A lab named ‘System Identification Lab’ is a compulsory part of the course ‘Modeling of Dynamical System’ given by School of Electrical Engineering, Automatic Control at KTH. System identification is an experimental method to derive a mathematical model from the input and the output data. The apparatus used in the lab is a fan and a hinged rectangular plate. Both fan and plate are mounted on aslide. The idea of this lab is to collect the input and output data from this process. The input data is theDC voltage sent to the fan and the output is the angular displacement of the plate due to air stream from the fan. This data is then used to derive a linear model by applying different theoretical methods. The main focus of this thesis work has been to design a user interface for this lab, and implement it in LabVIEW, which is an easy to use, integrated graphical environment with built-in compatibility across a broad range of data acquisition and control hardware devices. In short, the interface first lets the user choose the sampling time and then one can choose between different input signals. The input and output signals are displayed as plots on the screen and can also be saved to a file. A second, similar interface has also been implemented, where the process is replaced by a simulation model. The simulation model is based on an identified linear model with some added disturbances and non-linear effects. The idea with the simulation model is that the ‘System Identification Lab’ then can be done without using the lab process. This report also includes an introduction to system identification and a discussion about how to choose appropriate input signals for an identification experiment. These methods are used to derive the simulation model and in order to understand the lab process better, some step responses are done and the process is also modeled from physical principles.

The goal of the Master thesis was to construct an obstacle avoidance system. It was the intention that this system can be used as part of a bigger project performed at the lab. The system was implemented and tested for a Pioneer 2DX mobile robot in the lab environment. The positioning of the robot was done with an external ultrasound system provided by the lab. The Digiclops 3D camera from Gray Point research was used for main vision sensor. The code was written in C and implemented in Windows environment. The obstacle avoidance system contains image processing, map building, and control. For control, the Vector Field Histogram theory was studied and implemented. Fixed Decomposition was used to describe the surrounding world. The OAS system was tested for 6 different sets of obstacle environments: a single obstacle, a gate, multiple obstacles, mobile obstacles, a corridor, and a maze. Each set were designed to test the limits of the system. All tests were recorded on video, the position data and the map were saved. In all obstacle sets, the system proved successful. There was no risk for collision and the efficient path planning created a smooth path to the goal.

As the heavy duty truck market becomes more competitive the importance of quick and cheap repairs increases. However, to find and repair the faulty component constitutes cumbersome and expensive work and it is not uncommon that the troubleshooting process results in unnecessary expenses. To repair the truck in a cost effective fashion a troubleshooting strategy that chooses actions according to cost minimizing conditions is desirable. This thesis proposes algorithms that uses Bayesian networks to formulate cost minimizing troubleshooting strategies. The algorithms consider the effectiveness of observing components, performing tests and repairs to decide the best current action. The algorithms are investigated using three different Bayesian networks, out of which one is a model of a real life system. The results from simulation cases illustrate the effectiveness and properties of the algorithms.

This report describes the development of a test platform and a self study course for the process automation system DeltaV. Sandvik started using this process automation system in 2004. In order to educate personnel in the system without having to disrupt any production lines an educational test platform was needed. The test platform that was developed in this project made use of the same production instruments as the actual production process. The test platform consisted of a physical process automated with a control database developed with DeltaV control software. One important aspect to the development was that the platform was designed to be portable. This is still a part that could be improved.

One of the major challenges to design efficient Wireless Sensors Networks (WSN) is the scarcity of energy and computational resources. We address this problem with particular reference to algorithms for efficient source and channel coding. Distributed source coding (DSC) is a general framework which applies to highly correlated signals that are coded separately and decoded jointly. In WSN, DSC schemes provide closed loop algorithms that exploit the correlation of data sensed by the nodes to reduce the amount of information that each node transmits, thus saving energy. A study is herein carried out along with an implementation of the aforementioned algorithms with the Matlab environment. The stability of the closed loop algorithms is then tested by means of simulations. Minimum energy coding schemes can be superimposed on top of the previous DSC algorithm to achieve further gains. In our work, we investigate and extend two existent energy-efficient minimum energy coding schemes [1][2]. In this context, we analyze the performance of the WSN in terms of power consumption and bit error probability, where a detailed wireless channel description is taken into account. We characterize the problem of efficient coding by means of stochastic optimization problems. This more accurate model of the system (compared to those previously existent [1][2]) allows us to propose new solutions to reduce power consumption while ensuring adequate bit error probabilities. As a relevant part of our work, a test-bed has been set up by using the Berkeley Telos Motes, along with a Matlab application interface, for the adaptive source coding algorithm. According to the results obtained by the ABSTRACT experimental work we have carried out, the energy consumption can be effectively reduced.

In this thesis, a novel approach to the average consensus problem for multi-agent systems is followed. A new event-based control strategy is proposed, which incorporates event-based scheduling of state measurement broadcasts over the network. The control-laws are based on the resulting piecewise constant functions of these measurement values. This facilitates implementation on digital platforms such as microprocessors and reduces the number of interagent communications over the network. Starting from a basic problem setup with singleintegrator agents, fixed undirected connected communication topologies, and no time-delays, the novel strategy is developed. Different triggering conditions guaranteeing convergence to an adjustable region around the average consensus point or asymptotic convergence to this point, respectively, are discussed. Numerical simulations show the effectiveness of this approach, outperforming classical time-scheduled implementations of the consensus protocol in terms of load on the communication medium. Furthermore the problem class is extended to networks with directed communication links, switching topologies, and time-delays in the communication as well as to agents with double-integrator dynamics. As an illustrative example, the novel strategy is applied to a formation control problem of non-holonomic mobile robots in the plane.

Wireless control of 3D tower cranes is a topic of great industrial significance as it offers greater flexibility and lower maintenance cost. But the reduction in shared network resource utilization and the desired level of control performance against packet losses and delays are main concerns. Our first aim is to develop a mathematical model of the 3D tower crane for the control design purpose. The second aim is to design a model-based eventtriggered controller for wireless control of multiple 3D tower cranes to asymptotically track step reference inputs. The controller and cranes are connected in a star topology over simulated wireless sensor network with IEEE 802.15.4 MAC protocol for channel access. The third aim is to numerically evaluate and compare the performance of the event-triggered controller with that of a periodic controller under network induced delays, packet dropouts and with respect to scalability and the effect of MAC protocol on the stability. The dynamic model for the 3D tower crane has been identified through physical modeling followed by lumped parametric estimation. The proposed event-triggered control policy for tracking problem uses the integral control structure for which theoretical results have been derived using Lyapunov input-to-state stability theorem. Under nominal network settings, the event-triggered controller reduces the network resource utilization by 4 times but achieves almost the same control performance as compared to periodic controller executions. The network cost under event-triggered control policy not only depends on the point network induced delay enters into the system, but also increases almost monotonically with it. The event-triggered control policy utilizes the lower communication cost as compared to periodic case only up to certain amount of delay in a loop and hence must only be preferred over periodic case after considering the level and location of delay in the system. Under these conditions, up to 13 crane tasks are schedulable over network under event-triggered control policy using CSMA/CA random access. All the simulations have been done in MATLAB® Simulink using TrueTime toolbox.

Considering the potential benefits offered by Wireless Sensor Networks (WSNs), they have been becoming an interesting technology for process, manufacturing, and industrial control and Smart Grid applications. These applications motivate many companies, industrial communities and academy to focus and research in this direction. The IEEE 802.15.4 is the standard proposed to be use in low-power communication of which WSN is part. Even though there are many implementations of the standard for the selected operating system, TinyOS, they are not fully validated or fully implemented. Moreover, in spite of the existence of previous studies using the protocol, there is no sufficient analysis of the performance of this standard. In this thesis, a comparison between the two main implementations is done through the experiments to validate the feasibility of the implementations. Because of the fact that the selected implementation does not have the Guaranteed Time Slots (GTSs) mechanism developed, in this thesis are provided all the mechanisms necessary to transmit during the Contention-Free Period (CFP): allocation, expiration, reallocation and deallocation. Hence, a IEEE 802.15.4 implementation is provided with a comprehensive evaluation with which the behaviour is proven. The implementation is validated in terms of packet delivery rate and delay for different network configurations and different parameters. Owing to no practical results for the use of this protocol in control applications, a inverted pendulum process is introduced to show the benefits in wireless process control by using the IEEE 802.15.4 in a real-time control loop process. The extensive experimental results show that packets losses and delays are the essential factors to guarantee the stability of the system. Furthermore, we also demonstrate and analyse the benefits of using this protocol in a Home Smart Grid setup.

Estimation Theory has always been a very important and necessary tool in dealing with complex systems and control engineering, from its birth in 18th century. In the last decades, and by raising the hot topics of distributed systems, estimation over networks has been of great interest among the scientists, and lots of effort has been made to solve the various aspects of this problem. An important question in solving the estimation problems, either over networks or a single system, is how much the obtained estimation is reliable, or in the other words, how much close our estimation is to the subject being estimated. Undoubtedly, a good estimation is an estimation which produces the least error. This leads us to combine the estimation theory with optimization techniques to obtain the best estimation of a given variable, which it is called Optimal Estimation. In control systems theory, we can have the optimal estimation problem in a static system, which is not progressing in time, and also, we can have the optimal estimation problem in a dynamic system, which is changing by time. Moreover, from another point of view, we can divide the common problems into two different frameworks, Stochastic Estimation Problem, and Deterministic Estimation Problem, which less attention has been made on the latter. Actually, treating a problem in deterministic framework is tougher than stochastic case, since in deterministic case we are not allowed to use the nice properties of stochastic random variables. In this Master thesis, the optimal estimation problem over distributed systems consist of a finite number of players, in deterministic framework, and in static setting has been treated. We assume a special case of estimation problem, in which the measurements available for different players are completely decoupled from each other. In the other words, no player can have access to the other players’ information space. We will derive the mathematical conditions for this problem as well as the optimal estimation minimizing the given cost function. For ease of understanding, some numerical examples are also provided, and the performance of the given approach is derived. This thesis consists of five chapters. In chapter 1, a brief introduction about the considered problems in this thesis and their history is given. Chapter 2 introduces the reader with the mathematical tools used in the thesis through the solving a very classic problem in estimation theory. In chapter 3, we have treated the main part of this thesis which is static team estimation problem. In chapter 4, we have looked at the performance of derived estimators, and compare our results with the available numerical solutions. Chapter 5 is a short conclusion, stating the main results, and summarizing the main points of the thesis.

In this thesis, general problems are considered where a group of agents should autonomously position themselves in such a way that a global objective function is maximized, whereas each agent uses only the measurement of its own utility function. Specially constructed extremum seeking schemes for single and multi-agent systems are presented, where the agents have only access to the current value of their individual utility functions and do not know the analytical model of the global or local objectives. By using an approximative system that is calculated using a methodology based on Lie brackets, practical stability of an equilibrium point is proved for the single agent as well as for the multi-agent case. The motion dynamics of the agents are modeled as single integrators, double integrators and unicycles. A potential game approach is used in order to deduce conditions under which the whole group of agents converges to a region arbitrary close to the maximum of a global objective function, that coincides with the Nash equilibrium of the game. As an application of the proposed algorithms, the sensor coverage problem is introduced. In this problem, a group of autonomous sensors is meant to position themselves such that a certain region is covered optimally, in the sense that the amount of detected events appearing in this region, is maximized. The problem is interpreted as a potential game where individual utility functions for each sensor are constructed in a way suitable for the direct application of the proposed optimization methodology.