A model weighted adaptive neural backstepping sliding-mode controller for cooperative manipulator system. (21st November 2017)

• Record Type: Journal Article
• Authors: Wang, Jin; Xu, Fan; Lu, GuoDong
• Is Part Of: International journal of advanced robotic systems. Volume 14:Number 6(2017:Nov./Dec.)
• Publication Date: 2017-11-21
• Subjects: Cooperative manipulators -- radial basis function neural network -- backstepping sliding-mode control -- model weight -- internal force; Robotics -- Periodicals; Robotics; Periodicals; 629.892
• DOI: 10.1177/1729881417743028
• Languages: English
• ISSNs: 1729-8806

Abstract

Since dynamic model can be obtained through various approaches with certain different accuracies, the accuracy of model in simulation plant compared to reality is found to be influential but seldom concerned under robotic cooperation. This may result in acceptable simulation but unsatisfactory practical application. Based on different dynamics' demands or accuracies, an inverse model-based backstepping sliding-mode controller and another model-free controller are derived using feedforward radial basis function neural networks to compensate the uncertain dynamics of the system. Model-based algorithm is found to provide much more satisfactory contribution than that of model free. Thus, a model weight is introduced to adjust contribution of inverse dynamics and neural compensation, and a novel model weighted adaptive neural backstepping sliding-mode controller is proposed to cope with different dynamics' demands or accuracies. The neural networks require no off-line learning and it is shown that they can deal well with the dynamic uncertainty, joint friction, and external disturbances. With Lyapunov stability approach, these controllers are proved to be robust, both position/force convergence and tracking stability are guaranteed. Simulation and analysis indicate that these controllers provide good tracking performance in position and internal force. Moreover, the model weighted controller achieves better tracking accuracy and stability under various degrees of model errors.