Development of Modeling and Simulation Platform for Path-Planning and Control of Autonomous Underwater Vehicles in Three-Dimensional Spaces

Autonomous underwater vehicles (AUVs) operating in deep sea and littoral environments have diverse applications including marine biology exploration, ocean environment monitoring, search for plane crash sites, inspection of ship-hulls and pipelines, underwater oil rig maintenance, border patrol, etc. Achieving autonomy in underwater vehicles relies on a tight integration between modules of sensing, navigation, decision-making, path-planning, trajectory tracking, and low-level control. This system integration task benefits from testing the related algorithms and techniques in a simulated environment before implementation in a physical test bed. This thesis reports on the development of a modeling and simulation platform that supports the design and testing of path planning and control algorithms in a synthetic AUV, representing a simulated version of a physical AUV. The approach allows integration between path-planners and closed-loop controllers that enable the synthetic AUV to track dynamically feasible trajectories in three-dimensional spaces. The dynamical behavior of the AUV is modeled using the equations of motion that incorporate the effects of external forces (e.g., buoyancy, gravity, hydrodynamic drag, centripetal force, Coriolis force, etc.), thrust forces, and inertial forces acting on the AUV. The equations of motion are translated into a state space formulation and the S-function feature of the Simulink and MATLAB scripts are used to evolve the state trajectories from initial conditions. A three-dimensional visualization of the resulting AUV motion is achieved by feeding the corresponding position and orientation states into an animation code. Experimental validation is carried out by performing integrated waypoint planner (e.g., using the popular A* algorithm) and PD controller implementations that allow the traversal of the synthetic AUV in two-dimensional (XY, XZ, YZ) and three-dimensional spaces. An underwater pipe-line inspection task carried out by the AUV is demonstrated in a simulated environment. The simulation testbed holds a potential to support planner and controller design for implementation in physical AUVs, thereby allowing exploration of various research topics in the field

Distributed Neurodynamics-Based Backstepping Optimal Control for Robust Constrained Consensus of Underactuated Underwater Vehicles Fleet

Robust constrained formation tracking control of underactuated underwater vehicles (UUVs) fleet in three-dimensional space is a challenging but practical problem. To address this problem, this paper develops a novel consensus based optimal coordination protocol and a robust controller, which adopts a hierarchical architecture. On the top layer, the spherical coordinate transform is introduced to tackle the nonholonomic constraint, and then a distributed optimal motion coordination strategy is developed. As a result, the optimal formation tracking of UUVs fleet can be achieved, and the constraints are fulfilled. To realize the generated optimal commands better and, meanwhile, deal with the underactuation, at the lower-level control loop a neurodynamics based robust backstepping controller is designed, and in particular, the issue of "explosion of terms" appearing in conventional backstepping based controllers is avoided and control activities are improved. The stability of the overall UUVs formation system is established to ensure that all the states of the UUVs are uniformly ultimately bounded in the presence of unknown disturbances. Finally, extensive simulation comparisons are made to illustrate the superiority and effectiveness of the derived optimal formation tracking protocol.Comment: This paper is accepted by IEEE Transactions on Cybernetic

Prescribed performance control of underactuated surface vessels' trajectory using a neural network and integral time-delay sliding mode

summary:To tackle the underactuated surface vessel (USV) trajectory tracking challenge with input delays and composite disturbances, an integral time-delay sliding mode controller based on backstepping is discussed. First, the law of virtual velocity control is established by coordinate transformation and the position error is caused to converge utilizing the performance function. At the same time, based on the estimation of velocity vector by the high-gain observer (HGO), radial basis function (RBF) neural network is applied to compensate for both the uncertainty of model parameters and external disturbances. The longitudinal and heading control laws are presented in combination with the integral time-delay sliding mode control. Then, on the basis of Lyapunov - Krasovskii functional and stability proof, virtual velocity error is guaranteed to converge to 0 in finite time. Finally, the outcomes of the numerical simulation demonstrate the reliability and efficiency of the proposed approach

Autonomous homing and docking tasks for an underwater vehicle

This paper briefly introduces a strategy for autonomous homing and docking tasks using an autonomous underwater vehicle. The control and guidance based path following for those tasks are described in this work. A standard sliding mode for controller design is briefly given. The method provides robust motion control efforts for an underwater vehicle’s decoupled system whilst minimising chattering effects. In a guidance system, the vector field based on a conventional artificial potential field method gives a desired trajectory with a use of existing information from sensors in the network. A well structured Line-of-Sight method is used for an AUV to follow the path. It provides guidance for an AUV to follow the predefined trajectory to a required position with the final desired orientation at the dock. Integration of a control and guidance system provides a complete system for this application. Simulation studies are illustrated in the paper