<p>The purpose of this report is to evaluate different methods for identifying states in robot models. Both linear and non-linear filters exist among these methods and are compared to each other. Advantages, disadvantages and problems that can occur during tuning and running are presented. Additional measurements from accelerometers are added and their use with above mentioned methods for state estimation is evaluated. The evaluation of methods in this report is mainly based on simulations in Matlab, even though some experiments have been performed on laboratory equipment. </p><p>The conclusion indicates that simple non-linear models with few states can be more accurately estimated with a Kalman filter than with an extended Kalman filter, as long as only linear measurements are used. When non-linear measurements are used an extended Kalman filteris more accurate than a Kalman filter. Non-linear measurements are introduced through accelerometers with non-linear measurement equations. Using accelerometers generally leads to better state estimation when the measure equations have a simple relation to the model.</p>
| Identifer | oai:union.ndltd.org:UPSALLA/oai:DiVA.org:liu-820 |
| Date | January 2005 |
| Creators | Ankelhed, Daniel, Stenlind, Lars |
| Publisher | Linköping University, Department of Electrical Engineering, Linköping University, Department of Electrical Engineering, Institutionen för systemteknik |
| Source Sets | DiVA Archive at Upsalla University |
| Language | Swedish |
| Detected Language | English |
| Type | Student thesis, text |
Page generated in 0.0019 seconds