Optimal control of tension and thickness for tandem cold rolling process based on receding horizon control

Hu, Yunjian; Sun, Jie; Chen, Shu Zong; Zhang, Xin; Wen, Peng; Zhang, Dianhua

doi:10.1080/03019233.2019.1615813

Cited by 10 publications

(3 citation statements)

References 28 publications

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“…As in every manufacturing process, it is one of the critical issues to produce quality products by cold rolling [4,5]. Choosing the appropriate manufacturing parameters is also one of the crucial points in the quality production of the material.…”

Section: Introductionmentioning

confidence: 99%

Optimization of pickling and annealing parameters in cold rolling using the Taguchi method

Özakın

2023

Materialwissenschaft Werkst

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In this study, the optimum values of pickling and annealing parameters in cold rolling were obtained by using the Taguchi method. The materials taken from the hot rolling mill were subjected to pickling with different pickling times (3 min, 4 min, 5 min) parameters and then cold rolled. After this process, annealing was carried out with different annealing temperatures (700 °C, 750 °C, 800 °C), annealing times (5 min, 7.5 min, 10 min) in order to eliminate the cold working hardness. After these processes, sheet materials were subjected to skin‐pass rolling, which is the last step of the rolling process. Optimum conditions are minimum rolling force measured from the load cell positioned on the work rolls. In this direction, S/N ratios with objective function “smaller is better” and were calculated and the optimum levels of the parameters were determined as 3 min pickling time, 750 °C annealing temperature and 10 min annealing time. In line with the findings obtained from the analysis of variance, it was determined that the most important parameter was the annealing time with a rate of 48.99 %. Confirmation experiments were carried out and it was concluded that the optimization was valid.

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Section: Introductionmentioning

confidence: 99%

Optimization of pickling and annealing parameters in cold rolling using the Taguchi method

Özakın

2023

Materialwissenschaft Werkst

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“…To improve control performance and enhance system stability, many researchers have established mathematical models considering disturbance terms and applied them to the thickness tension control. Hu et al introduced a new state space model of the cold rolling process, proposed the multivariable optimization strategy based on inverse linear quadratic form and the control method based on receding horizon control strategy respectively to optimize the control performance of thickness and tension [8,9]. Koofigar et al [10 established a mathematical model considering the uncertainty of cold rolling process, and used the model to design a robust controller based on H ∞ control theory.…”

Section: Introductionmentioning

confidence: 99%

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

Zeng

Wang

Zhang

et al. 2022

Int J Adv Manuf Technol

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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.

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“…Some articles on the study of process control methods are also discussed. Yunjian Hu et al proposed an optimal tension and thickness control method based on the receding horizon control (RHC) strategy to improve control performance and enhance system robustness [18]. Ren et al studied a reduced-order extended state observer-(ESO-) based sliding mode control scheme for friction compensation of a three-wheeled omnidirectional mobile robot [19].…”

Section: Introductionmentioning

confidence: 99%

Model Predictive Control Method of Simulated Moving Bed Chromatographic Separation Process Based on Subspace System Identification

Yan

Wang

et al. 2019

Mathematical Problems in Engineering

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Simulated moving bed (SMB) chromatographic separation is a new type of separation technology based on traditional fixed bed adsorption operation and true moving bed (TMB) chromatographic separation technology, which includes inlet-outlet liquid, liquid circulation, and feed liquid separation. The input-output data matrices were constructed based on SMB chromatographic separation process data. The SMB chromatographic separation process was modeled by utilizing two subspace system identification algorithms: multivariable output-error state-space (MOESP) identification algorithm and numerical algorithms for subspace state-space system identification (N4SID), so as to obtain the 3rd-order and 4th-order state-space yield models of the SMB chromatographic separation process, respectively. The model predictive control method based on the established state-space models is used in the SMB chromatographic separation process. The influence of different control indicators on the predictive control system response performance is discussed. The output response curves of the yield models were obtained by changing the related parameters so that the yield model parameters are optimized set meanwhile. Finally, the simulation results showed that the yield models are successfully controlled based on the each control period and given yield range.

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Optimal control of tension and thickness for tandem cold rolling process based on receding horizon control

Cited by 10 publications

References 28 publications

Optimization of pickling and annealing parameters in cold rolling using the Taguchi method

Optimization of pickling and annealing parameters in cold rolling using the Taguchi method

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

Model Predictive Control Method of Simulated Moving Bed Chromatographic Separation Process Based on Subspace System Identification

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