Nonlinear Model Predictive Stabilization of DC–DC Boost Converters With Constant Power Loads

Andrés‐Martínez, Oswaldo; Flores‐Tlacuahuac, Antonio; Ruiz-Martinez, Omar F.; Mayo-Maldonado, Jonathan C.

doi:10.1109/jestpe.2020.2964674

Cited by 53 publications

(18 citation statements)

References 46 publications

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“…Moreover, the state constraints in problem (8) need to be augmented by a terminal constraint x(k + N p ) ∈ X f [170], [171]. In doing so, indirect MPC schemes with carrier-based PWM-either formulated as QP [111] or (M)IQP which employs explicit MPC [92], [172], [173]-that guarantee closed-loop stability can be designed.…”

Section: Stabilitymentioning

confidence: 99%

Model Predictive Control of Power Electronic Systems: Methods, Results, and Challenges

Καραμανάκος

Liegmann

Geyer

et al. 2020

IEEE Open J. Ind. Applicat.

256

130

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Model predictive control (MPC) has established itself as a promising control methodology in power electronics. This survey paper highlights the most relevant MPC techniques for power electronic systems. These can be classified into two major groups, namely, MPC without modulator, referred to as direct MPC, and MPC with a subsequent modulation stage, known as indirect MPC. Design choices and parameters that affect the system performance, closed-loop stability and controller robustness are discussed. Moreover, solvers and control platforms that can be employed for the real-time implementation of MPC algorithms are presented. Finally, the MPC schemes in question are assessed, among others, in terms of design and computational complexity, along with their performance and applicability depending on the power electronic system at hand.

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Section: Stabilitymentioning

confidence: 99%

Model Predictive Control of Power Electronic Systems: Methods, Results, and Challenges

Καραμανάκος

Liegmann

Geyer

et al. 2020

IEEE Open J. Ind. Applicat.

256

130

View full text Add to dashboard Cite

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“…Since the innerloop dynamics are usually designed much faster than the outer control loop, the inner control loop can be simplified to an unity gain; then the feedback inductor current can be considered to be equal to its reference, i.e., i L,k = i ref L,k [23]. Based on this, the closed-loop sliding-mode dynamics are obtained by substituting (20) into (11) as…”

Section: Inner-loop Current Controllermentioning

confidence: 99%

“…Given the nonlinear nature of power converters, nonlinear control algorithms have emerged as promising alternatives to stabilize dc MGs with CPLs under large-signal disturbances [10]- [13]. In [10], [11], model-predictive-control based CPLstabilization approaches are proposed, which are realized by optimizing a predefined cost function. However, a nonnegligible computational burden hinders the application of this approach.…”

Section: Introductionmentioning

confidence: 99%

Composite Robust Quasi-Sliding Mode Control of DC–DC Buck Converter With Constant Power Loads

Zheng

Dragičević

Zhang

et al. 2021

IEEE J. Emerg. Sel. Topics Power Electron.

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“…Small energy sources such as solar photovoltaics, fuel cells, batteries, and other renewable energy sources (RESes) [ 3 ] mainly have low output voltage and need to boost converters to increase the voltage levels up to the network reference. The most popular control techniques used to regulate the voltage are back stepping [ 5 ], sliding mode control (SMC) [ 6 , 7 ], model predictive control (MPC) [ 8 , 9 ], and passivity-based control [ 10 ]. These methods have the advantages of robustness, stability, optimality, and flexibility [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Resilient Consensus Control Design for DC Microgrids against False Data Injection Attacks Using a Distributed Bank of Sliding Mode Observers

Barzegari

Zarei

Razavi–Far

et al. 2022

Sensors

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This paper investigates the problem of false data injection attack (FDIA) detection in microgrids. The grid under study is a DC microgrid with distributed boost converters, where the false data are injected into the voltage data so as to investigate the effect of attacks. The proposed algorithm uses a bank of sliding mode observers that estimates the states of the neighbor agents. Each agent estimates the neighboring states and, according to the estimation and communication data, the detection mechanism reveals the presence of FDIA. The proposed control scheme provides resiliency to the system by replacing the conventional consensus rule with attack-resilient ones. In order to evaluate the efficiency of the proposed method, a real-time simulation with eight agents has been performed. Moreover, a verification experimental test with three boost converters has been utilized to confirm the simulation results. It is shown that the proposed algorithm is able to detect FDI attacks and it protects the consensus deviation against FDI attacks.

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Nonlinear Model Predictive Stabilization of DC–DC Boost Converters With Constant Power Loads

Cited by 53 publications

References 46 publications

Model Predictive Control of Power Electronic Systems: Methods, Results, and Challenges

Model Predictive Control of Power Electronic Systems: Methods, Results, and Challenges

Composite Robust Quasi-Sliding Mode Control of DC–DC Buck Converter With Constant Power Loads

Resilient Consensus Control Design for DC Microgrids against False Data Injection Attacks Using a Distributed Bank of Sliding Mode Observers

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