The Delft Center for Systems and Control (DCSC) 'Smart Optics' aim to achieve higher resolution imaging through Adaptive Optics (AO). Adaptive optics is a modern technique for detecting and correcting real-time wavefront aberrations and is widely used in biomedical imaging and astronomical imaging. Wavefront sensing lies at the core of Adaptive Optics and is known to pose some challenges. Measurement of the wavefront cannot be done directly and has to be estimated through an intensity distribution on a detector. One approach to wavefront sensing is by using a Shack-Hartmann (SH) sensor. A Shack-Hartmann sensor (a pupil-plane sensor) subdivides the wavefront into N spatial areas using sub-apertures. The individual slopes across all sub-apertures are integrated to reconstruct the wavefront. The major advantage of using a Shack-Hartmann sensor is its fast operation speed, caused by the linear relationship between local slopes and original wavefront. This enables real-time wavefront reconstruction. The Shack-Hartmann sensor however, has some limitations. Its ability to reconstruct higher-order aberrations is restricted by the amount of lenses within the micro-lens array. Furthermore, a centroiding algorithm is used to compute the local slopes. Going from spots to centroids decreases the amount of informative pixels and greatly limits its wavefront reconstruction potential. Moreover, these centroiding algorithms often add a measure of uncertainty since spots can have irregular shapes or cross-over/overlap. In this Master Thesis a novel approach to phase reconstruction from the raw SH measurement is proposed. Here, we show that Deep Learning techniques in combination with a micro-lens array can surpass traditional SH phase reconstruction methods and alleviate their current limitations. The proposed method uses the entire Shack-Hartmann Pattern (HP) as input to a neural network, supplying the network with more information than existing Deep Learning SHWR methods, which still rely on centroids. Using this approach, we can combine the accuracy of sensor-less techniques with the speed of a Shack-Hartmann sensor. Three different neural network architectures are considered in this thesis. Two of these neural networks (Alex-Net and Xception) are adapted to output a series of Zernike coefficients. Using these estimated Zernike coefficients, a wavefront can be reconstructed. The remaining neural network, U-Net, performs a direct pixel-wise estimation of the phase-map. The input Shack-Hartmann patterns are created using different micro-lens array (MLA) geometries, consisting of 25-, 256- or 900 lenses. The networks are evaluated on their ability to reconstruct a combination of 32- or 100- Zernike coefficients.

UnmannedAerial Vehicles(UAVs) have multi-domain applications and fixed-wing UAVs are a widely used class. There is ongoing research on topics in view to optimize the control and guidance of UAVs. This work explores the design, implementation and Software-in-the-Loop validation of an autopilot using adaptive guidance laws with emphasis on formation control of multiple fixed-wing UAVs. The work is done on Raspberry Pis in C++ which can be interfaced to standard autopilots as companion computers. The work splits a mission given by the user into primitive missions and uses an adaptive vector field approach for following it. For formation control, the work implements a discretized version of the Model Reference Adaptive Control synchronisation laws for multi-agent systems. Simulations are done in a distributed setting with a server program designed for the purpose. The server program handles the user inputs and configurations of the UAVs.

As a nonlinear alternative to the linear interpretation of arterial blood pressure waveform, soliton theory has been proposed to model arterial blood pressure by interpreting the pulsatile nature of pressure pulses in the viewpoint of soliton transmission. The existing solitary wave literature supports this interpretation by deriving Korteweg-de Vries (KdV) type dynamics from 1-D Navier-Stokes equations. In this paper, we explain and discuss the derivation of KdV type dynamics for arterial blood pressure from basics of fluid motion. As original work, we provide two verification tests for two of the existing KdV models in three case studies which are considered to be interconnected sections of a simplified arterial network. Finally,
using both KdV models and considering realistic inlet boundary conditions, we study arterial blood pressure waveforms using nonlinear Fourier analysis to extract physical information.

Fleet Cleaner B.V. is a company that specializes in the cleaning of ship hulls of oceanic trade vessels using a mobile robot. The mobile robot mainly operates out of sight of the operator, making accurate localization of the robot crucial to its operation. However, the absence of absolute localization and sources for error build-up like wheel slip and sensor noise, increase the error between the estimated location and true location over time. Therefore, the goal of this thesis is to minimize the amount of error build-up between the estimated position and the true position of the robot, to allow more efficient operation of the robot and to ultimately allow the robot to operate autonomously.

The main sources for error build-up are determined by simulating the position estimator with modeled sensor- and perturbation models and evaluating their individual effect on the position estimator. Algorithms that combat the error build-up are established by synthesizing working principles from literature and extensively testing these principles using simulated data and finally verifying them using real but limited data.

The analysis of the sensor noise and perturbations revealed that the EKF is best suited to estimate the position of the robot and that the main sources for error build-up are wheel slip and IMU heading drift. The addition of a slip detection and velocity correction algorithm reduced the average error build-up per meter traveled by a factor of four. The addition of the heading correction algorithm reduced the error build-up by a factor of three and mitigates the need for intermittent resetting of the robot heading by the operator.

The velocity and heading correction algorithms improve the position estimator allowing the operator to more efficiently control the robot. In order to further reduce the error build-up, it is recommended to add two more wheel encoders to the robot. However, the error will still be unbounded, making the position estimator unsuitable for autonomous operation.

The relation between the input of an ideal optical single-mode fiber and the corresponding fiber output constitutes a nonlinear system that can be described using the nonlinear Schrödinger equation. This nonlinear system has the interesting property that it can be solved analytically using nonlinear Fourier transforms. To utilize this property, new methods of optical communication are being developed by embedding information in the nonlinear Fourier domain and employing fast nonlinear Fourier transforms. Many of the recent works use a specialized form of inverse nonlinear Fourier transform to generate information-bearing fiber inputs in the form of so-called multi-soliton pulses. Recently, multiple fast inverse nonlinear Fourier transform algorithms that can generate multi-solitons have been proposed. The goal of this thesis is to study and improve these algorithms, in particular, with respect to their computational complexity.
Based on the literature survey, discrete Darboux transform combined with other discrete techniques is studied and a new algorithm is proposed. The algorithm employs a single-start approach in which discrete Darboux matrix is computed at only one sample point and rest of the samples are computed by evolution of the Darboux matrix. The algorithm is hence named as discrete Darboux evolution algorithm (DDE). The errors in the generated signal and run-time are studied by comparison with the classical Darboux transform (CDT). The DDE algorithm is shown to have floating point operations complexity of O(KN) for K eigenvalues and N samples. However, in a limited precision environment the number of samples that can be generated is found to be limited. To better understand the effects of machine precision, both the CDT and DDE algorithms are studied in a multi-precision environment. Certain insights from the study are used to develop two modifications to overcome the limitations. The first modification computes the signal using multiple single-start runs while the second one uses a multi-start approach. The second modification is shown to have errors comparable with other fast algorithms in literature. Additionally, in a qualitative comparison it is shown to be potentially faster than existing algorithms in a certain regime.

Automated driving, one of the rapidly growing research topics in the field of smart traffic, has proved to be a recognized solution for potentially improving road throughput and reducing vehicles' energy consumption by grouping individual vehicles into platoons controlled by one leading vehicle.

The advances in distributed inter-vehicle communication networks have stimulated a fruitful line of research in Cooperative Adaptive Cruise Control (CACC). In CACC, individual vehicles, grouped into platoons, must automatically adjust their own speed using on-board sensors and communication with the preceding vehicle so as to maintain a safe inter-vehicle distance. The importance of CACC lies in the fact that it enables small inter-vehicle time gaps, which leads to a major reduction of the aerodynamical drag force applied on vehicles in such a driving pattern. Consequently, vehicle emissions, which play a main role in trucks and heavy automobiles, are expected to be highly reduced.

However, a crucial limitation of the state-of-the-art research in this control scheme is that the string stability of the platoon can be proven only when the vehicles in the platoon have identical driveline dynamics and perfect engine performance (homogeneous platoon), and possibly an ideal communication channel.

Thus, the objective of this MSc thesis is to address the problem of CACC for heterogeneous platoons under realistic inter-vehicle network conditions. In the first part, we propose a novel CACC strategy that overcomes the homogeneity assumption and that is able to adapt its action and achieve string stability for uncertain heterogeneous platoons under ideal inter-vehicle network conditions. In the second part, in order to handle the inevitable communication losses, we formulate an extended average dwell-time framework and design an adaptive switched control strategy which activates an augmented CACC or an augmented Adaptive Cruise Control strategy depending on communication reliability.

Stability of the proposed control strategies is proven analytically and simulations are conducted to validate the theoretical analysis.

For wavefront reconstruction, often the combination of a Shack-Hartmann sensor and a reconstruction method utilizing the Cartesian derivatives of Zernike polynomials (the least-squares method) is used, which is known to introduce cross-talk. In Janssen (2014) a new wavefront reconstruction algorithm is introduced to be used with the Shack-Hartmann sensor that does not present cross-talk. To our knowledge, this method has never been demonstrated. In the current research, Janssen's method and the conventional least-squares method are compared on a modified Michelson interferometer setup with a spatial light modulator to first remove the aberrations of the complete system, and subsequently introduce specific aberrations for which the Zernike coefficients are reconstructed using both reconstruction methods. It is found that both methods work equally well with optimal fitting powers. When fitting fewer than the optimal amount of fitting powers, it was found that Janssen's method accurately recovered Zernike coeficient amn when n + 1 powers are fit, while the conventional method achieves the same accuracy only at n + 2 fitting powers, especially when aberrations introducing cross-talk are present. When more than the optimal amount Zernike powers are used, it is seen that Janssen's method presents aliasing at lower fitting powers. At the optimal fitting power, the RMS landscape for both methods is shown to be similar, and therefore they are equally susceptible to errors due to mis- estimation of the center position and radius of the beam on the ShackHartmann
sensor. Lastly, it is shown that theoretically, Janssen's method is at least 3.5 times slower than the conventional least-squares method due to the use of complex valued Zernike polynomials.