Robust system identification of an irrigation main canal

Rivas-Pérez, Raul; Feliu-Batlle, Vicente; Rodríguez, Luis Sánchez

doi:10.1016/j.advwatres.2007.02.002

Cited by 42 publications

(7 citation statements)

References 21 publications

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“…To establish controller design, the ID model of pools should first be determined. The two characteristics of the ID model, delay time, and storage area were calculated by applying the system identification technique [26,27]. The values are shown in Table 2.…”

Section: Water Level Difference Control Strategiesmentioning

confidence: 99%

A Model Predictive Water-Level Difference Control Method for Automatic Control of Irrigation Canals

Kong

Lei

Wang

et al. 2019

Water

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In this paper, automatic control of the water level in an irrigation canal by automatic regulation of intermediate gates was studied. Previous scholars have proposed a water level difference control strategy that works to keep relative deviations in all pools the same for a particular situation where the operator does not have full control over the canal inflow, with the centralized linear quadratic regulator (LQR) control method used. While in practice, the deviation tolerance of pools may differ in some canals which limits the applicability of the control strategy. In this work, a weight coefficient was added to the deviation and the algorithm was improved to keep the relative deviations to certain proportions. The model predictive control (MPC) method was then used with this improved control strategy and was compared to the LQR control method using the same control strategy. The results showed that the improved strategy can keep the water level deviations in all pools to certain proportions, as is our objective. Also, under this difference control strategy, the MPC method greatly improved the control performance compared to the LQR control method.

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Section: Water Level Difference Control Strategiesmentioning

confidence: 99%

A Model Predictive Water-Level Difference Control Method for Automatic Control of Irrigation Canals

Kong

Lei

Wang

et al. 2019

Water

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“…Using a robust recursive parameters estimation and model validation method, a delayed second-order model was developed in [32] to accurately approximate the dynamic behavior of a real main irrigation canal. Such model precisely reproduced the dynamics of the considered irrigation canal even with data that had not been used in the fitting procedure, and in spite of some unmodeled dynamics.…”

Section: Linear Models Of Integer Ordermentioning

confidence: 99%

A Fractional-Order Partially Non-Linear Model of a Laboratory Prototype of Hydraulic Canal System

Gharab

Feliu-Batlle

Rivas-Pérez

2019

Entropy

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This article addresses the identification of the nonlinear dynamics of the main pool of a laboratory hydraulic canal installed in the University of Castilla La Mancha. A new dynamic model has been developed by taking into account the measurement errors caused by the different parts of our experimental setup: (a) the nonlinearity associated to the input signal, which is caused by the movements of the upstream gate, is avoided by using a nonlinear equivalent upstream gate model, (b) the nonlinearity associated to the output signal, caused by the sensor’s resolution, is avoided by using a quantization model in the identification process, and (c) the nonlinear behaviour of the canal, which is related to the working flow regime, is taken into account considering two completely different models in function of the operating regime: the free and the submerged flows. The proposed technique of identification is based on the time-domain data. An input pseudo-random binary signal (PRBS) is designed depending on the parameters of an initially estimated linear model that was obtained by using a fundamental technique of identification. Fractional and integer order plus time delay models are used to approximate the responses of the main pool of the canal in its different flow regimes. An accurate model has been obtained, which is composed of two submodels: a first order plus time delay submodel that accurately describes the dynamics of the free flow and a fractional-order plus time delay submodel that properly describes the dynamics of the submerged flow.

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“…Substituting the particular form of each controller, (8), (9), (10) and (11), in conditions (13), and operating yields the following:…”

Section: Frequency Domain Tuningmentioning

confidence: 99%

“…. Also, second order plus time delay models have been used . All these models have the major drawback that their parameters may experience large changes when the canal pool discharge regime varies.…”

Section: Introductionmentioning

confidence: 99%

Time Domain Tuning of Fractional Order Controllers Combined With a Smith Predictor for Automation of Water Distribution in Irrigation Main Channel Pools

Castillo-García¹,

Feliu-Batlle²,

Rivas-Pérez³

2012

Asian Journal of Control

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This paper proposes a new technique for designing fractional order controllers applied to the automation of main canal pools of a water irrigation system. As large variation of all the plant parameters is present in such systems, a fractional order family of controllers combined with a Smith Predictor is designed using time domain specifications. The designed controllers are compared with the standard combination of a Smith Predictor with a proportional‐integral (PI) controller. All these controllers are tuned to fulfil the same time specifications in the case of canal nominal dynamics. In some canals the plant parameters may experience large changes that deteriorate the time response, and can even unstabilize the closed‐loop system. The fractional controllers are therefore designed to minimize the loss of performance of the system to variations in these parameters. Simulated results show the performance improvements achieved with these controllers compared with a conventional PI controller.

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Robust system identification of an irrigation main canal

Cited by 42 publications

References 21 publications

A Model Predictive Water-Level Difference Control Method for Automatic Control of Irrigation Canals

A Model Predictive Water-Level Difference Control Method for Automatic Control of Irrigation Canals

A Fractional-Order Partially Non-Linear Model of a Laboratory Prototype of Hydraulic Canal System

Time Domain Tuning of Fractional Order Controllers Combined With a Smith Predictor for Automation of Water Distribution in Irrigation Main Channel Pools

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