Weighting Factor Selection Techniques for Predictive Torque Control of Induction Motor Drives: A Comparison Study

Mamdouh, Mohamed; Abido, M. A.; Hamouz, Z.

doi:10.1007/s13369-017-2842-2

Cited by 47 publications

(31 citation statements)

References 29 publications

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“…Empirical relations described by the plant model are fundamentally used for the selection of weights. Investigation of general guidelines for the selection of weighting factors for the cost function has also been reported [15]. However, its performance degrades when there are uncertainties in plant parameters and plant-model mismatch.…”

Section: Related Workmentioning

confidence: 99%

Optimal Tuning of Model Predictive Controller Weights Using Genetic Algorithm with Interactive Decision Tree for Industrial Cement Kiln Process

et al. 2019

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Energy intense nature of cement kiln demands optimal operation to minimize the energy requirement. Optimal control of cement kiln is achieved by proper tuning of the model predictive controller (MPC), which is addressed in this work. Genetic algorithm (GA) is used to determine the MPC weights that minimize the overall energy utilization with reduced tracking error. Single objective function has been formulated using importance weighted performance metrics like energy utilization and integral absolute error in tracking the desired response. Importance weights are determined in specific to the control scenarios using an interactive decision tree (IDT). It interacts with the operator to detect the weaker metrics and raises the importance level for further improvement. The algorithm terminates after attending all the metrics with the consent from the operator. Five control scenarios that predominantly occur in industrial cement kiln have been considered in this study. It includes tracking, measured, and unmeasured disturbance rejection of pulse and Gaussian type noises. The results illustrate the minimized energy operation with the use of the proposed single objective function as compared with the multi-objective function-based GA tuning procedure.

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Section: Related Workmentioning

confidence: 99%

Optimal Tuning of Model Predictive Controller Weights Using Genetic Algorithm with Interactive Decision Tree for Industrial Cement Kiln Process

et al. 2019

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“…To tackle this problem, a number of proposals have appeared recently in the electrical systems literature. The new methods include neuronal approximations, elimination of weighting factors, fuzzy decision making, and other techniques (see the work of Mamdouh et al for a review). In the more general literature concerned with automatic control, different approaches can be found such as on‐line tuning …”

Section: Introductionmentioning

confidence: 99%

Cost function optimization for predictive control of a five‐phase IM drive

Arahal

Martín

Kowal

et al. 2019

Optim Control Appl Methods

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Summary Model predictive control has been used, for some time now, as a method to directly control power converters in electrical systems. The usual practice is tuning the cost function of the controller to obtain a certain compromise solution over the whole operating range of the system. This method is extended here to consider multiple, locally optimal, and tunings. The design objectives (tracking error, switching frequency, etc) are used to define a unique performance index that is locally optimized. In this way, the parameters of the cost function are linked to the current operating point. The tuning at each operating point is obtained numerically solving the optimization of the performance index. Although the idea can be applied to induction machines with any number of phases, in this paper, a five‐phase induction motor is considered for presentation. This system is a demanding case due to the extra number of phases compared with the traditional three‐phase motor. Simulation and experimental results are presented to assess the proposed predictive controller.

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“…A careful design of the cost function, which represents the control objectives of the FCS-MPC, can also help in the reduction of the harmonic content [11]. For example, a precise tuning of the weighting factors that weight each control objective can be decisive [4,6,12], as well as the limitation of the commutation frequency in the converter by the restriction of the available changes in the switches of the converter's legs [13]. However, these techniques generally increase the controller complexity and the computational requirements, another important handicap in the application of FCS-MPC methods to multiphase drives.…”

Section: Introductionmentioning

confidence: 99%

Model-Based Predictive Current Controllers in Multiphase Drives Dealing with Natural Reduction of Harmonic Distortion

et al. 2019

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An important drawback in the application of model-based predictive controllers for multiphase systems is the relatively high harmonic content. Harmonics arise due to the fixed sampling-time nature and the absence of modulation methods in the control technique. Recent research works have proposed different procedures to overcome this disadvantage at the expense of increasing the complexity of the controller and, in most cases, the computational requirements. There are, however, natural ways to face this harmonic generation that have been barely explored in the scientific literature. These alternatives include the use of variable sampling times or the application of the observer theory, whose utility has been stated without excessively increasing the computational cost of the controller. This paper presents the basis of both methodologies, analyzing their interest as natural alternatives to mitigate the generation of harmonic components in modern electrical drives when using predictive controllers. A five-phase induction machine is used as a case example to experimentally validate the study and draw conclusions.

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Weighting Factor Selection Techniques for Predictive Torque Control of Induction Motor Drives: A Comparison Study

Cited by 47 publications

References 29 publications

Optimal Tuning of Model Predictive Controller Weights Using Genetic Algorithm with Interactive Decision Tree for Industrial Cement Kiln Process

Optimal Tuning of Model Predictive Controller Weights Using Genetic Algorithm with Interactive Decision Tree for Industrial Cement Kiln Process

Cost function optimization for predictive control of a five‐phase IM drive

Model-Based Predictive Current Controllers in Multiphase Drives Dealing with Natural Reduction of Harmonic Distortion

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