The Dynamic Positioning (DP) systems currently available employ a feedback controller based on the position and heading error, associated with a wind feed-forward control action to keep the vessel position. This last technology improves the eficiency of the DP by anticipating wind loads. However, there is no consolidated technology to feed the controller with wave loads information and the major issue is related to the wave measurement process. The present thesis aims at filling this technological gap by proposing an alternative approach for the measuring of waves near the vessel. Image-based measurement methods are noninvasive and can produce a spatial and temporal description of the surface, making them suitable for recovering the geometry of liquid surfaces. Nevertheless, these surface reconstruction methods, particularly those applying stereoscopic approaches, have dificulty in measuring waves produced in laboratory facilities, mainly because, in such conditions, the water surface is smooth, translucent and highly specular. Alternatively, intensity based image methods are capable of dealing with these surface characteristics, if employed under controlled conditions and if a suitable reflectance model is selected. In the present study, a well-known reflectance model is applied to recover the parameters of regular waves produced in an offshore basin. Firstly, an experimental setup is proposed, designed to grant appropriate conditions for the application of the reflectance model, even under conventional laboratory illumination. Later, the second set of experiments with a model scale DP vessel are presented, which applied the image-based method developed before as a wave feed-forward system. Three control methods are evaluated, namely: PID, PD (Proportional-Derivative) and the PD with the wave feed-forward. Results demonstrated that the presence of the wave feed-forward reduced the steady error of the PD controller. These are the first steps towards the practical use of the wave feed-forward, and several aspects remain pending. However, the promising results and discussions regarding the future steps define the contribution of this work. / Os sistemas atuais de Posicionamento Dinâmico (DP) empregam técnicas de controle baseadas na realimentação da posição e do aproamento, associadas à compensação antecipada das cargas de vento (wind feed-forward) para manter a posição da embarcação. Esta tecnologia melhora a experiência do DP, pois antecipa a ação do vento. Entretanto, não há tecnologia consolidada para a pré-compensação de forças ondas (wave feed-forward) e o maior desafio reside na medição desses agentes ambientais. A presente tese tem como objetivo preencher essa lacuna tecnológica propondo uma abordagem alternativa para a medição das ondas próxima ao casco. Métodos de medição baseados em imagem são não invasivos e produzem descrições espaciais e temporais da superfície analisada, tornando-os particularmente adequados à medição de superfícies líquidas. Entretanto, os métodos comumente empregados, principalmente aqueles baseados em imagens estéreo, são incapazes de medir ondas produzidas em ambiente de laboratório, pois, nestas condições, a superfície da água é lisa, apresenta transparência e comporta-se como um espelho. Por outro lado, métodos baseados na intensidade de luz são capazes de lidar com tais características, se aplicados em condições adequadas e quando empregam modelos apropriados. Neste trabalho, um modelo de reetância amplamente conhecido é empregado para extrair os parâmetros principais de ondas regulares produzidas em um tanque de provas offshore. Inicialmente, propõe-se um arranjo experimental que permita a aplicação do modelo de refetância construído, mesmo sob as condições de iluminação naturais do laboratório. Posteriormente, um segundo conjunto de experimentos com um modelo de embarcação DP é proposto, no qual se aplica o método baseado em imagem, testado anteriormente, como sistema de pré-compensação de forças de onda. Três métodos de controle são avaliados, quais sejam: PID (Proporcional-Integral-Derivativo), PD (Proporcional-Derivativo) e o PD-WFF (PD associado ao wave feed-forward). Os resultados demonstram que a presença pré-conpensação de forças de onda reduz o erro em regime do controlador PD. Estes são os primeiros passos em direção da incorporação da pré-compensação de forças de onda nos sistemas DP reais e diversos aspectos técnicos ainda estão pendentes. Entretanto, os resultados promissores e discussões acerca dos futuros passos da pesquisa definem a contribuição do presente trabalho no campo do Posicionamento Dinâmico.

Optical fibers play a vital role in modern telecommunication systems and networks. An optical fiber link imposes some linear and nonlinear distortions on the propagating light-wave signal due to the inherent dispersive nature and nonlinear behavior of the fiber. These distortions impede the increasing demand for higher data rate transmission over longer distances. Developing efficient and computationally non-expensive digital signal processing (DSP) techniques to effectively compensate for the fiber impairments is therefore essential and of preeminent importance. This thesis proposes two DSP-based approaches for mitigating the induced distortions in short-reach and long-haul fiber-optic communication systems. The first approach introduces a powerful digital nonlinear feed-forward equalizer (NFFE), exploiting multilayer artificial neural network (ANN). The proposed ANN-NFFE mitigates nonlinear impairments of short-haul optical fiber communication systems, arising due to the nonlinearity introduced by direct photo-detection. In a direct detection system, the detection process is nonlinear due to the fact that the photo-current is proportional to the absolute square of the electric field intensity. The proposed equalizer provides the most efficient computational cost with high equalization performance. Its performance is comparable to the benchmark compensation performance achieved by maximum-likelihood sequence estimator. The equalizer trains an ANN to act as a nonlinear filter whose impulse response removes the intersymbol interference (ISI) distortions of the optical channel. Owing to the proposed extensive training of the equalizer, it achieves the ultimate performance limit of any feed-forward equalizer. The performance and efficiency of the equalizer are investigated by applying it to various practical short-reach fiber-optic transmission system scenarios. These scenarios are extracted from practical metro/media access networks and data center applications. The obtained results show that the ANN-NFFE compensates for the received BER degradation and significantly increases the tolerance to the chromatic dispersion distortion. The second approach is devoted for blindly combating impairments of long-haul fiber-optic systems and networks. A novel adjoint sensitivity analysis (ASA) approach for the nonlinear Schrödinger equation (NLSE) is proposed. The NLSE describes the light-wave propagation in optical fiber communication systems. The proposed ASA approach significantly accelerates the sensitivity calculations in any fiber-optic design problem. Using only one extra adjoint system simulation, all the sensitivities of a general objective function with respect to all fiber design parameters are estimated. We provide a full description of the solution to the derived adjoint problem. The accuracy and efficiency of our proposed algorithm are investigated through a comparison with the accurate but computationally expensive central finite-differences (CFD) approach. Numerical simulation results show that the proposed ASA algorithm has the same accuracy as the CFD approach but with a much lower computational cost. Moreover, we propose an efficient, robust, and accelerated adaptive digital back propagation (A-DBP) method based on adjoint optimization technique. Provided that the total transmission distance is known, the proposed A-DBP algorithm blindly compensates for the linear and nonlinear distortions of point-to-point long-reach optical fiber transmission systems or multi-point optical fiber transmission networks, without knowing the launch power and channel parameters. The NLSE-based ASA approach is extended for the sensitivity analysis of general multi-span DBP model. A modified split-step Fourier scheme method is introduced to solve the adjoint problem, and a complete analysis of its computational complexity is studied. An adjoint-based optimization (ABO) technique is introduced to significantly accelerate the parameters extraction of the A-DBP. The ABO algorithm utilizes a sequential quadratic programming (SQP) technique coupled with the extended ASA algorithm to rapidly solve the A-DBP training problem and optimize the design parameters using minimum overhead of extra system simulations. Regardless of the number of A-DBP design parameters, the derivatives of the training objective function with respect to all parameters are estimated using only one extra adjoint system simulation per optimization iterate. This is contrasted with the traditional finite-difference (FD)-based optimization methods whose sensitivity analysis calculations cost per iterate scales linearly with the number of parameters. The robustness, performance, and efficiency of the proposed A-DBP algorithm are demonstrated through applying it to mitigate the distortions of a 4-span optical fiber communication system scenario. Our results show that the proposed A-DBP achieves the optimal compensation performance obtained using an ideal fine-mesh DBP scheme utilizing the correct channel parameters. Compared to A-DBPs trained using SQP algorithms based on forward, backward, and central FD approaches, the proposed ABO algorithm trains the A-DBP with 2.02 times faster than the backward/forward FD-based optimizers, and with 3.63 times faster than the more accurate CFD-based optimizer. The achieved gain further increases as the number of design parameters increases. A coarse-mesh A-DBP with less number of spans is also adopted to significantly reduce the computational complexity, achieving compensation performance higher than that obtained using the coarse-mesh DBP with full number of spans. / Thesis / Doctor of Philosophy (PhD) / This thesis proposes two powerful and computationally efficient digital signal processing (DSP)-based techniques, namely, artificial neural network nonlinear feed forward equalizer (ANN-NFFE) and adaptive digital back propagation (A-DBP) equalizer, for mitigating the induced distortions in short-reach and long-haul fiber-optic communication systems, respectively. The ANN-NFFE combats nonlinear impairments of direct-detected short-haul optical fiber communication systems, achieving compensation performance comparable to the benchmark performance obtained using maximum-likelihood sequence estimator with much lower computational cost. A novel adjoint sensitivity analysis (ASA) approach is proposed to significantly accelerate sensitivity analyses of fiber-optic design problems. The A-DBP exploits a gradient-based optimization method coupled with the ASA algorithm to blindly compensate for the distortions of coherent-detected fiber-optic communication systems and networks, utilizing the minimum possible overhead of performed system simulations. The robustness and efficiency of the proposed equalizers are demonstrated using numerical simulations of varied examples extracted from practical optical fiber communication systems scenarios.

During the past years an increased focus on the development of autonomous solutions has resulted in driverless vehicles being used in numerous industries. Volvo Construction Equipment is currently developing the TA15, an autonomous hauler part of a larger transport solution. The transition to autonomous haulers have further increased the need for improved system condition monitoring in the strive for increased operational time. A method aiming to identify and isolate faults in the hydraulic steering system on the TA15 was therefore investigated in this thesis. Using fault tree analysis, five faults considered to be of importance regarding steering performance were selected. Two different methods for detecting the faults were compared to each other, data-driven and model based. Out of the two, data-driven was selected as the method of choice due to high modularity and relative simplicity regarding implementation. The data-driven approach consisted of Feed-Forward and Long Short Term Memory networks where the suitable inputs were decided to be a combination of pressure and position signals. Utilizing a simulation model of the steering system validated against the TA15, the selected faults were induced in the simulated system with various severity. Training the networks to classify and estimate fault severity in the simulated model resulted in satisfactory results using both networks. It was however concluded that in contrary to the Feed-Forward network, the LSTM network could achieve good performance using less amount of sensors. Although the diagnostic method showed promising result on a simulation model, test on the real TA15 needs to be performed in order to properly evaluate the method. The advantage of using a data-driven approach was specially noticeable when comparisons were made to the model based approach. The data-driven approach relies on labeling data rather than complete system knowledge. Meaning that the method developed therefore could be applied on practically any hydraulic system in construction equipment by changing the training data.

Student Number : 9811923K - PhD thesis - School of Mechanical Engineering - Faculty of Engineering and the Built Environment / Methods are presented for obtaining models used for predicting welded sample resistance and effective weld current (RMS) for desired weld diameter (weld quality) in the resistance spot welding process. These models were used to design predictive controllers for the welding process. A suitable process model forms an important step in the development and design of process controllers for achieving good weld quality with good reproducibility. Effective current, dynamic resistance and applied electrode force are identified as important input parameters necessary to predict the output weld diameter. These input parameters are used for the process model and design of a predictive controller. A three parameter empirical model with dependent and independent variables was used for curve fitting the nonlinear halfwave dynamic resistance. The estimates of the parameters were used to develop charts for determining overall resistance of samples for any desired weld diameter. Estimating resistance for samples welded in the machines from which dataset obtained were used to plot the chart yielded accurate results. However using these charts to estimate sample resistance for new and unknown machines yielded high estimation error. To improve the prediction accuracy the same set of data generated from the model were used to train four different neural network types. These were the Generalised Feed Forward (GFF) neural network, Multilayer Perceptron (MLP) network, Radial Basis Function (RBF) and Recurrent neural network (RNN). Of the four network types trained, the MLP had the least mean square error for training and cross validation of 0.00037 and 0.00039 respectively with linear correlation coefficient in testing of 0.999 and maximum estimation error range from 0.1% to 3%. A prediction accuracy of about 97% to 99.9%. This model was selected for the design and implementation of the controller for predicting overall sample resistance. Using this predicted overall sample resistance, and applied electrode force, a second model was developed for predicting required effective weld current for any desired weld diameter. The prediction accuracy of this model was in the range of 94% to 99%. The neural network predictive controller was designed using the MLP neural network models. The controller outputs effective current for any desired weld diameter and is observed to track the desired output accurately with same prediction accuracy of the model used which was about 94% to 99%. The controller works by utilizing the neural network output embedded in Microsoft Excel as a digital link library and is able to generate outputs for given inputs on activating the process by the push of a command button.

Precision landing capability is a necessary development that must take place before unmanned aircraft systems (UAS) can realize their full potential in today's modern society. Current multirotor UAS are heavily reliant on GPS data to provide positioning information for landing. While generally accurate to within several meters, much higher levels of accuracy are needed to ensure safe and trouble-free operations in several UAS applications that are currently being pursued. Examples of these applications include package delivery, automatic docking and recharging, and landing on moving vehicles. The specific problem we consider is that of precision landing of a multirotor unmanned aircraft on a small barge at sea---which presents several significant challenges. Not only must we land on a moving vehicle, but the vessel also experiences random rotational and translational motion as a result of waves and wind. Because maritime operations often span long periods of time, it is also desirable that precision landing can occur at any time---day or night.In this work we present a complete approach for precision shipboard landing and address each of the aforementioned challenges. Our method is enabled by leveraging an on-board camera and a specialized landing target which can be detected in light or dark conditions. Features belonging to the target are extracted from camera imagery and are used to compute image-based visual servoing velocity commands that lead to precise alignment between the multirotor and landing target. To enable the multirotor to match the horizontal velocities of the barge, an extended Kalman filter is used to generate feed-forward velocity reference commands. The complete landing procedure is guided by a state machine architecture that incorporates corrections to account for wind, and is also capable of quickly reacquiring the landing target in a loss event. Our approach is thoroughly validated through full-scale outdoor flight tests and is shown to be reliable, timely, and accurate to within 4 to 10 centimeters.

À la frontière entre neurosciences et intelligence artificielle, les neurosciences computationnelles tentent de comprendre les formidables capacités de calcul du cerveau, notamment l'efficacité du traitement de l'image par le système visuel. Mon travail est un double travail expérimental et de modélisation. Dans la partie expérimentale, je tente de déterminer les raisons qui font la précision et la rapidité des processus visuels. On présente brièvement (20-30 ms) des photographies contenant ou non des animaux au sujet qui doit relâcher un bouton quand l'image contient un animal. Le singe macaque réalise cette tâche avec une précision légèrement inférieure à celle de l'homme mais avec une plus grande rapidité. Je tente ensuite de contraindre la catégorisation pour déterminer le rôle à la fois des propriétés intrinsèques des images - couleur, luminance, nombre d'animaux présents, parties visibles de leurs corps, espèce de l'animal... - mais aussi de leurs propriétés extrinsèques - condition de présentation, effet de séquence, familiarité du stimulus, consigne... Bien que certaines conditions accélèrent la catégorisation, les réponses les plus précoces (dont on montre qu'elles ne sont pas spécifiques de certaines images), et les enregistrements EEGs correspondant au traitement de l'image ne sont que très peu affectés. Cela implique donc un traitement rapide massivement parallèle - quasiment automatique - des informations visuelles, où chaque neurone du système visuel peut difficilement émettre plus d'une décharge. À partir de ces contraintes, et de celles imposées par la structure du système visuel, j'ai construit un simulateur biologiquement plausible (SpikeNET) qui permet de simuler le comportement des neurones réels (de la détection de barres orientées jusqu'à la reconnaissance de visages). Les performances de ces modèles sont étonnantes du point de vue du traitement d'image et rivalisent avec les approches classiques en intelligence artificielle.

This thesis presents the design, implementation, and fabrication of an analog front-end (AFE) targeting 2x blind ADC-based receivers. The front-end consists of a combination of an anti-aliasing filter (AAF) and a 2-tap feed-forward equalizer (FFE) (AAF/FFE), the required clock generation circuitry (Ck Gen), 4 time-interleaved 4-b ADCs, and DeMUX. The contributions of this design are the AAF/FFE and the Ck Gen. The overall front-end optimizes the channel/filter characteristics for data-rates of 2-10 Gb/s. The bandwidth of the AAF is scalable with the data-rate and the analog 2-tap feed-forward equalizer (FFE) is designed without the need for noise-sensitive analog delay cells. The test-chip is implemented in 65-nm CMOS and the AAF/FFE occupies 152×86 μm2 and consumes 2.4 mW at 10 Gb/s. Measured frequency responses at data-rates of 10, 5, and 2 Gb/s confirm the scalability of the front-end bandwidth. FFE achieves 11 dB of high-frequency boost at 10 Gb/s.

This thesis presents the design, implementation, and fabrication of an analog front-end (AFE) targeting 2x blind ADC-based receivers. The front-end consists of a combination of an anti-aliasing filter (AAF) and a 2-tap feed-forward equalizer (FFE) (AAF/FFE), the required clock generation circuitry (Ck Gen), 4 time-interleaved 4-b ADCs, and DeMUX. The contributions of this design are the AAF/FFE and the Ck Gen. The overall front-end optimizes the channel/filter characteristics for data-rates of 2-10 Gb/s. The bandwidth of the AAF is scalable with the data-rate and the analog 2-tap feed-forward equalizer (FFE) is designed without the need for noise-sensitive analog delay cells. The test-chip is implemented in 65-nm CMOS and the AAF/FFE occupies 152×86 μm2 and consumes 2.4 mW at 10 Gb/s. Measured frequency responses at data-rates of 10, 5, and 2 Gb/s confirm the scalability of the front-end bandwidth. FFE achieves 11 dB of high-frequency boost at 10 Gb/s.

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