Optimized performance obtained from existing adaptive fuzzy optimal control methods comes at the cost of a intricate design procedure and a heavy computation of online parameter learning, and it is an under-explored problem on how to remove such a restriction. In this work, we tackle this problem and ensure the optimized performance using only one adaptive parameter. To this end, a direct adaptive fuzzy inverse approach is first proposed to design a switching-type inverse optimal controller and a one-parameter learning mechanism. It is proved that the proposed approach ensures the input-to-state stability of the control system and besides, the inverse optimality in regard to a meaningful cost functional is achieved. Illustrative examples verify the approach developed.