In industrial drives, electric motors are extensively utilized to impart
motion control and induction motors are the most familiar drive at present due
to its extensive performance characteristic similar with that of DC drives. Precise control of drives is the main attribute in
industries to optimize the performance and to increase its production rate. In
motion control, the major considerations are the torque and speed ripples. Design
of controllers has become increasingly complex to such systems for better management
of energy and raw materials to attain optimal performance. Meager parameter appraisal results are unsuitable, leading
to unstable operation. The rapid intensification of digital computer revolutionizes to practice precise
control and allows implementation of advanced control strategy to extremely
multifaceted systems. To solve complex control problems, model predictive
control is an authoritative scheme, which exploits an explicit model of the
process to be controlled. This paper presents a predictive control strategy by
a neural network predictive controller based single phase induction motor drive
to minimize the speed and torque ripples. The proposed method exhibits better
performance than the conventional controller and validity of the proposed
method is verified by the simulation results using MATLAB software.
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