统一设计方法,用于对具有多个约束的非线性系统进行次优控制
IEEE transactions on cybernetics
|November 5, 2025
概括
本研究提出了一种用于设计未知非线性系统的控制器的新方法,该系统具有状态,输入和输出约束. 该方法使用神经网络和综合强化学习来有效地管理约束.
科学领域:
- 控制理论 控制理论
- 非线性系统是非线性系统.
- 人工智能的人工智能
背景情况:
- 为具有多个约束 (状态,输入,输出) 的非线性系统设计控制器具有挑战性.
- 现有的方法经常与未知的系统动态和未建模的不确定性作斗争.
- 处理不平等的约束需要专门的技术.
研究的目的:
- 为未知的一般非线性系统提出统一的次优控制器设计方法.
- 为了有效地同时管理状态,输入和输出约束.
- 开发一种基于数据的方法,使用神经网络和综合强化学习.
主要方法:
- 将不平等约束转换为平等约束,使用slack函数和Pade近似.
- 定义一个包含原始系统动态和约束的不受约束的增强系统.
- 利用神经网络 (NN) 和综合强化学习 (IRL) 进行基于数据的控制设计.
主要成果:
- 增强系统的最佳控制器作为原始系统的次优控制器.
- 提出的方法有效地处理未知的动态和多重约束.
- 模拟结果证明了统一设计方法的有效性.
结论:
- 开发的方法为受约束的非线性系统中次优控制器设计提供了一个统一的框架.
- 神经网络和IRL为复杂的控制问题提供了强大的,基于数据的解决方案.
- 这种方法对于没有建模的动态和各种约束类型的系统有效.
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