Robotic vehicles are normally modeled as rigid bodies under general motion, combining translation and rotation motions. While such modeling results in motion controllers that are easy to implement, these controllers are also limited in the number of degrees of freedom (DOF) that can be controlled. The robotic vehicle with limited DOF operates well in structured terrain conditions with sufficient stability and friction. When the vehicle is operated in unstructured terrains, such as those that are sandy, snowy, or steep terrains, which might be slippery, such an approach fails to operate well. Since additional applications of robotic vehicles are in unstructured terrains, it is important to find alternative control models that will increase the number of controllable DOF and add more robustness and flexibility to the vehicle's performance. This thesis proposes the modeling of a vehicle as a system of particles centered at the wheels, with each particle controlled independent of one another in order to achieve the desired vehicle motion. In this work, the Particle Model Control approach was tested on the robotic platform BIBOT-1. The work illustrated the major vehicle kinematics under different steering modes and how the controls for the robotic motion can be formulated on the basis of Particle Modeling. A control system, based on Particle Modeling using decentralized control architecture, was designed and tested on BIBOT-1. The preliminary test results obtained from the trial runs were then analyzed on the basis of root mean square (R.M.S.) error performance factors. Some future work was also suggested in order to gather more results and validate the modeling approach.