Numerical analysis and graphic visualization with MATLAB
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Ant algorithms for discrete optimization
Artificial Life
Control Systems Theory with Engineering Applications
Control Systems Theory with Engineering Applications
Neural Systems for Control
Fuzzy Control
Modelling and Control of Robot Manipulators
Modelling and Control of Robot Manipulators
Foundations of Fuzzy Control
Bacterial foraging oriented by particle swarm optimization strategy for PID tuning
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
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ICYCS '08 Proceedings of the 2008 The 9th International Conference for Young Computer Scientists
A micro-bacterial foraging algorithm for high-dimensional optimization
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Dual-line PID controller based on PSO for speed control of DC motors
ISCIT'09 Proceedings of the 9th international conference on Communications and information technologies
An improved bacterial foraging optimization
ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
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ICICTA '10 Proceedings of the 2010 International Conference on Intelligent Computation Technology and Automation - Volume 02
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Introduction to Genetic Algorithms
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ICMTMA '11 Proceedings of the 2011 Third International Conference on Measuring Technology and Mechatronics Automation - Volume 03
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IEEE Transactions on Signal Processing
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IEEE Transactions on Neural Networks
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This paper introduces a bacterial foraging algorithm (BFA) based high performance speed control system for a DC motor. The rotor speed of the DC motor is being made to follow an arbitrary selected trajectory. The unknown nonlinear dynamics of the motor and the load are captured by BFA. The trained BFA identifier is used with a desired reference model to achieve trajectory control of DC motor. In this paper bacterial foraging algorithm (BFA) has been implemented for identification and control of DC motor. Simulation study on proposed system has been carried out in MATLAB. System nonlinearities alpha and beta have been estimated using BFA and compared with actual plant nonlinearities of dynamical system. In tracking of motor speed using BFA based controller the performance of the motor have been observed and compared with reference one. Performance study of DC motor has been carried out through genetic algorithm (GA) also. A comparison of performance analysis using BFA controller and that of GA for trajectory tracking shows that BFA based adaptive controller works effectively for tracking the desired trajectory in DC motor with less computational time.