Communication Dans Un Congrès Année : 2022

Stochastic model predictive control with randomly sampled measurements

Résumé

For dynamical systems arising in engineering and economics, we are often interested in addressing optimal control problems and whether our computational methods can provide reasonable solutions to this problem. Model Predictive Control (MPC) presents a technique for addressing an optimal control problem over a large, or infinite, time horizon by solving the problem on a smaller finite horizon, which is computationally tractable. When we implement the control actions obtained by solving the finite horizon problems, the resulting cost over the infinite horizon is larger than the minimal cost. An important question is to analyze how much the performance resulting from the computationally tractable algorithms has degraded compared to the theoretically optimal performance. One way to do so is by quantifying the increase in the value of the cost functional obtained by implementing MPC-based policies. In this regard, the problem of analyzing the performance of model predictive controllers in minimizing infinite-horizon cost functionals associated with stochastic dynamical systems when the measurements received by the controller are randomly sampled in time. In contrast to the standard model predictive control algorithms which rely on availability of the state measurements at all times, we compute control policies which minimize cost functionals over a (finite) rolling-horizon conditioned upon the information that arrives at random time instants; although a hard upper bound equal to the length of the optimization horizon is imposed on consecutive sampling instants. Sufficient conditions are provided on the system dynamics, the cost functionals, and the statistics of the sampling process, such that the proposed policies result in computable upper bounds on the infinite-horizon average cost. The case of linear time-varying system with quadratic cost functionals is studied for the illustration of our results.

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Dates et versions

hal-04912645 , version 1 (26-01-2025)

Identifiants

  • HAL Id : hal-04912645 , version 1

Citer

Aneel Tanwani. Stochastic model predictive control with randomly sampled measurements. 15th Viennese Conference on Optimal Control and Dynamic Games, TU Wien, Jul 2022, Vienna (AUSTRIA), Austria. ⟨hal-04912645⟩
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