@conference {822,
title = {Optimal Trajectory Design for Well-Conditioned Parameter Estimation},
booktitle = {IEEE Conference on Automation Science and Engineering (CASE)},
year = {2013},
pages = {13-19},
abstract = {When attempting to estimate parameters in a dynamical system, it is often beneficial to systematically design the experimental trajectory. This paper presents a method of generating trajectories using an extension of a nonlinear, infinite-dimensional, projection-based trajectory optimization algorithm. A reformulated objective function is derived for the algorithm to minimize the condition number of the Hessian of the batch-least squares identification method. The batch least-squares method is then used to estimate parameters of the nonlinear system. A simulation example is used to demonstrate that an arbitrarily designed trajectory can lead to an ill-conditioned Hessian matrix in the batch-least squares method, which in turn leads to a less precise set of identified parameters. An example using Monte-Carlo simulations of both trajectories shows a reduction in the variance of identified parameters for an example cart-pendulum system.},
doi = {10.1109/CoASE.2013.6653971},
author = {Andrew D Wilson and Todd D Murphey}
}