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Appendix D - Controllability and Observability...

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Unformatted text preview: Controllability and Observability Controllability and observability are important structural properties of dy- namic system. First identified and studied by Kalman (1960) and later by Kalman et al. (1962), these properties have continued to be examined dur- ing the last four decades. We will discuss only a few of the known results for linear constants systems with one input and one output. In the text we dis- cuss these concepts in connection with control law and estimator designs. For example, in Section 7.2 we suggest that if the square matrix given by e = [G FG F2 G FHG] (D1) is nonsingular, then by transformation of the state we can convert the given description into control canonical form. We can then construct a control law that will give the closed-loop system an arbitrary characteristic equation. 849 850 Appendix D Controllability and Observability Definition | Definition ll D.1 Controllability We begin our formal discussion of controllability with the first of four defini- tions: The system (F, G) is controllable if for any given nth-order polynomial 016(5) there exists a (unique) control law 14 = —Kx such that the characteristic poly- nomial of F — GK is ac(s). From the results of Ackermann’s formula (see Appendix E), we have the fol- lowing mathematical test for controllability: (F, G) is a controllable pair if and only if the rank of G is n. Definition I based on pole placement is a frequency—domain concept. Controllability can be equivalently defined in the time domain. The system (F, G) is controllable if there exists a (piecewise continuous) control signal u(t) that will take the state of the system from any initial state x0 to any desired final state x,» in a finite time interval. We will now show that the system is controllable by this definition if and only if ('3 is full-rank. We first assume that the system is controllable but that rank[G FG FZG F"‘1G] < 11. (D2) We can then find a vector v such that v[G FG FZG ... F”‘1G] = 0, (D3) or VG = vFG = vFZG = ... : vF"_1G = 0. (D4) The Cayley—Hamilton theorem states that F satisfies its own characteristic equa- tion, namely, —F" = alF"_1 + azFH + ~ - ~ + anI. (D5) Therefore, —vF"G = alvF”_1G + ava"_2G + . - . + anvG = 0. (D6) Byinduction, vF”+kG = 0fork = 0, 1, 2, . . .,oer’” G = 0form = 0, 1, 2, . . ., and thus 1 veFIG = v (I + Ft + 5W? + . . ) G = 0 (D7) for all t. However, the zero initial-condition response (x0 = 0) is t x(t) =/ veF("’)Gu(r)dr 0 t :5?th e'F’Gu(t)dt. (D.8) 0 Definition lil Section D.1 Controllability 851 Using Eq. (D.7), Eq. (D.8) becomes t vx(t) = / veF(’_’)Gu(t)dr = 0 (D9) 0 for all u(t) and t > 0. This implies that all points reachable from the origin are orthogonal to v. This restricts the reachable space and therefore contradicts the second definition of controllability. Thus if 6’ is singular, (F, G) is not controllable by Definition 11. Next we assume that 6’ is full-rank but (F, G) is uncontrollable by Definition 11. This means that there exists a nonzero vector v such that tf V/ eF(’f’T)Gu(1)dr = 0, (D10) 0 because the whole state space is not reachable. But Eq. (D.10) implies that veF(’f_T)G = 0, 0 5 r 5 if. (D.l 1) If we set I = If, we see that VG = 0. Also, differentiating Eq. (DH) and letting r = If gives vFG : 0. Continuing this process, we find that VG = vFG = VFZG = ... = vFHG = 0, (D.12) which contradicts the assumption that G is full-rank. We have now shown that the system is controllable by Definition II if and only if the rank of C” is n, exactly the same condition we found for pole assignment. Our third definition comes closest to the structural character of controllability: The system (F, G) is controllable if every mode of F is connected to the control input. Because of the generality of the modal structure of systems, we will only treat the case of systems for which F can be transformed to diagonal form. (The double-integration plant does not qualify.) Suppose we have a diagonal matrix Fd and its corresponding input matrix Gd with elements g). The structure of such a system is shown in Fig. D.l. By definition, for a controllable system the input must be connected to each mode so that the g, are all nonzero. However, this is not enough if the poles (ii) are not distinct. Suppose, for instance, that M = A2. The first two state equations are then 561d = )‘le1d + gilt, X201 = szd + gzu. (D.13) 852 Appendix D Controllability and Observability Figure D.1 Block diagram of a system with a diagonal matrix H l D g1 s4—A1 '— __ l‘ 8 2 _’ 1 g7] _.IY"A If we define a new state, S 2 gym — guy, the equation for S is 5-: £25610] - 5'15ch = gzkmd + 8281” - 8111M: — gigzu = MS, (D14) which does not include the control n; hence g is not controllable. The point is that if any two poles are equal in a diagonal Fd system with only one input, we effectively have a hidden mode that is not connected to the control, and the system is not controllable [Fig D.2(a)]. This is because the two state variables move together exactly, so we cannot independently control x 1,1 and xzd. There- fore, even in such a simple case, we have two conditions for controllability: 1. All eigenvalues of F d are distinct. 2. No element of Gd is zero. Now let us consider the controllability matrix of this diagonal system. By direct computation, 81 81M 81M 6’: £2 £212 g,1 gnkn gnkn n-l 2 ~l gl 0 1 A1 A1 A; 82 2 5 = . 1 Ag A2 . . (D15) 0 g" 1 A" xi A24 1* l l M Y +1 s —l . " (b) (c) Figure D.2 Examples of uncontrollable systems Section D.1 Controllability 853 Note that the controllability matrix C’ is the product of two matrices and is nonsingular if and only if both of these matrices are invertible. The first matrix has a determinant that is the product of the gi, and the second matrix (called a Vandermonde matrix) is nonsingular if and only if the A, are distinct. Thus Definition III is equivalent to having a nonsingular ('3 also. Important to the subject of controllability is the Popov—Hautus—Rosenbrock (PHR) test (see Rosenbrock, 1970, and Kailath, 1980), which is an alternate way to test the rank (or determinant) of G. The system (F, G) is controllable if the system of equations vT[s1 — F G] = 0T (D16) has only the trivial solution VT = 0T, that is, if the following matrix pencil is full-rank for all s rank[sI — F G]: n, (D.17) or if there is no nonzero vT such that1 VTF = svT, (D18) vTG = o. (D.19) This test is equivalent to the rank-of-G test. It is easy to show that if such a vector v exists, then ('3 is singular. For, if a nonzero v exists such that vTG = 0, then by Eqs. (D18) and (D.19) we have VTFG = svTG = 0. (D20) Then, multiplying by F G, we find that VTFZG = svTFG = 0, (13.21) and so on. Thus we determine that VTG 2 0T has a nontrivial solution, that 6’ is singular, and that the system is not controllable. To show that a nontrivial VT exists if C is singular requires more development, which we will not give here (see Kailath, 1980). We have given two pictures of uncontrollability. Either a mode is phys- ically disconnected from the input [Fig. D.2(b)], or else two parallel subsys- tems have identical characteristic roots [Fig D.2(a)]. The control engineer should be aware of the existence of a third simple situation, illustrated in Fig. D.2(c) namely, a pole-zero cancellation. Here the problem is that the mode at s = 1 appears to be connected to the input but is masked by the zero at s = 1 in the preceding subsystem; the result is an uncontrollable system. 1 VT is a left eigenvector of F. 854 Appendix D Controllability and Observability Definition IV This can be confirmed in several ways. First let us look at the controllability matrix. The system matrices are —1 0 —2 F-l1 ll G-l1 i so the controllability matrix is e=[G FG]=[_12 3]] (D22) which is clearly singular. The controllability matrix may be computed using the ctrb command in MATLAB [cc] = ctrb(F,G). If we compute the transfer function from u to xz, we find s—l I I H(S):s+1<s—1)=s+1' (D23) Because the natural mode at s = l disappears from the input—output descrip- tion, it is not connected to the input. Finally, if we consider the PHR test, (D24) [SI—F G]=[SJr1 0 4]. —1 S—1 1 and let s = 1, then we must test the rank of 20—2 —101’ which is clearly less than 2. This result means, again, that the system is uncontrollable. The asymptotically stable system (F, G) is controllable if the controllability Gramian, the square symmetric matrix 01.” given by the solution to the follow- ing Lyapunov equation Fe, + egFT + GGT = 0, (D25) is nonsingular. The controllability Gramian is also the solution to the following integral equation 00 e, = / eTFGGTe’FTdr. (D26) 0 One physical interpretation of the controllability Gramian is that if the input to the system is white gaussian noise, then 0,. is the covariance of the state. The controllability Gramian (for an asymptotically stable system) can be computed with the following command in MATLAB, [cg] = gram(F,G). Section D.2 Observability 855 In conclusion, the four definitions for controllability—pole assignment (Defini- tion I), state reachability (Definition II), mode coupling to the input (Definition III), and controllability Gramian (Definition IV)—are equivalent. The tests for any of these four properties are found in terms of the rank of the controllability or controllability Gramian matrices or the rank of the matrix pencil [sI - F G]. If G is nonsingular, then we can assign the closed-loop poles arbitrarily by state feedback, we can move the state to any point in the state space in a finite time, and every mode is connected to the control input.2 We have shown the latter for diagonal F only, but the result is true in general. D.2 Observability So far we have discussed only controllability. The concept of Observability is parallel to that of controllability, and all of the results we have discussed thus far may be transformed to statements about Observability by invoking the property of duality, as discussed in Section 7.5.1. The Observability definitions analogous to those for controllability are as follows: 1. Definition I . The system (F, H) is observable if, for any n th—order polynomial OMS), there exists an estimator gain L such that the characteristic equation of the state estimator error is 059(5). 2. Definition [1. The system (F, H) is observable if, for any x(0), there is a finite time I such that x(0) can be determined (uniquely) from u(t) and y(t) for 0 5 t 5 r. 3. Definition [11. The system (F, H) is observable if every dynamic mode in F is connected to the output through H. 4. Definition IV. The asymptotically stable system (F, H) is observable if the Observability Gramian is nonsingular. As we saw in the discussion for controllability, mathematical tests can be de- veloped for Observability. The system is observable if the Observability matrix H HF o = _ (D27) HFn~l is nonsingular. If we take the transpose of (9 and let HT 2 G and FT = F, then we find the controllability matrix of (F, G), another manifestation of duality. The Observability matrix, (9, may be computed using the obsv command 1n 2 WC have shown the latter for diagonal F only, but the result is true in general. 6 Appendix D Controllability and Observability Ackermann’s Formula for Pole Placement MATLAB [oo] = obsv(F, H). The system (F, H) is observable if the following matrix pencil is full-rank for all s: rank [511; F] = n. (D28) The observability Gramian, (9g, which is a symmetric matrix, and the solution to the integral equation 00 T (9g = / eTF HTHeTFdr, (13.29) 0 as well as the Lyapunov equation FTog + ogF + HTH = 0, (D30) can also be computed (for an asymptotically stable system) using the gram command in MATLAB [og] = gram(F’,H’). The observability Gramian has an in- terpretatlon as the mformation matrix 1n the context of estimatlon. Given the plant and state-variable equation X=Fx+Gu, (El) our objective is to find a state feedback control law it = —KX (E2) such that the closed—loop characteristic polynomial is (16(5) = det(sI — F + GK). (E3) First we have to select ac (s), which determines where the poles are to be shifted; then we have to find K such that Eq. (E3) will be satisfied. Our technique is i based on transforming the plant equation into control canonical form. We begin by considering the effect of an arbitrary nonsingular transforma- tion of the state, ‘ X = Ti, (E4) | where i is the new transformed state. The equations of motion in the new coordinates, from Eq. (E4), are X=T§=FX+GM =FTi+Gu, (E.5) i = T’IFTi—kT’lGu = Fi—l—Cu. (E6) 857 ...
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