Observer-Based adaptive neural network controller for uncertain nonlinear systems with unknown control directions subject to input time delay and saturation

• Published in: Information sciences Vol. 418-419; pp. 717 - 737
• Main Authors: Khajeh Talkhoncheh, Mahdi, Shahrokhi, Mohammad, Askari, Mohammad Reza
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.12.2017
• Subjects: Adaptive neural network; Dynamic surface control; Input delay; Input saturation; Neural network observer; Unknown control direction; Adaptive neural network; Input saturation; Input delay; Neural network observer; Unknown control direction; Dynamic surface control
• ISSN: 0020-0255; 1872-6291
• DOI: 10.1016/j.ins.2017.08.024

This paper addresses the design of an observer based adaptive neural controller for a class of strict-feedback nonlinear uncertain systems subject to input delay, saturation and unknown direction. The input delay has been handled using an integral compensator term in the controller design. A neural network observer has been developed to estimate the unmeasured states. In the observer design, the Lipschitz condition has been relaxed. To solve the problem of unknown control directions, the Nussbaum gain function has been applied in the backstepping controller design. “The explosion of complexity” occurred in the traditional backstepping technique has been avoided utilizing the dynamic surface control (DSC) technique and the designed controller is singularity free. It has been shown that all closed-loop signals are semi-globally uniformly ultimately bounded (SGUUB) and the output tracking error converges to a small neighborhood of the origin by choosing the design parameters appropriately. The numerical examples illustrate the effectiveness of the proposed control scheme.