A two-layer control architecture for operational management and hydroelectricity production maximization in inland waterways using model predictive control

Pour, Fatemeh Karimi; Segovia, P.; Duviella, E.; Puig, Vicenç

doi:10.1016/j.conengprac.2022.105172

Cited by 3 publications

(3 citation statements)

References 54 publications

(65 reference statements)

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“…The cost function weights in Equation ( 7) are defined in a similar manner for both low tide and high tide modes, as detailed below. Depending on the priority of each objective, these weights in MPC can be customized to assign greater importance to specific objectives (Karimi Pour et al, 2022). They are carefully adjusted through an iterative tuning process, as described in Section 3.…”

Section: Simulation and Resultsmentioning

confidence: 99%

“…This can be highly restrictive, especially considering the timing constraints, even if the time required for each internal run of the DT is on the order of tens of seconds. Segovia et al (2020) and Karimi Pour et al (2022) proposed similar control approaches wherein the main focus was to regulate the water levels and schedule the actuators. Although these works applied a different control strategy than Segovia et al (2019) by employing a two-layer controller, one consisting of an MPC and the other for pump scheduling, these papers did not perform simulations on a real database of disturbances included in the canal.…”

Section: Introductionmentioning

confidence: 99%

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Digital Twin of Calais Canal with Model Predictive Controller: A Simulation on a Real Database

Ranjbar,

Segovia,

Duviella

et al. 2024

J. Water Resour. Plann. Manage.

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This paper presents the design of a Model Predictive Control (MPC) for the Calais canal, located in the north of France for satisfactory management of the system. To estimate the unknown inputs/outputs arising from the uncontrolled pumps, a Digital Twin (DT) in the framework of a Matlab-SIC 2 is used to reproduce the dynamics of the canal, and the real database corresponding to a period of three days is employed to evaluate the control strategy. The canal is characterized by two operating modes due to high and low tides. As a consequence of this, time-varying constraints on the use of gates must be considered, which leads to the design of two multi-objective control problems, one for the high tide and another for the low tide. Furthermore, a moving horizon estimation (MHE) strategy is used to provide the MPC with unmeasured states. The simulation results show that the different objectives are met satisfactorily.

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Section: Simulation and Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Digital Twin of Calais Canal with Model Predictive Controller: A Simulation on a Real Database

Ranjbar,

Segovia,

Duviella

et al. 2024

J. Water Resour. Plann. Manage.

Self Cite

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“…For new semi-active hydro-pneumatic inerterbased suspension systems, a two-stage hierarchical control scheme based on MPC was proposed in [25] to achieve optimal dynamic performance. Moreover, MPC found applications across various industries [26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Nonlinear Model Predictive Motion Control of a Heavy-duty Bionic Caterpillar-like Robot

Li,

Lu,

Cheng

et al. 2024

J Bionic Eng

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This paper investigates the motion control of the heavy-duty Bionic Caterpillar-like Robot (BCR) for the maintenance of the China Fusion Engineering Test Reactor (CFETR). Initially, a comprehensive nonlinear mathematical model for the BCR system is formulated using a physics-based approach. The nonlinear components of the model are compensated through nonlinear feedback linearization. Subsequently, a fuzzy-based regulator is employed to enhance the receding horizon optimization process for achieving optimal results. A Deep Neural Network (DNN) is trained to address disturbances. Consequently, a novel hybrid controller incorporating Nonlinear Model Predictive Control (NMPC), the Fuzzy Regulator (FR), and Deep Neural Network Feedforward (DNNF), named NMPC-FRDNNF is developed. Finally, the efficacy of the control system is validated through simulations and experiments. The results indicate that the Root Mean Square Error (RMSE) of the controller with FR and DNNF decreases by 33.2 and 48.9%, respectively, compared to the controller without these enhancements. This research provides a theoretical foundation and practical insights for ensuring the future highly stable, safe, and efficient maintenance of blankets.

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A Multilayer Control Strategy for the Calais Canal

Segovia,

Puig,

Duviella

2024

IEEE Trans. Contr. Syst. Technol.

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A two-layer control architecture for operational management and hydroelectricity production maximization in inland waterways using model predictive control

Cited by 3 publications

References 54 publications

Digital Twin of Calais Canal with Model Predictive Controller: A Simulation on a Real Database

Digital Twin of Calais Canal with Model Predictive Controller: A Simulation on a Real Database

Hybrid Nonlinear Model Predictive Motion Control of a Heavy-duty Bionic Caterpillar-like Robot

A Multilayer Control Strategy for the Calais Canal

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