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.
����Ⱥ,���¹�.ֱ��������ʽ�ŷ��������о�[J].������ҵ��ѧѧ��, 2006, 24(3):308-312 Xia Liqun, Zhang Xinguo. Development of DDV (Direct Drive Valve) servo actuator[J]. Journal of Northwestern Polytechnical University, 2006, 24(3):308-312 (in Chinese)
Lin J S, Chen C L. Buck/boost servo amplifier for direct-drive-valve actuation[J].IEEE Transactions on Aerospace and Electronic Systems.1995, 31(3):960-967
Vieten K W, Snyder J D, Clark R P. Redundancy management concepts for advanced actuation systems . AIAA-93-1168, 1993
������,���Ҳ�,��ռ��.�ŷ�����ϵͳ����ȿ���[J].�������պ����ѧѧ��, 1999, 25(5):531-534 Fu Yongling, Pei Zhongcai, Wang Zhanlin. Redundance control of servo actuator system[J].Journal of Beijing University of Aeronautics and Astronautics, 1999, 25(5):531-534 (in Chinese)
Ҷ��,������.����������ŷ�����ϵͳ��ģ�붯��̬�����о�[J].����������, 2003, 26(1):71-75 Ye Hong, Zeng Guangshang. Modeling and analysis of static and dynamic characteristics of triplex redundance digital servo control system[J].Journal of Solid Rocket Technology, 2003, 26(1):71-75 (in Chinese)