适应性定时过控制设计,用于全状态受约束非线性系统
IEEE transactions on cybernetics
|November 8, 2024
概括
本研究介绍了一个适应性神经控制器,用于具有状态约束的非线性系统,确保在设定的时间内保持稳定. 新方法克服了复杂性问题,并扩大了约束的适用性.
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:
- 由于其复杂的动态,高阶非线性系统存在重大控制挑战.
- 这些系统的全态约束使控制器设计和稳定性分析复杂化.
- 现有的控制方法经常与计算复杂性和有限的约束适应性作斗争.
研究的目的:
- 为具有全态约束的高阶非线性系统开发一种适应性定时神经控制器.
- 为了解决传统的后退方法固有的"复杂性爆炸".
- 提高控制策略对各种状态约束类型的适应性.
主要方法:
- 设计一个有规定的时间限制的稳定性标准.
- 构建适应性定时过器,以管理过器错误稳定性.
- 发展一种转型方法,以更广泛地适应国家约束.
- 使用辐射基函数神经网络 (RBFNNs) 来近似未知的非线性函数.
主要成果:
- 拟议的适应性定时神经控制方案保证了闭环系统的定时稳定性.
- 所有系统状态都被证明保持在它们所定义的约束范围内.
- 比较模拟证明了开发的控制策略的有效性和优势.
结论:
- 开发的自适应性定时神经控制器有效地管理具有全态约束的高阶非线性系统.
- 与现有方法相比,这种方法提供了更好的稳定性保证和更广泛的适用性.
- 这项工作在规定的时间框架内为复杂的控制问题提供了可靠的解决方案.
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