Hsin-Jang Shieh

Modeling and Control of PV Charger System With SEPIC Converter

The photovoltaic (PV) stand-alone system requires a battery charger for energy storage. This paper presents the modeling and controller design of the PV charger system implemented with the single-ended primary inductance converter (SEPIC). The designed SEPIC employs the peak-current-mode control with the current command generated from the input PV voltage regulating loop, where the voltage command is determined by both the PV module maximum power point tracking (MPPT) control loop and the battery charging loop. The control objective is to balance the power flow from the PV module to the battery and the load such that the PV power is utilized effectively and the battery is charged with three charging stages. This paper gives a detailed modeling of the SEPIC with the PV module input and peak-current-mode control first. Accordingly, the PV voltage controller, as well as the adaptive MPPT controller, is designed. An 80-W prototype system is built. The effectiveness of the proposed methods is proved with some simulation and experimental results.

Nonlinear sliding-mode torque control with adaptive backstepping approach for induction motor drive

In this paper, the nonlinear sliding-mode torque and flux control combined with the adaptive backstepping approach for an induction motor drive is proposed. Based on the state-coordinates transformed model representing the torque and flux magnitude dynamics, the nonlinear sliding-mode control is designed to track a linear reference model. Furthermore, the adaptive backstepping control approach is utilized to obtain the robustness for mismatched parameter uncertainties. With the proposed control of torque and flux amplitude, the controlled induction motor drive possesses the advantages of good transient performance and robustness to parametric uncertainties, and the transient dynamics of the induction motor drive can be regulated through the design of a linear reference model which has the desired dynamic behaviors for the drive system. Finally, some experimental results are demonstrated to validate the proposed controllers.

A new switching surface sliding-mode speed control for induction motor drive systems

In this paper, a sliding-mode speed controller based on a new switching surface is proposed for induction motor systems. With this variable structure control switching surface, the exponential stability is guaranteed for the speed servo control and insensitivity to uncertainties and disturbances are obtained as well. Moreover, an adaptive variable structure speed control is studied to relax the need for the bound of disturbance in variable structure control. The insensitivity or robustness of the proposed method for general speed servo systems is maintained, and the dynamic performances are improved as well. Finally, the validity of proposed scheme is demonstrated by computer simulations and experimentations.

Adaptive wavelet neural network control with hysteresis estimation for piezo-positioning mechanism

An adaptive wavelet neural network (AWNN) control with hysteresis estimation is proposed in this study to improve the control performance of a piezo-positioning mechanism, which is always severely deteriorated due to hysteresis effect. First, the control system configuration of the piezo-positioning mechanism is introduced. Then, a new hysteretic model by integrating a modified hysteresis friction force function is proposed to represent the dynamics of the overall piezo-positioning mechanism. According to this developed dynamics, an AWNN controller with hysteresis estimation is proposed. In the proposed AWNN controller, a wavelet neural network (WNN) with accurate approximation capability is employed to approximate the part of the unknown function in the proposed dynamics of the piezo-positioning mechanism, and a robust compensator is proposed to confront the lumped uncertainty that comprises the inevitable approximation errors due to finite number of wavelet basis functions and disturbances, optimal parameter vectors, and higher order terms in Taylor series. Moreover, adaptive learning algorithms for the online learning of the parameters of the WNN are derived based on the Lyapunov stability theorem. Finally, the command tracking performance and the robustness to external load disturbance of the proposed AWNN control system are illustrated by some experimental results.

Adaptive control with hysteresis estimation and compensation using RFNN for piezo-actuator

Because the control performance of a piezoactuator is always severely deteriorated due to hysteresis effect, an adaptive control with hysteresis estimation and compensation using recurrent fuzzy neural network (RFNN) is proposed in this study to improve the control performance of the piezo-actuator. A new hysteresis model by modifying and parameterizing the hysteresis friction model is proposed. Then, the overall dynamics of the piezo-actuator is completed by integrating the parameterized hysteresis model into a mechanical motion dynamics. Based on this developed dynamics, an adaptive control with hysteresis estimation and compensation is proposed. However, in the designed adaptive controller, the lumped uncertainty E is difficult to obtain in practical application. Therefore, a RFNN is adopted as an uncertainty observer in order to adapt the value of the lumped uncertainty E on line. And, some experimental results show that the proposed controller provides high-performance dynamic characteristics and is robust to the variations of system parameters and external load

An adaptive recurrent-neural-network motion controller for X-Y table in CNC Machine

In this paper, an adaptive recurrent-neural-network (ARNN) motion control system for a biaxial motion mechanism driven by two field-oriented control permanent magnet synchronous motors (PMSMs) in the computer numerical control (CNC) machine is proposed. In the proposed ARNN control system, a RNN with accurate approximation capability is employed to approximate an unknown dynamic function, and the adaptive learning algorithms that can learn the parameters of the RNN on line are derived using Lyapunov stability theorem. Moreover, a robust controller is proposed to confront the uncertainties including approximation error, optimal parameter vectors, higher-order terms in Taylor series, external disturbances, cross-coupled interference and friction torque of the system. To relax the requirement for the value of lumped uncertainty in the robust controller, an adaptive lumped uncertainty estimation law is investigated. Using the proposed control, the position tracking performance is substantially improved and the robustness to uncertainties including cross-coupled interference and friction torque can be obtained as well. Finally, some experimental results of the tracking of various reference contours demonstrate the validity of the proposed design for practical applications.

Adaptive displacement control with hysteresis modeling for piezoactuated positioning mechanism

An adaptive displacement control with hysteresis modeling for a piezoactuated positioning mechanism is proposed in this paper because the dynamic performance of piezosystems is often severely deteriorated due to the hysteresis effect of piezoelectric elements. First, a new mathematical model based on the differential equation of a motion system with a parameterized hysteretic friction function is proposed to represent the dynamics of motion of the piezopositioning mechanism. As a result, the mathematical model describes a motion system with hysteresis behavior due to the hysteretic friction. Then, by using the developed mathematical model, the adaptive displacement tracking control with the adaptation algorithms of the parameterized hysteretic function and of an uncertain parameter is proposed. By using the proposed control approach on the displacement control of the piezopositioning mechanism, the advantages of the asymptotical stability in displacement tracking, high-performance displacement response, and robustness to the variations of system parameters and disturbance load can be provided. Finally, experimental results are illustrated to validate the proposed control approach for practical applications.

An Adaptive Approximator-Based Backstepping Control Approach for Piezoactuator-Driven Stages

This paper investigates precise trajectory tracking of a piezoactuator-driven stage with hysteresis behavior by using an approximator-based adaptive tracking control approach. Differential equations consisting of the dynamics of a linear motion system and a hysteresis function are first studied for describing the dynamics of motion of the piezoactuator-driven stage with hysteresis behavior. Then, a numerical optimization method is taken to identify the values of the parameters adopted in the differential equations. From the differential equations, an equivalent state-space model with an augmented integral input and with a defined hysteresis variable is established. Moreover, to approximate the unavailable hysteresis variable, an adaptive approximator that comprises a Gaussian radial-basis function network is adopted. Furthermore, from the state-space model, an adaptive approximator-based backstepping trajectory-tracking control is developed. Using the proposed control approach to trajectory tracking of the piezoactuator-driven stage, an improvement in transient performance and tracking errors, and robustness to the disturbance load, can be provided. Last, to show the validity of the proposed control approach, an implementation of the control algorithm on the computer-controlled single-axis piezoactuator-driven stage was developed. From the experimental results, the feasibility of the proposed control for practical applications can be confirmed.

Hybrid controller with recurrent neural network for magnetic levitation system

We propose a hybrid controller using a recurrent neural network (RNN) to control a levitated object in a magnetic levitation system. We describe a nonlinear dynamic model of the system and propose a computed force controller, based on feedback linearization, to control the position of the levitated object. To relax the requirement of the lumped uncertainty in the design of the computed force controller, an RNN functions as an uncertainty observer to adapt the lumped uncertainty on line. The computed force controller, the RNN uncertainty observer, and a compensated controller are embodied in a hybrid controller, which is based on Lyapunov stability. The computed force controller, with the RNN uncertainty observer, is the main tracking controller, and the compensated controller compensates the minimum approximation error of the RNN uncertainty observer. To ensure the convergence of the RNN, the adaptation law of the RNN is modified by using a projection algorithm. Experimental results illustrate the validity of the proposed control design for the magnetic levitation system.

Variable structure current control for induction motor drives by space voltage vector PWM

In this paper, a variable structure current controller based on a space voltage vector PWM scheme is presented for induction motor drives. In this current controller design, only the current sensors are employed and we attempt to force the stator currents to be exactly equal to the reference currents rapidly. This proposed current controller, which is based on the space voltage vector PWM drive, exhibits several advantages in terms of reduced switching frequency, robustness to parameter variations, elimination of current/torque ripple, and improved performance in induction motor drive. It shows that the current control laws can be demonstrated in theory. Finally, simulation and experimentation results verify the proposed control scheme.