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Convergence in Nonlinear Optimal Sampled-Data Control Problems
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DOI：10.1109/TAC.2024.3394650.png)
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摘要
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Consider, on the one part, a general nonlinear finite- dimensional optimal control problem and assume that it has a unique solution x*. On the other part, consider the sampled-data control version of it. Under appropriate assumptions, we prove that the optimal state of the sampled-data problem converges uniformly to x* as the norm of the partition tends to zero. Moreover, applying the Pontryagin maximum principle (PMP) to both problems, we prove that, if x* has a unique weak extremal lift with a costate p that is normal, then the costate of the sampled-data problem converges uniformly to p. In other words, under a normality assumption, control sampling commutes, at the limit of small partitions, with the application of the PMP.
Keyword:
Convergence
Optimal control
Aerospace electronics
Costs
Vectors
Trajectory
Standards
Filippov approach
Pontryagin maximum principle (PMP)
sampled-data control
期刊
IF:
7
论文数:
1.3W
被引数:
6.7W
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