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Person# Francisco Fernandes Castro Rego

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Related research domains (10)

Related publications (13)

Algorithm

In mathematics and computer science, an algorithm (ˈælɡərɪðəm) is a finite sequence of rigorous instructions, typically used to solve a class of specific problems or to perform a computation. Algorithms are used as specifications for performing calculations and data processing. More advanced algorithms can use conditionals to divert the code execution through various routes (referred to as automated decision-making) and deduce valid inferences (referred to as automated reasoning), achieving automation eventually.

Autonomous underwater vehicle

An autonomous underwater vehicle (AUV) is a robot that travels underwater without requiring continuous input from an operator. AUVs constitute part of a larger group of undersea systems known as unmanned underwater vehicles, a classification that includes non-autonomous remotely operated underwater vehicles (ROVs) – controlled and powered from the surface by an operator/pilot via an umbilical or using remote control. In military applications an AUV is more often referred to as an unmanned undersea vehicle (UUV).

Communication

Communication is usually defined as the transmission of information. The term can also refer to the message itself, or the field of inquiry studying these transmissions, also known as communication studies. The precise definition of communication is disputed. Controversial issues are whether unintentional or failed transmissions are included and whether communication does not just transmit meaning but also create it. Models of communication aim to provide a simplified overview of its main components and their interaction.

Francisco Fernandes Castro Rego

The main topics of this thesis are distributed estimation and cooperative path-following in the presence of communication constraints, with applications to autonomous marine vehicles. To this end, we study algorithms that take explicitly into account the constraints imposed by the communication channel, either by reducing the total number of messages per unit of time or quantizing the information with a reduced number of bits and transmitting it at a fixed rate. We develop a cooperative path following (CPF) algorithm with event-triggered communications and show both through simulations and sea trials with Medusa-class marine vehicles that the self-triggered cooperative path-following algorithm proposed yields adequate performance for formation control of autonomous marine vehicles, while reducing substantially the communications among the vehicles. By exploiting tools from quantized consensus theory, we also provide a method for cooperative path-following with quantized communications, and an algorithm for distributed estimation and control with quantized communications. The performance of the resulting systems is illustrated in simulations. A new methodology for the design of distributed estimators for linear systems is proposed that yields guaranteed stability in the case of collectively observable systems. The resulting algorithm only requires the broadcasting of each nodeâs state estimate at each discrete time instant. We show via simulations that for some particular conditions the algorithm has a lower estimation error norm than other methods that use the same bandwidth and yields stable estimation errors for unstable systems. This thesis also proposes a distributed estimation and control algorithm with progressive quantization. We show that with an appropriate parameter choice and given that the system is collective detectable, the algorithm proposed yields a bounded estimation error and state for every agent, with bounds proportional to the process and measurement noise of the system. Finally, it is shown in tests with model cars that distributed estimation with quantized consensus is a feasible strategy for formation control using only range measurements between the vehicles.

Colin Neil Jones, Ye Pu, Andrea Alessandretti, Francisco Fernandes Castro Rego

This article addresses the problem of simultaneous distributed state estimation, and control of linear systems with linear state feedback, subjected to process, and measurement noise, under the constraints of quantized, and rate-limited network data transmission. In the set-up adopted, sensors and actuators communicate through a network with a strongly connected topology. Unlike the case of centralized linear systems, for which the separation principle holds, the above practical assumption prevents the separate design of observers, and controller because each of the nodes does not necessarily have access to the control inputs generated at all the other nodes. We derive a linear distributed Luenberger observer, and a set of sufficient conditions that guarantee ultimate boundedness of the estimation error, and system state vectors, with bounds that depend on the L-infinity norm of the noise signals, and the number of bits used in the transmissions. A numerical example illustrates the performance and effectiveness of the proposed algorithm in controlling a network of open-loop unstable systems.

Colin Neil Jones, Francisco Fernandes Castro Rego

Motivated by the increasing availability and quality of miniaturized sensors, computers, and wireless communication devices arid given their enormous potential, the use of wireless sensor networks (WSN) has become widespread. Because in many applications of WSNs one is required to estimate at each local sensor unit the state of a system given the measurements acquired by multiple sensors, there has been a flurry of activity related to the theory of distributed state estimation. This article contains a literature survey of distributed state estimation for discrete-time linear time invariant systems. In order to obtain the proper historical context, we review the state of the art in this field and summarize previous work. To provide the mathematical intuition behind some of the methods, this survey paper reproduces some of the main results given in the literature. It also provides a critical appraisal of the state of the art and affords the reader a comprehensive presentation of the most relevant results published so far. (C) 2019 Elsevier Ltd. All rights reserved.

2019