Lecture 8. Chapter 5: Input-Output Stability Chapter 6: Passivity Chapter 14: Passivity-Based Control. Eugenio Schuster.

Transcription

1 Lecture 8 Chapter 5: Input-Output Stability Chapter 6: Passivity Chapter 14: Passivity-Based Control Eugenio Schuster schuster@lehigh.edu Mechanical Engineering and Mechanics Lehigh University Lecture 8 p. 1/34

2 Input-Output Stability Broader concept that Input-State Stability (ISS). We consider a system with input-output relation y = Hu, u : [0, ] R m Space of piecewise continuous, bounded functions: L m : u L = sup t 0 u(t) < Space of piecewise continuous, square-integrable functions: L m 2 : u L 2 = 0 u T (t)u(t)dt < Lecture 8 p. 2/34

3 Input-Output Stability More generally, the space L m p for 1 p < : u Lp = ( 0 u(t) p dt) 1/p < Truncated norm for 1 p : u τ Lp = { ( τ 0 u(t) p dt ) 1/p p < sup t [0,τ] u(t) p = allows for the definition of the extended space L m p,e, i.e., L m p,e = {u u τ L m p, τ [0, )} Example: Exponential functions are in this space. Lecture 8 p. 3/34

4 Input-Output Stability Note: We use L to denote any L p norm/space without committing to any particular p. Definition: A mapping H : L m e L q e is L stable if there is a class K function α and a nonnegative constant β such that (Hu) τ L α( u τ L )+β for all u L m e and τ [0, ). It is finite-gain L stable if there are nonnegative constants γ and β such that for all u L m e and τ [0, ). (Hu) τ L γ u τ L +β Lecture 8 p. 4/34

5 Input-Output Stability Let us consider a linear system. Let y = Hu be described by the convolution operator y(t) = t 0 h(t σ)u(σ)dσ Theorem (Young s Convolution Theorem): (See Example 5.2) y τ Lp = (h u) τ Lp h τ L1 u τ Lp, p [1, ] Linear systems are finite-gain L p stable with gain equal to the L 1 norm of the impulse response. Holder s Inequality: If p,q [0, ] and 1 p + 1 q = 1, then τ 0 f(t)g(t) dt ( τ 0 f(t) p dt ) 1/p ( τ 0 g(t) q dt) 1/q, t 0 Proof: Lecture 8 p. 5/34

6 Input-Output Stability Theorem 5.4: Consider the linear time-invariant system ẋ = Ax+Bu y = Cx+Du where A is Hurwitz. Let G(s) = C(sI A) 1 B +D. Then, the L 2 gain of the system is G(jω) H = sup ω R G(jω) 2. Fourier Transform: Parseval s Theorem: Proof: 0 Y(jω) = 0 y T (t)y(t)dt = 1 2π y(t)e jωt dt + Y (jω)y(jω)dω Lecture 8 p. 6/34

7 Input-Output Stability Theorem 5.5: Consider the nonlinear time-invariant system ẋ = f(x)+g(x)u, x ( 0) = x 0, y = h(x) where f(x) is locally Lipschitz, and G(x), h(x) are continuous over R n. The matrix G is n m and h : R n R q. The function f and h vanish at the origin; that is, f(0) = 0 and h(0) = 0. Let γ be a positive number and suppose there is a continuously differentiable, positive semidefinite function V(x) that satisfies the Hamilton-Jacobi inequality def V H(V,f,G,h,γ) = x f(x)+ 1 V 2γ 2 x G(x)GT (x) ( V x ) T ht (x)h(x) 0 for all x R n. For each x 0 R n, the system is finite-gain L 2 stable and its L 2 gain is less than or equal to γ. Proof: Lecture 8 p. 7/34

8 Input-Output Stability Theorem (Alternative to Theorem 5.4): time-invariant system Consider the linear ẋ = Ax+Bu y = Cx Suppose there is a positive semidefinite solution P of the Riccati equation PA+A T P + 1 γ 2PBBT P +C T C = 0 for some γ > 0. Then, the system is L 2 stable and its L 2 gain is less than or equal to γ. Proof: Trivial application of Theorem 5.5. See Example Lecture 8 p. 8/34

9 Input-Output Stability Feedback Systems: Consider the linear time-invariant system Figure 1: Feedback Connection. Lecture 8 p. 9/34

10 Input-Output Stability Theorem 5.6 (Small-Gain Theorem): Suppose H 1 and H 2 are finite-gain L stable with constants (γ 1,β 1 ) and (γ 2,β 2 ) respectively. Then, the feedback connection is finite-gain L stable if γ 1 γ 2 < 1. Proof: Notes: Phase does NOT matter at all. Magnitude of one of the gains dos NOT matter at all as long as the other gain is small enough to satisfy γ 1 γ 2 < 1. This theorem is the foundation of Robust Control. Lecture 8 p. 10/34

11 Passivity Definition 6.3: The system ẋ = f(x,u) y = h(x,u) is said to be passive if there exists a continuously differentiable positive semidefinite function V(x) (called the storage function) such that u T y V = V x f(x,u), (x,u) Rn R p Moreover, it is said to be lossless if u T y = V. Lecture 8 p. 11/34

12 Passivity input-feedforward passive if u T y V +u T φ(u) for some function φ. input strictly passive if u T y V +u T φ(u) and u T φ(u) > 0, for all u 0. output-feedback passive if u T y V +y T ρ(y) for some function ρ. output strictly passive if u T y V +y T ρ(y) and y T ρ(y) > 0, for all y 0. strictly passive if u T y V +ψ(x) for some positive definite function ψ. In all cases the inequality should hold for all (x,u). Lecture 8 p. 12/34

13 Passivity It is useful to write the passivity condition as V ρψ(x) δy T y ǫu T u+u T y The term u T y contains the phase information. The system is: lossless if ρ = δ = ǫ = 0, i.e., V u T y input strictly passive if ǫ > 0 output strictly passive if δ > 0 state strictly passive if ρ > 0 Note: V is allowed to be positive semidefinite. Lecture 8 p. 13/34

14 Passivity Example: Khalil 6.2. Lecture 8 p. 14/34

15 Passivity Lemma 6.5: If the system ẋ = f(x,u) y = h(x,u) with f(0,0) = h(0,0) = 0 is output strictly passive with u T y V +δy T y, for some δ > 0, then it is finite-gain L 2 stable and its L 2 gain is less than or equal to 1/δ. Proof: Lecture 8 p. 15/34

16 Passivity Lemma 6.6: If the system ẋ = f(x,u) y = h(x,u) with f(0,0) = h(0,0) = 0 is passive with a positive definite storage function V(x), then the origin of ẋ = f(x,0) is stable. Proof: Note: To show asymptotic stability of the origin of ẋ = f(x,0), we need to either show that V is negative definite or apply the invariance theorem. Lecture 8 p. 16/34

17 Passivity Definition 6.5: (1) (2) The system ẋ = f(x,u) y = h(x,u) with f(0,0) = h(0,0) = 0 is said to be zero-state observable if no solution of ẋ = f(x,0) can stay identically in S = {x R n h(x,0) = 0}, other than x(t) = 0. Lemma 6.7: Consider the system (1)-((2). The origin of ẋ = f(x,0) is asymptotically stable if the system is strictly passive output strictly passive and zero-state observable If the storage function is radially unbounded, the origin will be globally asymptotically stable. Proof: Lecture 8 p. 17/34

18 Passivity Example: Khalil 6.6. Lecture 8 p. 18/34

19 Passivity Feedback Systems: Consider the linear time-invariant system Figure 2: Feedback Connection. Lecture 8 p. 19/34

20 Passivity Each system can be represented by a state-space model ẋ i = f i (x i,e i ) y i = h i (x i,e i ) The closed-loop state model is ẋ = f(x,u) y = h(x,u) with f(0,0) = h(0,0) = 0 (assumption), where [ ] [ ] [ ] x = x 1 x 2, u = u 1 u 2, y = y 1 y 2 Lecture 8 p. 20/34

21 Passivity Theorem 6.1: The feedback connection of two passive systems is passive. Proof: Lecture 8 p. 21/34

22 Passivity Lemma 6.8: The feedback connection of two output strictly passive systems with e T i y i V i +δ i y T i y i, δ i > 0 is finite-gain L 2 stable and its L 2 gain is less than or equal to 1/min{δ 1,δ 2 }. Theorem 6.2: Consider the feedback connection of two time-invariant dynamical systems that satisfy e T i y i V i +ǫ i e T i e i +δ i y T i y i, ǫ i,δ i > 0 for some storage function V i (x i ). Then, the closed-loop map from u to y is finite-gain L 2 stable if ǫ 1 +δ 2 > 0 and ǫ 2 +δ 1 > 0. Lecture 8 p. 22/34

23 Passivity Theorem 6.3: Consider the feedback connection of two time-invariant dynamical systems. The origin of the close loop system (when u=0) is asymptotically stable if both feedback components are strictly passive both feedback components are output strictly passive and zero-state observable one component is strictly passive and the other one is output strictly passive and zero state observable Furthermore, if the storage function for each component is radially unbounded, the origin is globally asymptotically stable. Proof: Lecture 8 p. 23/34

24 Passivity ISS: SS Pasive: V α( x )+ρ( u ) V α( x )+u T y Neither one implies the other Feedback Interconnections: Small gain: Two stable systems that do not increase amplitude remains stable Passivity: Two stable systems that do not invert phase (sign) remains stable Lecture 8 p. 24/34

25 Passivity-Based Control We consider the p-input-p-output system (3) (4) ẋ = f(x,u) y = h(x) We assume that f(0,0) = 0 so that the origin is an open-loop equilibrium, and h(0) = 0. The system is said to be passive if there exists a continuously differentiable positive semidefinite function V(x) (called the storage function) such that u T y V = V x f(x,u), (x,u) Rn R p The system is zero-state observable if no solution of ẋ = f(x,0) can stay identically in the set {h(x) = 0} other than the trivial solution. Lecture 8 p. 25/34

26 Passivity-Based Control Theorem 14.4: If the system (3)-(4) is passive with a radially unbounded positive definite storage function and zero-state observable the the origin x = 0 can be globally stabilized by u = φ(y), where φ is any locally Lipschitz function such that φ(0) = 0 and y T φ(y) > 0 for all y 0. Proof: Lecture 8 p. 26/34

27 Passivity-Based Control The utility of Theorem 14.4 can be increased by transforming nonpassive systems into passive ones. We consider a special case of (3), where (5) ẋ = f(x)+g(x)u Suppose a radially unbounded, positive definite, continuously differentiable function V(x) exists such that V x If we take f(x) 0, x (origin is open-loop stable) y = h(x) = [ ] T V x G(x), the system with input u and output y is passive. If it is also zero-state observable, we can apply Theorem 14.4 Lecture 8 p. 27/34

28 Passivity-Based Control Allowing ourselves the freedom to choose the output function is useful, but we are still limited to state equations for which the origin is open-loop stable. If a feedback control u = α(x)+β(x)v and a function h(x) exist such that the system ẋ = f(x)+g(x)α(x)+g(x)β(x)v y = h(x) with input v and output y, satisfies the conditions of Theorem 14.4, we can globally stabilize the origin by using v = φ(y). The use of feedback to convert a nonpassive system into a passive one is known as feedback passivation. Lecture 8 p. 28/34

29 Passivity-Based Control Example: Khalil Lecture 8 p. 29/34

30 Passivity-Based Control Let us consider the cascade system (6) (7) (8) ż = f a (z)+f(z,y)y ẋ = f(x)+g(x)u y = h(x) If the driving-system (7)-(8) is passive with a radially unbounded positive definite storage function V(x), and if the origin of the driven-system (6) with y = 0, i.e., the origin of ż = f a (z) is stable with a radially unbounded Lyapunov function W(z) that satisfies W z f a(z) 0 z. Lecture 8 p. 30/34

31 Passivity-Based Control Then, the feedback control u = ( ) T W z F(z,y) +v makes the system (9) (10) (11) ż = f a (z)+f(z,y)y ( ) T W ẋ = f(x) G(x) z F(z,y) +G(x)v y = h(x) with input v and output y passive with U(z,x) = W(z)+V(x) as the storage function. If the system (9)-(11) is zero-state observable, we can apply Theorem 14.4 to globally stabilize the origin. Lecture 8 p. 31/34

32 Passivity-Based Control Checking zero-state observability of the system (9)-(11) can be avoided by strengthening the assumption on W(z). If the driving-system (7)-(8) zero-state observable and passive with a radially unbounded positive definite storage function V(x), and if the origin of the driven-system (6) with y = 0, i.e., the origin of ż = f a (z), is globally asymptotically stable with a radially unbounded Lyapunov function W(z) that satisfies W z f W a(z) < 0 z 0, (0) = 0. z The feedback control ( ) T W u = z F(z,y) φ(y) globally stabilizes the origin (z = 0,x = 0). Lecture 8 p. 32/34

33 Passivity-Based Control Proof: Lecture 8 p. 33/34

34 Passivity-Based Control Example: Khalil Lecture 8 p. 34/34