Predictive and Symbolic Control: Performance and Safety for Non-linear Systems

Abstract

Although Model Predictive Control (MPC) has proven its efficiency in the process operation and it is known for its high performance, it also suffers from design limitation. One of the fundamental issues for such an optimization-based technique is the difficulty to guarantee recursive feasibility in the absence of terminal constraints, or alternatively, the complexity of design when a certain basin of attraction or a controlled invariant set needs to be certified for the closed loop in the most general nonlinear setting. Based on symbolic control techniques, this paper proposes a simple and guaranteed solution for such problems. As a main result, a Symbolically guided Model Predictive Control scheme is developed. This controller is an improved version of the generic MPC approach such that the recursive feasibility is guaranteed through appending time-varying terminal constraints, carefully designed using the symbolic control approach, to the optimization problem of the original MPC formulation.