Objective. 1 Specification/modeling of the controlled system. 2 Specification of a performance criterion

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1 Optimal Control Problem Formulation Optimal Control Lectures 17-18: Problem Formulation Benoît Chachuat Department of Chemical Engineering Spring 2009 Objective Determine the control signals that will cause a controlled system to satisfy the physical constraints and, at the same time, minimize (or maximize) some performance criterion Main Steps of Problem Formulation: 1 Specification/modeling of the controlled system Admissible controls Response and dynamical model 2 Specification of a performance criterion Lagrange, Mayer and Bolza forms 3 Specification of physical constraints to be satisfied Path and terminal constraints Benoît Chachuat (McMaster University) Introduction & Formulation Optimal Control 1 / 15 Benoît Chachuat (McMaster University) Introduction & Formulation Optimal Control 2 / 15 Controlled System A controlled system is characterized by: State variables x = [x 1 x nx ] T, n x 1, describing its internal behavior phase space e.g., coordinates, velocities, concentrations, flow rates, etc. Control variables u = [u 1 u nu ] T, n u 1, describing the controller positions control space e.g., force, voltage, temperature, etc. Model Development Objective: Develop the simplest model that accurately predicts the system behavior to all foreseeable control decisions Controlled System System Response: A solution x(t;,x 0,u( )) to the differential equations is called a response to the control u( ) for the initial conditions x 0 at Important Questions: Does a response exist for a given control and initial conditions? Is this response unique? Typically, in the form of Ordinary Differential Equations (ODEs), ẋ(t) = f(t,x(t),u(t)), with specified initial conditions, x( ) = x 0 Benoît Chachuat (McMaster University) Introduction & Formulation Optimal Control 3 / 15 Benoît Chachuat (McMaster University) Introduction & Formulation Optimal Control 4 / 15

2 Admissible Controls Restriction to a certain control region, u(t) U IR nu E.g., U = {u IR n u : u j 1, j}, U = {u IR nu : (u) = 0} Which class for the time-varying controls? Continuous controls: u C[t0, t f ] nu may not be a large enough class More generally, piecewise continuous control: u Ĉ[t0, t f ] nu Admissible Controls Restriction to a certain control region, u(t) U IR nu E.g., U = {u IR n u : u j 1, j}, U = {u IR nu : (u) = 0} Which class for the time-varying controls? Continuous controls: u C[t0, t f ] nu may not be a large enough class More generally, piecewise continuous control: u Ĉ[t0, t f ] nu Finite partition: x(t),u(t) = θ 0 < < θ N < θ N+1 = t f and u [θk,θ k+1 ] C[θ k,θ k+1 ] nu θ 1 θ k θ N t f for each k = 0,...,N Allow instantaneous control jumps Underlying assumption: inertia-less controllers x( ) u( ) t Class of Admissible Controls U[, t f ] A piecewise continuous control u( ), defined on some time interval t t f, with range in the control region U, is said to be an admissible control u(t) U, t [,t f ], Every admissible control is bounded Do you see why? Benoît Chachuat (McMaster University) Introduction & Formulation Optimal Control 5 / 15 Benoît Chachuat (McMaster University) Introduction & Formulation Optimal Control 5 / 15 Performance Criterion A functional used for quantitative evaluation of a system s performance Can depend on both the control and state variables Can depend on the initial and/or terminal times too (if not fixed) Functional Forms Lagrange form: J(u) = l(t,x(t),u(t)) dt Mayer form: J(u) = ϕ(,x( ),t f,x(t f )) Bolza form: J(u) = ϕ(,x( ),t f,x(t f )) + l(t,x(t),u(t)) dt Lagrange, Mayer and Bolza functional forms are equivalent! Physical Constraints Functional equalities/inequalities restricting the range of values that can be assumed by control and/or state variables Types of Constraints Point constraints: ψ i ( t,x( t)) 0, ψ e ( t,x( t)) = 0, t [,t f ] E.g., terminal state constraint xk (t f ) x U,f k Isoperimetric (integral) constraints: κ i (t,x(t),u(t)) dt 0, κ e (t,x(t),u(t)) dt = 0 Can always be reformulated as a point constraints! (easier to handle) Path constraints: κ i (t,x(t),u(t)) 0, κ e (t,x(t),u(t)) = 0, t [,t f ] E.g., input path constraints u L u(t) u U, t [, t f ]; (pure) state path constraints x k (t) x U k, t [, t f ] Benoît Chachuat (McMaster University) Introduction & Formulation Optimal Control 6 / 15 Benoît Chachuat (McMaster University) Introduction & Formulation Optimal Control 7 / 15

3 A Quite General Optimal Control Formulation Open-Loop vs. Closed Loop Optimal Control Optimal Control Problem Determine u Ĉ 1 [,t f ] nu that Open-Loop Optimal Control An optimal control law of the form Closed-Loop Optimal Control A feedback control law of the form minimize: J(u) = (x(t f )) + l(t,x(t),u(t)) dt subject to: ẋ(t) = f(t,x(t),u(t)); x( ) = x 0 ψ i j(x(t f )) 0, ψ e j (x(t f)) = 0, κ i j(t,x(t),u(t)) 0, κ e j (t,x(t),u(t)) = 0, j u(t) [u L,u U ] j = 1,...,n i ψ j = 1,...,n e ψ j = 1,...,n i κ = 1,...,ne κ u (t) = ω(t;,x 0 ), t [,t f ], determined for a particular initial state value x( ) = x 0 Controller Process u (t) = ω(t,x(t)), t [,t f ], i.e., calculating the optimal control actions u (t) corresponding to the current state x(t) u (t) x(t) u (t) x(t) opens at Controller Process Pros and Cons? Benoît Chachuat (McMaster University) Introduction & Formulation Optimal Control 8 / 15 Benoît Chachuat (McMaster University) Introduction & Formulation Optimal Control 9 / 15 Class Exercise: Car Control Problem Class Exercise: Double Pendulum L 2 = 1 m θ 2 p( ) = p 0 p(t f ) = p f p m 2 = 1 kg, I 2 Optimal Control Formulation: Drive a car, initially parked at position p 0, to its final destination p f (parking), in minimum time State, p(t),ṗ(t): position and speed Control, u(t): force due to acceleration ( 0) or deceleration ( 0) L1 = 1 m m1 = 1 kg, I1 τ(t) = H 11 (t) θ 1 (t) + H 12 (t) θ 2 (t) 2h θ 1 (t) θ 2 (t) h θ 2 2 (t) 0 = H 22 (t) θ 2 (t) + H 12 (t) θ 1 (t) + h θ 2 1 (t) with: H 11 (t) = I 1 + I m 1L m [ 2 L L L 1L 2 cos θ 2 (t) ] H 12 (t) = I m 2L m 2L 1 L 2 cos θ 2 (t) H 22 (t) = I m 2L 2 2 h = 1 2 L 1L 2 sinθ 2 (t) θ 1 I 1 = 1 3 m 1L 2 1 τ I 2 = 1 3 m 2L 2 2 Control variable: torque, τ(t) Benoît Chachuat (McMaster University) Introduction & Formulation Optimal Control 10 / 15 Benoît Chachuat (McMaster University) Introduction & Formulation Optimal Control 11 / 15

4 Class Exercise: Double Pendulum θ 2 L 2 = 1 m m 2 = 1 kg, I 2 Feasibility and (Global) Optimality Feasible Control A control u U[,t f ] is said to be feasible if: 1 x(t;x 0,u( )) is defined for each t [,t f ] 2 u and x satisfy all of the physical constraints The set of feasible controls, Ω[,t f ], is defined as: L1 = 1 m m1 = 1 kg, I1 Optimal control problem: Bring the double pendulum to stationary, upper vertical position in minimum time, starting from stationary, lower vertical position Ω[,t f ] = {u U[,t f ] : u is feasible} (Globally) Optimal Control (Minimize Case) A control u U[,t f ] is said to be optimal if: θ 1 J(u ) J(u), u Ω[,t f ] τ Benoît Chachuat (McMaster University) Introduction & Formulation Optimal Control 11 / 15 This optimality condition is global in nature, i.e. it does not require consideration of a norm Benoît Chachuat (McMaster University) Introduction & Formulation Optimal Control 12 / 15 Class Exercise: Double Pendulum (cont d) A feasible solution: Class Exercise: Double Pendulum (cont d) The optimal solution: torque τ(t) (N m) feasible solution torque τ(t) (N m) feasible solution minimum-time solution time t (s) time t (s) Benoît Chachuat (McMaster University) Introduction & Formulation Optimal Control 13 / 15 Benoît Chachuat (McMaster University) Introduction & Formulation Optimal Control 13 / 15

5 Local Optimality Locally Optimal Control (Minimize Case) A control u U[,t f ] is said to be a local optimum if: δ > 0 such that: J(u ) J(u), u B δ (u ) Ω[,t f ] Specification of a Norm/Distance on U[,t f ]: Weak Minima: ( B 1, δ (u ) = u U[,t f ] : sup x(t) x (t) + u(t) u (t) < δ t S N k=0 (θ k,θ k+1 ) Similar considerations as with Euler s equation leads to the Euler-Lagrange equations Strong Minima: B δ (u ) = ( u U[,t f ] : sup x(t) x (t) < δ t S N k=0 (θ k,θ k+1 ) Similar considerations as with Weierstrass condition leads to the Pontryagin Maximum Principle Benoît Chachuat (McMaster University) Introduction & Formulation Optimal Control 14 / 15 ) ) Existence of an Optimal Solution Recalls: Weierstrass Theorem Let P IR n be a nonempty, compact set, and let : P IR be continuous on P. Then, the problem min{(p) : p P} attains its minimum, that is, there exists a minimizing solution to this problem. Why closedness of P? Why continuity of? Why boundedness of P? a (c) b a c b a (a) (b) (c) + Benoît Chachuat (McMaster University) Introduction & Formulation Optimal Control 15 / 15