The aim of this thesis is twofold. The first and major one is to develop an objectoriented Kaplan turbine model to support control design for hydropower systems. The model is developed in the Modelica language and implemented in the Dymola simulation environment. Using the equation-based modelling formalism of Modelica, the model exploits water flow and pressure balance equations to capture the relationship between the hydraulic and mechanical energy. A particular feature of the model is a detailed description of how the water flow and turbine torque are affected by the wicket gate and turbine runner blade angles. The second aim of the thesis is to demonstrate how the Kaplan turbine model can be used to design hydropower control systems. To this end, a relatively complete hydropower system model is developed using the model components and several dam control strategies are evaluated. This study clearly demonstrates the utility of the model in the design of hydropower control systems.

In a cell culture system, a lot of compounds are involved considering the ones contained in the medium (extracellular) and the intracellular environment. Studies of the different system behaviours adopted due to variations in the medium concentrations are typically based on empirical or statistical approaches when working with such an amount of compounds. In this paper is presented a method to develop a dynamical model of a cell culture system which can predict the evolution of the medium compounds and cell growth along the time for this kind of systems. The method has been implemented as a standardised tool that can be used for any kind of biological system when a pre-defined metabolic network and data of different states are available. In parallel to this project, a medium where the exact concentrations of all the compounds are known and should let the cells live properly have been developed by the KTH Division of Bioprocess. The system corresponding to the experiments carried out to develop the medium has been modelled using the proposed method. The results have been analysed and the conclusions, requirements and drawbacks of the model have been discussed.

Over the last few years, a change in one of the classical paradigms in Control Systems has been happening. Partially due to the increase of the computational power, there exists now the possibility of having intelligent sensors and actuators which may take actions in a distributed fashion. This way, there is no longer the need for a centralized controller or data fusion station, which is really well suited for Networked Control Systems. This led the way to the development of distributed control and sensor fusion frameworks and there is still a a great amount of research to be done in this field. Since there is no longer a central station containing all the information but instead there are several distinct agents controlling or monitoring the system, communication between these agents is now necessary. Thus one important aspect in these kind of systems is security, regarding not only the communications between agents but also a possible malfunction of one or several agents. The scope of this thesis is to study methods to detect possible misbehaviors and security breaches in the communications between agents from a control theoretical perspective. In this sense, a specific kind of distributed controller - the consensus problem - will be analyzed under the effect of faults and communication attacks. A Fault Detection and Isolation method will be used in order to detect these events using only the local information available. An example regarding power systems will also be given, where a decentralized state estimator is used in order to detect faults within an area, requiring only local information.

When system identification methods are used to construct mathematical models of real systems, it is important to collect data that reveal useful information about the systems dynamics. Experimental data are always corrupted by noise and this causes uncertainty in the model estimate. Therefore, design of input signals that guarantee a certain model accuracy is an important issue in system identification. This thesis studies input design problems for system identification where time domain constraints have to be considered. A finite Markov chain is used to model the input of the system. This allows to directly include input amplitude constraints into the input model, by properly choosing the state space of the Markov chain. The state space is defined so that the model generates a binary signal. The probability distribution of the Markov chain is shaped in order to minimize an objective function defined in the input design problem. Two identification issues are considered in this thesis: parameter estimation and NMP zeros estimation of linear systems. Stochastic approximation is needed to minimize the objective function in the parameter estimation problem, while an adaptive algorithm is used to consistently estimate NMP zeros. One of the main advantages of this approach is that the input signal can be easily generated by extracting samples from the designed optimal distribution. No spectral factorization techniques or realization algorithms are required to generate the input signal. Numerical examples show how these models can improve system identification with respect to other input realization techniques.

Industrial robots have been used as a key factor to improve productivity, quality and safety in manufacturing. Many tasks can be done by industrial robots and they usually play an important role in the system they are used, a robot stop or malfunction can compromise the whole plant as well as cause personal damages. The reliability of the system is therefore very important. Nevertheless, the tools available for maintenance of industrial robots are usually based on periodical inspection or a life time table, and do not consider the robot’s actual conditions. The use of condition monitoring and fault detection would then improve diagnosis. The main objective of this thesis is to define a parameter based diagnosis method for industrial robots. In the approach presented here, the friction phenomena is monitored and used to estimate relevant parameters that relate faults in the system. To achieve the task, the work first presents robot and friction models suitable to use in the diagnosis. The models are then identified with several different identification methods, considering the most suitable for the application sought. In order to gather knowledge about how disturbances and faults affect the friction phenomena, several experiments have been done revealing the main influences and their behavior. Finally, considering the effects caused by faults and disturbances, the models and estimation methods proposed, a fault detection scheme is built in order to detect three kind of behavioral modes of a robot (normal operation, increased friction and high increased friction), which is validated within some real scenarios.

With the widespread use of networked embedded systems operating over wireless sensor networks, a standardized architecture is required to enable the rapid development of applications. An embedded operating system serves as an important building block of the standardized architecture. The support of the most commonly used services and protocols should be made available in it as a system service to improve the development speed. Real-time services are commonly required by many time-sensitive applications, such as  automation control, real-time monitoring. Events need a global time notion or must happen within a deadline. Collected data should arrive at the destination before it becomes old and loses its meaning. But there is no common notion of time in a wireless sensor network in which all the nodes are physically separated and no global clock or common memory exists. Moreover, there is no guarantee that the sensed data will get to the destination before the deadline. To address these real-time issues, we develop real-time services including time synchronization and low-latency data collection to provide therapid development of time-critical applications. Meanwhile, security becomes an important issue to wireless sensor network due to the vulnerability of the wireless channel. The adversaries can simply capture and change the data and then resend it. The real-time services utilizing the wireless communication are vulnerable to the attacks and might be the weakest link for the whole system if it is not designed with security in mind. As the building block of real-time services, time synchronization comes into the first place to provide a global time scale for a distributed networking system. We study current time synchronization protocols for wireless sensor networks, propose our protocol design and implement it in the experimental platform, Contiki OS on the hardware platform Tmote Sky. To show the feasibility and performance of our protocol, we perform extensive experimental evaluation. Low-latency data collection services will also play a significant role for the time-critical applications. It aims to provide the guarantee of a time limit for the data collection. Based on the synchronized notion of time over the network, we implement a protocol for data collection aiming at low end-to-end latency for the same platform. To show the performance of data collection using this protocol, we test end-to-end latency in a multi-hop network and evaluate it based on the hop count and the estimation of the point-to-point delay in a single-hop communication. Security issues pose a great challenge to the applications as well as the underlying services due to vulnerability of the wireless channel, hostile environment as well as the severe resource constraint. To make the real-time services resilient to security attacks, we analyse the security attacks that might interrupt the services and present countermeasures to resist these security breaches. The hardware platform in use provides a crypto accelerator in the radio chip and frees the microcontroller from the long computation time for the security operations. We implement the security protocol utilizing hardware-assisted security operation to provide the link-layer security services. In addition, we provide data freshness service using authenticated MAC timestamping for each packet. Then we show how to secure the real-time services using these security services and integrate them into the protocol implementation.

Opticruise is an automated transmission system for manual gear boxes. The length stroke is a part of the Opticruise system, in which the mechanical gear shifting process is performed by pneumatic actuators. To reduce piston velocity before the cylinder walls are reached, an oil damper is attached to the pneumatic actuator. The purpose of this thesis is to evaluate if the oil damper can be replaced by a pneumatic feedback control law, a model predictive control scheme or an alternative mechanical construction; in the interest of cost reduction, robustness, shift comfort and shift time. To be able to do a systematic analysis, a mathematical model of the length stroke is developed. The model, developed using a length stroke that is not mounted on a truck, captures the essential dynamics of the system and is used to evaluate control strategies and investigate system properties. As the delay in feedback information from the different sensors is larger than the process time constant, a pneumatic feedback control law is discarded. A model predictive control law is found difficult to realize since the system properties, when the length stroke is placed on a truck, are different in every gearshift. Instead a mechanical construction proposing smaller final chamber volumes is discussed. This solution turns out to be a cost-effective way to achieve a good velocity reduction before the end position. Since feedback control is still an interesting option for future gear shifting systems, the requirements on sensors, controllers and actuators for achieving good controller performance is briefly discussed.

Inom industrin används olika testmetoder för att uppnå hög kvalitet i mjukvarukontrollerade system. Kvalitetskraven är särskilt höga för säkerhetskritiska system t.ex. styr- och reglersystem i fordon. I det här examensarbetet används två analysmetoder (Fault Mode and Effects Analysis och Fault Tree Analysis) för att ta fram ett nytt säkerhetstest för ett broms- och växlingssystem som Scania tillverkar. Det studeras även hur formella metoder kan användas för att ytterligare öka kvalitet ochh tillförlitlighet. Modellkontroll  med NuSMV och Computational Tree Logic (CTL) appliceras på viss funktionalitet av de studerade systemen och en formell verifiering av att funktionaliteten uppfyller specifikationen utförs. / In the industry various testing methods are used to achieve high quality in software controlled systems. The demands on quality are especially high for safety critical systems, for instance control systems in vehicles. In this thesis two analysis methods (Fault Mode and Effects Analysis and Fault Tree Analysis) are used to develop a new safety test for a brake and gearbox control system made by Scania. It is also analyzed how formal development methods can be used to further increase quality and reliability. Model checking using NuSMV and Computational Tree Logic (CTL) is applied to a part of the studied systems and it is formally verified that the implementation fulfills the specification.

To be able to do the correct action when a fault is detected, the fault isolation part must be precise and run in real time during operation of the process. In many cases can it be difficult to decide exactly where the fault is localized. In those cases, the isolation algorithm must rank the faults according to their probability to be the cause to the behavior. The masters thesis project aims at probabilistic methods and algorithms for fault isolation in embedded systems. Different kind of Bayesian Networks have been compared in this report and the comparison has been done on a literature defined “benchmark system”. Those Bayesian network models which have been implemented for fault isolation are: 1. Manually (on the basis of physical representations) 2. Two-layer structure continuous signals discreet signals 3. Via temporal causal graph (dynamical network) The algorithms should be compared in the following areas: computational complexity, isolation performance and degree of difficulty to construct the network on the basis of data. The evaluated algorithms showed good results. Even though the system data which have been used in the Bayesian Networks are not very accurate in the first place, it manage to give a fairly precise isolation of the faults. The continuous Bayesian Network manage to show a good isolation performance for different type of faults and the Dynamic Bayesian Network found most of the faults even for a rather complex network. / Detta examensarbete handlar om sannolikhetsbaserade metoder för felisolering. När ett fel uppstår ombord på en Scania lastbil kan man upptäcka det. I bästa fall kan en viss komponent pekas ut som orsak, men ofta kommer man att ha ett antal komponenter som kan vara orsaken. I många fall är det dock svårt att hitta var exakta felet finns. För att hantera dessa situationer vill man använda metoder för att beräkna sannolikheten att olika komponenter är trasiga. För att beräkna sannolikheten kan man använda en probabilistisk model, dvs. Bayesianska nätverk. I detta arbete har olika metoder för att skapa Bayesianska nätverk jämförts. Jämförelsen görs på ett litteratur väl definierat benchmark problem: diagnosar en två-tank system. De typer av Bayesiansk nätverks modeller som har implementerats för felisolering är: 1. Manuellt (ut ifrån fysikalisk modell) 2. Två-lagers struktur kontinuerliga signaler diskreta signaler 3. Via Bindningsgrafer (dynamiskt nätverk) Problemen som undersöktes var bland annat svårighet att bygga nätverket utifrån data, beräkningskomplexitet samt isolerings prestanda. En jämförelse mellan de Bayesianska metoderna för felisolering och samt dem befintliga standardmetoder har även gjorts. De undersökta algoritmerna visade goda resultat. Trots bristen på data, visade algoritmerna lovande resultat. Det Två-lagers Bayesianska nätverket visade en bra isoleringsprestanda på olika komponent fel och det Dynamiska Bayesianska nätverket upptäckte de flesta fel trots att det var ett ganska complext nätverk.

Model predictive control techniques are widely used in the process industry. They are considered methods that give good performance and are able to operate during long periods without almost any intervention. Model predictive control is also the only technique that is able to consider model restrictions. Almost all industrial processes have nonlinear dynamics, however most MPC applications are based on linear models. Linear models do not always give a sufficiently adequate representation of the system and therefore nonlinear model predictive control techniques have to be considered. Working with nonlinear models give rise to a wide range of difficulties such as, non convex optimization problems, slow processes and a different approach to guarantee stability . This project deals with nonlinear model predictive control and is written at the University of Seville at the department of Systems and Automatic control and at the department of Automatic Control at KTH. The first objective is to control the nonlinear Four Tank Process using nonlinear model predictive control. Objective number two is to investigate if and how the computational time and complexity can be reduced. Simulations show that a nonlinear model predictive control algorithm is developed with satisfactory results. The algorithm is fast enough and all restrictions are respected for initial state values inside of the terminal set as well as for initial state values outside of the terminal set. Feasibility and stability is ensured for both short as well as for longer prediction horizon, guaranteeing that the output reaches the reference. Hence the choice of a short respectively long prediction horizon is a trade off between shorter computational time versus better precision. Regarding the reduction of the computational time, penalty functions have been implemented in the optimization problem converting it to an unconstrained optimization problem including a PHASE-I problem. Results show that this implementation give approximately the same computational time as for the constrained optimization problem. Precision is good for implementations with penalty functions both for long and short prediction horizons and initial state values inside and outside of the terminal set.