| Summary: | 碩士 === 大同大學 === 電機工程學系(所) === 100 === The forces of drag, tire and road surface friction resistance, the drive motor characteristics, the hill climbing angle and other non-linear dynamic factors tremendously effect the performance of electric vehicles (Electric Vehicle, EV). The proposed design, self-construction of the type-2 fuzzy neural network (SCT2FNN) controller, based on robust typical type-2 fuzzy neural network (T2FNN) controller, with the help of self-construct parameter learning algorithms, and online learning algorithm to estimate the angular velocity of the motor operation to control the EV, can promptly track the speed of the EV and estimate the torque control of a DC motor. Applying the Mahalanobis distance (M-distance) method in the self-constructing learning algorithm to determine whether the T2FNN rules are generated or not, and the online learning algorithm, basing on back propagation method to adjust the parameter (mean, standard, deviation and weight) errors generated from T2FNN. The proposed SCT2FNN controller is identified more efficient while controlling the speed of EV, by comparing the PID controller , considering the difference of the climbing slope.
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