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- Barenthin Syberg, Märta, 1979-
(författare)
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Complexity Issues, Validation and Input Design for Control in System Identification
- 2008
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- System identification is about constructing and validating modelsfrom measured data. When designing system identificationexperiments in control applications, there are many aspects toconsider. One important aspect is the choice of model structure.Another crucial issue is the design of input signals. Once a modelof the system has been estimated, it is essential to validate theclosed loop performance if the feedback controller is based onthis model. In this thesis we consider the prediction-erroridentification method. We study model structure complexity issues,input design and model validation for control. To describe real-life systems with high accuracy, models of veryhigh complexity are typically needed. However, the variance of themodel estimate usually increases with the model order. In thisthesis we investigate why system identification, despite thisrather pessimistic observation, is successfully applied in theindustrial practise as a reliable modelling tool. It is shown thatby designing suitable input signals for the identificationexperiment, we obtain accurate estimates of the frequency functionalso for very complex systems. The input power spectrum can beused to shape the model quality. A key tool in input design is tointroduce a linear parametrization of the spectrum. With thisparametrization, several optimal input design problems can berewritten as convex optimization problems. Another problem considered is to design controllers withguaranteed robust stability and prescribed robust performanceusing models identified from experimental data. These models areuncertain due to process noise, measurement noise and unmodelleddynamics. In this thesis we only consider errors due tomeasurement noise. The model uncertainty is represented byellipsoidal confidence regions in the model parameter space. Wedevelop tools to cope with these ellipsoids for scalar andmultivariable models. These tools are used for designing robustcontrollers, for validating the closed loop performance and forimproving the model with input design. Therefore this thesis ispart of the research effort to connect prediction-erroridentification methods and robust control theory. The stability of the closed loop system can be validated using thesmall gain theorem. A critical issue is thus to have an accurateestimate of the L2-gain of the system. The key tosolve this problem is to find the input signal that maximizes thegain. One approach is to use a model of the system to design theinput signal. An alternative approach is to let the system itselfdetermine a suitable input sequence in repeated experiments. Insuch an approach no model of the system is required. Proceduresfor gain estimation of linear and nonlinear systems are discussedand compared.
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| 2. |
- Barenthin, Märta, 1979-
(författare)
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On input design in system identification for control
- 2006
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- There are many aspects to consider when designing system identification experiments in control applications. Input design is one important issue. This thesis considers input design both for identification of linear time-invariant models and for stability validation. Models obtained from system identification experiments are uncertain due to noise present in measurements. The input spectrum can be used to shape the model quality. A key tool in input design is to introduce a linear parametrization of the spectrum. With this parametrization a number of optimal input design problems can be formulated as convex optimization programs. An Achilles' heel in input design is that the solution depends on the system itself, and this problem can be handled by iterative procedures where the input design is based on a model of the system. Benefits of optimal input design are quantified for typical industrial applications. The result shows that the experiment time can be substantially shortened and that the input power can be reduced. Another contribution of the thesis is a procedure where input design is connected to robust control. For a certain system structure with uncertain parameters, it is shown that the existence of a feedback controller that guarantees a given performance specification can be formulated as a convex optimization program. Furthermore, a method for input design for multivariable systems is proposed. The constraint on the model quality is transformed to a linear matrix inequality using a separation of graphs theorem. The result indicates that in order to obtain a model suitable for control design, it is important to increase the power of the input in the low-gain direction of the system relative to the power in the high-gain direction. A critical issue when validating closed-loop stability is to obtain an accurate estimate of the maximum gain of the system. This problem boils down to finding the input signal that maximizes the gain. Procedures for gain estimation of nonlinear systems are proposed and compared. One approach uses a model of the system to design the optimal input. In other approaches, no model is required, and the system itself determines the optimal input sequence in repeated experiments.
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| 3. |
- Pernestål, Anna, 1978-
(författare)
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A Bayesian approach to fault isolation with application to diesel engine diagnosis
- 2007
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Users of heavy trucks, as well as legislation, put increasing demands on heavy trucks. The vehicles should be more comfortable, reliable and safe. Furthermore, they should consume less fuel and be more environmentally friendly. For example, this means that faults that cause the emissions to increase must be detected early. To meet these requirements on comfort and performance, advanced sensor-based computer control-systems are used. However, the increased complexity makes the vehicles more difficult for the workshop mechanic to maintain and repair. A diagnosis system that detects and localizes faults is thus needed, both as an aid in the repair process and for detecting and isolating (localizing) faults on-board, to guarantee that safety and environmental goals are satisfied. Reliable fault isolation is often a challenging task. Noise, disturbances and model errors can cause problems. Also, two different faults may lead to the same observed behavior of the system under diagnosis. This means that there are several faults, which could possibly explain the observed behavior of the vehicle. In this thesis, a Bayesian approach to fault isolation is proposed. The idea is to compute the probabilities, given ``all information at hand'', that certain faults are present in the system under diagnosis. By ``all information at hand'' we mean qualitative and quantitative information about how probable different faults are, and possibly also data which is collected during test drives with the vehicle when faults are present. The information may also include knowledge about which observed behavior that is to be expected when certain faults are present. The advantage of the Bayesian approach is the possibility to combine information of different characteristics, and also to facilitate isolation of previously unknown faults as well as faults from which only vague information is available. Furthermore, Bayesian probability theory combined with decision theory provide methods for determining the best action to perform to reduce the effects from faults. Using the Bayesian approach to fault isolation to diagnose large and complex systems may lead to computational and complexity problems. In this thesis, these problems are solved in three different ways. First, equivalence classes are introduced for different faults with equal probability distributions. Second, by using the structure of the computations, efficient storage methods can be used. Finally, if the previous two simplifications are not sufficient, it is shown how the problem can be approximated by partitioning it into a set of sub problems, which each can be efficiently solved using the presented methods. The Bayesian approach to fault isolation is applied to the diagnosis of the gas flow of an automotive diesel engine. Data collected from real driving situations with implemented faults, is used in the evaluation of the methods. Furthermore, the influences of important design parameters are investigated. The experiments show that the proposed Bayesian approach has promising potentials for vehicle diagnosis, and performs well on this real problem. Compared with more classical methods, e.g. structured residuals, the Bayesian approach used here gives higher probability of detection and isolation of the true underlying fault.
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| 4. |
- Sundvall, Paul, 1977-
(författare)
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Mobile robot fault detection using multiple localization modules
- 2006
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Most applications in service robotics require that the position of the robot is accurately known. Faults affecting the localization system can thus have serious effects on the overall performance. This includes internal hardware and software faults, but external disturbances and faults from the surrounding dynamical and complex environment are even more common in service robotics applications. This thesis makes two main contributions. The first one is a method for detecting faults affecting the localization system of a mobile robot. Most fault detection systems work with detailed models at sensor level, where sensor data is processed to decide if the system is in a faulty state or not. While this is often a powerful approach, it requires reliable models of the environment, sensor noise and the robot’s motion. The proposed approach is based on the observation that most of the modelling required for fault detection is shared with robot localization algorithms. The problems of localization and navigation have been extensively studied in the robotics community, and there exist many reliable methods and robust implementations of such systems. By combining the outputs from several high-level localization modules, and hence avoiding working with raw sensor data and detailed models, it is possible to detect faults affecting the robot. In this thesis, a low complexity model of such a combined system is proposed, and a detailed discussion of the corresponding design choices is given. An Extended Kalman filter is used to calculate the posterior probability distribution of the outputs of the localization modules. The alarm decision is made based on the Mahalanobis distance of the innovations and a CUSUM test. This approach is very flexible and does not need direct access to sensor data, nor modification of existing localization algorithms. The proposed method has been implemented and tested on an ActivMedia service robot. Odometry and a laser based scan matching method, described below, were used as position modules. The experimental results show that the approach works. The second contribution of this thesis is a method to increase the efficiency of point-to-point search in a scan matching algorithm. Scan matching is a method to estimate the relative displacement of a laser-scanning sensor (light radar) between data acquired at two positions. Scan matching is a good independent complement to other sensors like odometry and sonars. Here, scans are matched by maximization of a score function. This function is calculated from the distance between every point in the scan to be matched and the closes point in the reference scan. Straightforward search needs as many checks as the square of the number of points in the scan. A method to reduce the search space is presented that significantly reduces the effort for score calculation.
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