Prescribed Performance Adaptive Robust Control for Robotic Manipulators With Fuzzy Uncertainty
Faliang Wang, Ke Chen, Shengchao Zhen, Xiaofei Chen, Hongmei Zheng, Zhaodong Wang
Abstract
This article studies a prescribed performance adaptive robust control (PPARC) scheme for uncertain robotic manipulators. First, a state transformation is introduced to embed the predefined output constraints into the trajectory tracking servo constraints. Second, a PPARC is designed to fulfill the reference trajectory tracking and ensure the tracking errors within the predefined output constraints regardless of uncertainty and initial condition deviations. Then, Lyapunov stability analysis is conducted to prove the uniform boundedness and uniform ultimate boundedness of the tracking error. Moreover, the optimization of the control parameter is translated into an optimal design problem, and a fuzzy-based cost function is proposed for the optimal design problem. The existence and uniqueness of the solution to the optimal design problem are theoretically proved. Finally, the effectiveness and superiority of the proposed control scheme are demonstrated based on simulation studies.