An Improved Iterative Learning Control Approach Based on Random Forest and Whale Optimization for Urban Traffic Signals

Iterative learning control (ILC) has demonstrated effectiveness in urban traffic signal control systems. However, conventional ILC methods typically require infinite iterations to achieve theoretical convergence conditions, leading to neglect of convergence speed in practical applications. As convergence speed primarily depends on the initial control signal selection and learning gain configuration, current applications in urban traffic signal control face two limitations: 1) empirical initialization of control signals often induces significant initial iteration errors, and 2) fixed learning gains prevent real-time adaptation to system errors during iterations. In order to address the aforementioned issues, we propose a novel urban traffic signal iterative learning control method based on random forest and whale optimization, and a faster convergence speed is achieved by integrating the iterative initial value and learning gain optimization algorithms. In the initial iteration of ILC, a trained random forest regression model by collecting the historical iteration data is employed to predict the initial control signals. Subsequently, the whale optimization algorithm is used to determine the learning gain of each iteration, and online optimization is performed based on the error minimum principle. Finally, a rigorous proof of convergence is conducted for the proposed method, and ablation and comparative experiments are conducted using the road network between Changfeng Street and Xuefu Street in Taiyuan to verify the effectiveness of the proposed method. Compared with other control approaches, the proposed method achieves faster convergence speed and lower tracking error under varying disturbances. Note to Practitioners—The inspiration for this study stems from the convergence speed of ILC algorithms in urban traffic signal control. Similarly, this concept can be applied to ILC algorithms in other fields. The convergence proof of the existing ILC algorithms typically rely on an infinite number of iterations, leading to the neglect of the convergence speed. To fill this gap, this study proposes to build a historical iteration information dataset by collecting the historical iteration data, and the dataset is used to train a random forest regression model. Subsequently,the trained random forest regression model is used to predict the superior initial iteration control signal, which can accelerate the convergence speed of ILC. In addition, the whale optimization algorithm is also utilized to optimize the learning gain to further accelerate the convergence speed. Compared with existing methods, our proposed method has faster convergence speed and higher tracking accuracy.