Direct torque controlled space vector modulated method for line start permanent magnet synchronous and induction motors

Soreshjani, Mohsen Hosseinzadeh

doi:10.1177/0142331214554332

Cited by 5 publications

(2 citation statements)

References 23 publications

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“…Thus, this requires a timevarying solution of (10) to be updated at each time instant with the new values of ω s . However, ω s variations are almost insignificant during steady-steady as evident from (11), therefore proposed methodology skips model update during steady-state period, thereby, lessening the computational expense.…”

Section: Current Control Loopmentioning

confidence: 99%

“…The major limitations of DTC are sluggish response (during start and steady state) and lower steady-state performance. However, improved algorithms have been developed in the control arena such as DTC-SVM, Modified Direct Torque Control and 12-sector DTC to improve the performance of DTC [11]. Similarly, PI-control based designs for vector control applications [12] are also developed but those are relatively simple and possess many limitations ranging from static gains, parametric dependency, linearity and associated trade-offs [13].…”

Section: Introductionmentioning

confidence: 99%

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Brain Emotional Learning and Adaptive Model Predictive Controller for Induction Motor Drive: A New Cascaded Vector Control Topology

Affan

Uddin

2021

Int. J. Control Autom. Syst.

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With the development of high-speed microprocessors, it is now possible to implement mathematically complex vector control algorithms without compromising on the performance of motor drive. Among vector control techniques space vector proportional-integral (PI), direct-torque control (DTC), field-oriented control (FOC), model-predictive control (MPC) are being widely used in industries. But their limitations have urged researchers to develop more advance techniques. In this paper, a new technique learning and adaptive model — based predictive control (termed as LAMPC) is proposed for the vector control of three phase induction motor. In the proposed method, the dynamic model of induction motor is updated adaptively based on prediction (receding horizon principle) for the inner control loop (current control) while the brain emotional learning-based intelligent controller (BELIC) is used for the outer control loop (speed control). The proposed methodology offers desired dynamic response, precise tracking, good disturbance handling capability along with satisfactory steady-state performance. To show the effectiveness of the proposed approach, benchmark simulation results for various inputs are presented using MATLAB/Simulink. Finally, the detailed qualitative and quantitative comparison of the proposed LAMPC is made with the most relevant vector techniques to show its significance.

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Section: Current Control Loopmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Brain Emotional Learning and Adaptive Model Predictive Controller for Induction Motor Drive: A New Cascaded Vector Control Topology

Affan

Uddin

2021

Int. J. Control Autom. Syst.

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Variable structure MPP controller for photovoltaic pumping system

Farhat

Barambones

Aymen

et al. 2016

Transactions of the Institute of Measurement and Control

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This work is oriented to research performance in terms of optimal transfer of energy from a photovoltaic generator to a permeant magnet synchronous motor type used as a centrifuge pump driver. The performance improvement is done using a variable structure controller with sliding mode as a maximum power point tracker (MPPT). A stability analysis for the proposed MPPT is developed for a photovoltaic (PV) pumping system. The global stability of the variable structure algorithm is demonstrated by means of Lyapunov’s approach. The tracking algorithm leads the PV generator coordinates to the maximum power point by changing the pulse width modulation (PWM) signal frequency of the boost converter. The steady-state behaviour of the PV pumping system with variable structure control is characterized by a stable oscillation around the maximum power point. The effectiveness of the proposed MPPT scheme is demonstrated under internal and external disturbances.

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Mechanical parameter identification of two-mass drive system based on variable forgetting factor recursive least squares method

Chen

2018

Transactions of the Institute of Measurement and Control

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Alternating current (AC) motor drive systems are widely used and their performances are greatly affected by motor parameters and load disturbances, so it is necessary to identify and observe their moment of inertia, the viscous friction coefficient and load torque. This paper presents an identification method combined a Luenberger observer and the variable forgetting factor recursive least squares method (FFRLSM), which does not require the torque sensor but a position encoder and can identify the moment of inertia of the drive system, the viscous friction coefficient, and the load torque, simultaneously. The Luenberger observer takes the position and the actual current to estimate speed and angular position. This information, together with variable FFRLSM is used to identify the moment of inertia, viscous friction coefficient and load torque. The identified moment of inertia of the system is substituted into the Luenberger observer model to correct the error of the system model. According to a probability density function of a normal distribution, the proposed method estimates the identification target in a short time, with stable and high identification precision. Finally, a comparison is made between the proposed algorithm and the FFRLSM. Results show that the proposed identification method achieves intended purpose and promise application values with its short consuming-time and high identification accuracy.

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Direct torque controlled space vector modulated method for line start permanent magnet synchronous and induction motors

Cited by 5 publications

References 23 publications

Brain Emotional Learning and Adaptive Model Predictive Controller for Induction Motor Drive: A New Cascaded Vector Control Topology

Brain Emotional Learning and Adaptive Model Predictive Controller for Induction Motor Drive: A New Cascaded Vector Control Topology

Variable structure MPP controller for photovoltaic pumping system

Mechanical parameter identification of two-mass drive system based on variable forgetting factor recursive least squares method

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