通过改进的强化学习机制,通过转子辅助车辆的规定的性能路径跟踪控制
概括
本研究介绍了用于旋转器辅助车辆的强化学习 (RL) 的自适应性规定的性能控制算法. 这种新的方法通过管理控制错误和不确定性来确保节能巡航.
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
- 汽车工程 汽车工程
背景情况:
- 旋转器辅助的车辆需要先进的控制,以节能导航.
- 现有的路径控制器面临着模型不确定性和初始条件的挑战.
研究的目的:
- 为旋转器辅助车辆开发一个可适应的,遵循规定的性能路径的控制算法.
- 通过强化学习集成来加强节能巡航任务.
- 为了有效地解决模型不确定性和外部干扰.
主要方法:
- 设计了一个简洁的规定的性能控制 (PPC) 算法,具有转移功能.
- 集成后退方法和最佳控制与演员关键神经网络 (AC-NNs) 改进强化学习 (RL).
- 利用利亚普诺夫理论来保证半全球均终极边界 (SGUUB) 稳定性.
主要成果:
- 拟议的自适应RL算法有效地在定义的边界内限制输出错误.
- 演员-NN生成控制策略,而Critic-NN优化了成本-to-go功能.
- 数字实验验证了算法的优越性和可行性.
结论:
- 开发的自适应性规定的性能控制算法增强了旋转器辅助车辆的路径追踪能力.
- 将RL与AC-NNN集成,为不确定性和干扰提供了一个强大的解决方案.
- 该算法显示了节能自主任务的巨大潜力.
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