Adaptive Near Optimal Robust Control of Robot Based on Cerebellar Model Joint
In order to improve the control performance and reduce the training time and steady-state error,an adaptive near opti-mal robust control method based on cerebellar model joint was put forward.Firstly,the optimal weight set to overcome the non-linearity such as gravity was found,and the control function remained unchanged,so as to eliminate the steady-state error when there was a deviation term.Then the additional weight was set to realize the origin approximate optimal control,so that the penal-ty control effect would not lead to any steady-state error due to gravity when searching for optimization.The sum of the reactive power weights and the reactive power weights provided a monitoring terminal for robust weight updating.The Lyapunov method ensured the consistent ultimate boundedness of the signal and ensured that the weight drift and burst did not occur.Finally,the effectiveness of the proposed method is verified by the experiment of flexible joint robot.
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