文章基本信息

标题：Learning Stochastic Parametric Diferentiable Predictive Control Policies
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作者：Ján Drgoňa ; Sayak Mukherjee ; Aaron Tuor 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2022
卷号：55
期号：25
页码：121-126
DOI：10.1016/j.ifacol.2022.09.334
语种：English
出版社：Elsevier
摘要：AbstractThe problem of synthesizing stochastic explicit model predictive control policies is known to be quickly intractable even for systems of modest complexity when using classical control-theoretic methods. To address this challenge, we present a scalable alternative called stochastic parametric differentiable predictive control (SP-DPC) for unsupervised learning of neural control policies governing stochastic linear systems subject to nonlinear chance constraints. SP-DPC is formulated as a deterministic approximation to the stochastic parametric constrained optimal control problem. This formulation allows us to directly compute the policy gradients via automatic differentiation of the problem's value function, evaluated over sampled parameters and uncertainties. In particular, the computed expectation of the SP-DPC problem's value function is back propagated through the closed-loop system rollouts parametrized by a known nominal system dynamics model and neural control policy which allows for direct model-based policy optimization. We demonstrate the computational efficiency and scalability of the proposed policy optimization algorithm in three numerical examples, including systems with a large number of states or subject to nonlinear constraints.
关键词：KeywordsStochastic explicit model predictive controloffline model-based policy optimizationdeep neural networksdifferentiable programmingparametric programming