Computer-Aided Control Systems Design

Practical Applications Using MATLAB® and Simulink®

Cheng Siong Chin

• Publisher: CRC Press
• Year: 2017
• Print ISBN: 9781138073494
• eBook ISBN: 9781351831901
• Topic: Technology & Engineering
• Subtopic: Electrical Engineering & Telecommunications
• Index: Technology & Engineering

Computer-Aided Control Systems Design: Practical Applications Using MATLAB® and Simulink® supplies a solid foundation in applied control to help you bridge the gap between control theory and its real-world applications. Working from basic principles, the book delves into control systems design through the practical examples of the ALSTOM gasifier system in power stations and underwater robotic vehicles in the marine industry. It also shows how powerful software such as MATLAB® and Simulink® can aid in control systems design.

Make Control Engineering Come Alive with Computer-Aided Software

Emphasizing key aspects of the design process, the book covers the dynamic modeling, control structure design, controller design, implementation, and testing of control systems. It begins with the essential ideas of applied control engineering and a hands-on introduction to MATLAB and Simulink. It then discusses the analysis, model order reduction, and controller design for a power plant and the modeling, simulation, and control of a remotely operated vehicle (ROV) for pipeline tracking. The author explains how to obtain the ROV model and verify it by using computational fluid dynamic software before designing and implementing the control system. In addition, the book details the nonlinear subsystem modeling and linearization of the ROV at vertical plane equilibrium points. Throughout, the author delineates areas for further study. Appendices provide additional information on various simulation models and their results.

Learn How to Perform Simulations on Real Industry Systems

A step-by-step guide to computer-aided applied control design, this book supplies the knowledge to help you deal with control problems in industry. It is a valuable reference for anyone who wants a better understanding of the theory and practice of basic control systems design, analysis, and implementation.

An Overview of Applied Control Engineering

1.1 HISTORICAL REVIEW

In this section, we give a brief historical review of the major developments in the area of modern control systems. We shall attempt to discuss the development of classical control and its evolution into modern and robust control. The emphasis of our discussion is directed toward applying the control systems design method within which the framework of this book is based.

Applied control engineering is the engineering discipline that applies control theory to design systems with predictable behaviors. The practice uses sensors to measure the output performance of the device being controlled and those measurements can be used to give feedback to the input actuators that can make corrections to achieve the desired performance. When a device is designed to perform without the need of human inputs for correction, it is called automatic control.

Going backward in time, the Romans did use some elements of control theory in their aqueducts. Indeed, ingenious systems for regulating valves were used in these constructions in order to keep the water level constant. This certainly was a successful device as the water clocks were still being made in Baghdad when the Mongols captured the city in AD 1258. A variety of automatic devices have been used over the centuries to accomplish useful tasks. The latter includes the automata, popular in Europe in the 17th and 18th centuries. In 1788, J. Watt adapted these ideas when he invented the steam engine and this constituted a magnificent step in the industrial revolution. The British astronomer G. Airy was the first scientist to analyze mathematically the regulating system invented by Watt. But the first definitive mathematical description was given only in the works by J. C. Maxwell (who discovered the Maxwell electromagnetic field equations) in 1868, where some of the erratic behaviors encountered in the steam engine were described and some control mechanisms were proposed. He was able to explain instabilities exhibited by the flyball governor using differential equations. This demonstrated the importance and usefulness of mathematical models, and methods in understanding complex phenomena, and it signaled the beginning of mathematical control and systems theory. Elements of control theory had appeared earlier but not as dramatically and convincingly as in Maxwell’s analysis.

The issue of finding stability criteria [1–2] for certain linear systems was discussed first. This work was then extended to determine the stability of nonlinear systems by Lyapunov [3]. In the 1930s, work on feedback amplifier design at the Bell Telephone Laboratories was based on the concept of frequency response presented in the paper on “Regeneration Theory” [4], which basically described how to determine system stability using the frequency domain approach. This was later extended [5] during the next decade to give rise to one of the most widely applied control system design methodologies [6]. Based on the Root Locus method [2,7], a few designed techniques that allowed the roots of the characteristics equation to be displayed in a graphical form were subsequently proposed.

The invention of computers in the 1950s gave rise to the application of state-space equations that use vector matrix notation for machine computation. The concept of optimum [8] design was first proposed. The method of performing dynamic programming [9] was then developed at the same time as the maximum principle [10]. At the initial conference of the International Federation of Automatic Control, the concept of observability and controllability [11] was introduced. Around the same period, Kalman demonstrated that when the system dynamic equations are linear, performance criterion is quadratic and can be controlled using the LQ method. With the concept of the Kalman filter [12], which combined with an optimal controller, a linear-quadratic-Gaussian (LQG) control was introduced.

The 1980s showed great advances in control theory for the robust design of systems with uncertainties in their dynamic equations. The works on H-infinity norm and μ-synthesis theory [13, 14 and 15] demonstrated how uncertainty can be modeled in the system equations. A decade later, the concept of intelligent control systems was developed. An intelligent machine [16] that is able to give a better behavior under uncertainty condition was introduced. Intelligent control theory has the ideas laid down in the area of Artificial Intelligence (AI). Artificial Neural Networks [17, 18 and 19] uses many simple elements operating in parallel to emulate how their biological counterparts are used. Subsequently, the idea of fuzzy logic [20] was developed to allow computers to model human vagueness. The fuzzy logic [21, 22, 23 and 24] controllers offer some form of robust control without the requirement to model the system dynamic behavior.

Thus, control theories have made significant strides in the past 100 years in history. New mathematical techniques made it possible to more accurately control significantly more complex dynamical systems than the original flyball governor. These techniques include developments in optimal control in the 1950s and 1960s, followed by progress in stochastic, robust, adaptive, and optimal control methods in the 1970s and 1980s, and intelligent control in 1990s. Applications of control methodology have helped to make efficient power generation, space travel, communication satellites, aircraft, and underwater exploration possible.

1.2 COMPUTER-AIDED CONTROL SYSTEM DESIGN

One of the aspects of control systems that has received considerable attention is that of developing efficient and stable computational algorithms. Parallel with the advances in modern control, computer technology has made its own progress and has played a vital role in implementing the control algorithms. As a result, the field of control has been influenced by the revolution in computer technology. Now, most control engineers have easy access to a powerful computer package for system analysis and design. In fact, computers have become an integral part of control systems. With the progress in computing ability, the classes of problems which can be modeled, analyzed, and controlled are considerably larger than those previously treated.

One of the main goals of this book is to establish a relation between applied control engineering and computer software engineering. Applications of computers in control system design and/or implementation is commonly called computer-aided control system design (CACSD) or generally known as computer-aided applied control engineering (CAACE). Using a computer-aided design approach, all principles and techniques of control can be demonstrated in fairly simple fashion. The software in creating and solving problems is not limited to a particular one, rather a collection of powerful available packages. Even if the reader does not have access to these packages, it is still worthwhile to study the numerical results that have been presented. In this way certain trade-offs, trends, comparisons with experiment results, and other results will become apparent.

A major breakthrough in computer-aided control system design was the creation of a “matrix laboratory” for linear algebra. This software called MATLAB although not initially intended for control system design, was turned into a stepping-stone for many powerful CAACE programs in a relatively short period of time. In parallel to this effort, several other CAACE programs such as Mathematica^®, KEDDC^™ and TIMDOM^™ have been developed for a wide range of problems and classes of systems.

An important part of CAACE is simulating a dynamic system model to obtain theoretical behavior of the system before actual implementation on an application. However, a problem such as incompleteness and uncertainty in the dynamic model could happen. The system dynamics may change with time and thus a fixed control method does not work. For example, the mass of an airplane is different before and after the flight journey. This affects the dynamic model as the mass changes with time. Measurements may be contaminated with noise and external disturbance effects. Sensors, which provide accurate data, because of these difficulties in measuring the output data produce highly random and irrelevant information. Sometimes, the finest controller on a miserably designed system may not deliver the desired performance. However, advanced controllers are able to eke out better results for a badly designed system. But on this system, there is a definite end point, which can be approached by instrumentation.

With this in mind, there should be a unified approach to the design of the control systems. The first step is to perform a simulation of the theoretical dynamic system to obtain an insight to its behaviors and try to remodel it closer to the actual response before a controller is designed for the model. If modification of the model is difficult or sometimes unfeasible, the controller has to withstand the model inaccuracy without compromising the desired performance. The control system design is actually a combination of experience and techniques. Experience, however, only comes with time as one is exposed to more control applications. Usually, the techniques of designing a control system involve mostly a team of engineers. The process often emerges in a step-by-step design procedure as follows:

1. Study the system to be controlled.

2. Obtain information about the control objectives.

3. Simulate the system; simplify the model if necessary.

4. Analyze the resulting model.

5. Decide variables to be controlled.

6. Decide on measurements: what sensors and actuators will be used and where will they be placed?

7. Select the control configuration.

8. Decide on the type of controller to be used.

9. Decide on performance specifications, based on the overall control objectives.

10. Design a controller.

11. Analyze the resulting controlled system to see if the specifications are satisfied; and if they are not satisfied, modify the specifications or the type of controller.

12. Simulate the resulting controlled system on a computer or plant using hardware-in-the-loop.

13. Repeat step 3 or all previous steps, if necessary.

14. Choose hardware and software, and implement the controller.

15. Test and validate the control system, and fine-tune the controller if necessary.

Control engineering courses and textbooks usually focus on steps 10 and 11 in the above procedure; that is, on methods for controller design and control system analysis. Interestingly, some applications are designed without performing the simulation with the hardware or the controlled system. How the control algorithm can be coded in hardware after it completes the simulation is normally omitted in the control engineering course. A special feature of this book is the provision of computer-aided design steps for simulation and its subsequent implementation on the real-life applications such as ALSTOM gasifier and URV. This book also explains some tried and test applications using the available control system methods on these applications.

1.3 CONTROL SYSTEM FUNDAMENTALS

Before discussing the computer-aided applied control engineering applications, it is vital to define the term called system. Examples of a system can be physical systems such as underwater robotic vehicles, ships, submarines, planes, and robots. But, most systems have things in common. They need outputs and inputs to be specified. In the example of the underwater robotic vehicles, the inputs are the voltage and current to the thrusters, and the outputs a...

Table of contents

• Cover
• Half Title
• Title Page
• Copyright Page
• Table of Contents
• Foreword
• Preface
• Acknowledgments
• Chapter 1 An Overview of Applied Control Engineering
• Chapter 2 Introduction to MATLAB and Simulink
• Chapter 3 Analysis and Control of the ALSTOM Gasifier Problem
• Chapter 4 Modeling of a Remotely Operated Vehicle
• Chapter 5 Control of a Remotely Operated Vehicle
• References
• Appendix A1: State-Space Matrices for ALSTOM Gasifier System (Linear)
• Appendix A2: LQR Simulation Model and Results
• Appendix A3: LQG Simulation Model
• Appendix A4: LQG/LTR Simulation Model and Results
• Appendix A5: H2 Simulation Model and Results
• Appendix A6: H∞ Simulation Model and Results
• Index