Identification of continuous-time models with slowly time-varying parameters

Padilla, Arturo; Garnier, Hugues; Young, Peter C.; Chen, Fengwei; Yuz, Juan I.

doi:10.1016/j.conengprac.2019.104165

Cited by 12 publications

(4 citation statements)

References 25 publications

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“…Finally, in addition to the above focus on control design, various methods exist for the estimation of time-varying parameters and potentially uncertain time delays, including: [49], [50], [51]. For example, Tan [52] uses an ANN to construct a time-delay estimator to track the time-varying delay.…”

Section: Control Of Time-varying Delaysmentioning

confidence: 99%

Unknown and Time-Varying Time Delays in the Modelling and Control of Hydraulic Actuators: Literature Review

Albrecht

Taylor

2020

2020 Australian and New Zealand Control Conference (ANZCC)

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Uncertain time-delays can reduce the performance of hydraulic manipulator control systems. Variations in the time delay between, for example, an applied voltage and the associated manipulator movement, may be caused by the internal dynamics of the system and other nonlinear characteristics, such as fluid compressibility, dead-band of the pump, valve flow properties and friction characteristics. Robot control that addresses system time-delays, nonlinearities and uncertainty is the subject of much research but, whilst the specific concept of varying time delays in hydraulic systems is sometimes acknowledged, it appears to be less widely investigated than other types of uncertainty. The present article discuses some of the issues involved, exemplified by a dual manipulator device used in the laboratory for research into nuclear decommissioning, and presents a review of the relevant control literature in this area.Index Terms-hydraulic manipulators, uncertain time delays, time varying delays, robot control systems, literature review

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Section: Control Of Time-varying Delaysmentioning

confidence: 99%

Unknown and Time-Varying Time Delays in the Modelling and Control of Hydraulic Actuators: Literature Review

Albrecht

Taylor

2020

2020 Australian and New Zealand Control Conference (ANZCC)

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“…Step 1: Extract the set V F in (17) of steady-state data segments, the set W B in (18) of transient-state data segments, and the set D in (19) of special data segments from historical data samples.…”

Section: Steps Of the Proposed Approachmentioning

confidence: 99%

Identification of Multiple First-Order Continuous-Time Dynamic Models From Special Segments in Historical Data

et al. 2020

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Continuous-time dynamic models are often required for controller design, process monitoring, and operation optimization. This paper proposes an approach to estimate unknown parameters of multiple first-order continuous-time dynamic models from special segments in historical data. The approach is composed by two main steps. First, special segments are defined as the ones with two ends in steady states and the middle part having significant amplitude variations in transient states; the special segments are found automatically by exploiting a piece-wise linear representation technique from a large amount of historical data samples. Second, static gains are estimated by solving a set of linear equations based on steady-state values of inputs and outputs; sums of time constants and delays are obtained by solving another set of linear equations based on integrals of model output errors from data samples in transient states; the sums are used as an optimization constraint for maximizing the fitness value between measured and simulated outputs, from which separated estimates of time constants and delays are yielded. Numerical examples are provided to illustrate the proposed approach and compare with existing ones.INDEX TERMS Continuous-time model identification, steady states, transient states, model validation.

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“…The identification of LTV systems is typically done using recursive esti-mation method, where the forgetting factor or Kalman filter methods are considered to track the time-varying parameters [6]. Different studies have considered the estimation of LTV-OE models, that can be in DT [7] or continuous time (CT) [8].…”

Section: Introductionmentioning

confidence: 99%

Identification of Continuous-Time Linear Parameter Varying Systems With Noisy Scheduling Variable Using Local Regression

Padilla,

Garnier,

Yuz

et al. 2024

IEEE Access

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Some nonlinear systems can be represented through linear parameter varying models. In this work, we address the estimation of continuous-time linear parameter varying models in output error form, using a refined instrumental variable method. A distinguished feature of a linear parameter varying model is that it has parameters that depend on an external signal called the scheduling variable. In this paper, we assume that the scheduling variable is noisy, a condition which is often met in practice, but not frequently considered in the literature. On the other hand, there are applications in which the noise-free version of the scheduling variable is smooth. Under such scenario we can simply filter the scheduling variable before estimating the linear parameter model. Nonetheless, there are cases where special smoothing techniques are required. In this study, we consider one of these special cases, and we use the well-known local regression method as smoothing technique. A numerical example based on a Monte Carlo simulation shows the benefits of the proposed approach.INDEX TERMS Continuous-time system identification, instrumental variable method, linear parameter varying model, local regression, smoothing method.

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Identification of continuous-time models with slowly time-varying parameters

Cited by 12 publications

References 25 publications

Unknown and Time-Varying Time Delays in the Modelling and Control of Hydraulic Actuators: Literature Review

Unknown and Time-Varying Time Delays in the Modelling and Control of Hydraulic Actuators: Literature Review

Identification of Multiple First-Order Continuous-Time Dynamic Models From Special Segments in Historical Data

Identification of Continuous-Time Linear Parameter Varying Systems With Noisy Scheduling Variable Using Local Regression

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