21 Linear State-Space Representations

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1 ME 132, Spring 25, UC Berkeley, A Packard Linear State-Space Representations First, let s describe the most general type of dynamic system that we will consider/encounter in this class Systems may have many inputs, and many outputs Specifically, m inputs (denoted by d) and q outputs (denoted by e), and the input/output behavior is governed by a set of n, 1st order, coupled differential equations, of the form ẋ 1 (t) ẋ 2 (t) ẋ n (t) e 1 (t) e 2 (t) e q (t) = f 1 (x 1 (t), x 2 (t),, x n (t), d 1 (t), d 2 (t),, d m (t), t) f 2 (x 1 (t), x 2 (t),, x n (t), d 1 (t), d 2 (t),, d m (t), t) f n (x 1 (t), x 2 (t),, x n (t), d 1 (t), d 2 (t),, d m (t), t) h 1 (x 1 (t), x 2 (t),, x n (t), d 1 (t), d 2 (t),, d m (t), t) h 2 (x 1 (t), x 2 (t),, x n (t), d 1 (t), d 2 (t),, d m (t), t) h q (x 1 (t), x 2 (t),, x n (t), d 1 (t), d 2 (t),, d m (t), t) where the functions f i, h i are given functions of the n variables x 1, x 2,, x n, the m variables d 1, d 2,, d m and also explicit functions of t For shorthand, we write (79) as (79) ẋ(t) = f (x(t), d(t), t) e(t) = h (x(t), d(t), t) (8) Given an initial condition vector x, and a forcing function d(t) for t t, we wish to solve for the solutions x 1 (t) e 1 (t) x 2 (t) e 2 (t) x(t) = x n (t), e(t) = on the interval t, t F, given the initial condition and the input forcing function d( ) x (t ) = x e q (t) If the functions f and u are reasonably well behaved in x and t, then the solution exists, is continuous, and differentiable at all points 211 Linearity of solution Consider a vector differential equation ẋ(t) = A(t)x(t) + B(t)d(t) x(t ) = x (81)

2 ME 132, Spring 25, UC Berkeley, A Packard 188 where for each t, A(t) R n n, B(t) R n m, and for each time t, x(t) R n and d(t) R m Claim: ( The ) solution function x( ), on any interval t, t 1 is a linear function of the pair x, d(t) t t 1 The precise meaning of this statement is as follows: Pick any constants α, β R, if x 1 is the solution to (81) starting from initial condition x 1, (at t ) and forced with input d 1, and if x 2 is the solution to (81) starting from initial condition x 2, (at t ) and forced with input d 2, then αx 1 + βx 2 is the solution to (81) starting from initial condition αx 1, + βx 2, (at t ) and forced with input αd 1 (t) + βd 2 (t) This can be easily checked, note that for every time t, we have ẋ 1 (t) = A(t)x 1 (t) + B(t)d 1 (t) ẋ 2 (t) = A(t)x 2 (t) + B(t)d 2 (t) Multiply the first equation by the constant α and the second equation by β, and add them together, giving d dt 1(t) + βx 2 (t) = αẋ 1 (t) + βẋ 2 (t) = α Ax 1 (t) + Bd 1 (t) + β Ax 2 (t) + Bd 2 (t) = A αx 1 (t) + βx 2 (t) + B αd 1 (t) + βd 2 (t) which shows that the linear combination αx 1 +βx 2 does indeed solve the differential equation The initial condition is easily checked Finally, the existence and uniqueness theorem for differential equations tells us that this (αx 1 + βx 2 ) is the only solution which satisfies both the differential equation and the initial conditions Linearity of the solution in the pair (initial condition, forcing function) is often called The Principal of Superposition and is an extremely useful property of linear systems 212 Time-Invariance A separate issue, unrelated to linearity, is time-invariance A system described by ẋ(t) = f(x(t), d(t), t) is called time-invariant if (roughly) the behavior of the system does depend explicitly on the absolute time In other words, shifting the time axis does not affect solutions Precisely, suppose that x 1 is a solution for the system, starting from x 1 at t = t subject to the forcing d 1 (t), defined for t t

3 ME 132, Spring 25, UC Berkeley, A Packard 189 Now, let x be the solution to the equations starting from x at t = t +, subject to the forcing d(t) := d(t ), defined for t t + Suppose that for all choices of t,, x and d( ), the two responses are related by x(t) = x(t ) for all t t + Then the system described by ẋ(t) = f(x(t), d(t), t) is called time-invariant In practice, the easiest manner to recognize time-invariance is that the right-hand side of the state equations (the first-order differential equations governing the process) do not explicitly depend on time For instance, the system is nonlinear, yet time-invariant ẋ 1 (t) = 2 x 2 (t) sin x 1 (t)x 2 (t)d 2 (t) ẋ 2 (t) = x 2 (t) x 2 (t)d 1 (t) For the next few sections, we focus entirely on systems that are linear and time-invariant This is not any different than the systems governed by our standard ODE model, introduced in Section 7 However, the representation is different, namely lots of coupled 1st order equations, rather than one big n th order equation We will derive all of the same ideas from this perspective: general solution, stability, frequency response, transfer function

4 ME 132, Spring 25, UC Berkeley, A Packard Matrix Exponential Recall that for the scalar differential equation ẋ(t) = ax(t) + bu(t) x(t ) = x the solution for t t is given by the formula x(t) = e a(t t ) x + t t e a(t τ) bu(τ)dτ What makes this work is the special structure of the exponential function, namely that Now consider a vector differential equation d dt eat = ae at ẋ(t) = Ax(t) + Bu(t) x(t ) = x (82) where A R n n, B R n m are constant matrices, and for each time t, x(t) R n and u(t) R m The solution can be derived by proceeding analogously to the scalar case For a matrix A C n n define a matrix function of time, e At C n n as e At := t k k=k! = I + ta + t2 2! A2 + t3 3! A3 + This is exactly the same as the definition in the scalar case Now, for any T >, every element of this matrix power series converges absolutely and uniformly on the interval, T Hence, it can be differentiated term-by-term to correctly get the derivative Therefore d dt eat := k t k 1 A k k= k! = A + ta t2 2! A3 + = A ( I + ta + t2 2! A2 + ) = Ae At This is the most important property of the function e At Also, in deriving this result, A could have been pulled out of the summation on either side Summarizing these important identities, e A = I n, d dt eat = Ae At = e At A So, the matrix exponential has properties similar to the scalar exponential function However, there are two important facts to watch out for:

5 ME 132, Spring 25, UC Berkeley, A Packard 191 WARNING: Let a ij denote the (i, j)th entry of A The (i, j)th entry of e At IS NOT EQUAL TO e a ijt This is most convincingly seen with a nontrivial example Consider A = A few calculations show that A 2 = 1 2 A 3 = 1 3 A k = 1 k The definition for e At is e At = I + ta + t2 2! A2 + t3 3! A3 + Plugging in, and evaluating on a term-by-term basis gives e At = 1 + t + t2 2! 3! + t + 2 t2 + 3 t3 2! 3! t + t2 2! 3! The (1, 1) and (2, 2) entries are easily seen to be the power series for e t, and the (2, 1) entry is clearly After a few manipulations, the (1, 2) entry is te t Hence, in this case e At = et te t e t which is very different than the element-by-element exponentiation of At, WARNING: In general, ea 11t e a 21t e a 12t = et e t e a 22t 1 e t e (A 1+A 2 )t e A 1t e A 2t unless t = (trivial) or A 1 A 2 = A 2 A 1 However, the identity e A(t 1+t 2 ) = e At 1 e At 2 is always true Hence e At e At = e At e At = I for all matrices A and all t, and therefore for all A and t, e At is invertible

6 ME 132, Spring 25, UC Berkeley, A Packard Diagonal A If A C n n is diagonal, then e At is easy to compute Specifically, if A is diagonal, it is easy to see that for any k, β 1 β 1 k β A = 2 A k β k = 2 β n βn k In the power series definition for e At, any off-diagonal terms are identically zero, and the i th diagonal term is simply e At ii = 1 + tβ i + t2 2! β2 i + t3 3! β3 i + = e β it 2132 Block Diagonal A If A 1 C n 1 n 1 and A 2 C n 2 n 2, define A := A 1 A 2 Question: How is e At related to e A 1t and e A 2t? Very simply note that for any k, A k = A 1 A 2 k Hence, the power series definition for e At gives e At = ea 1t e A 2t = Ak 1 A k Effect of Similarity Transformations For any invertible T C n n, Equivalently, e T 1 AT t = T 1 e At T T e T 1 AT t T 1 = e At This can easily be shown from the power series definition e T 1 AT t = I + t ( T 1 AT ) + t2 ( T 1 AT ) 2 t 3 ( + T 1 AT ) 3 + 2! 3!

7 ME 132, Spring 25, UC Berkeley, A Packard 193 It is easy to verify that for every integer k ( T 1 AT ) k = T 1 A k T Hence, we have (with I written as T 1 IT ) e T 1 AT t = T 1 IT + tt 1 AT + t2 2! T 1 A 2 T + t3 3! T 1 A 3 T + Pull T 1 out on the left side, and T on the right to give ( ) e T 1 AT t = T 1 I + ta + t2 2! A2 + t3 3! A3 + T which is simply as desired e T 1 AT t = T 1 e At T 2134 Examples Given β R, define Calculate and hence for any k, A 2k = ( 1)k β 2k ( 1) k β 2k A := A 2 = β β β2 β 2, A 2k+1 ( 1) = k β 2k+1 ( 1) k+1 β 2k+1 Therefore, we can write out the first few terms of the power series for e At as e At 1 1 = 2 β2 t ! β4 t 4 βt 1 3! β3 t ! β5 t 5 βt + 1 3! β3 t 3 1 5! β5 t β2 t ! β4 t 4 which is recognized as e At = cos βt sin βt sin βt cos βt Similarly, suppose α, β R, and A := α β β α

8 ME 132, Spring 25, UC Berkeley, A Packard 194 Then, A can be decomposed as A = A 1 + A 2, where A 1 := α α, A 2 := Note: in this special case, A 1 A 2 = A 2 A 1, hence e (A 1+A 2 )t = e A 1t e A 2t β β Since A 1 is diagonal, we know e A 1t, and e A 2t follows from our previous example Hence e At = This is an important case to remember Finally, suppose λ F, and eαt cos βt e αt sin βt e αt sin βt e αt cos βt A = λ 1 λ We did a similar example in the first Warning of section 213 A few calculations show that A k = λk kλ k 1 λ k Hence, the power series gives e At = eλt te λt e λt 214 Problems 1 Determine e At for the matrices A = Plot the solution to ẋ(t) = Ax(t) from the initial conditions x() = 1, x() = 1, x() = 1, x() = 1 Plot this in 2 different manners: first, two plots (using subplot) of x 1 (t) versus t, and x 2 (t) versus t; and in another single plot of x 2 versus x 1 In each plot, there should be 4 lines Use different linetypes (or colors) and make sure they are consistent across all of the figures

9 ME 132, Spring 25, UC Berkeley, A Packard Solution To State Equations The matrix exponential is extremely important in the solution of the vector differential equation ẋ(t) = Ax(t) + Bu(t) (83) starting from the initial condition x(t ) = x Now, consider the original equation in (83) We can derive the solution to the forced equation using the integrating factor methid, proceeding in the same manner as in the scalar case, with extra care for the matrix-vector operations Suppose a function x satisfies (83) Multiply both sides by e At to give Move one term to the left, leaving, e At ẋ(t) = e At Ax(t) + e At Bu(t) e At Bu(t) = e At ẋ(t) e At Ax(t) = e At ẋ(t) Ae At x(t) = d dt e At x(t) (84) Since these two functions are equal at every time, we can integrate them over the interval t t 1 Note that the right-hand side of (84) is an exact derivative, giving t1 t1 e At d Bu(t)dt = e At x(t) dt t t dt = e At x(t) t 1 t = e At 1 x(t 1 ) e At x(t ) Note that x(t ) = x Also, multiply both sides by e At 1, to yield This is rearranged into e At t1 1 t e At Bu(t)dt = x(t 1 ) e A(t 1 t ) x x(t 1 ) = e A(t 1 t ) x + t1 t e A(t 1 t) Bu(t)dt Finally, switch variable names, letting τ be the variable of integration, and letting t be the right end point (as opposed to t 1 ) In these new letters, the expression for the solution of the (83) for t t, subject to initial condition x(t ) = x is x(t) = e A(t t ) x + t t e A(t τ) Bu(τ)dτ consisting of a free and forced response

10 ME 132, Spring 25, UC Berkeley, A Packard Eigenvalues, eigenvectors, stability 2151 Diagonalization: Motivation Recall two facts from Section 213: For diagonal matrices Λ F n n, λ 1 e λ 1t λ 2 Λ = e Λt e λ 2t = λ n e λnt and: If A F n n, and T F n n is invertible, and à := T 1 AT, then e At = T eãt T 1 Clearly, for a general A F n n, we need to study the invertible transformations T F n n such that T 1 AT is a diagonal matrix Suppose that T is invertible, and Λ is a diagonal matrix, and T 1 AT = Λ Moving the T 1 to the other side of the equality gives AT = T Λ Let t i denote the i th column of the matrix T SInce T is assumed to be invertible, none of the columns of T can be identically zero, hence t i Θ n Also, let λ i denote the (i, i) th entry of Λ The i th column of the matrix equation AT = T Λ is just At i = t i λ i = λ i t i This observation leads to the next section 2152 Eigenvalues Definition: Given a matrix A F n n A complex number λ is an eigenvalue of A if there is a nonzero vector v C n such that Av = vλ = λv The nonzero vector v is called an eigenvector of A associated with the eigenvalue λ Remark: Consider the differential equation ẋ(t) = Ax(t), with initial condition x() = v Then x(t) = ve λt is the solution (check that it satisfies initial condition and differential equation) So, an eigenvector is direction in the state-space such that if you start in the direction of the eigenvector, you stay in the direction of the eigenvector Note that if λ is an eigenvalue of A, then there is a vector v C n, v Θ n such that Av = vλ = (λi) v Hence (λi A) v = Θ n

11 ME 132, Spring 25, UC Berkeley, A Packard 197 Since v Θ n, it must be that det (λi A) = For an n n matrix A, define a polynomial, p A ( ), called the characteristic polynomial of A by p A (s) := det (λi A) Here, the symbol s is simply the indeterminate variable of the polynomial For example, take A = Straightforward manipulation gives p A (s) = s 3 s 2 + 3s 2 Hence, we have shown that the eigenvalues of A are necessarily roots of the equation For a general n n matrix A, we will write p A (s) = p A (s) = s n + a 1 s n a n 1 s + a n where the a 1, a 2,, a n are complicated products and sums involving the entries of A Since the characteristic polynomial of an n n matrix is a n th order polynomial, the equation p A (s) = has at most n distinct roots (some roots could be repeated) Therefore, a n n matrix A has at most n distinct eigenvalues Conversely, suppose that λ C is a root of the polynomial equation Question: Is λ an eigenvalue of A? p A (s) s=λ = Answer: Yes Since p A (λ) =, it means that det (λi A) = Hence, the matrix λi A is singular (not invertible) Therefore, by the matrix facts, the equation (λi A) v = Θ n has a nonzero solution vector v (which you can find by Gaussian elimination) This means that λv = Av for a nonzero vector v, which means that λ is an eigenvalue of A, and v is an associated eigenvector We summarize these facts as:

12 ME 132, Spring 25, UC Berkeley, A Packard 198 A is a n n matrix The characteristic polynomial of A is p A (s) := det (si A) = s n + a 1 s n a n 1 s + a n A complex number λ is an eigenvalue of A if and only if λ is a root of the characteristic equation p a (λ) = Next, we have a useful fact from linear algebra: Suppose A is a given n n matrix, and (λ 1, v 1 ), (λ 2, v 2 ),, (λ n, v n ) are eigenvalue/eigenvector pairs So, for each i, v i Θ n and Av i = v i λ i Fact: If all of the {λ i } n i=1 are distinct, then the set of vectors {v 1, v 2,, v n } are a linearly independent set In other words, the matrix V := v 1 v 2 v n C n n is invertible Proof: We ll prove this for 3 3 matrices check your linear algebra book for the generalization, which is basically the same proof Suppose that there are scalars α 1, α 2, α 3, such that This means that 3 α i v i = Θ 3 i=1 Θ 3 = (A λ 3 I) Θ 3 = (A λ 3 I) 3 i=1 α i v i (85) = α 1 (A λ 3 I) v 1 + α 2 (A λ 3 I) v 2 + α 3 (A λ 3 I) v 3 = α 1 (λ 1 λ 3 ) v 1 + α 2 (λ 2 λ 3 ) v 2 + Θ 3 Now multiply by (A λ 2 I), giving Θ 3 = (A λ 2 I) Θ 3 = (A λ 2 I) α 1 (λ 1 λ 3 )v 1 + α 2 (λ 2 λ 3 )v 2 = α 1 (λ 1 λ 3 )(λ 1 λ 2 )v 1 Since λ 1 λ 3, λ 1 λ 2, v 1 Θ 3, it must be that α 1 = Using equation (85), and the fact that λ 2 λ 3, v 2 Θ 3 we get that α 2 = Finally, α 1 v 1 + α 2 v 2 + α 3 v 3 = Θ 3 (by assumption), and v 3 Θ 3, so it must be that α 3 =

13 ME 132, Spring 25, UC Berkeley, A Packard Diagonalization Procedure In this section, we summarize all of the previous ideas into a step-by-step diagonalization procedure for n n matrices 1 Calculate the characteristic polynomial of A, p A (s) := det (si A) 2 Find the n roots of the equation p A (s) =, and call the roots λ 1, λ 2,, λ n 3 For each i, find a nonzero vector t i C n such that (A λ i I) t i = Θ n 4 Form the matrix T := t 1 t 2 t n C n n (note that if all of the {λ i } n i=1 are distinct from one another, then T is guaranteed to be invertible) 5 Note that AT = T Λ, where Λ = λ 1 λ 2 λ n 6 If T is invertible, then T 1 AT = Λ Hence e λ 1t e At = T e Λt T 1 e λ 2t = T e λnt T 1 We will talk about the case of nondistinct eigenvalues later 2154 e At as t For the remainder of the section, assume A has distinct eigenvalues if all of the eigenvalues (which may be complex) of A satisfy Re (λ i ) < then e λ it as t, so all entries of e At decay to zero

14 ME 132, Spring 25, UC Berkeley, A Packard 2 If there is one (or more) eigenvalues of A with then Re (λ i ) e λ it bounded as Hence, some of the entries of e At either do not decay to zero, or, in fact, diverge to So, the eigenvalues are an indicator (the key indicator) of stability of the differential equation ẋ(t) = Ax(t) if all of the eigenvalues of A have negative real parts, then from any initial condition x, the solution x(t) = e At x decays to Θ n as t (all coordinates of x(t) decay to as t ) In this case, A is said to be a Hurwitz matrix if any of the eigenvalues of A have nonnegative real parts, then from some initial conditions x, the solution to ẋ(t) = Ax(t) does not decay to zero 2155 Complex Eigenvalues In many systems, the eigenvalues are complex, rather than real This seems unusual, since the system itself (ie, the physical meaning of the state and input variables, the coefficients in the state equations, etc) is very much Real The procedure outlined in section 2153 for matrix diagonalization is applicable to both real and complex eigenvalues, and if A is real, all intermediate complex terms will cancel, resulting in e At being purely real, as expected However, in the case of complex eigenvalues it may be more advantageous to use a different similarity transformation, which does not lead to a diagonalized matrix Instead, it leads to a real block-diagonal matrix, whose structure is easily interpreted Let us consider a second order system, with complex eigenvalues where A R n n, x R 2 and ẋ(t) = Ax(t) (86) p A (λ) = det(λi 2 A) = (λ σ) 2 + ω 2 (87)

15 ME 132, Spring 25, UC Berkeley, A Packard 21 The two eigenvalues of A are λ 1 = σ + jω and λ 2 = σ jω, while the two eigenvectors of A are given by (λ 1 I 2 A)v 1 = Θ 2 (λ 2 I 2 A)v 2 = Θ 2 (88) Notice that, since λ 2 is the complex conjugate of λ 1, v 2 is the complex conjugate vector of v 1, ie if v 1 = v r + jv i, then v 2 = v r jv i This last fact can be verified as follows Assume that λ 1 = σ + jω, v 1 = v r + jv i and insert these expressions in the first of Eqs (88) (σ + jω)i 2 A) v r + jv i = Θ 2, Separating into its real and imaginary parts we obtain σv r ωv i + j σv i + ωv r = Av r + jav i σv r ωv i = Av r (89) σv i + ωv r = Av i Notice that Eqs (89) hold if we replace ω by ω and v i by v i Thus, if λ 1 = σ + jω and v 1 = v r + jv i are respectively an eigenvalue and eigenvector of A, then λ 2 = σ jω and v 2 = v r jv i are also respectively an eigenvalue and eigenvector Eqs (89) can be rewritten in matrix form as follows A σ ω v r v i = vr v i ω σ (9) Thus, we can define the similarity transformation matrix T = v r v i R 2 2 (91) and the matrix σ ω J c = ω σ (92) such that A = T J c T 1, e At = T e Jct T 1 (93) The matrix exponential e Jct is easy to calculate Notice that σ ω J c = = σi ω σ 2 + S 2 where I 2 = 1 1 and S 2 = ω ω

16 ME 132, Spring 25, UC Berkeley, A Packard 22 is skew-symmetric, ie S T 2 = S 2 Thus, cos(ωt) sin(ωt) e S2t = sin(ωt) cos(ωt) This last result can be verified by differentiating with respect to time both sides of the equation: d dt es 2t = S 2 e S 2t and d dt cos(ωt) sin(ωt) sin(ωt) cos(ωt) = = ω sin(ωt) ω cos(ωt) ω cos(ωt) ω sin(ωt) ω cos(ωt) sin(ωt) ω sin(ωt) cos(ωt) = S 2 e S 2t Since σi 2 S 2 = S 2 σi 2, then cos(ωt) sin(ωt) e Jct = e σt sin(ωt) cos(ωt) (94) 2156 Examples Consider the system ẋ(t) = Ax(t) (95) where x R 2 and A = The two eigenvalues of A are λ 1 = j and λ 2 = j Their corresponding eigenvectors are respectively j j v 1 = and v + 771j 2 = 771j Defining we obtain A T 2 = T 2 J c, where T = J c = 1 1 (96)

17 ME 132, Spring 25, UC Berkeley, A Packard 23 Thus, cos(t) sin(t) e Jct = sin(t) cos(t) and e At = T e Jct T 1 = cos(t) sin(t) sin(t) cos(t) Utilizing the coordinate transformation x = T 1 x x = T x, (97) where T is given by Eq (96), we can obtain from Eqs (97) and(95) ẋ (t) = { T 1 AT } x (t) (98) = J c x (t) Consider now the initial condition 4243 x() = = T x (), x () = 1 The phase plot for x (t) is given by Fig 8-(a), while that for x(t) is given by Fig 8-(b) x*2 x x*1 (a) ẋ = J c x x1 (b) ẋ = Ax Figure 8: Phase plots

18 ME 132, Spring 25, UC Berkeley, A Packard Jordan Form 2161 Motivation In the last section, we saw that if A F n n has distinct eigenvalues, then there exists an invertible matrix T C n n such that T 1 AT is diagonal In this sense, we say that A is diagonalizable by similarity transformation (the matrix T 1 AT is called a similarity transformation of A) Even some matrices that have repeated eigenvalues can be diagonalized For instance, take A = I 2 Both of the eigenvalues of A are at λ = 1, yet A is diagonal (in fact, any invertible T makes T 1 AT diagonal) On the other hand, there are other matrices that cannot be diagonalized Take, for instance, A = 1 The characteristic equation of A is p A (λ) = λ 2 Clearly, both of the eigenvalues of A are equal to Hence, if A can be diagonalized, the resulting diagonal matrix would be the matrix (recall that if A can be diagonalized, then the diagonal elements are necessarily roots of the equation p A (λ) = ) Hence, there would be an invertible matrix T such that T 1 AT = 2 2 Rearranging gives 1 t11 t 12 t 21 t 22 = t11 t 12 t 21 t 22 Multiplying out both sides gives t21 t 22 = But if t 21 = t 22 =, then T is not invertible Hence, this 2 2 matrix A cannot be diagonalized with a similarity transformation 2162 Details It turns out that every matrix can be almost diagonalized using similarity transformations Although this result is not difficult to prove, it will take us too far from the main flow of the course The proof can be found in any decent linear algebra book Here we simply outline the facts

19 ME 132, Spring 25, UC Berkeley, A Packard 25 Definition: If J C m m appears as λ 1 λ 1 J = λ 1 λ then J is called a Jordan block of dimension m, with associated eigenvalue λ Theorem: (Jordan Canonical Form) For every A F n n, there exists an invertible matrix T C n n such that J 1 T 1 J 2 AT = J k where each J i is a Jordan block If J F m m is a Jordan block, then using the power series definition, it is straightforward to show that e λt te λt t m 1 (m 1)! eλt e Jt = e λt t m 2 (m 2)! eλt e λt Note that if Re (λ) <, then every element of e Jt decays to zero, and if if Re (λ), then some elements of e Jt diverge to 217 Significance In general, there is no numerically reliable way to compute the Jordan form of a matrix It is a conceptual tool, which, among other things, shows us the extent to which e At contains terms other than exponentials Notice that if the real part of λ is negative, then for any finite integer m, { t m e λt} = lim t From this result, we see that even in the case of Jordan blocks, the signs of the real parts of the eigenvalues of A determine the stability of the linear differential equation ẋ(t) = Ax(t)

20 ME 132, Spring 25, UC Berkeley, A Packard Problems 1 Find, by hand calculation the eigenvalues, the eigenvectors and e At for the matrices 5 4 (a) A = (b) A = (c) A = (d) A = Find (by hand calculation) the eigenvalues and eigenvectors of the matrix A = Does e At have all of its terms decaying to zero? 3 Read about the MatLab command eig (use the MatLab manual, the Matlab primer, and/or the comnand >> help eig) Repeat problem 2 using eig, and explain any differences that you get If a matrix has distinct eigenvalues, in what sense are the eigenvectors not unique? 4 Consider the differential equation ẋ(t) = Ax(t), where x(t) is (3 1) and A is from problem (2) above Suppose that the initial condition is x() = Write the initial condition as a linear combination of the eigenvectors, and find the solution x(t) to the differential equation, written as a time-dependent, linear combination of the eigenvectors 5 Suppose that we have a 2nd order system (x(t) R 2 ) governed by the differential equation 2 ẋ(t) = x(t) 2 5 Let A denote the 2 2 matrix above (a) Find the eigenvalues an eigenvectors of A In this problem, the eigenvectors can be chosen to have all integer entries (recall that eigenvectors can be scaled) 1 1

21 ME 132, Spring 25, UC Berkeley, A Packard 27 (b) On the grid below (x 1 /x 2 space), draw the eigenvectors x 2 x 1 (c) A plot of x 2 (t) vs x 1 (t) is called a phase-plane plot The variable t is not explicitly plotted on an axis, rather it is the parameter of the tick marks along the plot On the grid above, using hand calculations, draw the solution to the equation ẋ(t) = Ax(t) for the initial conditions x() = 3 3, x() = 2 2, x() = 3 3, x() = HINT: Remember that if v i are eigenvectors, and λ i are eigenvalues of A, then the solution to ẋ(t) = Ax(t) from the initial condition x() = 2 i=1 α i v i is simply n x(t) = α i e λit v i i=1 (d) Use Matlab to create a similar picture with many (say 2) different initial conditions spread out in the x 1, x 2 plane 6 Suppose A is a real, n n matrix, and λ is an eigenvalue of A, and λ is not real, but λ is complex Suppose v C n is an eigenvector of A associated with this eigenvalue λ Use the notation λ = λ R + jλ I and v = v r + jv I for the real and imaginary parts of λ, and v ( j means 1) (a) By equating the real and imaginary parts of the equation Av = λv, find two equations that relate the various real and imaginary parts of λ and v (b) Show that λ (complex conjugate of λ) is also an eigenvalue of A What is the associated eigenvector? 4 2

22 ME 132, Spring 25, UC Berkeley, A Packard 28 (c) Consider the differential equation ẋ = Ax for the A described above, with the eigenvalue λ and eigenvector v Show that the function x(t) = e λ Rt cos (λ I t) v R sin (λ I t) v I satisfies the differential equation What is x() in this case? (d) Fill in this sentence: If A has complex eigenvalues, then if x(t) starts on the part of the eigenvector, the solution x(t) oscillates between the and parts of the eigenvector, with frequency associated with the part of the eigenvalue During the motion, the solution also increases/decreases exponentially, based on the part of the eigenvalue (e) Consider the matrix A It is possible to show that A j 1 2 j 1 2 = A = j 1 2 j j 1 2j Sketch the trajectory of the solution x(t) in R 2 to the differential equation ẋ = Ax 1 for the initial condition x() = (f) Find e At for the A given above NOTE: e At is real whenever A is real See the notes for tricks in making this easy 7 Consider the 2-state system governed by the equation ẋ(t) = Ax(t) Shown below are the phase-plane plots (x 1 (t)vsx 2 (t)) for 4 different cases Match the plots with the A matrices, and correctly draw in arrows indicating the evolution in time Put your answers on the enlarged graphs included in the solution packet A 1 = 3 2 1, A 2 = , A 3 = 3 1, A 4 =

23 ME 132, Spring 25, UC Berkeley, A Packard x2 x x x1 5 x2 x x x1

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