Journal of Zhejiang University-SCIENCE A

, Volume 12, Issue 3, pp 190–200 | Cite as

Multi-loop adaptive internal model control based on a dynamic partial least squares model

Article

Abstract

A multi-loop adaptive internal model control (IMC) strategy based on a dynamic partial least squares (PLS) framework is proposed to account for plant model errors caused by slow aging, drift in operational conditions, or environmental changes. Since PLS decomposition structure enables multi-loop controller design within latent spaces, a multivariable adaptive control scheme can be converted easily into several independent univariable control loops in the PLS space. In each latent subspace, once the model error exceeds a specific threshold, online adaptation rules are implemented separately to correct the plant model mismatch via a recursive least squares (RLS) algorithm. Because the IMC extracts the inverse of the minimum part of the internal model as its structure, the IMC controller is self-tuned by explicitly updating the parameters, which are parts of the internal model. Both parameter convergence and system stability are briefly analyzed, and proved to be effective. Finally, the proposed control scheme is tested and evaluated using a widely-used benchmark of a multi-input multi-output (MIMO) system with pure delay.

Key words

Partial least squares (PLS) Adaptive internal model control (IMC) Recursive least squares (RLS) 

CLC number

TP273 

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Copyright information

© Zhejiang University and Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.State Key Lab of Industrial Control Technology, Department of Control Science & EngineeringZhejiang UniversityHangzhouChina

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