限制性MIMO非线性系统的自适应神经控制与不对称的输入和和死区
IEEE transactions on neural networks and learning systems
|October 10, 2023
概括
本研究介绍了复杂非线性系统的自适应神经控制,解决了输入和和状态约束. 拟议的方法确保了系统稳定性和准确的跟踪,以提高性能.
科学领域:
- 控制系统工程 控制系统工程
- 非线性动力学是一种非线性动力学.
- 人工智能的人工智能
背景情况:
- 复杂的非线性系统经常表现出具有挑战性的特征,例如不对称的输入和和死区.
- 全态函数约束在现实应用中很常见,使控制设计复杂化.
- 现有的自适应控制方法可能会与这些结合的非线性和约束作斗争.
研究的目的:
- 为多输入多输出 (MIMO) 非线性系统开发强大的自适应神经控制策略.
- 为了有效地处理不对称的输入和,死区和全状态功能约束.
- 为了确保所有信号的边界性以及在一个定义区域内的跟踪误差的趋同.
主要方法:
- 使用辐射基函数 (RBF) 神经网络 (NN) 来近似未知非线性函数.
- 使用Nussbaum函数来管理未知的控制收益.
- 应用一个时间变化的屏障莱普诺夫函数 (BLF) 来解决状态函数约束.
- 为系统的控制方案整合后退设计方法.
主要成果:
- 拟议的控制方案成功地管理了不对称的输入和和死区.
- 严格执行国家功能约束,防止在闭环运行过程中违反.
- 闭环系统内的所有信号都被证明是有界的.
- 追踪错误汇聚到源头周围的一个小区域,证明了有效的控制.
结论:
- 提出的自适应神经控制方案为具有显著非线性和约束的MIMO非线性系统提供了强大的解决方案.
- 整合RBF NNs,Nussbaum函数和BLF,为先进的控制设计提供了一个强大的框架.
- 该方法的有效性通过模拟和应用到质量弹阻尼器系统来验证.
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