Title: Computationally efficient model predictive control for quasi-Z source inverter based on Lyapunov function
Authors: Minh-Khai Nguyen; Kim Anh Nguyen; Thi-Thanh-Van Phan; Van-Quang-Binh Ngo
Addresses: Department of Electrical and Electronics Engineering, Ho Chi Minh City University of Technology and Education, 70000, Vietnam ' The University of Danang-University of Science and Technology, 54 Nguyen Luong Bang, Danang, 50000, Vietnam ' The University of Danang-University of Technology and Education, 48 Cao Thang, Danang, 50000, Vietnam ' Faculty of Physics, University of Education, Hue University, Thua Thien Hue 49000, Vietnam
Abstract: This paper proposes a computationally efficient model predictive control strategy for quasi-Z source inverter. Unlike the classical finite control set model predictive control method, besides the ability of computational cost reduction, the proposed method considers the stability of the closed-loop system in the control design. At each sampling period, only feasible switch control inputs that satisfy the stability condition derived from a control Lyapunov function are taken into account in the minimisation of the cost function. Therefore, the computation time of the optimisation problem is decreased compared with the conventional algorithm. A comparison of the classical model predictive control method is investigated by Matlab software in various operating conditions of the system. The achieved results verify the benefit of the proposed approach for dealing with the stability and computational burden over the conventional method while maintaining high control performance.
Keywords: quasi-Z source inverter; finite control set model predictive control; delay compensation; computational burden; stability condition; control Lyapunov function; efficient optimisation approach; PV system; shoot-through (ST) state; non-ST state.
International Journal of Modelling, Identification and Control, 2020 Vol.36 No.4, pp.342 - 352
Received: 15 May 2020
Accepted: 27 Oct 2020
Published online: 06 Sep 2021 *