Disturbance-State-Based Event-Triggered Model Predictive Tracking Control for 4WIS-4WID Mobile Robot

Due to disturbances caused by ground environment uncertainty and noise, as well as the influence of model uncertainty, these effects lead to tracking error in the actual state, which in turn affects the design of the robot controller. To address this issue in the 4-Wheel Independent Steering and 4-Wheel Independent Driving (4WIS-4WID) robot system, a disturbance-state-based event-triggered Model Predictive Control (EMPC) algorithm was proposed in this paper. To address disturbances while ensuring the accuracy of the system state, a nonlinear extended state observer (NESO) is designed to concurrently estimate the state and perturbation of the 4WIS-4WID mobile robot. An event-triggered mechanism (ETM) is implemented with the state and disturbance observations from NESO, aiming to minimize the frequency of controller and alleviate the computational workload of the system. The simulation shows that the disturbance-state-based EMPC can achieve well tracking performance and lessening the computational load of the robot.