Based on sliding mode control (SMC), an expert fuzzy controller synthesized by a collection of fuzzy linguistic control rules is proposed in this paper. The control strategy of the proposed controller is characterized by the linguistic terms in the fuzzy control rules. The membership functions of consequent part in fuzzy control rules are adjusted according to some adaptive law for tracking objective of a control task. The membership functions of antecedent part are well defined to satisfy the stability requirement of control systems. In this paper, a well behavior of SMC-based fuzzy control system is synthesized through the following stages: first, develop an adaptive law to approximate the equivalent control of sliding mode control for improving its performance; second, append a hitting control term to achieve a stable control system and to translate arbitrary state toward an prespecified sliding surface; third, based on the principles of SMC, the fuzzy control rules are emulated; finally, apply genetic algorithms to learning membership functions for obtaining an optimal fuzzy control. A nonlinear simulation example is applied to confirm the validity of the proposed approach.