Tracking Control for Motion Constrained Robotic System via Dynamic Event-Sampled Intelligent Learning Method

This paper introduces a new event-sampled intelligent learning method to solve tracking control for motion constrained robotic system. In many applications, motion constraints on joint movements of a robot are needed for its safe operation or to accommodate limitations arising from its mechanical structures. To handle the control design problem, a state-transformation method is employed to guarantee motion constraints of the robot and obtain a corresponding unconstrained tracking error system. Next, the constrained control problem is transformed into a general optimal tracking control problem for the unconstrained error system. Finally, we implement neural networks based learning structures to obtain the optimal solution. An important highlight of our proposed algorithm is that we have integrated a dynamic event-sampled approach to the learning-based controller design, thus reducing the system state sampling times. Using the proposed event-sampled learning control method, the stability of the closed-loop system and the convergence of the weights of neural networks are both established. Lastly, the effectiveness of the proposed intelligent control method is verified via simulation.