Adaptive Neural Network Control for Euler-Lagrangian Systems with Uncertainties

• Published in: 东华大学学报（英文版） Vol. 39; no. 5; pp. 485 - 489
• Main Authors: CHENG Xin, LU Wenke, LIU Huashan
• Format: Journal Article
• Language: English
• Published: College of Information Science and Technology,Donghua University,Shanghai 201620,China%College of Information Science and Technology,Donghua University,Shanghai 201620,China 31.10.2022; Engineering Research Center of Digitized Textile and Fashion Technology,Ministry of Education,Shanghai 201620,China
• Subjects: adaptive control; neural network ( NN ); uncertainty; compensator; Euler-Lagrangian system(ELS)
• ISSN: 1672-5220
• DOI: 10.19884/j.1672-5220.202202993

TP273+.2; An adaptive controller involving a neural network (NN) compensator is proposed to resist the uncertainties in the Euler-Lagrangian system ( ELS ). Firstly, a proportional-differential(PD) control law is designed for the nominal model. Meanwhile, the uncertainties including model error and external disturbance are separated from the closed-loop system. Then, an adaptive NN compensator based on the online training mode is proposed to eliminate the adverse effect of the uncertainties. In addition, the stability of the closed-loop system is proved by Lyapunov theory. Finally, the effectiveness of the proposed approach is verified on a two-degree-of-freedom robot manipulator.