Hybrid position-force control for constrained reconfigurable manipulators based on adaptive neural network

Abstract

This article presents a hybrid position-force control method based on adaptive neural network for addressing the problems of position and force tracking of a constrained reconfigurable manipulator. The reduced-order dynamic model of a reconfigurable manipulator is formulated considering the model uncertainty and the external constraint. Combining decentralized control with centralized control scheme, a hybrid position-force controller is designed for controlling the position and force of the constrained reconfigurable manipulator. The dynamic model uncertainty and the dynamic coupling effect are compensated by radial basis function neural network. The stability of the closed-loop system is proved using the Lyapunov theory. Finally, simulations are performed to study the effectiveness of the proposed method.