基于学习的自动驾驶汽车控制使用适应性神经模糊推理系统和线性矩阵不平等方法
Mohammad Sheikhsamad1, Vicenç Puig1
1Institute of Robotics and Industrial Informatics (CSIC-UPC), Llorens i Artigas, 4-6, 08028 Barcelona, Spain.
Sensors (Basel, Switzerland)
|April 27, 2024
概括
本研究介绍了一种基于学习的自动驾驶汽车控制方法,使用自适应神经模糊推理系统 (ANFIS) 创建一个Takagi-Sugeno (TS) 控制器. 这种方法可以减少计算负载,以便在自动驾驶系统中实时实现.
科学领域:
- 机器人和控制系统 机器人和控制系统
- 人工智能的人工智能
- 模糊逻辑系统 模糊逻辑系统
背景情况:
- 自动驾驶汽车需要高效可靠的控制系统.
- 模型预测控制 (MPC) 提供先进的控制,但可能是计算密集的.
- 模糊逻辑系统,特别是塔卡吉-苏杰诺 (TS) 模型,为复杂的系统控制提供了一个框架.
研究的目的:
- 为自动驾驶汽车开发基于学习的控制方法.
- 为了减少与传统控制方法 (如MPC) 相关的计算负担.
- 为了实现实时实现自主系统的先进控制策略.
主要方法:
- 使用自适应神经模糊推理系统 (ANFIS) 算法,从现有控制器数据中学习明确的Takagi-Sugeno (TS) 控制器.
- 在TS表格中识别车辆的动态模型.
- 使用利亚普诺夫理论和线性矩阵不等式 (LMIs) 评估闭环稳定性.
- 从模型预测控制 (MPC) 控制器学习控制规律,以消除在线优化.
主要成果:
- 成功地从MPC数据中学习了一个明确的TS控制器,而没有在线优化.
- 与传统的MPC相比,显著减少了计算负载.
- 通过对小型自主赛车模型的模拟来验证该方法.
- 通过理论分析确认了闭环稳定性.
结论:
- 提出的基于学习的方法有效地为自动驾驶汽车创建了一个明确的TS控制器.
- 通过基于ANFIS的学习消除在线优化,促进实时实现,减少计算需求.
- 该方法在提高自动驾驶汽车控制系统的效率和实用性方面具有前景.
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