Enhancing Active Disturbance Rejection Control for a Vehicle Active Stabiliser Bar with an Improved Chicken Flock Optimisation Algorithm

IF 2.8 4区 工程技术 Q2 ENGINEERING, CHEMICAL Processes Pub Date : 2024-09-13 DOI:10.3390/pr12091979
Zhenglin Tang, Qiang Zhao, Duc Truong Pham, Xuesong Zhang
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Abstract

An active stabiliser bar significantly enhances the anti-roll capabilities of vehicles. The control strategy is a crucial factor in enabling the active stabiliser bar to function effectively. This paper investigates an active disturbance rejection control (ADRC) strategy. Given the numerous parameters of the ADRC and their significant mutual influence, optimising these parameters is challenging. To address this, an improved chicken flock optimisation algorithm is proposed to optimise the ADRC parameters and enhance its performance. First, a three-degree-of-freedom dynamic model of the vehicle is established, and an active disturbance rejection control-based optimisation model utilising a chicken flock optimisation algorithm is constructed. To tackle the issues of getting stuck in local optima and low precision when dealing with complex problems in the traditional chicken flock optimisation (CFO) algorithm, several strategies, including improved Lévy flight, have been adopted. Subsequently, the twelve parameters of the ADRC are optimised using the improved chicken flock optimisation algorithm. Comprehensive testing on multiple benchmark functions demonstrates that the improved chicken flock optimisation (ICFO) algorithm is distinctly superior to other advanced algorithms in terms of solution quality and robustness. Simulation results show that the ICFO-ADRC controller is significantly superior. In four different complex road condition tests, the ICFO-ADRC controller shows an average performance improvement of 8% compared to the fuzzy PI-PD controller, an average improvement of 82% compared to the non-optimised ADRC controller, and an average improvement of 18% compared to the CFO-ADRC controller. Our findings confirm that this paper was able to provide new solutions for vehicle stability control whilst opening up new possibilities for the application of metaheuristic algorithms.
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用改进的鸡群优化算法加强车辆主动稳定杆的主动干扰抑制控制
主动稳定杆可大大增强车辆的抗侧倾能力。控制策略是使主动稳定杆有效发挥作用的关键因素。本文研究了一种主动干扰抑制控制(ADRC)策略。鉴于 ADRC 的参数众多且相互影响巨大,优化这些参数具有挑战性。为此,本文提出了一种改进的鸡群优化算法来优化 ADRC 参数并提高其性能。首先,建立了车辆的三自由度动态模型,并利用鸡群优化算法构建了基于主动干扰抑制控制的优化模型。针对传统鸡群优化算法(CFO)在处理复杂问题时陷入局部最优和精度低的问题,采用了包括改进的莱维飞行在内的多种策略。随后,使用改进的鸡群优化算法对 ADRC 的 12 个参数进行了优化。对多个基准函数的全面测试表明,改进的鸡群优化算法(ICFO)在解决方案质量和鲁棒性方面明显优于其他先进算法。仿真结果表明,ICFO-ADRC 控制器明显优于其他算法。在四个不同的复杂路况测试中,ICFO-ADRC 控制器的性能与模糊 PI-PD 控制器相比平均提高了 8%,与非优化 ADRC 控制器相比平均提高了 82%,与 CFO-ADRC 控制器相比平均提高了 18%。我们的研究结果证实,本文能够为车辆稳定性控制提供新的解决方案,同时也为元启发式算法的应用开辟了新的可能性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Processes
Processes Chemical Engineering-Bioengineering
CiteScore
5.10
自引率
11.40%
发文量
2239
审稿时长
14.11 days
期刊介绍: Processes (ISSN 2227-9717) provides an advanced forum for process related research in chemistry, biology and allied engineering fields. The journal publishes regular research papers, communications, letters, short notes and reviews. Our aim is to encourage researchers to publish their experimental, theoretical and computational results in as much detail as necessary. There is no restriction on paper length or number of figures and tables.
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