Adaptive Neural Output Feedback Control of Output-Constrained Nonlinear Systems With Unknown Output Nonlinearity

• Published in: IEEE transaction on neural networks and learning systems Vol. 26; no. 8; pp. 1789 - 1802
• Main Authors: Zhi Liu, Guanyu Lai, Yun Zhang, Chen, Chun Lung Philip
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.08.2015
• Subjects: Adaptation models; Adaptive control; Adaptive systems; Algorithms; Artificial neural networks; barrier Lyapunov function (BLF); Bouc-Wen hysteresis model; Closed loop systems; Computer Simulation; Feedback; Hysteresis; Magnetic hysteresis; Neural Networks (Computer); neural networks (NNs); Nonlinear Dynamics; Nonlinear systems; Research Design - statistics & numerical data; barrier Lyapunov function (BLF); neural networks (NNs); Bouc–Wen hysteresis model; Adaptive control
• ISSN: 2162-237X; 2162-2388
• DOI: 10.1109/TNNLS.2015.2420661

This paper addresses the problem of adaptive neural output-feedback control for a class of special nonlinear systems with the hysteretic output mechanism and the unmeasured states. A modified Bouc-Wen model is first employed to capture the output hysteresis phenomenon in the design procedure. For its fusion with the neural networks and the Nussbaum-type function, two key lemmas are established using some extended properties of this model. To avoid the bad system performance caused by the output nonlinearity, a barrier Lyapunov function technique is introduced to guarantee the prescribed constraint of the tracking error. In addition, a robust filtering method is designed to cancel the restriction that all the system states require to be measured. Based on the Lyapunov synthesis, a new neural adaptive controller is constructed to guarantee the prescribed convergence of the tracking error and the semiglobal uniform ultimate boundedness of all the signals in the closed-loop system. Simulations are implemented to evaluate the performance of the proposed neural control algorithm in this paper.