Industrial feedforward control technology: a review

Liu, Lu; Tian, Siyuan; Xue, Dingyü; Zhang, Tao; Chen, YangQuan

doi:10.1007/s10845-018-1399-6

Cited by 48 publications

(15 citation statements)

References 73 publications

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“…The detailed system information and decoupling process can be referred to [28,29]. One of the on-site temperature control profiles of this etching chamber is shown in Fig.…”

Section: The Process Modelmentioning

confidence: 99%

General type industrial temperature system control based on fuzzy fractional-order PID controller

Liu

Xue

Zhang

2021

Complex Intell. Syst.

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A fuzzy fractional-order PID control algorithm for a general type industrial temperature control system is proposed in this paper. In order to improve the production quality and controlled model accuracy, a fractional-order elementary system is used to describe the temperature control process. The gain coefficients of the proposed fractional-order PID controller is updated online based on a set of fractional-order fuzzy rules which are defined by Mittag–Leffler functions and follow fat-tailed distributions. Therefore, the proposed controller parameters could be auto-tuned according to model uncertainties, noise disturbance, random delay, and etc. Examples of the studied temperature control systems are shown to verify the effectiveness of the proposed controller. The superiority of fractional calculus is fully explored in the presented control methodology. The controlled temperature profile with the proposed algorithm could realize more satisfactory dynamic performance, better robustness respect to environment changes caused by internal and external disturbance.

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“…The detailed system information and decoupling process can be referred to [28,29]. One of the on-site temperature control profiles of this etching chamber is shown in Fig.…”

Section: The Process Modelmentioning

confidence: 99%

General type industrial temperature system control based on fuzzy fractional-order PID controller

Liu

Xue

Zhang

2021

Complex Intell. Syst.

Self Cite

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“…The feedforward controller can effectively decrease the impact of disturbance as compared to feedback control [35]. The feedforward controller has an advantage that is to remove disturbance before entering systems while in a feedback controller disturbances travel through the whole system and are rejected by error of feedback signal [36]. The current system derives a trajectory-based feedforward controller, which uses the dynamics of the mathematical model of the proposed system.…”

Section: Feedforward Controlmentioning

confidence: 99%

Model-Based Design Approach to Improve Performance Characteristics of Hydrostatic Bearing Using Multivariable Optimization

et al. 2021

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Research in the field of tribo-mechatronics has been gaining popularity in recent decades. The objective of the current research is to improve static/dynamics characteristics of hydrostatic bearings. Hydrostatic bearings always work in harsh environmental conditions that effect their performance, and which may even result in their failure. The current research proposes a mathematical model-based system for hydrostatic bearings that helps to improve its static/dynamic characteristics under varying conditions of performance-influencing variables such as temperature, spindle speed, external load, and clearance gap. To achieve these objectives, the capillary restrictors are replaced with servo valves, and a mathematical model is developed along with robust control design systems. The control system consists of feedforward and feedback control techniques that have not been applied before for hydrostatic bearings in the published literature. The feedforward control tries to remove a disturbance before it enters the system while feedback control achieves the objective of disturbance rejection and improves steady-state characteristics. The feedforward control is a trajectory-based controller and the feedback controller is a sliding mode controller with a PID sliding surface. The particle swarm optimization algorithm is used to tune the 6-dimensional vector of the tuning parameters with multi-objective performance criteria. Numerical investigations have been carried out to check the performance of the proposed system under varying conditions of viscosity, clearance gap, external load and the spindle speed. The comparison of our results with the published literature shows the effectiveness of the proposed system.

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“…Predictive machine learning models have been applied to several industrial problems, such as error detection and diagnosis [29][30][31], quality manufacturing [32][33][34], process monitoring [4,35], and manufacturing automation [36,37]. These machine learning models, which include regression methods, decision trees, support vector machines, and artificial neural networks, have shown better performance compared with conventional methods in various situations.…”

Section: Machine Learning Applications To Industrial Problemsmentioning

confidence: 99%

Motor Load Balancing with Roll Force Prediction for a Cold-Rolling Setup with Neural Networks

Lee

Son

2021

Mathematics

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The use of machine learning algorithms to improve productivity and quality and to maximize efficiency in the steel industry has recently become a major trend. In this paper, we propose an algorithm that automates the setup in the cold-rolling process and maximizes productivity by predicting the roll forces and motor loads with multi-layer perceptron networks in addition to balancing the motor loads to increase production speed. The proposed method first constructs multilayer perceptron models with all available information from the components, the hot-rolling process, and the cold-rolling process. Then, the cold-rolling variables related to the normal part set-up are adjusted to balance the motor loads among the rolling stands. To validate the proposed method, we used a data set with 70,533 instances of 128 types of steels with 78 variables, extracted from the actual manufacturing process. The proposed method was found to be superior to the physical prediction model currently used for setups with regard to the prediction accuracy, motor load balancing, and production speed.

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Industrial feedforward control technology: a review

Cited by 48 publications

References 73 publications

General type industrial temperature system control based on fuzzy fractional-order PID controller

General type industrial temperature system control based on fuzzy fractional-order PID controller

Model-Based Design Approach to Improve Performance Characteristics of Hydrostatic Bearing Using Multivariable Optimization

Motor Load Balancing with Roll Force Prediction for a Cold-Rolling Setup with Neural Networks

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