Designing Digital Filters for Servo Systems

The aim of this work was to select an appropriate digital filter for a servo application and to filter the noise from the measurement devices. Low pass filter attenuates the high frequency noise beyond the specified cut-off frequency. Digital lowpass filters in both IIR and FIR responses were designed and experimentally compared to understand their characteristics from the corresponding step responses of the system. Kaiser Windowing and Equiripple methods were selected for FIR response, whereas Butterworth, Chebyshev, InverseChebyshev and Elliptic methods were designed for IIR case. Limitations in digital filter design for a servo system were analysed. Especially the dynamic influences of each designed filter on the control stabilityof the electrical servo drive were observed. The criterion for the selection ofparameters in designing digital filters for servo systems was studied. Control system dynamics was given significant importance and the use of FIR and IIR responses in different situations were compared to justify the selection of suitableresponse in each case. The software used in the filter design was MatLab/Simulink® and dSPACE's DSP application. A speed controlled Permanent Magnet Linear synchronous Motor was used in the experimental work.

Stability Analysis in Multicriteria Discrete Portfolio Optimization.

Almost every problem of design, planning and management in the technical and organizational systems has several conflicting goals or interests. Nowadays, multicriteria decision models represent a rapidly developing area of operation research. While solving practical optimization problems, it is necessary to take into account various kinds of uncertainty due to lack of data, inadequacy of mathematical models to real-time processes, calculation errors, etc. In practice, this uncertainty usually leads to undesirable outcomes where the solutions are very sensitive to any changes in the input parameters. An example is the investment managing. Stability analysis of multicriteria discrete optimization problems investigates how the found solutions behave in response to changes in the initial data (input parameters). This thesis is devoted to the stability analysis in the problem of selecting investment project portfolios, which are optimized by considering different types of risk and efficiency of the investment projects. The stability analysis is carried out in two approaches: qualitative and quantitative. The qualitative approach describes the behavior of solutions in conditions with small perturbations in the initial data. The stability of solutions is defined in terms of existence a neighborhood in the initial data space. Any perturbed problem from this neighborhood has stability with respect to the set of efficient solutions of the initial problem. The other approach in the stability analysis studies quantitative measures such as stability radius. This approach gives information about the limits of perturbations in the input parameters, which do not lead to changes in the set of efficient solutions. In present thesis several results were obtained including attainable bounds for the stability radii of Pareto optimal and lexicographically optimal portfolios of the investment problem with Savage's, Wald's criteria and criteria of extreme optimism. In addition, special classes of the problem when the stability radii are expressed by the formulae were indicated. Investigations were completed using different combinations of Chebyshev's, Manhattan and Hölder's metrics, which allowed monitoring input parameters perturbations differently.