Optimal Tracking Current Control of Switched Reluctance Motor Drives Using Reinforcement Q-Learning Scheduling

Abstract

In this paper, a novel Q-learning scheduling method for the current controller of a switched reluctance motor (SRM) drive is investigated. The Q-learning algorithm is a class of reinforcement learning approaches that can find the best forward-in-time solution of a linear control problem. An augmented system is constructed based on the reference current signal and the SRM model to allow for solving the algebraic Riccati equation of the current-tracking problem. This paper introduces a new scheduled-Q-learning algorithm that utilizes a table of Q-cores that lies on the nonlinear surface of an SRM model without involving any information about the model parameters to track the reference current trajectory by scheduling the infinite horizon linear quadratic trackers (LQT) handled by Q-learning algorithms. Additionally, a linear interpolation algorithm is proposed to improve the transition of the LQT between trained Q-cores to ensure a smooth response as state variables evolve on the nonlinear surface of the model. Lastly, simulation and experimental results are provided to validate the effectiveness of the proposed control scheme.

Artificial Intelligence in Electric Machine Drives: Advances and Trends

“…Additionally, there is a wide variety of deep RL algorithms and high flexibility at the implementation level, such as the design of state space, the action space, and the reward function, etc. Despite their widespread applications in AlphaGo, robots, and self-driving cars, RL has only fairly recently been introduced to the control electric machines [67]- [77].…”

“…Besides the cutting-edge work mentioned above, readers are also kindly referred to other state-of-the-art work employing RL to PM machine [74]- [76] and switched reluctance machine drives [77] in the literature for more details.…”

“…However, the entire field of electric machine drives remains pretty much silent on the resurgence of AI in this deep 1 learning era, when compared with its continued success and widespread application in condition monitoring [38]- [44], design optimization [45]- [64], and manufacturing [65], [66] of various types of electric machines. It wasn't until in the last few years that research efforts have begun to gradually catch up with the trend [67]- [77]. It is anticipated that with the rapid progress in deep learning models and hardware embedded platforms, AI-based data-driven approaches will become increasingly popular for the high-performance control of electric machine drives, as envisioned in Fig.…”

Análise e Modelagem do Motor de Relutância Variável Monofásico Através da Utilização do Método dos Elementos Finitos

“…(5) A relação do diâmetro do rotor Dr pelo diâmetro externo do estator Ds deve variar entre 0,4 e 0,7. Segundo alguns trabalhos na literatura de projetos de motores de relutância variável (Alharkan et al 2021), (Heidari et al 2021), (Mamede, 2016), usa-se o valor médio de 0,55 para o cálculo,…”

“…Isso pode ser uma característica importante, pois esse fato implica que todas as perdas resistivas de enrolamento ocorrem no estator. Como muitas vezes o estator pode ser facilmente refrigerado com mais eficiência que o rotor, o resultado em geral é um motor menor para uma determinada especificação de potência e tamanho (Mamede et al 2016), (Alharkan et al 2021), (El-Nemr et al 2021), (Heidari et al 2021), (Patel et al 2021).…”