  In this paper, we analyze the robustness of the PSO-based approach to
parameter estimation of robot dynamics presented in Part One. We have made
attempts to make the PSO method more robust by experimenting with potential
cost functions. The simulated system is a cylindrical robot; through
simulation, the robot is excited, samples are taken, error is added to the
samples, and the noisy samples are used for estimating the robot parameters
through the presented method. Comparisons are made with the least squares,
total least squares, and robust least squares methods of estimation.
