Adaptive depth control algorithms for an autonomous underwater vehicle

Abstract

Research on Autonomous Underwater Vehicle (AUV) has been increasing in the recent years. These robotics have the ability to revolutionize access to the oceans to address critical problems facing the marine community such as underwater search and mapping, water column observations climate change assessment, marine habitat monitoring, and underwater mine detection. In this thesis an adaptive nonlinear controller for steering the dynamic model of an autonomous underwater vehicle (AUV) onto a predefined path at a constant forward speed along a desired path is being presented. In this we have used two different controllers for dive plane control of an AUV. In one case, the diving dynamics of an AUV is derived under the assumptions that the pitch angle of AUV is small in the diving plane motion of the vehicle. Autonomous Underwater Vehicles’ dynamics are highly nonlinear and the hydrodynamic coefficients of the vehicles are difficult to determine due to the variations of the hydrodynamic coefficients with different operating conditions of the environment. These kinds of difficulties cause modeling inaccuracies of AUV’s dynamics. So in order to achieve robustness against parameter uncertainty, system identification technique based on Model Reference Adaptive Controller (MRAC) using MIT rule and Fuzzy Logic Controller (FLC) is employed for modeling the AUV dynamics. In this thesis MRAC technique is being proposed for the dynamics control and for the kinematics control of an Autonomous Underwater Vehicle we have used the FLC .Simulation results are being shown which shows effective dive-plane control in spite of the dynamic uncertainties. However in the second case we have proposed a dive plane controller based on Lyapunov theory and Backstepping techniques, where the dynamics of the AUV is being considered without keeping any restricting assumptions on AUV’s pitch angle.