# EE 580 Linear Control Systems VI. State Transition Matrix

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1 EE 580 Linear Control Systems VI. State Transition Matrix Department of Electrical Engineering Pennsylvania State University Fall Introduction Typical signal spaces are (infinite-dimensional vector spaces. Consider the space C(J, R n of R n - valued continuous functions on an interval J = (a,b R. For f, g C(J, R n, define f + g C(J, R n to be the function satisfying (f + g(t = f(t + g(t for all t J. Also, for α R and f C(J, R n, define αf C(J, R n to be the function satisfying (αf(t = αf(t for all t J. Then it is straightforward to show that C(J, R n is a vector space over the real field, where the origin 0 C(J, R n is the function which is identically zero. Under this definition, a function f C(J, R n is a linear combination of functions g 1,..., g m C(J, R n if there are α 1,..., α m R such that f(t = (α 1 g 1 + +α m g m (t for all t J. A nonempty finite subset {g 1,...,g m } of C(J, R n is then linearly independent if and only if (α 1 g α m g m (t = 0 for all t J implies α 1 = = α m = 0. For simplicity, we will focus on LTI systems of the form ẋ(t = Ax(t. However, all the theorems up to Section inclusive carry over to LTV systems of the form ẋ(t = A(tx(t as long as A( is continuous; see [1, Sections 2.3 & 2.4] and [2, Pages 50 73]. These results hold true even if A( is piecewise continuous; a function is said to be piecewise continuous if, on each bounded interval in its domain, it is continuous except possibly for a finite number of discontinuities. If A( is piecewise continuous (or piecewise constant in particular, then one can solve ẋ(t = A(tx(t piece by piece and then put together the results to obtain an overall solution. 6.2 Fundamental Matrices [3, Section 3.2.1] Solution Space Consider the homogeneous linear time-invariant system ẋ(t = Ax(t, t R, (6.1 where A R n n is a constant matrix. We have seen that this system, subject to any initial condition x(t 0 = x 0, has a unique solution φ on R; that is, there exists a unique φ C(R, R n such that φ(t = Aφ(t for all t R and such that φ(t 0 = x 0. It turns out that the set of solutions to (6.1 is a finite-dimensional subspace of C(R, R n, which is infinite dimensional. 1

2 EE 580 Linear Control Systems 2 Theorem 6.1 The set of all solutions to (6.1 forms an n-dimensional linear subspace of C(R, R n. Proof. Let V be the space of all solutions of (6.1 on R. It is easy to verify that V is a linear subspace of C(R, R n. Choose a set of n linearly independent vectors {x 1,...,x n } in R n. If t 0 R, then there exist n solutions φ 1,..., φ n of (6.1 such that φ 1 (t 0 = x 1,..., φ n (t 0 = x n. The linear independence of {φ 1,...,φ n } follows from that of {x 1,...,x n }. It remains to show that {φ 1,...,φ n } spans V. Let φ be any solution of (6.1 on R such that φ(t 0 = x 0. There exist unique scalars α 1,..., α n R such that x 0 = α 1 x α n x n, and thus that ψ = α 1 φ α n φ n is a solution of (6.1 on R such that ψ(t 0 = x 0. However, because of the uniqueness of a solution satisfying the given initial data, we have that φ = α 1 φ α n φ n. This shows that every solution of (6.1 is a linear combination of φ 1,..., φ n, and hence that {φ 1,...,φ n } is a basis of the solution space Matrix Differential Equations With A R n n as before, consider the matrix differential equation Ẋ(t = AX(t. (6.2 If the columns of X(t R n n are denoted x 1 (t,..., x n (t R n, then (6.2 is equivalent to ẋ 1 (t A 0 x 1 (t. =......, ẋ n (t 0 A x n (t so a matrix differential equation of the form (6.2 is a system of n 2 differential equations. Definition 6.2 A set {φ 1,...,φ n } C(R, R n of n linearly independent solutions of (6.1 is called a fundamental set of solutions of (6.1, and the R n n -valued function Φ = [ φ 1 φ n ] is called a fundamental matrix of (6.1. Theorem 6.3 A fundamental matrix Φ of (6.1 satisfies the matrix equation (6.2. Proof. The result is immediate from Φ(t = [ φ1 (t φn (t ] = [ Aφ 1 (t Aφ n (t ] = A [ φ 1 (t φ n (t ] = AΦ(t. Therefore, a fundamental matrix is a matrix-valued function whose columns span the space of solutions to the system of n differential equations ( Characterizations of Fundamental Matrices It can be shown that, if Φ is a solution of the matrix differential equation (6.2, then detφ(t = e (tra(t τ detφ(τ (6.3 for all t, τ R. This result is called Abel s formula and proved in [1, Theorem 2.3.3]. Its immediate consequence is that, since t, τ are arbitrary, we have either detφ(t = 0 for all t R or detφ(t 0 for any t R. In fact, we have the following characterizations of fundamental matrices, which we obtain without using Abel s formula.

3 EE 580 Linear Control Systems 3 Theorem 6.4 A solution Φ of the matrix differential equation (6.2 is a fundamental matrix of (6.1 if and only if Φ(t is nonsingular for all t R. Proof. To show necessity by contradiction, suppose that Φ = [φ 1 φ n ] is a fundamental matrix for (6.1 and that detφ(t 0 = 0 for some t 0 R. Then, since Φ(t 0 is singular, the set {φ 1 (t 0,...,φ n (t 0 } R n is linearly dependent (over the real field, so that there are α 1,..., α n R, not all zero, such that n i=1 α iφ i (t 0 = 0. Every linear combination of the columns of a fundamental matrix is a solution of (6.1, and so n i=1 α iφ i is a solution of (6.1. Due to the uniqueness of the solution, this, along with the initial condition n i=1 α iφ i (t 0 = 0, implies that n i=1 α iφ i is identically zero, which contradicts the fact that φ 1,..., φ n are linearly independent. Thus, we conclude that detφ(t 0 for all t R. To show sufficiency, let Φ be a solution of (6.2 and suppose that detφ(t 0 for all t R. Then the columns of Φ form a linearly independent set of vectors for all t R. Hence, Φ is a fundamental matrix of (6.1.. For example, consider three R 3 3 -valued functions 1 2t t 2 1 t t 2 1 t t 2 Φ 1 (t = 0 1 t, Φ 2 (t = 0 1 t, Φ 3 (t = 0 1 t t, t R. If we write Φ 1 = [φ 1 φ 2 φ 3 ], where φ i C(R, R 3 are given by φ 1 (t = [1 ] T, φ 2 (t = [2t 1 0] T, and φ 3 (t = [t 2 t 1] T for all t R, then {φ 1,φ 2,φ 3 } is a linearly independent set in C(R, R 3 because 3 i=1 α iφ i being identically zero implies all α i equal 0. Similarly, the columns of Φ 2 are linearly independent in C(R, R 3 and so are those of Φ 3. Thus, Φ 1, Φ 2, and Φ 3 are potentially fundamental matrices. We have detφ 1 (t = detφ 2 (t = 1 and detφ 3 (t = t for all t R. Since detφ 3 (0 = 0, however, Theorem 6.4 tells us that Φ 3 is not a fundamental matrix of (6.1 or even its time-varying version ẋ(t = A(tx(t (as the proof of Theorem 6.4 carries over to the timevarying case. On the other hand, since Φ 1 (t and Φ 2 (t are nonsingular for all t, it remains that Φ 1 and Φ 2 are fundamental matrices provided that they solve the matrix differential equation (6.2. Solving Φ 1 (t = AΦ 1 (t for A R 3 3 yields that 0 2 2t 1 2t t A = Φ 1 (tφ 1 (t 1 = t = 0 so indeed Φ 1 is a fundamental matrix of the LTI system (6.1. However, since 0 1 2t 1 t t Φ 2 (tφ 2 (t 1 = 0 1 t = 0 0 is a function of t, the matrix-valued function Φ 2 is not a fundamental matrix of (6.1. Nevertheless, Φ 2 is a fundamental matrix of the LTV system ẋ(t = A(tx(t, where A(t = Φ 2 (tφ 2 (t 1. Theorem 6.5 Let Φ be a fundamental matrix of (6.1. Then Ψ is a fundamental matrix of (6.1 if and only if there exists a nonsingular matrix P R n n such that Ψ(t = Φ(tP for all t R. Proof. Suppose P is invertible and Ψ(t = Φ(tP for all t. Then it is easy to check that Ψ is a solution of (6.2. Moreover, since det Φ(t 0 for all t and since det P 0, we have det Φ(tP = detφ(tdetp 0 for all t. Thus by Theorem 6.4, Ψ is a fundamental matrix. Conversely, suppose that Ψ is a fundamental matrix. As Φ(tΦ(t 1 = I for all t, the chain rule gives d dt( ΦΦ 1 (t = 0 d dt( Φ 1 (t = Φ(t 1 d dt( Φ (tφ(t 1 = Φ(t 1 A,

4 EE 580 Linear Control Systems 4 for all t R. Using this equality, we obtain d dt( Φ 1 Ψ (t = Φ(t 1 d ( dt Ψ (t + d dt( Φ 1 (tψ(t = Φ(t 1 AΨ(t Φ(t 1 AΨ(t = 0 for all t. Hence Φ( 1 Ψ( is constant, which implies the existence of a matrix P such that Φ(t 1 Ψ(t = P for all t; since Φ(t 1 and Ψ(t are both nonsingular, P is nonsingular. 6.3 State Transition Matrix [3, Sections & 3.3.1] Definitions Solving the matrix differential equation (6.2 subject to the initial condition X(t 0 = I R n n is equivalent to solving the initial-value problems ẋ i (t = Ax i (t, t R; x i (t 0 = e i R n, (6.4 separately over all i = 1,..., n, where e i denotes the ith standard basis vector (i.e., ith column of the identity matrix I. That is, if φ 1,..., φ n C(R, R n are the unique solutions to (6.4 such that φ i (t 0 = e i and φ(t = Aφ(t for all t R and for each i = 1,..., n, then the fundamental matrix Φ(,t 0 = [φ 1 ( φ n ( ] is the unique solution to the matrix differential equation subject to t Φ(t,t 0 = AΦ(t,t 0 Φ(t 0,t 0 = I. The R n n -valued function Φ(,t 0 is called the state transition matrix of (6.1. Every x 0 R n is a linear combination of the standard basis vectors e 1,..., e n (i.e., x 0 = Ix 0, and so, as we already know, the solution to the system of differential equations (6.1 subject to the initial condition x(t 0 = x 0 has a unique solution given by φ( = Φ(,t 0 x 0, which is the same linear combination of the columns of the state transition matrix. Moreover, Φ(t,t 0 is determined by the Peano-Baker series, which in the case of LTI systems reduces to the matrix exponential given by Φ(t,t 0 = e A(t t 0 = A k (t t 0 k for all t, t 0 R with the convention that A 0 = I and 0! = Properties of State Transition Matrix Theorem 6.6 Let Φ(t,t 0 be the state transition matrix of (6.1. If P is invertible, then the state variable change P x(t = x(t leads to the equivalent state equation k! x(t = Ā x(t, t R; Ā = P 1 AP. (6.5 If Φ(,t 0 is the state transition matrix of (6.1, then the state transition matrix of (6.5 is Φ(t,t 0 = P 1 Φ(t,t 0 P, t, t 0 R. (6.6 Proof. Equation (6.5 follows from x(t = P 1 x(t and d dt( P 1 x (t = (P 1 APP 1 x(t, and equation (6.6 from t( P 1 Φ(t,t 0 P = (P 1 APP 1 Φ(t,t 0 P and P 1 Φ(t 0,t 0 P = I.

5 EE 580 Linear Control Systems 5 Theorem 6.7 Let Φ(,t 0 be the state transition matrix of (6.1. Then the following hold: (a If Ψ is any fundamental matrix of (6.1, then Φ(t,t 0 = Ψ(tΨ(t 0 1 for all t, t 0 R; (b Φ(t,t 0 is nonsingular and Φ(t,t 0 1 = Φ(t 0,t for all t, t 0 R; (c Φ(t,t 0 = Φ(t,sΦ(s,t 0 for all t, s, t 0 R; (semigroup property Proof. Part (a follows from t( Ψ(tΨ(t0 1 = AΨ(tΨ(t 0 1 and Ψ(t 0 Ψ(t 0 1 = I. Choose any fundamental matrix Ψ. Then detψ(t 0 for all t, so (a implies Φ(t,t 0 is nonsingular for all t, t 0 because detφ(t,t 0 = det ( Ψ(tΨ(t 0 1 = detψ(tdetψ(t Moreover, Φ(t,t 0 1 = ( Ψ(tΨ(t = Ψ(t0 Ψ(t 1, so (b holds. Finally, for any fundamental matrix Ψ, we have Φ(t,t 0 = Ψ(tΨ(t 0 1 = ( Ψ(tΨ(s 1( Ψ(sΨ(t 0 1, so (c holds. As an example, consider the homogeneous LTI system ẋ(t = Ax(t given by ẋ 1 (t x 1 (t ẋ 2 (t = x 2 (t. ẋ 3 (t 0 x 3 (t It follows readily from ẋ 3 (t = 0, ẋ 2 (t = x 3 (t, and ẋ 1 (t = 2x 2 (t that φ 1 (t = [1 ] T, φ 2 (t = [2t 1 0] T, and φ 3 (t = [t 2 t 1] T are solutions of the system among infinitely many others. Moreover, {φ 1,φ 2,φ 3 } is a linearly independent set in C(R, R 3. Thus Ψ = [φ 1 φ 2 φ 3 ] is a fundamental matrix of the system. Then by part (a of Theorem 6.7 we have that the state transition matrix of the system is 1 2t t 2 1 2(t t 0 (t t 0 2 Φ(t,t 0 = Ψ(tΨ(t 0 1 = 0 1 t 1 2t 0 t t 0 = 0 1 t t 0 Indeed, we have Φ(t,t 0 = e A(t t0 for all t, t 0 R. As another example, consider the homogeneous LTV system ẋ(t = A(tx(t given by ẋ 1 (t 0 1 t x 1 (t ẋ 2 (t = x 2 (t. ẋ 3 (t 0 x 3 (t As φ 1 (t = [1 ] T, φ 2 (t = [t 1 0] T, and φ 3 (t = [t 2 t 1] T are linearly independent solutions to this LTV system, the matrix-valued function Ψ = [φ 1 φ 2 φ 3 ] is a fundamental matrix of the LTV system (but not of any LTI system. Therefore, the state transition matrix of the LTV system is 1 t t 2 1 t t t 0 t(t t 0 Φ(t,t 0 = Ψ(tΨ(t 0 1 = 0 1 t 0 1 t 0 = 0 1 t t 0. That is, t Φ(t,t 0 = A(tΦ(t,t 0 for all t R with Φ(t 0,t 0 = I Properties of Matrix Exponentials Theorem 6.8 Let A R n n. Then the following hold: (a e At 1 e At 2 = e A(t 1+t 2 for all t 1, t 2 R;.

6 EE 580 Linear Control Systems 6 (b Ae At = e At A for all t R; (c ( e At 1 = e At for all t R; (d det e At = e (trat for all t R; (e If AB = BA, then e At e Bt = e (A+Bt for all t R. Proof. Part (a holds because e At 1 e At 2 = Φ(t 1,0Φ(0, t 2 = Φ(t, t 2 = e A(t 1+t 2, where the second equality follows from Theorem 6.7(c. Part (b follows from ( m A A lim k t k m ( A m k! = lim k+1 t k m A m k! = lim k t k m k! A. Part (c is immediate from Theorem 6.7(b. If Ψ is a fundamental matrix of (6.1, then Theorem 6.7(a gives dete At = det ( Ψ(tΨ(0 1 = detψ(t/detψ(0, where the last equality follows from 1 = deti = det ( Ψ(0Ψ(0 1 = detψ(0detψ(0 1. Thus Abel s formula (6.3 gives (d. Finally, if AB = BA, then A i B j = B j A i and hence we have ( m lim m i=0 A i t i i! ( lim l l B j t j j! = ( l lim l which implies (e. In general, matrices do not commute. For example, if [ ] 0 1 A = and B = then AB BA. In this case, [ ] e At 1 t =, e Bt = 0 1 so e At e Bt e (A+Bt. [ ] 1 0, e (A+Bt = t 1 [ ], 1 0 ( B j t j m j! lim m i=0 [ 1 2 (et + e t 1 2 (et e t ] 1 2 (et e t 1 2 (et + e t, 6.4 How to Determine Matrix Exponentials [3, Section 3.3.2] A i t i i!, We have already seen that the state transition matrix of a linear (time-invariant or time-varying system can be obtained from a fundamental matrix, whose columns are linearly independent solutions to the given homogeneous system. Other methods to obtain the state transition matrix of an LTI system are summarized in this section Infinite Series Method Given a matrix A R n n, evaluate the partial sum m t k S m (t = k! Ak = I + ta + t2 2 A + + tm m! Am for m = 0, 1,.... Then, since S m (t e At uniformly on any bounded interval J in R, it is guaranteed that e At S m (t for all t J and for all sufficiently large m. If A is nilpotent, then the partial sum S m (t, with m = n 1, gives a closed-form expression for e At. For example, [ ] [ ] [ ] 0 α A = A 2 = e At 1 αt = I + ta =. 0 1

7 EE 580 Linear Control Systems Similarity Transformation Method Diagonalizable Case. If A R n n has n linearly independent eigenvectors v 1,..., v n corresponding to its (not necessarily distinct eigenvalues λ 1,..., λ n, then let P = [v 1 v n ]. Then, since J = P 1 AP = diag{λ 1,...,λ n }, Theorem 6.6 gives e λ 1t 0 e At = Pe Jt P 1 = P... 0 e λnt P 1. General Case. Generate n linearly independent generalized eigenvectors v 1,..., v n of A R n n such that P = [v 1 v n ] takes A into the Jordan canonical form J = P 1 AP = diag{j 0,...,J m } C n n, where J 1,..., J m are Jordan blocks of varying dimensions. Then e J 1t 0 e At = Pe Jt P 1 = P... 0 e Jmt P 1. A Jordan block is the sum of a diagonal matrix and a nilpotent matrix; that is, J k = Λ k +N k with λ k λ k 0 Λ k = and N k =... 1, λ k where λ k is some eigenvalue of A for each k. Let J k C n k n k for each k = 1,..., m, so that m k=1 n k = n. Then we have N n k k = 0, and so the series defining e Nkt terminates for each k. Moreover, direct computation yields Λ k N k = N k Λ k for each k. Therefore, Theorem 6.8(e yields 1 t t 2 /2 t nk 1 /(n k 1! 0 1 t t nk 2 /(n k 2! e Jkt = e Λkt e Nkt = e λ kt t nk 3 /(n k 3!, k = 1,..., m. ( Note that, if λ k in (6.7 is complex, then Euler s formula gives e λ kt = e (σ k+iω k t = e σ kt (cos ω k t + isin ω k t. For example, if α 0, then [ ] A = J = P 1 AP = α 0 [ ] 0 1, P = Cayley-Hamilton Theorem Method [ ] 0 1 α 0 e At = P [ ] 1 t P 1 = 0 1 [ ] 1 0. αt 1 In view of the Cayley-Hamilton Theorem, there exist β i (m; C(R, R for i = 0,..., n 1 and m = 1, 2,...such that, for A R n n, e At = lim m m t k n 1 k! Ak = lim m i=0 β i (m;ta i = n 1 i=0 lim β i(m;ta i. m

8 EE 580 Linear Control Systems 8 Then, letting β i (t = lim m β i (m;t for i = 0,..., n 1 and t R, we obtain n 1 e At = β i (ta i, t R. (6.8 i=0 Thus one can determine e At by obtaining β i (t for all i and t. Let f(s and g(s be two analytic functions (i.e., functions locally given by a convergent power series; e.g., polynomials, exponential functions, trigonometric functions, logarithms, etc.. Let p(s = p j=1 (s λ j m k be the characteristic polynomial of A. Then f(a = g(a if d k f ds k (λ j = dk g ds k (λ j, k = 0,..., m j 1, k = 1,..., p, (6.9 where p j=1 m j = n. (Proof. Equations (6.9 imply that f(s g(s has p(s as a factor, so the result follows from the Cayley-Hamilton Theorem. Thus the terms β i (t in (6.8 can be determined by letting f(s = e st and g(s = β 0 (t + β 1 (ts + + β n 1 (ts n 1. For example, if A = [ α 0 ], then the characteristic polynomial of A is p(s = det(si A = s2. Let f(s = e st and g(s = β 0 (t + β 1 (ts. Then, as f(0 = g(0 and f g s (0 = s (0 imply β 0(t = 1 and β 1 (t = t, we obtain that e At = g(a = I + ta Laplace Transform Method We know that e At is the inverse Laplace transform of (si A 1 for A R n n. The partial fraction expansion of each entry in (si A 1 gives (si A 1 = p j=1 m j 1 k! (s λ j k+1a jk, where λ 1,..., λ p are the distinct eigenvalues of A, with corresponding algebraic multiplicities m 1,..., m p such that p j=1 m i = n, and where each A jk C n n is a matrix of partial fraction expansion coefficients. Taking the inverse Laplace transform gives e At = p j=1 m j 1 t k e λ jt A jk. (6.10 If some eigenvalues are complex, conjugate terms on the right side of (6.10 can be combined using Euler s formula to give a real representation. If A jk is nonzero, then t k e λjt is called a mode of the system ẋ(t = Ax(t, t R. It is easily seen that x(t 0 as t under arbitrary initial conditions if each mode of the system converges to zero (i.e., each λ j has negative real part. For example, if A = [ α 0 ], then eat is the inverse Laplace transform of (si A 1 = modes of the system ẋ(t = Ax(t in this example are 1 and t. 6.5 Discrete-Time Case State Transition Matrix Given an A R n n, consider the discrete-time initial-value problem of solving [ 1/s 0 α/s 2 1/s ]. The x(t + 1 = Ax(t, t = t 0, t 0 + 1,...; x(t 0 = x 0. (6.11

9 EE 580 Linear Control Systems 9 The discrete-time state transition matrix is defined to be the unique solution Φ(,t 0 R n n of the matrix difference equation subject to Φ(t + 1,t 0 = AΦ(t,t 0, t = t 0, t 0 + 1,..., Φ(t 0,t 0 = I. That is, Φ(t,t 0 = A t t 0 for all t, t 0 Z with t t 0, and as we have seen earlier the unique solution to (6.11 is given by x(t = Φ(t,t 0 x 0, t t 0. Unlike the continuous-time case, the difference equation (6.11 cannot be solved backward in time unless A is nonsingular. This is because, unless A is invertible, the discrete-time state transition matrix Φ(t,t 0 is not invertible. An immediate consequence is that the semigroup property holds only forward in time; that is, Φ(t,t 0 = Φ(t,sΦ(s,t 0 for t s t 0. Due to time-invariance, we have Φ(t,t 0 = Φ(t t 0, How to Determine Powers of Matrices Given an A R n n, if J = P 1 AP is a Jordan canonical form of A, then we have A t = (PJP 1 t = PJ t P 1. In particular, if J = diag{λ 1,...,λ n }, then Φ(t,0 = A t = Pdiag{λ t 1,...,λt n}p 1. Also, by the Cayley-Hamilton Theorem there exist β i (, i = 1,..., n 1, such that n 1 A t = β i (ta i, t = 0, 1,... (6.12 Thus, as in the continuous-time case, one can determine A t by letting i=0 f(s = s t and g(s = β 0 (t + β 1 (ts + + β n 1 (ts n 1, and then solving (6.9 for the terms β i (t. Finally, one can use the fact that Φ(,0 is the inverse z- transform of z(zi A 1 = (I z 1 A 1 to determine A t. If λ 1,..., λ p are the distinct eigenvalues of A, with corresponding algebraic multiplicities m 1,..., m p such that p i=1 m i = n, we have and hence z(zi A 1 = z A t = p j=1 p j=1 min{m j 1,t} m j 1 k! (z λ j k+1a jk t! (t k! λt k j A jk, where A jk C n n are the matrices of partial fraction expansion coefficients. If A jk is nonzero, t! then (t k! λt k j is called a mode of the system x(t+1 = Ax(t. We have x(t 0 under arbitrary initial conditions if each mode converges to zero (i.e., each λ j has a magnitude less than 1. References [1] P. J. Antsaklis and A. N. Michel, Linear Systems, 2nd ed. Boston, MA: Birkhäuser, [2] W. J. Rugh, Linear System Theory. Englewood Cliffs, NJ: Prentice-Hall, [3] P. J. Antsaklis and A. N. Michel, A Linear Systems Primer. Boston, MA: Birkhäuser, 2007.

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