Adaptive Neural-Network-Based Control for a Class of Nonlinear Systems With Unknown Output Disturbance and Time Delays

• Published in: IEEE access Vol. 7; pp. 7702 - 7716
• Main Authors: Chen, Chao-Yang, Tang, Yang, Wu, Liang-Hong, Lu, Ming, Zhan, Xi-Sheng, Li, Xiong, Huang, Cai-Lun, Gui, Wei-Hua
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation models; Adaptive control; Backstepping; Control methods; Control systems design; Controllers; Delay effects; Neural networks; Nonlinear systems; output disturbance; Robust control; Time lag; time-delay; Tracking control; Tracking errors
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2018.2889969

This paper pays close attention to the adaptive neural network tracking control. Aiming at a class of uncertain nonlinear systems with completely unknown output disturbance and unknown time delay, a corresponding robust control method is proposed based on the backstepping design technology. Neural network approximation is introduced as a very effective estimation technique for modeling uncertain partitions in the design process of virtual controller. The suitable Lyapunov-Krasovskii function is constructed, and by using the organic combination of Young's inequality, unknown time delays are compensated. Nussbaum function is used to handle unknown virtual control directions. A practical robust control method is proposed to deal with the controller singularity problems. A priori knowledge is not required for this method. In this method, all signals achieve semi-global uniform ultimate boundedness, and it is demonstrated that the tracking error eventually converges the region around the origin. The simulation results verify this method's feasibility and effectiveness.