Nonlinear Control Lecture 9: Feedback Linearization

Transcription

1 Nonlinear Control Lecture 9: Feedback Linearization Farzaneh Abdollahi Department of Electrical Engineering Amirkabir University of Technology Fall 2011 Farzaneh Abdollahi Nonlinear Control Lecture 9 1/75

2 Feedback Linearzation Input-State Linearization Input-Output Linearization Internal Dynamics of Linear Systems Zero-Dynamics Preliminary Mathematics Diffeomorphism Frobenius Theorem Input-State Linearization Control Design Input-Output Linearization Well Defined Relative Degree Undefined Relative Degree Normal Form Zero-Dynamics Local Asymptotic Stabilization Global Asymptotic Stabilization Tracking Control Farzaneh Abdollahi Nonlinear Control Lecture 9 2/75

3 Feedback Linearzation The main idea is: algebraically transform a nonlinear system dynamics into a (fully or partly) linear one, so that linear control techniques can be applied. In its simplest form, feedback linearization cancels the nonlinearities in a nonlinear system so that the closed-loop dynamics is in a linear form. Example: Controlling the fluid level in a tank Objective: controlling of the level h of fluid in a tank to a specified level h d, using control input u the initial level is h 0. Farzaneh Abdollahi Nonlinear Control Lecture 9 3/75

4 Example Cont d The dynamics: A(h)ḣ(t) = u a 2gh where A(h) is the cross section of the tank and a is the cross section of the outlet pipe. Choose u = a 2gh + A(h)v ḣ = v Choose the equivalent input v: v = α h where h = h(t) h d is error level, α a pos. const. resulting closed-loop dynamics: ḣ + α h = 0 h 0 as t The actual input flow: u = a 2gh + A(h)α h First term provides output flow a 2gh Second term raises the fluid level according to the desired linear dynamics If h d is time-varying: v = ḣ d (t) α h h 0 as t Farzaneh Abdollahi Nonlinear Control Lecture 9 4/75

5 Canceling the nonlinearities and imposing a desired linear dynamics, can be simply applied to a class of nonlinear systems, so-called companion form, or controllability canonical form: A system in companion form: x (n) (t) = f (x) + b(x)u (1) u is the scalar control input x is the scalar output;x = [x, ẋ,..., x (n 1) ] is the state vector. f (x) and b(x) are nonlinear functions of the states. no derivative of input u presents. (1) can be presented as controllability canonical form d dt x 1. x n 1 = x n x 2. x n f (x) + b(x)u for nonzero b, define control input: u = 1 b [v f ] Farzaneh Abdollahi Nonlinear Control Lecture 9 5/75

6 Feedback Linearzation the control law: v = k 0 x k 1 ẋ... k n 1 x (n 1) k i is chosen s.t. the roots of s n + k n 1 s n k 0 are strictly in LHP. Thus: x (n) + k n 1 x (n 1) k 0 = 0 is e.s. For tracking desired output x d, the control law is: v = x (n) d k 0 e k 1 ė... k n 1 e (n 1) Exponentially convergent tracking, e = x x d 0. This method is extendable when the scalar x was replaced by a vector and the scalar b by an invertible square matrix. When u is replaced by an invertible function g(u) u = g 1 ( 1 b [v f ]), Farzaneh Abdollahi Nonlinear Control Lecture 9 6/75

7 Example: Feedback Linearization of a Two-link Robot A two-link robot: each joint equipped with a motor for providing input torque an encoder for measuring joint position a tachometer for measuring joint velocity objective: the joint positions q l and q 2 follow desired position histories q dl (t) and q d2 (t) For example when a robot manipulator is required to move along a specified path, e.g., to draw circles. Farzaneh Abdollahi Nonlinear Control Lecture 9 7/75

8 Using [ the Lagrangian ] [ ] equations [ the robotic ] dynamics [ ] are: H11 H 12 q1 h q2 h q + 2 h q 1 q1 + H 21 H 22 q 2 h q 1 0 q 2 [ g1 g 2 ] = [ τ1 τ 2 ] where q = [q 1 q 2 ] T : the two joint angles, τ = [τ 1 τ 2 ] T : the joint inputs, and H 11 = m 1 l 2 c1 + l 1 + m 2 [l l 2 c2 + 2l 1 l c2 cos q 2 ] + I 2 H 22 = m 2 lc2 2 + I 2 H 12 = H 21 = m 2 l 1 l c2 cos q 2 + m 2 lc2 2 + I 2 g 1 = m 1 l c1 cos q 1 + m 2 g[l c2 cos(q 1 + q 2 ) + l 1 cos q 1 ] g 2 = m 2 l c2 g cos(q 1 + q 2 ), h = m 2 l 1 l c2 sin q 2 Control law for tracking, (computed torque): [ ] [ ] [ ] [ ] [ τ1 H11 H = 12 v1 h q2 h q + 2 h q 1 q1 τ 2 H 21 H 22 v 2 h q 1 0 q 2 ] [ g1 + g 2 ] where v = q d 2λ q λ 2 q, q = q q d : position tracking error, λ: pos. const. q d + 2λ q + λ 2 q = 0 where q converge to zero exponentially. This method is applicable for arbitrary # of links Farzaneh Abdollahi Nonlinear Control Lecture 9 8/75

9 Input-State Linearization When the nonlinear dynamics is not in a controllability canonical form, use algebraic transformations Consider the SISO system ẋ = f (x, u) In input-state linearization technique: 1. finds a state transformation z = z(x) and an input transformation u = u(x, v) s.t. the nonlinear system dynamics is transformed into ż = Az + bv 2. Use standard linear techniques (such as pole placement) to design v. Farzaneh Abdollahi Nonlinear Control Lecture 9 9/75

10 Example: Consider ẋ 1 = 2x 1 + ax 2 + sin x 1 ẋ 2 = x 2 cos x 1 + u cos(2x 1 ) Equ. pt. (0, 0) The nonlinearity cannot be directly canceled by the control input u Define a new set of variables: z 1 = x 1 z 2 = ax 2 + sin x 1 ż 1 = 2z 1 + z 2 ż 2 = 2z 1 cos z 1 + cos z 1 sin z 1 + au cos(2z 1 ) The Equ. pt. is still (0, 0). The control law: u = 1 a cos(2z (v cos z 1) 1 sin z 1 + 2z 1 cos z 1 ) The new dynamics is linear and controllable: ż1 = 2z 1 + z 2, ż 2 = v By proper choice of feedback gains k1 and k 2 in v = k 1 z 1 k 2 z 2, place the poles properly. Both z 1 and z 2 converge to zero, the original state x converges to zero Farzaneh Abdollahi Nonlinear Control Lecture 9 10/75

11 The result is not global. The result is not valid when xl = (π/4 ± kπ/2), k = 0, 1, 2,... The input-state linearization is achieved by a combination of a state transformation and an input transformation with state feedback used in both. To implement the control law, the new states (z 1, z 2 ) must be available. If they are not physically meaningful or measurable, they should be computed by measurable original state x. If there is uncertainty in the model, e.g., on a error in the computation of new state z as well as control input u. For tracking control, the desired motion needs to be expressed in terms of the new state vector. Two questions arise for more generalizations which will be answered in next lectures: What classes of nonlinear systems can be transformed into linear systems? How to find the proper transformations for those which can? Farzaneh Abdollahi Nonlinear Control Lecture 9 11/75

12 Input-Output Linearization Consider ẋ = f (x, u) y = h(x) Objective: tracking a desired trajectory y d (t), while keeping the whole state bounded y d (t) and its time derivatives up to a sufficiently high order are known and bounded. The difficulty: output y is only indirectly related to the input u it is not easy to see how the input u can be designed to control the tracking behavior of the output y. Input-output linearization approach: 1. Generating a linear input-output relation 2. Formulating a controller based on linear control Farzaneh Abdollahi Nonlinear Control Lecture 9 12/75

13 Example: Consider ẋ 1 = sin x 2 + (x 2 + 1)x 3 ẋ 2 = x1 5 + x 3 ẋ 3 = x1 2 + u y = x 1 To generate a direct relationship between the output y and the input u, differentiate the output ẏ = ẋ 1 = sin x 2 + (x 2 + 1)x 3 No direct relationship differentiate again: ÿ = (x 2 + 1)u + f (x), where f (x) = (x1 5 + x 3)(x 3 + cos x 2 ) + (x 2 + 1)x1 2 Control input law: u = 1 x 2 +1 (v f ). Choose v = ÿ d k 1 e k 2 ė, where e = y y d is tracking error, k 1 and k 2 are pos. const. The closed-loop system: ë + k 2 ė + k 1 e = 0 e.s. of tracking error Farzaneh Abdollahi Nonlinear Control Lecture 9 13/75

14 Example Cont d The control law is defined everywhere except at singularity points s.t. x 2 = 1 To implement the control law, full state measurement is necessary, because the computations of both the derivative y and the input transformation need the value of x. If the output of a system should be differentiated r times to generate an explicit relation between y and u, the system is said to have relative degree r. For linear systems this terminology expressed as # poles # zeros. For any controllable system of order n, by taking at most n differentiations, the control input will appear to any output, i.e., r n. If the control input never appears after more than n differentiations, the system would not be controllable. Farzaneh Abdollahi Nonlinear Control Lecture 9 14/75

15 Feedback Linearzation Internal dynamics: a part of dynamics which is unobservable in the input-output linearization. In the example it can be ẋ 3 = x x 2+1 (ÿ d(t) k 1 e k 2 ė + f ) The desired performance of the control based on the reduced-order model depends on the stability of the internal dynamics. stability in BIBO sense Example: Consider [ ] [ ] ẋ1 x 3 = 2 + u ẋ 2 u y = x 1 Control objective: y tracks y d. First differentiations of y linear I/O relation The control law u = x2 3 e(t) + ẏ d(t) exp. convergence of e : ė + e = 0 Internal dynamics: ẋ2 + x2 3 = ẏ d e Since e and ẏd are bounded (ẏ d (t) e D) x 2 is ultimately bounded. Farzaneh Abdollahi Nonlinear Control Lecture 9 15/75 (2)

16 I/O linearization can also be applied to stabilization (y d (t) 0): For previous example the objective will be y and ẏ will be driven to zero and stable internal dynamics guarantee stability of the whole system. No restriction to choose physically meaningful h(x) in y = h(x) Different choices of output function leads to different internal dynamics which some of them may be unstable. When the relative degree of a system is the same as its order: There is no internal dynamics The problem will be input-state linearization Farzaneh Abdollahi Nonlinear Control Lecture 9 16/75

17 Summary Feedback linearization cancels the nonlinearities in a nonlinear system s.t. the closed-loop dynamics is in a linear form. Canceling the nonlinearities and imposing a desired linear dynamics, can be applied to a class of nonlinear systems, named companion form, or controllability canonical form. When the nonlinear dynamics is not in a controllability canonical form, input-state linearization technique is employed: 1. Transform input and state into companion canonical form 2. Use standard linear techniques to design controller For tracking a desired traj, when y is not directly related to u, I/O linearizaton is applied: 1. Generating a linear input-output relation (take derivative of y r n times) 2. Formulating a controller based on linear control Relative degree: # of differentiating y to find explicate relation to u. If r n, there are n r internal dynamics that their stability be checked. Farzaneh Abdollahi Nonlinear Control Lecture 9 17/75

18 Internal Dynamics of Linear Systems In general, directly determining the stability of the internal dynamics is not easy since it is nonlinear. nonautonomous, and coupled to the external closed-loop dynamics. We are seeking to translate the concept of internal dynamics to the more familiar context of linear systems. Example: Consider the controllable, observable system [ ] [ ] ẋ1 x2 + u = ẋ 2 u y = x 1 Control objective: y tracks y d. First differentiations of y ẏ = x 2 + u The control law u = x 2 e(t) + ẏ d (t) exp. convergence of e : ė + e = 0 Internal dynamics: ẋ2 + x 2 = ẏ d e e and ẏd are bounded x 2 and therefore u are bounded. Farzaneh Abdollahi Nonlinear Control Lecture 9 18/75 (3)

19 Now consider a little different dynamics [ ] [ ] ẋ1 x2 + u = ẋ 2 u y = x 1 (4) using the same control law yields the following internal dynamics ẋ 2 x 2 = e(t) ẏ d Although y d and y are bounded, x 2 and u diverge to as t why the same tracking design method yields different results? Transfer function of (3) is: W1 (s) = s+1 s. 2 s. 2 Transfer function of (4) is: W 2 (s) = s 1 Both have the same poles but different zeros The system W1 which is minimum-phase tracks the desired trajectory perfectly. The system W2 which is nonminimum-phase requires infinite effort for tracking. Farzaneh Abdollahi Nonlinear Control Lecture 9 19/75

20 Internal Dynamics Consider a third-order linear system with one zero Its transfer function is: y = ẋ = Ax + bu, y = c T x (5) b 0 +b 1 s a 0 +a 1 s+a 2 s 2 +s 3 u First transform it into the companion form: ż z 1 ż 2 = z 2 ż 3 a 0 a 1 a 2 z 3 y = [b 0 b 1 0] z 1 z 2 z u (6) Farzaneh Abdollahi Nonlinear Control Lecture 9 20/75

21 In second derivation of y, u appears: ÿ = b 0 z 3 + b 1 ( a 0 z 1 a 1 z 2 a 2 z 3 + u) Required number of differentiations (the relative degree) is indeed the same as # of poles- # of zeros Note that: the I/O relation is independent of the choice of state variables two differentiations is required for u to appear if we use (5). The control law: u = (a 0 z 1 + a 1 z 2 + a 2 z 3 b 0 b 1 z 3 ) + 1 b 1 ( k 1 e k 2 ė + ÿ d ) an exp. stable tracking is guaranteed The internal dynamics can be described by only one state equation z1 can complete the state vector,( z 1, y, and ẏ are related to z 1, z 2 and z 3 through a one-to-one transformation). z1 = z 2 = 1 b 1 (y b 0 z 1 ) y is bounded stability of the internal dynamics depends on b 0 b 1 If the system is minimum phase the internal dynamics is stable (independent of initial conditions and magnitude of desired trajectory) Farzaneh Abdollahi Nonlinear Control Lecture 9 21/75

22 Zero-Dynamics For linear systems the stability of the internal dynamics is determined by the locations of the zeros. To extend the results for nonlinear systems the concept of zero should be modified. Extending the notion of zeros to nonlinear systems is not trivial In linear systems I/O relation is described by transfer function which zeros and poles are its fundamental components. But in nonlinear systems we cannot define transfer function Zeros are intrinsic properties of a linear plant. But for nonlinear systems the stability of the internal dynamics may depend on the specific control input. Zero dynamics: is defined to be the internal dynamics of the system when the system output is kept at zero by the input.(output and all of its derivatives) Farzaneh Abdollahi Nonlinear Control Lecture 9 22/75

23 For dynamics (2), the zero dynamics is ẋ 2 + x2 3 = 0 we find input u to maintain the system output at zero uniquely (keep x1 zero in this example), By Layap. Fcn V = x2 2 it can be shown it is a.s For linear system (5), the zero dynamics is ż 1 + (b 0 /b 1 )z 1 = 0 The poles of the zero-dynamics are exactly the zeros of the system. In linear systems, if all zeros are in LHP g.a.s. of the zero-dynamics g.s. of internal dynamics. In nonlinear systems, no results on the global stability only local stability is guaranteed for the internal dynamics even if the zero-dynamics is g.e.s. Zero-dynamics is an intrinsic feature of a nonlinear system, which does not depend on the choice of control law or the desired trajectories. Examining the stability of zero-dynamics is easier than examining the stability of internal dynamics,but the result is local. Zero-dynamics only involves the internal states Internal dynamics is coupled to the external dynamics and desired trajs. Farzaneh Abdollahi Nonlinear Control Lecture 9 23/75

24 Zero-Dynamics Similar to the linear case, a nonlinear system whose zero dynamics is asymptotically stable is called an asymptotically minimum phase system, If the zero-dyiamics is unstable, different control strategies should be sought As summary control design based on input-output linearization is in three steps: 1. Differentiate the output y until the input u appears 2. Choose u to cancel the nonlinearities and guarantee tracking convergence 3. Study the stability of the internal dynamics If the relative degree associated with the input-output linearization is the same as the order of the system the nonlinear system is fully linearized satisfactory controller Otherwise, the nonlinear system is only partly linearized whether or not the controller is applicable depends on the stability of the internal dynamics. Farzaneh Abdollahi Nonlinear Control Lecture 9 24/75

25 Preliminary Mathematics Vector function f : R n R n is called a vector field in R n. Smooth vector field: function f(x) has continuous partial derivatives of any required order. Gradient of a smooth scalar function h(x) is denoted by a row vector h = h x, where ( h) j = h x j Jacobian of a vector field f(x):an n n matrix f = f x, where ( f) ij = f i x j Lie derivative of h with respect to f is a scalar function defined by L f h = hf, where h : R n R: a smooth scalar, f : R n R n : a smooth vector field. If g is another vector field: L g L f h = (L f h)g L 0 f h = h; Li f h = L f (L i 1 f h) = (L i 1 f h)f Farzaneh Abdollahi Nonlinear Control Lecture 9 25/75

26 Example: For single output system ẋ = f(x), ẏ = h x ẋ = L fh ÿ = [L fh] x ẋ = L2 f h y = h(x) then If V is a Lyap. fcn candidate, its derivative V can be written as L f V. Lie bracket of f and g is a third vector field defined by [f, g] = gf f g, where f and g two vector field on R n. The Lie bracket [f, g] is also written as ad f g (ad stands for adjoint ). adf 0g = g; ad f i i 1 g = [f, adf g], i = 1,... Example: [ Consider ẋ = f(x) ] + g(x)u [ where ] 2x1 + ax f = 2 + sin x 1 0, g = x 2 cos x 1 cos(2x 1 ) So the Lie [ bracket is: ] a cos(2x [f, g] = 1 ) cos x 1 cos(2x 1 ) 2 sin(2x 1 )( 2x 1 + ax 2 + sin x 1 ) Farzaneh Abdollahi Nonlinear Control Lecture 9 26/75

27 Lemma: Lie brackets have the following properties: 1. bilinearity: [α 1 f 1 + α 2 f 2, g] = α 1 [f 1, g] + α 2 [f 2, g] [f, α 1 g 1 + α 2 g 2 ] = α 1 [f, g 1 ] + α 2 [f, g 2 ] where f, f 1, f 2, g g 1, g 2 are smooth vector fields and α 1 and α 2 are constant scalars. 2. skew-commutativity: 3. Jacobi identity where h is a smooth fcn. [f, g] = [g, f] L adf gh = L f L g h L g L f h Farzaneh Abdollahi Nonlinear Control Lecture 9 27/75

28 Diffeomorphism The concept of diffeomorphism can be applied to transform a nonlinear system into another nonlinear system in terms of a new set of states. Definition: A function φ : R n R n defined in a region Ω is called a diffeomorphism if it is smooth, and if its inverse φ 1 exists and is smooth. If the region Ω is the whole space R n φ(x) is global diffeomorphism Global diffeomorphisms are rare,we are looking for local diffeomorphisms. Lemma: Let φ(x) be a smooth function defined in a region Ω in R n. If the Jacobian matrix φ is non-singular at a point x = x 0 of Ω, then φ(x) defines a local diffeomorphism in a subregion of Ω Farzaneh Abdollahi Nonlinear Control Lecture 9 28/75

29 Diffeomorphism Consider the dynamic system described by ẋ = f (x) + g(x)u, y = h(x) Let the new set of states z = φ(x) ż = φ x ẋ = φ x (f (x) + g(x)u) The new state-space representation where x = φ 1 (z). ż = f (z) + g (z)u, y = h (z) Example [ ] of a non-global [ diffeomorphism: Consider z1 2x1 + 5x = φ(x) = 1 x 2 ] 2 z 2 3 sin x 2 [ ] φ 2 + 5x Its Jacobian matrix: x = x 1 x cos x 2 rank is 2 at x = (0, 0) local diffeomorphism around the origin where Ω = {(x 1, x 2 ), x 2 < π/2}. outside the region, the inverse of φ does not uniquely exist. Farzaneh Abdollahi Nonlinear Control Lecture 9 29/75

30 Frobenius Theorem An important tool in feedback linearization Provide necess. and suff. conditions for solvability of PDEs. Consider a PDE with (n=3): h f 1 + h f 2 + h f 3 x 1 x 2 x 3 = 0 h g 1 + h g 2 + h g 3 = 0 (7) x 1 x 2 x 3 where f i (x 1, x 2, x 3 ), g i (x 1, x 2, x 3 ) (i = 1, 2, 3) are known scalar fcns and h(x 1, x 2, x 3 ) is an unknown function. This set of PDEs is uniquely determined by the two vectors f = [f 1 f 2 f 3 ] T, g = [g 1 g 2 g 3 ] T. If the solution h(x 1, x 2, x 3 ) exists, the set of vector fields {f, g} is completely integrable. When the equations are solvable? Farzaneh Abdollahi Nonlinear Control Lecture 9 30/75

31 Frobenius Theorem Frobenius theorem states that Equation (7) has a solution h(x 1, x 2, x 3 ) iff there exists scalar functions α 1 (x 1, x 2, x 3 ) and α 2 (x 1, x 2, x 3 ) such that [f, g] = α 1 f + α 2 g i.e., if the Lie bracket of f and g can be expressed as a linear combination of f and g This condition is called involutivity of the vector fields {f, g}. Geometrically, it means that the vector field [f, g] is in the plane formed by the two vectors f and g The set of vector fields {f, g} is completely integrable iff it is involutive. Definition (Complete Integrability): A linearly independent set of vector fields {f 1, f 2,..., f m } on R n is said to be completely integrable, iff, there exist n m scalar fcns h 1 (x), h 2 (x),..., h n m (x) satisfying the system of PDEs: h i f j = 0 where 1 i n m, 1 j m and h i are linearly independent. Farzaneh Abdollahi Nonlinear Control Lecture 9 31/75

32 Number of vectors: m, dimension of the vectors: n, number of unknown scalar fcns h i : (n-m), number of PDEs: m(n-m) Definition (Involutivity): A linearly independent set of vector fields {f 1, f 2,..., f m } on R n is said to be involutive iff, there exist scalar fcns α ijk : R N R s.t. m [f i, f j ](x) = α ijk (x) f k (x) i, j k=i i.e., the Lie bracket of any two vector fields from the set {f 1, f 2,..., f m } can be expressed as the linear combination of the vectors from the set. Constant vector fields are involutive since their Lie brackets are zero A set composed of a single vector is involutive: [f, f ] = ( f )f ( f )f = 0 Involutivity means: rank (f 1 (x)... f m (x)) = rank (f 1 (x)... f m (x) [f i, f j ](x)) for all x and for all i, j. Farzaneh Abdollahi Nonlinear Control Lecture 9 32/75

33 Frobenius Theorem Theorem (Frobenius): Let f 1, f 2,..., f m be a set of linearly independent vector fields. The set is completely integrable iff it is involutive. Example: Consider the set of PDEs: 4x 3 h x 1 h x 2 = 0 x 1 h x 1 + (x 2 3 3x 2 ) h x 2 + 2x 3 h x 3 = 0 The associated vector fields are {f 1, f 2 } f 1 = [4x 3 1 0] T f 2 = [ x 1 (x 2 3 3x 2 ) 2x 3 ] T We have [f 1, f 2 ] = [ 12x 3 3 0] T Since [f 1, f 2 ] = 3f 1 + 0f 2, the set {f 1, f 2 } is involutive and the set of PDEs are solvable. Farzaneh Abdollahi Nonlinear Control Lecture 9 33/75

34 Input-State Linearization Consider the following SISO nonlinear system ẋ = f (x) + g(x)u (8) where f and g are smooth vector fields The above system is also called linear in control or affine If we deal with the following class of systems: ẋ = f (x) + g(x)w(u + φ(x)) where w is an invertible scalar fcn and φ is an arbitrary fcn We can use v = w(u + φ(x)) to get the form (8). Control design is based on v and u can be obtained by inverting w: u = w 1 (v) φ(x) Now we are looking for Conditions for system linearizability by an input-state transformation A technique to find such transformations A method to design a controller based on such linearization technique Farzaneh Abdollahi Nonlinear Control Lecture 9 34/75

35 Input-State Linearization Definition: Input-State Linearization The nonlinear system (8) where f (x) and g(x) are smooth vector fields in R n is input-state linearizable if there exist region Ω in R n, a diffeomorphism mapping φ : Ω R n, and a control law: u = α(x) + β(x)v s.t. new state variable z = φ(x) and new input variable v satisfy an LTI relation: ż = Az + Bv A = B =. (9) The new state z is called the linearizing state and the control law u is called the linearizing control law Let z = z(x) Farzaneh Abdollahi Nonlinear Control Lecture 9 35/75

36 Input-State Linearization (9) is the so-called linear controllability or companion form This linear companion form can be obtained from any linear controllable system by a transformation if u leads to a linear system, (9) can be obtained by another transformation easily. This form is an special case of Input-Output linearization leading to relative degree r = n. Hence, if the system I/O linearizable with r = n, it is also I/S linearizable. On the other hand, if the system is I/S linearizable, it is also I/O linearizable with y = z, r = n. Farzaneh Abdollahi Nonlinear Control Lecture 9 36/75

37 Input-State Linearization Lemma: An n th order nonlinear system is I/S linearizable iff there exists a scalar fcn z 1 (x) for which the system is I/O linearizable with r = n. Still no guidance on how to find the z 1 (x). Conditions for Input-State Linearization: Theorem: The nonlinear system (8) with f (x) and g(x) being smooth vector field is input-state linearizable iff there exists a region Ω s.t. the following conditions hold: The vector fields {g, adf g,... ad f n 1 g} are linearly independent in Ω The set {g, adf g,... ad f n 2 g} is involutive in Ω The first condition: can be interpreted as a controllability condition For linear system, the vector field above becomes {B, AB,... A n 1 B} Linear independency invertibility of controllability matrix Farzaneh Abdollahi Nonlinear Control Lecture 9 37/75

38 The second condition is always satisfied for linear systems since the vector fields are constant, but for nonlinear system is not necessarily satisfied. It is necessary according to Ferobenius theorem for existence of z1 (x). Lemma: If z(x) is a smooth vector field in Ω, then the set of equations L g z = L g L f z =... = L g L k f z = 0 is equivalent to L g z = L adf g z =... = L adf k g z = 0 Proof: Let k = 1, from Jacobi s identity, we have L adf g z = L f L g z L g L f z = 0 0 = 0 When k = 2, we have from Jacobi s identity: L adf 2 g z = L f 2 L g z 2L f L g L f z + L g L f 2 z = = 0 Farzaneh Abdollahi Nonlinear Control Lecture 9 38/75

39 Proof of the linearization theorem: Necessity: Suppose state transformation z = z(x) and input transformation u = α(x) + β(x)v s.t. z and v satisfy (9), i.e. similarly: ż 1 = z 1 x (f + gu) = z 2 z 1 x f + z 1 x gu = z 2 z 2 x f + z 2 x gu = z 3 z n x f + z n x gu = v. Farzaneh Abdollahi Nonlinear Control Lecture 9 39/75

40 z 1,..., z n 1 are independent of u, L g z 1 = L g z 2 =... L g z n 1 = 0, L g z n 0 L f z i = z i+1, i = 1, 2,..., n 1 Use, z = [z 1 L f z 1,... L f n 1 z 1 ] T to get ż k = z k+1, k = 1,... n 1 ż n = L f n z 1 + L g L f n 1 z 1 u The above equations can be expressed in terms of z 1 only z 1 ad f k g = 0, k = 0, 1, 2,..., n 2 (10) z 1 ad f n 1 g = ( 1) n 1 L g z n (11) First note that for above eqs to hold, the vector field g, ad f g,..., ad f n 1 g must be linearly independent. If for some i(i n 1) there exist scalar fcns α 1 (x),... α i 1 (x) s.t. i 1 ad i f g = α k ad k f g k=0 Farzaneh Abdollahi Nonlinear Control Lecture 9 40/75

41 We, then have: ad f n 1 g = z 1 ad f n 1 g = Contradicts with (11). n 2 k=n i 1 n 2 k=n i 1 α k ad f k g α k z 1 ad f k g = 0 (12) The second property is that a scalar fcn z 1 that satisfy n 1 PDEs z 1 ad f k g = 0 From the necessity part of Frobenius theorem, we conclude that the set of vector field must be involutive. Sufficient condition Involutivity condition = Frobenius theorem, a scalar fcn z1 (x): L g z 1 = L adf g z 1 =... L adf k g z 1 = 0, implying L g z 1 = L g L f z 1 =... L g L f k z 1 = 0 Farzaneh Abdollahi Nonlinear Control Lecture 9 41/75

42 Define the new sets of variable as z = [z 1 L f z 1... L n 1 f z 1 ] T, to get ż k = z k+1 k = 1,..., n 1 ż n = L n f z 1 + L g L n 1 f z 1 u (13) The question is whether L g L n 1 f z 1 can be equal to zero. Since {g, ad f g,..., ad n 1 f g} are linearly independent in Ω: L g L n 1 f z 1 = ( 1) n 1 L adf n 1 g z 1 We must have L adf n 1 g z 1 0, otherwise the nonzero vector z 1 satisfies z 1 [g, ad f g,..., ad f n 1 g] = 0 i.e. z 1 is normal to n linearly independent vector = impossible Now, we have: ż n = L n f z 1 + L g L n 1 f z 1 u = a(x) + b(x)u Farzaneh Abdollahi Nonlinear Control Lecture 9 42/75

43 Now, select u = 1 b(x) ( a(x) + v) to get: ż n = v implying input-state linearization is obtained. Summary: how to perform input-state Linearization 1. Construct the vector fields g, ad f g,... ad f n 1 g 2. Check the controllability and involutivity conditions 3. If the conditions hold, obtain the first state z 1 from: z 1 ad f i g = 0 i = 0,..., n 2 z 1 ad f n 1 g 0 4. Compute the state transformation z(x) = [z 1 L f z 1... L n 1 f z 1 ] T and the input transformation u = α(x) + β(x)v: L f n z 1 α(x) = L g L n 1 f z 1 1 β(x) = L g L n 1 f z 1 Farzaneh Abdollahi Nonlinear Control Lecture 9 43/75

44 Example:A single-link flexible-joint manipulator: The link is connected to the motor shaft via a torsional spring Equations of motion: I q1 + MgLsinq 1 + K(q 1 q 2 ) = 0 J q 2 K(q 1 q 2 ) = u Farzaneh Abdollahi Nonlinear Control Lecture 9 44/75

45 Example:A single-link flexible-joint manipulator: Equations of motion: I q 1 + MgLsinq 1 + K(q 1 q 2 ) = 0 J q 2 K(q 1 q 2 ) = u nonlinearities appear in the first equation and torque is in the second equation Let: x = q 1 q 1 q 2 q 2, f = x 2 MgL I sinx 1 K I (x 1 x 3 ) x 4 K J (x 1 x 3 ), g = Controllability and involutivity conditions: K IJ [g ad f g ad 2 f g ad 3 K f g] = 0 0 IJ K J 0 J 2 1 J 0 K J 0 2 Farzaneh Abdollahi Nonlinear Control Lecture 9 45/ J

46 Example: Cont d It s full rank for k > 0 and IJ < = vector fields are linearly independent Vector fields are constant = involutive The system is input-state linearizable Computing z = z(x), u = α(x) + β(x)v z 1 z x 2 = 0, 1 z x 3 = 0, 1 z x 4 = 0, 1 x 1 0 Hence, z 1 is the fcn of x 1 only. Let z 1 = x 1, then z 2 = z 1 f = x 2 z 3 = z 2 f = MgL sinx 1 K I I (x 1 x 3 ) z 4 = z 3 f = MgL x 2 cosx 1 K I I (x 2 x 4 ) Farzaneh Abdollahi Nonlinear Control Lecture 9 46/75

47 Example: Cont d The input transformation is given by: u = (v z 4 f )/( z 4 g) = IJ (v a(x)) K a(x) = MgL I + K I (x 1 x 3 )( K I sinx 1 (x2 2 + MgL cosx 1 + K I I ) + K J + MgL cosx 1 ) I As a result, we get the following set of linear equations ż 1 = z 2, ż 2 = z 3 ż 3 = z 4, ż 4 = v The inverse of the state transformation is given by: x 1 = z 1, x 2 = z 2 x 3 = z 1 + I ( z 3 + MgL ) sinz 1 K I x 4 = z 2 + I ( z 4 + MgL ) z 2 cosz 1 K I Farzaneh Abdollahi Nonlinear Control Lecture 9 47/75

48 Example Cont d State and input transformations are defined globally In this example, transformed state have physical meaning, z 1 : link position, z 2 : link velocity, z 3 : link acceleration, z 4 : link jerk. It could be obtained by I/O linearization, i.e. by differentiating the output q 1. (4 times) We can transform the inequality (11) to a normalized equation by setting z 1 ad n 1 f g = 1 resulting in: z 1 0 x 1 0 [ad 0 f g ad 1 f g... ad n 2 f g ad n 1 f g ]. =. z 1 0 x n 1 Farzaneh Abdollahi Nonlinear Control Lecture 9 48/75

49 Control Design Once, the linearized dynamics is obtained, either a tracking or stabilization problem can be solved For instance, in flexible-joint manipulator case, we have z (4) 1 = v Then, a tracking controller can be obtained as v = z (4) d1 a 3 z (3) 1 a 2 z 1 a 1 z 1 a 0 z 1 where z 1 = z 1 z d1. The error dynamics is then given by: z (4) 1 + a 3 z (3) 1 + a 2 z 1 + a 1 z 1 + a 0 z 1 = 0 The above dynamics is exponentially stable if a i are selected s.t. s 4 + a 3 s 3 + a 2 s 2 + a 1 s + a 0 is Hurwitz Farzaneh Abdollahi Nonlinear Control Lecture 9 49/75

50 Input-Output Linearization Consider the system: ẋ = f (x) + g(x)u y = h(x) (14) Input-output linearization yields a linear relationship between the output y and the input v (similar to v in I/S Lin.) How to generate a linear I/O relation for such systems? What are the internal dynamics and zero-dynamics associated with this I/O linearization How to design a stable controller based on this technique? Performing I/O Linearization The basic approach is to differentiate the output y until the input u appears, then design u to cancel nonlinearities Sometime, cancelation might not be possible due to the undefined relative degree. Farzaneh Abdollahi Nonlinear Control Lecture 9 50/75

51 Well Defined Relative Degree Differentiate y and express it in the form of Lie derivative: ẏ = h(f + gu) = L f h(x) + L g h(x)u if L g h(x) 0 for some x = x 0 in Ω x, then continuity implies that L g h(x) 0 in some neighborhood Ω of x 0. Then, the input transformation 1 u = L g h(x) ( L f h(x) + v) results in a linear relationship between y and v, namely ẏ = v. If L g h(x) = 0 for all x Ω x, differentiate ẏ to obtain ÿ = L f 2 h(x) + L g L f h(x)u If L g L f h(x) = 0 for all x Ω x, keep differentiating until for some integer r, L g L r 1 f h(x) 0 for some x = x 0 Ω x Farzaneh Abdollahi Nonlinear Control Lecture 9 51/75

52 Hence, we have and the control law y (r) = L f r h(x) + L g L f r 1 h(x)u (15) u = 1 L g L f r 1 h(x) ( L f r h(x) + v) yields a linear mapping: y (r) = v The number r of differentiation required for u to appear is called the relative degree of the system. r n, if r = n, the input-state realization is obtained with z 1 = y. Definition: The SISO system is said to have a relative degree r in Ω if: L g L f i h(x) = 0 0 i r 2 L g L f r 1 h(x) 0 Farzaneh Abdollahi Nonlinear Control Lecture 9 52/75

53 Undefined Relative Degree Sometimes, we are interested in the properties of a system about a specific operating point x 0. Then, we say the system has relative degree r at x 0 if L g L f r 1 h(x 0 ) 0 However, it might happen that L g L f r 1 h(x) is zero at x 0, but nonzero in a close neighborhood of x 0. The relative degree of the nonlinear system is then undefined at x 0. Example: ẍ = ρ(x, ẋ) + u where ρ is a smooth nonlinear fcn. Define x = [x ẋ] T and let y = x = the system is in companion form with r = 2. Farzaneh Abdollahi Nonlinear Control Lecture 9 53/75

54 However, if we define y = x 2, then: ẏ = 2xẋ ÿ = 2xẍ + 2ẋ 2 = 2xρ(x, ẋ) + 2xu + 2ẋ 2 = L g L f h = 2x (16) The system has neither relative degree 1 nor 2 at x 0 = 0. Sometime, change of output leads us to a solvable problem. We assume that the relative degree is well defined. Normal Forms When, the relative degree is defined as r n, using y, ẏ,..., y (r 1), we can transform the system into the so-called normal form. Normal form allows a formal treatment of the notion of internal dynamics and zero dynamics. Let µ = [µ 1 µ 2... µ r ] T = [y ẏ... y (r 1) ] T in a neighborhood Ω of a point x 0. Farzaneh Abdollahi Nonlinear Control Lecture 9 54/75

55 Normal Form The normal form of the system can be written as µ = µ 2. µ r a(µ, Ψ) + b(µ, Ψ)u (17) Ψ = w(µ, Ψ) (18) y = µ 1 The µ i and Ψ j are called normal coordinate or normal states. The first part of the Normal form, (17) is another form of (15), however in (18) the input u does not appear. The system can be transformed to this form if the state transformation φ(x) is a local diffeomorphism: φ(µ 1... µ r Ψ 1... Ψ n r ) T To show that φ is a diffeomorphism, we must show that the Jacobian is invertible, i.e. µ i and Ψ i are all linearly independent. Farzaneh Abdollahi Nonlinear Control Lecture 9 55/75

56 Normal Form µ i are linearly independent = µ can be part of state variables, (µ is output and its r 1 derivatives) There exist n r other vector fields that complete the transformation Note that u does not appear in (18), hence: Ψ j g = 0 1 j n r Ψ can be obtained by solving n r PDE above. Generally, internal dynamics can be obtained simpler by intuition. Zero Dynamics System dynamics have two parts: 1. external dynamics µ 2. internal dynamics Ψ For tracking problems (y yd ), one can easily design v once the linear relation is obtained. The question is whether the internal dynamics remain bounded Farzaneh Abdollahi Nonlinear Control Lecture 9 56/75

57 Zero-Dynamics Stability of the zero dynamics (i.e. internal dynamics when y is kept 0) gives an idea about the stability of internal dynamics u is selected s.t. y remains zero at all time. In normal form: y (r) (t) = L r f h(x) + L g L r 1 f h(x)u 0 0 = u 0 (x) = L r f h(x) L g L r 1 f h(x) { µ = 0 Ψ = w(0, Ψ) u 0 (Ψ) = a(0, Ψ) b(0, Ψ) (19) Farzaneh Abdollahi Nonlinear Control Lecture 9 57/75

58 Example Consider ẋ = x 1 2x 1 x 2 + sinx 2 2x 2 + e 2x 2 1/2 0 u y = h(x) = x 3 We have ẏ = 2x 2 ÿ = 2ẋ 2 = 2(2x 1 x 2 + sinx 2 ) + u The system has relative degree r = 2 and L f h(x) = 2x 2 L g h(x) = 0 L g L f h(x) = 1 Farzaneh Abdollahi Nonlinear Control Lecture 9 58/75

59 Example Cont d To obtain the normal form µ 1 = h(x) = x 3 µ 2 = L f h(x) = 2x 2 The third function Ψ(x) is obtained by L g Ψ = Ψ e 2x x 1 2 One solution is Ψ(x) = 1 + x 1 e 2x 2 Ψ = 0 x 2 Consider the jacobian of state transformation z = [µ 1 µ 2 Ψ] T. The Jacobian matrix is e 2x 2 0 Farzaneh Abdollahi Nonlinear Control Lecture 9 59/75

60 Example Cont d The Jacobian is non-singular for any x. In fact, inverse transformation is given by: x 1 = 1 + Ψ + e µ2 x 2 = 1 2 µ 2 x 3 = µ 1 State transformation is valid globally and the normal form is given by: µ 1 = µ 2 µ 2 = 2( 1 + Ψ + e µ2 )µ 2 + 2sin(µ 2 /2) + u Ψ = (1 Ψ e µ2 )(1 + 2µ 2 e µ2 ) 2sin(µ 2 /2)e µ2 (20) Zero dynamics is obtained by setting µ 1 = µ 2 = 0 = Ψ = Ψ (21) Farzaneh Abdollahi Nonlinear Control Lecture 9 60/75

61 Zero-Dynamics In order to obtain the zero dynamics, it is not necessary to put the system into normal form since µ is known, we can intuitively find n r vector to complete the transformation. As mention before, zero dynamics is obtained by substituting u 0 for u in internal dynamics. Definition: A nonlinear system with asymptotically stable zero dynamics is called asymptotically minimum phase If the zero dynamics is stable for all x, the system is globally minimum phase, otherwise the results are local. Farzaneh Abdollahi Nonlinear Control Lecture 9 61/75

62 Local Asymptotic Stabilization Consider again the nonlinear system ẋ = f (x) + g(x)u y = h(x) (22) Assume that the system is I/O linearized, i.e. y (r) = L f r h(x) + L g L f r 1 h(x)u (23) and the control law yields a linear mapping: u = L g L f 1 r 1 h(x) ( L f r h(x) + v) (24) y (r) = v Now let v be chosen as v = k r 1 y (r 1)... k 1 ẏ k 0 y (25) where k i are selected s.t. K(s) = s r + k r 1 s r k 1 s + k 0 is Hurwitz Farzaneh Abdollahi Nonlinear Control Lecture 9 62/75

63 Then, provided that the zero-dynamics is asymptotically stable, the control law (24) and (25) locally stabilize the whole system: Theorem: Suppose the nonlinear system (22) has a well defined relative degree r and its associated zero-dynamics is locally asymptotically stable. Now, if k i are selected s.t. K(s) = s r + k r 1 s r k 1 s + k 0 is Hurwitz, then the control law (24) and (25) yields a locally asymptotically stable system. Proof: First, write the closed-loop system in a normal form: µ = = Aµ k 0 k 1 k 2... k r 1 Ψ = w(µ, Ψ) = A 1 µ + A 2 Ψ + h.o.t. h.o.t. is higher order terms in the Taylor expansion about x 0 = 0. The above Eq. can [ be written ] as: [ ] [ ] d µ A 0 µ = + h.o.t. dt Ψ A 1 A 2 Ψ Farzaneh Abdollahi Nonlinear Control Lecture 9 63/75

64 Now, since the zero dynamics is asymptotically stable, its linearization Ψ = A 2 Ψ is either asymptotically stable or marginally stable. If A 2 is asymptotically stable, then all eigenvalues of the above system matrix are in LHP and the linearized system is stable and the nonlinear system is locally asymptotically stable If A2 is marginally stable, asymptotic stability of the closed-loop system was shown in (Byrnes and Isidori, 1988). Comparing the above method to local stabilization and using linear control: the above stabilization method can treat systems whose linearizations contain uncontrollable but marginally stable modes, while linear control methods requires the linearized system to be strictly stabilizable Farzaneh Abdollahi Nonlinear Control Lecture 9 64/75

65 For stabilization where state convergence is required, we can freely choose y = h(x) to make zero-dynamics a.s. Example: Consider the nonlinear system: ẋ 1 = x1 2 x 2 ẋ 2 = 3x 2 + u System linearization at x = 0: ẋ 1 = 0 thus has an uncontrollable mode ẋ 2 = 3x 2 + u Farzaneh Abdollahi Nonlinear Control Lecture 9 65/75

66 Example (cont d) Define y = 2x 1 x 2 = ẏ = 2ẋ 1 ẋ 2 = 2x 2 1 x 2 3x 2 u Hence, the relative degree r = 1 and the associated zero-dynamics is ẋ 1 = 2x 3 1 The zero-dynamics is asymptotically stable, hence the control law u = 2x 2 1 x 2 4x 2 2x 1 locally stabilizes the system Global Asymptotic Stabilization Stability of the zero-dynamics only guarantees local stability unless relative degree is n in which case there is no internal dynamics Can the idea of I/O linearization be used for global stabilization problem? Can the idea of I/O linearization be used for systems with unstable zero dynamics? Farzaneh Abdollahi Nonlinear Control Lecture 9 66/75

67 Global Asymptotic Stabilization Global stabilization approach based on partial feedback linearization is to simply regard the problem as a standard Lyapunov controller design problem But simplified by the fact that in normal form part of the system dynamics is now linear. The basic idea is to view µ as the input to the internal dynamics and Ψ as its output. The first step: find the control law µ0 = µ 0 (Ψ) which stabilizes the internal dynamics with the corresponding Lyapunov fcn V 0. Then: find a Lyapunov fcn candidate for the whole system (as a modified version of V 0 ) and choose the control input v s.t. V be a Lyapunov fcn for the whole closed-loop dynamics. Farzaneh Abdollahi Nonlinear Control Lecture 9 67/75

68 Example: Consider a nonlinear system with the normal form: ẏ = v z + ż 3 + yz = 0 (26) where v is the control input and Ψ = [z ż] T Considering y as an input to internal dynamics (26), it would be asymptotically stabilized by the choice of y = y 0 = z 2 Let V 0 be a Lyap. fcn: V 0 = 1 2ż z4 Differentiating V0 along the actual dynamics results in V 0 = ż 4 zż(y z 2 ) Farzaneh Abdollahi Nonlinear Control Lecture 9 68/75

69 Example Cont d Consider the Lyap. fcn candidate, obtained by adding a quadratic error term in y y 0 to V 0 V = V (y z2 ) 2 V = ż 4 + (y z 2 )(v 3zż) The following choice of control action will then make V n.d. v = y + z 2 + 3zż V = ż 4 (y z 2 ) 2 Application of Invariant-set theorem shows all states converges to zero Farzaneh Abdollahi Nonlinear Control Lecture 9 69/75

70 Example: A non-minimum phase system Consider the system dynamics ẏ = v z + ż 3 z 5 + yz = 0 where again Ψ = [z ż] T The system is non-minimum phase since its zero-dynamics is unstable The zero-dynamics would be stable if we select y = 2z 4 : V 0 = 1 2ż z6 V 0 = ż 4 zż(y 2z 4 ) Consider the Lyap. fcn candidate V = V (y 2z4 ) 2 V = ż 4 + (y 2z 4 )(v 8z 3 ż zż) suggesting the following choice of control law v = y + 2z 4 + 8z 3 ż + zż V = ż 4 (y 2z 4 ) 2 Application of Invariant-set theorem shows all states converges to zero Farzaneh Abdollahi Nonlinear Control Lecture 9 70/75

71 Tracking Control I/O linearization can be used in tracking problem Let µ d = [y d ẏ d... y (r 1) d ] T and the tracking error µ(t) = µ(t) µ d (t) Theorem: Assume the system (22) has a well defined relative degree r and µ d is smooth and bounded and that the solution Ψ d : Ψ d = w(µ d, Ψ d ), Ψ d (0) = 0 exists and bounded and is uniformly asymptotically stable. Choose k i s.t K(s) = s r + k r 1 s r k 1 s + k 0 is Hurwitz, then by using 1 u = L g L r 1 [ L r f µ 1 + y (r) f µ d k r 1 µ r... k 0 µ 1 ] (27) 1 the whole system remains bounded and the tracking error µ converge to zero exponentially. Proof: Refer to Isidori (1989). For perfect tracking µ(0) µ d (0) Farzaneh Abdollahi Nonlinear Control Lecture 9 71/75

72 Tracking Control for Non-minimum Phase Systems: The tracking control (27) cannot be applied to non-minimum phase systems since they cannot be inverted Hence we cannot have perfect or asymptotic tracking and should seek controllers that yields small tracking errors One approach is the so-called Output redefinition The new output y1 is defined s.t. the associated zero-dynamics is stable y1 is defined s.t. it is close to the original output y in the frequency range of interest Then, tracking y 1 also implies good tracking the original output y Example: Consider a linear ( system ) 1 s b B0 (s) y = u b > 0 A(s) Perfect/asymptotic tracking is impossible due to the presence of s = b Farzaneh Abdollahi Nonlinear Control Lecture 9 72/75

73 Example Cont d Let us redefine the output as y 1 = B 0(s) A(s) u with the desired output for y 1 be simply y d A controller can be found s.t. y 1 asymptotically tracks y d. What about the actual tracking error? ( y(s) = 1 s ) ( y 1 = 1 s ) y d b b Thus, the tracking error is proportional to the desired velocity ẏ d : y(t) y d (t) = ẏd(t) b Tracking error is bounded as long as ẏ d is bounded, it is small when the frequency content of y d is well below b Farzaneh Abdollahi Nonlinear Control Lecture 9 73/75

74 Example Cont d An alternative output, motivated by (1 s b ) 1/(1 + s b ) for small s /b: y(s) = y 2 = B 0 (s) A(s)(1 + s b )u ( 1 s ) ( 1 + s ) y d = b b ) (1 s2 b 2 y d Thus, the tracking error is proportional to the desired acceleration ÿ d : y(t) y d (t) = ÿd(t) b 2 Small tracking error if the frequency content of y d is below b Farzaneh Abdollahi Nonlinear Control Lecture 9 74/75

75 Tracking Control Another approximate tracking (Hauser, 1989) can be obtained by When performing I/O linearization, using successive differentiation, simply neglect the terms containing the input Keep differentiating n timed (system order) Approximately, there is no zero dynamics It is meaningful if the coefficients of u at the intermediate steps are small or the system is weakly non-minimum phase system The approach is similar to neglecting fast RHP zeros in linear systems. Zero-dynamics is the property of the plant, choice of input and output and desired Trajectory. It cannot be changed by feedback: Modify the plant (distribution of control surface on an aircraft or the mass and stiffness in a flexible robot) Change the output (or the location of sensor) Change the input (or the location of actuator) Change the desired Traj. Farzaneh Abdollahi Nonlinear Control Lecture 9 75/75