The knowledge-based control of autonomous vehicles allows efficient hierarchical structures that utilize linguistic sensory data at various levels of resolution and exactness. This is mainly due to the fact that the control is based on a collection of rules, rather than an analytical controller. Each rule in the controller prescribes the control for a specific situation. In this paper an experimental method of control rule derivation is described. A two stage identification process is employed. First, the structure of the mobile robot is determined and the state variables are defined. Then the relations that are essential for the derivation of optimal control rules are found by experimentation. A minimum-time control algorithm for mobile robot position control is also included in the paper.