Inference engines for fuzzy rule-based control

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. The applicability of a rule in an observed situation involves inexactness, which is modeled using fuzzy sets. The control rules can be obtained analytically, experimentally, or from an expert. All of these approaches involve certainty levels of possible control commands, and the rule bases can best be represented as fuzzy relations. The experimental identification of a mobile robot behavior is described in this paper as a two-step process. These steps are the determination of the vocabulary of representation and the derivation of fuzzy control rules. The experiments and the derived rules are geared towards minimum-time control of the robot motion. The combination of uncertainties that exist in the rules and observations gives rise to an inference mechanism based on the extension principle. Although computationally straightforward, the sequential max and min operations involved in the inferencing are too time-consuming and may prohibit real-time operation. In this paper two architectures for the parallel computation of max-min operations are described and their applicabilities to rule-based control are compared.