Nonlinear Wasserstein Distributionally Robust Optimal Control
Keywords: Optimal transport, Distributionally robsut optimal control, Predictive control for nonlinear systems, Stochastic systems
TL;DR: The paper presents a new algorithm for distributionally robust nonlinear model predictive control, providing a practical method for dynamic ambiguity control with a theoretical guarantee.
Abstract: This paper presents a novel approach to addressing the distributionally robust nonlinear model predictive control (DRNMPC) problem. Current literature primarily focuses on the static Wasserstein distributionally robust optimal control problem with a prespecified ambiguity set of uncertain system states. Although a few studies have tackled the dynamic setting, a practical algorithm remains elusive. To bridge this gap, we introduce a DRNMPC scheme that dynamically controls the propagation of ambiguity, based on the constrained iterative linear quadratic regulator. The theoretical results are also provided to characterize the stochastic error reachable sets under ambiguity. We evaluate the effectiveness of our proposed iterative DRMPC algorithm by comparing the closed-loop performance of feedback and open-loop on a mass-spring system, and demonstrate in numerical experiments that our algorithm controls the propagated Wasserstein ambiguity.
Submission Number: 52
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