This thesis is concerned with the development, design and implementation of a novel hybrid multi-agent orientated control architecture for navigation of multiple autonomous mobile robots operating in an unknown and unstructured environment populated by static and/or dynamic obstacles. The proposed hybrid control architecture is modular and draws its design from competitive tasks architecture, production rules architecture, connectionist architecture, dynamic system architecture, multi-agent architecture and subsumption architecture. The reasoning of the control architecture is both deliberative and reactive. The proposed reactive behaviours are modelled using fuzzy logic, neural networks and hybrid behavioural encoding incorporating stateflow-fuzzy logic and stateflow-neural networks. The deliberative system is comprised of finite state machines. The processing is achieved in a centralised and/or decentralised manner using the proposed controller-agent concept from the field of multi-agent systems. The framework of the control architecture is suitable for adaptation in single and multiple robot navigation. The control architecture has been implemented in MATLAB/Simulink using the full non-linear model of the MIABOT V2 mobile robots. It is evaluated incrementally in order to verify its overall control performance and the performance of each subsystem. Results show that the control architecture's modularity, distribution, reactivity and behaviourbased structure provided the overall control system with robustness in all cases of navigation tasks utilising either single or multiple mobile robots. Furthermore the results obtained show the effectiveness of the control architecture in navigation tasks involving up to five mobile robots operating in unknown static and dynamic environments. The results demonstrate that the control strategy chosen for navigation of multiple mobile robots is efficient and also established the robustness of the control system architecture against the desired requirements, such as supervision, decision-making and co-ordination of internal control structures (subsystems). The autonomous mobile robots were exposed to a complex and highly dynamic environment and successfully achieved every control objective. Their trajectories were smooth despite the interaction between several behaviours and the presence of unexpected static and dynamic obstacles. The main contributions of this thesis are: development of a novel hybrid multi-agent based control architecture called CARDS; novel approach for identification of direction of moving obstacles (other robots) using finite state machines; novel approach for behavioural encoding using hybrid solutions such as stateflow-fuzzy and stateflow-neural for autonomous robot navigation; proposed a design methodology for developing integrated solutions for autonomous mobile robotic systems and classification of the main design methodology (properties) of control systems architectures for autonomous mobile robots. Less significance contributions are: literature survey on approaches/methods related to the development of intelligent control architectures for navigation of multiple autonomous mobile robots; modelling of MIABOT V2 mobile robots; comparison between PI, fuzzy and neural controllers and algorithmic methodology for discovery of fuzzy/neural local models from observation data; identification of the relationship of the most important requirements/properties of control architecture versus the main control architecture specifications using the Quality Function Deployment tool; modular approach for modelling and evaluation of three types of sensor and sensor sensitivity.

The rapidly increasing amounts of space debris orbiting Earth is threatening to reach a critical level, where the near-Earth environment becomes so overfilled with junk that many missions simply become unfeasible. Long-term active debris removal operations appear to be a necessity, but due to the scale of the problem this will likely be an expensive affair spanning decades or even centuries. Many of the mission-related costs can be significantly reduced by making use of a smaller spacecraft, such as the rapidly developing CubeSat standard. An issue with this approach is the limited actuation capabilities, as that makes it very difficult to perform orbital maneuvers in a fuel-efficient manner. Rather than making a few high-impulse thrusts over the course of the mission, the thrust must be applied continuously for several hundred hours. This thesis attempts to solve the problem by using a non-linear Model Predictive Control strategy to implement an Orbital and Attitude Control system for a small satellite. This was done in MATLAB, using the fast-NMPC package MATMPC recently developed by Yutao Chen at Padova University. The controller was tested in a realistic model of the near-Earth environment, where disturbances such as drag and gravitational perturbations are simulated. It was shown through simulations that this method can successfully be used to perform a fuel-efficient rendezvous maneuver with an uncontrollable object, a critical step in any Active Debris Removal operation. Using a 4 kg CubeSat with a 30 µN thruster mounted on each of its six surfaces, the total mass consumption for a phasing maneuver of 10 degrees at 300 km altitude was less than 0.1% of the spacecraft mass. This assumes an Isp of 1,150 s, which is the specific impulse of the S-iEPS Electrostatic thruster flown on the Aero-Cube-8 IMPACT mission. This is made possible through prediction horizons spanning several days, which in turn forces the controller to operate at sampling rates as low as 1/100 Hz due to the computational load. Fully realizing the potential of this technique would likely require the inclusion of a low-level controller that uses the generated trajectories as input values, as this would negate many of the issues associated with a heavy computational load. The urgency of the space debris problem, and the endless list of other CubeSat applications that would benefit from a flexible and fuel-efficient AOCS, makes this an interesting to topic to consider for further research.

This thesis considers the application of neural networks to automotive suspension systems. In particular their ability to learn non-linear feedback control relationships. The speed of processing, once trained, means that neural networks open up new opportunities and allow increased complexity in the control strategies employed. The suitability of neural networks for this task is demonstrated here using multilayer perceptron, (MLP) feed forward neural networks applied to a quarter vehicle simulation model. Initially neural networks are trained from a training data set created using a non-linear optimal control strategy, the complexity of which prohibits its direct use. They are shown to be successful in learning the relationship between the current system states and the optimal control.

Energy efficient control of server rooms in modern data centers can help reducing the energy usage of this fast growing industry. Efficient control, however, cannot be achieved without: i) continuously monitoring in real-time the behaviour of the basic thermal nodes within these infras- tructures, i.e., the servers; ii) analyzing the acquired data to model the thermal dynamics within the data center. Accurate data and accurate models are indeed instrumental for implementing efficient data centers cooling strategies. In this thesis we focus on Open Compute Servers, a class of servers designed in an open-source fashion and used by big players like Facebook. We thus propose a set of appropriate methods for collecting real-time data from these platforms and a dedicated thermal model describing the thermal dynamics of the CPUs and RAMs of these servers as a function of both controllable and non-controllable inputs (e.g., the CPU utilization levels and the air mass flow of the server’s fans). We also identify this model from real data and provide the results so to be reusable by other researchers.

Poorly operating control loops cause loss in productivity in almost every industry worldwide. Therefore performance monitoring has been an active area of research for the past decades. In this work, a newly developed fault detection method is applied to the monitoring of lambda-tuned control loops. The lambda-tuning method has, due to its simple use, become very popular in the pulp and paper industry and is now spreading to other industries. A model-based fault detection algorithm assuming uncertain process parameters is used for detecting changes in the process. The algorithm consists of two parts, a residual and a time-varying threshold. The a priori information obtained from lambda-tuning is used to create an observer, which is used as residual generator. Known process inputs are used together with upper bounds for the uncertainties and upper bounds for disturbances when calculating the detection threshold. The observer may have integral action in order to make the threshold tight to the residual. Upper bounds for the uncertainties in the process parameters and upper bounds for disturbances are tuning parameters in the algorithm. Two different methods for finding those parameter values are proposed. The first is an approach based on allowed loss in phase margin and the second consists of solving a nonlinear optimization problem to minimize the difference between the residual and the threshold. The algorithm is tested in a simple water tank system. The threshold handles step changes in reference value without giving any false alarms. A fault is introduced by widening of the output in the water tank which simulates a change in the process parameters. The residual then gets larger than the threshold and the fault is detected. / <p>Godkänd; 2006; 20061120 (pafi)</p>

This work is about interaction. Research in the field of information visualization has traditionally been slanted more towards presentation; this even though humans are interactive creatures. We observe our surroundings, make decisions based on what we see and react back into the world. It may all have had its modest beginnings on the primordial Xerox Parc desktop but user interfaces of today are becoming more and more faithful representations of our everyday environments. Between the promise of augmented reality and ubiquitous computing, who knows where we are headed. One thing is certain however; whatever world we end up in, we will continue to interact with visual devices. Three papers make up this thesis: Three-dimensional user interfaces are often considered worse than two dimensional from a usability standpoint due to the difficulties imposed by the need to navigate in three dimensions. The first paper of this thesis compares two user interfaces for the visualization of hierarchies, one two-dimensional and one three-dimensional. As we expected the three-dimensional tool excelled at tasks related to hierarchical depth while, surprisingly, the two tools perform comparably on most other tasks. A possible explanation for this is that we restrict manipulation in such a way that the user simply cannot get lost while navigating. Abrupt transitions cause confusion. This is a well known fact and in our second user study we set out to investigate what effect animated transitions would have on navigation in a 3D file system visualizer. The study we conducted failed to show an effect on user performance but did find an effect on navigation patterns. Participants were tasked to navigate stepwise into a directory and then, starting at the root, try to return to it (whichever way they chose). Participants in the animated treatment group were nearly four times as likely to take a shortcut back while participants in the non-animated group overwhelmingly favored a stepwise return strategy. The stepwise approach appears safer in that a percentage of shortcut attempts fail; failures which it seems difficult to recover from. The third and last paper is a survey and taxonomy of interactive animation in user interfaces. User interface animations are becoming more and more prevalent and advanced. Yet there is little in the way of research showing when you should animate and why. In the model underlying the taxonomy a number of aspects of animation are identified. Animation generally has a purpose, animation may catch attention, explain, smooth out abrupt transitions as well as reveal the progress of otherwise hidden processes. There is also a difference between transitional animations that preserve context and those that do not; a fact which is often overlooked. Of the user studies surveyed many show some benefit of animation but a significant number also show distinctly negative effects. Animation has been found to lead to superficial learning and poor retention when applied to teaching, as well as when it is used to smooth out abrupt transitions. / <p>Godkänd; 2006; 20070109 (haneit)</p>

In society today the production of energy has aimed its focus towards the resources that are renewable as a result of their ability to avoid negative effects of the environment. In the future it is desired to have replaced the current market entirely with these options which generates a demand to optimize these resources and their capabilities to produce energy. The current situation of production require separate systems to generate energy from a solar system and windmills which is a clear economic disadvantage.  Therefore it is highly desired to establish a solution to combine these two systems into one where they can generate energy collectively, this would result in the possibility to avoid unnecessary costs when these are desiderated to combine. Through development and executed testing’s a prototype has been created to counter this demand which will be presented in this thesis along with the positive effects this will have on the market of energy production concerning the environment as well as financially. The prototype has been created with the possibility to integrate a windmill into a solar system. Tests have been conducted in order to ensure that the project goals have been achieved. Through the execution of this project it has been determined that integration between a solar system and a windmill is feasible without major expenses. / I dagens samhälle har energiproduktionen riktat fokus mot de energikällor som är förnybara på grund av dess förmåga att undvika negativa effekter på miljön. I framtiden hoppas energiproduktionens nuvarande marknad vara helt ersatta med dessa alternativ vilket genererar ett behov av optimering av dessa energikällor och de möjligheter dessa innehar att producera energi. Den situation som vi befinner oss i idag kräver enskilda system för att möjliggöra utvinning av energi från vindkraftverk och solcellssystem. Då detta är ekonomiskt ofördelaktigt är det önskvärt att en lösning till detta problem etableras och på så sätt kan avsevärda kostnader undvikas i situationer där dessa system önskas kombineras vid elproduktion. I detta projekt har en prototyp framtagits som kombinerar ett solcellssystem med ett vindkraftverk. Det har gjorts tester för att se hur det befintliga solcellssystemet agerar vid anslutning av vindkraftverk och hur det går att anpassa vissa delar i systemet för att uppnå önskad funktionalitet. I slutet av projektet har det blivit klart att en integration mellan ett befintligt solcellssystem och ett vindkraftverk är möjligt. Systemet består av ett vindkraftverk som samverkar med ytterligare komponenter för att utvinna energi till en låg kostnad. Prototypen har mött de mål och krav som har ställts inom projektet med en möjlighet till vidareutveckling i framtiden.

This thesis presents a purposed navigation and control solution for autonomous ying of a quadrotor in conned spaces where positioning with GPS is notfeasible. The problem and related application considered is autonomous ying of a quadrotor in mineshafts for 3D modeling and inspection. Theoryand test results are presented including positioning experiments with a cameracombined with a UWB-ranging radio and a P-PI regulator for position control.It is argued that this is a viable and cost eective solution that can be usedto determine the position of the copter and control its position to within 0.2meters with stable controlled velocity.

Inertial sensors and magnetometers are abundant in today's society, where they can be found in many of our everyday electronic devices, such as smart phones or smart watches. Their primary function is to measure the movement and orientation of the device and provide this information for the apps that request it. This licenciate thesis explores the use of these types of sensors in biomedical applications. Specifically, how these sensors can be used to analyze human movement and work as a tool for assessment of human balance and movement disorders. The methods presented in this thesis deal with mathematical modeling of the sensors, their relationship to the biomechanical models that are used to describe the dynamics of human movement and how we can combine these models to describe the mechanisms behind human balance and quantify the symptoms of movement disorders. The main contributions come in the form of four papers. A practical calibration method for accelerometers is presented in Paper I, that deals with compensation of intrinsic sensor errors that are common for relatively cheap sensors that are used in e.g. smart phones. In Paper II we present an experimental evaluation and minor extension of methods that are used to determine the position of the joints in the biomecanical model, using inertial sensor data alone. Paper III deals with system identification of nonlinear controllers operating in closed loop, which is a method that can be used to model the neuromuscular control mechanisms behind human balance. In Paper IV we propose a novel method for quantification of hand tremor, a primary symptom of neurological disorders such as Parkinson's disease (PD) or Essential tremor (ET), where we make use of data collected from sensors in a smart phone. The thesis also contains an introduction to the sensors, biomechanical modeling, neuromuscular control and the various estimation and modeling techniques that are used throughout the thesis.

Wastewater treatment involves many processes and methods which make a treatment plant a large-scaled and complex system. A fundamental challenge is how to maintain a high process efficiency while keeping the operational costs low. The variety in plant configurations, the nonlinear behaviour, the large time delays and saturations present in the system contribute to making automation and monitoring a demanding task. The biological part of a wastewater treatment process includes an aeration of the water and this process has been shown to often result in the highest energy consumption of the plant. Oxygen supply is a fundamental part of the activated sludge process used for aerobic microorganisms growing. The concentration of the dissolved oxygen should be high enough to maintain a sufficient level of biological oxidation. However, if the concentration is too high the process efficiency is significantly reduced leading to a too high energy consumption. Hence, there are two motivations behind the aeration control task: process efficiency and economy. One of the possible strategies to adjust the dissolved oxygen level in a nitrifying activated sludge process is to use ammonium feedback measurements. In this thesis, an activated sludge process is modelled and analysed in terms of dissolved oxygen to ammonium dynamics. First, the data obtained from a simplified Benchmark Simulation Model no.1 was used to identify the system. Both linear and nonlinear models were evaluated. A model with a Hammerstein structure where the nonlinearity was described by a Monod function was chosen for a more thorough study. Here, a feedback controller was designed to achieve L2-stability. The stability region was pre-computed to determine the maximum allowed time delay for the closed loop system. Finally, a feedforward controller was added to the system, and shown to significantly improve the disturbance rejection properties.