This paper presents an adaptive pseudo reduced-order Takagi-Sugeno (T-S) fuzzy flux estimator for the induction motor direct field orientation control system. The estimator gain can be obtained by solving a set of linear matrix inequalities (LMIs) to estimate the rotor flux accurately. It is well known that, because of changes in temperature, variations of stator and rotor resistances affect the accuracy of rotor flux estimation. To resolve this problem, a cerebellar model articulation proportional integral controller (CMAPIC) is proposed to estimate the stator and rotor resistances during temperature variations. These estimated quantities, including stator and rotor resistances, are taken as the T-S fuzzy flux estimator inputs, so that the flux estimation is uninfluenced by these parameter variations. Thus the estimators enhance the robustness of the system. Moreover, this work uses a cerebellar model articulation controller to estimate the rotor speed, which is fed back to the adaptive supervisory fuzzy cerebellar model articulation speed controller (ASFCMAC) to achieve the speed sensor-less control.

This study adopts the fuzzy control theory to design a self-tuning fuzzy controller (STFC), which allows adjustment to overcome the controller design difficulty caused by switched reluctance motor (SRM) nonlinearity. Based on the torque sharing function (TSF), the proposed STFC was implanted into an SRM direct torque control (DTC) drive system to develop a system with superior speed and electromagnetic torque dynamic responses. In addition, the control strategy possessed excellent electromagnetic torque response, and effectively improved the dynamic response of the system. Keywords: fuzzy control theory, switched reluctance motor (SRM), torque sharing strategy.

This study proposed an intelligent rotary fault diagnosis systems for motors. A sensorless rotational speed detection method and a dynamic structural neural network (DSNN) were used. This method can be employed to detect the rotary frequencies of motors with varying speeds and can enhance the discrimination of motor faults. To conduct the experiments, this work used wireless sensor nodes to transmit vibration data, and employed MATLAB to write codes for functional modules, including signal processing, sensorless rotational speed estimation, and neural networks. Additionally, Visual Basic was used to create an integrated human-machine interface. The experimental results regarding test equipment faults indicated that the proposed method can effectively estimate rotational speeds and provide superior discrimination of motor faults.

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The purpose of this study was to develop a hybrid fuzzy-sliding controller with fuzzy self-tuning (HFSC). This controller used a fuzzy supervisory system to allocate the output proportions of a sliding-mode controller and a fuzzy controller (FC). The sliding-mode controller primarily provides rapid control efforts in the transient state, and the FC mainly offers smooth control in the steady state and decreases the chatter phenomenon caused by the sliding-mode controller. Finally, the proposed HFSC was implemented in the vector controlled drive system of induction motor as the speed controller. The experimental results showed that the tracking performance and effects of the HFSC were superior to those of the FC.