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In this brief, a new adaptive control framework to compensate for uncertain nonlinear parameters in robot manipulators is developed. The designed adaptive controllers possess a linear parameter structure, guarantee global boundedness of the closed-loop system as well as tracking of a given trajectory within any prescribed accuracy. Our design approach takes advantage of a Lipschitzian property with...
In this paper, a neural network based controller is proposed for robot manipulators. By considering the second order term of the Taylor expansion of the robot dynamics, the weight tuning algorithm can guarantee the tracking performance of the robot with unknown dynamics. The generic structure selection problem for the neural network controller is addressed by using the structural learning with forgetting,...
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