Self-Triggered Robust MPC with ISM for Constrained Nonlinear Input-Affine Systems

This paper investigates the self-triggered model predictive control (MPC) with integral sliding mode method (ISM) of networked nonlinear continuous-time system subject to state and input constraints with additive disturbances and uncertainties. In the proposed scheme, the constrained optimization problem is solved aperiodically to generate control signals and the next execution time, leading to possible reductions in both computation and communication. The motivation of using ISM approach is to reject matched uncertainties. First, a self-triggered condition that involves comparing the cost function values with different execution periods is derived. Second, the robust MPC with ISM control strategy is rigorously studied depending on the self-triggered scheme. Finally, a numerical example is used to test the proposed algorithm and to verify our theoretical findings.