Aperiodic event-triggered model predictive control for perturbed LTI systems: A PID based approach

A new event-triggered model predictive control (ET-MPC) framework is proposed for perturbed linear time-invariant (LTI) systems with input and state constraints. Firstly, we propose a novel aperiodic sampling strategy, which determines the upcoming update instants based on the proportion, integral and differential (PID) of the error between the actual state and its optimal prediction to effectively reduce the computational and communication burden of the system. Then, a PID based ET-MPC algorithm is developed and its feasibility as well as the closed-loop stability are strictly proved. Finally, simulation and comparison studies are conducted based on the cart-damper-spring system to illustrate the effectiveness of the proposed method.