Mini-AUV Hydrodynamic Parameters Identification via CFD Simulations and Their Application on Control Performance Evaluation

Linear matrix genetic programming as a tool for data-driven black-box control-oriented modeling in conditions of limited access to training data

In the paper, a new evolutionary technique called Linear Matrix Genetic Programming (LMGP) is proposed. It is a matrix extension of Linear Genetic Programming and its application is data-driven black-box control-oriented modeling in conditions of limited access to training data. In LMGP, the model is in the form of an evolutionarily-shaped program which is a sequence of matrix operations. Since the program has a hidden state, running it for a sequence of input data has a similar effect to using well-known recurrent neural networks such as Long Short-Term Memory (LSTM) or Gated Recurrent Unit (GRU). To verify the effectiveness of the LMGP, it was compared with different types of neural networks. The task of all the compared techniques was to reproduce the behavior of a nonlinear model of an underwater vehicle. The results of the comparative tests are reported in the paper and they show that the LMGP can quickly find an effective and very simple solution to the given problem. Moreover, a detailed comparison of models, generated by LMGP and LSTM/GRU, revealed that the former are up to four times more accurate than the latter in reproducing vehicle behavior.

Fuzzy logic controller for UAV with gains optimized via genetic algorithm

A gains optimizer of a fuzzy controller system for an Unmanned Aerial Vehicle (UAV) based on a metaheuristic algorithm is developed in the present investigation. The contribution of the work is the adjustment by the Genetic Algorithm (GA) to tune the gains at the input of a fuzzy controller. First, a typical fuzzy controller was modeled, designed, and implemented in a mathematical model obtained by Newton-Euler methodology. Subsequently, the control gains were optimized using a metaheuristic algorithm. The control objective is that the UAV consumes the least amount of energy. With this basis, the Genetic Algorithm finds the necessary gains to meet the design parameters. The tests were performed using the Matlab-Simulink environment. The results indicate an improvement, reducing the error in tracking trajectories from 30% in some tasks and following trajectories that could not be completed without a tuned controller in other tasks.

Numerical Investigation on Hydrodynamic Performance of a Portable AUV

This paper conducts a numerical investigation on the hydrodynamic performance of a portable autonomous underwater vehicle (AUV). The portable AUV is designed to cruise and perform some tasks autonomously in the underwater world. However, its dynamic performance is strongly affected by hydrodynamic effects. Therefore, it is crucial to investigate the hydrodynamic performance of the portable AUV for its accurate dynamic modeling and control. In this work, based on the designed portable AUV, a comprehensive hydrodynamic performance investigation was conducted by adopting the computational fluid dynamics (CFD) method. Firstly, the mechanical structure of the portable AUV was briefly introduced, and the dynamic model of the AUV, including the hydrodynamic term, was established. Then, the unknown hydrodynamic coefficients in the dynamic model were estimated through the towing experiment and the plane-motion-mechanism (PMM) experiment simulation. In addition, considering that the portable AUV was affected by wave forces when cruising near the water surface, the influence of surface waves on the hydrodynamic performance of the AUV under different wave conditions and submerged depths was analyzed. Finally, the effectiveness of our method was verified by experiments on the standard models, and a physical experiment platform was built in this work to facilitate hydrodynamic performance investigations of some portable small-size AUVs.

Dynamic Neural Network-Based Adaptive Tracking Control for an Autonomous Underwater Vehicle Subject to Modeling and Parametric Uncertainties

This research presents a way to improve the autonomous maneuvering capability of a four-degrees-of-freedom (4DOF) autonomous underwater vehicle (AUV) to perform trajectory tracking tasks in a disturbed underwater environment. This study considers four second-order input-affine nonlinear equations for the translational (x,y,z) and rotational (heading) dynamics of a real AUV subject to hydrodynamic parameter uncertainties (added mass and damping coefficients), unknown damping dynamics, and external disturbances. We proposed an identification-control scheme for each dynamic named Dynamic Neural Control System (DNCS) as a combination of an adaptive neural controller based on nonparametric identification of the effect of unknown dynamics and external disturbances, and on parametric estimation of the added mass dependent input gain. Several numerical simulations validate the satisfactory performance of the proposed DNCS tracking reference trajectories in comparison with a conventional feedback controller with no adaptive compensation. Some graphics showing dynamic approximation of the lumped disturbance as well as estimation of the parametric uncertainty are depicted, validating effective operation of the proposed DNCS when the system is almost completely unknown.