基于粒子群优化的知识控制器在自主重型车辆自适应路径跟踪控制中的应用

IF 1.3 Q4 AUTOMATION & CONTROL SYSTEMS
N. H. Amer, K. Hudha, H. Zamzuri, V. R. Aparow, A. F. Z. Abidin, Zulkiffli Abd Kadir, M. Murrad
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引用次数: 1

摘要

本章讨论了一种基于知识监督算法的重型自动驾驶车辆自适应路径跟踪控制器的研制。该控制器是在几何/运动学控制器Stanley控制器的基础上开发的。任何几何/运动学控制器最广为人知的问题之一是,适当调整的控制器可能在不同的操作区域无效,而不是它被调整/优化的区域。因此,本研究提出一种自适应算法,根据机动和车辆状况自动选择最优控制器参数。开发了一个最优知识库,用于自适应算法自动获取基于车速v和航向误差φ的参数值。用不同的轨迹和速度进行了多次仿真,以评估控制器相对于其前身(即Stanley和非自适应改进Stanley (Mod St)控制器)的有效性。然后将模拟的转向动作与人类驾驶员沿着预定义路径的实验数据进行比较。结果表明,所提出的自适应算法成功地引导了重型车辆,并适应了不同车速的各种轨迹,同时与原始Stanley控制器相比,记录的横向误差改善高达82%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Knowledge-Based Controller Optimised with Particle Swarm Optimisation for Adaptive Path Tracking Control of an Autonomous Heavy Vehicle
This chapter discusses the development of an adaptive path tracking controller equipped with a knowledge-based supervisory algorithm for an autonomous heavy vehicle. The controller was developed based on a geometric/kinematic controller, the Stanley controller. One of the mostly known issues with any geometric/kinematic controller is that a properly tuned controller may not be valid in a different operating region than the one it was being tuned/optimised on. Therefore, this study proposes an adaptive algorithm to automatically choose an optimal controller parameter depending on the manoeuvring and vehicle conditions. An optimal knowledge database is developed for an adaptive algorithm to automatically obtain the parameter values based on the vehicle speed, v, and heading error, ϕ. Several simulations are carried out with different trajectories and speeds to evaluate the effectiveness of the controller against its predecessors, namely, Stanley and the non-adaptive modified Stanley (Mod St) controllers. The simulated steering actions are then compared against human driver’s experimental data along the predefined paths. It was shown that the proposed adaptive algorithm managed to guide the heavy vehicle successfully and adapt to various trajectories with different vehicle speeds while recording lateral error improvement of up to 82% compared to the original Stanley controller.
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来源期刊
International Journal of Automation and Control
International Journal of Automation and Control AUTOMATION & CONTROL SYSTEMS-
自引率
41.70%
发文量
50
期刊介绍: IJAAC addresses the evolution and realisation of the theory, algorithms, techniques, schemes and tools for any kind of automation and control platforms including macro, micro and nano scale machineries and systems, with emphasis on implications that state-of-the-art technology choices have on both the feasibility and practicability of the intended applications. This perspective acknowledges the complexity of the automation, instrumentation and process control methods and delineates itself as an interface between the theory and practice existing in parallel over diverse spheres. Topics covered include: -Control theory and practice- Identification and modelling- Mechatronics- Application of soft computing- Real-time issues- Distributed control and remote monitoring- System integration- Fault detection and isolation (FDI)- Virtual instrumentation and control- Fieldbus technology and interfaces- Agriculture, environment, health applications- Industry, military, space applications
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