Numerical analysis of time discretization of optimal control problems

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The simplest optimal time problem II • Solution: Bang-bang optimal control, at most one switching time • Discretized solution of same nature (costate affine function of time) • Exact integrators control constant over a time: mid point rule • In that case, error only due to the switching time step • Expected error: at most O( ˜ h), with ˜ h = time step a switching time. Ref. for LQ bang-bang problems Alt, Baier, Gerdts, Lempio, Error bounds for Euler approximation of linear-quadratic control problems with bang-bang solutions. Preprint, 2010. 7
Fuller’s problem I (work with J. Laurent-Varin) Same dynamics: ¨xt = ut ∈ [−1,1]; Integral cost T 0 x2 tdt. 1.0 0.8 0.6 0.4 0.2 0.0 −0.2 −0.4 −0.6 −0.8 −1.0 0 1 2 3 4 5 6 7 8 Figure 2: Fuller problem: optimal control, logarithmic penalty 8
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: The simplest optimal time problem I
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 and 20: Reduction by elimination of the con
Page 21 and 22: Shooting problem: discretized probl
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 61 and 62:
Second-order optimality conditions
Page 63 and 64:
Second-order optimality conditions
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Euler discretization ⎧ Min φ(yN)
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Geometrical hypotheses: junction po
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Junction points for the discretized
Page 71 and 72:
Connection with stability analysis
Page 73 and 74:
Connection with stability analysis
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High-order RK schemes and control c
Page 77 and 78:
Abstract stability result (in view
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Since F1 is strongly coercive, it f
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Examples: high orders are natural
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Academic example: first-order state
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Academic example: first boundary ar
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Academic example: three boundary ar
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First-order state constraint: Min
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Third-order state constraint: Min
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In the sequel: we discuss only firs
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Condition at junction point Case of
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Homotopy path For θ ∈ [0,1]: Min
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Proof. We have that 0 = pθ k+1fu(u
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and similarly 0 ≥ gi(y θ k−1)
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For ε > 0 small enough, we deduce
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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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