The document provides an introduction to feedback control systems. It defines feedback control as measuring the controlled variable and using that information to influence the value of the controlled variable. It then discusses control theory and its multi-disciplinary nature, covering areas like math, electrical engineering, and mechanical engineering. The document uses an example of maintaining room temperature to illustrate key components of a feedback control system like the plant, sensors, actuator, and compensator. It also outlines the typical methodology used in control system design.
218001 control system technology lecture 1Toàn Hữu
The document provides an introduction to control systems, covering key topics such as:
- The basic components of a control system including sensors, controllers, actuators, and the plant.
- The differences between open-loop and closed-loop control systems. Open-loop systems do not use feedback while closed-loop systems incorporate feedback to reduce errors.
- Examples of early control systems throughout history as well as modern applications in fields like aerospace, robotics, manufacturing, and more. Mathematical control theory has also been applied to non-engineering domains.
This document provides an introduction to control engineering. It discusses several key points:
1) Control engineering deals with designing systems to control dynamic processes and improve response speed, accuracy, and stability. This includes analyzing both classical and modern control methods.
2) Modern control engineering uses state-space and eigenvector approaches to model multi-input multi-output systems as sets of first-order differential equations.
3) Automatic control systems are commonly used, where a controlled variable is measured and compared to a setpoint to generate an output that achieves the desired result. This reduces costs and improves quality and productivity over manual control.
Control system note for 6th sem electricalMustafaNasser9
This document provides an overview of the course EC 6405 – Control System Engineering. It includes 5 units that cover various topics in control systems including:
1) Control system modeling using block diagrams, transfer functions, and modeling different physical systems.
2) Time response analysis using concepts like impulse response, step response, and stability analysis tools.
3) Frequency response analysis using tools like Bode plots, Nyquist plots, and Nichol's chart.
4) Stability analysis using tools like the Routh-Hurwitz criterion and root locus analysis.
5) State variable analysis and digital control systems including discrete-time systems and sampled data control systems.
The document lists textbooks and references for
This document outlines the course objectives, topics, and structure for a System Dynamics and Controls course taught by Dr. Nguyen Duy Anh. The course aims to help students understand system modeling, analysis, stability, and controller design. Key topics include transfer functions, root locus, state space modeling, and PID control. The course consists of 8 chapters, homework assignments, midterm and final exams, and student feedback. The grading scheme is also provided.
This document provides an introduction to control systems engineering. It defines the basic components of a control system as an input, control system, and output. It describes open and closed loop control systems, with open loop systems having no feedback and closed loop using feedback to compensate for disturbances. Examples of open and closed loop antenna positioning systems are given. The document also discusses different types of feedback control systems including SISO, MIMO, linear, nonlinear, continuous, discrete, time-varying, and time-invariant systems.
Solutions control system sengineering by normannice 6ed 130502172814-phpapp02Khalil Abu Haltam
This document is a solutions manual for an unknown textbook. It contains 20 solved problems related to control systems and feedback loops. The problems cover various concepts like modeling systems using differential equations, determining transfer functions and state-space representations, analyzing stability, and designing feedback loops. John Wiley & Sons is identified as the publisher.
The document provides an introduction to control system design. It discusses that a control system manages the behavior of devices through feedback loops. The summary discusses the main types of control system designs:
1. Open loop and closed loop systems, with closed loop using feedback to regulate outputs based on inputs.
2. Adaptive control systems that can adapt to uncertain or changing parameters.
3. Nonlinear control systems that can handle systems with nonlinear characteristics.
The document outlines common control system components like inputs, outputs, and processing devices. It also provides examples of control systems for applications like temperature control and vehicle cruise control.
Introduction, Feature of Control System, Requirement of Good Control System, Types of Control System, Open-loop control system, Closed-loop control system, Comparison of Closed-Loop and Open-Loop Control System, Signal flow graph, Conversion of Block Diagrams into Signal Flow Graphs, and Questions.
218001 control system technology lecture 1Toàn Hữu
The document provides an introduction to control systems, covering key topics such as:
- The basic components of a control system including sensors, controllers, actuators, and the plant.
- The differences between open-loop and closed-loop control systems. Open-loop systems do not use feedback while closed-loop systems incorporate feedback to reduce errors.
- Examples of early control systems throughout history as well as modern applications in fields like aerospace, robotics, manufacturing, and more. Mathematical control theory has also been applied to non-engineering domains.
This document provides an introduction to control engineering. It discusses several key points:
1) Control engineering deals with designing systems to control dynamic processes and improve response speed, accuracy, and stability. This includes analyzing both classical and modern control methods.
2) Modern control engineering uses state-space and eigenvector approaches to model multi-input multi-output systems as sets of first-order differential equations.
3) Automatic control systems are commonly used, where a controlled variable is measured and compared to a setpoint to generate an output that achieves the desired result. This reduces costs and improves quality and productivity over manual control.
Control system note for 6th sem electricalMustafaNasser9
This document provides an overview of the course EC 6405 – Control System Engineering. It includes 5 units that cover various topics in control systems including:
1) Control system modeling using block diagrams, transfer functions, and modeling different physical systems.
2) Time response analysis using concepts like impulse response, step response, and stability analysis tools.
3) Frequency response analysis using tools like Bode plots, Nyquist plots, and Nichol's chart.
4) Stability analysis using tools like the Routh-Hurwitz criterion and root locus analysis.
5) State variable analysis and digital control systems including discrete-time systems and sampled data control systems.
The document lists textbooks and references for
This document outlines the course objectives, topics, and structure for a System Dynamics and Controls course taught by Dr. Nguyen Duy Anh. The course aims to help students understand system modeling, analysis, stability, and controller design. Key topics include transfer functions, root locus, state space modeling, and PID control. The course consists of 8 chapters, homework assignments, midterm and final exams, and student feedback. The grading scheme is also provided.
This document provides an introduction to control systems engineering. It defines the basic components of a control system as an input, control system, and output. It describes open and closed loop control systems, with open loop systems having no feedback and closed loop using feedback to compensate for disturbances. Examples of open and closed loop antenna positioning systems are given. The document also discusses different types of feedback control systems including SISO, MIMO, linear, nonlinear, continuous, discrete, time-varying, and time-invariant systems.
Solutions control system sengineering by normannice 6ed 130502172814-phpapp02Khalil Abu Haltam
This document is a solutions manual for an unknown textbook. It contains 20 solved problems related to control systems and feedback loops. The problems cover various concepts like modeling systems using differential equations, determining transfer functions and state-space representations, analyzing stability, and designing feedback loops. John Wiley & Sons is identified as the publisher.
The document provides an introduction to control system design. It discusses that a control system manages the behavior of devices through feedback loops. The summary discusses the main types of control system designs:
1. Open loop and closed loop systems, with closed loop using feedback to regulate outputs based on inputs.
2. Adaptive control systems that can adapt to uncertain or changing parameters.
3. Nonlinear control systems that can handle systems with nonlinear characteristics.
The document outlines common control system components like inputs, outputs, and processing devices. It also provides examples of control systems for applications like temperature control and vehicle cruise control.
Introduction, Feature of Control System, Requirement of Good Control System, Types of Control System, Open-loop control system, Closed-loop control system, Comparison of Closed-Loop and Open-Loop Control System, Signal flow graph, Conversion of Block Diagrams into Signal Flow Graphs, and Questions.
ME-314 Introduction to Control Engineering is a course taught to Mechanical Engineering senior undergrads. The course is taught by Dr. Bilal Siddiqui at DHA Suffa University. This lecture is introduction to the field.
Control Algorithms for Evaluating Seismic Performance of StructuresIRJET Journal
This document discusses and compares different control algorithms for evaluating the seismic performance of structures, including active control systems. It specifically examines three algorithms: linear quadratic regulator (LQR) control, filtered-x least mean square (LMS) adaptive control, and a hybrid feedback LMS control. LQR is a feedback control algorithm while filtered-x LMS is an adaptive control algorithm. The hybrid feedback LMS combines feedback and adaptive control. It also provides background on active tuned mass dampers and reviews literature analyzing the seismic response of structures using various active control systems and algorithms.
The document discusses concepts related to automatic control systems including open loop and closed loop systems. It covers topics such as feedback, controllers like proportional, integral and proportional integral differential controllers. It also provides examples of automatic control systems used in various industries and applications. The document consists of lecture slides on control systems for a class.
This document provides an introduction to control systems. It defines a control system as a system used to achieve a desired output. The basic components of a control system are identified as a plant, controller, actuator, sensor, and disturbance. Control systems are classified as open-loop or closed-loop based on whether feedback is used. A brief history of control is provided, highlighting early examples and the development of modern control theory. Requirements for control systems like stability, quickness, and accuracy are also discussed.
Okay, let's solve this step-by-step:
* Set point (Io) = 12 rpm
* Range = 15 - 10 = 5 rpm
* Initial controller output = 22%
* KI = -0.15%/s/% error
* Error = Actual - Set point = ?
* Given: Initial output is 22%
* To find: What is the actual speed?
Using the integral control equation:
Iout = Io - KI * ∫edt
22% = 12rpm - 0.15%/s/% * ∫e dt
∫e dt = (22% - 12rpm)/0.15%/s/% = 40%*
This document provides an overview of control systems engineering. It discusses:
- The basics of control theory including open and closed loop control systems.
- Examples of control systems in real life including manual vs automatic control of a car.
- Classification of control systems as open loop or closed loop and the processes of each.
- Applications of control systems including temperature regulation and motor speed control.
- The purpose of control systems is to cause a system variable to conform to a desired value through feedback.
Industrial Control Systems - PID ControllersBehzad Samadi
The document discusses industrial control systems and PID controllers. It provides an overview of feedback control loops, on-off control, and the proportional, integral and derivative actions of PID controllers. It describes how proportional control introduces steady-state error, how integral control eliminates this error, and how derivative control can improve system response by being predictive of future errors. PID controllers combine all three actions and the document discusses different structures for PID controllers, including ideal, series and parallel forms.
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.
A feedback control system uses feedback to regulate a process variable by comparing its actual output to its desired setpoint. It manipulates the input to minimize this error. There are three main types of feedback controllers - proportional (P), proportional-integral (PI), and proportional-integral-derivative (PID). A PID controller combines the advantages of P, PI, and PD control to provide faster response, eliminate offset, and increase stability. Fuzzy logic controllers provide an alternative approach to control systems using fuzzy set theory rather than mathematical equations.
This document discusses bioreactor control systems. It describes different types of control systems including manual control, automatic control, two-position controllers, proportional control, integral control, and derivative control. It explains that automatic control systems use four basic components: a measuring element, controller, final control element, and the process to be controlled. The document also summarizes different combinations of control methods, such as proportional plus integral control and proportional plus integral plus derivative control.
This document contains a question bank for control systems with questions about various control system concepts. Some key points:
1. It defines open loop and closed loop control systems, with closed loop systems having feedback to correct errors and maintain desired output values.
2. The components of a feedback control system are identified as the plant, feedback path, error detector, actuator and controller.
3. Different types of controllers are listed, including proportional, PI, PD and PID controllers. The proportional controller produces an output proportional to the error signal.
This document discusses different types of control systems including proportional (P), proportional-integral (PI), proportional-derivative (PD), and proportional-integral-derivative (PID) control. It provides block diagrams and explanations of how each type of controller works, the effects on steady state and transient response, and limitations. It also provides examples of applying these controllers to a mass-spring-damper system and speed control of a DC motor. Tuning of PID controllers is identified as difficult and alternative future controllers like fuzzy logic are mentioned.
Chapter 8 Pid controllers and modified pid controllers. From the book (Ogata Modern Control Engineering 5th).
8-1 introduction
8-2 Ziegler-Nichols rules for tuning pid controllers .
The document describes research into developing a novel controller for stabilizing an inverted pendulum. It begins with reviewing previous work that derived the differential equations governing the dynamic behavior of an inverted pendulum. The researcher then uses Laplace transforms to solve these equations and derive a transfer function relating the input and output. An output-feedback PID controller is designed using this transfer function. Additionally, a stochastic method is examined for initializing the weighting matrices in linear quadratic regulation to minimize the performance index and optimize controller design. Upon simulation, the new output-feedback controller is shown to effectively control pendulum stability without integral gain.
The document describes a PID controller. It discusses:
1) The three terms - proportional, integral and derivative - that make up a PID controller and how each term reacts to the error signal.
2) Block diagrams showing how a PID controller works in a closed loop feedback system.
3) Equations for the PID controller in both time and frequency domains.
4) An example of applying a PID controller to a mass-spring-damper system and analyzing the response.
This document summarizes a study comparing three control methods - PID, IMC, and IMC-PID - for controlling a first-order motor-tachometer system. The key findings are:
1) IMC performs better than PID when near system limitations, as PID can exhibit reset windup causing poor control. IMC avoids this issue.
2) Both IMC and IMC-PID effectively control the system under normal operation. However, IMC has less noise than IMC-PID.
3) When a large disturbance occurs, IMC returns to the setpoint faster than IMC-PID, which overshoots due to integral windup.
This document presents a novel adaptive PID control scheme for flexible joint robot manipulators with known upper bounds on external disturbances. The control law combines PID feedback control with robust adaptive compensation of disturbances and unknown parameters. Lyapunov stability theory and Barbalat's lemma are used to prove global asymptotic stability of the closed-loop system. Simulation results on a two-degree-of-freedom flexible joint robot illustrate the effectiveness of the proposed controller in improving trajectory tracking accuracy and dynamic performance compared to existing adaptive PD control methods.
This document provides an introduction to a course on Process Instrumentation and Control. It defines process control as dealing with mechanisms, architectures, and algorithms for controlling processes. Examples of controlled processes include controlling temperature with steam addition and maintaining fluid levels in tanks. The objectives of control are to maintain operational conditions, transition between conditions, and define key terminology like manipulated and disturbance inputs, control and output variables, and common control structures like SISO, MIMO, and PID controllers.
Pe 3032 wk 1 introduction to control system march 04eCharlton Inao
This document outlines the course PE-3032 Introduction to Control Systems Engineering taught by Professor Charlton S. Inao at Defence Engineering University College in Ethiopia in 2012. The course covers topics such as open and closed loop control, Laplace transformations, stability analysis, root locus, frequency response, PID controllers, and digital control. Students are expected to develop abilities in applying mathematical principles to control systems, obtaining mathematical models of systems, deriving transfer functions and state space models, and performing time and frequency domain analysis. Assessment includes a midterm, final exam, lab assessments, and assignments. Recommended textbooks and references are also provided.
Control engineering module 1 part-a 18me71Mohammed Imran
Control engineering module 1 part-a
Part-A
Introduction: Components of a control system, Open loop and closed loop systems.
Types of controllers: Proportional, Integral, Differential, Proportional-Integral, and Proportional- Integral Differential controllers.
Part-B
Modelling of Physical Systems: Mathematical Models of Mechanical, Electrical, Thermal, Hydraulic Systems.
ME-314 Introduction to Control Engineering is a course taught to Mechanical Engineering senior undergrads. The course is taught by Dr. Bilal Siddiqui at DHA Suffa University. This lecture is introduction to the field.
Control Algorithms for Evaluating Seismic Performance of StructuresIRJET Journal
This document discusses and compares different control algorithms for evaluating the seismic performance of structures, including active control systems. It specifically examines three algorithms: linear quadratic regulator (LQR) control, filtered-x least mean square (LMS) adaptive control, and a hybrid feedback LMS control. LQR is a feedback control algorithm while filtered-x LMS is an adaptive control algorithm. The hybrid feedback LMS combines feedback and adaptive control. It also provides background on active tuned mass dampers and reviews literature analyzing the seismic response of structures using various active control systems and algorithms.
The document discusses concepts related to automatic control systems including open loop and closed loop systems. It covers topics such as feedback, controllers like proportional, integral and proportional integral differential controllers. It also provides examples of automatic control systems used in various industries and applications. The document consists of lecture slides on control systems for a class.
This document provides an introduction to control systems. It defines a control system as a system used to achieve a desired output. The basic components of a control system are identified as a plant, controller, actuator, sensor, and disturbance. Control systems are classified as open-loop or closed-loop based on whether feedback is used. A brief history of control is provided, highlighting early examples and the development of modern control theory. Requirements for control systems like stability, quickness, and accuracy are also discussed.
Okay, let's solve this step-by-step:
* Set point (Io) = 12 rpm
* Range = 15 - 10 = 5 rpm
* Initial controller output = 22%
* KI = -0.15%/s/% error
* Error = Actual - Set point = ?
* Given: Initial output is 22%
* To find: What is the actual speed?
Using the integral control equation:
Iout = Io - KI * ∫edt
22% = 12rpm - 0.15%/s/% * ∫e dt
∫e dt = (22% - 12rpm)/0.15%/s/% = 40%*
This document provides an overview of control systems engineering. It discusses:
- The basics of control theory including open and closed loop control systems.
- Examples of control systems in real life including manual vs automatic control of a car.
- Classification of control systems as open loop or closed loop and the processes of each.
- Applications of control systems including temperature regulation and motor speed control.
- The purpose of control systems is to cause a system variable to conform to a desired value through feedback.
Industrial Control Systems - PID ControllersBehzad Samadi
The document discusses industrial control systems and PID controllers. It provides an overview of feedback control loops, on-off control, and the proportional, integral and derivative actions of PID controllers. It describes how proportional control introduces steady-state error, how integral control eliminates this error, and how derivative control can improve system response by being predictive of future errors. PID controllers combine all three actions and the document discusses different structures for PID controllers, including ideal, series and parallel forms.
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.
A feedback control system uses feedback to regulate a process variable by comparing its actual output to its desired setpoint. It manipulates the input to minimize this error. There are three main types of feedback controllers - proportional (P), proportional-integral (PI), and proportional-integral-derivative (PID). A PID controller combines the advantages of P, PI, and PD control to provide faster response, eliminate offset, and increase stability. Fuzzy logic controllers provide an alternative approach to control systems using fuzzy set theory rather than mathematical equations.
This document discusses bioreactor control systems. It describes different types of control systems including manual control, automatic control, two-position controllers, proportional control, integral control, and derivative control. It explains that automatic control systems use four basic components: a measuring element, controller, final control element, and the process to be controlled. The document also summarizes different combinations of control methods, such as proportional plus integral control and proportional plus integral plus derivative control.
This document contains a question bank for control systems with questions about various control system concepts. Some key points:
1. It defines open loop and closed loop control systems, with closed loop systems having feedback to correct errors and maintain desired output values.
2. The components of a feedback control system are identified as the plant, feedback path, error detector, actuator and controller.
3. Different types of controllers are listed, including proportional, PI, PD and PID controllers. The proportional controller produces an output proportional to the error signal.
This document discusses different types of control systems including proportional (P), proportional-integral (PI), proportional-derivative (PD), and proportional-integral-derivative (PID) control. It provides block diagrams and explanations of how each type of controller works, the effects on steady state and transient response, and limitations. It also provides examples of applying these controllers to a mass-spring-damper system and speed control of a DC motor. Tuning of PID controllers is identified as difficult and alternative future controllers like fuzzy logic are mentioned.
Chapter 8 Pid controllers and modified pid controllers. From the book (Ogata Modern Control Engineering 5th).
8-1 introduction
8-2 Ziegler-Nichols rules for tuning pid controllers .
The document describes research into developing a novel controller for stabilizing an inverted pendulum. It begins with reviewing previous work that derived the differential equations governing the dynamic behavior of an inverted pendulum. The researcher then uses Laplace transforms to solve these equations and derive a transfer function relating the input and output. An output-feedback PID controller is designed using this transfer function. Additionally, a stochastic method is examined for initializing the weighting matrices in linear quadratic regulation to minimize the performance index and optimize controller design. Upon simulation, the new output-feedback controller is shown to effectively control pendulum stability without integral gain.
The document describes a PID controller. It discusses:
1) The three terms - proportional, integral and derivative - that make up a PID controller and how each term reacts to the error signal.
2) Block diagrams showing how a PID controller works in a closed loop feedback system.
3) Equations for the PID controller in both time and frequency domains.
4) An example of applying a PID controller to a mass-spring-damper system and analyzing the response.
This document summarizes a study comparing three control methods - PID, IMC, and IMC-PID - for controlling a first-order motor-tachometer system. The key findings are:
1) IMC performs better than PID when near system limitations, as PID can exhibit reset windup causing poor control. IMC avoids this issue.
2) Both IMC and IMC-PID effectively control the system under normal operation. However, IMC has less noise than IMC-PID.
3) When a large disturbance occurs, IMC returns to the setpoint faster than IMC-PID, which overshoots due to integral windup.
This document presents a novel adaptive PID control scheme for flexible joint robot manipulators with known upper bounds on external disturbances. The control law combines PID feedback control with robust adaptive compensation of disturbances and unknown parameters. Lyapunov stability theory and Barbalat's lemma are used to prove global asymptotic stability of the closed-loop system. Simulation results on a two-degree-of-freedom flexible joint robot illustrate the effectiveness of the proposed controller in improving trajectory tracking accuracy and dynamic performance compared to existing adaptive PD control methods.
This document provides an introduction to a course on Process Instrumentation and Control. It defines process control as dealing with mechanisms, architectures, and algorithms for controlling processes. Examples of controlled processes include controlling temperature with steam addition and maintaining fluid levels in tanks. The objectives of control are to maintain operational conditions, transition between conditions, and define key terminology like manipulated and disturbance inputs, control and output variables, and common control structures like SISO, MIMO, and PID controllers.
Pe 3032 wk 1 introduction to control system march 04eCharlton Inao
This document outlines the course PE-3032 Introduction to Control Systems Engineering taught by Professor Charlton S. Inao at Defence Engineering University College in Ethiopia in 2012. The course covers topics such as open and closed loop control, Laplace transformations, stability analysis, root locus, frequency response, PID controllers, and digital control. Students are expected to develop abilities in applying mathematical principles to control systems, obtaining mathematical models of systems, deriving transfer functions and state space models, and performing time and frequency domain analysis. Assessment includes a midterm, final exam, lab assessments, and assignments. Recommended textbooks and references are also provided.
Control engineering module 1 part-a 18me71Mohammed Imran
Control engineering module 1 part-a
Part-A
Introduction: Components of a control system, Open loop and closed loop systems.
Types of controllers: Proportional, Integral, Differential, Proportional-Integral, and Proportional- Integral Differential controllers.
Part-B
Modelling of Physical Systems: Mathematical Models of Mechanical, Electrical, Thermal, Hydraulic Systems.
The document provides an introduction to control systems, including:
- Control systems are integral parts of modern society and are found in applications like rockets, manufacturing machines, and self-driving vehicles.
- The chapter defines a control system and describes their basic features and configurations, including open-loop and closed-loop systems.
- The objectives of control system analysis and design are described as producing the desired transient response, reducing steady-state error, and achieving stability.
- The design process for control systems is outlined in six steps: determining requirements, drawing block diagrams, creating schematics, developing mathematical models, reducing block diagrams, and analyzing and designing the system.
This document provides an introduction and overview of control systems engineering. It outlines the topics that will be covered in the course, including modeling control systems in the frequency and time domains, time response analysis, stability, and design techniques like root locus and Bode plots. The course aims to introduce fundamental concepts in control systems and their applications. Key concepts discussed include open-loop and closed-loop systems, modeling using Laplace transforms and transfer functions, and time and frequency domain analysis methods. Assessment will include assignments, quizzes, tests, and a final exam.
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 provides an introduction to automatic control systems. It discusses:
1. The objectives of understanding basic control concepts, mathematical modeling using block diagrams, and studying systems in time and frequency domains.
2. The differences between manual and automatic control systems, with examples of driverless cars versus manual driving.
3. A brief history of automatic control, including James Watt's flyball governor and Ivan Polzunov's water-level regulator.
4. An overview of control system components and their representation in block diagrams.
The document provides an introduction to control systems engineering, defining key terms like controlled variable, manipulated variable, and feedback control. It describes the basic components and configurations of control systems, including open-loop and closed-loop designs. The introduction also covers analysis and design objectives for control systems, like transient response, steady-state response, and stability. It outlines the typical design process and gives examples of control system applications in various fields like aerospace, manufacturing, and power generation.
1 MODULE 1 INTRODUCTION TO SIMULATION Module out.docxjeremylockett77
1
MODULE 1: INTRODUCTION TO SIMULATION
Module outline:
• What is Simulation?
• Simulation Terminology
• Components of a System
• Models in Simulation
• Typical applications
• References
WHAT IS SIMULATION?
simulation may be defined as a technique that imitates the operation of a real world
system or processes as it evolves over time. It involves the generation of an artificial
history of the system and observation of that artificial history to obtain information and
draw inferences about the operating characteristics of the real system. Simulation
educates us on how a system operates and how the system might respond to changes. It
enables us to test alternative courses of action to determine their impact on system
performance. Before an alternative is implemented, it must be tested. Although
performing tests with the “real thing” would be ideal. This is seldom practically feasible.
The cost associated with changing/improving a system may be very high both in the
term of capital required to implement the change and losses due to interruption in
production operations and other losses. In most cases experimentation with the
proposed alternative is practically impossible. In addition, as the cost of proposed
changes (alternative solutions) increase, so does the cost of physically experimenting.
As an example, suppose a heavy-duty conveyor is being considered as an alternative to
the existing material handling method (by trucks) for improving productivity and
speeding up the production operations in a factory (seeFigre3). It is obvious that
installing the proposed conveyor on a test basis would probably not be cost effective.
Therefore, experimentation with alternative configurations would be practically
impossible. In stead, experimentation with a representative model of the system would
probably make more sense.
Simulation is a means of experimenting with a detailed model of a real system to
Determine how the system will respond to changes in its environment, structure, and its
underlying assumption [Harrel (1996)]. Management Scientist uses a wide variety of
analytical tools to model, analyze, and solve complex decision problems. These tool
include linear programming, decision analysis, forecasting, Queuing theory and
Alternative 1: Use lift-truck
2
Point A Point B
(Warehouse) (Factory)
Alternative 2: use a conveyor
Point A
(warehouse ) . . . . . . . . Point B
...
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.
Chapter 1 introduction to control systemLenchoDuguma
This chapter introduces control systems and covers the following topics:
1. It defines open-loop and closed-loop control systems, with open-loop systems having no feedback and closed-loop systems using feedback to reduce errors between the output and desired input.
2. It discusses the history of control systems from the 18th century to present day, including developments in areas like stability analysis, frequency response methods, and state-space methods.
3. It compares classical and modern control theory, noting that modern control theory can handle more complex multi-input, multi-output systems through time-domain analysis of differential equations.
The document discusses various topics related to mechatronics and mechanical systems. It begins by defining mechatronics as the application of electronics and computer technology to control mechanical systems. It then discusses some key aspects of mechatronics like its multidisciplinary approach and concurrent use of electrical, mechanical, control and computer engineering. Examples provided include electronic fuel injection systems replacing mechanical ones. The document also covers topics like sensors, transducers, gears, cams, hydraulic systems and their applications.
Synthesis of the Decentralized Control System for Robot-Manipulator with Inpu...ITIIIndustries
It is discussed the problem of synthesis of the decentralized adaptive-periodic control system for two degrees of freedom robotic manipulator which have an input limitations. The solution of the problem is based on the use of hyperstability criterion, L-dissipativity conditions and dynamic filter-corrector.
PC Based Industrial Automation With AVR Atmega 16 - Project ReportRobo India
Robo India Presents A project Report on PC Based Industrial Automation using AVR family's Atmel Atmega 16 microcontroller.
It uses Serial communication technology to communicate between PC and embedded system.
You will learn following aspects-
1. serial communication
2. Input output programming
3. Embedded system
4. AVR atmega 16
6. Controlling
This report also contains complete codding of the project.
Automation or automatic control is the use of various control systems for operating equipment such as machinery, processes in factories, boilers and heat treating ovens, switching in telephone networks, steering and stabilization of ships, aircraft and other applications with minimal or reduced human intervention. Some processes have been completely automated.
The biggest benefit of automation is that it saves labour, however, it is also used to save energy and materials and to improve quality, accuracy and precision.
Please share your views and queries, we are found at-
Website- http://roboindia.com
mail- info@roboindia.com
Biomedical Control Systems - Time Response Analysis (Short Questions & Answers)Mathankumar S
Biomedical Control Systems - Time Response Analysis (Short Questions & Answers) - Its detailed about Standard Test Signals, Time Response Analysis of First and Second Order Systems, Steady state errors and Error constants, Effects of Adding Zero to a system, Damping System and PD & PID Controller.
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.
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Analogous Electrical Model of Water Processing Plant as a Tool to Study “The ...ijctcm
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Analogous Electrical Model of Water Processing Plant as a Tool to Study “The ...ijctcm
This paper presents an analogous electrical model for water processing plant. The three major components of the plant; the connecting pipes, the water tanks and the filter have been modeled here by resistors, capacitors and inductors of an electrical circuit as a model of the plant. The mechanical properties of these components are thus represented by equivalent electrical properties as resistance, capacitance and inductance, respectively. In these representations the resistance is expressed as a function of both the cross sectional area (A) and the length (L) of the pipes; and the capacitance is expressed as a function of the Area of the tanks (capacity of tank), while the inductance (constriction to debris) is expressed as a function of number of wound string on the filter cartridge. From the results of the simulation, it was observed that by varying the electrical parameters (resistors, capacitors, inductor), it is possible to study the way the manipulation of equivalent parameters of the analogous mechanical components (the connecting pipes, the water tanks and the water filter) of the water plant could influence the rate of water flow in the production process. This work does demonstrate the possibility of knowing from the beginning the various sizes of pipes, tanks and filter to be used and how these will affect the flow of water in the plant before going into the physical construction of the plant. This method could be used in a more complex system.
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This document provides information about the ECE4510/ECE5510 Feedback Control Systems course. It includes details about the instructor, textbook, topics to be covered over the semester, evaluation criteria, homework policies, and course objectives. Students will learn linear systems analysis and design techniques including block diagrams, root locus, Bode plots, and state-space methods. Evaluation will include homework, exams, and an optional project for ECE5510 students. The course expects significant weekly workload outside of class.
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Ece4510 notes01
1. ECE4510/5510: Feedback Control Systems. 1–1
INTRODUCTION TO FEEDBACK CONTROL
1.1: What is feedback control?
I Control-system engineers often face this question (or, “What is it that
you do, anyway?”) when trying to explain their professional field.
I Loosely speaking, control is the process of getting “something” to do
what you want it to do (or “not do,” as the case may be).
• The “something” can be almost anything. Some obvious
examples: aircraft, spacecraft, cars, machines, robots, radars,
telescopes, etc.
• Some less obvious examples: energy systems, the economy,
biological systems, the human body. . .
I Control is a very common concept.
e.g., Human-machine interaction: Driving a car.
® Manual control.
e.g., Independent machine: Room temperature control. Furnace in
winter, air conditioner in summer. Both controlled (turned “on”/“off”)
by thermostat. (We’ll look at this example more in Topic 1.2.)
® Automatic control (our focus in this course).
DEFINITION: Control is the process of causing a system variable to
conform to some desired value, called a reference value. (e.g.,
variable = temperature for a climate-control system)
Lecture notes prepared by and copyright c 1998–2013, Gregory L. Plett and M. Scott Trimboli
2. ECE4510/ECE5510, INTRODUCTION TO FEEDBACK CONTROL 1–2
I Usually defined in terms
of the system’s step re-
sponse, as we’ll see in
notes Chapter 3. 0.1
0.9
1
ttr
Mptp
ts
DEFINITION: Feedback is the process of measuring the controlled
variable (e.g., temperature) and using that information to influence the
value of the controlled variable.
I Feedback is not necessary for control. But, it is necessary to cater for
system uncertainty, which is the principal role of feedback.
I Control theory is truly a multi-disciplinary study that cuts across
boundaries of many disciplines.
• Math is the cornerstone of control theory.
• Electrical engineering is also an important building block, though
mostly from the aspect of signals and systems, and less from
circuits.
• Mechanical engineering is likewise an important fundamental,
chiefly from the standpoint of dynamic system behavior.
• Other important foundational elements come from: physics
(conservation of energy), thermodynamics (entropy), and other
scientific first principles.
I Control engineers bring value across a very wide array of industries:
• Automotive, aerospace, electronic, medical, financial. . . (see
next page for a synopsis).
Lecture notes prepared by and copyright c 1998–2013, Gregory L. Plett and M. Scott Trimboli
3. ECE4510/ECE5510, INTRODUCTION TO FEEDBACK CONTROL 1–3
Some applications of feedback control
Categories Specific applications
Ecological Wildlife and forest growth management (reference = population);
control of plant chemical wastes via monitoring lakes and rivers; air
pollution abatement (reference = chemical concentrations); water
control and distribution; flood control via dams and reservoirs (ref-
erence = water flow rate).
Medical Medical instrumentation for monitoring and control (reference =
dosage); artificial limbs (prosthesis; reference = limb position).
Home
appliances
Home heating, refrigeration, and air conditioning via thermostatic
control; electronic sensing and control in clothes dryers; humidity
controllers; temperature control of ovens (references = desired tem-
perature, humidity).
Power/energy Power system control and planning; feedback instrumentation in oil
recovery; optimal control of windmill blade and solar panel surfaces;
optimal power distribution via power factor control; power electron-
ics for dc-dc conversion, battery chargers, motor controls (refer-
ences = power level, optimization criteria).
Transportation Control of roadway vehicle traffic flows using sensors; automatic
speed control devices on automobiles; propulsion control in rail tran-
sit systems; building elevators and escalators (references = vehicle
flow rate, speed, position and speed together).
Manufacturing Sensor-equipped robots for cutting, drilling die casting, forging,
welding, packaging, and assembling; chemical process control; ten-
sion control windup processes in textile mills; conveyor speed con-
trol with optical pyrometer sensing in hot steel rolling mills (refer-
ences = position–speed profiles).
Aerospace and
military
Missile guidance and control; automatic piloting; spacecraft control;
tracking systems; nuclear submarine navigation and control; fire-
control systems (references = position and attitude).
(adapted from: J.R. Rowland, “Linear Control Systems: Modeling, Analysis, and Design,” John Wiley & Sons, (New York: 1986), p. 6.)
Lecture notes prepared by and copyright c 1998–2013, Gregory L. Plett and M. Scott Trimboli
4. ECE4510/ECE5510, INTRODUCTION TO FEEDBACK CONTROL 1–4
1.2: An illustrative example of a feedback control system
I We proceed by looking at an example that allows us to define some
important terminology.
I Consider the system designed to maintain the temperature of your
house/apartment.
I We can draw a block diagram, which identifies the major components
of the system, and omits unnecessary details. It also highlights the
information/energy flows:
Desired
Temp.
Room
Temp.
Heat Loss (Qout)
Qin
HouseFurnaceGas
Valve
Thermo-
stat
I Central component = process or plant, one of whose variables we
want to control. e.g., Plant = ; Variable = .
I Disturbance = some system input that we do not control.
e.g., Disturbance = .
I Actuator = device that influences controlled variable.
e.g., Actuator = .
I Reference sensor measures desired system output.
I Output sensor measures actual system output.
I Compensator or controller = device that computes the control effort
to apply the the actuator, based on sensor readings.
e.g., combines the last three functions.
Lecture notes prepared by and copyright c 1998–2013, Gregory L. Plett and M. Scott Trimboli
5. ECE4510/ECE5510, INTRODUCTION TO FEEDBACK CONTROL 1–5
A more abstract block diagram
I The detail in the prior block diagram is helpful to identify the physical
components of the particular system of interest.
I However, we can abstract the ideas from the prior diagram to arrive at
a more general block diagram that is applicable to most scenarios of
interest:
Ref.
Value
Output
Disturbance
PlantReference
Sensor
Output
Sensor
Compen-
sator
Actuator
error
“Negative Feedback”
An even more abstract block diagram
I Finally, by assuming the presence of (perfect) sensors and actuators
(absorbed into the plant), we can make an even more abstract block
diagram.
Ref.
Value
Output
Disturbance
Plant
Compen-
sator
I This is the picture of a feedback control system that you should have
in mind for the remainder of this course.
Lecture notes prepared by and copyright c 1998–2013, Gregory L. Plett and M. Scott Trimboli
6. ECE4510/ECE5510, INTRODUCTION TO FEEDBACK CONTROL 1–6
The control problem/solution methodology
I Now that we have identified the main components of a feedback
control system, we consider the problem that a control system is
designed to solve, and the methodology for solution.
I The objectives of any control-system design include:
• “Reject” disturbance (plant response to disturbance input
minimized).
• Acceptable steady state errors (response after a “long” time).
• Acceptable transient response (short-term dynamics).
• Minimize sensitivity to plant parameter changes (“robustness”).
I Solutions are reached via the methodology:
1. Choosing output sensors.
2. Choosing actuators.
*3. Developing plant, actuator, sensor equations (models).
*4. Designing compensator based on the models and design criteria.
*5. Evaluating design analytically, with simulation and prototype.
*6. Iteration!! (In part because actual system will be different from
model.)
I Steps 1 and 2 are sometimes out of our purview. Our focus in this
course is on steps 3–6.
I In the next section, we will look at a lengthy example of these six
steps.
Lecture notes prepared by and copyright c 1998–2013, Gregory L. Plett and M. Scott Trimboli
7. ECE4510/ECE5510, INTRODUCTION TO FEEDBACK CONTROL 1–7
1.3: A first analysis: Auto cruise control
I As an example, suppose that we wish to design a cruise-control
system for an automobile.
I Generically, we wish to control the car’s speed (and possibly
acceleration profile).
I Following our design methodology, steps 1–2 are:
1. Output sensor = speedometer.
2. Actuator = throttle and engine.
I Block diagram:
Desired
Speed
Actual
Speed
Road Grade
Plant
Auto Body
Output
Sensor
Speedometer
Sensor Noise
Compen-
sator
Actuator
Engine
Throttle
Control
Variable
Measured
Speed
3. Model of system:
1. Operate system ≈ 55 MPH. Assume linear response near 55 MPH.
2. Measure: 1 % change in throttle « 10 MPH change in speed.
3. Measure: 1 % change in grade « 5 MPH change in speed.
4. Measure: Speedometer accurate to a fraction of 1 MPH, so is
assumed exact.
Lecture notes prepared by and copyright c 1998–2013, Gregory L. Plett and M. Scott Trimboli
8. ECE4510/ECE5510, INTRODUCTION TO FEEDBACK CONTROL 1–8
5. Functional block diagram:
Compen-
sator
r(t) y(t)
w(t)
u(t)
10
0.5
r(t)—reference speed, MPH.
u(t)—throttle posn., %.
y(t)—actual speed, MPH.
w(t)—road grade, %.
4. Design compensator/controller
• First attempt = “open loop” controller.
Compensator
1/10r(t) yol(t)
w(t)
u(t)
10
0.5
yol(t) = 10 (u(t) − 0.5w(t))
= 10
r(t)
10
− 0.5w(t)
= r(t) − 5w(t).
5. Evaluate design: Percent error is computed as 100
r(t) − yol(t)
r(t)
• r(t) = 55, w(t) = 0 « yol(t) = 55. No error. . . Good.
• r(t) = 55, w(t) = 1 « yol(t) = 50. ≈ 10 % error. . . Not good.
• r(t) = 55, w(t) = 2 « yol(t) = 45. ≈ 20 % error. . . Not good.
Lecture notes prepared by and copyright c 1998–2013, Gregory L. Plett and M. Scott Trimboli
9. ECE4510/ECE5510, INTRODUCTION TO FEEDBACK CONTROL 1–9
• Suppose you load the trunk with all your ECE4510/ECE5510 notes
and the car becomes more sluggish.
e.g., the gain “10” becomes “9”
• r(t) = 55, w(t) = 0 « yol(t) = 49.5. ≈ 10 % error. . . Not good.
• r(t) = 55, w(t) = 1 « yol(t) = . . .
6. Iterate design. Go to step #4.
Auto cruise-control example (attempt #2)
I Our first attempt at the cruise-control design didn’t go too well.
I So, we try again, with a different approach.
4. Second attempt = Feedback controller
Compensator
100r(t) ycl(t)
w(t)
u(t)
10
0.5
I Multiply your output error by 100; feedback gain = 100
ycl(t) = 10u(t) − 5w(t)
u(t) = 100(r(t) − ycl(t))
so, ycl(t) = 1000r(t) − 1000ycl(t) − 5w(t)
1001ycl(t) = 1000r(t) − 5w(t)
ycl(t) = 0.999r(t) − 0.005w(t)
Lecture notes prepared by and copyright c 1998–2013, Gregory L. Plett and M. Scott Trimboli
10. ECE4510/ECE5510, INTRODUCTION TO FEEDBACK CONTROL 1–10
5. Evaluate design.
I r(t) = 55, w(t) = 0 « ycl(t) = 54.945
I r(t) = 55, w(t) = 1 « ycl(t) = 54.94
I r(t) = 55, w(t) = 2 « ycl(t) = 54.935
I r(t) = 55, w(t) = 10 « ycl(t) = 54.895 . . . ≈ 0.2 % error!
® Feedback system rejects disturbances.
® Feedback system has steady state error.
I r(t) = 55, w(t) = 0, plant=“9”, not “10” « ycl(t) = 54.939
® Feedback system less sensitive to system parameter values.
NOTE! High feedback gain = good performance here. Not always
true! e.g., Public address amplifier.
Auto cruise-control example (attempt #3)
4. Third attempt. Try to get rid of steady state error.
Compensator
α
β
r(t) ycl(t)
w(t)
u(t)
10
0.5
ycl(t) = 10 α (r(t) − βycl(t)) − 5w(t)
Lecture notes prepared by and copyright c 1998–2013, Gregory L. Plett and M. Scott Trimboli
11. ECE4510/ECE5510, INTRODUCTION TO FEEDBACK CONTROL 1–11
= 10αr(t) − 10αβycl(t) − 5w(t)
ycl(t) =
10αr(t) − 5w(t)
1 + 10αβ
=
10α
1 + 10αβ
set to 1: β=1− 1
10α
r(t) −
5
1 + 10αβ
w(t)
= r(t) −
5
10α
w(t).
I Best results yet! (Try for yourself with α = 100.)
Lecture notes prepared by and copyright c 1998–2013, Gregory L. Plett and M. Scott Trimboli
12. ECE4510/ECE5510, INTRODUCTION TO FEEDBACK CONTROL 1–12
1.4: Examples of senior-design/MSEE controls topics
I As we conclude this chapter of notes, we consider how you might
benefit from learning about control systems in your educational
journey.
I The thermostat example seems pretty “low tech,” but recently the Nest
Learning Thermostat has made quite a splash in the tech industry.
I Control-system design is an integral component to a lot of innovation,
and is very often a basic element of senior-design projects and MSEE
thesis topics.
I A few prior senior-design projects in the controls area include:
• N. Shaffer, J. Alvarez, J. Valenzuela, R. Hindley, “Autonomous Robot Delivery System.”
• B. Lessard, K. Stetzel, “Novel Solar Tracking Charge Device.”
• S. Bretzke, G. Deemer, “Deetzke Coffee-Bean Roaster.”
• R. Gallegos, T. McCorkle, L. Morgan, “Mag-pulsion Track Vehicle.”
• M. Anderson, S. Hopp, C. Cotey, “Automated Personal Use Bartender (A.P.U.B.).”
• T. Bouma, E. Silva, “Automatic Temperature Regulated Vehicle Power Window Controller.”
• A. Levasseur, P. Cotey, C. Runyan, “P.O.T.T. (Phone Operated Toy Truck).”
I Several past MSEE thesis/project topics in the controls area include:
• R. Jobman, “Implementation of a Quad-Rotor Control System.”
• H. Shane, “Model Predictive Control of the Magnetic Levitation Device.”
• J.A. Stewart, “Linear Optimal Control of a Two-Stage Hydraulic Valve Actuator.”
• R. Murray, “Pole Placement and LQR Methods to Control a Focus Actuator of An Optical Disk
Drive.”
• J.D. Musick, “Target-Tracking a Non-Linear Target Path Using Kalman Predictive Algorithm.”
• M. Seil, “Adaptive Neural Network Control of Cylinder Position Utilizing Digitally Latching
Pneumatic Poppet Valves.”
• I. Rueda, “Regeneration Control via Traction Estimation in E.V.”
• H. Böttrich, “Adaptive Inverse Control of Nonlinear Systems Using Recurrent Neural Networks.”
Lecture notes prepared by and copyright c 1998–2013, Gregory L. Plett and M. Scott Trimboli
13. ECE4510/ECE5510, INTRODUCTION TO FEEDBACK CONTROL 1–13
Our research area: xEV applications of control systems
I At UCCS, our research is focused on applications of control systems
to hybrid- and electric-vehicle battery management.
• The battery is the heaviest and most expensive component in the
electric drivetrain.
• Battery packs are presently over-designed/underutilized in an
effort to extend life.
• Understanding the physical mechanisms that degrade life, and
controlling battery packs to avoid conditions that accelerate these
mechanisms will extend life.
I In the past, we have had projects in topics including:
• Collecting data from laboratory tests to understand ideal-cell and
degradation dynamics.
• Modeling hysteresis effects in battery cell voltage.
• Making reduced-order models of full-order physics models of cells.
• Using model-predictive controls for fast-charge.
I Presently, we have projects in topics including:
• Optimal balancing of cells in a large battery pack.
• Building a hardware battery pack simulator.
• Building a hardware battery management system.
• Internal state estimation of battery cells.
• Reduced-order degradation models of battery cells.
• System identification of battery cell model parameters.
• Physics-based power limits to optimize life.
Lecture notes prepared by and copyright c 1998–2013, Gregory L. Plett and M. Scott Trimboli
14. ECE4510/ECE5510, INTRODUCTION TO FEEDBACK CONTROL 1–14
Control systems in the ECE Department at UCCS
I The following graphic shows what control-systems courses are
offered by ECE at UCCS:
I We also have a Graduate Certificate Program in Electric Drivetrain
Technology, and an MSEE option in Battery Controls.
• GATE Fellowships are available to qualified students enrolled in
either of these two programs.
• cf. http://mocha-java.uccs.edu/IDEATE/
Lecture notes prepared by and copyright c 1998–2013, Gregory L. Plett and M. Scott Trimboli