Robust nonlinear model predictive control algorithm based on reduced precision solution criteria

This paper discusses the robustness of nonlinear model predictive control (NMPC) based on sub-optimal solution obtained under reduced precision solution (RPS) criteria. NMPC needs to solve the optimal control problem (OCP) quickly and the input is injected to the controlled plant in time. Traditional convergence criteria in optimization algorithms usually cost excessive long computation time with little improvement of solution, which results in degradation of control performance eventually. RPS criteria are new convergence criteria for deciding whether the current iterate is good enough and whether the optimization procedure should be terminated. It can terminate the optimization process timely. This work pays special attention to robustness of the closed-loop system controlled by NMPC with RPS criteria when model plant mismatch exists. Simulations demonstrate that the proposed algorithm owns good robustness and stability.