Data-Driven Safe Predictive Control Using Spatial Temporal Filter-based Function Approximators

Abstract

Model predictive control (MPC) is a state-of-the-art control method that can explicitly tackle system constraints. However, its high computational cost still remains an open challenge for embedded systems. To achieve satisfactory performance with manageable computational complexity, a spatial temporal filter (STF)-based data-driven predictive control framework is developed to systematically identify system dynamics and subsequently learn the MPC policy using STF-based function approximations. Specifically, an online nonlinear system identification method that satisfies persistence of excitation (PE) is developed by using a discrete-time concurrent learning technique. An STF-based function approximation is then employed to learn the nonlinear MPC (NMPC) policy based on the identified model. Furthermore, a discrete-time robust control barrier function (RCBF) is introduced to guarantee system safety in the presence of additive disturbances and system identification errors. Finally, simulations on the cart inverted pendulum are performed to demonstrate the efficacy of the proposed control synthesis.