Online real-time scheduled model predictive feedforward control for impounded river reaches applied to the Moselle river

Amann, Kai-Uwe; Arnold, Eckhard; Sawodny, Oliver

doi:10.1109/coase.2016.7743554

Cited by 4 publications

(7 citation statements)

References 9 publications

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“…The concept allows fast reference tracking, is insensitive to the cyclic disturbances of the engine torque and is capable of taking advantage of the full input range without exceeding their limits. The method was introduced in [14] and utilized in simulation studies for example to regulate the water level of impounded river reaches [15]. In [16], it was used without any additional feedback controller to control the outlet water temperature of a solar collector plant.…”

Section: Introductionmentioning

confidence: 99%

Model Predictive Feedforward Control for High-Dynamic Speed Control of Combustion Engine Test Beds

Erdogan

Jakubek

Mayr

et al. 2021

IEEE Open J. Ind. Applicat.

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Hardware-in-the-Loop tests have become a key factor in decreasing development time of various drive systems. In this context, high performance test beds, where feedback control plays a major role, are necessary to meet the demanding requirements such as real driving emissions in the automotive industry. Particularly for combustion engine test beds, periodic combustion strokes cause large oscillations in different measurement signals on engine test beds, which causes severe problems since conventional controllers try to compensate for these periodic disturbances. The control settings are a trade-off between fast reference tracking and undesirably strong disturbance rejection, which may compromise test results. An ordinary two-degree-of-freedom (2-DOF) control with flatness-based feedforward control produces relief in separating reference and disturbance behavior but cannot handle input constraints. Thus, a model predictive feedforward controller (MPFFC) in combination with the 2-DOF strategy is introduced for high-dynamic speed control in this contribution. The MPFFC generates optimal input and output trajectories, which respect constraints of the system. Fast reference tracking is achieved without the risk of falsification by high feedback. Comparisons between the proposed method, a simple feedback and a standard 2-DOF controller with a conventional flatness-based feedforward control are performed on a state-of-the-art 4-cylinder engine test bed. The experiments constitute a rare implementation of a MPFFC on a real system and demonstrate its capabilities of separating reference and disturbance response as well as taking advantage of certain situations to even further improve reference tracking speed. Index Terms-Engine test beds, hardware-in-the-loop, model predictive control, speed control, two-degree-of-freedom control.

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Section: Introductionmentioning

confidence: 99%

Model Predictive Feedforward Control for High-Dynamic Speed Control of Combustion Engine Test Beds

Erdogan

Jakubek

Mayr

et al. 2021

IEEE Open J. Ind. Applicat.

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“…Using simulations based on 1D Saint-Venant models, the authors showed in [8] that the developed MPFFC strategy is suitable for water level and discharge control in a chain of barrages. In [6] it was shown that the use of multiple linearized models is necessary to cover the full discharge range. A prototype implementation of the developed control system was deployed on the control hardware on site.…”

Section: Discussionmentioning

confidence: 99%

“…In a joint research project with the University of Stuttgart, a model predictive feed forward control (MPFFC) strategy is currently being developed and deployed to achieve these goals [6]. The chosen approach is based on a control scheme presented in [7], where a full model predictive controller based on linearized 1D Saint-Venant equations is proposed.…”

Section: Figure 1 Locations Of the 25 Barrages At Moselle Rivermentioning

confidence: 99%

“…Using a linearized Saint-Venant model, an optimal discharge trajectory is found, which compensates the predicted inflow and fulfills an optimized desired water level trajectory. To cover the full discharge range of the Moselle River, the used model is switched at runtime, where each model has a different nominal discharge [6]. To allow for a smooth transition between two models, the results of the models are interpolated in an overlap region.…”

Section: Model Predictive Feed Forward Controlmentioning

confidence: 99%

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On Migrating to Advanced Model Predictive Control Strategies at the Moselle River

Simons¹,

Kasper²,

Amann³

et al.

EPiC Series in Engineering

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Along the German and Luxembourgian part of the Moselle River, eleven distributed local water level and discharge controllers ensure safe navigation by guaranteeing a water level within a specified tolerance and by reducing variations in the river discharge. The current control scheme is based on gain scheduled PI control with a feed forward disturbance compensation element. Both were parametrized using a 1D Saint-Venant model. Due to advancements in control strategies and processing power, the current scheme will be upgraded by adding a model predictive feed forward component (MPFFC) which improves local control and links the isolated local controllers to coordinate their efforts. The authors want to report on this process from an operator's point of view and share their insights from the ongoing testing procedure prior to actual service. A prototype implementation was deployed on the target hardware and linked to the data acquisition system to verify real time operation. The logged results are then verified using a simulation model.

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“…In this way, errors in discharge predictions are compensated. As shown in [10], the MPFFC avoids violations of the water level deviation limits and considerably reduces discharge peaks.…”

Section: Figure 3: Prediction Horizon T P and Control Horizon T C Of mentioning

confidence: 98%

Enhancing Automated Water Level Control at Navigable Waterways by High-Resolution Weather Predictions

Kasper¹,

Pranner²,

Simons³

et al.

EPiC Series in Engineering

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Heavy rainfall can cause large variations in the water level of navigable waterways when a lot of urban runoff is generated on sealed surfaces and discharged into the river. Due to climate change, extreme weather events will increase in intensity and frequency demanding a better automated water level control at impounded waterways. Highresolution forecasts of catchment rainfall are intended to serve as input to a rainfallrunoff model. Based on the resulting discharge forecasts, a model predictive feed forward controller calculates the ideal water level and discharge across the barrage. The control system is completed by a PI control loop. In this way water level deviations and discharge peaks resulting from stormwater overflow events can be reduced, which enhances the safety of shipping. Regarding the uncertainties of weather predictions, the consequences of an underestimated or overestimated overflow discharge are investigated.Engineering

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Online real-time scheduled model predictive feedforward control for impounded river reaches applied to the Moselle river

Cited by 4 publications

References 9 publications

Model Predictive Feedforward Control for High-Dynamic Speed Control of Combustion Engine Test Beds

Model Predictive Feedforward Control for High-Dynamic Speed Control of Combustion Engine Test Beds

On Migrating to Advanced Model Predictive Control Strategies at the Moselle River

Enhancing Automated Water Level Control at Navigable Waterways by High-Resolution Weather Predictions

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