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Robust adaptive dead zone technology for fault-tolerant control of robot manipulators using neural networks. (English) Zbl 1047.93546
Summary: A multi-layered feed-forward neural network is trained on-line by the robust adaptive dead zone scheme to identify simulated faults occurring in a robot system and reconfigure the control law to prevent the tracking performance from deteriorating in the presence of system uncertainty. Considering the fact that system uncertainty can not be known a priori, the proposed robust adaptive dead zone scheme can estimate the upper bound of system uncertainty on-line to ensure convergence of the training algorithm, and in turn the stability of the control system. A discrete-time robust weight-tuning algorithm using the adaptive dead zone scheme is presented with a complete convergence proof. The effectiveness of the proposed methodology has been shown by simulations for a two-link robot manipulator.

MSC:
93C85 Automated systems (robots, etc.) in control theory
68T40 Artificial intelligence for robotics
68T05 Learning and adaptive systems in artificial intelligence
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