Jen-Hsiang Chou

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.

Design and Implementation of Adaptive Fuzzy Cerebellar Model Articulation Controller for Direct Torque Control System

This study implemented fuzzy control theory with a cerebellar controller to design a fuzzy cerebellar model articulation controller (FCMAC), ensuring system stability by deriving an adaptive FCMAC weight update rule from the Lyapunov theory. The speed estimator design in this study is based on the structure of an adaptive stator flux observer (ASFO) to achieve rapid sensorless control. According to results from experiments, installing the adaptive FCMAC speed controller in the induction motor’s direct torque control system demonstrates excellent speed dynamic response.

Recognition of packet loss speech using the most reliable reduced-frame-rate data

In a client-server distributed speech recognition (DSR) application, speech features are extracted and quantized at the client-end, and are sent to a remote back-end server for recognition. Although the bandwidth constrains are mostly eliminated, data packets may be lost over error prone channels. In order to reduce the performance degradation because of frame missing, a frequently used error concealment approach is to restore a full frame rate (FFR) observation sequence for recognition at the back-end. In this paper, an alternative approach is proposed to deal with observations with lost frames. This approach at first extracts the most reliable reconstructed reduced-frame-rate (RFR) observation sequence from the received data at the back-end, and then decodes it with an adapted hidden Markov model (HMM) that compensates the mismatch between the FFR trained model and the RFR test data. Experimental results show that a DSR system using the proposed method can achieve the same level of accuracy as an FFR data reconstruction method and significantly lessens the computation time. From the viewpoint of user capacity of a DSR system, we find that the proposed method is capable of serving much more client users without any extra cost of installing new equipment.

A Motor Rotary Fault Diagnosis System Using Dynamic Structural Neural Network

This study proposed an intelligent rotary fault diagnosis systems for motors. A sensor less 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, sensor less 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.

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.

Analysis of a Quadrotor with a Two-Degree-of-Freedom Robotic Arm

The classical kinematic approach used in analyzing systems of a small number of interconnected bodies is unsuitable for researching complex kinematics. Moreover, solving nonlinear algebraic equations that involve trigonometric functions is difficult. However, the computational kinematic approach involves using a set of algebraic kinematic equations that describe the joint connectivity between the bodies of a system as well as the specified motion trajectories. This paper presents an analytical approach for a quadrotor with a two-degree-of-freedom (DOF) robotic arm. The approach, which is based on the computational kinematic approach, was adopted to model algebraic equations to obtain the values of position, velocity, acceleration, and the trajectories of motion of the joint and end effector. MATLAB was employed to simulate and solve the kinematic equations. The results showed that the analysis approach is feasible for the quadrotor with a two-DOF robotic arm.