Mathematical Modeling of Control Systems

1 213 Mathematical Modeling of Control Systems2 1 INTRODUCTIONIn studying Control Systems the reader must be able to model dynamic Systems in math - ematical terms and analyze their dynamic characteristics. A Mathematical model of a dy-namic system is defined as a set of equations that represents the dynamics of the systemaccurately, or at least fairly well. Note that a Mathematical model is not unique to agiven system . A system may be represented in many different ways and, therefore, mayhave many Mathematical models, depending on one s dynamics of many Systems , whether they are mechanical, electrical, thermal,economic, biological, and so on, may be described in terms of differential differential equations may be obtained by using physical laws governing a partic-ular system for example, Newton s laws for mechanical Systems and Kirchhoff s lawsfor electrical Systems .

2 We must always keep in mind that deriving reasonable mathe-matical models is the most important part of the entire analysis of Control this book we assume that the principle of causality applies to the systemsconsidered. This means that the current output of the system (the output at time t=0)depends on the past input (the input for t<0) but does not depend on the future input(the input for t>0). Mathematical models may assume many different on the particular system and the particular circumstances, one mathemati-cal model may be better suited than other models. For example, in optimal Control prob-lems, it is advantageous to use state-space representations.

3 On the other hand, for theOGATA-CH02-013-062hr 7/14/09 1:51 PM Page 1314 Chapter 2 / Mathematical Modeling of Control Systemstransient-response or frequency-response analysis of single-input, single-output, linear,time-invariant Systems , the transfer-function representation may be more convenientthan any other. Once a Mathematical model of a system is obtained, various analyticaland computer tools can be used for analysis and synthesis Versus obtaining a Mathematical model, we must makea compromise between the simplicity of the model and the accuracy of the results ofthe analysis. In deriving a reasonably simplified Mathematical model, we frequently findit necessary to ignore certain inherent physical properties of the system .

4 In particular,if a linear lumped-parameter Mathematical model (that is, one employing ordinary dif-ferential equations) is desired, it is always necessary to ignore certain nonlinearities anddistributed parameters that may be present in the physical system . If the effects thatthese ignored properties have on the response are small, good agreement will be obtainedbetween the results of the analysis of a Mathematical model and the results of theexperimental study of the physical general, in solving a new problem, it is desirable to build a simplified model so thatwe can get a general feeling for the solution.

5 A more complete Mathematical model maythen be built and used for a more accurate must be well aware that a linear lumped-parameter model, which may be valid inlow-frequency operations, may not be valid at sufficiently high frequencies, since the neg-lected property of distributed parameters may become an important factor in the dynamicbehavior of the system . For example, the mass of a spring may be neglected in low-frequency operations, but it becomes an important property of the system at high fre-quencies. (For the case where a Mathematical model involves considerable errors, robustcontrol theory may be applied.)

6 Robust Control theory is presented in Chapter 10.)Linear system is called linear if the principle of superpositionapplies. The principle of superposition states that the response produced by thesimultaneous application of two different forcing functions is the sum of the twoindividual responses. Hence, for the linear system , the response to several inputs canbe calculated by treating one input at a time and adding the results. It is this principlethat allows one to build up complicated solutions to the linear differential equationfrom simple an experimental investigation of a dynamic system , if cause and effect are pro-portional, thus implying that the principle of superposition holds, then the system canbe considered Time-Invariant Systems and Linear Time-Varying differ-ential equation is linear if the coefficients are constants or functions only of the in-dependent variable.

7 Dynamic Systems that are composed of linear time-invariantlumped-parameter components may be described by linear time-invariant differen-tial equations that is, constant-coefficient differential equations. Such Systems arecalled linear time-invariant(or linear constant-coefficient) Systems . Systems thatare represented by differential equations whose coefficients are functions of timeare called linear time-varyingsystems. An example of a time-varying Control sys-tem is a spacecraft Control system . (The mass of a spacecraft changes due to fuelconsumption.)OGATA-CH02-013-062hr 7/14/09 1:51 PM Page 14 Section 2 2 / Transfer Function and Impulse-Response Function15 Outline of the 2 1 has presented an introduction to the math - ematical Modeling of dynamic Systems .

8 Section 2 2 presents the transfer function andimpulse-response function. Section 2 3 introduces automatic Control Systems and Sec-tion 2 4 discusses concepts of Modeling in state space. Section 2 5 presents state-spacerepresentation of dynamic Systems . Section 2 6 discusses transformation of mathemat-ical models with MATLAB. Finally, Section 2 7 discusses linearization of nonlinearmathematical 2 TRANSFER FUNCTION AND IMPULSE-RESPONSE FUNCTIONIn Control theory, functions called transfer functions are commonly used to character-ize the input-output relationships of components or Systems that can be described by lin-ear, time-invariant, differential equations.

9 We begin by defining the transfer functionand follow with a derivation of the transfer function of a differential equation we discuss the impulse-response transfer functionof a linear, time-invariant, differentialequation system is defined as the ratio of the Laplace transform of the output (responsefunction) to the Laplace transform of the input (driving function) under the assumptionthat all initial conditions are the linear time-invariant system defined by the following differential equation:where yis the output of the system and xis the input. The transfer function of this sys-tem is the ratio of the Laplace transformed output to the Laplace transformed inputwhen all initial conditions are zero, orBy using the concept of transfer function, it is possible to represent system dynam-ics by algebraic equations in s.

10 If the highest power of sin the denominator of the trans-fer function is equal to n, the system is called an nth-order on Transfer applicability of the concept of the trans-fer function is limited to linear, time-invariant, differential equation Systems . The trans-fer function approach, however, is extensively used in the analysis and design of suchsystems. In what follows, we shall list important comments concerning the transfer func-tion. (Note that a system referred to in the list is one described by a linear, time-invariant,differential equation.) =Y(s)X(s)=b0 sm+b1 sm-1+p+bm-1 s+bma0 sn+a1 sn-1+p+an-1 s+an Transfer function=G(s)=l[output]l[input] 2 zero initial conditions= b0 x(m)+ b1x(m-1)+p+bm-1 x#+bm x (n m)a0 y(n)+ a1y(n-1)+p+an-1 y#+an yOGATA-CH02-013-062hr 7/14/09 1.