Investigation of Parameter Estimation of a Car-Trailer System Using Condition Numbers

In this research we investigate condition numbers obtained from least squares estimation for a car-trailer system to characterize estimation performance. In this case, we can select better parameter estimation methods or well-posed measured data sets for the car-trailer system using condition numbers. We calculate condition numbers from several different linear model-based least squares methods which use four linear regression models and three least squares methods to estimate trailer parameters. We also consider three different observed data sets in ideal and non-ideal sensor scenarios for simulation tests.