國立臺灣師範大學電機工程學系G.-M. ChenW.-Y. WangT.-T. LeeC.-W. Tao2014-10-302014-10-302006-12-011562-2479http://rportal.lib.ntnu.edu.tw/handle/20.500.12235/31950In this paper, an observer-based direct adaptive fuzzy-neural controller (ODAFNC) for an anti-lock braking system (ABS) is developed under the constraint that only the system output, i.e., the wheel slip ratio, is measurable. The main control strategy is to force the wheel slip ratio to well track the optimal value, which may vary with the environment. The observer-based output feedback control law and update law for on-line tuning of the weighting factors of the direct adaptive fuzzy-neural controller are derived. By using the strictly-positive-real (SPR) Lyapunov theory, the stability of the closed-loop system can be guaranteed. Simulation results demonstrate the effectiveness of the proposed control scheme forABS control.anti-lock braking systemslip ratiofuzzy controlneural networksnonlinear systemsadaptive controlobserver