Plant Friendly Input Design: Convex Relaxation and Quality

Narasimhan, Sridharakumar; Rengaswamy, Raghunathan

doi:10.1109/tac.2011.2132290

Cited by 16 publications

(16 citation statements)

References 13 publications

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“…The plant-friendly experiment design is comparable to the application-oriented input design technique. The objective of this experiment is to find a trade-off between minimal departure from real working conditions and the precision of the model parameters to be identified [ 10 , 11 ]. For this purpose, the concept of the performance degradation minimization instead of the variance minimization should be considered.…”

Section: Introductionmentioning

confidence: 99%

Robustness Analysis of the Estimators for the Nonlinear System Identification

Jakowluk

Godlewski

2020

Entropy

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The main objective of the system identification is to deliver maximum information about the system dynamics, while still ensuring an acceptable cost of the identification experiment. The focus of such an idea is to design an appropriate experiment so that the departure from normal working conditions during the identification task is minimized. In this paper, the adaptive filtering (AF) scheme for plant model parameter estimation is proposed. The experimental results are obtained using the nonlinear interacting water tanks system. The adaptive filtering is expressed by minimizing the error between the system and the plant model outputs subject to the white noise signal affecting system output. This measurement error is quantified by the comparison of Minimum Error Entropy Renyi (MEER), Least Entropy Like (LEL), Least Squares (LS), and Least Absolute Deviation (LAD) estimators, respectively. The plant model parameters were obtained using sequential quadratic programming (SQP) algorithm. The robustness tests for the double-tank water system parameter estimators are performed using the ellipsoidal confidence regions. The effectiveness analysis for the above-mentioned estimators relies on the total number of iterations and the number of function evaluation comparisons. The main contribution of this paper is the evaluation of different estimation methods for the nonlinear system identification using various excitation signals. The proposed empirical study is illustrated by the numerical examples, and the simulation results are discussed.

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Section: Introductionmentioning

confidence: 99%

Robustness Analysis of the Estimators for the Nonlinear System Identification

Jakowluk

Godlewski

2020

Entropy

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“…The plant-friendly input design is classified as the application-oriented methodology. The aim of such an identification experiment is to find a trade-off between the minimal disruption to the normal operation of the system, and the most precise identification experiment [10,11]. There have been some reports that plant friendliness constraints often disturb a precise model parameters estimation while a set of harmonically related sinusoids with high peak-to-peak values can destroy an identified model [3,12].…”

Section: Introductionmentioning

confidence: 99%

Free Final Time Input Design Problem for Robust Entropy-Like System Parameter Estimation

Jakowluk

2018

Entropy

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In this paper, a novel method is proposed to design a free final time input signal, which is then used in the robust system identification process. The solution of the constrained optimal input design problem is based on the minimization of an extra state variable representing the free final time scaling factor, formulated in the Bolza functional form, subject to the D-efficiency constraint as well as the input energy constraint. The objective function used for the model of the system identification provides robustness regarding the outlying data and was constructed using the so-called Entropy-like estimator. The perturbation time interval has a significant impact on the cost of the real-life system identification experiment. The contribution of this work is to examine the economic aspects between the imposed constraints on the input signal design, and the experiment duration while undertaking an identification experiment in the real operating conditions. The methodology is applicable to the general class of systems and was supported by numerical examples. Illustrative examples of the Least Squares, and the Entropy-Like estimators for the system parameter data validation where measurements include additive white noise are compared using ellipsoidal confidence regions.

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“…Prior work for data-centric estimation addressed distribution of only the outputs using Weyl’s criterion [12], [13]. Constraints on the input and output are incorporated in this approach to achieve plant-friendly operation [11], [13]–[15]. For the purposes of this paper, the focus is specifically on the requirements for Model-on-Demand by considering linear time-invariant models under amplitude constraints on the input.…”

Section: Introductionmentioning

confidence: 99%

Optimal input signal design for data-centric estimation methods

Deshpande

Rivera

2013

2013 American Control Conference

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Data-centric estimation methods such as Model-on-Demand and Direct Weight Optimization form attractive techniques for estimating unknown functions from noisy data. These methods rely on generating a local function approximation from a database of regressors at the current operating point with the process repeated at each new operating point. This paper examines the design of optimal input signals formulated to produce informative data to be used by local modeling procedures. The proposed method specifically addresses the distribution of the regressor vectors. The design is examined for a linear time-invariant system under amplitude constraints on the input. The resulting optimization problem is solved using semidefinite relaxation methods. Numerical examples show the benefits in comparison to a classical PRBS input design.

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Plant Friendly Input Design: Convex Relaxation and Quality

Cited by 16 publications

References 13 publications

Robustness Analysis of the Estimators for the Nonlinear System Identification

Robustness Analysis of the Estimators for the Nonlinear System Identification

Free Final Time Input Design Problem for Robust Entropy-Like System Parameter Estimation

Optimal input signal design for data-centric estimation methods

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