Optimizing the settling time with iterative feedback tuning

Lequin, Olivier; Gevers, Michel; Triest, Lionel

doi:10.1016/s1474-6670(17)56794-4

Cited by 31 publications

(30 citation statements)

References 2 publications

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“…optimization in Iterative Feedback Tuning is attractive since it provides a systematic method for handling ill conditioned Hessians, which otherwise can lead to large steps in Gauss-Newton optimization that may render the loop unstable. This problem was encountered in [24] for optimization of step responses. The solution chosen by the authors was to gradually truncate the initial part of the time horizon in the calculation of the cost function and thereby changing the curvature of the performance cost with respect to the controller parameters.…”

Section: Control Parameter Updatementioning

confidence: 99%

“…The solution chosen by the authors was to gradually truncate the initial part of the time horizon in the calculation of the cost function and thereby changing the curvature of the performance cost with respect to the controller parameters. The cost function used in [24] was…”

Section: Control Parameter Updatementioning

confidence: 99%

“…This process model was used in [24] The initial controller is chosen identical to the example in [24], which gives a very poorly tuned loop, but helps to illustrate some of the inherent problems in Iterative Feedback Tuning.…”

Section: Simulation Examplesmentioning

confidence: 99%

“…As a solution to this problem, and in order to avoid local minima, [24] uses the cost function (23) Table 1, where the control parameters are presented with the corresponding value of the cost function from the first experiment in the iteration and the corresponding mask width. Table 1 Control parameters and the value of the performance cost function for each iteration with the corresponding mask as suggested by [24].…”

Section: Optimizing Of Settling Timementioning

confidence: 99%

“…Table 1 Control parameters and the value of the performance cost function for each iteration with the corresponding mask as suggested by [24]. The cost function is evaluated based on the first experiment in the iterations.…”

Section: Optimizing Of Settling Timementioning

confidence: 99%

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Improving convergence of Iterative Feedback Tuning

Huusom

Poulsen

Jørgensen

2009

Journal of Process Control

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Iterative Feedback Tuning constitutes an attractive control loop tuning method for processes in the absence of an accurate process model. It is a purely data driven approach aiming at optimizing the closed loop performance. The standard formulation ensures an unbiased estimate of the loop performance cost function gradient with respect to the control parameters. This gradient is important in a search algorithm. The extension presented in this paper further ensures informative data to improve the convergence properties of the method and hence reduce the total number of required plant experiments especially when tuning for disturbance rejection. Informative data is achieved through application of an external probing signal in the tuning algorithm. The probing signal is designed based on a constrained optimization which utilizes an approximate black box model of the process. This model estimate is further used to guarantee nominal stability and to improve the parameter update using a line search algorithm for determining the iteration step size. The proposed algorithm is compared to the classical formulation in a simulation study of a disturbance rejection problem. This type of problem is notoriously difficult for Iterative Feedback Tuning due to the lack of excitation from the reference.

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Section: Control Parameter Updatementioning

confidence: 99%

Section: Control Parameter Updatementioning

confidence: 99%

Section: Simulation Examplesmentioning

confidence: 99%

Section: Optimizing Of Settling Timementioning

confidence: 99%

Section: Optimizing Of Settling Timementioning

confidence: 99%

See 3 more Smart Citations

Improving convergence of Iterative Feedback Tuning

Huusom

Poulsen

Jørgensen

2009

Journal of Process Control

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Iterative feedback tuning—an overview

Hjalmarsson

2002

Adaptive Control & Signal

381

228

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Adaptive and iterative control algorithms based on explicit criterion minimization are briefly reviewed and an overview of one such algorithm, iterative feedback tuning (IFT) , is presented. The basic IFT algorithm is reviewed for both single-input/single-output and multi-input/multi-output systems. Subsequently the application to non-linear systems is discussed. Stability and robustness aspects are covered. A survey of existing extensions, applications and related methods is also provided. ; 16:373-395 H. HJALMARSSON Sensitivity modelsLater it was recognized that for linear time-invariant, processes, the sensitivity of any signal, sðtÞ say, in the system w.r.t. any scalar parameter r in the system, i.e. the partial derivative @sðtÞ=@r;

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On-line Model-Free Methods

Crowe

Johnson

PID Control

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Learning Objectives 3.1 Introduction 3.2 Iterative Feedback Tuning 3.3 The Controller Parameter Cycling Tuning Method 3.4 Summary and Future Directions = ∂ 2 ∂r ∂rAlgorithm 3.1 serves as a fundamental framework for this chapter, since in the sequel two methods are described for obtaining the gradient. The first is that of the Iterative Feedback Tuning technique and the second is the method of controller parameter cycling. Introduction 113

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Optimizing the settling time with iterative feedback tuning

Cited by 31 publications

References 2 publications

Improving convergence of Iterative Feedback Tuning

Improving convergence of Iterative Feedback Tuning

Iterative feedback tuning—an overview

On-line Model-Free Methods

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