Neural Network-Based Self-Tuning PID Control for Underwater Vehicles.
For decades, PID (Proportional + Integral + Derivative)-like controllers have been successfully used in academia and industry for many kinds of plants. This is thanks to its simplicity and suitable performance in linear or linearized plants, and under certain conditions, in nonlinear ones. A number of PID controller gains tuning approaches have been proposed in the literature in the last decades; most of them off-line techniques. However, in those cases wherein plants are subject to continuous parametric changes or external disturbances, online gains tuning is a desirable choice. This is the case of modular underwater ROVs (Remotely Operated Vehicles) where parameters (weight, buoyancy, added mass, among others) change according to the tool it is fitted with. In practice, some amount of time is dedicated to tune the PID gains of a ROV. Once the best set of gains has been achieved the ROV is ready to work. However, when the vehicle changes its tool or it is subject to ocean currents, its performance deteriorates since the fixed set of gains is no longer valid for the new conditions. Thus, an online PID gains tuning algorithm should be implemented to overcome this problem. In this paper, an auto-tune PID-like controller based on Neural Networks (NN) is proposed. The NN plays the role of automatically estimating the suitable set of PID gains that achieves stability of the system. The NN adjusts online the controller gains that attain the smaller position tracking error. Simulation results are given considering an underactuated 6 DOF (degrees of freedom) underwater ROV. Real time experiments on an underactuated mini ROV are conducted to show the effectiveness of the proposed scheme.
Energy Division, Center for Engineering and Industrial Development-CIDESI, Santiago de Queretaro, Queretaro 76125, Mexico. rodrigoherz@gmail.com.
Rodrigo
R
Hernández-Alvarado
Energy Division, Center for Engineering and Industrial Development-CIDESI, Santiago de Queretaro, Queretaro 76125, Mexico. ggarcia@cidesi.edu.mx.
Luis Govinda
LG
García-Valdovinos
Energy Division, Center for Engineering and Industrial Development-CIDESI, Santiago de Queretaro, Queretaro 76125, Mexico. tsalgado@cidesi.edu.mx.
Tomás
T
Salgado-Jiménez
Tecnologico de Monterrey, Campus Queretaro, Ave. Epigmenio González 500, Fracc. San Pablo, Santiago de Queretaro, Queretaro 76130, Mexico. agomeze@itesm.mx.
Alfonso
A
Gómez-Espinosa
Energy Division, Center for Engineering and Industrial Development-CIDESI, Santiago de Queretaro, Queretaro 76125, Mexico. ffonseca@posgrado.cidesi.edu.mx.
Fernando
F
Fonseca-Navarro
ROV control; auto-tuning PID; disturbances; neural networks