U-model-based adaptive control for a class of stochastic non-linear dynamic plants with unknown parameters

Wu, Xueli; Liu, Lei; Zhu, Quanmin; Du, Wenxia; Wang, Bin; Zhang, Jianhua

Abstract

In this paper, an adaptive control algorithm within a U-model framework is developed for controlling a class of stochastic non-linear discrete-time models with unknown parameters. With the authors' previous justification, the control-oriented model not only represents a wide range of smooth (polynomial) non-linear dynamic plants (without using linearisation approximation at all), but also makes almost all linear control system design techniques directly applicable to non-linear dynamic plants (with a root solver bridging the linear design and calculation of controller output). A new recursive least squares algorithm is derived and its convergence is proved for the online estimation of time-varying parameters. For initial bench test, a pole placement controller for non-linear stochastic polynomial models is designed using the corresponding linear design technique. Accordingly a step by step procedure is listed to implement the adaptive control operation. A number of simulated case studies are conducted to illustrate the efficiency of the claimed insight and design procedure. Copyright © 2011 Inderscience Enterprises Ltd.

• Journal Article Type: Article
• Publication Date: Jul 1, 2011
• Deposit Date: Feb 4, 2013
• Journal: International Journal of Modelling, Identification and Control
• Print ISSN: 1746-6172
• Electronic ISSN: 1746-6180
• Publisher: Inderscience
• Peer Reviewed: Peer Reviewed
• Volume: 13
• Issue: 3
• Pages: 135-143
• DOI: https://doi.org/10.1504/IJMIC.2011.041300
• Keywords: U-model, online estimation, nonlinear systems, stochastic dynamic systems, pole placement controller, time varying parameters, adaptive control, unknown parameters, discrete-time models, recursive least squares
• Public URL: https://uwe-repository.worktribe.com/output/960880
• Publisher URL: http://dx.doi.org/10.1504/IJMIC.2011.041300
• Contract Date: May 28, 2016