1 

A comparative performance evaluation of nonlinear observers for a fedbatch evaporative crystallization process
Different nonlinear observers are compared throughout this work where they are part of an NMPC framework used to control a fedbatch crystallization process . We study which observeroptimizer pair offers the best control performance while maintaining adequate computational burden so that a posterior realtime implementation is feasible. At the same time, the relationship between state estimation accuracy and control performance is covered. Along the way we distinguish between stochastic and deterministic observers and compare which class is more suitable for our case study. The observers we make use of are: the moving horizon estimator (MHE), a nonlinear version of a Luenberger observer (extended Luenberger observer, ELO) and nonlinear variants of the Kalman filter such as extended Kalman filter(EKF), unscented Kalman filter (UKF) and ensemble Kalman filter (EnKF). Special variants of UKF and EKF that make use of a non constant system covariance matrix, which according to some literature is suitable to describe uncertainty distribution in batch processes, are also included in the analysis. The analysis focuses on how four main error sources such as unmeasured disturbances, uncertain initial conditions, model mismatch, and stochastic disturbances may impact observer estimation accuracy as well as their repercussion on control effectiveness and consequently on process performance. Results show that unmeasured disturbances are the most detrimental to observer and process performance in our case study. In spite of this finding, we present a methodology to tackle and solve this problem. All the analysis is first made under an openloop configuration and then moves onto a closedloop setup. All testing is based on computer simulations of the crystallization process. The evaluation criterion is based on the magnitude of a normalized rootmean squared error throughout 50 batch runs. The results are then used to identify if a link between estimation accuracy and control performance exists. The computational burden is also evaluated along 50 batch simulations, and is measured on the basis of CPU time required by every observer at every estimation stage.

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2 

Identification and Feedforward Control of a Dropondemand Inkjet Printhead

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3 

Identification of Models of Industrial Batch Cooling Crystallization system

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4 

Error detection and reduction within DriftLessTM

file embargo until: 20160603

5 

Formal guarantees on controller performance for autonomous vehicles during highway lane changing manoeuvres
Annually over 60.000 people die and another 3.7 million get injured in car accidents in the United States and Europe combined. Automation of vehicles can reduce the number of accidents by 90%. Therefore, it is of great interest in academia and industry. For the automated vehicles that are being presented in industry however, proofs of safe performance are generally not found. Therefore, this thesis investigates approaches to automatically assess automated vehicle controller performance such as deviation overshoot and obtained lateral speeds.
More specifically, this thesis studies socalled formal verification of the performance of a highway lane change controller. Because of its robustness properties, a Sliding Mode Control (SMC) strategy is selected and in this research several variations are designed. The availability of a lower level controller was assumed to generate the required steering torque, which results in a third order linear vehicle model. Nonlinear vehicle models with higher order dynamics were considered as well but showed to be too complex for the verification approach. Due to the switching nature of SMC, the closed loop system is modelled as a Hybrid System and as such verified using multiple verification packages.
The abstraction based environment HSolver can provide full formal guarantees that account for intervals of parameter uncertainty, in this case initial deviation and lateral speed. However, in comparison with simulations using the Matlab Toolbox Breach–which only propagates distinct values and cannot provide formal guarantees for entire intervals–it suffers from a large overapproximation. Still, for a limited initial interval, HSolver analyses show that a SMC variant called Quasi Sliding Mode Control (QSMC) results in a safe lane change in terms of deviation overshoot. More advanced controllers show promising performance in Breach simulations. This suggests that techniques to reduce the overapproximation in HSolver, or developments in similar tools, can be very interesting to verify safety for more general situations in future research. Alternatively, other performance criteria or different parametric uncertainties could be formally verified as well.

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6 

Optimal Control for Race Car Minimum Time Maneuvering
Minimizing the time needed to travel a prescribed distance is the main development goal in motorsports. In racing car development, simulations are used to predict the effect of design parameter changes on vehicle performance. If approached as an optimal trajectory planning problem, a maneuver simulation can be used to determine not only the maneuver time, but also to identify the performance limitations on the system. This thesis describes the development and implementation of an optimal trajectory planning method using optimal control for short maneuvers. The requirements and modeling decisions are based on the application of the method to example problems related to TC design.
The model for the method is based on a study of steadystate acceleration limits and stability. The rigid twotrack model resulting from this study includes lateral and longitudinal load transfer, a nonlinear tire model, a limitedslip differential and aerodynamic downforce. An important contribution is the omission of wheel rotational velocities from the model, reducing the number of states by four and relaxing the requirements on the discretization interval. Possible misuse of this formulation is prevented by a constraint representing wheel rotational stability limitations. The formulation is validated by comparison to a reference model which includes wheel rotational velocities.
The optimal trajectory planning method is formulated as an optimal control problem. The cost function is the maneuver time, and the constraints consist of the system dynamics and maneuver boundaries. The timebased dynamics are transformed into spatial dynamics, and a curvilinear coordinate system is used.
The optimal control problem is discretized using a full collocation method, and the state and input trajectories are parametrized in terms of Bspline coefficients. The resulting problem is solved using a NLP solver. Interiorpoint solver IPOPT and SQP solver SNOPT are compared on various small problems. For this application IPOPT appears to be superior over SNOPT. The first order derivative information of the constraints required for IPOPT is approximated using sparsefinite differences, and the cost function gradient is calculated analytically. The precision of the method is assessed in a study of maneuver time dependency on mass. It appears that precision is mainly affected by convergence of the solver to various local minima. As such, the use of distancedependent constraints and warmstart are employed for improving precision.
The optimal trajectory for a hairpin with various radii is studied in detail. Special attention is paid to tire friction potential utilization and vehicle stability according the Lyapunov's First Method. For the given parameters it is shown that the optimal solution involves instances of overdriving either the front or rear axle. It is also shown that the vehicle is openloop locally unstable on intervals along the optimal trajectory.
In another simulation study, the reaction of the control inputs to temporary reductions in tireroad friction and perturbations to the yaw rate and body slip angle on turnexit are evaluated. The most important result of this study is that the longitudinal control was found to be the primary means for rejecting such disturbances. The study also showed that steering angle changes are used as additional means for disturbance rejection if the perturbation is large enough to saturate the reduction of longitudinal control.
The sensitivity of maneuver time and optimal trajectory to vehicle mass is studied by the use of socalled sensitivity differentials. This is done using a welldeveloped theoretical framework for parametric sensitivity for barrier methods, implemented in the software package sIPOPT. The sensitivity study can be seen as a proof of concept of the sensitivity differential approach for the race car MTM application.

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7 

Design and analysis of a model based low level slip controller based on a hybrid braking system
Treats the design an ABS control strategy for a hybrid braking system.

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8 

Neural network based condition monitoring for track circuits
Railway track circuits are electrical systems that are used for train detection. The identification of faults in these track circuits and the estimation of the severity of these faults is crucial for the safety and availability of railway networks. In this thesis a method is proposed to solve these tasks based on the commonly available measurement signals.
By considering the signals from multiple track circuits in a geographic area, faults can be identified from their spatial and temporal dependencies. In this thesis, artificial neural networks are used to learn these dependencies from historical data. Although not enough measurement data was available during the writing of this thesis, it is more than likely that reasonable amounts of (unlabeled) measurement data will become available at a later time, as the required measurement equipment has already been installed. To train the networks in this thesis, the small available dataset is analyzed and used together with the currently available understanding of the fault dependencies to make a generative model. The synthetic data produced by this model are used to train and test the neural networks in this thesis.
Artificial neural networks have recently achieved state of the art performance on difficult pattern recognition problems in several different fields such as image recognition and speech recognition. These recent successes can be largely attributed to the combination of large networks and large datasets. In the condition monitoring domain large datasets are generally not available. This prevents the use of the large neural networks that have become so successful in other fields. Inspite of this, some of the ideas that have become popular in other domains might still have value in the condition monitoring domain. This thesis focuses on bringing the LongShort Term Memory architecture and the concept of endtoend learning to the condition monitoring domain. To address the fact that only a limited amount of labeled data will be available, an unsupervised learning strategy is investigated. This strategy will use unlabeled data to pretrain a network so that it can more efficiently learn from the scarce labeled data.
For the fault isolation task, it is shown in this thesis that when a large amount of labeled training data is available, the endtoend learning strategy can detect and diagnose faults in the data from the generative model very accurately. When only a small amount of labeled data is available, it is shown that using a pretrained network works better than using endtoend learning.

file embargo until: 20200526
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9 

Synchronization of Wireless Sensor Networks
Wireless sensor networks of the type discussed in this MSc project play a crucial role in many envisionings of the Internet of Things, a trend that is thought to play a major role in the technological innovations of the near future. These wireless, adhoc, scalable mesh networks provide the infrastructure for numerous sensing and control applications. A key requirement for such networks is energy efficiency. Synchronization of nodes can significantly improve energy efficiency by enabling a tighter communication schedule.
In this MSc project an improved synchronization algorithm for an existing MAC protocol is developed. After establishing a clock model and surveying the literature for existing algorithms, the synchronization problem is modelled from a control theoretic viewpoint. It is shown that the synchronization problem closely resembles the consensus problem, which is extensively covered in literature. This insight is used to prove stability of a class of synchronization algorithms  including the existing algorithm  under requirements on the communication topology that are easily satisfied.
A set of improved algorithms is developed, and their performance is assessed in simulations. The best performers were tested experimentally on networks with up to 300 nodes.
In conclusion, we have been able to create a substantial improvement over the existing synchronization algorithm, attaining a higher throughput and longer network lifetime. Experiments have shown however, that very large networks (>150 nodes) are not adequately described by our models and can display unexpected dynamics.

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10 

Well testing in the framework of system identification
Well testing has been subject of research for many decades. Well testing is performed to estimate reservoir properties. These estimations are needed to predict the amount of oil that can be produced, and to determine a strategy how to produce this predicted amount. The currently used procedure to estimate the properties seems cumbersome and inefficient and uncertainty regions of the estimated properties are not yet available. In this report the focus is on evaluating the current well test methods from a system identification point of view and on improving the well test analysis. The latest and best performing well test method in literature is evaluated by simulations and the results are compared with results of a new well test analysis method using Prediction Error Identification (PEI). PEI is a black box identification method. Both methods contribute to improvements in the field of well testing. The new method estimates a full expression for the system, consisting of the reservoir and well bore. Direct property estimation, without the interpretation of so called type curves, reliable uncertainty regions and large cost reduction are now within reach.

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11 

Scheduling using maxplus algebra
In industry, discrete models can be used to describe and analyze a class of event driven systems. These so called discrete event systems (DESs) contain a finite number of resources that are shared by different users called jobs. All of these resources and jobs contribute to the achievement of some common goal. Deciding on how to allocate a set of jobs to limited resources over time in an optimal way is called scheduling, where the basic types of control decisions are routing, ordering and synchronization. The scheduling of DESs is crucial in many applications, such as railway networks, production systems, baggage handling, legged locomotion, container handling and paper handling in printers.
Most of the models that describe the behavior of DESs are nonlinear in the conventional algebra that uses addition and multiplication operators. There is however a class of DESs that can be described by a model that is linear in the maxplus algebra, which uses maximization and addition as its main operators. In such a maxplus linear (MPL) system the model structure is fixed, whereby changes in the structure of the system cannot be modeled. However, the model dynamics can be influenced by allowing switching between different modes. An MPL system where switching is allowed is called a switching maxplus linear (SMPL) system. By switching to another mode, the route, order and/or synchronization is changed.
A general framework on how to model these SMPL systems is obtained, where the topology graph is used. A topology graph of a system describes all possible connections between resources but does not tell which route or order is chosen. The routes can all be constructed by assigning maxplus binary control variables to each arc. By looking at the number of incoming arcs specific ordering constraints for each node are obtained. A problem that remains are circuits where a job can reenter multiple times. These circuits result in duplicated nodes whereby the ordering constraints change. The method on how to model such circuits is described. Once the model is obtained, the scheduling problem always results in finding the optimal maxplus binary variables related to routing and ordering. This control problem is solved by using a Model Predictive Control (MPC) strategy, which is recast as a Mixed Integer Linear Programming problem.
Using the general framework, a simple and more complex baggage handling system is modeled and controlled. Adding a due date reference for the complex baggage handling system shows that the bags arrive in time. Also the switching between different cycles can clearly be seen.

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12 

Performance Assessment for Advanced Process Control
Model predictive control (MPC) is a control technique that is frequently used in the process industry. Two advantages of model predictive controllers is their ability to predict the impact of disturbances, and to easily account for constraints. However, a disadvantage of model predictive controllers is that they rely on mathematical models of the controlled processes. Changes in process conditions may cause the model to no longer accurately represent the real process, which in turn may result in a drop in performance. Detecting performance drops and taking subsequent action is thus highly desirable.
This thesis aims at assessing current methods that detect and revert these performance drops due to modelplant mismatch. To this end, a benchmark distillation column model representing a typical industrial process was designed. For two different control configurations, models were identified using predictionerror identification, and modelpredictive controllers were subsequently designed for reference tracking and rejection of disturbances. Performance of these modelpredictive controllers has been compared, and results show that the so called doubleratio configuration is better at disturbance rejection, and shows more robust performance. Further, a performance index that tracks the average variance of the controlled output, computed over a timerange, was developed. A methodology is shown which can be used to compute the optimal time range, when the historic variance, a desired false alarm rate, and threshold is known.
In the event a performance drop due to modelplant mismatch is detected, the plant should be reidentified in order to restore nominal performance. This can however be a costly proce dure. The second part of this thesis therefore focuses on leastcostly identification methods. Recently, a new experiment design method was developed that is aimed at minimizing the length of an identification experiment, while constraints on the minimal accuracy of the tobe identified model and the maximum values of the in and output signals are honoured Analysis of this new minimaltime algorithm shows that the identification experiment time can be reduced by up to 56 % when compared with conventional leastcostly identification methods. Further, MonteCarlo simulations have been performed for different initializations and it has been shown that the minimaltime algorithm manages to find its global minimum for various initial conditions, although the probability to obtain it from an arbitrary simulation is different from system to system.
The last part of the thesis discusses the relevance and applicability of performance monitoring and reidentification in practice. While literature assumes tracking the variance of the controlled output is a good indicator, in reality wrong limits on the control inputs, a suboptimal economic function in the modelpredictive controller, and uptime of the modelpredictive controllers are more important factors that determine the economic benefits gained from a model predictive control system.

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13 

Iterative feedback tuning of feedforward IPC for twobladed wind turbines: A comparison with conventional IPC
The development of sustainable energy production methods is an important aspect in lowering the emission of greenhouse gases and exhaustion of fossil fuels. Wind energy is recognized globally as one of the most promising sustainable forms of electricity generation, but the cost of offshore wind energy does not meet the level of fossilbased energy sources. An opportunity for wind energy cost reduction is the deployment of twobladed wind turbines at offshore locations. Twobladed turbines save the mass and cost of one rotor blade, which allows the entire wind turbine construction to be designed lighter, and in effect leads to lower initial costs. Moreover, reduction of harmonic fatigue loads on blades and other turbine parts using Individual Pitch Control (IPC) is a way to extend the turbine lifetime. This type of control, using a feedback control structure incorporating the MultiBlade Coordinate (MBC) transformation, is capable of mitigating the most dominant periodic loads. It is generally known that significant turbine load reductions can be achieved using IPC, however, it is unclear to what extent the MBC pitch signal is optimal in terms of load alleviations. The main goal of this thesis is to develop a selflearning feedforward IPC strategy for a stateofthe art twobladed wind turbine. This IPC strategy will be compared to the conventional feedback IPC implementation.
By making use of properties of the MBC transformation, implementations of yaw control by IPC in different configurations are evaluated in terms of performance and stability. As a preparation for the comparison between conventional feedback and selflearning feedforward IPC strategies, a linear controloriented model from blade pitch angles to harmonic blade loads is identified and used throughout this work for two main purposes. The first purpose is to reveal the level of interaction between both blades, which turns out to be negligible. On the basis of this reasoning, an appropriate costfunction is implemented for optimization of the feedforward controller. The second purpose of the linear model is the simplified and faster development of the Iterative Feedback Tuning (IFT) algorithm, which is later implemented in highfidelity nonlinear wind turbine simulation software. IFT is a selflearning modelfree algorithm, and is used to optimize the rotorposition dependent feedforward IPC implementation. It is shown that the selflearning algorithm succeeds in optimization of the feedforward controller at all constant wind speeds, but also in more realistic turbulent wind conditions in the aboverated region. As the feedforward controller generates a constant amplitude pitch signal for each wind speed, the amplitude of the feedforward pitch signal is gainscheduled on exogenous load signals, in an effort to improve feedforward performance.
Results show that the conventional IPC strategy is optimal in terms of load reductions in steady state wind conditions, as the IFT algorithm optimizes to the exact same pitch signal at various constant wind speeds. In turbulent wind conditions, performance results indicate that the constant amplitude feedforward controller is able to attain significant load reductions, but that the performance of the conventional feedback control method is still superior. Comparing the pitch signals of both controllers in turbulent wind conditions, reveals that the conventional method continuously changes the phase of the implemented pitch signal, which is not driven by the varying rotor speed. To see how these changing pitch periodics have an effect on the load reduction capabilities, the feedforward (rotor speed dependent) pitch signal amplitude is scheduled on exogenous signals in various ways. This scheduling shows only minor performance improvements, and it can be concluded that the frequency changes in the pitch signal imposed by MBC, help to actively mitigate periodic blade loads. Using both the azimuth and blade load measurements, the conventional IPC strategy seems to actively track and mitigate the current present blade load harmonic, and it appears to be a serious challenge to develop control strategies that can improve performance already attained by MBC.

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14 

RealTime Optimistic Planning for the Control of Nonlinear Systems
Optimistic Planning is a modelbased online planning algorithm that guarantees nearoptimal actions for the control arbitrarily nonlinear systems. Planning algorithms aim to find optimal actions by starting from the current state and developing a tree representation of sequences of actions and resulting states, using a model to simulate statetransitions. Typically, online planning algorithms return a sequence of actions, apply the first action (or several actions at the start of the sequence) and start planning again from the new state, resembling the receding horizon principle as seen in Model Predictive Control.
Several optimistic planning algorithms exist, of which in this work only Optimistic Planning for Deterministic systems (OPD) is considered. OPD works for large, possibly infinite state spaces, but only for finite, discrete action spaces. Unfortunately, while OPD shows good theoretical nearoptimality guarantees, there is no record yet of OPD being applied to control nonlinear physical systems in realtime. This is because of the (long) computation required by OPD.
This work analyzes two main methods that can be used to make OPD suitable for realtime applications. The first approach is to increase the computational speed of the planning process by parallelizing the algorithm. Unfortunately, while parallelization has been proven to be able to increase the computational speed in classical planning, in experiments no improvement is found yet for OPD using parallelization. However, a potential benefit from creating a parallel version of OPD is not ruled out and it is expected that more research and more efficient implementations could still lead to an increase in the computational speed.
The second approach is to apply sequences of actions instead of single actions, which increases the time available for the planning process. Replanning starts immediately after a sequence is returned, using as initial state a prediction of the state at the end of the previous sequence. The resulting algorithm is called RealTime Optimistic Planning with Action Sequences (RTOPS). Extensive analysis is performed to find restrictions on the parameters of the algorithm that, when met, can guarantee realtime applicability. Additionally, the effect of using sequences of actions on the performance of the algorithm is investigated and bounds are put on the maximum performance loss.
The performance of RTOPS has been tested in various experiments on different problems: a cartpole simulation, an acrobot simulation and a real inverted pendulum. Different settings are compared and, overall, RTOPS proves to perform well, without violating realtime constraints. The experiments prove that RTOPS allows for the use of optimistic planning for realtime control of physical nonlinear systems.
Future work should focus on applying the ideas used to develop RTOPS to other optimistic planning algorithms, such as those that allow for continuous actions or stochastic systems. Furthermore, a parallelization of RTOPS could be developed that increases its computational speed.

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15 

Haptic feedback on the steering wheel near the vehicle’s handling limits using wheel load sensing
Research in vehicle dynamics and vehicle control systems has increased a lot over the last decades due to the focus towards safety, driving comfort and emissions and the opportunities
that come with the development of hybrid and electric powertrains. Modern vehicles are equipped with many automated systems but automation can have disadvantages such as overreliance, complacency, nonvigilance, deskilling and confusing the driver. The goal of this thesis is to prevent loss of control by improving haptic feedback on the steering wheel near the vehicle’s handling limits using wheel load sensing. Predicting when a vehicle starts to over or understeer is a difficult task since it depends on the road surface and will only be revealed by vehicle states once it is already too late. The aligning moment of the tire drops before the lateral force actually saturates because of a decrease in pneumatic trail caused by tire contact patch deformation. This drop in aligning moment can be felt on the steering wheel and is an indication that a vehicle is close to the limit. However, the large ratio of mechanical to pneumatic trail and the increased power steering in modern vehicles makes the drop in aligning moment difficult to feel. If vehicles become steerbywire there is no feedback reaching the driver through the steering wheel at all.
The first part of this thesis consists of identification of the lateral force and aligning moment of the tires with Load Sensing Bearings (LSBs) from SKF. Estimating the lateral force has been done before and the results here show that estimation of the aligning moment is also possible. A Multiple Linear Regression Analysis (MLRA) is used to find first order linear models reconstructing the lateral force and aligning moment from measurements with the LSBs. Different models are derived based on measurements of strain gauges and hall effect sensors on the bearing. The second part of this thesis consists of experiments done at the Prodrive test track to investigate the improvement of haptic feedback using LSBs. The work is a followup study based on the Haptic Support Near the Limits (HSNL) system developed in a previous research project. The drop in aligning moment is measured with LSBs and amplified on the steering wheel. The results show that with this feedback drivers are indeed better capable of preventing saturation of the front tires but further research is needed on how the system can increase safety. The results show a decrease in control effort and workload with feedback which can increase driving pleasure and comfort for the driver.

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16 

Improving winch control performance in Kite Power Systems using gain scheduling and a compliant element
Rising demands in energy consumption necessitate the development of lowcost renewable power generation. A Kite Power Sytem (KPS) is a novel approach to harvest wind energy with kites at higher altitudes than is possible with conventional wind turbines, at a lower cost. In this thesis, an approach to improve the winch controller of a KPS will be proposed in order to increase the power output. Measurements of the test system revealed an especially poor performance during the reel out phase, where the tether force was constrained to a maximum. It was found that a propagation delay is present on the system input. A force tracking controller for the reel out phase therefore needs to be developed, which accounts for the system’s propagation delays. A nonlinear KPS model that can be used in control algorithm design was presented. To control the nonlinear system across its full operating region, a gain scheduled feedback controller was proposed. It was found that the stability of the modeled original system was compromised when the system delay is high enough. By extending the system with a compliant element, a larger delay can be allowed before instability occurs. Within the boundary conditions of the nonlinear KPS model, by applying gain scheduled feedback control with integral action and extending the system with a compliant element, the winch controller can asymptotically track a force reference across its operating region in case of system delays. Given that the correct force reference is supplied, this will increase the power output of the KPS.

file embargo until: 20161204
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17 

Cogging compensation in embedded brushless motor control for haptics applications
For the development of a space qualified, 7 degrees of freedom, haptic arm exoskeleton a brushless DC motor with high torque production, power density and efficiency was selected. However, the attraction of the rotor magnets to the stator teeth introduces an additive, position dependent, torque disturbance on the motor shaft called cogging torque. In haptic applications this disturbance impacts the realism of the force reflection and limits control fidelity during masterslave teleoperation. This thesis investigates possible solutions within the constraints of the envisioned application. Due to volume, torque production and efficiency requirements the chosen motor is not to be changed. This rules out the use of motor design based cogging minimization techniques. For this reason only controlbased methods are considered. Two methods to identify the cogging waveform were developed. This data was used to do feedforward compensation using a lookuptable approach and a Fourier series approximation. For comparison, a PID feedback compensation and hybrid approach were also tested. Identification of the cogging torque and testing of compensation methods is done using a custom build measurement setup. A reduction of the RMS cogging of 39% was achieved using the feedforward approach, while the PID feedback loop resulted in a 46% reduction. A combination of these two methods in the hybrid approach resulted in a reduction of 75%.
Thesis done as part of a 'double degree' with Systems & Control at 3mE (DCSC) and Embedded Systems at EWI.

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18 

Gaining by forgetting: Towards longterm mobile robot autonomy in large scale environments using a novel hybrid metrictopological mapping system
The emerging of mobile robots in everyday life scenarios, such as in the case of domestic care robots, is highly anticipated. Much research has been carried out to make robots more capable of performing tasks in our everyday environments. Despite major progress over the last decades, many hurdles are still to be taken. In the field of robotic mapping, which studies how robots can generate a map (internal representation) of their environment, modern Simultaneous Localization and Mapping (SLAM) methods allow robots to map their environment and be aware of where they are in that environment. Such maps can then be used for robot navigation, which allows a robot to travel from one place to another safely and autonomously. State of the art SLAM methods still show large limitations in their real world applicability. First and foremost, they are limited in the size of environment they can handle as maps grow inconsistent when environments get too large, or they cannot handle multistory buildings for example because they are designed to only map in 2D. Performance even becomes significantly worse if one limits oneself to using affordable sensors. Secondly, modern SLAM algorithms still struggle with the tasks of building a map that is metrically consistent with the real world (that is, the map and a ground truth floor plan should align). Thirdly, the generated maps show obstacles (like walls), but do not give any other semantic details on them. For example, the map does not tell what places are rooms and what places are a corridor.
In this thesis, it is investigated how robotic mapping and robot navigation could benefit from a human inspired approach to these tasks. Humans do not create floor plans, but remember their environments in terms of concepts. These concepts are then linked in a relative way, and places are connected by fuzzy, relative defined connections. The relatively new study of semantic mapping aims at integrating these concepts (semantics) into robotic mapping. However, so far these systems have been built on top of a traditional SLAM method.
Parallel to this new development of semantic mapping, this thesis proposes an architecture, which we named LEMTOMap (Large Environment Metric TOpological Mapping system), that generates and handles maps in a relative way. It specifies mapping, localization and navigation in a way in which metric consistency of the map is no longer a requirement on a larger scale (e.g. that of a faculty building or larger).
The main contributions of this thesis are captured by the LEMTOMap architecture. LEMTOMap introduces a new topological mapping paradigm that allows the robot to generate a map that is metrically consistent on a local scale, but does not require metric consistency on a larger scale. This way, the main challenge of modern SLAM  limiting metric inconsistency  is reduced to a challenge of subordinate importance. Additionally, a new grid map SLAM algorithm is introduced, named Rolling Window GMapping (RWGMapping).
To verify the expected performance enhancements of the LEMTOMap system architecture, LEMTOMap has been partially implemented and tested in simulated experiments. The experiments confirm the main benefits of LEMTOMap, mostly in terms of improved overall time and space complexity.
The thesis concludes with a range of advices for future work. Part is aimed at the further implementation of LEMTOMap, and part at improving LEMTOMap beyond its current specification. Also, a performance issue of the original GMapping algorithm was detected and suggestions are made on how this should be improved.

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19 

Filtering & Identification for Spline based Wavefront Reconstruction in Adaptive Optics
Without any form of compensation, atmospheric turbulence blurs the images obtained by groundbased telescopes. An Adaptive Optics (AO) system compensates for the optical wavefront distortions introduced in a light beam as it propagates through a turbulent medium. The wavefront phase errors are measured with a Wavefront Sensor (WFS) and corrected by adding the conjugated phase with an actuator such as a Deformable Mirror (DM). This graduation project focuses on the reconstruction of the wavefront using a ShackHartmann (SH) WFS, while taking its spatial and temporal dynamics into account.
The recently introduced Spline based ABerration REconstruction (SABRE) is used to model the spatial dynamics using the approximated slopes. It has been shown that using the measured intensity pattern of the WFS, rather than the approximated slopes (which are obtained using a centroid algorithm), the WFR can be improved, because the intensity distribution contains more information than the approximated slopes. This, however, has been demonstrated using a Hartmann sensor. The first contribution of this thesis was to adapt the method for the SH WFS, which is the commonly used sensor in astronomy. This is achieved by using an additional image of a SH WFS under the same conditions, but with an additional known aberration. The prescribed algorithms are tested with the AO simulation tool Yao. It is shown that for small aberrations, SABRE with intensity measurements provides more accurate reconstructions of the wavefront.
Because of a delay, caused by the WFS and WFR, an error is introduced resulting from the temporal dynamics of the wavefront. The second goal of this thesis is to predict the wavefront aberrations, such that the temporal dynamics are taken into account. Furthermore, the prediction should exploit the local nature of SABRE, such that it is applicable for parallel programming. Subspace Identification (SID) is employed for estimating the model of the temporal dynamics. The estimated model is used by a Kalman Filter (KF) to predict the wavefront aberration. The SID and KF are adapted to methods which are compliant with the local nature of SABRE and therefore, the presented SID and KF are suitable for parallel programming. The SID and KF are tested and tuned with both methods of SABRE, i.e. SABRE with the approximated slopes and SABRE with the measured intensities. It is demonstrated that the KF predicts the aberration significantly more accurate compared to the delayed reconstruction and at times even outperforms the reconstruction without delay.

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20 

LTL specifications for Highway Lane Changing Maneuvers of Highly Automated Vehicles
Since the interest in autonomous driving solutions is massively increasing, the need for good and reliable control algorithms is growing every day. This project studies the performance of safe lane changes of a highly autonomous vehicle given the currently available perception of the environment, vehicle dynamics and desired comfort and speed requirements from the user. Also focus will be on when the vehicle decides to overtake other vehicles to move closer to its desired prescribed speed, while respecting the "rules of the road", i.e. not causing unexpected actions in relation to the other road participants. These requirements will then be converted into linear temporal logic statements for the purpose of automated synthesis of a receding horizon controller for longitudinal and lateral control of the vehicle. Thereby allowing it to make adjustments to the desired system behavior and computing a new control strategy, relatively easy and by definition, the resulting controller is formally guaranteed to meet the safety specifications at all times. Besides this search for formal specifications, a comparison is made with more conventional control techniques by reviewing a model predictive controller that was developed parallel to this project, showing its capabilities and discussing possible safety issues.

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