期刊
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
卷 51, 期 3, 页码 1735-1746出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSMC.2019.2901277
关键词
Neural networks; Adaptive systems; Nonlinear systems; Manipulator dynamics; Uncertainty; adaptive control; asymmetrically bounded inputs; neural networks; robotic manipulator
资金
- National Natural Science Foundation of China [61873298, 61873268, 61633016, 61751310, 61573147]
- Anhui Science and Technology Major Program [17030901029]
- Beijing Science and Technology Project [Z181100003118006]
- Beijing Municipal Natural Science Foundation [L182060]
- Engineering and Physical Sciences Research Council [EP/S001913]
An adaptive neural bounded control scheme is proposed for an n-link rigid robotic manipulator with unknown dynamics, guaranteeing tracking performance and compensating for unknown robotic dynamics. The designed controller bounds are known a priori and determined by controller gains, applicable within actuator limitations, with all signals ultimately bounded uniformly proven via Lyapunov stability theory. Simulations verify the effectiveness of the scheme.
In this paper, an adaptive neural bounded control scheme is proposed for an n-link rigid robotic manipulator with unknown dynamics. With the combination of the neural approximation and backstepping technique, an adaptive neural network control policy is developed to guarantee the tracking performance of the robot. Different from the existing results, the bounds of the designed controller are known a priori, and they are determined by controller gains, making them applicable within actuator limitations. Furthermore, the designed controller is also able to compensate the effect of unknown robotic dynamics. Via the Lyapunov stability theory, it can be proved that all the signals are uniformly ultimately bounded. Simulations are carried out to verify the effectiveness of the proposed scheme.
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