Diss. Uppsala : Univ., 2008.

Diss. Linköping : Linköpings universitet, 2008.

Diss. Luleå : Luleå tekniska univ., 2003.

Diss. Linköping : Linköpings universitet, 2006.

Licentiatavhandling Linköping : Linköpings universitet, 2009.

Licentiatavhandling Linköping : Linköpings universitet, 2004.

<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 time-invariant 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 low-gain direction of the system relative to the power in the high-gain direction.</p><p>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.</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 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. / QC 20101109

<p>The first part of the thesis consists of a brief introduction to the general principles of target detection and the sensor-systems used. In the following part there is a theoretical description of the algorithms this thesis focuses on. The detection algorithms described in this paper are called Cell Average, Ordered Statistics, 2parameter and Gammadetector. Two different discriminators called Extended Fractal Features and Quadratic Gamma Discriminator are also described. The algorithms are tested on three different types of data, simulated SAR-pictures, authentic SAR-targets and IR-pictures. The last part account for the results, both those achieved with pictures and those results achieved when doing statistical tests, in this case MonteCarlo- simulations and Reciever Operating Characteristics-curves. The results show that the Gamma- detector and the QGD-algorithm perform best on the tests done in this thesis.</p>

<p>To enable the enhancement of the driver's environment in a forklift, an alternative form of steering has been evaluated. An earlier concept with a combined driving and steering wheel has been exchanged to a concept with two driving wheels. A link wheel has replaced the combined driving and steering wheel. The steering is done through differentiation of the velocities of the two driving wheels. </p><p>The scope of this master thesis is to create a simulation model, create a control program and to evaluate the concept of two driving wheels. </p><p>The simulation model was created in Matlab/Simulink with the aid of enclosed functions written in C. The model is constructed so that the same control program can be used in the model as well as in the physical forklift prototype, which the model is based on. Validation of the simulation model shows that the model is in accordance with reality in a satisfactory manner. </p><p>The control program is a compromise between a gentle behaviour of the forklift and a quick response to the driver's steering commands. </p><p>Evaluation of the concept shows that steering through differentiation of the wheel velocities is possible although there are some deficiencies. The two main deficiencies are that, when driving slowly, the link wheel will affect the steering of the forklift in a negative manner, and that there are limitations regarding motor power.</p>