Implementation and Performance Analysis of Digital Controllers for Brushless DC Motor Drives

Shanmugasundram, R.; Zakariah, K. Muhammad; Yadaiah, N.

doi:10.1109/tmech.2012.2226469

Cited by 112 publications

(43 citation statements)

References 25 publications

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“…, r, j = 1, 2) based on the current sampling data can be determined in terms of Table II. Parameter σ ij was determined in terms of (35) [23] σ ij = n k =1 μ ik (x kj − ν ij ) 2 n k =1 (μ ik ) m , i = 1, . .…”

Section: B Unknown Parametersmentioning

confidence: 99%

Modeling and Parameter Identification of Raw Meal Calcination Process

Qiao

Chai

2015

IEEE/ASME Trans. Mechatron.

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Raw meal calcination system is a complex mechanical and electrical integration system. This system is different from the traditional machining process without a chemical reaction. Moreover, raw meal calcination in calciner is a complex physical-chemical process, and its dynamic characteristics were rarely researched. In order to verify the rationality of the mechanical structure designed, the dynamic characteristics of the raw meal calcination system need to be analyzed in advance. Therefore, a mathematical model of this process is being called for. The most important variables to be controlled are the calciner temperature and the outlet temperature of preheater C1 (i.e., the no. 1 preheater). Because of the big time constant associated with thermal dynamic characteristics, controlling the two variables is a thorny problem. This paper presents a modeling and parameter identification procedure for a raw meal calcination process. In addition, a parameter identification method based on the T-S fuzzy model and radial basis function was proposed because the specific heat capacity of pulverized coal particles, raw meal, and exhaust cannot be measured. The performance index of parameter identification is to minimize the difference of force response between the simulation and the experiment. Finally, both modeling and parameter identification methods were validated by comparing the results of simulation and experiments.Index Terms-Calciner temperature, outlet temperature of preheater C1 (i.e., the no. 1 preheater), raw meal calcination process, T-S fuzzy model.

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Section: B Unknown Parametersmentioning

confidence: 99%

Modeling and Parameter Identification of Raw Meal Calcination Process

Qiao

Chai

2015

IEEE/ASME Trans. Mechatron.

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“…The trapezoidal current control is usually implemented by hysteresis control, PI control, fuzzy logic control or feed forward control [7][8][9][10]. However, in most of the control algorithms outgoing phase current is without control and its decay rate is only determined by DC bus voltage and back EMF.…”

Section: Stepping Vector Control Of Bldc Motormentioning

confidence: 99%

Stepping Vector Control of BLDC Motor

Liu

Wang

et al. 2014

2014 IEEE Conference and Expo Transportation Electrification Asia-Pacific (ITEC Asia-Pacific)

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“…The performance of traditional motor controllers such as proportional integral (PI) and PID controllers are affected by parameter variations, load disturbances and speed variations [3] - [5]. Hence, researchers have explored numerous other controllers, which account for nonlinearity and be adaptable to load and speed variations.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Self Tuned Fuzzy PID controller for speed control of Brushless DC Motor

2016

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Original scientific paperThe objective of the proposed work is to investigate the performance of hybrid self tuned fuzzy proportional integral derivative (STFPID) controller for brushless DC (BLDC) motor drive. The proposed hybrid STFPID controller includes a proportional integral derivative (PID) controller at steady state, a PID type self tuned fuzzy logic (FL) controller (STFLC) at transient state thereby combining the merits of both the controllers. The switching function incorporated in the controller ensures desired control response at various operating conditions by appropriately switching between PID and STFPID based on speed error. A detailed simulation study and performance comparison with other control approaches is performed to highlight the merits of the proposed work. The simulation results indicate that the proposed controller is robust with fast tracking capability and less steady state error. The experimental results are provided to validate the simulation study.Key words: BLDC motor drive, speed control, self tuned fuzzy logic controller, Hybrid controller, PID controller.Hibridni samopodešavajući neizraziti PID regulator za upravljanje brzinom bezkolektorskog istosmjernog motora.Cilj ovog rada je istražiti performance hibridnog samopodešavajućeg regulatora za bezkolektorski istosmjerni motor. Predloženi hibridni samopodešavajući neizraziti regulator uključuje PID regulator u stacionarnom stanju i samopodešavajući neizraziti PID regulator (STFLC) za vrijeme trajanja prijelazne pojave kombinirajući prednosti oba regulatora. Funkcija prekapčanja regulatora omogućava upravljanje u različitim uvjetima odgovarajućim odabirom izme u PID i samopodešavajućeg neizrazitog PID regulatora na temelju brzine pogreške. Provedena je detaljna simulacijska analiza i usporedba performansi s ostalim metodama upravljanja kako bi se istaknule prednosti predloženog rada. Iz simulacijskih rezultata je vidljivo je robusno svojstvo predloženenog regulatora te smanjena pogreška u stacionarnom stanju. Sustav pravljanja testiran je i eksperimentalno kao potvrda simulacijskih rezultata.

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Implementation and Performance Analysis of Digital Controllers for Brushless DC Motor Drives

Cited by 112 publications

References 25 publications

Modeling and Parameter Identification of Raw Meal Calcination Process

Modeling and Parameter Identification of Raw Meal Calcination Process

Stepping Vector Control of BLDC Motor

Hybrid Self Tuned Fuzzy PID controller for speed control of Brushless DC Motor

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