Weighting Factors Optimization of Predictive Torque Control of Induction Motor by Multiobjective Genetic Algorithm

Guazzelli, Paulo Roberto Ubaldo; Pereira, William César de Andrade; Oliveira, Carlos Matheus Rodrigues de; Castro, Allan Gregori de; Aguiar, Manoel Luís de

doi:10.1109/tpel.2018.2834304

Cited by 134 publications

(94 citation statements)

References 41 publications

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“…To avoid heuristics, there have been some methods that choose the weighting factors based on analytical expressions, thus avoiding tuning altogether [115], [151], [152]. Moreover, emerging methods for the weighting factors tuning utilize techniques from artificial intelligence, such as neural networks and genetic algorithms [143], [153]- [155]. In this way the tuning process is automated and the weighting factors can be adjusted in real time.…”

Section: B Tuning Parametersmentioning

confidence: 99%

Model Predictive Control of Power Electronic Systems: Methods, Results, and Challenges

Καραμανάκος

Liegmann

Geyer

et al. 2020

IEEE Open J. Ind. Applicat.

300

130

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Model predictive control (MPC) has established itself as a promising control methodology in power electronics. This survey paper highlights the most relevant MPC techniques for power electronic systems. These can be classified into two major groups, namely, MPC without modulator, referred to as direct MPC, and MPC with a subsequent modulation stage, known as indirect MPC. Design choices and parameters that affect the system performance, closed-loop stability and controller robustness are discussed. Moreover, solvers and control platforms that can be employed for the real-time implementation of MPC algorithms are presented. Finally, the MPC schemes in question are assessed, among others, in terms of design and computational complexity, along with their performance and applicability depending on the power electronic system at hand.

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Section: B Tuning Parametersmentioning

confidence: 99%

Model Predictive Control of Power Electronic Systems: Methods, Results, and Challenges

Καραμανάκος

Liegmann

Geyer

et al. 2020

IEEE Open J. Ind. Applicat.

300

130

View full text Add to dashboard Cite

show abstract

“…The stator reference frame induction machine dynamic model considering stator current and rotor flux as the state variables, is expressed as follow [32], [33]:…”

Section: A Induction Machinementioning

confidence: 99%

“…where V dc is the DC link voltage, S UVW is the T5MLC switching function and T Cl is Clarke transformation that is defined by (10) [32],…”

Section: B T5mlc Modellingmentioning

confidence: 99%

Design, Implementation and Control of a SiC-Based T5MLC Induction Drive System

Salem

2020

IEEE Access

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This paper presents and evaluate a prototype of an advanced topology of five-level T-type MLC, namely ''T5MLC'', implemented within a complete AC induction drive. The evaluation is based on the ability of balancing the converter DC link capacitor voltages, the torque and flux ripples reduction of the induction motor under control, the harmonic analysis and the efficiency of the inverter and the overall drive system. The converter implementation and design considerations using Silicon Carbide (SiC) discrete MOSFETs are discussed. The operation and the analytical model of T5MLC using switching-functions are presented. The model predictive control (MPC), as a preferred AC drive control approach, is selected to adapt the machine torque and flux, and to balance the DC link capacitor voltages. The T5MLC drive system is tested at different scenarios; i.e. suddenly load changing and speed reversal, to ensure the converter capability and excellent performance in AC drive applications. The experimental results show that the MPC could achieve an excellent performance in terms of low harmonic distortions, reduced flux and torque ripples and the ability of balanced DC link capacitors. In addition, the loss analysis showed that the T5MLC is 5.5 % to 24.8 % efficient compared to dual T-type MLC at different loading conditions.

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“…However, Rodriguez et al [10] state that research efforts in the parameter sensitivity of this strategy, adjustment of the weight factor in the cost function, limitation of the switching frequency, and computational optimization are required to develop more efficient FCS-PTC strategies. For this purpose, some efforts have been made on determining or eliminating the weighting factor [11][12][13], reducing or fixing the switching frequency [12][13][14][15], reducing torque and current harmonics [12,[14][15][16], overcurrent protection [11,[14][15][16], minimizing electrical power losses [17], robustness of parameter variation [17,18], dead time compensation [11,15,16,18], and optimizing computational complexity [12,14,16].…”

Section: Introductionmentioning

confidence: 99%

Speed-sensorless predictive torque controlled induction motor drive with feed-forward control of load torque for electric vehicle applications

Zerdali¹,

Demir²

2021

Turk J Elec Eng & Comp Sci

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Nowadays, the global trend is towards reducing CO2 emissions and one solution is to replace internal combustion vehicles with electric vehicles. To this end, electric drive system, the most crucial part of an electric vehicle, has gained importance and has become a major research field. The induction motor (IM) is one of the best candidates for electric vehicle applications due to its advantages such as simple and robust design, low cost and maintenance requirements, the ability to operate in harsh environments. However, it has a highly nonlinear model with time-varying electrical and mechanical parameters making them difficult to control. Finite control set-predictive torque control (FCS-PTC) is an inherently suitable and a promising control method for the IM because FCS-PTC is easy to implement and has the ability to handle nonlinearities with the inclusion of constraints. In addition, the elimination of speed sensors increases the reliability of electric motor drives while reducing cost and hardware complexity. In this paper, a speedsensorless FCS-PTC based IM drive system is designed in order to combine the aforementioned advantages. Unlike the current literature, to improve the torque response of conventional FCS-PTC, the load torque is also estimated by an adaptive fading extended Kalman filter and is fed back into the torque control loop. The results show that improved control performance is achieved.

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Weighting Factors Optimization of Predictive Torque Control of Induction Motor by Multiobjective Genetic Algorithm

Cited by 134 publications

References 41 publications

Model Predictive Control of Power Electronic Systems: Methods, Results, and Challenges

Model Predictive Control of Power Electronic Systems: Methods, Results, and Challenges

Design, Implementation and Control of a SiC-Based T5MLC Induction Drive System

Speed-sensorless predictive torque controlled induction motor drive with feed-forward control of load torque for electric vehicle applications

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