This paper develops a robust nonlinear controller that asymptotically drives the dynamic model of an autonomous underwater vehicle (AUV) onto a predefined path at a constant forward speed. A kinematic controller is first derived, and extended to cope with vehicle dynamics by resorting to backstepping and Lyapunov-based techniques. Robustness to vehicle parameter uncertainty is addressed by incorporating a hybrid parameter adaptation scheme. The resulting nonlinear adaptive control system is formally shown and it yields asymptotic convergence of the vehicle to the path. Simulations illustrate the performance of the derived controller .

Robust Nonlinear Path-Following Control of an AUV

This report presents a survey of computer based simulators for unmanned vehicles. The simulators examined cover a wide spectrum of vehicles including unmanned aerial vehicles, both full scale and micro size; unmanned surface and subsurface vehicles; and unmanned ground vehicles. The majority of simulators use simple numerical simulation and simplistic visualization using custom OpenGL code. An emerging trend is to used modified commercial game engines for physical simulation and visualization. The game engines that are commercially available today are capable of physical simulations providing basic physical properties and interactions between objects. Newer and/or specialized engines such as the flight simulator X-Plane or Ageia PhysX and Havok physics engines, are capable of simulating more complex physical interactions between objects. Researchers in need of a simulator have a choice of using game engines or available open source and commercially available simulators, allowing resources to be focused on research instead of building a new simulator. We conclude that it is no longer necessary to build a new simulator from scratch.

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A Survey of Commercial & Open Source Unmanned Vehicle Simulators

In this paper, a new concept of formation learning control is introduced to the field of formation control of multiple autonomous underwater vehicles (AUVs), which specifies a joint objective of distributed formation tracking control and learning/identification of nonlinear uncertain AUV dynamics. A novel two-layer distributed formation learning control scheme is proposed, which consists of an upper-layer distributed adaptive observer and a lower-layer decentralized deterministic learning controller. This new formation learning control scheme advances existing techniques in three important ways: 1) the multi-AUV system under consideration has heterogeneous nonlinear uncertain dynamics; 2) the formation learning control protocol can be designed and implemented by each local AUV agent in a fully distributed fashion without using any global information; and 3) in addition to the formation control performance, the distributed control protocol is also capable of accurately identifying the AUVs’ heterogeneous nonlinear uncertain dynamics and utilizing experiences to improve formation control performance. Extensive simulations have been conducted to demonstrate the effectiveness of the proposed results.

Formation Learning Control of Multiple Autonomous Underwater Vehicles With Heterogeneous Nonlinear Uncertain Dynamics

Design of an adaptive nonlinear controller for depth control of an autonomous underwater vehicle

This paper focuses on design of a new self-adaptive fuzzy PID controller based on nonlinear MIMO structure for an AUV. Complexity and highly coupled dynamics, time-variance, and difficulty in hydrodynamic modeling and simulation, complicates the AUV modeling process and the design of proper and acceptable controller. In this work, the comprehensive nonlinear model of AUV is derived through kinematics and dynamic equations and then its treatment in open-loop is verified. In proposed controller, the PID parameters are adjusted by Mamdani fuzzy rules. Combined adaptive methods and dual PID controllers can improve solving of the uncertainty challenge in the PID parameters and AUV modeling uncertainty. The simulation results indicate that developed control system is stable, competent, and efficient enough to control the AUV in tracking the two channels of heading and depth with stabilized speed. Obtained results show that the proposed controller is not only robust, but also gives excellent dynamic, stunning steady-state characteristics and robust stability compared with a classically tuned PID controller.

Modeling and control of autonomous underwater vehicle (AUV) in heading and depth attitude via self-adaptive fuzzy PID controller

Improving the performance of modular, low-cost autonomous underwater vehicles (AUVs) in such applications as long-range oceanographic survey, autonomous docking, and shallow-water mine countermeasures requires improving the vehicles' maneuvering precision and battery life. These goals can be achieved through the improvement of the vehicle control system. A vehicle dynamics model based on a combination of theory and empirical data would provide an efficient platform for vehicle control system development, and an alternative to the typical trial-and-error method of vehicle control system field tuning. As there exists no standard procedure for vehicle modeling in industry, the simulation of each vehicle system represents a new challenge. Developed by von Alt and associates at the Woods Hole Oceanographic Institute, the REMUS AUV is a small, low-cost platform serving in a range of oceanographic applications. This thesis describes the development and verification of a six degree of freedom, non-linear simulation model for the REMUS vehicle, the first such model for this platform. In this model, the external forces and moments resulting from hydrostatics, hydrodynamic lift and drag, added mass, and the control inputs of the vehicle propeller and fins are all defined in terms of vehicle coefficients. This thesis describes the derivation of these coefficients in detail. The equations determining the coefficients, as well as those describing the vehicle rigid-body dynamics, are left in non-linear form to better simulate the inherently non-linear behavior of the vehicle. Simulation of the vehicle motion is achieved through numeric integration of the equations of motion. The simulator output is then checked against vehicle dynamics data collected in experiments performed at sea. The simulator is shown to accurately model the motion of the vehicle. Thesis Supervisor: Jerome Milgram Title: Professor of Ocean Engineering, MIT Thesis Supervisor: Kamal Youcef-Toumi Title: Professor of Mechanical Engineering, MIT Thesis Supervisor: Christopher von Alt Title: Principal Engineer, WHOI Candide had been wounded by some splinters of stone; he was stretched out in the street and covered with debris. He said to Pangloss: "Alas, get me a little wine and oil, I am dying." "This earthquake is not a new thing," replied Pangloss. "The town of Lima suffered the same shocks in America last year; same causes, same effects; there is certainly a vein of sulfur underground from Lima to Lisbon." "Nothing is more probable," said Candide, "but for the love of God, a little oil and wine." "What do you mean, probable?" replied the philosopher. "I maintain that the matter is proved." Candide lost consciousness.

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Timothy Prestero

Papers

Verification of a six-degree of freedom simulation model for the REMUS autonomous underwater vehicle