Real-time Nonlinear Model Predictive Control using One-step Optimizations and Reachable Sets

Abstract

Model predictive control allows solving complex control tasks with control and state constraints. However, an optimal control problem must be solved in real-time to predict the future system behavior, which is hardly possible on embedded hardware. To solve this problem, this paper proposes to compute a sequence of one-step optimizations aided by pre-computed inner approximations of reachable sets rather than solving the full-horizon optimal control problem at once. This feature can be used to virtually predict the future system behavior with a low computational footprint. Proofs for recursive feasibility and for the sufficient conditions for asymptotic stability under mild assumptions are given. The presented approach is demonstrated in simulation for functional verification.