Adaptive Neural Network Based Variable Stiffness Control of Uncertain Robotic Systems Using Disturbance Observer

• Published in: IEEE transactions on industrial electronics (1982) Vol. 64; no. 3; pp. 2236 - 2245
• Main Authors: Zhang, Longbin, Li, Zhijun, Yang, Chenguang
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.03.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Actuators; Adaptive control; Adaptive neural network; Adaptive systems; Control design; disturbance observer; Disturbance observers; Dynamical systems; Feedback control; high-dimensional integral Lyapunov; Liapunov functions; Network control; Neural networks; Nonlinear dynamics; Robot control; Robots; Stiffness; Torque; Uncertainty; variable stiffness actuator (VSA)
• ISSN: 0278-0046; 1557-9948
• DOI: 10.1109/TIE.2016.2624260

The variable stiffness actuator (VSA) has been equipped on many new generations of robots because of its superior performance in terms of safety, robustness, and flexibility. However, the control of robots with joints driven by VSAs is challenging due to the inherited highly nonlinear dynamics. In this paper, a novel disturbance observer based adaptive neural network control is proposed for robotic systems with variable stiffness joints and subject to model uncertainties. By utilizing a high-dimensional integral Lyapunov function, adaptive neural network control is designed to compensate for the model uncertainties, and a disturbance observer is integrated to compensate for the nonlinear VSA dynamics, as well as the neural network approximation errors and external disturbance. The semiglobally uniformly ultimately boundness of the closed-loop control system has been theoretically established. Simulation and extensive experimental studies have also been presented to verify the effectiveness of the proposed approach.