Adaptive Predictive Control With Recurrent Neural Network for Industrial Processes: An Application to Temperature Control of a Variable-Frequency Oil-Cooling Machine

Lu, Chi‐Huang; Tsai, Ching‐Chih

doi:10.1109/tie.2007.896492

Cited by 114 publications

(52 citation statements)

References 22 publications

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“…The recurrent NN has received increasing attention due to its structural advantage in the modeling of the nonlinear system and dynamic control of the system [19][20][21][22][23][24]. These networks are capable of effective identification and control of complex process dynamics, but with the expense of large computational complexity.…”

Section: Chih-hong Linmentioning

confidence: 99%

Dynamic Response of Novel Adaptive Modified Recurrent Legendre Neural Network Control for PMSM Servo-Drive Electric Scooter

Lin

2015

Automatika

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Original scientific paperBecause an electric scooter driven by permanent magnet synchronous motor (PMSM) servo-driven system has the unknown nonlinearity and the time-varying characteristics, its accurate dynamic model is difficult to establish for the design of the linear controller in whole system. In order to conquer this difficulty and raise robustness, a novel adaptive modified recurrent Legendre neural network (NN) control system, which has fast convergence and provide high accuracy, is proposed to control for PMSM servo-driven electric scooter under the external disturbances and parameter variations in this study. The novel adaptive modified recurrent Legendre NN control system consists of a modified recurrent Legendre NN control with adaptation law and a remunerated control with estimation law. In addition, the online parameter tuning methodology of the modified recurrent Legendre NN control and the estimation law of the remunerated control can be derived by using the Lyapunov stability theorem and the gradient descent method. Furthermore, the modified recurrent Legendre NN with variable learning rate is proposed to raise convergence speed. Finally, comparative studies are demonstrated by experimental results in order to show the effectiveness of the proposed control scheme.Key words: Permanent magnet synchronous motor, Legendre neural network, Lyapunov stability Dinamički odziv nove adaptivne modificirane povratne Legendrove neuronske mreže upravljanja sinkronim motorom s permanentnim magnetima za električni skuter. S obzirom da električni skuter pogonjen servo sustavom sa sinkroni motor s permanentnim magnetima ima nelinearnu dinamiku i vremenski promjenjive parametre, njegov dinamički model nije jednostavno odrediti u svrhu dizajniranja linearnog regulatora. Kako bi se riješio taj problem te povećala robusnost predložen je sustav upravljanja korištenjem adaptivne modificirane povratne Legendrove neuronske mreže za upravljanje skuterom pogonjenim servo sustavom sa sinkronim motorom uz prisustvo vanjskog poremećaja i promjenjivih parametara. Predloženo upravljanje ima brzu konvergenciju i visoku preciznost. Sustav upravljanja sastoji se od modificirane povratne Legendrove neuronske moreže s adaptivnim zakonom upravljanja i estimacijom. Dodatno, on-line podešavanje parametara takvog sustava može se dobiti korištenjem Ljapunovljevog teorema o stabilnosti sustava i gradijente metode. Modificirana povratne Legendrove neuronska mreža s promjenjivim vremenom učenja predložena je za povećanje brzine konvergencije. Ispravnost predložene sheme upravljanja provjerena je eksperimentalno.Ključne riječi: sinkroni motor s permanentnim magnetima, Legendrova neuronska mreža, stabilnost po Ljaponovu

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Section: Chih-hong Linmentioning

confidence: 99%

Dynamic Response of Novel Adaptive Modified Recurrent Legendre Neural Network Control for PMSM Servo-Drive Electric Scooter

Lin

2015

Automatika

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“…This includes maintenance and upgrade of already existing profiles. Scientific challenges can be seen in the ever increasing complexity of systems and in the intelligent evaluation of the enormous amounts of collected data [23]- [26]. Another potential for improvement is the interface between fieldbuses, which currently creates unnecessary additional costs.…”

Section: Fieldbus Systems-historymentioning

confidence: 99%

“…The amount of data to be transmitted is fortunately quite small-in the case of simple sensors in the range of some bytes every 10 min. On the other hand, more sophisticated sensors as finger print sensors, cameras [38], or the like produce considerably more data [23]- [26]. Altogether, these data soon sum up to considerable amounts and quickly go beyond the capacity of standard computers in the imaginary case of data collection on a single computer.…”

Section: Future Prospectsmentioning

confidence: 99%

Communication and Computation in Buildings: A Short Introduction and Overview

Dietrich

Bruckner

Zucker

et al. 2010

IEEE Trans. Ind. Electron.

105

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Abstract-Building automation (BA) and smart homes (SHs) have traditionally not been a unified field but varied by their origins, legal foundations, different applications, different goals, and national funding programs for basic research. Only within the last years that an international common focus appeared. The following overview gives not only an introduction into the topic of BA but also the distinction to other areas of automation, in which networks of the field level (the sensor and actuator level) play an important role. Finally, the scientific challenges will be mentioned. SHs are referred to when the differences to BA have to be explicitly stressed. This paper is an introduction for the special IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS section on BA and shall introduce the reader to this new topic. BA not only has a huge economic potential but also is of significant academic interest today.

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“…The recurrent NN has received increasing attention due to its structural advantage in the modelling of the nonlinear system and dynamic control of the system [23][24][25][26][27]. These networks are capable of effective identification and control of complex process dynamics, but with the expense of large computational complexity.…”

Section: Introductionmentioning

confidence: 99%

“…However, in the complicated nonlinear dynamic system such as the V-belt CVT system driven by PMSM with the flux linkage and external force interference is always an important factor. Hence, if each neuron in the recurrent neural networks is considered as a state in the nonlinear dynamic systems, the self-connection feedback type is able to approximate the dynamic systems efficiently [23][24][25][26][27]. In order to improve the ability of identifying high order systems and reduce computational complexity, the recurrent Chebyshev NN has been proposed in this study.…”

Section: Introductionmentioning

confidence: 99%

PMSM Servo Drive for V-Belt Continuously Variable Transmission System Using Hybrid Recurrent Chebyshev NN Control System

Lin¹

2015

Journal of Electrical Engineering and Technology

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-Because the wheel of V-belt continuously variable transmission (CVT) system driven by permanent magnet synchronous motor (PMSM) has much unknown nonlinear and time-varying characteristics, the better control performance design for the linear control design is a time consuming job. In order to overcome difficulties for design of the linear controllers, a hybrid recurrent Chebyshev neural network (NN) control system is proposed to control for a PMSM servo-driven V-belt CVT system under the occurrence of the lumped nonlinear load disturbances. The hybrid recurrent Chebyshev NN control system consists of an inspector control, a recurrent Chebyshev NN control with adaptive law and a recouped control. Moreover, the online parameters tuning methodology of adaptive law in the recurrent Chebyshev NN can be derived according to the Lyapunov stability theorem and the gradient descent method. Furthermore, the optimal learning rate of the parameters based on discrete-type Lyapunov function is derived to achieve fast convergence. The recurrent Chebyshev NN with fast convergence has the online learning ability to respond to the system's nonlinear and timevarying behaviors. Finally, to show the effectiveness of the proposed control scheme, comparative studies are demonstrated by experimental results.

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Adaptive Predictive Control With Recurrent Neural Network for Industrial Processes: An Application to Temperature Control of a Variable-Frequency Oil-Cooling Machine

Cited by 114 publications

References 22 publications

Dynamic Response of Novel Adaptive Modified Recurrent Legendre Neural Network Control for PMSM Servo-Drive Electric Scooter

Dynamic Response of Novel Adaptive Modified Recurrent Legendre Neural Network Control for PMSM Servo-Drive Electric Scooter

Communication and Computation in Buildings: A Short Introduction and Overview

PMSM Servo Drive for V-Belt Continuously Variable Transmission System Using Hybrid Recurrent Chebyshev NN Control System

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