One of the primary advantages of model‐free predictive control over conventional methods is that it does not use any mathematical models and relies only utilizes measured input/output data from the storage. The capability of model‐free predictive control has been already demonstrated in nonlinear systems using linear and polynomial regression for data storage. However, identifying the appropriate order that aligns with the actual system order remains a primary challenge, selecting an incorrect order may result in increasing redundant terms, ultimately leading to instability issues. In this study, we employed the Singular Value Decomposition (SVD) order selection technique, combined with the Bayesian Information Criterion (BIC), to identify the appropriate input and output orders of the system as well as the optimal horizon order in predictive control. This combined technique was subsequently applied to determine the appropriate order for model‐free predictive control. Our findings confirmed the effectiveness of the proposed method using numerical simulations in both linear and nonlinear systems. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

中文翻译：

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无模型预测控制中阶次选择的两阶段方法

与传统方法相比，无模型预测控制的主要优点之一是它不使用任何数学模型，并且仅依赖于存储中测量的输入/输出数据。无模型预测控制的能力已经在使用线性和多项式回归进行数据存储的非线性系统中得到了证明。然而，确定与实际系统顺序相符的适当顺序仍然是一个主要挑战，选择不正确的顺序可能会导致冗余项增加，最终导致不稳定问题。在本研究中，我们采用奇异值分解（SVD）阶次选择技术，结合贝叶斯信息准则（BIC），来确定系统适当的输入和输出阶数以及预测控制中的最佳水平阶数。随后应用这种组合技术来确定无模型预测控制的适当顺序。我们的研究结果证实了所提出的方法在线性和非线性系统中使用数值模拟的有效性。© 2024 日本电气工程师协会和 Wiley periodicals LLC。