Open AccessAN ADAPTIVE PROPORTIONAL-DERIVATIVE CONTROL METHOD FOR ROBOT MANIPULATOR
Author(s) -
Mai Thang Long,
Tran Huu Toan,
Tran Van Hung,
Tran Ngoc Anh,
NGUYEN HOANG HIEU
Publication year - 2022
Publication title -
khoa học và công nghệ
Language(s) - English
Resource type - Journals
ISSN - 2525-2267
DOI - 10.46242/jstiuh.v52i05.4116
Subject(s) - control theory (sociology) , controller (irrigation) , stability (learning theory) , computer science , artificial neural network , lyapunov function , lyapunov stability , tracking error , proportional control , adaptive control , derivative (finance) , robot , tracking (education) , control system , control (management) , control engineering , engineering , nonlinear system , artificial intelligence , psychology , pedagogy , physics , electrical engineering , quantum mechanics , machine learning , financial economics , agronomy , economics , biology
This research presents an improved control method for the robot manipulator system based on the proportional-derivative technique and neural networks. In the proposed strategy, the proportional-derivative controller based on the filtered tracking error technique has been modified such that the proportional-derivative gain parameters are adaptively updated. Similar to the conventional intelligent control methods, the neural networks approximator is applied to relax the unknown dynamics of the robot control system. In addition, the compensator-typed robust controller is also considered to eliminate inevitable approximating errors and unknown disturbances of the control system. By using the Lyapunov stability theorem for the proposed control design procedure, the tracking control and stability are guaranteed. The comparative simulation results will provide clearly the evident to prove effectiveness of the proposed approach.