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基于深度神经网络的结构化后退轨迹跟踪控制,用于拉格朗系统
本研究介绍了针对拉格朗系统的结构化深度神经网络 (DNN) 控制器,以确保闭环稳定性和改善轨迹跟踪性能. 这种方法甚至在未知的系统动态和外部干扰的情况下也保证了稳定性.
科学领域:
- 机器人和控制系统 机器人和控制系统
- 机器学习应用 机器学习应用
- 非线性控制理论 不线性控制理论
背景情况:
- 深度神经网络 (DNN) 为控制提供了强大的功能近似,但由于它们的黑盒性质,缺乏固有的稳定性保证.
- 确保复杂系统中基于DNN的控制器的闭环稳定性和性能分析仍然是一个重大挑战.
研究的目的:
- 开发一个结构化的基于深度神经网络 (DNN) 的控制器,用于拉格朗系统的轨迹跟踪.
- 为拟议的DNN控制器的闭环稳定性和性能分析提供正式保证.
- 处理未知系统动态和外部干扰的场景.
主要方法:
- 采用了支持技术来设计一个结构化的DNN控制器,用于拉格朗的系统.
- 嵌入了基于控制器参数的追踪错误的明确上限.
- 提出了一个改进的拉格朗日神经网络 (LNN) 结构,用于当模型未知时的学习系统动态.
主要成果:
- 结构化的DNN控制器确保了任何兼容的神经网络参数的闭环稳定性.
- 通过神经网络参数优化,可以实现更好的控制性能.
- 可以限制跟踪错误,通过选择适当的控制器参数来实现所需的性能.
- 尽管存在模型近似错误和外部干扰,但仍然保持了闭环稳定性和跟踪性能.
结论:
- 拟议的基于DNN的结构控制器有效地解决了传统DNN控制器对拉格朗系统的稳定性和性能限制.
- 该方法为轨迹跟踪提供了一个强大的解决方案,即使存在模型不确定性和干扰.
- 明确的误差界限和参数选择为实现所需的控制性能提供了实际指导方针.
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