灰狼算法优化的DC/DC降压变换器自适应反步控制器设计

IF 1.6 Q4 ENERGY & FUELS IET Energy Systems Integration Pub Date : 2023-04-19 DOI:10.1049/esi2.12098
Seyyed Morteza Ghamari, Fatemeh Khavari, Hasan Mollaee
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引用次数: 0

摘要

为功率降压转换器设计了一种基于 Lypunov 的自适应反步进控制 (ABSC) 方法。该策略是利用 Lyapunov 稳定函数的后步法的高级版本,可在实际应用中达到更高的稳定性和更好的干扰抑制性能。此外,为了减轻计算负担并提高植入的简便性,假设系统没有精确的数学模型,则考虑采用黑盒技术。然而,在实时环境中,包括电源电压变化、参数变化和噪声在内的较大范围的干扰会对该方法的运行产生负面影响。为了弥补这一问题,应重新调整控制器的增益,以更好地适应工作条件。因此,为了满足这一需求并提高控制器的性能,在控制方案中采用了一种名为灰狼优化(GWO)算法的元启发式算法。GWO 是一种受大自然启发的算法,与不同的优化算法相比,它具有更快的决策动态和更高的精确度。为了更好地阐述这种方法的优点,还设计了传统的 BSM 和基于 PSO 的 PID 方案,并在不同情况下进行了测试。
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Adaptive backstepping controller design for DC/DC buck converter optimised by grey wolf algorithm

A Lypunov-based Adaptive Backstepping Control (ABSC) approach is designed for a power Buck converter. This strategy is an advanced version of the Backstepping method utilising Lyapunov stability function to reach a higher stability and a better disturbance rejection behaviour in the practical applications. In addition, to reduce the computational burden and increase ease of implantation, Black-box technique is considered assuming no accurate mathematical model for the system. Nonetheless, in real-time environments, disturbances with wider ranges including: supply voltage variation, parametric variation, and noise can negatively impact the operation of this method. To compensate for this problem, the gains of the controller should be tuned again for better adaptability with the working condition. Therefore, to satisfy this need and enhance the controller's performance, a metaheuristic algorithm is applied in the control scheme called Grey Wolf Optimisation (GWO) algorithm. GWO is a well-behaved nature-inspired algorithm with faster decision-making dynamics along with more accuracy over different optimisation algorithms. To better elaborate the merits of this approach, conventional BSM and PSO-based PID schemes are also designed and tested in different situations.

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来源期刊
IET Energy Systems Integration
IET Energy Systems Integration Engineering-Engineering (miscellaneous)
CiteScore
5.90
自引率
8.30%
发文量
29
审稿时长
11 weeks
期刊最新文献
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