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In dieser Dissertation wird die Exploration und Untersuchung von Umweltfeldern mit Mikro-Unterwasserfahrzeugen (μAUVs) behandelt. Der Entwurf einer unteraktuierten μAUV-Plattform wird präsentiert. Weiterhin werden Regelkonzepte vorgestellt, die es dem μAUV erlauben Trajektorien und Pfaden zu folgen. Es werden zwei Methoden zur Unterwasserlokalisierung von μAUVs vorgestellt. Die erste Methode beruht auf der Ausbreitungsdauer akustischer Signale und die zweite Methode auf der Dämpfung elektromagnetischer Wellen im Wasser. Weiterhin wird ein Regelungskonzept entwickelt, welches einem Flottenverband von μAUVs es erlaubt, Umweltfelder zu erkunden. Das Konzept vereint stochastische optimale Steuerung mit Gauß-Markov-Zufallsfeldern., This dissertation covers exploration and monitoring of environmental fields with micro underwater vehicles (μAUVs). The design of an underactuated μAUV is presented. Moreover, control concepts are introduced for trajectory tracking and path following. Two methods for underwater self-localization are presented, which are suitable for μAUVs. The first approach is based on time delays of arrival of acoustic signals. The second approach exploits attenuation of electro-magnetic signals in water. Lastly, an information theoretic control method is developed, which allows a fleet of μAUVs to explore environmental fields. The method combines stochastic optimal control with Gaussian Markov random fields.

Autonomous exploration of environmental fields is one of the most promising tasks to be performed by fleets of mobile underwater robots. The goal is to maximize the information gain during the exploration process by integrating an information-metric into the path-planning and control step. Therefore, the system maintains an internal belief representation of the environmental field which incorporates previously collected measurements from the real field. In contrast to surface robots, mobile underwater systems are forced to run all computations on-board due to the limited communication bandwidth in underwater domains. Thus, reducing the computational cost of field exploration algorithms constitutes a key challenge for in-field implementations on micro underwater robot teams. In this work, we present a computationally efficient exploration algorithm which utilizes field belief models based on Gaussian Processes, such as Gaussian Markov random fields or Kalman regression, to enable field estimation with constant computational cost over time. We extend the belief models by the use of weighted shape functions to directly incorporate spatially continuous field observations. The developed belief models function as information-theoretic value functions to enable path planning through stochastic optimal control with path integrals. We demonstrate the efficiency of our exploration algorithm in a series of simulations including the case of a stationary spatio-temporal field.

Multi-degree-of-freedom ship motion and ship stability in random seas are of major interest for the development of new advanced intact stability criteria. The purpose of this research is to improve the safety of new ship designs, but the results are relevant also for other engineering systems involving multiple scales. We focus on roll-pitch and roll-heave motion in random seas. The random wave excitation is modeled by a non-white stationary process. This process is derived from a spectral description of the random seaway using traveling effective wave. © 2013 The Authors.

Mechatronic systems are becoming an intrinsic part of our daily life, and the adopted control approach in turn plays an essential role in the emulation of the intelligent behavior. In this paper, a framework for the development of intelligent controllers is proposed. We highlight that robustness, prediction, adaptation, and learning, which may be considered the most fundamental traits of all intelligent biological systems, should be taken into account within the project of the control scheme. Hence, the proposed framework is based on the fusion of a nonlinear control scheme with computational intelligence and also allows mechatronic systems to be able to make reasonable predictions about its dynamic behavior, adapt itself to changes in the plant, learn by interacting with the environment, and be robust to both structured and unstructured uncertainties. In order to illustrate the implementation of the control law within the proposed framework, a new intelligent depth controller is designed for a microdiving agent. On this basis, sliding mode control is combined with an adaptive neural network to provide the basic intelligent features. Online learning by minimizing a composite error signal, instead of supervised off-line training, is adopted to update the weight vector of the neural network. The boundedness and convergence properties of all closed-loop signals are proved using a Lyapunov-like stability analysis. Numerical simulations and experimental results obtained with the microdiving agent demonstrate the efficacy of the proposed approach and its suitableness for both stabilization and trajectory tracking problems., Peer Reviewed

We are interested in perturbed Hamiltonian systems in a plane, which are damped and excited by an absolutely regular non-white Gaussian process. We use two methods for the determination of analytical and semi-analytical solutions to such nonlinear stochastic differential equations (SDE). The first method is based on a limit theorem by Khashminskii, from which a class of methods was derived known as stochastic averaging. From the drift and diffusion of the resulting averaged process, probability density functions and mean exit times can be easily obtained. The second method enables the determination of a Gaussian mixture representation for probability density functions of SDE's. This method was proposed by Pradlwarter and is known as Local Statistical Linearization. The error evolution of such Gaussian mixture shows promising results for further research.

The development of accurate control systems for underwater robotic vehicles relies on the adequate compensation of thruster dynamics. Without compensation, the closed-loop positioning system can exhibit limit cycles. This undesired behaviour may compromise the overall system stability. In this work, a fuzzy sliding-mode compensation scheme is proposed for electrically actuated bladed thrusters, which are commonly employed in the dynamic positioning of underwater vehicles. The boundedness and convergence properties of the tracking error are analytically proven. The numerical results suggest that this approach shows a greatly improved performance when compared with an uncompensated counterpart.

The dead-zone is one of the most common hard nonlinearities in industrial actuators and its presence may drastically compromise control systems stability and performance. Due to the possibility to express specialist knowledge in an algorithmic manner, fuzzy logic has been largely employed in the last decades to both control and identification of uncertain dynamical systems. In spite of the simplicity of this heuristic approach, in some situations a more rigorous mathematical treatment of the problem is required. In this work, an adaptive fuzzy controller is proposed for nonlinear systems subject to dead-zone input. The boundedness of all closed-loop signals and the convergence properties of the tracking error are proven using Lyapunov stability theory and Barbalat's lemma. An application of this adaptive fuzzy scheme to an electro-hydraulic servo-system is introduced to illustrate the controller design method. Numerical results are also presented in order to demonstrate the control system performance.

For a vessel in open seas, the sudden indirect excitation of roll motions due to waves from the front or rear leads to dangerous situations, sometimes even capsizing. We derive a general non-linear model, which is appropriate for the analysis of parametric excited roll motions in head or following random seas. The irregular waves are modelled in terms of a continuous time autoregressive moving average process. The resulting model of stochastic differential equations is investigated numerically by Local Statistical Linearization. The necessary stochastic moments and their derivatives are computed using Itoˆ’s differential rule and Gaussian closure.

Self-localization is one of the most challenging problems for deploying micro autonomous underwater vehicles ( AUV) in confined underwater environments. This paper extends a recently-developed self-localization method that is based on the attenuation of electro-magnetic waves, to the AUV domain. We demonstrate a compact, low-cost architecture that is able to perform all signal processing steps present in the original method. The system is passive with one-way signal transmission and scales to possibly large AUV fleets. It is based on the spherical localization concept. We present results from static and dynamic position estimation experiments and discuss the tradeoffs of the system., Self-localization is one of the most challenging problems for deploying micro autonomous underwater vehicles (μAUV) in confined underwater environments. This paper extends a recently-developed self-localization method that is based on the attenuation of electro-magnetic waves, to the μAUV domain. We demonstrate a compact, low-cost architecture that is able to perform all signal processing steps present in the original method. The system is passive with one-way signal transmission and scales to possibly large μAUV fleets. It is based on the spherical localization concept. We present results from static and dynamic position estimation experiments and discuss the tradeoffs of the system.