Periodic Event‐Triggered Predictive Tracking Control for Mobile Robots With Unknown Disturbances

ABSTRACT This paper investigates the trajectory tracking problem for a disturbed mobile robot system. We propose a periodic event‐triggered model predictive control (PETMPC) scheme based on an extended state observer (ESO). First, an event‐triggering condition constructed by the error between the system state and its corresponding optimal prediction, reduces the computational load required to solve optimization problems. Then, a periodic checking mechanism is proposed to periodically check the triggering condition based on a specified time interval. Between two detection intervals, the corresponding input is calculated by the adjacent triggering instant. By incorporating the dual‐model control method, the control law switches to a state feedback control law when the predefined switching condition is met. Thus, the computational burden of the system can be reduced as the frequency of solving the optimization problem decreases. Additionally, an ESO‐based disturbance compensation mechanism is developed to mitigate the effect of unknown disturbances. Both the feasibility of the algorithm and the stability of the closed‐loop system are derived for the mobile robot with unknown disturbances. Simulation results demonstrate the effectiveness and practicality of the proposed PETMPC strategy.