LEARNING
RBF neural networks based robot non-smooth adaptive control
Dongya Zhao, Zhu Quanmin, LI Shao-yuan
- Year
- 2013
- Citations
- 3
Abstract
A novel non-smooth adaptive robot control is proposed in light of general error decimal power law and RBF neural networks. The corresponding stability analysis is presented to lay a foundation to the safe operation in practice. An illustrative example is used to validate the effectiveness of the proposed approach.
Keywords
DecimalArtificial neural networkComputer scienceAdaptive controlStability (learning theory)RobotControl theory (sociology)Artificial intelligenceError detection and correctionControl engineering
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