Design of a closed-loop statistical data controller for a servomechanism using a self-tuning technique
Keywords:
Statistics, Data, Control, Self-tuning, ServomechanismAbstract
This work proposes and applies a technique based on statistical data for the design of a Proportional and a Proportional Integral controller for speed, both capable of self-tuning their gains. The self-tuning technique is based on the statistical analysis of the system output, performing a sampling to calculate the mean and standard deviation, and using the value of z according to the central limit theorem. From this analysis, the probability level is estimated, which allows a mapping from the probabilistic space to the controller gain space, using a sigmoid function to adjust the gains online. The controller performance is evaluated and compared in two first-order systems, one stable and the other unstable, as well as in a real-time speed control servomechanism. The self-tuned statistical proportional-integral controller is compared with equivalent controllers tuned using Matlab's pidtune, highlighting the effectiveness of the proposed technique in the automatic adaptation of the controller.
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