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

The increasing complexity of modern systems can introduce significant uncertainties to the models that describe them, which poses a great challenge to safe model-based control. This paper presents a learning-based stochastic model predictive control (LB-SMPC) strategy with chance constraints for offset-free trajectory tracking. The LB-SMPC strategy systematically handles plant-model 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 predictive control of safety-critical systems with hard-to-model and/or time-varying dynamics.