适应性抽样对受约束系统的非零和游戏进行人工实际控制
1Beijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China.
概括
本研究介绍了使用改进的埃尔曼动态神经网络 (EDNNs) 的输入约束的非线性系统的人工实际控制. 它开发了自适应动态编程 (ADP) 和新型采样机制,以确保系统稳定性和优化控制.
科学领域:
- 控制系统工程 控制系统工程
- 人工智能的人工智能
- 非线性动力学是一种非线性动力学.
背景情况:
- 执行器的物理约束对控制连续非线性系统构成挑战.
- 现有的控制方法在非零和游戏中与对称和不对称的输入约束作斗争.
- 预测系统状态和近似系统动态对于有效控制至关重要.
研究的目的:
- 为具有输入约束的连续非线性系统开发人工实际控制策略.
- 用对称和非对称输入约束来解决非零和游戏.
- 通过非周期性采样机制提高控制效率和数据通信.
主要方法:
- 利用改进的Elman动态神经网络 (EDNNs) 进行人工系统建模和状态预测.
- 设计一个非二次数值函数来处理各种输入约束.
- 使用多项式参数化的自适应动态编程 (ADP) 来解决合的汉密尔顿-雅各比方程 (HJEs).
- 引入事件触发机制 (ETM),动态ETM (DETM) 和自动触发机制 (STM) 进行适应性采样.
主要成果:
- 两个玩家的最佳控制规律是使用ADP获得的.
- 拟议的采样机制 (ETM,DETM,STM) 确保了系统的稳定性,并避免了Zeno现象.
- 模拟验证了人工实际控制算法的有效性.
- 突出了每个采样触发模式的独特特征.
结论:
- 人工实际控制方法有效地管理非线性系统中的输入约束.
- 适应性动态编程和新型采样策略提供了强大而高效的控制解决方案.
- 开发的方法为先进的控制系统设计提供了一个有希望的方向.
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