Adaptive neural control of MIMO nonlinear systems with a block-triangular pure-feedback control structure

Chen, Zhenfeng, Ge, Shuzhi Sam, Zhang, Yun and Li, Yanan (2014) Adaptive neural control of MIMO nonlinear systems with a block-triangular pure-feedback control structure. IEEE Transactions on Neural Networks and Learning Systems, 25 (11). pp. 2017-2029. ISSN 2162-237X

[img] PDF - Accepted Version
Download (2MB)


This paper presents adaptive neural tracking control for a class of uncertain multi-input-multi-output (MIMO) nonlinear systems in block-triangular form. All subsystems within these MIMO nonlinear systems are of completely nonaffine purefeedback form and allowed to have different orders. To deal with the nonaffine appearance of the control variables, the mean value theorem (MVT) is employed to transform the systems into a block-triangular strict-feedback form with control coefficients being couplings among various inputs and outputs. A systematic procedure is proposed for the design of a new singularityfree adaptive neural tracking control strategy. Such a design procedure can remove the couplings among subsystems and hence avoids the possible circular control construction problem. As a consequence, all the signals in the closed-loop system are guaranteed to be semiglobally uniformly ultimately bounded (SGUUB). Moreover, the outputs of the systems are ensured to converge to a small neighborhood of the desired trajectories. Simulation studies verify the theoretical findings revealed in this work.

Item Type: Article
Additional Information: (c) 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Schools and Departments: School of Engineering and Informatics > Engineering and Design
Depositing User: Yanan Li
Date Deposited: 15 Dec 2017 11:19
Last Modified: 02 Jul 2019 17:04

View download statistics for this item

📧 Request an update