Modelling and model predictive control of a DC-DC Boost converter

Bououden, Sofiane; Hazil, O.; Filali, S.; Chadli, Mohammed

doi:10.1109/sta.2014.7086663

Cited by 26 publications

(7 citation statements)

References 9 publications

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“…Note that there is staircase‐like rise as shown in Figs. 9–11 when the reference value increased slowly, where similar phenomenon can be seen in [20, 30]. In fact, by simulation research, it can be found that slowly increased current reference may lead to similar phenomenon and it is hard to completely eliminated just by adjusting the gains

K_{p}

and

K_{i}

.…”

Section: Experimental Validationsupporting

confidence: 53%

Composite adaptive model predictive control for DC–DC boost converters

Tianying

et al. 2018

IET Power Electronics

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A composite adaptive model predictive control (MPC) scheme is proposed to mitigate model mismatches due to parameter uncertainties and disturbances in controller designs for DC-DC boost converters. First, a model-based feedforward compensator based on a cascade proportional-integral (PI) MPC controller was developed as a current reference to improve the dynamic response performance. Then, an observer was employed to construct an accurate model and thereby avoid overcompensation and poor prediction due to model mismatch. Finally, a finite control set MPC was implemented to select a suitable switching state with which to realise output voltage regulation. Compared with a traditional PI MPC, the proposed controller has a rapid dynamic response, and compared with an unknown offset free MPC, the proposed controller is well suited to a simplified model. The proposed methods and benefits were validated by simulations in MATLAB/Simulink and by experiments on an NI Compact RIO rapid control prototype test bench.

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K_{p}

and

K_{i}

.…”

Section: Experimental Validationsupporting

confidence: 53%

Composite adaptive model predictive control for DC–DC boost converters

Tianying

et al. 2018

IET Power Electronics

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“…For this case, a discrete state-space model of the boost converter is obtained by means of the forward Euler approximation [111] given in Equation (12). Therefore, the discretetime state-space model of the boost converter can be written as Equation (13).…”

Section: Model Predictive Controlmentioning

confidence: 99%

Maximum Power Point Tracking Techniques for Photovoltaic Panel: A Review and Experimental Applications

et al. 2021

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This article contains a review of essential control techniques for maximum power point tracking (MPPT) to be applied in photovoltaic (PV) panel systems. These devices are distinguished by their capability to transform solar energy into electricity without emissions. Nevertheless, the efficiency can be enhanced provided that a suitable MPPT algorithm is well designed to obtain the maximum performance. From the analyzed MPPT algorithms, four different types were chosen for an experimental evaluation over a commercial PV system linked to a boost converter. As the reference that corresponds to the maximum power is depended on the irradiation and temperature, an artificial neural network (ANN) was used as a reference generator where a high accuracy was achieved based on real data. This was used as a tool for the implementation of sliding mode controller (SMC), fuzzy logic controller (FLC) and model predictive control (MPC). The outcomes allowed different conclusions where each controller has different advantages and disadvantages depending on the various factors related to hardware and software.

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“…A challenge associated with solving MPC problems is that it is dependent on the accuracy of the physical system realisation (model) of industrial systems that generally have multiple degrees of freedom [20], [21]. Another challenge impeding the performance of MPC systems pertains to the optimal controller, the limitation of the number of recorded measurements of the observable state and the constrained number of actuations available.…”

Section: Model Predictive Controlmentioning

confidence: 99%

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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Modelling and model predictive control of a DC-DC Boost converter

Cited by 26 publications

References 9 publications

Composite adaptive model predictive control for DC–DC boost converters

Composite adaptive model predictive control for DC–DC boost converters

Maximum Power Point Tracking Techniques for Photovoltaic Panel: A Review and Experimental Applications

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

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