GoRoSo: Feedforward Control Algorithm for Irrigation Canals Based on Sequential Quadratic Programming

Soler, Jordi; Gómez, Manuel; Rodellar, José

doi:10.1061/(asce)ir.1943-4774.0000507

Cited by 18 publications

(11 citation statements)

References 5 publications

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“…As the model tuning is a general problem for all deterministic models [20] and former study has shown that automatic control methods have a good robustness [14,21,22], the model tuning is not As the last pool delivers much more flow to a larger city, the disturbance of the last pool should be less than that of other pools. Thus, the weight coefficient of the last pool should be larger than that of the other pools.…”

Section: Simulation Modelmentioning

confidence: 99%

“…As the model tuning is a general problem for all deterministic models [20] and former study has shown that automatic control methods have a good robustness [14,21,22], the model tuning is not discussed in this paper. The downstream boundary was set as a constant water level boundary, with a water depth of 3 m. The upstream boundary was similarly set as a constant water level boundary, with a water depth of 7 m. The basic parameters and initial flow condition of each of the pools are shown in Table 1.…”

Section: Simulation Modelmentioning

confidence: 99%

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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: Simulation Modelmentioning

confidence: 99%

“…As the model tuning is a general problem for all deterministic models [20] and former study has shown that automatic control methods have a good robustness [14,21,22], the model tuning is not discussed in this paper. The downstream boundary was set as a constant water level boundary, with a water depth of 3 m. The upstream boundary was similarly set as a constant water level boundary, with a water depth of 7 m. The basic parameters and initial flow condition of each of the pools are shown in Table 1.…”

Section: Simulation Modelmentioning

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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show abstract

“…So these disturbances are known in advance, and these could be mitigated by an open loop controller (feedforward controller) as GoRoSo (Soler et al 2013), Bautista et al (2003), Wahlin et al (2006),...…”

Section: Models For Instance Mcg (General Circulation Model) or Mcr mentioning

confidence: 99%

GoRoSoBo: an overall control diagram to improve the efficiency of water transport systems in real time

Bonet

Gómez²,

Yubero³

et al. 2017

Journal of Hydroinformatics

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Abstract:The agriculture holds an important part of the food chain and the water resources for agriculture are essential. The problem is the water transport systems present low efficiencies in practice. The yield agriculture has to be optimized, because the goal of an operational water manager is to deliver the water to the irrigation sites accurately and efficiently. In order to fulfill this objective, we propose a centralized overall control diagram to optimize the management of the canal. Our control diagram in real-time is mainly composed by two algorithms, CSE and GoRoSoBo. The first one is a powerful tool in canal management, so that it is able to estimate the real extracted flow in the canal and the hydrodynamic canal state from measured level data at selected points. The second one is an essential tool in the management of a canal, a feedback control algorithm operating in real-time. The GoRoSoBo algorithm (Gómez, Rodellar, Soler, Bonet) is able to calculate the optimum gates trajectories for a predictive horizon taking into account the current canal state and the real extracted flow both obtained by CSE. Powered by Editorial Manager® and ProduXion Manager® from Aries Systems CorporationTitle: GOROSOBO: An overall control diagram to improve the efficiency of water transport systems in real time Manuscript Click here to download Manuscript PaperGRSB_17_10_2016. doc 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 ABSTRACTThe agriculture holds an important part of the food chain and the water resources for agriculture are essential. The problem is the water transport systems present low efficiencies in practice. The yield agriculture has to be optimized, because the goal of an operational water manager is to deliver the water to the irrigation sites accurately and efficiently. In order to fulfill this objective, we propose a centralized overall control diagram to optimize the management of the canal. Our control diagram in real-time is mainly composed by two algorithms, CSE and GoRoSoBo. The first one is a powerful tool in canal management, so that it is able to estimate the real extracted flow in the canal and the hydrodynamic canal state from measured level data at selected points. The second one is an essential tool in the management of a canal, a feedback control algorithm operating in real-time. The GoRoSoBo algorithm (Gómez, Rodellar, Soler, Bonet) is able to calculate the optimum gates trajectories for a predictive horizon taking into account the current canal state and the real extracted flow both obtained by CSE.

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“…Combined with the actual project, Wei simulated the influence of different feedforward control time on the water level fluctuation at the intake, and proposed a feedforward control time calculation method to effectively reduce the water level fluctuation at the intake [6]. In order to solve the nonlinear optimization problem with constraints on the gate movements in feedforward control, the sequential quadratic problem (SQP) method is used [7]. In order to shorten the time necessary to stabilize the new flow rate at the buffer reservoir in a traditional automated upstream controlled canal, the method is proposed which requires calculated, remote manual adjustments to all the canal check structure gate positions in addition to two flow rate changes made at the head of the canal, followed by a return to automated upstream control [8].…”

Section: Introductionmentioning

confidence: 99%

Exploring Explicit Delay Time for Volume Compensation in Feedforward Control of Canal Systems

Liao

Guan

Tian

2019

Water

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In the open channel control algorithm, good feed-forward controllers will reduce the transition time of the canal and improve performance. Feedforward control algorithms based on active storage compensation are greatly affected by delay time. However, there is no literature comparing the three most commonly used algorithms, namely volume step compensation, dynamic wave principle and water balance models, under the operation mode of constant water level downstream. In order to compare the existing three algorithms, and to avoid storage calculation by calculating the constant non-uniform water surface line or identification of relevant parameters, combined with the open channel constant gradient flow theory with the storage compensation algorithm, a delay time explicit algorithm is proposed in this study. Tested on the first canal pool of the American Society of Civil Engineers (ASCE) Test Canal 2, the performance of the delay time explicit algorithm is assessed and compared to that of the three conventional algorithms. In the current water intake plan, i.e., in the second hour, the intake begins to take 1.2 m3/s, and the upstream flow of the canal pool changes from 6 m3/s to 7.2 m3/s, among the three existing algorithms, the volume step compensation algorithm has better performance in terms of time to achieve stability, i.e., 1.25 h. The actual adjusted storage accounts for 99.6% of the target adjusted storage, which can basically meet the requirement of compensated storage of the canal pool. The delay time explicit algorithm only needs 1.47 h to stabilize the regulation system. The fluctuation of water level and discharge in the regulation process is small. The actual adjusted storage accounts for 99.6% of the target adjusted storage, which can basically meet the requirement of compensated storage for the canal pool. The delay time calculated by explicit algorithm can provide references for the determination of delay time in feedforward control.

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GoRoSo: Feedforward Control Algorithm for Irrigation Canals Based on Sequential Quadratic Programming

Cited by 18 publications

References 5 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

GoRoSoBo: an overall control diagram to improve the efficiency of water transport systems in real time

Exploring Explicit Delay Time for Volume Compensation in Feedforward Control of Canal Systems

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