Customer: SACO CONTROLS Prep by: Sam Larizza Project #: P7-03-01 BILL OF MATERIALS Date: July 11, 2003 P.O. #: 2003/10781 Project: Page: 1 of 4
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Preface iii,1 Introduction 1,1 1 Issues in Control System Design 1. 1 2 What Is in This Book 7,2 Norms for Signals and Systems 13. 2 1 Norms for Signals 13,2 2 Norms for Systems 15,2 3 Input Output Relationships 18. 2 4 Power Analysis Optional 19,2 5 Proofs for Tables 2 1 and 2 2 Optional 21. 2 6 Computing by State Space Methods Optional 24,3 Basic Concepts 31. 3 1 Basic Feedback Loop 31,3 2 Internal Stability 34. 3 3 Asymptotic Tracking 38,3 4 Performance 40,4 Uncertainty and Robustness 45. 4 1 Plant Uncertainty 45,4 2 Robust Stability 50,4 3 Robust Performance 53. 4 4 Robust Performance More Generally 58,4 5 Conclusion 59. 5 Stabilization 63,5 1 Controller Parametrization Stable Plant 63. 5 2 Coprime Factorization 65, 5 3 Coprime Factorization by State Space Methods Optional 69. 5 4 Controller Parametrization General Plant 71,5 5 Asymptotic Properties 73. 5 6 Strong and Simultaneous Stabilization 75,5 7 Cart Pendulum Example 81. 6 Design Constraints 87,6 1 Algebraic Constraints 87. 6 2 Analytic Constraints 88,7 Loopshaping 101,7 1 The Basic Technique of Loopshaping 101. 7 2 The Phase Formula Optional 105,7 3 Examples 108. 8 Advanced Loopshaping 117,8 1 Optimal Controllers 117. 8 2 Loopshaping with C 118,8 3 Plants with RHP Poles and Zeros 126. 8 4 Shaping S T or Q 135,8 5 Further Notions of Optimality 138. 9 Model Matching 149,9 1 The Model Matching Problem 149. 9 2 The Nevanlinna Pick Problem 150,9 3 Nevanlinna s Algorithm 154. 9 4 Solution of the Model Matching Problem 158,9 5 State Space Solution Optional 160. 10 Design for Performance 163,10 1 P 1 Stable 163,10 2 P 1 Unstable 168. 10 3 Design Example Flexible Beam 170,10 4 2 Norm Minimization 175. 11 Stability Margin Optimization 181,11 1 Optimal Robust Stability 181. 11 2 Conformal Mapping 185,11 3 Gain Margin Optimization 187. 11 4 Phase Margin Optimization 192,12 Design for Robust Performance 195. 12 1 The Modified Problem 195,12 2 Spectral Factorization 196. 12 3 Solution of the Modified Problem 198,12 4 Design Example Flexible Beam Continued 204. References 209, Striking developments have taken place since 1980 in feedback control theory The subject has be. come both more rigorous and more applicable The rigor is not for its own sake but rather that even. in an engineering discipline rigor can lead to clarity and to methodical solutions to problems The. applicability is a consequence both of new problem formulations and new mathematical solutions. to these problems Moreover computers and software have changed the way engineering design is. done These developments suggest a fresh presentation of the subject one that exploits these new. developments while emphasizing their connection with classical control. Control systems are designed so that certain designated signals such as tracking errors and. actuator inputs do not exceed pre specified levels Hindering the achievement of this goal are. uncertainty about the plant to be controlled the mathematical models that we use in representing. real physical systems are idealizations and errors in measuring signals sensors can measure signals. only to a certain accuracy Despite the seemingly obvious requirement of bringing plant uncertainty. explicitly into control problems it was only in the early 1980s that control researchers re established. the link to the classical work of Bode and others by formulating a tractable mathematical notion. of uncertainty in an input output framework and developing rigorous mathematical techniques to. cope with it This book formulates a precise problem called the robust performance problem with. the goal of achieving specified signal levels in the face of plant uncertainty. The book is addressed to students in engineering who have had an undergraduate course in. signals and systems including an introduction to frequency domain methods of analyzing feedback. control systems namely Bode plots and the Nyquist criterion A prior course on state space theory. would be advantageous for some optional sections but is not necessary To keep the development. elementary the systems are single input single output and linear operating in continuous time. Chapters 1 to 7 are intended as the core for a one semester senior course they would need. supplementing with additional examples These chapters constitute a basic treatment of feedback. design containing a detailed formulation of the control design problem the fundamental issue. of performance stability robustness tradeoff and the graphical design technique of loopshaping. suitable for benign plants stable minimum phase Chapters 8 to 12 are more advanced and. are intended for a first graduate course Chapter 8 is a bridge to the latter half of the book. extending the loopshaping technique and connecting it with notions of optimality Chapters 9 to. 12 treat controller design via optimization The approach in these latter chapters is mathematical. rather than graphical using elementary tools involving interpolation by analytic functions This. mathematical approach is most useful for multivariable systems where graphical techniques usually. break down Nevertheless we believe the setting of single input single output systems is where this. new approach should be learned, There are many people to whom we are grateful for their help in this book Dale Enns for. sharing his expertise in loopshaping Raymond Kwong and Boyd Pearson for class testing the book. and Munther Dahleh Ciprian Foias and Karen Rudie for reading earlier drafts Numerous Caltech. students also struggled with various versions of this material Gary Balas Carolyn Beck Bobby. Bodenheimer and Roy Smith had particularly helpful suggestions Finally we would like to thank. the AFOSR ARO NSERC NSF and ONR for partial financial support during the writing of this. Introduction, Without control systems there could be no manufacturing no vehicles no computers no regulated. environment in short no technology Control systems are what make machines in the broadest. sense of the term function as intended Control systems are most often based on the principle. of feedback whereby the signal to be controlled is compared to a desired reference signal and the. discrepancy used to compute corrective control action The goal of this book is to present a theory. of feedback control system design that captures the essential issues can be applied to a wide range. of practical problems and is as simple as possible. 1 1 Issues in Control System Design, The process of designing a control system generally involves many steps A typical scenario is as. 1 Study the system to be controlled and decide what types of sensors and actuators will be used. and where they will be placed,2 Model the resulting system to be controlled. 3 Simplify the model if necessary so that it is tractable. 4 Analyze the resulting model determine its properties. 5 Decide on performance specifications,6 Decide on the type of controller to be used. 7 Design a controller to meet the specs if possible if not modify the specs or generalize the. type of controller sought, 8 Simulate the resulting controlled system either on a computer or in a pilot plant. 9 Repeat from step 1 if necessary, 10 Choose hardware and software and implement the controller. 11 Tune the controller on line if necessary,2 CHAPTER 1 INTRODUCTION. It must be kept in mind that a control engineer s role is not merely one of designing control. systems for fixed plants of simply wrapping a little feedback around an already fixed physical. system It also involves assisting in the choice and configuration of hardware by taking a system. wide view of performance For this reason it is important that a theory of feedback not only lead. to good designs when these are possible but also indicate directly and unambiguously when the. performance objectives cannot be met, It is also important to realize at the outset that practical problems have uncertain non. minimum phase plants non minimum phase means the existence of right half plane zeros so the. inverse is unstable that there are inevitably unmodeled dynamics that produce substantial un. certainty usually at high frequency and that sensor noise and input signal level constraints limit. the achievable benefits of feedback A theory that excludes some of these practical issues can. still be useful in limited application domains For example many process control problems are so. dominated by plant uncertainty and right half plane zeros that sensor noise and input signal level. constraints can be neglected Some spacecraft problems on the other hand are so dominated by. tradeoffs between sensor noise disturbance rejection and input signal level e g fuel consumption. that plant uncertainty and non minimum phase effects are negligible Nevertheless any general. theory should be able to treat all these issues explicitly and give quantitative and qualitative results. about their impact on system performance, In the present section we look at two issues involved in the design process deciding on perfor. mance specifications and modeling We begin with an example to illustrate these two issues. Example A very interesting engineering system is the Keck astronomical telescope currently. under construction on Mauna Kea in Hawaii When completed it will be the world s largest The. basic objective of the telescope is to collect and focus starlight using a large concave mirror The. shape of the mirror determines the quality of the observed image The larger the mirror the more. light that can be collected and hence the dimmer the star that can be observed The diameter of. the mirror on the Keck telescope will be 10 m To make such a large high precision mirror out of. a single piece of glass would be very difficult and costly Instead the mirror on the Keck telescope. will be a mosaic of 36 hexagonal small mirrors These 36 segments must then be aligned so that. the composite mirror has the desired shape, The control system to do this is illustrated in Figure 1 1 As shown the mirror segments. are subject to two types of forces disturbance forces described below and forces from actuators. Behind each segment are three piston type actuators applying forces at three points on the segment. to effect its orientation In controlling the mirror s shape it suffices to control the misalignment. between adjacent mirror segments In the gap between every two adjacent segments are capacitor. type sensors measuring local displacements between the two segments These local displacements. are stacked into the vector labeled y this is what is to be controlled For the mirror to have the. ideal shape these displacements should have certain ideal values that can be pre computed these. are the components of the vector r The controller must be designed so that in the closed loop. system y is held close to r despite the disturbance forces Notice that the signals are vector valued. Such a system is multivariable, Our uncertainty about the plant arises from disturbance sources. As the telescope turns to track a star the direction of the force of gravity on the mirror. During the night when astronomical observations are made the ambient temperature changes. 1 1 ISSUES IN CONTROL SYSTEM DESIGN 3,disturbance forces. r u mirror y,controller actuators, Figure 1 1 Block diagram of Keck telescope control system. The telescope is susceptible to wind gusts,and from uncertain plant dynamics. The dynamic behavior of the components mirror segments actuators sensors cannot be. modeled with infinite precision, Now we continue with a discussion of the issues in general. Control Objectives, Generally speaking the objective in a control system is to make some output say y behave in a. desired way by manipulating some input say u The simplest objective might be to keep y small. or close to some equilibrium point a regulator problem or to keep y r small for r a reference. or command signal in some set a servomechanism or servo problem Examples. On a commercial airplane the vertical acceleration should be less than a certain value for. passenger comfort, In an audio amplifier the power of noise signals at the output must be sufficiently small for. high fidelity, In papermaking the moisture content must be kept between prescribed values. There might be the side constraint of keeping u itself small as well because it might be constrained. e g the flow rate from a valve has a maximum value determined when the valve is fully open. or it might be too expensive to use a large input But what is small for a signal It is natural to. introduce norms for signals then y small means kyk small Which norm is appropriate depends. on the particular application, In summary performance objectives of a control system naturally lead to the introduction of. norms then the specs are given as norm bounds on certain key signals of interest. 4 CHAPTER 1 INTRODUCTION, Before discussing the issue of modeling a physical system it is important to distinguish among four. different objects,1 Real physical system the one out there. 2 Ideal physical model obtained by schematically decomposing the real physical system into. ideal building blocks composed of resistors masses beams kilns isotropic media Newtonian. fluids electrons and so on, 3 Ideal mathematical model obtained by applying natural laws to the ideal physical model. composed of nonlinear partial differential equations and so on. 4 Reduced mathematical model obtained from the ideal mathematical model by linearization. lumping and so on usually a rational transfer function. Sometimes language makes a fuzzy distinction between the real physical system and the ideal. physical model For example the word resistor applies to both the actual piece of ceramic and. metal and the ideal object satisfying Ohm s law Of course the adjectives real and ideal could be. used to disambiguate, No mathematical system can precisely model a real physical system there is always uncertainty. Uncertainty means that we cannot predict exactly what the output of a real physical system will. be even if we know the input so we are uncertain about the system Uncertainty arises from two. sources unknown or unpredictable inputs disturbance noise etc and unpredictable dynamics. What should a model provide It should predict the input output response in such a way that. we can use it to design a control system and then be confident that the resulting design will work. on the real physical system Of course this is not possible A leap of faith will always be required. on the part of the engineer This cannot be eliminated but it can be made more manageable with. the use of effective modeling analysis and design techniques. Mathematical Models in This Book, The models in this book are finite dimensional linear and time invariant The main reason for this. is that they are the simplest models for treating the fundamental issues in control system design. The resulting design techniques work remarkably well for a large class of engineering problems. partly because most systems are built to be as close to linear time invariant as possible so that they. are more easily controlled Also a good controller will keep the system in its linear regime The. uncertainty description is as simple as possible as well. The basic form of the plant model in this book is, Here y is the output u the input and P the nominal plant transfer function The model uncertainty. comes in two forms,n unknown noise or disturbance,unknown plant perturbation. 1 1 ISSUES IN CONTROL SYSTEM DESIGN 5, Both n and will be assumed to belong to sets that is some a priori information is assumed. about n and Then every input u is capable of producing a set of outputs namely the set of. all outputs P u n as n and range over their sets Models capable of producing sets of. outputs for a single input are said to be nondeterministic There are two main ways of obtaining. models as described next,Models from Science, The usual way of getting a model is by applying the laws of physics chemistry and so on Consider. the Keck telescope example One can write down differential equations based on physical principles. e g Newton s laws and making idealizing assumptions e g the mirror segments are rigid The. coefficients in the differential equations will depend on physical constants such as masses and. physical dimensions These can be measured This method of applying physical laws and taking. measurements is most successful in electromechanical systems such as aerospace vehicles and robots. Some systems are difficult to model in this way either because they are too complex or because. their governing laws are unknown,Models from Experimental Data. The second way of getting a model is by doing experiments on the physical system Let s start. with a simple thought experiment one that captures many essential aspects of the relationships. between physical systems and their models and the issues in obtaining models from experimental. data Consider a real physical system the plant to be controlled with one input u and one. output y To design a control system for this plant we must understand how u affects y. The experiment runs like this Suppose that the real physical system is in a rest state before. an input u is applied i e u y 0 Now apply some input signal u resulting in some output. signal y Observe the pair u y Repeat this experiment several times Pretend that these data. pairs are all we know about the real physical system This is the black box scenario Usually we. know something about the internal workings of the system. After doing this experiment we will notice several things First the same input signal at different. times produces different output signals Second if we hold u 0 y will fluctuate in an unpredictable. manner Thus the real physical system produces just one output for any given input so it itself. is deterministic However we observers are uncertain because we cannot predict what that output. Ideally the model should cover the data in the sense that it should be capable of producing. every experimentally observed input output pair Of course it would be better to cover not just. the data observed in a finite number of experiments but anything that can be produced by the real. physical system Obviously this is impossible If nondeterminism that reasonably covers the range. of expected data is not built into the model we will not trust that designs based on such models. will work on the real system, In summary for a useful theory of control design plant models must be nondeterministic having. uncertainty built in explicitly,Synthesis Problem, A synthesis problem is a theoretical problem precise and unambiguous Its purpose is primarily. pedagogical It gives us something clear to focus on for the purpose of study The hope is that. 6 CHAPTER 1 INTRODUCTION, the principles learned from studying a formal synthesis problem will be useful when it comes to. designing a real control system, The most general block diagram of a control system is shown in Figure 1 2 The generalized plant. generalized,controller,Figure 1 2 Most general control system. consists of everything that is fixed at the start of the control design exercise the plant actuators. that generate inputs to the plant sensors measuring certain signals analog to digital and digital. to analog converters and so on The controller consists of the designable part it may be an electric. circuit a programmable logic controller a general purpose computer or some other such device. The signals w z y and u are in general vector valued functions of time The components of w. are all the exogenous inputs references disturbances sensor noises and so on The components of. z are all the signals we wish to control tracking errors between reference signals and plant outputs. actuator signals whose values must be kept between certain limits and so on The vector y contains. the outputs of all sensors Finally u contains all controlled inputs to the generalized plant Even. open loop control fits in the generalized plant would be so defined that y is always constant. Very rarely is the exogenous input w a fixed known signal One of these rare instances is where. a robot manipulator is required to trace out a definite path as in welding Usually w is not fixed. but belongs to a set that can be characterized to some degree Some examples. In a thermostat controlled temperature regulator for a house the reference signal is always. piecewise constant at certain times during the day the thermostat is set to a new value The. temperature of the outside air is not piecewise constant but varies slowly within bounds. In a vehicle such as an airplane or ship the pilot s commands on the steering wheel throttle. pedals and so on come from a predictable set and the gusts and wave motions have amplitudes. and frequencies that can be bounded with some degree of confidence. The load power drawn on an electric power system has predictable characteristics. Sometimes the designer does not attempt to model the exogenous inputs Instead she or he. designs for a suitable response to a test input such as a step a sinusoid or white noise The. designer may know from past experience how this correlates with actual performance in the field. Desired properties of z generally relate to how large it is according to various measures as discussed. 1 2 WHAT IS IN THIS BOOK 7, Finally the output of the design exercise is a mathematical model of a controller This must. be implementable in hardware If the controller you design is governed by a nonlinear partial. differential equation how are you going to implement it A linear ordinary differential equation. with constant coefficients representing a finite dimensional time invariant linear system can be. simulated via an analog circuit or approximated by a digital computer so this is the most common. type of control law, The synthesis problem can now be stated as follows Given a set of generalized plants a set. of exogenous inputs and an upper bound on the size of z design an implementable controller to. achieve this bound How the size of z is to be measured e g power or maximum amplitude. depends on the context This book focuses on an elementary version of this problem. 1 2 What Is in This Book, Since this book is for a first course on this subject attention is restricted to systems whose models. are single input single output finite dimensional linear and time invariant Thus they have trans. fer functions that are rational in the Laplace variable s The general layout of the book is that. Chapters 2 to 4 and 6 are devoted to analysis of control systems that is the controller is already. specified and Chapters 5 and 7 to 12 to design, Performance of a control system is specified in terms of the size of certain signals of interest For. example the performance of a tracking system could be measured by the size of the error signal. Chapter 2 Norms for Signals and Systems looks at several ways of defining norms for a signal u t. in particular the 2 norm associated with energy,the norm maximum absolute value. and the square root of the average power actually not quite a norm. lim u t dt, Also introduced are two norms for a system s transfer function G s the 2 norm. kGk2 G j d,and the norm,kGk max G j, Notice that kGk equals the peak amplitude on the Bode magnitude plot of G Then two very. useful tables are presented summarizing input output norm relationships For example one table. gives a bound on the 2 norm of the output knowing the 2 norm of the input and the norm of the. 8 CHAPTER 1 INTRODUCTION,Figure 1 3 Single loop feedback system. transfer function Such results are very useful in predicting for example the effect a disturbance. will have on the output of a feedback system, Chapters 3 and 4 are the most fundamental in the book The system under consideration is. shown in Figure 1 3 where P and C are the plant and controller transfer functions The signals are. as follows,r reference or command input,e tracking error. u control signal controller output,d plant disturbance. y plant output,n sensor noise, In Chapter 3 Basic Concepts internal stability is defined and characterized Then the system is. analyzed for its ability to track a single reference signal r a step or a ramp asymptotically as. time increases Finally we look at tracking a set of reference signals The transfer function from. reference input r to tracking error e is denoted S the sensitivity function It is argued that a useful. tracking performance criterion is kW1 Sk 1 where W1 is a transfer function which can be tuned. by the control system designer, Since no mathematical system can exactly model a physical system we must be aware of how. modeling errors might adversely affect the performance of a control system Chapter 4 Uncertainty. and Robustness begins with a treatment of various models of plant uncertainty The basic technique. is to model the plant as belonging to a set P Such a set can be either structured for example. there are a finite number of uncertain parameters or unstructured the frequency response lies in. a set in the complex plane for every frequency For us unstructured is more important because it. leads to a simple and useful design theory In particular multiplicative perturbation is chosen for. detailed study it being typical In this uncertainty model there is a nominal plant P and the family. P consists of all perturbed plants P such that at each frequency the ratio P j P j lies in a. disk in the complex plane with center 1 This notion of disk like uncertainty is key because of it. the mathematical problems are tractable, Generally speaking the notion of robustness means that some characteristic of the feedback. system holds for every plant in the set P A controller C provides robust stability if it provides. internal stability for every plant in P Chapter 4 develops a test for robust stability for the multi. plicative perturbation model a test involving C and P The test is kW2 T k 1 Here T is the. 1 2 WHAT IS IN THIS BOOK 9, complementary sensitivity function equal to 1 S or the transfer function from r to y and W2. is a transfer function whose magnitude at frequency equals the radius of the uncertainty disk at. that frequency, The final topic in Chapter 4 is robust performance guaranteed tracking in the face of plant. uncertainty The main result is that the tracking performance spec kW1 Sk 1 is satisfied for all. plants in the multiplicative perturbation set if and only if the magnitude of W1 S W2 T is less. than 1 for all frequencies that is,k W1 S W2 T k 1 1 1. This is an analysis result It tells exactly when some candidate controller provides robust perfor. Chapter 5 Stabilization is the first on design Most synthesis problems can be formulated like. this Given P design C so that the feedback system 1 is internally stable and 2 acquires some. additional desired property or properties for example the output y asymptotically tracks a step. input r The method of solution presented here is to parametrize all Cs for which 1 is true and. then to find a parameter for which 2 holds In this chapter such a parametrization is derived it. has the form, where N M X and Y are fixed stable proper transfer functions and Q is the parameter an. arbitrary stable proper transfer function The usefulness of this parametrization derives from the. fact that all closed loop transfer functions are very simple functions of Q for instance the sensitivity. function S while a nonlinear function of C equals simply M Y M N Q This parametrization. is then applied to three problems achieving asymptotic performance specs such as tracking a. step internal stabilization by a stable controller and simultaneous stabilization of two plants by a. common controller, Before we see how to design control systems for the robust performance specification it is. important to understand the basic limitations on achievable performance Why can t we achieve. both arbitrarily good performance and stability robustness at the same time In Chapter 6 Design. Constraints we study design constraints arising from two sources from algebraic relationships that. must hold among various transfer functions and from the fact that closed loop transfer functions. must be stable that is analytic in the right half plane The main conclusion is that feedback control. design always involves a tradeoff between performance and stability robustness. Chapter 7 Loopshaping presents a graphical technique for designing a controller to achieve. robust performance This method is the most common in engineering practice It is especially. suitable for today s CAD packages in view of their graphics capabilities The loop transfer function. is L P C The idea is to shape the Bode magnitude plot of L so that 1 1 is achieved at. least approximately and then to back solve for C via C L P When P or P 1 is not stable L. must contain P s unstable poles and zeros for internal stability of the feedback loop an awkward. constraint For this reason it is assumed in Chapter 7 that P and P 1 are both stable. Thus Chapters 2 to 7 constitute a basic treatment of feedback design containing a detailed. formulation of the control design problem the fundamental issue of performance stability robustness. tradeoff and a graphical design technique suitable for benign plants stable minimum phase. Chapters 8 to 12 are more advanced,10 CHAPTER 1 INTRODUCTION. Chapter 8 Advanced Loopshaping is a bridge between the two halves of the book it extends the. loopshaping technique and connects it with the notion of optimal designs Loopshaping in Chapter 7. focuses on L but other quantities such as C S T or the Q parameter in the stabilization results. of Chapter 5 may also be shaped to achieve the same end For many problems these alternatives. are more convenient Chapter 8 also offers some suggestions on how to extend loopshaping to handle. right half plane poles and zeros, Optimal controllers are introduced in a formal way in Chapter 8 Several different notions of. optimality are considered with an aim toward understanding in what way loopshaping controllers. can be said to be optimal It is shown that loopshaping controllers satisfy a very strong type. of optimality called self optimality The implication of this result is that when loopshaping is. successful at finding an adequate controller it cannot be improved upon uniformly. Chapters 9 to 12 present a recently developed approach to the robust performance design prob. lem The approach is mathematical rather than graphical using elementary tools involving interpo. lation by analytic functions This mathematical approach is most useful for multivariable systems. where graphical techniques usually break down Nevertheless the setting of single input single. output systems is where this new approach should be learned Besides present day software for. control design e g MATLAB and Program CC incorporate this approach. Chapter 9 Model Matching studies a hypothetical control problem called the model matching. problem Given stable proper transfer functions T1 and T2 find a stable transfer function Q to. minimize kT1 T2 Qk The interpretation is this T1 is a model T2 is a plant and Q is a cascade. controller to be designed so that T2 Q approximates T1 Thus T1 T2 Q is the error transfer function. This problem is turned into a special interpolation problem Given points ai in the right half. plane and values bi also complex numbers find a stable transfer function G so that kGk 1. and G ai bi that is G interpolates the value bi at the point ai When such a G exists and how. to find one utilizes some beautiful mathematics due to Nevanlinna and Pick. Chapter 10 Design for Performance treats the problem of designing a controller to achieve the. performance criterion kW1 Sk 1 alone that is with no plant uncertainty When does such a. controller exist and how can it be computed These questions are easy when the inverse of the. plant transfer function is stable When the inverse is unstable i e P is non minimum phase the. questions are more interesting The solutions presented in this chapter use model matching theory. The procedure is applied to designing a controller for a flexible beam The desired performance is. given in terms of step response specs overshoot and settling time It is shown how to choose the. weight W1 to accommodate these time domain specs Also treated in Chapter 10 is minimization. of the 2 norm of some closed loop transfer function e g kW1 Sk2. Next in Chapter 11 Stability Margin Optimization is considered the problem of designing a. controller whose sole purpose is to maximize the stability margin that is performance is ignored. The maximum obtainable stability margin is a measure of how difficult the plant is to control. Three measures of stability margin are treated the norm of a multiplicative perturbation gain. margin and phase margin It is shown that the problem of optimizing these stability margins can. also be reduced to a model matching problem, Chapter 12 Design for Robust Performance returns to the robust performance problem of. designing a controller to achieve 1 1 Chapter 7 proposed loopshaping as a graphical method. when P and P 1 are stable Without these assumptions loopshaping can be awkward and the. methodical procedure in this chapter can be used Actually 1 1 is too hard for mathematical.
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Customer: SACO CONTROLS Prep by: Sam Larizza Project #: P7-03-01 BILL OF MATERIALS Date: July 11, 2003 P.O. #: 2003/10781 Project: Page: 1 of 4
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