Adaptive Neural Network Control of an Uncertain Robot With Full-State Constraints

• Published in: IEEE transactions on cybernetics Vol. 46; no. 3; pp. 620 - 629
• Main Authors: He, Wei, Chen, Yuhao, Yin, Zhao
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.03.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive control; Adaptive control systems; Adaptive systems; Artificial neural networks; barrier Lyapunov function (BLF); Closed loop systems; Computer simulation; Control design; Control systems; Disturbances; full-state constraint; Neural networks; neural networks (NNs); nonlinear system; Nonlinear systems; robot; Robotics; Robots; Tracking control; robot; barrier Lyapunov function (BLF); full-state constraint; neural networks (NNs); nonlinear system; Adaptive control
• ISSN: 2168-2267; 2168-2275
• DOI: 10.1109/TCYB.2015.2411285

This paper studies the tracking control problem for an uncertain <inline-formula> <tex-math notation="LaTeX">{n} </tex-math></inline-formula>-link robot with full-state constraints. The rigid robotic manipulator is described as a multiinput and multioutput system. Adaptive neural network (NN) control for the robotic system with full-state constraints is designed. In the control design, the adaptive NNs are adopted to handle system uncertainties and disturbances. The Moore-Penrose inverse term is employed in order to prevent the violation of the full-state constraints. A barrier Lyapunov function is used to guarantee the uniform ultimate boundedness of the closed-loop system. The control performance of the closed-loop system is guaranteed by appropriately choosing the design parameters. Simulation studies are performed to illustrate the effectiveness of the proposed control.