African vulture optimizer algorithm based vector control induction motor drive system

This study describes a new optimization approach for three-phase induction motor speed drive to minimize the integral square error for speed controller and improve the dynamic speed performance. The new proposed algorithm, African vulture optimizer algorithm (AVOA) optimizes internal controller parameters of a fuzzy like proportional differential (PD) speed controller. The AVOA is notable for its ease of implementation, minimal number of design parameters, high convergence speed, and low computing burden. This study compares fuzzy-like PD speed controllers optimized with AVOA to adaptive fuzzy logic speed regulators, fuzzy-like PD optimized with genetic algorithm (GA), and proportional integral (PI) speed regulators optimized with AVOA to provide speed control for an induction motor drive system. The drive system is simulated using MATLAB/Simulink and laboratory prototype is implemented using DSP-DS1104 board. The results demonstrate that the suggested fuzzy-like PD speed controller optimized with AVOA, with a speed steady state error performance of 0.5% compared to the adaptive fuzzy logic speed regulator’s 0.7%, is the optimum alternative for speed controller. The results clarify the effectiveness of the controllers based on fuzzy like PD speed controller optimized with AVOA for each performance index as it provides lower overshoot, lowers rising time, and high dynamic response

Experimental Investigation Of Different Rules Size Of Fuzzy Logic Controller For Vector Control Of Induction Motor Drives

There is lack of performance comparison investigation between 49,25 and 9 rules size speed controller of Induction Motor (IM) drives of the rules size toward the motor performance. Furthermore, no study was conducted based on the computation burden time affects by the rules experimentally fuzzy rules sizes in terms of performance based on simulations and experimental analysis as well as the execution time.MATLAB/SIMULINK and dSPACE DSl104 controller platform are used for the analysis. Variation in performance with the shape and number of membership functions.Based on the experimental results,it can be concluded that,higher number of rules increase the Computational Time (CT), hence bigger sampling time is required which will There is lack of performance comparison investigation between 49,25 and 9 rules size speed controller of Induction Motor (IM) drives.Thus,it is difficult to understand the effect of the rules size toward the motor performance.Furthermore, o study was conducted based on the computation burden time affects by the rules experimentally.This paper compares the fuzzy rules sizes in terms of performance based on simulations and experimental analysis as well as the execution time. MATLAB/SIMULINK and dSPACE DSl104 controller platform are used for the analysis.Variation in performance with different rule size may occur due to the shape and number of membership functions.Based on the experimental results,it can be concluded that,higher number of rules increase the Computational Time (CT),hence bigger sampling time is required which will affect the performance.There is lack of performance comparison investigation between 49,25 and 9 rules sizefor the. Thus, it is difficult to understand the effect of the rules size toward the motor performance.Furthermore,no study was conducted based.This paper compares the fuzzy rules sizes in terms of performance based on simulations and experimental analysis as well as the execution time. MATLAB/SIMULINK and dSPACE DSl104 controller platform different rule size may occur due to the shape and number of membership functions.Based on the experimental results, it can be concluded that, higher number of rules increase the Computational Time (CT),hence bigger computational time (CT)

Fuzzy Membership Functions Tuning For Speed Controller Of Induction Motor Drive: Performance Improvement.

Fuzzy logic controller (FLC) has gained high interest in the field of speed control of machine drives in both academic and industrial communities. This is due to the features of FLC of handling non-linearity and variations. FLC system consists of three main elements: scaling factors (SFs), membership functions (MFs), and rule-base. Fuzzy MFs can be designed with different types and sizes. For induction motor (IM) speed control, (3x3), (5x5) and (7x7) MFs are the most used MFs sizes, and normally designed based on symmetrical distribution. However, changing the width and peak position of MFs design enhance the performance. In this paper, tuning of MFs of FLC speed control of IM drives is considered. Considering (3x3), (5x5) and (7x7) MFs sizes, the widths and peak positions of these MFs are asymmetrically distributed to improve the performance of IM drive. Based on these MFs sizes, the widths and peak positions are moved toward the origin (zero), negative and positive side that produces a controller less sensitive to the small error variations. Based on simulation and performance evaluations, improvement of 5% in settling time (Ts), 0.5% in rise time and 20% of steady-state improvement achieved with the tuned MFs compared to original MFs

Locating and extracting acoustic and neural signals

This dissertation presents innovate methodologies for locating, extracting, and separating multiple incoherent sound sources in three-dimensional (3D) space; and applications of the time reversal (TR) algorithm to pinpoint the hyper active neural activities inside the brain auditory structure that are correlated to the tinnitus pathology. Specifically, an acoustic modeling based method is developed for locating arbitrary and incoherent sound sources in 3D space in real time by using a minimal number of microphones, and the Point Source Separation (PSS) method is developed for extracting target signals from directly measured mixed signals. Combining these two approaches leads to a novel technology known as Blind Sources Localization and Separation (BSLS) that enables one to locate multiple incoherent sound signals in 3D space and separate original individual sources simultaneously, based on the directly measured mixed signals. These technologies have been validated through numerical simulations and experiments conducted in various non-ideal environments where there are non-negligible, unspecified sound reflections and reverberation as well as interferences from random background noise. Another innovation presented in this dissertation is concerned with applications of the TR algorithm to pinpoint the exact locations of hyper-active neurons in the brain auditory structure that are directly correlated to the tinnitus perception. Benchmark tests conducted on normal rats have confirmed the localization results provided by the TR algorithm. Results demonstrate that the spatial resolution of this source localization can be as high as the micrometer level. This high precision localization may lead to a paradigm shift in tinnitus diagnosis, which may in turn produce a more cost-effective treatment for tinnitus than any of the existing ones