对受到干扰的状态受约束的非线性系统进行神经预分配性能控制
IEEE transactions on neural networks and learning systems
|March 27, 2024
概括
本研究介绍了对具有约束和干扰的非线性系统的有限时间神经预定义性能控制 (PPC). 该方法确保追踪错误在设定的时间内满足性能指标,增强系统的稳定性.
科学领域:
- 控制系统工程 控制系统工程
- 非线性系统动态 非线性系统动态
- 人工智能在控制中
背景情况:
- 由于固有的局限性,受国家约束的非线性系统 (NSs) 难以控制.
- 外源干扰会降低系统的性能和稳定性.
- 在有限的时间内实现预定义的性能需要先进的控制策略.
研究的目的:
- 为状态受约束的非线性系统 (NSs) 开发一个有限时间的神经预定义性能控制 (PPC).
- 确保追踪错误在有限的时间内满足预定义的绩效指标 (PPI).
- 为了增强干扰排斥和系统稳定性.
主要方法:
- 集成预定义时间性能函数 (PTPFs) 和屏障Lyapunov函数 (BLFs) 的时间变化的约束.
- 应用非线性干扰观察技术 (NDOT) 来估计和补偿干扰.
- 使用动态表面控制 (DSC) 进行递归控制器设计.
主要成果:
- 一个新的有限时间神经适应PPC策略被成功设计出来.
- 拟议的控制器确保满足全状态约束.
- 闭环系统展示了半全球实际有限时间稳定性 (SPFS),并实现了所需的PPI.
- 模拟结果验证了该方法的有效性和对干扰的稳定性.
结论:
- 开发的有限时间神经适应PPC策略有效地处理非线性系统中的状态约束和干扰.
- 该方法在有限的时间内保证了预定义的性能,提高了控制精度和稳定性.
- 该方法为复杂的控制问题提供了可行的解决方案,需要高强度和性能.
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