Closed-loop identification revisited

The links between identification and control are examined. The main trends in this research area are summarized, with particular focus on the design of low complexity controllers from a statistical perspective. It is argued that a guiding principle should be to model as well as possible before any model or controller simplifications are made as this ensures the best statistical accuracy. This does not necessarily mean that a full-order model always is necessary as well designed experiments allow for restricted complexity models to be near-optimal. Experiment design can therefore be seen as the key to successful applications. For this reason, particular attention is given to the interaction between experimental constraints and performance specifications.

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“…An alternative expression which characterizes the bias introduced by an erroneous noise model can be found in Forssell and Ljung (1999).…”

Section: The Bias Errormentioning

confidence: 99%

From experiment design to closed-loop control

2005

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“…In contrast to the standard identification problem, the PEM estimate is not consistent here unless the noise model is identified correctly. Similar to the case of closed-loop identification [9], a tailor-made parameterization can be used to find a consistent estimate. If a tailor-made parametrization is used, where H(θ, ρ) = K(ρ)H(θ), the estimation error is asymptotically…”

Section: Identification Of the Controllermentioning

confidence: 99%

On identification methods for direct data‐driven controller tuning

Heusden¹,

Karimi²,

Söderström³

2010

Adaptive Control & Signal

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SUMMARYIn non-iterative data-driven controller tuning, a set of measured input/output data of the plant is used directly to identify the optimal controller that minimizes some control criterion. This approach allows the design of fixed-order controllers, but leads to an identification problem where the input is affected by noise, and not the output as in standard identification problems. Several solutions that deal with the effect of measurement noise in this specific identification problem have been proposed in literature. The consistency and statistical efficiency of these methods are discussed in this paper and the performance of the different methods is compared. The conclusions offer a guideline on how to solve efficiently the data-driven controller tuning problem .

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“…[1]) typically requires a stabilizing controller. The selection of the controller needs some knowledge on the system prior to the identification experiment.…”

Section: Introductionmentioning

confidence: 99%

Grey-box modeling of an inverted pendulum system via identification of a PD-LTI system

Tanaka

Ohta

2016

Stochastic Systems Theory and its Applications (SSS)

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A rotary-type unstable inverted pendulum system is modeled by means of a parameter-dependent lineartime-invariant (PD-LTI) system. The pendulum systems operating at upright and downright positions are modeled by a PD-LTI system with a scheduling parameter that is fixed in each operation. The unknown parameters are estimated by taking a grey-box modeling approach to the pendulum system operating at the downright position. The obtained model is verified by designing an H ∞ controller.

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Closed-loop identification revisited

Abstract: Technical reports from the Automatic Control group in Linkping are available by anonymous ftp at the address 130.236.20.24 (ftp.control.isy.liu.se). This report is contained in the compressed postscript le 1959.ps.Z. Closed-loop Identi cation Revisited ?

Cited by 630 publications

References 31 publications

From experiment design to closed-loop control

From experiment design to closed-loop control

On identification methods for direct data‐driven controller tuning

Grey-box modeling of an inverted pendulum system via identification of a PD-LTI system

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