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Research Papers

A New Approach to Adaptive Control of Multi-Input Multi-Output Systems Using Multiple Models

[+] Author and Article Information
Narjes Ahmadian

Faculty of Computer and Electrical Engineering,
Babol Noushirvani University of Technology,
Babol 71167-47148, Iran

Alireza Khosravi

Faculty of Computer and Electrical Engineering,
Babol Noushirvani University of Technology,
Babol 71167-47148, Iran
e-mail: akhosravi@nit.ac.ir

Pouria Sarhadi

Department of Control Engineering,
Islamic Azad University,
South Tehran Branch,
Tehran 4435/11365, Iran

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received September 21, 2014; final manuscript received April 29, 2015; published online June 24, 2015. Assoc. Editor: Sergey Nersesov.

J. Dyn. Sys., Meas., Control 137(9), 091009 (Sep 01, 2015) (10 pages) Paper No: DS-14-1386; doi: 10.1115/1.4030611 History: Received September 21, 2014; Revised April 29, 2015; Online June 24, 2015

This paper proposes a novel method as second level adaptation using multiple models to identify and control of a class of multi-input multi-output (MIMO) systems. Different uncertain environments change the system parameters and create multiple operating conditions. These conditions are designed as multiple identification models in a model bank using adaptive laws. These models are evaluated using some estimated weighting factors based on the errors between each of the models and the actual plant. The evaluated models are effectively used in identification and control process. Bounded signals, proper closed-loop tracking performance, and rapid and accurate parameter convergence to their real values are achieved through simulation results.

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Figures

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Fig. 1

Second level adaptation using multiple models

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Fig. 2

Closed-loop system tracking performance using second level adaptation for nominal case

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Fig. 3

Control signals using second level adaptation for nominal case

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Fig. 4

Tracking errors using second level adaptation for nominal case

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Fig. 5

Closed-loop system tracking performance using second level adaptation in the presence of the 50% decrease in system states

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Fig. 6

Control signals using second level adaptation in the presence of the 50% decrease in system states

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Fig. 7

Tracking errors using second level adaptation in the presence of the 50% decrease in system states

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Fig. 8

Closed-loop system tracking performance using second level adaptation in the presence of the 50% increase in system states

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Fig. 9

Control signals using second level adaptation in the presence of the 50% increase in system states

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Fig. 10

Tracking errors using second level adaptation in the presence of the 50% increase in system states

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Fig. 11

Parameter estimation using second level adaptation

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