Shun-Yuan Wang

Design and Implementation of a Single-Stage High-Efficacy LED Driver with Dynamic Voltage Regulation

This paper proposes a single-stage high-efficacy fly back power-factor-correction (PFC) converter with optimization efficiency control for driving multi-string light-emitting diodes (LEDs). The LED driver consists of a fly back converter with PFC mechanism and a constant-current drive circuit with functionalities of efficiency optimization and dimming control. The proposed single-stage LED driver topology features benefits of high power factor and low total harmonic distortion (THD) in low-cost outlay. The pulse width modulation (PWM) dimming mechanism cooperating with the constant-current control circuit completes the LED dimming control. In order to reduce the power dissipations of the dimming circuits, a dynamic voltage regulation (DVR) control is presented to regulate the LED supply voltage by means of the sensing of the drain voltage of the MOSFET in the current controller. While maintaining the desired LED brightness, the DVR technique can minimize the voltage drop of the dimming circuit to enhance the LED driver efficiency further. A 30W white LED driver is devised and a corresponding driver prototype is realized. Testing results are shown experimentally to verify the effectiveness and performance improved of the proposed scheme.

An adaptive supervisory sliding fuzzy cerebellar model articulation controller for sensorless vector-controlled induction motor drive systems.

This paper presents the implementation of an adaptive supervisory sliding fuzzy cerebellar model articulation controller (FCMAC) in the speed sensorless vector control of an induction motor (IM) drive system. The proposed adaptive supervisory sliding FCMAC comprised a supervisory controller, integral sliding surface, and an adaptive FCMAC. The integral sliding surface was employed to eliminate steady-state errors and enhance the responsiveness of the system. The adaptive FCMAC incorporated an FCMAC with a compensating controller to perform a desired control action. The proposed controller was derived using the Lyapunov approach, which guarantees learning-error convergence. The implementation of three intelligent control schemes—the adaptive supervisory sliding FCMAC, adaptive sliding FCMAC, and adaptive sliding CMAC—were experimentally investigated under various conditions in a realistic sensorless vector-controlled IM drive system. The root mean square error (RMSE) was used as a performance index to evaluate the experimental results of each control scheme. The analysis results indicated that the proposed adaptive supervisory sliding FCMAC substantially improved the system performance compared with the other control schemes.

Estimator-based fuzzy credit-assigned cerebellar model articulation controller design for vector-controlled induction motor drives

This study has designed and implemented a novel speed controller and multi-estimator in a sensorless field-oriented control system for controlling induction motor (IM) speed. The speed controller was designed based on a fuzzy credit-assigned cerebellar model articulation controller (FCA-CMAC) to provide the online learning ability required for IM speed control. In contrast to the fuzzy cerebellar model articulation controller, the FCA-CMAC provides a faster convergence speed in the learning process for approximating a nonlinear function. Additionally, the multi-estimator provides a real-time adaptive estimation of motor speed and rotor resistance for achieving robustness for the IM controller against varying motor parameters. The multi-estimator is implemented by designing a cerebellar model articulation controller (CMAC) PI controller based on model reference adaptive system theory to adjust the adaptive pseudo-reduced-order flux observer parameters. Experiments performed on a 3-hp IM confirmed the effectiveness of the proposed approach. The experimental results confirm that the proposed control scheme achieves excellent dynamic and tracking responses to varying motor parameters.

A Diagnostic System for Speed-Varying Motor Rotary Faults

This study proposed an intelligent rotary fault diagnostic system for motors. A sensorless rotational speed detection method and an improved dynamic structural neural network are used. Moreover, to increase the convergence speed of training, a terminal attractor method and a hybrid discriminant analysis are also adopted. The proposed 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 study used wireless sensor nodes to transmit vibration data and employed MATLAB to write codes for functional modules, including the signal processing, sensorless rotational speed estimation, neural network, and stochastic process control chart. Additionally, Visual Basic software was used to create an integrated human-machine interface. The experimental results regarding the test of equipment faults indicated that the proposed novel diagnostic system can effectively estimate rotational speeds and provide superior ability of motor fault discrimination with fast training convergence.

A HMI/GUI control in a steam-pretreated process for agriculture wastes reuses

A microprocessor-based touch-screen Human-Machine Interface (HMI) and Programmable Logic Controller (PLC) standard libraries are applied to construct models for creating supervisory control software. This software, in turn, is developed and presented for the Graphical User Interface (GUI) functions used in agriculture wastes reuses process of steam-boiling pretreated to become efficiently produce agricultural biotechnology material and liquid products, such as xylose, xylooligosaccharides, and animal feed, thereby providing excellent GUI-based monitoring and control functions. This HMI/GUI approach was presented a very cost-effective technique. Also, the HMI/GUI operation effectively provides user-friendly and reliable interactions.

Adaptive TSK fuzzy sliding mode control design for switched reluctance motor DTC drive systems with torque sensorless strategy

Abstract This study proposes a novel Adaptive Takagi-Sugeno-Kang (TSK) Fuzzy Sliding Mode controller (abbreviated as AFSC) and investigates the application for a switched reluctance motor (SRM) direct torque control (DTC) drive system without a torque sensor. The sliding mode controller (SMC) is used for reducing the influence of uncertainties and external disturbances, and it performs fast responses. The parameters of the adaptive TSK fuzzy controller (AFC) are adjusted online for further reducing error residues after applying the SMC. Lyapunov stability theory is used for deriving the stability condition of the SMC and the adaptive update law of the AFC. The stability of the overall closed-loop system is also analyzed. To verify the performance and practicality of the controller developed for this study, the AFSC is employed as the speed controller in a SRM DTC drive system. The experimental results reveal that the steady-state speed error is maintained between ±2 rpm when the motor load torque is 1 Nm, and the motor operates at low, medium, high, and variable speeds. Comparing with the conventional SMC, a faster and smoother speed and torque responses are achieved. Moreover, the proposed control strategy is superior to the conventional SMC with respect to robustness for external disturbances.

Design on Sliding Mode Controller with Adaptive Fuzzy Compensation for Switched Reluctance Motor Drive Systems

In this study, the sliding mode controller (SMC) and the Takagi-Sugeno-Kang (TSK) fuzzy system were employed to design the proposed sliding mode controller with adaptive fuzzy compensation (AFSC). This design can improve the speed control performance of switched reluctance motor drive systems. Lyapunov function was derived to ensure the stability of the controller in the motor drive systems. To verify the performance and feasibility of the controller developed in this study, the AFSC was employed as the speed controller in the switched reluctance motor drive systems. The experiment results verify that the proposed AFSC control strategy can effectively improve the system dynamic response and achieve satisfactory robustness against external disturbances.

Robust H-infinity Control for Uncertain Type-2 Fuzzy Singular Systems with Input and State Delays

This paper investigates the robust H controller design for a class of nonlinear singular time-delay systems. The parametric uncertainties are also considered in this work. To solve the problem, firstly, the nonlinear system is modeled by using the interval type-2 T-S fuzzy theory. To analyze the stability, by choosing a proper Lyapunov functional, the delay dependent characteristics are taken into account. Moreover, free weighting matrices are introduced to reduce the conservativeness. Consequently, using the linear matrix inequality technique, a sufficient condition for the stability of singular delayed control system is obtained. As to the controller design, this work introduces the concept of duality. Along with the stability condition, the desired robust H8 controller design can be derived. Finally, a numerical example is simulated and analyzed by using the Mat lab LMI toolbox. According to the simulation results, the designed controller stabilizes the nonlinear systems and achieves the H8 performance. It verifies the correctness of the proposed controller design method.

Speed controller design of direct torque control system for induction motors by using adaptive supervisory Fuzzy-CMAC

A model-free approach was used to develop an adaptive supervisory Fuzzy-cerebellar model articulation controller (ASFCMAC) for a direct torque control system for an induction motor without shaft encoder. The two parts of the ASFCMAC are a supervisory controller for limiting tracking error to a bounded range and a Fuzzy-cerebellar model articulation controller subsystem for learning and approximating system dynamics. The ASFCMAC parameters are tuned according to adaptive rules derived from Lyapunov stability theory. Simulations and experimental comparisons with adaptive Fuzzy-cerebellar model articulation controller, adaptive cerebellar model articulation controller, fuzzy logic control, and proportional–integral control show that the proposed ASFCMAC has a superior root mean square error in operation over a wide range of speeds.

An intelligent motor rotary fault diagnosis system using Taguchi method

This paper applies the Taguchi method to filter out the number of input neurons and increases the training efficiency of the dynamic structural neural networks. In order to avoid that omitting the harmonics may affect the fault diagnosis result, this work establishes an index for the fault identification which is based on the features of the first and second harmonics. Together with the identification results of dynamic structural neural network, the diagnosis can be done. The experimental results indicate the proposed method can reduce the iterations dramatically.