In this paper, a new compound control scheme is proposed for manipulator based on radial basis function neural network (RBFNNs), sliding mode controller (SMC) and adaptive proportional–integral (API) controller. In this control scheme, the filtered tracking error is the input of the RBFNNs update laws, SMC and API controller. The RBFNNs uses three-layer to approximate uncertain nonlinear manipulator dynamics. A robust sliding function is selected as a second controller to guarantee the stability and robustness under various environments. By using additional API controllers, the goal of manipulator tuning is to minimize tracking performance, overshoot and completely removes the chattering signal caused by the sign function in the SMC. Simulation results highlight performance of the controller to compensate the approximate errors and its simpleness in the adaptive parameter tuning process. To be concluded, the controller is suitable for robust adaptive control and can be used as supplementary of traditional neural network (NN) controllers.
Năm 2024 
