Design and Application of Fractional Order Predictive Functional Control for Industrial Heating Furnace

Hu, Xiaomin; Zou, Qingze; Zou, Hongbo

doi:10.1109/access.2018.2878554

Cited by 12 publications

(10 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [22], the authors have successfully applied the fractional-order predictive functional control into industrial heating furnace. According to this reference, we can implement the proposed PEO-FOPFC for real-world engineering problem described as follows: after using the PEO algorithm, we can obtain the optimized FOPFC.…”

Section: Remarkmentioning

confidence: 99%

“…Sanatizadeh and Bigdeli [21] designed the fractional-order predictive functional controller for unstable systems with time delay. In [22], the authors have successfully applied the fractional-order PFC into industrial heating furnace, and the experimental results on the temperature process showed the improvement of the fractionalorder PFC. In all aforementioned examples, fractional-order methods have shown better performance than the corresponding traditional integer-order methods.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Fractional-Order Predictive Functional Control of Industrial Processes with Partial Actuator Failures

Dai

et al. 2020

Complexity

View full text Add to dashboard Cite

As the actuator faults in an industrial process cause damage or performance deterioration, the design issue of an optimal controller against these failures is of great importance. In this paper, a fractional-order predictive functional control method based on population extremal optimization is proposed to maintain the control performance against partial actuator failures. The proposed control strategy consists of two key ideas. The first one is the application of fractional-order calculus into the cost function of predictive functional control. Since the knowledge of analytical parameters including the prediction horizon, fractional-order parameter, and smoothing factor in fractional-order predictive functional control is not known, population extremal optimization is employed as the second key technique to search for these parameters. The effectiveness of the proposed controller is examined on two industrial processes, e.g., injection modeling batch process and process flow of coke furnace under constant faults, time-varying faults, and nonrepetitive unknown disturbance. The comprehensive simulation results demonstrate the performance of the proposed control method by comparing with a recently developed predictive functional control, genetic algorithm, and particle swarm optimization-based versions in terms of four performance indices.

show abstract

Section: Remarkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fractional-Order Predictive Functional Control of Industrial Processes with Partial Actuator Failures

Dai

et al. 2020

Complexity

View full text Add to dashboard Cite

show abstract

“…Prediction error often occurs between the prediction model output

y_{m} false(k false)

and the actual output

y_{p} false(k false)

of the system:

e false(k false) = y_{p} false(k false) - y_{m} false(k false)

Usually, the k + i time prediction error is presented as [22, 35]

e false(k + i false) = e false(k false) = y_{p} false(k false) - y_{m} false(k false)

The real predictive output after correction can be expressed as

y_{p} false(k + i false) = y_{m} false(k + i false) + e false(k + i false)

…”

Section: Fuzzy Predictive Functional Controller Designmentioning

confidence: 99%

Fuzzy predictive functional control of a class of non‐linear systems

Wang

Yang

et al. 2019

IET Control Theory & Applications

View full text Add to dashboard Cite

“…Most of the temperature control processes were modelled or approximated by the first-order elementary model in the previous studies [2][3][4]. However, with the growing demand of production quality and efficiency, some of the complex temperature control stages may not be described adequately by this kind of elementary model.…”

Section: Introductionmentioning

confidence: 99%

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

Liu

Xue

Zhang

2021

Complex Intell. Syst.

View full text Add to dashboard Cite

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.

show abstract

Design and Application of Fractional Order Predictive Functional Control for Industrial Heating Furnace

Cited by 12 publications

References 36 publications

Fractional-Order Predictive Functional Control of Industrial Processes with Partial Actuator Failures

Fractional-Order Predictive Functional Control of Industrial Processes with Partial Actuator Failures

Fuzzy predictive functional control of a class of non‐linear systems

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

Contact Info

Product

Resources

About