路径跟踪的最佳控制 车辆处理问题 逆动力学
Yingjie Liu1, Dawei Cui1, Wen Peng2
1School of Machinery and Automation, Weifang University, Weifang 261061, China.
Sensors (Basel, Switzerland)
|August 12, 2023
概括
一种新的简单的伪光谱方法 (SPM) 提高了自动驾驶汽车的车辆路径跟踪精度. 这种先进的算法比现有方法提供了更高的性能,提高了机动性和控制.
科学领域:
- 控制理论 控制理论
- 机器人技术 机器人技术 机器人技术
- 计算数学 计算数学 计算数学
背景情况:
- 车辆路径跟踪对于机动性和自动驾驶汽车研究至关重要.
- 该问题通常通过将其转化为最佳控制问题来解决.
- 现有的方法可能在准确性和适用性方面存在局限性.
研究的目的:
- 提出一种高效准确的方法来解决车辆路径跟踪中的非线性最佳控制问题.
- 引入一种新的Symplectic伪光谱法 (SPM) 来加强车辆控制.
- 将SPM的性能与已知方法 (如高斯伪谱法 (GPM)) 进行比较.
主要方法:
- 开发一种利用第三代交联理论的交联伪谱法 (SPM).
- 伪光谱离散的应用,以有效解决问题.
- 通过模拟,对SPM与高斯伪光谱法 (GPM) 的比较分析.
主要成果:
- 拟议的SPM有效地解决了车辆路径跟踪的非线性最佳控制问题.
- 模拟表明,与GPM相比,SPM在路径跟踪方面实现了更高的准确性.
- SPM显示出更大的适用性和更好的车辆机动性.
结论:
- 综合性伪光谱方法 (SPM) 是用于车辆路径跟踪的高效算法.
- SPM提供了卓越的准确性和适用性,推进了自动驾驶汽车控制能力.
- 这种方法为机器人学中复杂的非线性最佳控制挑战提供了强大的解决方案.
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