Adaptive computational chemotaxis in bacterial foraging optimization: an analysis
IEEE Transactions on Evolutionary Computation
Stability analysis of the reproduction operator in bacterial foraging optimization
Theoretical Computer Science
Automatic circle detection on digital images with an adaptive bacterial foraging algorithm
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Applied Computational Intelligence and Soft Computing
Controller parameter optimization for nonlinear systems using enhanced bacteria foraging algorithm
Applied Computational Intelligence and Soft Computing
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In this paper, a method to tune the two Degree Of Freedom PID controller for a class of time delayed unstable systems using Bacterial Foraging Optimization algorithm is proposed. In this algorithm, limits are assigned for the algorithm and search parameters to minimize the convergence time during the optimization exploration. The problem considered in this study is to aptly tune the controller in order to enhance the overall closed loop performance of the time delayed unstable process. The BFO algorithm is focused to search the best possible controller parameter values such as 'Kp, Ki, Kd' and controller weighting parameters'α, β' by minimizing the multiple objective function which monitor the optimization search. A comparative study is presented between the feed forward and the feedback 2DOF PID structures. The robustness of the BFO tuned 2DOF PID controller is tested by introducing model uncertainty in the process model parameters. The simulation results evident that, the proposed procedure helps to accomplish enhanced system performance such as smooth reference tracking, satisfactory disturbance rejection, and error minimization for a class of unstable systems.