In this paper, the uncertain dynamic parameters of an experimental target tracker robot are identified through the
application of genetic algorithm. The considered serial robot is a two-degree-of-freedom dynamic system with two
revolute joints in which damping coefficients and inertia terms are uncertain. First, dynamic equations governing the
robot system are extracted and then, simulated numerically. Next, an open-loop experiment with finite duration step
inputs is implemented on the experimental setup to collect practical output data. Accordingly, a desired objective
function is defined as the sum of discrepancy between the experimental and simulated output data. Subsequently, a
genetic algorithm is employed to explore the best damping coefficients and inertia terms of the simulation scheme so as
to minimize the presented cost function and taking into account the same input data for both simulation and experiment.
Finally, the simulated output data based on the identified robot parameters reveal an acceptable agreement with the
measured outputs through which validity of the identification scheme is affirmed.