Observability. Dynamic Systems. Lecture 2 Observability. Observability, continuous time: Observability, discrete time: = h (2) (x, u, u)

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1 Observability Dynamic Systems Lecture 2 Observability Continuous time model: Discrete time model: ẋ(t) = f (x(t), u(t)), y(t) = h(x(t), u(t)) x(t + 1) = f (x(t), u(t)), y(t) = h(x(t)) Reglerteknik, ISY, Linköpings Universitet Can you compute the state x from the output y and the input u? Observability, discrete time: Observability, continuous time: Look at several points in time to extract more information y(t) = h(x(t)) y(t + 1) = h(x(t + 1)) = h(f (x(t), u(t)) = h (1) (x(t), u(t)) y(t + 2) = h (1) (x(t + 1), u(t + 1)) = h (1) (f (x(t), u(t)), u(t + 1)) = h (2) (x(t), u(t), u(t + 1)) yt + N) = h (N) (x(t), u(t), u(t + 1),, u(t + N 1)) This is a system of nonlinear equations in x(t) Solvability implies observability Differentiate the output to extract more information y = h(x) ẏ = h x (x)ẋ = h x (x)f (x, u) = h (1) (x, u) ÿ = h (1) x (x, u)ẋ + h (1) u (x, u) u = h (1) x (x, u)f (x, u) + h (1) u (x, u) u = = h (2) (x, u, u) y (N) = h (N) (x, u, u,, u (N 1) ) This is a system of nonlinear equations in x Solvability implies observability

2 No input Alternative description Solvability usually depends on u Define Then L f = f i (x) x i Example: ẋ 1 = x 2 u, ẋ 2 = x 1 x 2, y = x 1 y = h(x) ẏ = (L f h)(x) ÿ = (L 2 f h)(x) The system of equations y = x 1 ẏ = x 2 u y (N) = (L N f h)(x) can not be solved for x 2 if u = 0 Mathematics of equation solving Implicit function theorem Consider the equation f (x, y) = 0 Linear equations: solvability determined by rank test Polynomial equations: extensive (and difficult) mathematical theory (ideals, Gröbner bases, characteristic sets, elimination theory, cylindrical algebraic decomposition) Very high computational complexity Local properties of general equations: Implicit function theorem where the dimensions of x and f are equal (same number of unknowns and equations) Assume that f (x o, y o ) = 0, f x (x o, y o ) nonsingular (f x is the Jacobian of f with respect to x) Then, for all y close to y o the equation has a solution x which is locally unique

3 Local observability Linear time invariant systems Compute the Jacobian J = h x (x) h (1) x (x, u, u) h (N) x (x, u, u,, u (N 1) ) J full rank at x 0 x is uniquely determined by u, y and their derivatives in a neighborhood of x 0 (implicit function theorem) ẋ = Ax + Bu, y = Cx The Jacobian J is constant and has the form C CA J = CA N 1 J full rank x can be solved uniquely (globally) This is the classical observability test for linear systems Cayley-Hamilton no need to take N > n Linear time-varying systems Quantitative measure of observability ẋ = A(t)x + B(t)u, y = C(t)x The Jacobian J has the form C J = t C + CA See the exercises ẋ(t) = A(t)x(t), y(t) = C(t)x(t) The energy in the output is given by t1 y(t) T y(t) dt = x(t 0 ) T M(t 0, t 1 )x(t 0 ) t 0 where M is the Observability Gramian: M(t 0, t 1 ) = t1 t 0 Φ T (t, t 0 )C T (t)c(t)φ(t, t 0 ) dt

4 Observability and the Gramian Observability, discrete time From the definition of the observability Gramian: t1 t 0 Φ T (t, t 0 )C T (t)y(t) dt = M(t 0, t 1 )x(t 0 ) Theorem: observability on [t 0, t 1 ] M(t 0, t 1 ) nonsingular Note: This is a smoothing estimate of x(t 0 ) based on y(t), t 0 t t 1 Iterating the state equation with u = 0 gives y(t 0 ) C(t 0 ) y(t 0 + 1) = C(t 0 +1)A(t 0 ) x(t 0 ) y(t 1 1) C(t 1 1)A(t 1 2) A(t 0 ) {{ O(t 0,t 1 ) x(t 0 ) can be computed from y(t 0 ),, y(t 1 1) if O(t 0, t 1 ) has full rank O T O is the discrete time observability Gramian Change of state variables For constant A and C one can define C CA O = CA n 1 in both continuous and discrete time Theorem The range and null spaces of M(t 0, t 1 ) coincide with the range and null spaces of O T O for all t 1 > t 0 gives x = T(z), T one-to-one T z nonsingular ż = T 1 z f (T(z), u) = f (z, u), y = h(t(z)) = h(z) To test observability, we have to compute h (1) (z, u) = h z (z) f (z, u) = h x (T(z))T z T 1 z f (T(z), u) = h (1) (T(z), u) In general h (j) (z, u, u,, u (j 1) ) = h (j) (T(z), u, u,, u (j 1) ) The corresponding discrete time result is trivial

5 Change of state variables, cont d Change of state variables, linear systems The Jacobian is h z (z) J = h (1) z (z, u, u) h (N) z (z, u, u,, u (N 1) ) = h (N) x h x (T(z))T z h (1) x (T(z), u, u)t z (T(z), u, u,, u (N 1) )T z Since T z is nonsingular, the rank of J is the same in both coordinate systems = JT z gives x = Tz, T nonsingular matrix ż = T AT z + T B u, y = {{ CT z à B C C CT Õ = Cà = CTT 1 AT = OT Unobservability and observers Canonical form for observability Consider the systems ẋ = y = [ 1 [ ] 0 1 x ] x Both systems are unobservable ẋ = y = [ 1 One of them can be given a converging observer For the other one this is not possible Why? [ ] 0 1 x ] x ż = T AT z + T B u, y = {{ CT z à B C Theorem Let the rank of O be r Then T can be chosen so that ) (Ã11 0 à =, C = ( C à 21 à 11 0 ) 22 where à 11 is r r, C 11 is p r and à 11, C 11 observable Non-unique! Note: The partition of eigenvalues between à 11 and à 22 is unique: it makes sense to speak of observable and unobservable eigenvalues Similar theorem for time-varying systems, see Rugh

6 PBH tests Detectability Popov 1966, Belevitch 1968, Hautus 1969: Theorem: A, C observable if and only if Ap = λp, Cp = 0 p = 0 Theorem: A, C observable if and only if ( ) C rank = n si A for all complex s Detectable system: All eigenvalues of the unobservable part lie strictly in left half plane The following are equivalent: The system is detectable It is possible to choose the observer gain K so that A KC has all its eigenvalues strictly in the left half plane Proof: PBH and canonical form More on nonlinear observability Some terminology The definition of nonlinear observability is tricky Is it for instance reasonable to say that the following system is observable? ẋ = 1 { 0 x < y = (x ) 2 x ẋ = f (x, u), y = h(x), x(0) = x o has solution x(t) = π(t, x o, u) x 1, x 2 indistinguishable: h(π(t; x 1, u)) = h(π(t; x 2, u)), all t 0, all u I(x) = all points indistinguishable from x system observable at x 0 : I(x 0 ) = {x 0 system observable: I(x 0 ) = {x 0 all x 0

7 More terminology Local weak observability Often sufficient to distinguish points that are close: system weakly observable at x 0 : exists neighborhood (nbh) U such that I(x 0 ) U = {x 0 Often necessary to distinguish points without moving to far: x 1, x 2 U-indistinguishable if they are distinguishable as long as both trajectories lie entirely in U I U (x) = all points U-indistinguishable from x System locally observable at x 0 : I U (x 0 ) = {x 0 every open nbh U of x 0 System locally weakly observable at x 0 : Exists open nbh U of x 0 such that I V (x 0 ) = {x 0 for every open nbh V of x 0 with V U System locally weakly observable: locally weakly observable at every x 0 ; x can instantaneously be distinguished from its neighbors Relationships: locally observable observable locally weakly observable weakly observable For a linear system they are all equivalent Lie derivative evaluation (piecewise constant u) Test for local weak observability Define f 1 (x) = f (x, u 1 ) (L f1 h)(x) = h x (x)f 1 (x) = ẏ The Lie-derivative in the direction f 1 More generally, if f = f 1 for t 1 units of time, f = f 2 for t 2 units of time, ( k y(t 1 + t t t 1 k )) = t k t1 ( L f1 L f2 L fk h ) (x 0 ) = =t k =0 Consider all elements of the form h x, (L f1 h) x, (L f2 h) x,, (L f1 L f2 h) x,, (L f1 L f2 L fk h) x, for all possible choices of u Observability rank condition at x 0 : n linearly independent rows among these elements Hermann and Krener 1977: Observability rank condition at x 0 local weak observability at x 0 Local weak observability for all x observability rank condition generic (satisfied on open dense subset)