A Constrained MPC Approach Applied to Buck DC-DC Converter for Greenhouse Powered by Photovoltaic Source

Boutchich, Noureddine; Moufid, Ayoub; Bennis, Najib; Hani, Soumia El

doi:10.1109/iceit48248.2020.9113197

Cited by 3 publications

(2 citation statements)

References 12 publications

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“…To design an appropriate control law within constraints limits, we use 𝛥𝑢(𝑘) = 𝑢(𝑘) − 𝑢(𝑘 − 1) in (6) to design a new state-space representation (8). In the prediction horizon the output prediction is represented in matrix form ( 9 .…”

Section: Constrained Mpc Design 31 Objectives and Motivationmentioning

confidence: 99%

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A constrained model predictive control for the building thermal management with optimal setting design

Boutchich

Moufid

Bennis

2023

IJECE

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<span lang="EN-US">Today, the building sector is the most important consumer of energy. The main challenge in building management is to obtain the desired performance taking into account many aspects such as comfort requirements, variation of building physical characteristics, system constraints, and energy management. For this purpose, a predictive control approach applied to the building thermal has been designed to achieve desired performances combined with an energy optimization approach based on intrinsic system parameters. The developed approach is applied with an online identification system for effective predictive control to take into account the reel building characteristics and to choose the optimal tuning parameters. The simulation results show good performances in terms of accuracy and robustness face to internal and external disturbances with respect to system constraints.</span>

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Section: Constrained Mpc Design 31 Objectives and Motivationmentioning

confidence: 99%

“…A constrained model predictive control for the building thermal … (Noureddine Boutchich) 135 problems. It has been applied in several fields of biology, electrical equipment control [8], mecanique control [9], magnetic system [10], agriculture [11], [12], thermal control [13], [14].…”

Section: Introductionmentioning

confidence: 99%

A constrained model predictive control for the building thermal management with optimal setting design

Boutchich

Moufid

Bennis

2023

IJECE

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Proximal policy optimization-based reinforcement learning approach for DC-DC boost converter control: A comparative evaluation against traditional control techniques

Saha,

Jawad,

Shahria

et al. 2024

Heliyon

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Data-Driven Model Predictive Control of DC-to-DC Buck-Boost Converter

2021

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The data-driven model predictive control (DDMPC) scheme is proposed to obtain fast convergence to a desired reference and to be utilised to mitigate the destabilising effects that a DC-to-DC buck-boost converter (BBC) with an active load experiences. The DDMPC strategy uses the observed state to derive an optimal control policy using a reinforcement learning (RL) algorithm. The employed Proximal Policy Optimisation (PPO) algorithm's performance is benchmarked against the PI controller. From the simulated results obtained using the MATLAB Simulink solver, the most robust methods for short settling time and stability were the hybrid methods. These methods take advantage of the short settling time provided by the PPO algorithm and the stability provided by the PI controller or the filtering mechanism over the transient time. The source code for this study is available on GitHub to support reproducible research in industrial electronics society.INDEX TERMS Adaptive control, data-driven model predictive control, DC-to-DC buck-boost converter, proximal policy optimisation, reinforcement learning.

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A Constrained MPC Approach Applied to Buck DC-DC Converter for Greenhouse Powered by Photovoltaic Source

Cited by 3 publications

References 12 publications

A constrained model predictive control for the building thermal management with optimal setting design

A constrained model predictive control for the building thermal management with optimal setting design

Proximal policy optimization-based reinforcement learning approach for DC-DC boost converter control: A comparative evaluation against traditional control techniques

Data-Driven Model Predictive Control of DC-to-DC Buck-Boost Converter

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