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Model Free Adaptive Control (MFAC) has been used to reject the sensor noise measuring the controlled output. The MFAC is trained using feedback error learning law. The measurement noise makes the control error (used by feedback error learning) very noisy which results in increased control activity as well as corruption of the past learning. The MFAC is modeled as a Fuzzy Relational Model (FRM). FRM can be trained to represent the sensor noise occurring at the plant output in the fuzzy control signal. This representation of the sensor noise can be effectively utilized to reduce the control activity; hence increasing the life span of an actuator and contributing towards enhanced control performance. The control activity can be reduced by using conditional defuzzification which defuzzifies the fuzzy control signal by taking into consideration the uncertainty representation. By employing conditional defuzzification the controller will not respond to the noisy measurement of the plant output. The superiority of the conditional defuzzification used in conjunction with MFAC is demonstrated by the fact that with increased sensor noise the control activity reduces while maintaining the same control performance.