Data-Driven Model-Free Adaptive Control of Z-Source Inverters

Sensors

Yasin Asadi¹  Behnam Mohammadi-ivatloo²  Sasan Mohammadi³  Amirhossein Ahmadi⁴  Mousa Marzband⁵  Ali Moradi Amani⁶ 

[1] Department of Electrical Engineering, Shahid Bahonar University of Kerman, Kerman 7616913439, Iran;Department of Electrical Engineering, University of Tabriz, Tabriz 5166616471, Iran;Department of Electrical and Computer Engineering, Sharif University of Technology, Tehran 1136511155, Iran;Department of Electrical and Software Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada;Department of Mathematics, Physics and Electrical Engineering, Northumbria University, Newcastle NE7 7XA, UK;School of Engineering, Royal Melbourne Institute of Technology, Melbourne 2476, Australia;

关键词: Z-source;    non-minimum phase;    data-driven;    model-free adaptive control;    uncertainties;   

DOI  :  10.3390/s21227438

来源: DOAJ

【 摘 要 】

The universal paradigm shift towards green energy has accelerated the development of modern algorithms and technologies, among them converters such as Z-Source Inverters (ZSI) are playing an important role. ZSIs are single-stage inverters which are capable of performing both buck and boost operations through an impedance network that enables the shoot-through state. Despite all advantages, these inverters are associated with the non-minimum phase feature imposing heavy restrictions on their closed-loop response. Moreover, uncertainties such as parameter perturbation, unmodeled dynamics, and load disturbances may degrade their performance or even lead to instability, especially when model-based controllers are applied. To tackle these issues, a data-driven model-free adaptive controller is proposed in this paper which guarantees stability and the desired performance of the inverter in the presence of uncertainties. It performs the control action in two steps: First, a model of the system is updated using the current input and output signals of the system. Based on this updated model, the control action is re-tuned to achieve the desired performance. The convergence and stability of the proposed control system are proved in the Lyapunov sense. Experiments corroborate the effectiveness and superiority of the presented method over model-based controllers including PI, state feedback, and optimal robust linear quadratic integral controllers in terms of various metrics.

【 授权许可】

Unknown