Disturbance-observer-based event-triggered model predictive control of nonlinear input-affine systems

This paper proposes a novel disturbance-observer-based event-triggered model predictive control (DEMPC) framework for a class of nonlinear input-affine systems with state and control input constraints as well as unmatched disturbances to simultaneously enhance the robustness of standard MPC and reduce computational resource utilization. First, a nonlinear disturbance observer is designed to compensate for disturbances actively. Next, a constant-threshold-type event-triggering mechanism (CTETM) is designed in terms of the state prediction deviation caused by the remaining disturbances. Subsequently, a DEMPC algorithm is constructed using the disturbance estimation information, the CTETM, and the so-called dual-mode scheme. Furthermore, rigorous theoretical analysis is provided, involving robust constraint fulfillment, Zeno phenomenon prevention, recursive feasibility, and stability. Particularly for systems with only matched disturbances, it is proven that continuous disturbance compensation would result in a possibly lower triggering frequency and enhanced control performance for the DEMPC compared to conventional EMPC without introducing additional conservatism into the state constraints. In the end, simulation studies are performed to illustrate the effectiveness of the proposed framework.