Adaptive neural network second-order sliding mode control of dual arm robots
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An adaptive robust control system is considered for dual-arm manipulators (DAM) using the combination of second-order sliding mode control (SOSMC) and neural networks. The SOSMC deals with the system robustness when faced with external disturbances and parametric uncertainties. Meanwhile, the radial basis function network (RBFN) is to constitute an adaptation mechanism for approximating the unknown dynamic model of DAM. The stability of model estimator-integrated controller is analyzed using Lyapuov theory. To show the effectiveness of proposed controller, a four DOFs-DAM is applied as an illustrating example. The results reveal that the controller works well, excellently adapt to no information of robot modeling.
KeywordsDual-arm manipulator modelling estimation neural network robust adaptive control
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