Numerical analysis of time discretization of optimal control problems

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Shooting problem: continuous problem • Let p[p0], y[p0] denote the solution of the previous ODE with initial condition (y 0 ,p0). • Shooting function: S(p0) = pT[p0]−φ ′ (yT[p0]). • Optimality system ⇔ Shooting equation: S(p0) = 0. • Assume ¯p0 well-posed solution: S(¯p0) = 0, and S ′ (¯p0) invertible. • Then locally: ¯p0 unique solution, Newton’s method converges, and sensitivity analysis thanks to IFT. 19
Shooting problem: discretized problem • Reduced formulation by elimination of the control: ⎧ ⎪⎨ ⎪⎩ yk+1−yk hk pk −pk+1 hk = f(Υ(yk,pk),yk), = pk+1fy(Υ(yk,pk),yk), k = 0,...,N −1, y0 = y 0 ; pN = φ ′ (yN). • Let p h [p0], y h [p0] denote the solution of the previous ODE with initial condition (y 0 ,p0) (well-defined if p0 close to ¯p0 and ¯ h small) • Optimality system ⇔ S h (p0) := p h T [p0]−φ ′ (y h T [p0]) = 0. 20
Page 1 and 2: Numerical analysis of time discreti
Page 3 and 4: This class of problems is quite lar
Page 5 and 6: The Euler discretization • N: num
Page 7 and 8: The simplest optimal time problem I
Page 9 and 10: Fuller’s problem I (work with J.
Page 11 and 12: Robbins state constrained problem I
Page 13 and 14: Robbins state constrained problem I
Page 15 and 16: Beam problem: a second-order state
Page 17 and 18: Discretization by Euler’s method
Page 19: Reduction by elimination of the con
Page 23 and 24: Error analysis • F: set of C 1 ma
Page 25 and 26: Second-order optimality conditions
Page 27 and 28: Link with the shooting formulation
Page 29 and 30: A priori parameterized control •
Page 31 and 32: Example: mid-point rule • If uk c
Page 33 and 34: Order computation “by hand” Den
Page 35 and 36: Order of a RK scheme I • We forma
Page 37 and 38: Exercice: order 2 yk +hyk+1,1+ 1 2h
Page 39 and 40: s Lagrangian (contracting yk +hk j=
Page 41 and 42: Partitionned Runge-Kutta (PRK) sche
Page 43 and 44: Conditions for order 1 to 3: dj :=
Page 45 and 46: Conditions for order 5 Table 5: Ord
Page 47 and 48: Number of conditions for each order
Page 49 and 50: Definition 1. (i) A linear mapping
Page 51 and 52: Back to the mid-point rule • The
Page 53 and 54: Synthesis of part II: unconstrained
Page 55 and 56: III: CONTROL CONSTRAINTS (Hager, Do
Page 57 and 58: Active constraints Denote the set o
Page 59 and 60: Minimization of the Hamiltonian •
Page 65 and 66: Euler discretization ⎧ Min φ(yN)
Page 67 and 68: Geometrical hypotheses: junction po
Page 69 and 70: Junction points for the discretized
Page 71 and 72:
Connection with stability analysis
Page 73 and 74:
Connection with stability analysis
Page 75 and 76:
High-order RK schemes and control c
Page 77 and 78:
Abstract stability result (in view
Page 79 and 80:
Since F1 is strongly coercive, it f
Page 81 and 82:
Examples: high orders are natural
Page 83 and 84:
Academic example: first-order state
Page 85 and 86:
Academic example: first boundary ar
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Academic example: three boundary ar
Page 89 and 90:
First-order state constraint: Min
Page 91 and 92:
Third-order state constraint: Min
Page 93 and 94:
In the sequel: we discuss only firs
Page 95 and 96:
Condition at junction point Case of
Page 97 and 98:
Homotopy path For θ ∈ [0,1]: Min
Page 99 and 100:
Proof. We have that 0 = pθ k+1fu(u
Page 101 and 102:
and similarly 0 ≥ gi(y θ k−1)
Page 103 and 104:
For ε > 0 small enough, we deduce
Page 105 and 106:
OPEN PROBLEMS • First-order state
Page 107 and 108:
Sources • J.F. Bonnans, J. Lauren
Page 109:
The End ! 108
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Numerical analysis of time discretization of optimal control problems

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