基于ADP的在线补偿层次的滑动模式控制,用于部分未知的开关非线性系统,执行器故障
Tengda Wang1, Ben Niu2, Ning Xu3
1College of Control Science and Engineering, Bohai University, Jinzhou 121013, Liaoning, China.
ISA transactions
|September 20, 2024
概括
本研究介绍了一种适应性动态编程控制,用于执行器故障的非线性系统,使用识别器关键神经网络进行在线补偿并确保系统稳定性.
科学领域:
- 控制系统工程 控制系统工程
- 人工智能的人工智能
- 非线性动力学是一种非线性动力学.
背景情况:
- 交换非线性系统在各种工程应用中很普遍.
- 执行器故障和不确定的干扰对系统控制构成重大挑战.
- 现有的自适应动态编程方法在处理未知的内部动态和近似误差方面存在局限性.
研究的目的:
- 为部分未知的交换非线性系统开发基于自适应动态编程的在线补偿控制策略.
- 在不依赖故障检测和隔离单元的情况下解决执行器故障和不确定的干扰.
- 为了提高控制系统的稳定性和稳定性,使用识别器关键神经网络架构.
主要方法:
- 通过引入与分层滑动模式表面相关的成本函数,将控制问题制定为最佳控制问题.
- 使用具有标识符关键神经网络的自适应动态编程解决汉密尔顿-雅各比-贝尔曼方程.
- 采用梯度下降和体验重播用于关键网络重量调整,放松激发条件的持久性.
- 纳入基于层次的滑动模式表面的执行器故障的补偿术语.
主要成果:
- 标识符关键的网络架构有效地克服了未知的内部动态和演员网络近似错误的局限性.
- 拟议的控制策略成功地在线补偿执行器故障,而无需故障检测和隔离单元.
- 利亚普诺夫稳定理论证实,闭环非线性系统的所有状态最终都具有统一的边界.
- 数字和实例证实了在线薪酬控制战略的有效性.
结论:
- 开发的自适应动态编程控制策略为发生执行器故障的开关非线性系统提供了强大的解决方案.
- 识别器关键的神经网络架构在传统方法上提供了显著的优势.
- 该方法确保了系统稳定性和可靠的性能,在存在不确定性和故障的情况下.
相关概念视频
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