Controller Synthesis Free of Analytical Models: Three Term Controllers

Keel, L.H.; Bhattacharyya, S.P.

doi:10.1109/tac.2008.925810

Cited by 140 publications

(108 citation statements)

References 24 publications

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“…The basic idea is to represent the robust performance constraints in (8) in the Nyquist diagram and give a set of linear or convex constraints which guarantee that the robust performance condition is satisfied. This way, the controller design is represented by a convex feasibility problem.…”

Section: Robust Performance Constraintsmentioning

confidence: 99%

“…For unstable and nonminimum-phase systems and systems with parametric and frequency-domain uncertainty, more advanced methods should be used. Recently, it has been shown that the set of all stabilizing PID controllers achieving a desired gain and phase margin or H ∞ norm can be obtained using only the frequency-domain data [8]. Another frequency-domain method is the well-known Quantitative Feedback Theory (QFT) [9] which is based on loop shaping in the Nichols chart.…”

Section: Introductionmentioning

confidence: 99%

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Fixed-order H∞ controller design for nonparametric models by convex optimization

Karimi¹,

Galdos²

2010

Automatica

128

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A new approach for robust fixed-order H∞ controller design by convex optimization is proposed. Linear time-invariant singleinput single-output systems represented by nonparametric models in the frequency domain are considered. It is shown that the H∞ robust performance condition can be represented by a set of linear or convex constraints with respect to the parameters of a linearly parameterized controller in the Nyquist diagram. Multimodel and frequency-domain uncertainty can be considered straightforwardly in the proposed approach. The proposed method is compared with the standard H∞ control problem by a simulation example on an unstable system.

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Section: Robust Performance Constraintsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fixed-order H∞ controller design for nonparametric models by convex optimization

Karimi¹,

Galdos²

2010

Automatica

128

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“…In this case, the linear controller is similar to a lead/lag compensator, and the closed-loop stabilising gains of the controller are determined from the frequency response functions (FRFs) of the open-loop system. Later, the theory for designing PID controllers was presented [12], whose equations for finding the stabilising gains were formally deduced in [13]. By knowing the gains that stabilise the closed-loop system, one can find sets of gains that optimise any performance criteria, thus achieving closed-loop stability and robustness [14,15].…”

Section: Introductionmentioning

confidence: 99%

PD controller synthesis from open-loop response measurements of rotating system

Buttini

Nicoletti

2012

IET Control Theory &Amp; Applications

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Abstract:In this work, a method of computing PD stabilising gains for rotating systems is presented based on the D-decomposition technique, which requires the sole knowledge of frequency response functions. By applying this method to a rotating system with electromagnetic actuators, it is demonstrated that the stability boundary locus in the plane of feedback gains can be easily plotted, and the most suitable gains can be found to minimise the resonant peak of the system. Experimental results for a Laval rotor show the feasibility of not only controlling lateral shaft vibration and assuring stability, but also helps in predicting the final vibration level achieved by the closed-loop system. These results are obtained based solely on the input-output response information of the system as a whole.

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“…Whereas a local modeling is continuously updated via the knowledge of the input-output behavior. In [5,6], it was shown that the complete set of stabilizing PID and first-order controllers for a linear time-invariant (LTI) plant with time delay could be calculated directly from the frequency response data. Nonparametric model based design uses the explicit identification of significant modelpoints but doest not use a parametric model.…”

Section: Introductionmentioning

confidence: 99%

Data Based Lower-Order Controller Design: Moment Matching Approach

Kim¹,

Jin²

2012

The Transactions of The Korean Institute of Electrical Engineer

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-This paper presents a data based low-order controller design algorithm for a linear time-invariant process with a time delay. The algorithm is composed by combining an identification step based on open loop pulse test with a low-order controller design step to obtain the entire set of controllers achieving multiple performance specifications. The initial information necessary for this algorithm are merely the width and amplitude of a rectangular pulse, a controller of four types (PI, PD, PID, first-order), and design objectives. Various parametric approaches that have been developed are merged in the controller design algorithm. The resulting controller set satisfying the design objectives are displayed on the 2D and 3D graphics and thus it is very easy for us to pick a controller inside the admissible set because we can check the corresponding closed-loop performances visually.

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Controller Synthesis Free of Analytical Models: Three Term Controllers

Cited by 140 publications

References 24 publications

Fixed-order H∞ controller design for nonparametric models by convex optimization

Fixed-order H∞ controller design for nonparametric models by convex optimization

PD controller synthesis from open-loop response measurements of rotating system

Data Based Lower-Order Controller Design: Moment Matching Approach

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