ILC-RBNNF-Based Vibration Control of a Rotatable Manipulator With Time-Varying Output Constraints

• Published in: IEEE transactions on systems, man, and cybernetics. Systems Vol. 53; no. 10; pp. 1 - 10
• Main Authors: Mei, Yanfang, Liu, Yu
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-10-2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive control; Backstepping technique; Boundary control; flexible manipulator; iterative learning control (ILC); Liapunov functions; Lyapunov methods; Manipulators; Neural networks; Observers; Parameter uncertainty; radial basis neural network functions (RBNNFs); Robust control; Systems stability; time-varying output constraint; Uncertain systems; Uncertainty; Vibration control; vibration suppression; Vibrations
• ISSN: 2168-2216; 2168-2232
• DOI: 10.1109/TSMC.2023.3283492

This article focuses on the problem of vibration suppression and attitude tracking of a flexible rotatable manipulator. For the manipulator system suffering from parameter uncertainties, input saturations, time-varying output constraints, and periodic boundary disturbances, a new type of robust adaptive boundary control scheme is proposed. To cope with system parameter uncertainties and input saturations, radial basis neural network functions (RBNNFs) are introduced. To compensate for the periodic disturbance errors, the iterative learning control (ILC) is designed. In order to obtain a controller to guarantee the system stability, the backstepping technique is employed. Then, a modified Lyapunov function is constructed and system stability and uniform boundedness of output variables are proved. By conducting simulation experiment, the robustness and prescribed performance of the adaptive ILC-RBNNF-based controllers are testified.