Model predictive control with stage cost shaping inspired by reinforcement learning
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Publication:6320091
arXiv1906.02580MaRDI QIDQ6320091
Stefan Streif, Lukas Beckenbach, Pavel Osinenko
Publication date: 6 June 2019
Abstract: This work presents a suboptimality study of a particular model predictive control with a stage cost shaping based on the ideas of reinforcement learning. The focus of the suboptimality study is to derive quantities relating the infinite-horizon cost function under the said variant of model predictive control to the respective infinite-horizon value function. The basis control scheme involves usual stabilizing constraints comprising of a terminal set and a terminal cost in the form of a local Lyapunov function. The stage cost is adapted using the principles of Q-learning, a particular approach to reinforcement learning. The work is concluded by case studies with two systems for wide ranges of initial conditions.
Has companion code repository: https://github.com/pavel-osinenko/rcognita
Nonlinear systems in control theory (93C10) Adaptive control/observation systems (93C40) Discrete-time control/observation systems (93C55)
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