Neuroadaptive Robotic Control Under Time-Varying Asymmetric Motion Constraints: A Feasibility-Condition-Free Approach

• Published in: IEEE transactions on cybernetics Vol. 50; no. 1; pp. 15 - 24
• Main Authors: Zhao, Kai, Song, Yongduan
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive neural control; Algorithms; Artificial neural networks; Asymmetry; Computer simulation; Control algorithms; Feasibility; feasibility conditions; Manipulators; Neural networks; Nonlinear systems; position and velocity constraints; Robot arms; Robot control; Robot kinematics; robotic manipulator; Robotics; Symmetric matrices; Time-varying systems; Tracking control
• ISSN: 2168-2267; 2168-2275; 2168-2275
• DOI: 10.1109/TCYB.2018.2856747

This paper presents a neuroadaptive tracking control approach for uncertain robotic manipulators subject to asymmetric yet time-varying full-state constraints without involving feasibility conditions. Existing control algorithms either ignore motion constraints or impose additional feasibility conditions. In this paper, by integrating a nonlinear state-dependent transformation into each step of backstepping design, we develop a control scheme that not only directly accommodates asymmetric yet time-varying motion (position and velocity) constraints but also removes the feasibility conditions on virtual controllers, simplifying design process, and making implementation less demanding. Neural network (NN) unit accounting for system uncertainties is included in the loop during the entire system operational envelope in which the precondition on the NN training inputs is always ensured. The effectiveness and benefits of the proposed control method for robotic manipulator are validated via computer simulation.