Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
Volume 31, Issue 5, Pages 1735-1746Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2019.2923241
Keywords
Manipulator dynamics; Robot kinematics; Neural networks; Kinematics; Force; Adaptive neural network control; distubance observer; input saturation; multi-manipulator collaborative control; robot
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Funding
- National Natural Science Foundation of China [61873298]
- Beijing Natural Science Foundation [4172041]
- Joint Funds of Equipment Pre-Research and Ministry of Education of China [6141A02033339]
- Engineering and Physical Sciences Research Council (EPSRC) [EP/S001913]
- EPSRC [EP/S001913/2] Funding Source: UKRI
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In this paper, the complex problems of internal forces and position control are studied simultaneously and a disturbance observer-based radial basis function neural network (RBFNN) control scheme is proposed to: 1) estimate the unknown parameters accurately; 2) approximate the disturbance experienced by the system due to input saturation; and 3) simultaneously improve the robustness of the system. More specifically, the proposed scheme utilizes disturbance observers, neural network (NN) collaborative control with an adaptive law, and full state feedback. Utilizing Lyapunov stability principles, it is shown that semiglobally uniformly bounded stability is guaranteed for all controlled signals of the closed-loop system. The effectiveness of the proposed controller as predicted by the theoretical analysis is verified by comparative experimental studies.
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