Robust Self-Triggered MPC with Adaptive Prediction Horizon for Perturbed Nonlinear Systems

This paper proposes a robust self-triggered model predictive control (MPC) with an adaptive prediction horizon scheme for constrained nonlinear discrete-time systems subject to additive disturbances. At each triggering instant, the controller provides an optimal control sequence by solving an optimal control problem (OCP), and at the same time, determines the next triggering time and prediction horizon. By implementing the algorithm, the average sampling frequency is reduced and the prediction horizon is adaptively decreased as the system state approaches a terminal region. Meanwhile, an upper bound of performance loss is guaranteed when compared with a nominal periodic sampling MPC. Feasibility of the OCP and stability of the closed-loop system are established. Simulation results verify the effectiveness of the scheme.