Robust Neural Networks Compensating Motion Control of Reconfigurable Manipulator

There are many uncertainties in real dynamics system of reconfigurable manipulator that makes PID etc. traditional methods control imprecisely. Thus, in this paper, to enhance computed torque control (CTC) based method, robust neural networks (RNN) compensating control scheme is developed to compensate structured and unstructured uncertainties. The controller for a RRP reconfigurable manipulator is designed, uniformly ultimately bounded (UUB) stability is proved by Lyapunov theory and simulations show its effectiveness on satisfactory tracking performance

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