How to Apply Fuzzy MISO PID in the Industry? An Empirical Study Case on Simulation of Crane Relocating Containers

Abstract

The proportional-integral-derivative (PID) algorithm automatically adjusts the control output based on the difference between a set point and a measured process variable. The classical approach is broadly used in the majority of control systems. However, in complex problems, this approach is not efficient, especially when the exact mathematical formula is difficult to specify. Besides, it was already proven that highly nonlinear situations are also significantly limiting the usage of the PID algorithm, in contrast to the fuzzy algorithms, which often work correctly under such conditions. In the case of multidimensional objects, where many independently operating PID algorithms are currently used, it is worth considering the use of one fuzzy algorithm with many-input single-output (MISO) or many-input many-output (MIMO) structure. In this work, a MISO type chip is investigated in the study case on simulation of crane relocating container with the external distribution. It is an example of control objects that due to badly conditioned dynamic features (strong non-linearities) require the operator’s intervention in manual or semi-automatic mode. The possibility of fuzzy algorithm synthesis is analyzed with two linguistic variable inputs (distance from −100 to 500 mm and angle from −45° to 45°). The output signal is the speed which is modelled as a linguistic power variable (in the domain from −100% to 100%). Based on 36 fuzzy rules, we present the main contribution, the control system with external disturbance, to show the effectiveness of the identified fuzzy PID approach with different gain values. The fuzzy control system and PID control are implemented and compared concerning the time taken for the container to reach the set point. The results show that fuzzy MISO PID is more effective than the classical one because fuzzy set theory helps to deal with the environmental uncertainty. The container’s angle deviations are taken into consideration, as mitigating them and simultaneously maintaining the fastest speed possible is an essential factor of this challenge.

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