首页|Adaptive predictive control of periodic non-linear auto-regressive moving average systems using nearest-neighbour compensation

Adaptive predictive control of periodic non-linear auto-regressive moving average systems using nearest-neighbour compensation

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  • NETL
  • NSTL
  • Inst Engineering Technology-Iet
Many practical non-linear systems can be described by non-linear auto-regressive moving average (NARMA) system models, whose stabilisation problem is challenging in the presence of large parametric uncertainties and non-parametric uncertainties. In this work, to address this challenging problem for a wide class of discrete-time NARMA systems, in which there are uncertain periodic parameters as well as uncertain non-linear part with unknown periodic time delays, we develop adaptive predictive control laws using the key ideas of `future outputs prediction' and `nearest-neighbour compensation', among which the former is carried out to overcome the non-causalness problem and the latter novel idea is proposed to completely compensate for the effect of non-linear uncertainties as well as unknown time delays. To achieve the desired asymptotic tracking performance in the presence of semi-parametric uncertainties with time delays, an `n-step parameter update law' is first designed, based on which an `one-step update law' is then elaborately constructed to obtain smoother closed-loop signals. This study in general develops a systematic adaptive control framework for periodic NARMA systems with guaranteed boundedness stability and asymptotic tracking performance, which are established by rigorous theoretic proof and verified by simulation studies.

uncertain systemsadaptive controlasymptotic stabilityautoregressive moving average processesclosed loop systemscompensationcontrol system synthesisdelaysdiscrete time systemsnonlinear control systemsperiodic controlpredictive controltime-varying systems

Chenguang Yang、Hongbin Ma、Mengyin Fu

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School of Computing and Mathematics, Plymouth University

Key Laboratory of Intelligent Control and Decision of Complex Systems, Beijing Institute of Technology

2013

10.1049/iet-cta.2012.0809

IET control theory & applications

IET control theory & applications

ISSN：1751-8644
年,卷(期)：2013.7(7)
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