Abstract
Automated Guided Vehicles (AGVs) are essential to settle the Industry 4.0 paradigm, along with other robotic systems. These autonomous vehicles are usually controlled with a PID controller. But the accuracy of the path following strongly depends on the PID performance, and hence, of the fine tuning of the regulator parameters. Even more, this adjustment also depends on the system dynamics. Thus, in this work the use of a Soft Computing evolutive technique, genetic algorithms (GA), is proposed in order to obtain the optimal parameters of a PID regulator. The dynamic model of the AGV and of the guiding sensor are used. Different trajectories have been tested. A qualitative analysis of different system configurations using this optimization procedure is carried out. Some conclusions regarding the sensor design and the inner power train system are obtained. They may be useful for robotic engineers and companies manufacturing this kind of industrial autonomous vehicles.
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Acknowledgement
This work was partially supported by the Spanish Ministry of Science, Innovation and Universities under MCI/AEI/FEDER Project number RTI2018–094902-B-C21.
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Abajo, M.R., Sierra-García, J.E., Santos, M. (2022). Evolutive Tuning Optimization of a PID Controller for Autonomous Path-Following Robot. In: Sanjurjo González, H., Pastor López, I., García Bringas, P., Quintián, H., Corchado, E. (eds) 16th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2021). SOCO 2021. Advances in Intelligent Systems and Computing, vol 1401. Springer, Cham. https://doi.org/10.1007/978-3-030-87869-6_43
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