This paper present a robust and efficient way of tuning PID controller using three variants of swam intelligence algorithms for disturbance attenuation, and control of a positioning system. While many tuning algorithms focuses on getting the best PID gains that will enable the system to track the command input, and little or no attention is paid on the effect of those gains on disturbance resulting from external natural and artificial sources. Out of the three variants considered, comprehensive learning particle swarm optimization (CLPSO) appear to be more promising in rapidly attenuating (mitigating) the effect of disturbance on the system with a maximum disturbance response amplitude of 0.000329, and peak overshoot of 0.00635 (0.635%), rise time of 0.01s, and setting time of 0.01s. The second most promising algorithm is toroidal bound CLPSO with disturbance response amplitude of 0.000518, and peak overshoot of 0.0812 (8.12%). These results depicts the robustness of swarm intelligence algorithm variants implemented, in combating the effects of external disturbance on the position-controlled system, and at the same time achieving a very low peak overshoot, rise time and settling time.
Iliya et al. (Mon,) studied this question.
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