Robust Adaptive Fuzzy Control of Chaos in the Permanent Magnet Synchronous Motor

Yu, Jinpeng; Gao, Junwei; Ma, Yajie; Yu, Haisheng; Pan, Songfeng

doi:10.1155/2010/269283

Cited by 15 publications

(12 citation statements)

References 12 publications

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“…With a qualitative approach, fuzzy systems offer a methodology to simulate a human expert operational behaviour and allow using available data from these experts' knowledge. Fuzzy expert systems have been largely used in control systems (Benyakhlef&Radouane, 2008;Chiu &Lian, 2009;Yu et al, 2010;Feng, 2010;Wang et al, 2011), since when Mamdani and Assilian developed a fuzzy controller for a boiler (Mamdani&Assilan, 1975).…”

Section: Usage Of Fuzzy Logic In Aluminum Industrymentioning

confidence: 99%

Fuzzy Control Applied to Aluminum Smelting

Pereira¹,

Oliveira²,

Soares³

2012

Fuzzy Logic - Controls, Concepts, Theories and Applications

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Fuzzy Logic-Controls, Concepts, Theories and Applications 254 20 smelters are responsible for 65% or world production. Most companies work only with Aluminum, but 20% of them work with Aluminum with other metals or mines. Half of Aluminum production is done by companies vertically integrated, from bauxite mining to metal recycling (IAI, 2010). For all these reasons, the Aluminum can be considered a highly important metal, and therefore, its production is a target for many research activities. Researchers all around the world make efforts in making Aluminum production a less costly process, since it spends a lot of energy, and is very complex. In this chapter, we are going to present the whole context, and why and where fuzzy control is important to assist plant operators. The impact and consequences of this work is the use of rules defined by process operators indirectly through the huge database which provides historic information including control decisions made by them. Since this strategy emulates process operator, it can be said that an expert system can provide this personnel more time to concentrate on other activities. Moreover, this technique will be continually improved by revising its rules and evaluation, provided that fuzzy decisions will also have an impact, and this should be analysed and adjusted. 1.1 Aluminum production process Aluminum has been produced through the Hall-Héroult process, named after its inventors. So far, this is the only industrial way to produce this metal. Primary Aluminum is produced in a liquid form, through an electrolytic reduction of alumina (Al 2 O 3) in a cryolite bath (Na 3 AlF 6). This reaction takes place in electrolytic pots, as shown in Figure 2. Fig. 2. Sketch of an Alumina reduction pot of prebake type (adapted from Kola & Store, 2009). www.intechopen.com Fuzzy Control Applied to Aluminum Smelting 255 Inside these pots, also often called cells, Alumina is fed through silo and it is electrically consumed by the carbon anodes (Solheim, 2005), and as shown in the equation (1), the anode is also consumed during the electrolytic process.

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Section: Usage Of Fuzzy Logic In Aluminum Industrymentioning

confidence: 99%

Fuzzy Control Applied to Aluminum Smelting

Pereira¹,

Oliveira²,

Soares³

2012

Fuzzy Logic - Controls, Concepts, Theories and Applications

View full text Add to dashboard Cite

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“…Chaos control in PMSM has been implemented with several methods. Feedback control method [2,12,14], adaptive fuzzy control [5,21,37,38], simple sliding mode adaptive control [7,23], adaptive neural sliding mode control [33], optimal Lyapunov exponents' placement [1], passive control [25], impulsive control [6,10] and finite-time stability theory [27] were used for the control of chaotic behavior in PMSM. However, the methods mentioned above have some shortcomings.…”

Section: Introductionmentioning

confidence: 99%

Permanent magnet synchronous motor: chaos control using single controller, synchronization and circuit implementation

et al. 2020

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The aim of this paper is to address the problem of the electronic implementation of chaos control using a single controller and synchronization of chaotic permanent magnet synchronous motor (PMSM). Firstly, different dynamical behaviors of the PMSM including steady state, periodic and chaotic behaviors are found using numerical methods such as twodimensional largest Lyapunov exponents graph associated with two parameters of PMSM. Secondly, two simple and single controllers are designed and added to the chaotic PMSM in order to suppress chaotic behavior. The performance of the two proposed simple and single controllers is illustrated by numerical simulations. Thirdly, controllers are designed to achieve synchronization of unidirectional coupled identical chaotic PMSMs. Numerical simulations are also used to verify the effectiveness of the synchronization. Finally, the existence of chaos in PMSM and the physical feasibility of the proposed two simple and single controllers as well as the chaos synchronization are validated through the circuit implementation on OrCAD-PSpice software. The circuit implementation results comply fairly with those of the numerical simulation results and establish that the existence of chaotic behavior in the PMSM and the achievement of chaos synchronization in unidirectional coupled identical PMSMs designed and the two single controllers designed are effective and successful in suppressing chaotic behavior in PMSM.

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“…The PMSM is characterized by complexity, high nonlinearity, time-varying dynamics, inaccessibility of some states, and output for measurements; hence, it can be considered as a challenging engineering problem [1,2]. It is found that the PMSM is experiencing chaotic behavior at specific parameters and working conditions [3,4]. Then, the intermittent oscillation of torque and rotational speed, irregular current noise of the system, and unstable control performance appear in the PMSM, which seriously affect the stability and safety.…”

Section: Introductionmentioning

confidence: 99%

Output Feedback Adaptive Dynamic Surface Control of Permanent Magnet Synchronous Motor with Uncertain Time Delays via RBFNN

Luo

Wang

Shi

et al. 2014

Discrete Dynamics in Nature and Society

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This paper focuses on an adaptive dynamic surface control based on the Radial Basis Function Neural Network for a fourth-order permanent magnet synchronous motor system wherein the unknown parameters, disturbances, chaos, and uncertain time delays are presented. Neural Network systems are used to approximate the nonlinearities and an adaptive law is employed to estimate accurate parameters. Then, a simple and effective controller has been obtained by introducing dynamic surface control technique on the basis of first-order filters. Asymptotically tracking stability in the sense of uniformly ultimate boundedness is achieved in a short time. Finally, the performance of the proposed control has been illustrated through simulation results.

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Robust Adaptive Fuzzy Control of Chaos in the Permanent Magnet Synchronous Motor

Cited by 15 publications

References 12 publications

Fuzzy Control Applied to Aluminum Smelting

Fuzzy Control Applied to Aluminum Smelting

Permanent magnet synchronous motor: chaos control using single controller, synchronization and circuit implementation

Output Feedback Adaptive Dynamic Surface Control of Permanent Magnet Synchronous Motor with Uncertain Time Delays via RBFNN

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