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1 
Nonlinear identification and control with solar energy applications /Brus, Linda, January 2008 (has links)
Diss. Uppsala : Univ., 2008.

2 
Fluid power applications using selforganising maps in condition monitoring /Zachrison, Anders, January 2008 (has links)
Diss. Linköping : Linköpings universitet, 2008.

3 
Modeling control and analysis of complex dynamic chemical systems /Ding, Limei. January 2003 (has links)
Diss. Luleå : Luleå tekniska univ., 2003.

4 
Frequency domain identification of continuoustime systems : reconstruction and robustness /Gillberg, Jonas January 2006 (has links)
Diss. Linköping : Linköpings universitet, 2006.

5 
On low order controller synthesis using rational constraints /Ankelhed, Daniel, January 2009 (has links)
Licentiatavhandling Linköping : Linköpings universitet, 2009.

6 
Methods for frequency domain estimation of continuoustime models /Gillberg, Jonas January 2004 (has links)
Licentiatavhandling Linköping : Linköpings universitet, 2004.

7 
On input design in system identification for controlBarenthin, Märta January 2006 (has links)
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 timeinvariant 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 lowgain direction of the system relative to the power in the highgain direction. A critical issue when validating closedloop 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. / QC 20101109

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On input design in system identification for controlBarenthin, Märta January 2006 (has links)
<p>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 timeinvariant models and for stability validation.</p><p>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.</p><p>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 lowgain direction of the system relative to the power in the highgain direction.</p><p>A critical issue when validating closedloop 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.</p>

9 
Experiment design with applications in identification for controlJansson, Henrik January 2004 (has links)
The main part of this thesis focuses on optimal experiment design for system identification within the prediction error framework. A rather flexible framework for translating optimal experiment design into tractable convex programs is presented. The design variables are the spectral properties of the external excitations. The framework allows for any linear and finitedimensional parametrization of the design spectrum or a partial expansion thereof. This includes both continuous and discrete spectra. Constraints on these spectra can be included in the design formulation, either in terms of power bounds or as frequency wise constraints. As quality constraints, general linear functions of the asymptotic covariance matrix of the estimated parameters can be included. Here, different types of frequencybyfrequency constraints on the frequency function estimate are expected to be an important contribution to the area of identification and control. For a certain class of linearly parameterized frequency functions it is possible to derive variance expressions that are exact for finite sample sizes. Based on these variance expressions it is shown that the optimization over the square of the Discrete Fourier Transform (DFT) coefficients of the input leads to convex optimization problems. The optimal input design are compared to the use of standard identification input signals for two benchmark problems. The results show significant benefits of appropriate input designs. Knowledge of the location of nonminimum phase zeros is very useful when designing controllers. Both analytical and numerical results on input design for accurate identification of nonminimum phase zeros are presented. A method is presented for the computation of an upper bound on the maximum over the frequencies of a worst case quality measure, e.g. the worst case performance achieved by a controller in an ellipsoidal uncertainty region. This problem has until now been solved by using a frequency gridding and, here, this is avoided by using the KalmanYakubovichPopovlemma. The last chapter studies experiment design from the perspective of controller tuning based on experimental data. Iterative Feedback Tuning (IFT) is an algorithm that utilizes sensitivity information from closedloop experiments for controller tuning. This method is experimentally costly when multivariable systems are considered. Several methods are proposed to reduce the experimental time by approximating the gradient of the cost function. One of these methods uses the same technique of shifting the order of operators as is used in IFT for scalar systems. This method is further analyzed and sufficient conditions for local convergence are derived.

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TCP/IP i taktiska ad hocnät / TCP/IP in tactical ad hoc networksPersson, Katarina January 2002 (has links)
TCP (Transmission Control Protocol) is a transport protocol designed for the wired Internet. In wireless networks packet losses occur more frequently due to the unreliability of the physical link. The main problem is that TCP treats all losses as congestion, which leads to a lower throughput. Ad hoc networks are multihop wireless networks of mobile nodes, where each node can allow other packets to pass through it. Topology changes often occur and may lead to packet losses and delays, which TCP misinterprets as congestion. We want to modify TCP to recognize the differences between link failure and congestion to improve the capacity. In our model we have built a connection in an ad hoc network where packet losses and partitions can be made. Simulation experiments show that we didn't get the problems we expected. This can be explained by low delays and because we buffered the packets during link failure. A simple modification of TCP was made and simulated, and showed that an improvement of performance is possible. More research should be done to make a modification of TCP that would further affect the throughput.

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