对于具有非对称输入约束和不匹配干扰的非线性系统,基于临界学习的安全最佳控制
Chunbin Qin1, Kaijun Jiang1, Jishi Zhang2
1School of Artificial Intelligence, Henan University, Zhengzhou 450000, China.
Entropy (Basel, Switzerland)
|July 29, 2023
概括
本研究介绍了面临不对称输入约束和干扰的非线性系统的安全最佳控制方法. 适应式动态编程方法提高了安全性和最佳性,同时降低了计算负载.
科学领域:
- 控制理论 控制理论
- 人工智能的人工智能
- 系统工程 系统工程
背景情况:
- 安全关键系统需要在复杂的约束条件下进行可靠的控制.
- 不对称的输入约束和无与伦比的干扰对传统的控制方法构成重大挑战.
- 适应动态编程 (ADP) 为最佳控制提供了一个框架,但需要加强安全保证.
研究的目的:
- 为连续时间非线性安全关键系统开发安全的最佳控制方法.
- 为了有效地解决不对称的输入约束和不匹配的干扰.
- 为了减少实现安全最佳控制的计算复杂性.
主要方法:
- 一个新的非二次式形式函数用于管理不对称的输入约束.
- 这个问题是作为一个两个玩家的零和游戏 (ZSG) 框架,以减轻干扰.
- 一个新的汉密尔顿 - 雅各比 - 艾萨克斯 (HJI) 方程将控制屏障功能 (CBF) 与安全成本功能相结合.
- 缓冲因子被纳入CBF,以平衡安全性和性能.
- 使用单个关键神经网络 (CNN) 来解决HJI方程,从而减少计算负担.
主要成果:
- 拟议的方法有效地处理不对称的输入约束和不匹配的干扰.
- 将CBF与HJI方程集成成功地惩罚了不安全的行为.
- 单一的CNN方法显著降低了计算负载,而不是演员批判方法.
- 利亚普诺夫稳定性分析证实,系统状态和CNN参数具有统一的最终界限 (UUB).
结论:
- 开发的安全最佳控制方法对于连续时间非线性安全关键系统是有效的.
- 该方法为复杂的控制问题提供了计算效率高的解决方案.
- 该方法在处理系统不确定性和约束方面表现出强大的性能,并通过示例验证.
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