基于预测信息的参数优化迭代学习控制
Parameter-Optimal Iterative Learning Control Based on Predictive Information
摘要: 针对一类单输入单输出离散线性时不变系统，本文提出一种预测参数优化迭代学习控制算法。该算法不仅利用历史信息，还结合预测信息进行学习，构建了性能指标函数。研究表明，通过引入未来预测信息，所设计的迭代学习控制器能够突破传统迭代学习控制算法仅依赖历史数据的局限性，加入预测信息，可获得更优越的控制性能。该方法在收敛速度方面有了一定程度的提高。最后，通过数值仿真验证了所提出方法的有效性。
Abstract: For a class of single-input single-output discrete linear time-invariant systems, this paper proposes a predictive parameter optimized iterative learning control algorithm. The algorithm not only utilizes historical information but also incorporates predictive information for learning, thereby constructing a performance index function. Research demonstrates that by introducing future predictive information, the designed iterative learning controller can overcome the limitation of traditional iterative learning control algorithms that rely solely on historical data. The inclusion of predictive information leads to superior control performance. This method achieves a certain degree of improvement in convergence speed. Finally, the effectiveness of the proposed approach is verified through numerical simulations.
文章引用：胡珍珍, 杨静. 基于预测信息的参数优化迭代学习控制[J]. 理论数学, 2025, 15(11): 64-75. https://doi.org/10.12677/pm.2025.1511269

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