Model Reference Adaptive System based Speed Sensorless Control of Induction Motor using Fuzzy-PI Controller

Saranya, U.; Allirani, S.

doi:10.5120/19313-0772

Cited by 3 publications

(3 citation statements)

References 12 publications

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“…In this case, the fuzzy logic calculations need a long time and addition efforts by try and error is performed to obtain normalizing gains selection [18]. So, this study resort to the MRAS to self-tuning the TSMFOPID online where it has simple structure, easy to implement and fast calculations [19,20]. The model-reference adaptive system (MRAS) presents one of the best adaptive control techniques.…”

Section: Introductionmentioning

confidence: 99%

“…The model-reference adaptive system (MRAS) presents one of the best adaptive control techniques. It may be regarded as an adaptive servo system in which the desired performance is expressed in terms of a reference model, which gives the desired response to a command signal [20]. It forces the overall system to follow the behavior of preselected model reference.…”

Section: Introductionmentioning

confidence: 99%

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Model reference self-tuning fractional order PID control based on for a power system stabilizer

Ghany¹,

Shamseldin²

2020

IJPEDS

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<p><span lang="EN-US">This paper presents a novel approach of self-tuning for a Modified Fractional Order PID (MFOPID) depends on the Model Reference Adaptive System (MRAS). The proposed self-tuning controller is applied to Power System Stabilizer (PSS). Takaji-Sugeno (TS) fuzzy logic technique is used to construct the MFOPID controller. The objective of MRAS is to update the five parameters of Takaji-Sugeno Modified FOPID (TSMFOPID) controller online. For different operating points of PSS, MRAS is applied to investigate the effectiveness of proposed controllers. The harmony optimization technique used to obtain the optimal parameters of TSMFOPID controllers and MRAS parameters. For different operating points with different disturbance under parameters variations the simulation results are obtained. This is to show that Self-Tuning of TSMFOPID based on (MRAS) have better performance than the fixed parameters TSMOFOPID controller.</span></p>

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Model reference self-tuning fractional order PID control based on for a power system stabilizer

Ghany¹,

Shamseldin²

2020

IJPEDS

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“…Then a Linear Parameter Varying model (LPV) has been obtained and developed in several works. Reference [21] is dedicate to design a nonlinear method for a fault diagnosis method based on a polytypic linear parameter varying (LPV) formulation, and [12] is interested in a stability analysis of the double feed induction machine using LPVs. On the other hand, in [4] a design of a linear parameter varying model is used to control the speed of the shaft angle of an induction motor.…”

Section: Introductionmentioning

confidence: 99%

Actuator Fault Estimation Based on LPV Unknown Input Observer for Induction Machine

Amrane¹,

Larabi²,

Aïtouche³

2017

STUD INFORM CONTROL

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This machine is not easy to control due to the nonlinearity of its dynamic model. Moreover, its state variables are not all measurable and it is under the influence of parametric variations. Therefore, a good control is necessary to guaranty the stability of its proper functioning. In the literature, most of the works deal with steady state vector control [8-10, 21, 25], Direct Torque Control (DTC) [19], Sliding Mode Control (SMC) [13,27]. In addition, nonlinear control applied to induction machine has been developed such in [14].The advantages of such approaches are their robustness to disturbances and their simplicity of implementation. The major disadvantage of the set-up is that all controls rely on the reliability of the measurement provided by sensors, Abstract: This paper presents an unknown inputs observer (UIO) applied to an induction machine (IM) in order to estimate the actuator faults and the state variables. Knowing that the machine used is highly nonlinear, a model based on LPV (linear parameter varying) system of the machine is used. Indeed, using the induction machine based on LPV model, we develop the structure of an observer where the actuator faults are the unknown inputs. The conditions for convergence are based on the Lyapunov theory that will ensure the stability. Based on the LMI (Linear Matrix Inequalities), the gains of the UIO subject to actuator faults are confirmed and then ensure the efficiency of our approach. The obtained results through simulations demonstrate the effectiveness of the proposed approach.

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MRAS and Luenberger observers using a SIFLC controller in adaptive mechanism based sensorless fuzzy logic control of induction motor

Chalawane

Essadki

Nasser

2016

2016 International Conference on Electrical and Information Technologies (ICEIT)

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Model Reference Adaptive System based Speed Sensorless Control of Induction Motor using Fuzzy-PI Controller

Cited by 3 publications

References 12 publications

Model reference self-tuning fractional order PID control based on for a power system stabilizer

Model reference self-tuning fractional order PID control based on for a power system stabilizer

Actuator Fault Estimation Based on LPV Unknown Input Observer for Induction Machine

MRAS and Luenberger observers using a SIFLC controller in adaptive mechanism based sensorless fuzzy logic control of induction motor

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