在线反向最佳控制时间变化的成本权重
Sheng Cao1, Zhiwei Luo1, Changqin Quan1
1Graduate School of System Informatics, Kobe University, 1-1 Rokkodai-cho, Nada-ku, Kobe 657-8501, Japan.
本研究介绍了一种使用神经网络的自适应在线反向最佳控制方法,以从专家演示中恢复时间变化的成本权重. 该方法确保了稳定的趋同,并且适合实时应用.
科学领域:
- 控制理论 控制理论
- 机器学习 机器学习
- 机器人技术 机器人技术 机器人技术
背景情况:
- 传统的反向最佳控制假设恒定的成本权重,限制现实世界的适用性.
- 许多实际场景涉及随时间变化的动态成本函数.
- 恢复这些时间变化的成本权重是控制系统的一个重大挑战.
研究的目的:
- 开发一种自适应的在线反向最佳控制方法,用于回收时间变化的成本权重.
- 为了解决假定恒定成本参数的现有方法的局限性.
- 为了使在动态环境中更准确地建模专家行为.
主要方法:
- 使用神经网络近似来建模时间变化的成本权重.
- 进行良好位置分析,以确保问题可解决性.
- 提出一个新的权重更新法,以实现解决方案的稳定趋同.
- 通过对线性系统的模拟来验证方法.
主要成果:
- 证明了拟议的自适应在线逆最佳控制策略的有效性.
- 建立了神经网络权重的独特性条件,用于反向最佳控制问题.
- 通过拟议的更新法律,确保解决方案的稳定性和趋同.
- 在模拟中成功恢复了时间变化的成本权重.
结论:
- 拟议的基于神经网络的自适应在线反向最佳控制有效地处理时间变化的成本权重.
- 该方法为实时逆最佳控制问题提供了强大的解决方案.
- 适用于广泛的动态系统和专家演示.
- 在复杂的现实应用中推进逆最佳控制领域.
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