Redundancy control of direct-drive-valve servo system

A novel neural network adaptive sliding mode control strategy was proposed, which was applied to ensure tracking capability to direct-drive-valve (DDV) servo system in the presence of degrading redundancy. A radial basis function neural network (RBFNN) was adopted to realize sliding mode control. By means of compensating varieties of the system with adaptive learning algorithm, the control based on RBFNN decreased the tracking error and enhanced the robustness. Meanwhile, a proportional-derivative (PD) controller was designed as the other parallel control part, which improved the convergence of RBFNN, and enhanced the stability of system. Simulation results show that the proposed control scheme solves the problems brought by the degrading of redundancy effectively. It possesses better tracking performance than switching proportional- integral-derivative (PID) control, and can be designed easily.