The qualities of measured signals are always affected by the measurement systems and noise. If these effects can be modelled, the original signals can be restored. Two methods for estimation of signals are studied in this work, the causal Wiener deconvolution filter and the Kalman filter. The methods are first applied to two theoretical test cases and then to a pressure signal, recorded during a reactor process. The measurement system works basically as a lowpass filter, and therefore, the high frequency contents of the measured signal consist mostly of uninteresting noise. Because of this, it will not be possible to restore the true high frequency properties of the signal. However, most of the interesting information is in the low frequency range. One conclusion from this work is that some peaks that may indicate interesting dynamics can appear more distinctly in the power spectrum of the restored signal than in that of the measured signal. The methods recover the signals in the time domain, which is useful e.g. when physical models between different quantities are analysed. Finally, the Wiener filter is more straightforward to use in this application, than the Kalman filter is.

Automatic control design and Hi-Fi loudspeakers are two areas that not very often are combined. In 1976 Karl Erik Ståhl performed a master thesis project at KTH where he, with analog circuits, made a positive feedback loop to manipulate the mechanical parameters of a loudspeaker. That project introduced the idea to use control design when constructing loudspeakers. In this project this idea is pursued. For a subwoofer, the interesting thing from a control perspective is that it is the low frequency range that has to be controlled as opposed to the high frequency range which is normally the case in disturbance and servo problems. This master thesis project will present a solution to this problem where a digital signal processor is used to handle the feed back information. The IMC controller implemented in the processor is based on models derived from data, measured in the tailor made laboratory set-up that was built for the project. In order to satisfy the sampling rate requirements, the complexity of the control algorithm had to be restricted. Despite this limitation in the equipment, the frequency response of the loudspeaker was improved significantly at low frequencies.

This thesis deals with the problem of prototyping a vehicle, made up by a motorized body and two passive trailers using LOGO Mindstorms, and provide a hybrid control system supporting the driver while reversing the vehicle. The goal of the hybrid system is to switch between different linear controllers, each designed for a specific purpose, to follow a generic path. The approach, when designing the hybrid system, is similar to Dubin’s car problem. The prototype will be designed to perform as a stand-alone system and be controlled by its own computer. Information about the instant condition of the vehicle is collected by means of a set of onboard sensors. Two angle sensors are used to measure the relative angles between the different parts of the structure, one rotation sensor is used to measure the steering wheel angle and two rotation sensors are used to transform the wheel’s revolution into orientation of a specific point of the vehicle. The implementation of the controller consist in a java-program written on a host computer and then downloaded to a robotics command explorer (RCX), equipped with an input-output board that allows to generate the control signal and acquire data from sensors.

When drivers overnight in the truck lots of electric energy is consumed by heaters, fans, lights and other extra equipment. If the engine is not running, this energy is taken from the lead/acid batteries of the truck which then needs to be recharged during operating hours. The fact that the charge acceptance of the lead/acid batteries is dependent on the temperature makes it hard to fully recharge the batteries at lower temperatures. A negative charge balance can lead to starting problems within a few days use in cold climate. In this thesis work, the effects of different battery charging concept are evaluated on the most common Scania battery (175Ah). A battery model is implemented in Matlab and some simulations of charging are made. Some tests are made in a real truck but the evaluation of the charging methods is mainly based on battery tests made in the laboratory. The methods and procedures for battery testing that were derived during this work are an important part of the results. The variations of charge acceptance with temperature are investigated. The effects of charging at a higher voltage than used today is also evaluated. We find that it is better to raise the temperature of the batteries rather than raise the charging voltage, even though both lead to a better charge acceptance. The internal heating of the batteries during charging are examined and the preliminary results indicate a thermal loss of approximately 10% of the energy the battery is charged with. The thermal loss in percent is the same at room temperature as at -10°C and -20°C. / När förare övernattar i lastbilen förbrukas stora mängder el av värmare, fläktar, belysning och annan komfortutrustning. När inte motorn går tas elenergin ur bilens batterier som sedan måste återladdas under körning. Då lastbilsbatteriernas laddningsmottaglighet försämras avsevärt med sjunkande temperatur är det inte säkert att batterierna blir återladdade fullt ut. En negativ laddbalans gör att föraren kan få startproblem redan efter ett par dagars drift och övernattningar i vintermiljö. Examensarbetet utgör en del i arbetet att komma tillrätta med problematiken kring laddningsmottaglighet vid lägre temperaturer. Olika laddningskoncept utvärderas på Scanias vanligast förekommande batteri (175Ah) med utgångspunkt från dagens elsystem. En batterimodell har implementerats i Matlab och försök till parameteranpassning och simulering har gjorts. Huvuddelen av utvärderingen har skett genom försök i laboratoriemiljö och enstaka försök har gjorts på en provlastbil. Ett viktigt resultat av arbetet är de provmetoder som tagits fram under arbetets gång. Laddningsmottaglighetens beroende av temperatur har undersökts liksom effekterna av enhöjning av laddningsspänningen. Det är eftersträvansvärt att i första hand försöka höja temperaturen även om en höjning av laddningsspänningen också ger förbättrad laddningsmottaglighet. Batteriets egen uppvärmning har undersökts och de preliminära resultaten visar på att så mycket som 10 % av energin batteriet laddas med omvandlas till värme i batteriet. Intressant är att denna termiska verkningsgrad är densamma vid rumstemperatur som vid -10°C och -20°C.

Disturbing noise is a growing problem in the society. Also in the home environment noise making devices exist, for example the vacuum cleaner. A simple way to decrease the annoyance from a vacuum cleaner is to use personal passive ear defenders. A problem with passive ear defenders is that they also attenuate wanted signals, such as speech or music. In this thesis a pair of prototype active ear defenders for vacuum cleaner noise attenuation have been developed and evaluated. Active noise control technology was used, which solved the problem with wanted signal attenuation. A measured noise reference was used with a pair of open earphones as actuator. The overall cancellation performance of the prototype system was quite low for vacuum cleaner noise. Due to that the coherence between the measured noise reference and the unwanted noise was low. Wanted signals were shown to be just slightly affected by the prototype system.

Modern control designs are, with few exceptions, in some way model based. In particular, predictive control has rapidly become a popular control strategy, implemented in a large number of industrial plants. Model predictive control (MPC) uses a model to predict the impact of future control inputs on the controlled plant. The quality of the model can have a large impact on the achievable control performance. It is widely reported that modeling is the single most time and cost consuming part of the commissioning of an industrial MPC and therefore an important research issue. This thesis addresses the need for good modeling for MPC by introducing an optimal input design and identification method tailored to the specifics of predictive control. Parametric models are used and the influence of the individual parameters on the control performance is measured through a cost function. This leads to a set of parameters that are deemed acceptable. Optimal input design is used to ensure, with high probability, that the estimated parameters are in the set of acceptable parameters while keeping experimental cost low. It is shown that optimal input design can lead to a significant reduction of the experimental cost while still guaranteeing acceptable control performance. A toolbox for optimal input design in Matlab is also presented. Real world systems tend to be nonlinear and sometimes it is necessary to model them as such. Input design for two types of nonlinear systems with finite memory is considered. Similarities and differences compared to the linear case are pointed out and exploited. Convex formulations of the optimal input design problem are presented. It is shown by example that the resulting optimal design can differ greatly compared to designs for linear models. / QC 20111013

Large-scale control systems are often composed of several smaller interconnected units. For these systems, it is common to employ local controllers, which observe and act locally. At the heart of common control design procedures for distributed systems lies the often implicit assumption that the designer has access to the global plant model information when designing a local controller. However, there are several reasons why such plant model information would not be globally known. One reason could be that the designer wants the parameters of each local controller to only depend on local model information, so that the controllers are not modified if the model parameters of a particular subsystem change. It might also be the case that the design of each local controller is done by individual designers with no access to the global plant model, for instance, due to the fact that the designers refuse to share their model information since they consider it private. This class of problems, which we refer to as limited model information control design, is the topic of the thesis. First, we investigate the achievable closed-loop performance of discrete-time linear time-invariant plants under a separable quadratic cost performance with structured static state-feedback controllers. To do so, we introduce control design strategies as mappings, which construct controllers by accessing the plant model information in a constrained way according to a given design graph. We compare control design strategies using the competitive ratio as a performance metric, that is, we compare the worst case control performance for a given design strategy normalized with the optimal control performance based on full model information. An explicit minimizer of the competitive ratio is sought. As this minimizer might not be unique, we further search for the ones that are undominated, that is, there is no other control design strategy in the set of limited model information design strategies with a better closed-loop performance for all possible plants while maintaining the same worst-case ratio. We study the trade-off between the amount of model information exploited by a control design strategy and the best possible closed-loop performance. We generalize this setup to structured dynamic state-feedback controllers for H_2-performance. Surprisingly, the optimal control design strategy with limited model information is still a static one. This is the case even though the optimal decentralized state-feedback controller with full model information is dynamic. Finally, we discuss the design of dynamic controllers for disturbance accommodation under limited model information. This problem is of special interest because the best limited model information control design in this case is a dynamic control design strategy. The optimal controller can be separated into a static feedback law and a dynamic disturbance observer. For constant disturbances, it is shown that this structure corresponds to proportional-integral control. / C 20120201

Recent advances in small and cheap communication and sensing have opened up for large scale systems with intricate interconnections and interactions. These applications pose new challenges for analysis and control design. To keep up with the increasing demand on performance and efficiency, accurate models of these systems are needed.  Often some prior knowledge of the system, such as system structure, is available.Prior knowledge should whenever possible be used in system identification to improve the model estimate.This thesis addresses the problem of using prior information about the overall structure of the system in system identification.  Two special structures are considered, the cascade and the parallel serial structure.  The motivation for looking at these structures are two folded; they are common in industrial applications and they can be used to build up almost all interesting feedforward interconnected systems.  The effect of sensor placement, input signals and common dynamics of the subsystems on the quality of the estimated models for these two structures is considered.In many control applications it is vital that the model has a physical interpretation. Hence, it is important that the system identification method retains the physical interpretation of the identified model. However, it has proven hard to incorporate prior knowledge of structure in subspace methods. This thesis presents two methods for identifying systems with known structures using subspace methods.  The first method utilizes that the state-space matrices of a system on cascade form have a certain structure. The idea is to find a transform that takes the identified system back to this form. The second method uses the known structure of the extended observability matrix. The state-space matrices for the subsystems can then be found by solving linear least squares problems. However, the method is only applicable if the second subsystem has order one. But this is a common case in practice. The two methods are applied to a two tank lab process with promising results.Nonparametric estimates of the frequency response function of systems are used in most engineering fields. The second contribution of this thesis is a new method for estimating the frequency response. The method uses the known structure of the transient or leakage error. The feasibility of the method is tested in simulations. For the two cases considered, one with a large amount of random systems, the second with a resonant system, the method shows good performance compared to current state of the art methods. / QC 20120416

There are many different approaches to fault detection and isolation suitable for different kind of problems. In this thesis the properties and effectiveness of six different algorithms for isolation are investigated. To be able to compare the algorithms, performance measures are developed and implementations of the algorithms on a pre-defined benchmark system are done. The benchmark consists of a system, which is assembled by two tanks connected with various components. The components can be simulated faulty in different ways. The isolation algorithms that are used are classic methods for isolation from the automatic control field and the AI field as well as more recent approaches, such as Bayesian methods. Based on the results of the benchmark, recommendations are made for which type of isolation problems the respective algorithm suits best.

An optimal velocity trajectory for a heavy duty vehicle, obtained with the aid of modern GPS and digital map devices, depends on several variables. Curvature speed limitations, road grade, and posted road speed are common constraints imposed by the road travelled. This thesis presents a method for modelling and analysing a switching controller through the use of the former mentioned constraints. A non-linear model for the heavy duty vehicle is derived, enabling suitable control methods to be applied. Pontryagin’s Principal and LQR are discussed to get a profound understanding of how the controller should be designed. It is discovered that a switching controller based on optimal control and engineering experience is most favourable for the problem at hand. The controller is designed to address the main objectives set in this paper of minimising fuel consumption, travelling time, and brake wear. Gauss-Newtons’s algorithm for non-linear equations is used to estimate curve radii. Other input parameters are presumed to be available. GPS data error is discussed to perform a sensitivity analysis. An electronic horizon is produced on three road segments, entailed with data of the future road topology. Finally the switching controller is applied to the road segments. Experimental results show that the controller produces a velocity trajectory, which reduces fuel consumption by 5-15% and brake wear by 15-35%, while the travelling time is only increased by 1-2%.