Model reference robust control for MIMO systems

Ambrose, Hollis; Qu, Zhipeng

doi:10.1080/002071797223541

Cited by 19 publications

(6 citation statements)

References 8 publications

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“…Model reference robust control (MRRC) was introduced by Qu et al [1] and Qu and Dawson [2] as a new means of I/O-based controller design for linear time invariant plants with input disturbance and has been extended to MIMO, nonminimum phase plants and the case in the presence of unmodeled dynamics [3][4][5]. For plants with relative degree greater than one, the method can be considered as an extension of backstepping design [6], where the final control signal is obtained through a series of fictitious control signals designed recursively.…”

Section: Introductionmentioning

confidence: 99%

A model reference robust control with unknown high‐frequency gain sign

Lin

Cheng

2010

Intl J Robust & Nonlinear

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SUMMARYIn this paper, we discuss the model reference robust control (MRRC) for plants with unknown high-frequency gain sign. Based on an appropriate monitoring function, a switching scheme is proposed so that after a finite number of switching, for plants with relative degree one, the tracking error converges to zero exponentially, while for plants with relative degree greater than one, it converges exponentially to a residual set that can be made arbitrarily small by properly choosing some design parameters.

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Section: Introductionmentioning

confidence: 99%

A model reference robust control with unknown high‐frequency gain sign

Lin

Cheng

2010

Intl J Robust & Nonlinear

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“…For systems with slow‐varying parameters, several techniques, such as the family of Kalman filters, recursive (sequential) forms, probabilistic frameworks, adaptive filters, soft computing methods, and artificial intelligent algorithms, can be implemented successfully in real applications. Owing to the structure of the aforementioned identification techniques, if the parameters of a system vary faster than a corresponding endurance limit, the tracking of parameters fails completely, which eventually destroys the precision of the designed control system .…”

Section: Introductionmentioning

confidence: 99%

Short‐Time Linear Quadratic Form Technique for Estimating Fast‐Varying Parameters in Feedback Loops

Homaeinezhad¹,

Moghaddam²,

Khakpour³

et al. 2015

Asian Journal of Control

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The precision of a closed‐loop controller system designed for an uncertain plant depends strongly upon the maximum extent to which it is possible to track the trend of time‐varying parameters of the plant. The aim of this study is to describe a new parameter estimation algorithm that is able to follow fast‐varying parameters in closed‐loop systems. The short‐time linear quadratic form (STLQF) estimation algorithm introduced in this paper is a technique for tracking time‐varying parameters based on short‐time analysis of the regressing variables in order to minimize locally a linear quadratic form cost function. The established cost function produces a linear combination of errors with several delays. To meet this objective, mathematical development of the STLQF estimation algorithm is described. To implement the STLQF algorithm, the algorithm is applied to a planar mobile robot with fast‐varying parameters of inertia and viscous and coulomb frictions. Next, performance of the proposed algorithm is assessed against noise effects and variation in the type of parameters.

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“…In [4] the model reference robust control of single-input single-output systems is introduced as a new tools of designing robust control. The design of this algorithm is an extension of the recursive backstepping design keeping in mind that a nonlinear dynamic control (not static) is generated recursively.…”

Section: Introductionmentioning

confidence: 99%