Computationally Efficient Fixed Switching Frequency Direct Model Predictive Control

Yang, Qifan; Καραμανάκος, Πέτρος; Tian, Wei; Gao, Xiaonan; Li, Xinyue; Geyer, Tobias; Kennel, Ralph

doi:10.1109/tpel.2021.3114979

Cited by 24 publications

(12 citation statements)

References 42 publications

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“…The time that is mostly affected by the timevarying size of the GP 3 C problem is-as expected-the time t QP required by the solver to solve the QP (8). However, owing to the adopted computationally efficient solver [22], which fully exploits the geometry of the underlying optimization problem, the associated computation time remains relatively modest, even when a four-dimensional QP needs to be solved. This greatly facilitates the real-time implementation of the proposed control algorithm.…”

Section: Computational Burden and Timing Analysismentioning

confidence: 96%

“…As the optimization problem ( 8) is a convex QP, it can be solved in a computationally efficient manner in real time with the gradient projection-based solver described in [22]. The Intel i7 processor solves the QP at every sampling interval and supplies the calculated switching time instants t * and corresponding switching pattern U within the first step of the prediction horizon to the FPGA.…”

Section: A Platform and Test Benchmentioning

confidence: 99%

“…15 and 16, respectively. Considering that in the worst-case scenario a four-dimensional QP needs to be solved in real time, the average number of the required iterations, i.e., 18, is modest, indicating the efficacy of the adopted solver [22]. Moreover, as can be seen in Fig.…”

Section: Computational Burden and Timing Analysismentioning

confidence: 99%

“…Hence, the wrong estimation of ψ r results in the partial demagnetization of the machine, and, consequently, steadystate tracking error of the torque. Such issues, however, can be tackled by employing an observer of integrating nature, e.g., a Kalman filter [22], or an optimal observer, such as a moving horizon estimator [29]. Moreover, activating the outer loop will remedy, to some extent, this issue.…”

Section: A Robustness To Machine Parameter Variationsmentioning

confidence: 99%

See 3 more Smart Citations

Gradient-Based Predictive Pulse Pattern Control of Medium-Voltage Drives—Part II: Performance Assessment

Begh

Καραμανάκος

Geyer

et al. 2022

IEEE Trans. Power Electron.

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In this paper, the performance of gradient-based predictive pulse pattern control (GP 3 C) is evaluated for a medium-voltage variable speed drive consisting of a threelevel neutral point clamped (NPC) inverter and a mediumvoltage induction machine. To this end, real-time tests are performed in a hardware-in-the-loop (HIL) environment, which along with extensive simulation studies, elucidate the potential of and performance gains achieved with GP 3 C. As shown, by manipulating offline-computed optimized pulse patterns (OPPs) in real time such that the stator current of the machine follows a pre-calculated optimal current trajectory, superior steady-state and transient performance can be achieved. Specifically, the current total demand distortion (TDD) is significantly reduced compared with established control methods, such as field oriented control (FOC) with space vector modulation (SVM), while shorter settling times during transients are achieved. Finally, to complete the assessment of the control method of interest, real-time implementation aspects are discussed in detail.

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Section: Computational Burden and Timing Analysismentioning

confidence: 96%

Section: A Platform and Test Benchmentioning

confidence: 99%

Section: Computational Burden and Timing Analysismentioning

confidence: 99%

Section: A Robustness To Machine Parameter Variationsmentioning

confidence: 99%

See 2 more Smart Citations

Gradient-Based Predictive Pulse Pattern Control of Medium-Voltage Drives—Part II: Performance Assessment

Begh

Καραμανάκος

Geyer

et al. 2022

IEEE Trans. Power Electron.

Self Cite

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show abstract

“…MPC tries to produce proper output signals by predicting the states of the system and the future value of that. MPC can be deployed in different types of applications, e.g., energy management of an electrified powertrain to climate control [42], control of three-phase dual-active-bridge converters [43], control of drive systems [44], control of the energy storage systems [45], improvement of the cybersecurity of DC microgrids [46], and sensorless control of DC microgrids [47]. There are some types of MPC-based controllers, and based on the application, the proper type of MPC can be selected.…”

Section: Control Of Pe Applicationsmentioning

confidence: 99%

Power and Energy Applications Based on Quantum Computing: The Possible Potentials of Grover’s Algorithm

et al. 2022

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In quantum computing, calculations are achieved using quantum mechanics. Typically, two main phenomena of quantum mechanics (i.e., superposition and entanglement) allow quantum computing to solve some problems more efficiently than classical algorithms. The most well-known advantage of quantum computing is the speedup of some of the calculations, which have been performed before by classical applications. Scientists and engineers are attempting to use quantum computing in different fields of science, e.g., drug discovery, chemistry, computer science, etc. However, there are few attempts to use quantum computing in power and energy applications. This paper tries to highlight this gap by discussing one of the most famous quantum computing algorithms (i.e., Grover’s algorithm) and discussing the potential applications of this algorithm in power and energy systems, which can serve as one of the starting points for using Grover’s algorithm in power and energy systems.

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A new floor function single‐carrier‐based modulated model predictive current control technique for single‐phase PUC5 inverter topology

Bhanuchandar,

Murthy

2023

Circuit Theory & Apps

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SummaryIn this paper, a new floor function (F2) single‐carrier (SC)‐based modulated model predictive current control technique (M2PC2T) has been proposed by considering the conventional single‐phase five‐level packed U‐cell (PUC5) inverter topology. Generally, to regulate the output current, the finite control set‐based MPC technique without a modulator inherently operates with variable switching frequency, and it is unsuitable for industrial applications. To alleviate this, authors have implemented M2PC2T, which provides constant switching frequency of operation. In this technique, generally, the predictive algorithm has been integrated with any modulation stage. However, while the implementation of pulse width modulation (PWM) techniques, most authors have used conventional PWM techniques which further increases the control complexity as the level number increases. In this manuscript, a simple and generalized F2‐SC‐PWM technique has been proposed and integrated with the predictive algorithm. This PWM technique implementation process is very simple as compared with all conventional PWM techniques since even if the level number increases, the number of modulating and carrier signals is always only one. Finally, the complete closed‐loop control technique is called a generalized F2‐SC‐M2PC2T which is suitable for all stiff direct current (dc) sources and switched capacitor‐type multilevel inverter (MLI) topologies. The proposed control technique (PCT) has been experimentally verified with different transient and steady‐state case studies by considering resistive‐inductive load.

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Computationally Efficient Fixed Switching Frequency Direct Model Predictive Control

Cited by 24 publications

References 42 publications

Gradient-Based Predictive Pulse Pattern Control of Medium-Voltage Drives—Part II: Performance Assessment

Gradient-Based Predictive Pulse Pattern Control of Medium-Voltage Drives—Part II: Performance Assessment

Power and Energy Applications Based on Quantum Computing: The Possible Potentials of Grover’s Algorithm

A new floor function single‐carrier‐based modulated model predictive current control technique for single‐phase PUC5 inverter topology

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