Approximation-Based Self-Triggered Model Predictive Control for Perturbed Nonlinear Systems

This paper proposes an approximation-based self-triggered model predictive control strategy for nonlinear sampled-data systems with additive disturbance and system constraints. In our strategy, the finite horizon optimal control problem (FHOCP) and the triggering condition are designed based on approximate models in a discrete-time manner. By implementing the strategy, the computation problem of the FHOCP becomes tractable since it is computed in a discrete-time framework. Meanwhile, the next triggering instant is predetermined by the triggering condition, reducing the sensing cost and the computing frequency of the FHOCP. Furthermore, feasibility of the FHOCP and stability of the overall system are analyzed. Finally, a simulation example verifies the effectiveness of the strategy.