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关键参与者强化学习,以优化多非全学轮式移动车辆的合作形成
Lixia Liu1, Peiyong Duan1, Xiaoyu Liu1
1School of Mathematics and Statistics, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250353, China.
ISA transactions
|January 19, 2026
概括
本研究介绍了一种优化的形成控制策略,用于多个非全方位的轮式移动车辆,使用关键演员强化学习和自适应神经网络. 该方法有效地管理了合作导航的复杂动态.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 控制系统 控制系统
- 人工智能的人工智能
背景情况:
- 多车辆系统的形成控制是复杂的,因为不足的执行和非全方位的约束.
- 解决汉密尔顿 - 雅各比 - 贝尔曼方程,以优化形成协议是具有挑战性的.
- 现有的方法在拉格朗日动力学中的不确定性方面扎.
研究的目的:
- 为多个非全方位轮式移动车辆 (MNWMVs) 开发一个优化的领导跟随者形成控制方案.
- 将批评者-演员强化学习 (RL) 与自适应神经网络 (NN) 整合起来,以实现强大的训练跟踪.
- 解决汉密尔顿-雅各比-贝尔曼方程的非线性和合性特征.
主要方法:
- 一个分布式的合作形成方案,结合了动力学和动态扭矩控制器.
- 在关键演员RL策略中整合适应识别器,以处理拉格朗日动态的不确定性.
- 为了简化,利用从正函数的负梯度中得出的RL训练规律.
主要成果:
- 为MNWMVs开发了一种新的优化形成跟踪算法.
- 适应式标识符有效地弥补了拉格朗奇动态中的不确定性.
- 劳动力培训法简化了整体培训计划.
结论:
- 拟议的分布式合作社组建计划对MNWMVs有效.
- 整合RL和自适应NNs为复杂的形成控制问题提供了强大的解决方案.
- 数字模拟和物理实验验证实了理论发现.
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