`` Direct Search'' Solution of Numerical and Statistical Problems
Journal of the ACM (JACM)
Modern Control Engineering
Simultaneous generalized hill climbing algorithms for addressing sets of discrete optimization problems
Design of optimal disturbance rejection PID controllers usinggenetic algorithms
IEEE Transactions on Evolutionary Computation
Tuning of a neuro-fuzzy controller by genetic algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Proportional---Integral---Derivative (PID) controllers are mainly used in industrial control systems, motor drives, process control, and instrumentation. Optimum tuning of PID controller parameters is a big challenge for researchers and plant operators. This paper describes the 'Local Unimodal Sampling (LUS)' algorithm to tune the PID controller for an Automatic Voltage Regulator (AVR) system to determine the optimum controller parameters. Many optimization techniques use local sampling with a fixed sampling range. Therefore, there is a risk of getting stuck in the local optima. This problem can be overcome by using LUS algorithm which decreases the sampling range as optimization progresses. Compared with the artificial bee colony (ABC), the proposed method is found to be more robust and efficient in improving the step response of an AVR system. Transient response analysis, root locus analysis and bode analysis are used to compare the performance of both the algorithms.