scholarly journals DDPG-Based Continuous Thickness and Tension Coupling Control for the Unsteady Cold Rolling Process

2021 ◽  
Author(s):  
Wenying Zeng ◽  
Jinkuan Wang ◽  
Yan Zhang ◽  
Yinghua Han ◽  
Qiang Zhao
Keyword(s):  
Cold Rolling ◽  
Product Quality ◽  
State Space ◽  
State Space Model ◽  
Rolling Process ◽  
Tension Control ◽  
Learning Performance ◽  
Random Disturbance ◽  
Iron And Steel ◽  
Model Free

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.

Finite Element Analysis of Four-Roll Pass Cold Rolling

1992 ◽  
Vol 206 (2) ◽  
pp. 133-141 ◽  
Author(s):  
S W Wen ◽  
P Hartley ◽  
I Pillinger ◽  
C E N Sturgess
Keyword(s):  
Finite Element Analysis ◽  
Finite Element ◽  
Cold Rolling ◽  
Cross Sections ◽  
Rolling Process ◽  
Roll Pass ◽  
Element Analysis ◽  
Finite Element Program ◽  
Iron And Steel ◽  
Element Program

This paper presents a study of the mechanics of deformation of the four-roll pass cold rolling using an elastic-plastic finite element program. This process has been developed at the Anshan Institute of Iron and Steel Technology, People's Republic of China, where a new four-roll pass small section cold rolling mill has been built. The initial finite element analysis has been carried out for the rolling of 8 mm square section bar from 10 mm diameter round stock under dry friction conditions. The results show clearly how the areas of plastic deformation develop during the rolling process. The distributions of the generalized stress and the generalized plastic strain, both on the longitudinal symmetrical plane and on the transverse cross-sections of the workpiece, have been obtained, and the pressure distribution along the arc of contact has been determined. In addition, the roll separation force and the pass elongation of the workpiece predicted by the finite element program have been compared with the corresponding values measured in experiments when rolling 6.5 mm square section bar from 8 mm round material with machine oil lubrication. Good agreement has been obtained.

scholarly journals Robust Tension Control of Strip for 5-Stand Tandem Cold Mills

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Behrooz Shafiei ◽  
Mohsen Ekramian ◽  
Khoshnam Shojaei
Keyword(s):  
Cold Rolling ◽  
Rolling Process ◽  
Tension Control ◽  
External Disturbances ◽  
Rolling Mills ◽  
Tandem Cold Rolling ◽  
Cold Rolling Mills ◽  
Hankel Norm ◽  
Norm Approximation

Tandem cold rolling process is a nonlinear complex system with external and internal uncertainties and significant disturbances. The improvement in the quality of the final output depends on the control strategy of centerline thickness and interstand tension. This paper focuses on interstand tension control problem in 5-stand tandem cold rolling mills. Tension dynamics can be described by a nominal model perturbed by parametric uncertainties. In order to overcome the model uncertainties and external disturbances, suboptimalH∞andμcontrollers are proposed and the Hankel-norm approximation is used to reduce the order ofμcontroller. The performance of the proposed controllers is demonstrated by some simulations.

scholarly journals Predictive Controller Design for a Cement Ball Mill Grinding Process under Larger Heterogeneities in Clinker Using State-Space Models

Designs ◽  
2020 ◽  
Vol 4 (3) ◽  
pp. 36
Author(s):  
Sivanandam Venkatesh ◽  
Kannan Ramkumar ◽  
Rengarajan Amirtharajan
Keyword(s):  
Product Quality ◽  
State Space ◽  
Real Time ◽  
Controller Design ◽  
State Space Model ◽  
Grinding Process ◽  
Process Industries ◽  
Viable Solution ◽  
Predictive Controller ◽  
Cement Grinding

Chemical process industries are running under severe constraints, and it is essential to maintain the end-product quality under disturbances. Maintaining the product quality in the cement grinding process in the presence of clinker heterogeneity is a challenging task. The model predictive controller (MPC) poses a viable solution to handle the variability. This paper addresses the design of predictive controller for the cement grinding process using the state-space model and the implementation of this industrially prevalent predictive controller in a real-time cement plant simulator. The real-time simulator provides a realistic environment for testing the controllers. Both the designed state-space predictive controller (SSMPC) in this work and the generalised predictive controller (GPC) are tested in an industrially recognized real-time simulator ECS/CEMulator available at FLSmidthPvt. Ltd., Chennai, by introducing a grindability factor from 33 to 27 (the lower the grindability factor, the harder the clinker) to the clinkers. Both the predictive controllers can maintain product quality for the hardest clinkers, whereas the existing controller maintains the product quality only up to the grindability factor of 30.

scholarly journals State-Space Model of Quasi-Z-Source Inverter-PV Systems for Transient Dynamics Studies and Network Stability Assessment

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4150
Author(s):  
Lluís Monjo ◽  
Luis Sainz ◽  
Juan José Mesas ◽  
Joaquín Pedra
Keyword(s):  
Power Systems ◽  
State Space ◽  
State Space Model ◽  
Pv System ◽  
Pv Systems ◽  
Renewable Power ◽  
Ac Networks ◽  
Transient Interactions ◽  
The Stability ◽  
Averaged Model

Photovoltaic (PV) power systems are increasingly being used as renewable power generation sources. Quasi-Z-source inverters (qZSI) are a recent, high-potential technology that can be used to integrate PV power systems into AC networks. Simultaneously, concerns regarding the stability of PV power systems are increasing. Converters reduce the damping of grid-connected converter systems, leading to instability. Several studies have analyzed the stability and dynamics of qZSI, although the characterization of qZSI-PV system dynamics in order to study transient interactions and stability has not yet been properly completed. This paper contributes a small-signal, state-space-averaged model of qZSI-PV systems in order to study these issues. The model is also applied to investigate the stability of PV power systems by analyzing the influence of system parameters. Moreover, solutions to mitigate the instabilities are proposed and the stability is verified using PSCAD time domain simulations.

Development of L1-norm sliding mode observer for sensor fault diagnosis of an industrial gas turbine

2021 ◽  
pp. 095965182199617
Author(s):  
Mahyar Akbari ◽  
Abdol Majid Khoshnood ◽  
Saied Irani
Keyword(s):  
Fault Detection ◽  
State Space ◽  
Gas Turbine ◽  
Sliding Mode ◽  
State Space Model ◽  
Sensor Fault ◽  
Space Model ◽  
Decision Logic ◽  
Sensor Fault Detection ◽  
Industrial Gas Turbine

In this article, a novel approach for model-based sensor fault detection and estimation of gas turbine is presented. The proposed method includes driving a state-space model of gas turbine, designing a novel L1-norm Lyapunov-based observer, and a decision logic which is based on bank of observers. The novel observer is designed using multiple Lyapunov functions based on L1-norm, reducing the estimation noise while increasing the accuracy. The L1-norm observer is similar to sliding mode observer in switching time. The proposed observer also acts as a low-pass filter, subsequently reducing estimation chattering. Since a bank of observers is required in model-based sensor fault detection, a bank of L1-norm observers is designed in this article. Corresponding to the use of the bank of observers, a two-step fault detection decision logic is developed. Furthermore, the proposed state-space model is a hybrid data-driven model which is divided into two models for steady-state and transient conditions, according to the nature of the gas turbine. The model is developed by applying a subspace algorithm to the real field data of SGT-600 (an industrial gas turbine). The proposed model was validated by applying to two other similar gas turbines with different ambient and operational conditions. The results of the proposed approach implementation demonstrate precise gas turbine sensor fault detection and estimation.

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