extreme learning machine; finite-time sliding mode control; model uncertainty; prescribed performance; robot manipulators; Mechanical Engineering; Control and Optimization; Artificial Intelligence
Abstract :
[en] This study aims to provide a robust trajectory tracking controller which guarantees the prescribed performance of a robot manipulator, both in transient and steady-state modes, experiencing parametric uncertainties. The main core of the controller is designed based on the adaptive finite-time sliding mode control (SMC) and extreme learning machine (ELM) methods to collectively estimate the parametric model uncertainties and enhance the quality of tracking performance. Accordingly, the global estimation with a fast convergence rate is achieved while the tracking error and the impact of chattering on the control input are mitigated significantly. Following the control design, the stability of the overall control system along with the finite-time convergence rate is proved, and the effectiveness of the proposed method is investigated via extensive simulation studies. The results of simulations confirm that the prescribed transient and steady-state performances are obtained with enough accuracy, fast convergence rate, robustness, and smooth control input which are all required for practical implementation and applications.
Disciplines :
Electrical & electronics engineering
Author, co-author :
Raoufi, Mona; Department of Electrical Engineering, Hamedan University of Technology, Hamedan, Iran
HABIBI, Hamed ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > Automation
Yazdani, Amirmehdi; College of Science, Health, Engineering and Education, Murdoch University, Perth, Australia
Wang, Hai ; College of Science, Health, Engineering and Education, Murdoch University, Perth, Australia
External co-authors :
yes
Language :
English
Title :
Robust Prescribed Trajectory Tracking Control of a Robot Manipulator Using Adaptive Finite-Time Sliding Mode and Extreme Learning Machine Method
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