Deep brain emotional learning-based intelligent controller applied to an inverted pendulum system

Silva, Jeydson; Aquino, Ronaldo R. B. de; Ferreira, Aida A.; Marques, Davidson C.

doi:10.1007/s11227-021-04200-w

Cited by 5 publications

(1 citation statement)

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“…They have a good dynamic performance, but when practically applied they add complexity to the architecture and need the use of fast digital signal processors to execute various controller computations [24]- [27]. Simulation and experimental findings are investigated and PI-based SVM DTC strategies evaluate the output of the proposed controller.…”

Section: Introductionmentioning

confidence: 99%

Modified Brain Emotional Controller-Based Ripple Minimization for SVM-DTC of Sensorless Induction Motor Drive

Savarapu

Qutubuddin²,

Yadaiah

2022

IEEE Access

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This paper presents the Modified Brain Emotional Controller (MBEC) technique for the minimization of torque and flux ripples of sensorless Induction Motor (IM) drive. In low-speed operation, IM is very sensitive due to variations in torque and flux ripples. These variations inert low order harmonics in stator currents that adversely affect the performance of the IM drive. The drive's performance can be improved by integrating a controller that not only minimizes harmonic presence but also increases the drive's performance. In this paper a biological inspired intelligent speed controller namely, MBEC is proposed to obtain improved performance from the drive. The input to the MBEC is an error of the reference speed and the actual speed of the motor that is estimated using the Model Reference Adaptive System (MRAS). SVM based topology is used to develop the inverter and IM is operated in sensorless DTC technique. Stability analysis of MBEC based IM drive is presented in this paper to hold the effectiveness. The proposed control configuration is tested with different operating conditions and obtained results are verified through real-time experimentation using Opal-RT OP5600. The performance of the proposed control algorithm is compared with the BEC and PI techniques to hold the effectiveness in terms of low flux ripples and torque ripples in different operating conditions.

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Section: Introductionmentioning

confidence: 99%