Composite Learning Enhanced Neural Control for Robot Manipulator With Output Error Constraints

This article presents a control scheme for robot manipulators with the consideration of output error constraints, unknown dynamics, and bounded disturbances. A modified virtual input variable in the second stage design of the dynamic surface control scheme is proposed, which can enhance the robustness of the controller. Bounded disturbances due to the situations that the base is not well fixed if the robot manipulator is mounted at a mobile platform are considered and suppressed. Besides, the detailed implementation process of the composite learning laws adopted for enhancing the radial basis function neural network is presented. Lyapunov stability analysis verifies that the proposed control scheme ensures the trajectory tracking errors stay within predefined boundaries and parameter estimate errors converge without a stringent condition termed persistent excitation. Experimental results show the superiority of the proposed controller regarding parameter estimation and tracking capabilities.

Automated summary

This article introduces a control scheme for robot manipulators that takes into account output error constraints, unknown dynamics, and bounded disturbances. By proposing a modified virtual input variable and implementing composite learning laws for enhancing neural networks, the controller's robustness is improved. Experimental results demonstrate the superiority of the proposed controller in terms of parameter estimation and tracking capabilities.