Abstract

This paper describes the strategy for the implementation of model predictive control (MPC) to a three-phase multilevel inverter. Though MPC has been active since the 1960s, it has only gained traction in recent years owing to the increased processing capabilities of modern devices which now help to remove the obstacle of large delays in this scheme. Moreover, this method is far superior in control of multiple input multiple output systems, could handle constraints of operation, and removed the necessity of a modulator, which was required in the popular pulse width modulation technique (PWM). In this paper, to achieve this scheme, we developed a novel cost function to reduce distortions in current output along with switching frequency reduction and capacitor voltage balancing. This was achieved by the iterative nature of the MPC that provides the least error which was then implemented in the next step. The action of prediction for the MPC was further tuned by adjusting the sampling time. In the process of implementation of MPC, the mathematical model of the converter and load was developed and simulations were performed on MATLAB/Simulink to understand the effect of changing the weighting factor on the system response. In this manner, this paper demonstrated the degree of control over DC-link capacitor voltages and switching frequency. The merit of such a predictive control over conventional PWM-based proportional integral (PWM-PI) control was analyzed by comparing the distortions in their respective waveforms and dynamic responses. It is observed that our strategy is more effective in reference tracking and gives a faster dynamic response as compared to PWM-PI control. Also, MPC was implemented using Intel’s DE0-Nano board, a field programmable gate array (FPGA) platform, and the results were analyzed to prove the feasibility of the strategy for field use.

Keyword

Least square method, Model predictive controller, Multi-objective control, MATLAB, Neutral point clamped inverter.

Cite this article

Cite this Article

Refference

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[13]Riad AM, Elminir HK, Shohieb SM. Hand gesture recognition system based on a geometric model and rule based classifier. British Journal of Applied Science & Technology. 2014; 4(9):1432-44.

[14]Mariappan HM, Gomathi V. Real-time recognition of Indian sign language. In international conference on computational intelligence in data science (ICCIDS) 2019 (pp. 1-6). IEEE.

[15]Wu J, Sun L, Jafari R. A wearable system for recognizing American sign language in real-time using IMU and surface EMG sensors. IEEE Journal of Biomedical and Health Informatics. 2016; 20(5):1281-90.

[16]Rekha J, Bhattacharya J, Majumder S. Hand gesture recognition for sign language: a new hybrid approach. In proceedings of the international conference on image processing, computer vision, and pattern recognition (IPCV) 2011 (pp. 1-7). WorldComp.

[17]Huu PN, Phung NT. Hand gesture recognition algorithm using SVM and HOG model for control of robotic system. Journal of Robotics. 2021; 2021:1-3.

[18]Shinde P, Shinde P, Shinde S, Shinde S, Shinde S. Augmented reptile feeder. In Pune section international conference (PuneCon) 2022 (pp. 1-4). IEEE.

[19]Ismail MH, Dawwd SA, Ali FH. A review on Arabic sign language recognition. Journal of Advances in Computer and Electronics Engineering. 2021; 6(12):1-12.

[20]Michele A, Colin V, Santika DD. Mobilenet convolutional neural networks and support vector machines for palmprint recognition. Procedia Computer Science. 2019; 157:110-7.

[21]Carney M, Webster B, Alvarado I, Phillips K, Howell N, Griffith J, et al. Teachable machine: approachable web-based tool for exploring machine learning classification. In extended abstracts of the 2020 CHI conference on human factors in computing systems 2020 (pp. 1-8). ACM.

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