DDPG-based continuous thickness and tension coupling control for the unsteady cold rolling process

Abstract

Cold rolling is an important part of the iron and steel industry, and the unsteady rolling process of cold rolling usually brings significant influences on the stability of product quality. In the unsteady rolling process, various disturbances and uncertainties such as variable lubrication state, variable equipment working conditions lead to difficulties in the establishment of state space model of thickness and tension, which has become a thorny problem in thickness and tension control. In this paper, we present a model-free controller based on Deep Deterministic Policy Gradient(DDPG), which can continuously control the thickness tension of the unsteady rolling process without the mathematical model. We first formulate the thickness and tension control problem to Markov Decision Process(MDP). We apply strategies such as dividing state space variables with mechanism model, defining reward function and state normalization, the random disturbance and complex uncertainties of unsteady cold rolling process are coped with by utilizing the DDPG controller. In addition, these strategies also ensure the learning performance and stability of the DDPG controller under random disturbance. Simulations and experiments show that the proposed the DDPG controller does not require any prior knowledge of uncertain parameters and can operate without knowing unsteady rolling mathematical models, which has better accuracy, stability and rapidity for thickness and tension in the unsteady rolling process than proportional integral(PI) controller. The artificial intelligence-based controller brings both product quality improvement and intelligence to cold rolling.

Generative AI in the Manufacturing Process: Theoretical Considerations

“…The rapid advancement of information technologies makes it crucial to utilise them for monitoring and achieving stable, precise control over industrial processes and product quality (Xu et al, 2024). To address challenges in industrial process monitoring, fault diagnosis, and product quality control, experts and scholars have proposed the application of AI (Hartung et al, 2022;Zeng et al, 2022;Xu et al, 2024), including GAI as evidenced in recent studies (Narasimhan, 2023;Raja, 2023;Wang et al, 2019). The utilisation of GAI holds the potential to enhance quality control processes by effectively detecting and identifying defects and anomalies in various products.…”