomechanism design, it is required that the output vector tracks a certain reference
vector while satisfying some desired transient specifications, for this purpose a 2DOF control law
consisting of state feedback gain and feedforward scaling gain is proposed. The control law is designed
using block pole placement technique by assigning a set of desired Block poles in different canonical forms.
The resulting control is simulated for linearized model of the HAVE DASH II BTT missile; numerical
results are analyzed and compared in terms of transient response, gain magnitude, performance
robustness, stability robustness and tracking. The suitable structure for this case study is then selected.
This document contains a lesson plan for Module 1 of a control systems course. The module covers topics such as introduction to control systems, types of control systems, effect of feedback systems, and differential equations of physical, mechanical, and electrical systems. Examples of various control systems like thermostats, traffic signals, and aircraft are provided. Terminology used in control systems like command input, reference input, and disturbance are defined. The types of control systems - open loop and closed loop - are described along with their characteristics. General considerations for designing control systems like stability, accuracy, and speed of response are also outlined.
TEST GENERATION FOR ANALOG AND MIXED-SIGNAL CIRCUITS USING HYBRID SYSTEM MODELSVLSICS Design
In this paper we propose an approach for testing time-domain properties of analog and mixed-signal circuits. The approach is based on an adaptation of a recently developed test generation technique for hybrid systems and a new concept of coverage for such systems. The approach is illustrated by its application to some benchmark circuits.
Test Generation for Analog and Mixed-Signal Circuits Using Hybrid System Mode...VLSICS Design
In this paper we propose an approach for testing time-domain properties of analog and mixed-signal circuits. The approach is based on an adaptation of a recently developed test generation technique for hybrid systems and a new concept of coverage for such systems. The approach is illustrated by its application to some benchmark circuits.
SLIDING MODE CONTROLLER DESIGN FOR GLOBAL CHAOS SYNCHRONIZATION OF COULLET SY...ijistjournal
This paper derives new results for the design of sliding mode controller for the global chaos synchronization of identical Coullet systems (1981). The synchronizer results derived in this paper for the complete chaos synchronization of identical hyperchaotic systems are established using sliding control theory and Lyapunov stability theory. Since the Lyapunov exponents are not required for these calculations, the sliding mode control method is very effective and convenient to achieve global chaos synchronization of the identical Coullet systems. Numerical simulations are shown to illustrate and validate the synchronization schemes derived in this paper for the identical Coullet systems.
SLIDING MODE CONTROLLER DESIGN FOR GLOBAL CHAOS SYNCHRONIZATION OF COULLET SY...ijistjournal
This paper derives new results for the design of sliding mode controller for the global chaos synchronization of identical Coullet systems (1981). The synchronizer results derived in this paper for the complete chaos synchronization of identical hyperchaotic systems are established using sliding control theory and Lyapunov stability theory. Since the Lyapunov exponents are not required for these calculations, the sliding mode control method is very effective and convenient to achieve global chaos synchronization of the identical Coullet systems. Numerical simulations are shown to illustrate and validate the synchronization schemes derived in this paper for the identical Coullet systems.
1) The document analyzes the boundedness and domain of attraction of a fractional-order wireless power transfer (WPT) system.
2) It establishes a fractional-order piecewise affine model of the WPT system and derives sufficient conditions for boundedness using Lyapunov functions and inequality techniques.
3) The results provide a way to estimate the domain of attraction of the fractional-order WPT system and systems with periodically intermittent control.
2-DOF BLOCK POLE PLACEMENT CONTROL APPLICATION TO:HAVE-DASH-IIBTT MISSILEZac Darcy
In a multivariable servomechanism design, it is required that the output vector tracks a certain reference
vector while satisfying some desired transient specifications, for this purpose a 2DOF control law
consisting of state feedback gain and feedforward scaling gain is proposed. The control law is designed
using block pole placement technique by assigning a set of desired Block poles in different canonical forms.
The resulting control is simulated for linearized model of the HAVE DASH II BTT missile; numerical
results are analyzed and compared in terms of transient response, gain magnitude, performance
robustness, stability robustness and tracking. The suitable structure for this case study is then selected.
1) Control theory deals with analyzing and designing closed-loop control systems to achieve desired output behaviors.
2) The document provides examples of modeling control systems using transfer functions and state-space representations. These include modeling an RF control system and passive and active filter circuits.
3) State-space representation involves expressing higher-order differential equations as a set of first-order equations and representing the system using matrix equations that can be analyzed and simulated on computers. This allows visualization and analysis of dynamic systems.
Here are the summaries for the 3 questions:
1. An open-loop control system operates without feedback of the output, while a closed-loop control system uses feedback of the output to the input. An example of open-loop is a basic speed control system, while cruise control in cars is closed-loop as it senses and adjusts speed based on feedback.
2. The main design objectives of any control system are: achieving the desired transient response, minimizing steady-state error, ensuring stability, and making the system robust to parameter variations.
3. The total system response C is found using superposition as C = CR + CU1 + CU2, where CR, CU1, and CU2 are the individual
The document discusses dynamic systems and modeling dynamic systems. It provides examples of dynamic systems from various domains like mechanical, electrical, biological, and economic systems. It defines dynamic systems as systems where the present output depends on both present and past inputs. The document discusses modeling dynamic systems using mathematical models and provides guidelines for formulating control-oriented models, including defining the objective, system boundaries, relevant stocks, conservation laws, and relations between flows and levels. It provides examples of modeling a room's thermal dynamics and a vehicle's motion and energy consumption.
The document discusses control systems and provides examples. It begins by describing the general process for designing a control system and examines examples throughout history. Modern control engineering includes strategies to improve manufacturing, energy efficiency, automobiles, and other applications. The document also discusses the gap between physical systems and their models in control system design and how an iterative process can effectively address this gap.
This document provides an overview of a Wikibook on control systems. It discusses both classical and modern control techniques, as well as analog and digital systems. The book covers topics such as system modeling, transforms, stability analysis, controllers, optimal control, and nonlinear systems. It is intended for undergraduate and graduate engineering students and requires knowledge of calculus, differential equations, and linear algebra. The document provides context on the history and applications of control systems, and how the book is organized.
This document describes the design and implementation of a controller for an inverted pendulum on a cart system. It provides the nonlinear and linearized models of the system and designs a PID controller using root locus analysis. Simulation results show the uncompensated system is unstable but the controlled system with PID controller and pre-compensator meets design specifications with less than 0.2 seconds settling time and 8% overshoot for a unit step input.
Event triggered control design of linear networked systems with quantizationsISA Interchange
This paper is concerned with the control design problem of event-triggered networked systems with both state and control input quantizations. Firstly, an innovative delay system model is proposed that describes the network conditions, state and control input quantizations, and event-triggering mechanism in a unified framework. Secondly, based on this model, the criteria for the asymptotical stability analysis and control synthesis of event-triggered networked control systems are established in terms of linear matrix inequalities (LMIs). Simulation results are given to illustrate the effectiveness of the proposed method.
Computer Applications in Power Systems 2023 SECOND.pdfhussenbelew
The document discusses real-time applications of computers in power systems. It describes how SCADA systems are used for monitoring, control, and management of electric power grids. Key functions of SCADA include data acquisition, remote control, supervision, historical data analysis, and various control applications specific to power generation, transmission, and distribution. Real-time monitoring and control allow for faster response to disturbances, optimized system operation, and more reliable power delivery.
The document discusses various aspects of power system reliability including adequacy, security, and stability. It defines adequacy as relating to having sufficient generation and transmission facilities to meet customer demand. Security pertains to how the system responds to disturbances like loss of generation or transmission. Stability refers to generators staying synchronized during disturbances. The document also discusses reliability assessment techniques like loss of load probability and expectation indices used to evaluate generation adequacy. Distribution reliability is assessed using indices that consider customer interruptions and outage times.
1) Research methodology involves systematically studying a problem and applying scientific processes to understand it. This may involve gathering new data or analyzing existing data.
2) There are various types of research including descriptive, analytical, applied, fundamental, quantitative, qualitative, conceptual, and empirical research. Research can also be one-time, longitudinal, diagnostic, exploratory, or experimental.
3) The scientific method relies on empirical evidence and objective consideration to formulate theories, make probabilistic predictions, and aim to solve problems through proper observation and experimentation. Research requires clearly defining the problem, designing the methodology, and justifying conclusions.
Plant design involves planning the physical requirements of a manufacturing facility including product design, process design, capital acquisition, sales planning, and plant location selection. Key factors in selecting a plant location include proximity to markets and raw materials, transportation infrastructure, availability of utilities, labor costs, and environmental impact. Location analysis methodologies help evaluate potential sites, such as using weighted factors to rate locations or calculating the center of gravity based on existing facility distances and production volumes.
1) Water turbines are used to convert the kinetic and potential energy of falling water into mechanical energy for power generation.
2) There are two main types of water turbines - reaction turbines which operate submerged in water and impulse turbines which utilize the kinetic energy of a water jet.
3) Common types include the Francis turbine suited for medium heads, the Kaplan turbine for low heads and large flows, and the Pelton wheel impulse turbine for high heads.
Off grid electrical power systems can be single-source using solar, wind, hydro, or generators, or hybrid combining sources. Hybrid systems supply AC or DC power using conversion devices. Generated electricity is stored in batteries to provide power at night or without sun/wind. Rural electrification brings power to remote areas, allowing mechanization to increase farming productivity and reducing costs. It allows activities after dark like education, and improves safety, healthcare, and reduces isolation with telecoms.
The document discusses the components, sizing, and design of a solar photovoltaic (PV) system for a residence in Ethiopia. It describes measuring solar radiation, the major system components including solar panels, charge controller, inverter, batteries, and loads. It then sizes each component for the specific site based on energy demand calculations and equipment specifications. Components are selected, such as eight 180W solar panels, a 60A charge controller, 3000W inverter, and eight deep cycle batteries. A block diagram shows how all the parts connect and work together to power the home.
This document discusses different types of thermal power plants. It begins by describing the basic components and working principles of thermal power plants, which convert heat from fuels into electricity. It then provides details on specific thermal power cycles like the Rankine, Brayton, and combined cycles. The Rankine cycle uses steam to power a turbine, while the Brayton cycle uses gas turbines. A combined cycle uses both a gas turbine and steam turbine for improved efficiency. The document concludes by listing some advantages and disadvantages of thermal power plants.
This document discusses different types of spillways used in dam engineering projects. It describes spillways as important structures that allow for the controlled or uncontrolled release of excess water to ensure dam safety. The key types of spillways mentioned include overflow, side channel, shaft, siphon, chute, and emergency spillways. For each type, the document provides details on how they function and the types of dams they are best suited for. Maintaining adequate spillway capacity and proper location are emphasized as critical factors for dam safety.
5. Conduits, Intake, Power house and Accessories.ppthussenbelew
This document provides information on various components used in hydroelectric power generation systems, including conduits, intakes, power houses, and accessories. It describes the different types of conduits like canals, tunnels, pipelines and penstocks used to transport water. Intakes allow water to flow into conduits while preventing debris. Power houses house the generating equipment and can be surface or underground. Accessories include surge tanks to prevent water hammer in penstocks, which are closed conduits that supply water under pressure to turbines.
This document discusses different types of hydropower developments including run-of-river developments, diversion and canal developments, storage regulation developments, pumped storage developments, tidal power developments, single-purpose developments, and multipurpose developments. It also discusses classification of hydropower plants based on design capacity, design head, design type, supply type, and operation. Hydropower efficiency ranges from 75-95% depending on losses from friction and turbulence of flow and friction in the turbine and generator. Advantages of hydropower include no fuel costs and low maintenance costs, while disadvantages include large initial investments, transmission line requirements, and potential social and environmental impacts.
3. Hydrologic and Hydraulic Design Concept.ppthussenbelew
Hydrology is the scientific study of the movement and distribution of water on Earth. The water cycle is driven by energy from the sun, which causes evaporation of water from oceans, lakes, rivers, and soil into water vapor in the atmosphere. Water vapor then condenses to form clouds and precipitation falls back to the ground through rain or snow, completing the cycle. A hydrograph provides information on discharge, maximum and minimum run-off, average run-off, and total discharge volume during a given period by showing the flow rate over time.
The document provides details about the BSC6910 product, including:
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Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Batteries -Introduction – Types of Batteries – discharging and charging of battery - characteristics of battery –battery rating- various tests on battery- – Primary battery: silver button cell- Secondary battery :Ni-Cd battery-modern battery: lithium ion battery-maintenance of batteries-choices of batteries for electric vehicle applications.
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Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.
Understanding Inductive Bias in Machine LearningSUTEJAS
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The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
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Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
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The smart irrigation system represents an innovative approach to optimize water usage in agricultural and landscaping practices. The integration of cutting-edge technologies, including sensors, actuators, and data analysis, empowers this system to provide accurate monitoring and control of irrigation processes by leveraging real-time environmental conditions. The main objective of a smart irrigation system is to optimize water efficiency, minimize expenses, and foster the adoption of sustainable water management methods. This paper conducts a systematic risk assessment by exploring the key components/assets and their functionalities in the smart irrigation system. The crucial role of sensors in gathering data on soil moisture, weather patterns, and plant well-being is emphasized in this system. These sensors enable intelligent decision-making in irrigation scheduling and water distribution, leading to enhanced water efficiency and sustainable water management practices. Actuators enable automated control of irrigation devices, ensuring precise and targeted water delivery to plants. Additionally, the paper addresses the potential threat and vulnerabilities associated with smart irrigation systems. It discusses limitations of the system, such as power constraints and computational capabilities, and calculates the potential security risks. The paper suggests possible risk treatment methods for effective secure system operation. In conclusion, the paper emphasizes the significant benefits of implementing smart irrigation systems, including improved water conservation, increased crop yield, and reduced environmental impact. Additionally, based on the security analysis conducted, the paper recommends the implementation of countermeasures and security approaches to address vulnerabilities and ensure the integrity and reliability of the system. By incorporating these measures, smart irrigation technology can revolutionize water management practices in agriculture, promoting sustainability, resource efficiency, and safeguarding against potential security threats.
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ISPM 15 Heat Treated Wood Stamps and why your shipping must have one
Chapter one(1).pdf
1. Modern Control System (ECEg4321)
By:Yeshambel Fentahun
yeshfe21@gmail.com
Jigjiga University
Jigjiga Institute of Technology
School of Electrical and Computer Engineering
power stream
April 8, 2022
2. Modern Vs. Classical control Systems Open-Loop Vs. Closed-Loop Control Systems. Mathematical M
Chapter 1
PRESENTATION OUTLINES
1 Modern Vs. Classical control Systems
2 Open-Loop Vs. Closed-Loop Control Systems.
3 Mathematical Modeling of Control Systems.
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3. Modern Vs. Classical control
Systems Modern Vs. Classical control Systems Open-Loop Vs. Closed-Loop Control Systems. Mathematical M
Modern Vs. Classical control Systems
Modern control theory:
It is applicable to: multiple-input, multiple-output sys-
tems, which may be linear or nonlinear, time invariant or
time varying.
Also, modern control theory is essentially time-domain
approach and frequency domain approach (in certain cases
such as H-infinity control).
Classical control theory:
It is applicable only to linear, time-invariant, single-input,
single-output systems.It is also a complex frequency-domain
approach.
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4. Open-Loop Vs. Closed-Loop
Control Systems. Modern Vs. Classical control Systems Open-Loop Vs. Closed-Loop Control Systems. Mathematical M
Open-Loop Vs. Closed-Loop Control Systems.
Open-Loop Control Systems: Asystem in which con-
trol action does not depend on output is known Open-
Loop Control Systems.
Figure:1 Open loop control system
The major advantages of open-loop control systems are
as follows:
1. Simple construction and ease of maintenance.
2. Less expensive than a corresponding closed-loop sys-
tem.
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5. Open-Loop Vs. Closed-Loop
Control Systems. Modern Vs. Classical control Systems Open-Loop Vs. Closed-Loop Control Systems. Mathematical M
Open-Loop Vs. Closed-Loop Control Systems.
Cont...
Closed -Loop Control Systems: Asystem in which con-
trol action does depend on output is known clesd-Loop
Control Systems.
It is feed back control system.
Figure:2 Closed loop control system
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6. Open-Loop Vs. Closed-Loop
Control Systems. Modern Vs. Classical control Systems Open-Loop Vs. Closed-Loop Control Systems. Mathematical M
Open-Loop Vs. Closed-Loop Control Systems.
Cont...
An advantage of the closed loop control system is:
The fact that the use of feedback makes the system response rela-
tively insensitive to external disturbances and internal variations in
system parameters.
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7. Mathematical Modeling of Control
Systems. Modern Vs. Classical control Systems Open-Loop Vs. Closed-Loop Control Systems. Mathematical M
Mathematical Modeling of Control Systems:
State?
The state of a dynamic system is the smallest set of variables (called
state variables) such that knowledge of these variables at t=t0 , to-
gether with knowledge of the input for t > t0, completely determines
the behavior of the system for any time t > t0 .
State variable: The state variables of a dynamic system are the
variables making up the smallest set of variables that determine the
state of the dynamic system.
State vector: If n state variables are needed to completely describe
the behavior of a given system, then these n state variables can be
considered the n components of a vector x. Such a vector is called a
state vector.
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8. Mathematical Modeling of Control
Systems. Modern Vs. Classical control Systems Open-Loop Vs. Closed-Loop Control Systems. Mathematical M
Mathematical Modeling of Control Systems
(state space based)
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9. Mathematical Modeling of Control
Systems. Modern Vs. Classical control Systems Open-Loop Vs. Closed-Loop Control Systems. Mathematical M
Mathematical Modeling of Control Systems
(state space based)
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10. Mathematical Modeling of Control
Systems. Modern Vs. Classical control Systems Open-Loop Vs. Closed-Loop Control Systems. Mathematical M
Mathematical Modeling of Control Systems
(state space based)
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11. Mathematical Modeling of Control
Systems. Modern Vs. Classical control Systems Open-Loop Vs. Closed-Loop Control Systems. Mathematical M
Mathematical Modeling of Control Systems
(state space based)
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12. Mathematical Modeling of Control
Systems. Modern Vs. Classical control Systems Open-Loop Vs. Closed-Loop Control Systems. Mathematical M
Mathematical Modeling of Control Systems
(state space based)
State space:The n-dimensional space whose coordinate axes consist
of the x1 axis, x2 axis,...,xn axis, where x1 ,x2,..., xn are state
variables, is called a state space. Any state can be represented by a
point in the state space.
State space equations:State-space analysis is concerned with three
types of variables that are involved in the modeling of dynamic sys-
tems: input variables, output variables, and state variables.
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13. Mathematical Modeling of Control
Systems. Modern Vs. Classical control Systems Open-Loop Vs. Closed-Loop Control Systems. Mathematical M
Mathematical Modeling of Control Systems
(state space based)
If vector functions f and/or g involve time t explicitly, then the sys-
tem is called a time-varying system.
If eqn (1) & (2)are linearized about the operating state, then we have
the following linearized state equation and output equation:
Where is A(t) called state matrix, B(t) the input matrix,C(t) the out-
put matrix and D(t) direct transmission matrix.
Block diagram representation:
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14. Mathematical Modeling of Control
Systems. Modern Vs. Classical control Systems Open-Loop Vs. Closed-Loop Control Systems. Mathematical M
Mathematical Modeling of Control Systems
(state space based)
If vector functions f and g do not involve time t explicitly then the
system is called a time-invariant system. And eqn(1) & (2) is sim-
plified as:
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15. Mathematical Modeling of Control
Systems. Modern Vs. Classical control Systems Open-Loop Vs. Closed-Loop Control Systems. Mathematical M
Mathematical Modeling of Control Systems
In short;
• Nonlinear time variant system
• Nonlinear time invariant system
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16. Mathematical Modeling of Control
Systems. Modern Vs. Classical control Systems Open-Loop Vs. Closed-Loop Control Systems. Mathematical M
Mathematical Modeling of Control Systems
Cont...
• Linear time variant system
• Linear time invariant system
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17. Mathematical Modeling of Control
Systems. Modern Vs. Classical control Systems Open-Loop Vs. Closed-Loop Control Systems. Mathematical M
Mathematical Modeling of Control Systems
Example 1: For the mechanical system shown below
(assume that the system is linear), The external force u(t)
is the input to the system, and the displacement y(t) of
the mass is the output. The displacement y(t) is mea-
sured from the equilibrium position in the absence of the
external force.
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18. Mathematical Modeling of Control
Systems. Modern Vs. Classical control Systems Open-Loop Vs. Closed-Loop Control Systems. Mathematical M
Mathematical Modeling of Control Systems
Cont...
Figure 2
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19. Mathematical Modeling of Control
Systems. Modern Vs. Classical control Systems Open-Loop Vs. Closed-Loop Control Systems. Mathematical M
Mathematical Modeling of Control Systems
Cont...
The system equation from the diagram:
This system is of second order. which means that the system in-
volves two integrators. Let us define state variables x1(t) and x2(t)
as:
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20. Mathematical Modeling of Control
Systems. Modern Vs. Classical control Systems Open-Loop Vs. Closed-Loop Control Systems. Mathematical M
Mathematical Modeling of Control Systems
Cont...
The output equation is:
y=x1
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21. Mathematical Modeling of Control
Systems. Modern Vs. Classical control Systems Open-Loop Vs. Closed-Loop Control Systems. Mathematical M
Mathematical Modeling of Control Systems
The state equation in vector-matrix form:
and, the output equation can be written as:
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22. Mathematical Modeling of Control
Systems. Modern Vs. Classical control Systems Open-Loop Vs. Closed-Loop Control Systems. Mathematical M
Mathematical Modeling of Control Systems
Cont...
The above equation is in standard form:
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23. Mathematical Modeling of Control
Systems. Modern Vs. Classical control Systems Open-Loop Vs. Closed-Loop Control Systems. Mathematical M
Mathematical Modeling of Control Systems
.
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24. Mathematical Modeling of Control
Systems. Modern Vs. Classical control Systems Open-Loop Vs. Closed-Loop Control Systems. Mathematical M
Mathematical Modeling of Control Systems
Example 2: RLC circuit formulate the state space representation
,state space equation by vecter form and output equation. The state
of this system can be described in terms of a set of variables [x1
x2], where x1 is the capacitor voltage vc(t) and x2 is equal to the
inductor current iL(t).
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25. Mathematical Modeling of Control
Systems. Modern Vs. Classical control Systems Open-Loop Vs. Closed-Loop Control Systems. Mathematical M
Mathematical Modeling of Control Systems
Utilizing KCL at the junction, we obtain a first order differential
equation by describing the rate of change of capacitor voltage
. KVL for the right-hand loop provides the equation describing the
rate of change of inducator current as
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26. Mathematical Modeling of Control
Systems. Modern Vs. Classical control Systems Open-Loop Vs. Closed-Loop Control Systems. Mathematical M
Mathematical Modeling of Control Systems
The output of the system is represented by the linear algebraic equa-
tion
. Rearranging to write in the form of vector-matrix
.
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27. Mathematical Modeling of Control
Systems. Modern Vs. Classical control Systems Open-Loop Vs. Closed-Loop Control Systems. Mathematical M
Mathematical Modeling of Control Systems
The output signal is then
.
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28. Mathematical Modeling of Control
Systems. Modern Vs. Classical control Systems Open-Loop Vs. Closed-Loop Control Systems. Mathematical M
Mathematical Modeling of Control Systems
Exercise:1.The 2 mass system shown figure be-
low,find the state and output equation when the
state variabeles are the position and velocity of
each mass.
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29. Mathematical Modeling of Control
Systems. Modern Vs. Classical control Systems Open-Loop Vs. Closed-Loop Control Systems. Mathematical M
Mathematical Modeling of Control Systems
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30. Mathematical Modeling of Control
Systems. Modern Vs. Classical control Systems Open-Loop Vs. Closed-Loop Control Systems. Mathematical M
Mathematical Modeling of Control Systems
The state space representation from the abeve is called contrrollabel
canonical form and the out put equation is
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31. Mathematical Modeling of Control
Systems. Modern Vs. Classical control Systems Open-Loop Vs. Closed-Loop Control Systems. Mathematical M
Mathematical Modeling of Control Systems
Example 1:find the stae and out put equation for
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32. Mathematical Modeling of Control
Systems. Modern Vs. Classical control Systems Open-Loop Vs. Closed-Loop Control Systems. Mathematical M
Mathematical Modeling of Control Systems
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33. Mathematical Modeling of Control
Systems. Modern Vs. Classical control Systems Open-Loop Vs. Closed-Loop Control Systems. Mathematical M
Mathematical Modeling of Control Systems
Considering a differential equation:
One way to obtain a state equation and output equation for this case
is defining n state variables as:
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34. Mathematical Modeling of Control
Systems. Modern Vs. Classical control Systems Open-Loop Vs. Closed-Loop Control Systems. Mathematical M
Mathematical Modeling of Control Systems
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35. Mathematical Modeling of Control
Systems. Modern Vs. Classical control Systems Open-Loop Vs. Closed-Loop Control Systems. Mathematical M
Mathematical Modeling of Control Systems
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36. Mathematical Modeling of Control
Systems. Modern Vs. Classical control Systems Open-Loop Vs. Closed-Loop Control Systems. Mathematical M
Mathematical Modeling of Control Systems
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37. Mathematical Modeling of Control
Systems. Modern Vs. Classical control Systems Open-Loop Vs. Closed-Loop Control Systems. Mathematical M
Mathematical Modeling of Control Systems
Exercise:2.Obtain a state-space equation and output equa-
tion for the system defined by
Hint:
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38. Mathematical Modeling of Control
Systems. Modern Vs. Classical control Systems Open-Loop Vs. Closed-Loop Control Systems. Mathematical M
Transfer function of SISO system from state
space equations
If we have a state space equation as:
It can be calculated in the form of TF as:
Taking the Laplace transform of equation (1)
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39. Mathematical Modeling of Control
Systems. Modern Vs. Classical control Systems Open-Loop Vs. Closed-Loop Control Systems. Mathematical M
Transfer function of SISO system from state
space equations
Substituting equation (6) into (4)
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40. Mathematical Modeling of Control
Systems. Modern Vs. Classical control Systems Open-Loop Vs. Closed-Loop Control Systems. Mathematical M
Transfer function of SISO system from state
space equations
Exampel:Find the transfer function of the system from
the following stat space and output equation:
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41. Mathematical Modeling of Control
Systems. Modern Vs. Classical control Systems Open-Loop Vs. Closed-Loop Control Systems. Mathematical M
Transfer function of SISO system from state
space equations
Exercise:3.Find the transfer function of the system from
the following stat space and output equation:
Answer
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42. Mathematical Modeling of Control
Systems. Modern Vs. Classical control Systems Open-Loop Vs. Closed-Loop Control Systems. Mathematical M
Eigenvalues and Eigenvectors
Definition:A nonzero vector x is an eigenvector. A square
matrix A if there exists a scalar λ such that Ax = λ. Then
λ is an eigenvalue.
Note:The zero vector can not be an eigenvector even
though A0 = λ0. But λ = 0 can be an eigenvalue. Let
x be an eigenvector of the matrix A. Then there must ex-
ist an eigenvalue λ such that Ax = λx or, equivalently,
Ax - λx = 0 or (A – λI)x = 0
Example 1: Find the eigenvalues and eigenvectors of
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43. Mathematical Modeling of Control
Systems. Modern Vs. Classical control Systems Open-Loop Vs. Closed-Loop Control Systems. Mathematical M
Computation of the State Transition Matrix
linear time-invariant state equation
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44. Mathematical Modeling of Control
Systems. Modern Vs. Classical control Systems Open-Loop Vs. Closed-Loop Control Systems. Mathematical M
Computation of the State Transition Matrix
linear time-invariant state equation
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45. Mathematical Modeling of Control
Systems. Modern Vs. Classical control Systems Open-Loop Vs. Closed-Loop Control Systems. Mathematical M
Computation of the State Transition Matrix
Example: calculate:
a.The state transition matrix φ(s) and φ(t)
b.The transient response of the state variabel fromm the
set of intial conditions for following system.
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46. Mathematical Modeling of Control
Systems. Modern Vs. Classical control Systems Open-Loop Vs. Closed-Loop Control Systems. Mathematical M
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