MATLAB与Simulink在控制系统设计中的实践应用

4星 · 超过85%的资源需积分: 9 105 浏览量更新于2024-07-23 1 收藏 17.42MB PDF 举报

"Computer Aided Control Systems Design" 是一本专注于应用控制基础和实践能力培养的书籍，旨在帮助工程师和学生弥合理论与实际应用之间的差距。本书适用于初学者、研究生、博士生，以及希望掌握基本控制系统设计、分析和实现的实践工程师。书中使用MATLAB和Simulink作为工具进行讲解，第一部分可以作为MATLAB的入门教材或相关计算课程的补充材料。本书主要知识点包括： 1. **控制系统的概念和设计**：深入浅出地介绍了控制系统的基本原理，包括系统的稳定性、性能指标（如稳态误差、上升时间、超调量等）以及如何通过数学模型分析和设计控制系统。 2. **MATLAB基础**：提供了MATLAB的基础教程，适合初学者了解和学习如何使用MATLAB进行数值计算、符号运算和数据分析，以及创建脚本和函数。 3. **Simulink仿真**：Simulink是用于动态系统建模和仿真的图形化环境，本书将介绍如何使用Simulink构建控制系统模型，进行时域和频域分析，以及系统辨识和优化。 4. **控制系统的分析与设计**：涵盖经典控制理论，如根轨迹法、频率响应法、PID控制器设计等，并结合MATLAB和Simulink进行实例演示和练习，以提升读者的实际操作能力。 5. **现代控制理论的应用**：可能涉及状态空间法、李雅普诺夫稳定性理论、最优控制和自适应控制等，这些内容有助于理解更高级的控制策略。 6. **工程实践**：强调理论知识与工业实际问题的结合，书中包含多个实际控制问题的案例，帮助读者解决实际工作中遇到的问题。 7. **软件许可和使用**：书中明确指出MATLAB和Simulink的商标权属于The MathWorks公司，并非对其教育方法或产品使用的背书或赞助，而是作为教学和学习的工具。 8. **资料来源和引用**：书中引用了权威和高度认可的资料来源，确保内容的准确性和专业性。通过阅读本书，读者不仅可以掌握控制系统的理论知识，还能获得使用MATLAB和Simulink解决实际控制问题的技能，从而在学术研究或工业实践中得到提升。

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 cor-

rections 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 magnicent step in the

industrial revolution. The British astronomer G. Airy was the rst scientist to analyze

mathematically the regulating system invented by Watt. But the rst denitive math-

ematical description was given only in the works by J. C. Maxwell (who discovered

the Maxwell electromagnetic eld equations) in 1868, where some of the erratic

behaviors encountered in the steam engine were described and some control mecha-

nisms were proposed. He was able to explain instabilities exhibited by the yball

governor using differential equations. This demonstrated the importance and useful-

ness 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 nding stability criteria [1–2] for certain linear systems was discussed

rst. This work was then extended to determine the stability of nonlinear systems by

Lyapunov [3]. In the 1930s, work on feedback amplier 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

2 Computer-Aided Control Systems Design

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 rst 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 lter [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-innity norm

and μ-synthesis theory [13–15] demonstrated how uncertainty can be modeled in the

system equations. A decade later, the concept of intelligent control systems was devel-

oped. An intelligent machine [16] that is able to give a better behavior under uncer-

tainty condition was introduced. Intelligent control theory has the ideas laid down in

the area of Articial Intelligence (AI). Articial Neural Networks [17–19] 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 [21–24] controllers offer some form of

robust control without the requirement to model the system dynamic behavior.

Thus, control theories have made signicant strides in the past 100 years in

history. New mathematical techniques made it possible to more accurately control

signicantly more complex dynamical systems than the original yball 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 meth-

odology have helped to make efcient 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 efcient 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 eld of control

has been inuenced 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.

3An Overview of Applied Control Engineering

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 prin-

ciples 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 xed con-

trol method does not work. For example, the mass of an airplane is different before

and after the ight journey. This affects the dynamic model as the mass changes

with time. Measurements may be contaminated with noise and external distur-

bance effects. Sensors, which provide accurate data, because of these difculties

in measuring the output data produce highly random and irrelevant information.

Sometimes, the nest 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 denite end point, which

can be approached by instrumentation.

With this in mind, there should be a unied approach to the design of the control

systems. The rst 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 modication of the model is difcult

or sometimes unfeasible, the controller has to withstand the model inaccuracy without

compromising the desired performance. The control system design is actually a com-

bination 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.

4 Computer-Aided Control Systems Design

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 conguration.

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

9. Decide on performance specications, based on the overall control

objectives.

10. Design a controller.

11. Analyze the resulting controlled system to see if the specications are

satised; and if they are not satised, modify the specications 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 ne-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 analy-

sis. 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 engi-

neering 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 appli-

cations such as ALSTOM gasier 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 dene 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 specied. In the

example of the underwater robotic vehicles, the inputs are the voltage and current

to the thrusters, and the outputs are the position, velocity, and acceleration of the

vehicles. The system to be controlled is known as the plant and is represented by

a block diagram. The control engineers may have direct control on the inputs, and

they are known as controllable inputs. But, there are some inputs over which the

control engineers have no control and they are called disturbance inputs. A typical

closed-loop system is shown in Figure1.1; it consists of the forward path controlled

by a controller and the feedback path.

As seen in Figure 1.1, to attenuate the disturbance, a controller (or embedded

controller) is used to provide control signal to the plant. The most popular control-

ler used in industry is the Proportional-Integral-Derivative (PID) controller. It gen-

erates P-action, I-action, and D-action and combines them to produce the control

signal. The derivative mode is added to the PI controller to make the response

5An Overview of Applied Control Engineering

speed faster and less oscillatory. This type of control provides zero offset and faster

response but can be quite poor in attenuating the disturbance. The control signal will

actuate the plant via an interfacing circuit (e.g., amplier or digital-to-analog con-

verter) and actuators such as motors. To obtain the actual outputs, sensors are used.

For  example, an altimeter measures the depth of the water and a magnetic compass

measures the heading angle of the vehicle. Difference between the actual outputs

and desired value (or set point) is called error signal. The error signal is used for an

input to the controller to generate control signal.

In control system design, the responses of system outputs to the system inputs are

important to the engineers. The control engineers often try to determine the system

response by using a mathematical model that represents the system behavior. With

the system model obtained, the system outputs are computed. Comparisons can be

made with the theoretical results to see whether it matches the mathematical model.

In order to obtain the system inputs, instrumentation and measurement devices

such as communication cables, data acquisition cards, measurement devices, and a

microprocessor are needed.

In the example of a ship seen in Figure1.2, the n or rudder and diesel engines

are the control inputs, which can be changed to control the outputs such as the ship’s

heading and surge velocity. The wave, wind, and ocean current are disturbance

inputs that create errors in the controlled variables such as the roll, pitch, heave,

forward, sway, and heading or turning velocity.

sired

Value

Error

Signal

–

Embedded

Controller

(Microprocessor,

PC104, desktop PC)

Interfacing Circuit

(Ampliﬁer,

motor driver)

Control

Signal

Actual

Output

Feed Forward Path

Actuators

(Motor, linear

actuator)

Sensors

(Altimeter,

compass)

Plant

Controlle

Variable

Disturbance

(Vibration,

load variation)

Feedback Path

FIGURE 1.1 Closed-loop control system block diagram.

Forward Velocity

Heading

Ship Motion

(roll, pitch, hea

ve)

Ship

Engines

Rudder

rusters

Wind

Waves

Current

Disturbance

FIGURE 1.2 A ship as a control system.

剩余383页未读，继续阅读

Bigningbo

粉丝: 0
资源: 1

MATLAB与Simulink在控制系统设计中的实践应用

SAP AG's TestWorkbench与Computer Aided Test Tool

"MATLAB控制系统仿真课程设计及实验报告要求

"OrCAD概述及电路图绘制技巧详解

multivariable control

Digital Control Application Illustrated with Matlab

cigre_b457_dc_test_systems.zip

Modern control system -12th (book and solution)

Master MATLAB Control Systems from Scratch: Full Process Analysis and Practical Exercises

"计算机集成制造概念及相关技术简介PPT教程

036GraphTheory(图论) matlab代码.rar

最新资源