Abstract
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changes its tool or it is subject to ocean currents, its performance deteriorates since the fixed set of gains is no longer valid for the new conditions.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.
Highlights
Underwater Remotely Operated Vehicles (ROVs) have been widely used in many subsea tasks, ranging from inspection to repair of underwater structures related mainly to the power and oil industry.Very often, according to the task, the ROV is required to continuously change its operating tool and/or to pick up and release loads causing a change in behavior
The controls were evaluated by performing a data capture of 3 m, once the ROV was placed 1m underwater
Reached the stability and the PID gains, computed by the Neural Networks (NN), became stationary, these gains were programmed into the conventional PID as Kp, Kd, and Ki
Summary
Underwater Remotely Operated Vehicles (ROVs) have been widely used in many subsea tasks, ranging from inspection to repair of underwater structures related mainly to the power and oil industry.Very often, according to the task, the ROV is required to continuously change its operating tool and/or to pick up and release loads causing a change in behavior. ROVs have to deal with the highly dynamical underwater environment represented in the form of ocean currents and waves in shallow water. With this in mind, when the dynamic characteristics of the system are time dependent or the operating conditions of the system vary, Sensors 2016, 16, 1429; doi:10.3390/s16091429 www.mdpi.com/journal/sensors. Sensors 2016, 16, 1429 it is necessary to re-tune the gains to obtain the desired performance, resulting in time consumption. The self-tuning mechanism will avoid time consuming manual tuning of the PID controller and promises better results by providing PID controller settings as the system dynamics or operating points change
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