加速智能关键跟踪预测控制与数据体验重播用于未知的非线性系统.
IEEE transactions on cybernetics
|November 12, 2025
概括
这项研究引入了针对非线性系统的加速智能批评跟踪预测控制与数据体验重复 (AICTPC-DER) 框架. 在轨迹跟踪任务中,AICTPC-DER算法表现出卓越的控制性能.
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:
- 在具有未知动态的非线性系统中,轨迹跟踪带来了重大的控制挑战.
- 现有的自适应式批评设计需要改进,以提高在线优化效率.
研究的目的:
- 开发一个先进的控制框架,用于解决未知动态的非线性系统中的轨迹跟踪问题.
- 提高控制系统在线政策优化的效率和性能.
主要方法:
- 将模型预测控制的退缩优化与智能批评方案集成.
- 使用时间序列数据开发深度神经网络预测模型.
- 建立一个带有经验重复的加速批评架构 (AICTPC-DER),以改善在线优化.
主要成果:
- AICTPC-DER算法有效地解决了非线性系统中的轨迹跟踪问题.
- 模拟结果验证了算法的有效性,渐进性和卓越的控制性能.
- 加速因子和数据体验重复 (DER) 机制的好处得到了明确的证明.
结论:
- 拟议的AICTPC-DER框架为复杂的非线性系统的轨迹跟踪提供了强大而高效的解决方案.
- 集成先进的人工智能技术显著提高了控制性能和优化效率.
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