Dorsal and pectoral fin control of a biorobotic autonomous underwater vehicle

This thesis involves an in-depth research on the maneuvering of bio-robotic autonomous undersea vehicles (BAUVs) using bio-mimetic swimming mechanisms. Motivation was derived from the amazing flexibility and agility the fish inherit with the help of their pectoral and dorsal fins; In the first part of the thesis, control of BAUVs using dorsal fins is considered. The force produced by the cambering of the dorsal fins is used for control. An indirect adaptive controller is designed for depth tracking along constant trajectories even when the system parameters are not known. Next, for following time-varying trajectories, an adaptive control system for yaw plane control of BAUVs is developed. It is capable of working efficiently even when large uncertainties in the system parameters are present and system nonlinearities are dominant; In the second part of the thesis, pectoral fin control of BAUVs is considered. The flapping of these oscillating fins provides the necessary force and moment for control. A discrete-time optimal controller for set point (constant path) control and inverse controller for tracking time varying trajectories in the yaw plane are derived. Further, an indirect adaptive control system that can accomplish depth trajectory tracking even when the model paramters are completely unknown is developed; The performance evaluation of the controllers is done by simulation using matlab/simulink

Nonlinear suboptimal and adaptive pectoral fin control of autonomous underwater vehicle

Autonomous underwater vehicles (AUVs) are used for numerous applications in the deep sea, such as hydrographic survey, sea bed mining and oceanographic mapping, etc. Presently, significant amount of effort, is being made in developing biorobotic AUVs (BAUVs) with biologically inspired control surfaces. However, the dynamics of AUVs and BAUVs are highly nonlinear and the hydrodynamic coefficients are not precisely known. As such the development of nonlinear and adaptive control systems is of considerable importance; We consider the suboptimal dive plane control of AUVs using the state-dependent Riccati equation (SDRE) technique. This method provides effective means of designing nonlinear control systems for minimum as well as nonminimum phase AUV models. Moreover, hard control constraints are included in the design process; We also attempt to design adaptive control systems for BAUVs using biologically-inspired pectoral-like fins. The fins are assumed to be oscillating harmonically with a combined linear (sway) and angular (yaw) motion. The bias (mean) angle of the angular motion of the fin is used as a control input. Using discrete-time state variable representation of the BAUV, adaptive sampled-data control systems for the trajectory control are derived using state feedback as well as output feedback. We develop direct as well as indirect adaptive control systems for BAUVs. The advantage of the indirect adaptive law lies in its applicability to minimum as well as nonminimum phase systems. Simulation results are presented to evaluate the performance of each control system

Dynamic response and maneuvering strategies of a hybrid autonomous underwater vehicle in hovering

Thesis (S.M. in Ocean Engineering)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2009.Includes bibliographical references (p. 87-93).The Odyssey IV autonomous underwater vehicle (AUV) is the next generation of unmanned subsurface robots from the MIT Sea Grant AUV Laboratory. The Odyssey IV AUV has a novel propulsion system, which includes a pair of azimuthing thrusters for maneuvering in surge and heave. An analytical model was developed to describe the complex nonlinear vehicle dynamics, and experiments were performed to refine this model. The fluid dynamics of unsteady azimuthing marine propulsors are largely unstudied, especially for small vehicles like the Odyssey IV AUV. Experiments suggest that thrust developed by an azimuthing propulsor is dependent on the azimuth angle rate of change, and can substantially affect vehicle dynamics. A simple model capturing the effects of azimuthing on the thruster dynamics is developed, and is shown to improve behavior of the model.The use of azimuthing thrusters presents interesting problems and opportunities in maneuvering and control. Nonlinear model predictive control (MPC) is a technique that consists of the real-time optimization of a nonlinear dynamic system model, with the ability to handle constraints and nonlinearities. In this work, several variations of simulated and experimental MPC-based controllers are investigated. The primary challenge in applying nonlinear MPC to the Odyssey IV is solving the time intensive trajectory optimization problem online. Simulations suggest that MPC is able to capitalize on its knowledge of the system, allowing more aggressive trajectories than a traditional PID controller.by Lauren Alise Cooney.S.M.in Ocean Engineerin

Pectoral fin control of a biorobotic Auv in the dive pLane

Maneuvering of biologically inspired robotic undersea vehicles (BAUVs) is considered in the dive plane using pectoral-like oscillating fins. Firstly, an open-loop and optimal feedback control system is designed to control a biorobotic AUV in the dive pLane Next, an inverse control system for dive-plane control is derived based on a discrete-time AUV model. An approximate minimum phase system with a new output variable is derived for the purpose of design; Computational fluid dynamics (CFD) is used to parameterize the forces generated by a mechanical oscillatory flapping foil, which attempts to mimic the pectoral fin of a fish. Finally, a control system for the independent asymptotic control of the lateral and rotational motion of a 2-D hydrofoil based on the internal model principle (servomechanism theory) is derived

Underwater Vehicles

For the latest twenty to thirty years, a significant number of AUVs has been created for the solving of wide spectrum of scientific and applied tasks of ocean development and research. For the short time period the AUVs have shown the efficiency at performance of complex search and inspection works and opened a number of new important applications. Initially the information about AUVs had mainly review-advertising character but now more attention is paid to practical achievements, problems and systems technologies. AUVs are losing their prototype status and have become a fully operational, reliable and effective tool and modern multi-purpose AUVs represent the new class of underwater robotic objects with inherent tasks and practical applications, particular features of technology, systems structure and functional properties

Biologically inspired learning system

Learning Systems used on robots require either a-priori knowledge in the form of models, rules of thumb or databases or require that robot to physically execute multitudes of trial solutions. The first requirement limits the robot’s ability to operate in unstructured changing environments, and the second limits the robot’s service life and resources. In this research a generalized approach to learning was developed through a series of algorithms that can be used for construction of behaviors that are able to cope with unstructured environments through adaptation of both internal parameters and system structure as a result of a goal based supervisory mechanism. Four main learning algorithms have been developed, along with a goal directed random exploration routine. These algorithms all use the concept of learning from a recent memory in order to save the robot/agent from having to exhaustively execute all trial solutions. The first algorithm is a reactive online learning algorithm that uses a supervised learning to find the sensor/action combinations that promote realization of a preprogrammed goal. It produces a feed forward neural network controller that is used to control the robot. The second algorithm is similar to first in that it uses a supervised learning strategy, but it produces a neural network that considers past values, thus providing a non-reactive solution. The third algorithm is a departure from the first two in that uses a non-supervised learning technique to learn the best actions for each situation the robot encounters. The last algorithm builds a graph of the situations encountered by agent/robot in order to learn to associate the best actions with sensor inputs. It uses an unsupervised learning approach based on shortest paths to a goal situation in the graph in order to generate a non-reactive feed forward neural network. Test results were good, the first and third algorithms were tested in a formation maneuvering task in both simulation and onboard mobile robots, while the second and fourth were tested simulation

Dynamics and Control of Non-smooth Systems with Applications to Supercavitating Vehicles

The subject matter of this dissertation relates to the dynamics of non-smooth vehicle systems, and in particular, supercavitating vehicles. These high-speed underwater vehicles are designed to have sustained vaporous or ventilated gas cavities that form over the entire vehicle. In terms of the modeling, the system non-smoothness is caused by the interaction forces generated when the vehicle contacts the cavity. These planing interactions can cause stable and unstable dynamics, some of which could be limit-cycle dynamics. Here, planing forces are considered on the basis of non-cylindrical cavity shapes that include shifts induced by the cavitator angle of attack. Incorporating these realistic physical effects into a vehicle system model generates a unique hydrodynamic non-smoothness that is characterized by non-constant switching boundaries and non-constant switched dynamics. Nonlinear stability analyses are carried out, Hopf bifurcations of equilibrium solutions are identified, and stabilizing control is investigated. Also considered is partially cavitating system dynamics, where active fin forces are used to support the vehicle. Non-steady planing is also considered, which accounts for vehicle motions into the cavity, and this planing provides a damping-like component in the planing force formulation. Modeled with non-steady planing is a physical time delay relating to the fact that the cavity, where planing occurs, is based on the previous cavitator position and orientation data. This delay is found to be stabilizing for certain values of speed. Maneuvering is considered by using inner-loop and outer-loop control schemes. A feedback inner-loop scheme helps reject fast planing instabilities, while a numeric optimal control approach is used to generate outer-loop commands to guide the vehicle through desired maneuvers. The maneuvers are considered for operations with tight body to cavity clearance, and in which planing is prevalent. Simple search algorithms along with a penalty method for handling the constraints are found to work the best due to the complexity of the non-smooth system dynamics

Hybrid state estimators for the control of remotely operated underwater vehicles

Submitted in partial fulfillment of the requirements for the degree of Ocean Engineer at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution August 1988This paper explores the use of 'hybrid' state estimators to increase the accuracy and flexibility of acoustic position measurement systems used in the control of underwater vehicles. Two different approaches to extend the range of acoustic position measurement systems are explored. The first approach is the use of an inexpensive Strapdown Inertial Measurement System (SIMS) to augment the acoustic with inertial position information. This approach is based on the experience gained using an attitude and inertial measurement package fielded on the JASON JUNIOR Remotely Operated Vehicle (ROV). The second approach is the use of a mobile, platform-mounted, acoustic net in conjunction with a platform tracking system. This second investigation used the JASON ROV as the basis for the simulation work. As motivation, some of the theoretical and practical difficulties encountered when range is extended using an unaugmented system are explored. Simulation results are used to demonstrate the effects of these difficulties on position estimation accuracy and on the performance in closed loop control of the vehicle. Using measured sensor noise characteristics, a hybrid Kalman filter is developed for each approach to take the greatest advantage of the available information. Formulation of the Kalman filter is different for each case. In the second case, the geographic position of the ROV is the sum of the acoustic net's geographic position, measured at a different interval by an RF positioning system, and the position of the ROV relative to the net, as measured acoustically. Closed loop vehicle performance evaluations are made for representative noise levels and update rates with and without the augmentation discussed in the first approach. Finally, conclusions are drawn about the benefits and applications of the hybrid Kalman filter to the control of Remotely Operated Vehicles