Adaptive fuzzy systems and control: design and stability analysis
Adaptive fuzzy systems and control: design and stability analysis
Design and Control of Autonomous Underwater Robots: A Survey
Autonomous Robots
Underwater Robots: Motion and Force Control of Vehicle-Manipulator Systems (Springer Tracts in Advanced Robotics)
An adaptive fuzzy sliding mode controller for remotely operated underwater vehicles
Robotics and Autonomous Systems
Journal of Intelligent and Robotic Systems
Observer: Assisted Adaptive Tracking Control of an Underactuated Autonomous Underwater Vehicle
Proceedings of Conference on Advances In Robotics
Fuzzy sliding mode autopilot design for nonminimum phase and nonlinear UAV
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Recent Advances in Soft Computing: Theories and Applications
| Hi-index | 0.00 |
This paper address the kinematic variables control problem for the low-speed manoeuvring of a low cost and underactuated underwater vehicle. Control of underwater vehicles is not simple, mainly due to the non-linear and coupled character of system equations, the lack of a precise model of vehicle dynamics and parameters, as well as the appearance of internal and external perturbations. The proposed methodology is an approach included in the control areas of non-linear feedback linearization, model-based and uncertainties consideration, making use of a pioneering algorithm in underwater vehicles. It is based on the fusion of a sliding mode controller and an adaptive fuzzy system, including the advantages of both systems. The main advantage of this methodology is that it relaxes the required knowledge of vehicle model, reducing the cost of its design. The described controller is part of a modular and simple 2D guidance and control architecture. The controller makes use of a semi-decoupled non-linear plant model of the Snorkel vehicle and it is compounded by three independent controllers, each one for the three controllable DOFs of the vehicle. The experimental results demonstrate the good performance of the proposed controller, within the constraints of the sensorial system and the uncertainty of vehicle theoretical models.