Abstract
Abstract This paper proposed a learning predictive control method for wheeled mobile robots with communication delays. In consideration of the property in the Networked Control System (NCS), communication delay was divided into two types: delay from sensor to controller and delay from controller to actuator. For each type of delay, state prediction and state augmentation techniques were utilized respectively to diminish their passive impact on the control of WMR. Then receding horizon reinforcement learning approach was adopted to learn the optimal policy based on the delay compensation. Finally, simulations were performed applying the proposed method to the trajectory tracking of WMR and showed the validity of the proposed scheme.
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