Learning-based Stochastic Model Predictive Control with State-Dependent Uncertainty

Angelo Domenico Bonzanini, Ali Mesbah

Abstract: The increasing complexity of modern engineering systems can introduce a great deal of uncertainty in our knowledge of system dynamics, which can, in turn, pose a major challenge to safe model-based control. This paper presents a learning-based stochastic model predictive control (LB-SMPC) strategy with chance constraints for tracking. The LB-SMPC strategy systematically handles mismatch between the actual system dynamics and a system model via a state-dependent uncertainty term that is intended to correct model predictions at each sampling time. A chance constraint handling method is presented to ensure state constraint satisfaction to a desired level for the case of state-dependent model uncertainty. Closed-loop simulations demonstrate the usefulness of LB-SMPC for control of a safety-critical plasma system for processing of (bio)materials with hard-to-model and time-varying dynamics.