Neural Network-Based Self-Tuning PID Control for Underwater Vehicles

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changes its tool or it is subject to ocean currents, its performance deteriorates since the fixed set of gains is no longer valid for the new conditions.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.

Trajectory Tracking Control Study of a New Parallel Mechanism with Redundant Actuation

“…The nonredundant limbs are controlled by the BP neural network PID controller. Figure 3 shows the block diagram of the BP neural network model which is employed in the intelligent gain tuning by online learning [37][38][39][40][41]. In this model, the NN has four input layers, eight hidden layers, and three output layers, and w o and w i are the weight factors of input layers and output layers that can be continuously updated by machine learning.…”

Trajectory tracking control for manned submersible system with disturbances via disturbance characterization index approach

“…Therefore, the complete nonlinear model of the manned submersible consists of the kinematics Equation (1) and dynamics Equation (5). In this paper, we will design a control input vector u(t) to drive the trajectory (t) of the manned submersible to track the desired trajectory d (t) even in the presence of the lumped disturbances d l , which means that lim t→∞ ( (t) − d (t)) = 0.…”

Improvement of the inverse dynamics method for high-precision control of nonlinear objects under conditions of uncertainty

“…Study [6] proposed solving a task on the motion control of ROV by using ACS based on a multidimensional adaptive PID controller. Parameters for the controller are set by a neural network.…”

“…where k is the coefficient that defines the speed of performance of the circuit to compensate for uncertainties; σ is the parameter that implements the rule of signs, which ensures stability of the circuit to compensate for uncertainties [14]. Performance speed of circuit (7) must be much higher than the performance speed of reference model (6). However, a significant increase in the coefficient k can cause the loss of stability in the process of solving numerically the differential equations of the circuit to compensate for uncertainties.…”

“…Thus, parameter k is chosen so that the performance speed of circuit (7) is much slower than the time quantization period in the computer implementation of ACS controller. Next, we set the performance speed for the reference model of a compensation circuit (6). And the last step is to assign the performance speed of a reference model of ACS (1); it should be much lower than the performance speed for the reference model of a compensation circuit (6).…”