半自主驾驶辅助系统反馈控制的量子粒子群算法

Che-Cheng Chang, Jichiang Tsai, Shi-Jia Pei
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引用次数: 3

摘要

随着人口的老龄化,汽车技术的发展对于预防老年司机造成的交通事故变得越来越重要。作为老年人驾驶辅助系统的基础研究,提出了一种新的转向辅助控制设计方法。在这种方法中,我们考虑了系统的稳定性和局限性,使老年驾驶员能够更加安全舒适地驾驶。在此背景下,我们首先使用基于粒子群优化(PSO)的方法在约束条件下搜索最优反馈增益以实现跟踪控制。为了进一步缩短收敛时间,将粒子群优化算法与量子计算相结合。仿真结果还表明,所提出的基于量子粒子群优化(QPSO)的反馈控制器能够为弹道跟踪和稳定提供满意的计算性能。
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A quantum PSO algorithm for feedback control of semi-autonomous driver assistance systems
With the aging of the population, the development of automotive technology has become increasingly important for preventing car accident caused by senior drivers. As a basic research of driver assistance system for senior drivers, we propose a novel control design method for steering support. In this method, we consider the stability and limitation of a system, so that senior drivers can driver more safely and comfortably. In the context, we first use a particle swarm optimization (PSO) based method to search the optimal feedback gain under constraints for achieving tracking control. Moreover, to reduce the convergence time further, the particle swarm optimization algorithm is combined with quantum computing for the purpose. Simulation results also indicate that the proposed feedback controller based on the quantum particle swarm optimization (QPSO) is capable of providing satisfactory computational performance for the trajectory tracking and stabilization.
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