对于神经网络近似的建设性方法,设置了严格反系统的自适应控制
IEEE transactions on cybernetics
|April 21, 2025
概括
这项研究引入了一种使用神经网络 (NN) 来适应控制不确定的系统的新方法. 该方法提前确定了NN近似集,改进了控制系统设计.
科学领域:
- 控制工程 控制工程 控制工程
- 人工智能的人工智能
- 系统动力学系统动力学
背景情况:
- 对不确定的系统进行自适应控制是具有挑战性的.
- 神经网络 (NN) 用于近似的系统功能.
- 确定严格反系统的NN近似集是一个关键问题.
研究的目的:
- 提出一种用于确定神经网络近似集的新方法,用于严格反不确定系统的自适应控制.
- 确保状态误差界限,并计算NN重量估计器的确切界限.
- 通过说明性示例来验证拟议的方法.
主要方法:
- 信号替换技术用于将系统状态转换为状态错误变量.
- 屏障函数 (BFs) 限制状态错误,并使绑定计算成为可能.
- 退步方法与适应性控制的NN相结合.
主要成果:
- 提前成功确定了NN的近似集.
- 使用屏障函数实现了受限制状态错误.
- 计算了NN重量估计器的确切边界.
- 通过示例验证了拟议方法的有效性.
结论:
- 提出的方法为严格反不确定系统的自适应控制提供了一个建设性的解决方案.
- 信号替换,屏障函数和NN的集成提供了对状态错误和NN近似集的精确控制.
- 这种方法增强了自适应控制系统的设计和稳定性分析.
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