Adaptive Neural Control for Robotic Manipulators With Output Constraints and Uncertainties

• Published in: IEEE transaction on neural networks and learning systems Vol. 29; no. 11; pp. 5554 - 5564
• Main Authors: Zhang, Shuang, Dong, Yiting, Ouyang, Yuncheng, Yin, Zhao, Peng, Kaixiang
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.11.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive control; Artificial neural networks; barrier Lyapunov function (BLF); Computer simulation; Constraint modelling; constraints; Control design; Control methods; Control stability; Feasibility studies; Feedback; Feedback control; Liapunov functions; Manipulator dynamics; Manipulators; Neural networks; neural networks (NNs); Output feedback; robot; Robot arms; Robots; Stability analysis; State feedback; Systems stability; Uncertainty
• ISSN: 2162-237X; 2162-2388
• DOI: 10.1109/TNNLS.2018.2803827

This paper investigates adaptive neural control methods for robotic manipulators, subject to uncertain plant dynamics and constraints on the joint position. The barrier Lyapunov function is employed to guarantee that the joint constraints are not violated, in which the Moore-Penrose pseudo-inverse term is used in the control design. To handle the unmodeled dynamics, the neural network (NN) is adopted to approximate the uncertain dynamics. The NN control based on full-state feedback for robots is proposed when all states of the closed loop are known. Subsequently, only the robot joint is measurable in practice; output feedback control is designed with a high-gain observer to estimate unmeasurable states. Through the Lyapunov stability analysis, system stability is achieved with the proposed control, and the system output achieves convergence without violation of the joint constraints. Simulation is conducted to approve the feasibility and superiority of the proposed NN control.