Simulering av simulinkmodeller medExtended Kalman FilterTidsoptimering av integrering

Sammanfattning: Simulation of different systems can be done using a graphical model description of the system based on block diagrams. The simulation software Matlab/Simulink offers a lot of possibilities to describe a huge number of systems, which can be linear as well as nonlinear. The simulation using Matlab/Simulink is done by integration based on different kinds of numerical methods. When nonlinear Simulink-models are simulated, it is often interesting to apply a Kalman filter to handle the noise that may occur within the process as well as corrupting the measurement. However, application of such a filter leads to an extensive time-consumption despite using fast computers. The reason is that the existing Matlab functions are not constructed for time optimal evaluation. The time-consuming procedures partly consist of the integration between the sample points and partly of the calculation of the correction term in the Kalman filter. To calculate the correction term and correct the states for the influence of the noise, the integration has to be interrupted for each sampling point. This interruption makes new call to the model at each sampling point concerning the measurement data. The function call to the model is the main reason for the large time-consumption. The goal for this thesis work is to reduce the time consumption for the integrating part of the model simulation. This can be done by the development of new methods that carry out the filtering simulation without a frequent call to the Simulink-model. This can be possible to perform by a conversion from the Simulink-model description to a state-model description. The optimized parts regarding to time, consisting of the integration and filtering part, will furthermore be brought together in a routine for filtering simulation (the filtering part of the simulation is being managed in a complementary report). The time-optimized routine is then able to perform the filtering simulation of nonlinear Simulink-models, but with a time consumption that is manageable. The optimized integration and filtering parts, has in the final routine made it possible to make the filtering simulation with a time-consumption that is reduced by approximately a factor of ten. The goal, to reduce the current time consumption to be more manageable, has thereby been accomplished. The integrating part is in the final routine reducing the time-consumption with approximately a factor two, in relation to if Matlab built in integration-functions is being used.