Search

This study analyzes the power coupling in a weak power grid, and designs a novel decoupling method for the coupling power with multi harmonic frequency on the dc bus. By introducing a ratio factor for the dc-bus capacitors, the decoupling ability can be further improved. Moreover, the sliding-mode controller will enhance the system robustness and be adaptive on the varied condition of the power coupling. The proposed control strategy will be effective under the ideal power grid or even the non-ideal power grid. In addition, numerical simulations are provided to verify the effectiveness of the proposed method.

This paper focuses on the design of a dynamic Petri recurrent fuzzy neural network (DPRFNN), and this network structure is applied to the path-tracking control of a nonholonomic mobile robot for verifying its validity. In the DPRFNN, the concept of a Petri net and the recurrent frame of internal-feedback loops are incorporated into a traditional FNN to alleviate the computation burden of parameter learning and to enhance the dynamic mapping of network ability. Moreover, the supervised gradient-descent method is used to develop the online-training algorithm for the DPRFNN control. In order to guarantee the convergence of path-tracking errors, analytical methods based on a discrete-type Lyapunov function are proposed to determine varied learning rates for DPRFNN. In addition, the effectiveness of the proposed DPRFNN control scheme under different moving paths is verified by experimental results, and its superiority is indicated in comparison with FNN, RFNN, Petri FNN, and PRFNN control systems. Index Terms--Fuzzy neural network (FNN), nonholonomic mobile robot, Petri net (PN), recurrent structure, varied learning rates.

The objective of this paper is to develop a high step-up isolated converter with two input power sources. The proposed converter has two input ports for simultaneously converting two different input power sources with low voltages to a stable output power with a high voltage. Moreover, the demand of bidirectional power flow, which is dependent on the power management for charging and discharging power storage mechanisms, can also be satisfied in the proposed converter. According to various situations, the operational states of the proposed converter can be divided into three states, including a stand-alone state, a united power supply state, and a charge and discharge state. The effectiveness of the designed circuit topology is verified by experimental results, and the goals of high-efficiency conversion, high step-up ratio, and bidirectional power flow can be achieved by the proposed converter operation. Index Terms--Bidirectional power flow, high efficiency, high step-up ratio, isolation, two input power sources.

This paper mainly focuses on the development of a high-efficiency power conversion system for kilowatt-level stand-alone generation units with a low output voltage, such as photovoltaic modules, fuel cells, and small-scale wind generators, and it aims at having the same output ac voltage, i.e., 110 [V.sub.rms]/ 60 Hz as the utility power for the utilization of a stand-alone power supply. This high-efficiency power conversion system includes one high-efficiency high-step-up dc-dc converter and one soft-switching dc-ac current-source inverter. This dc-dc converter is capable of solving the voltage spike problem while the switch is turned off, and it can achieve the objectives of high efficiency and high voltage gain. Because the techniques of soft switching and voltage clamping are used in the dc-ac current-source inverter, the conversion efficiency could greatly be improved. The effectiveness of the designed circuits is verified by experimentation, and the maximum efficiency of the entire high-efficiency power conversion system is over 91% based on the experimental measurements. Index Terms--Current-source inverter, high step-up, soft switching, stand-alone generation units, voltage clamping.

This paper focuses on the development of adaptive fuzzy neural network control (AFNNC), including indirect and direct frameworks for an n-link robot manipulator, to achieve high-precision position tracking. In general, it is difficult to adopt a model-based design to achieve this control objective due to the uncertainties in practical applications, such as friction forces, external disturbances, and parameter variations. In order to cope with this problem, an indirect AFNNC (IAFNNC) scheme and a direct AFNNC (DAFNNC) strategy are investigated without the requirement of prior system information. In these model-free control topologies, a continuous-time Takagi-Sugeno (T-S) dynamic fuzzy model with online learning ability is constructed to represent the system dynamics of an n-link robot manipulator. In the IAFNNC, an FNN estimator is designed to tune the nonlinear dynamic function vector in fuzzy local models, and then, the estimative vector is used to indirectly develop a stable IAFNNC law. In the DAFNNC, an FNN controller is directly designed to imitate a predetermined model-based stabilizing control law, and then, the stable control performance can be achieved by only using joint position information. All the IAFNNC and DAFNNC laws and the corresponding adaptive tuning algorithms for FNN weights are established in the sense of Lyapunov stability analyses to ensure the stable control performance. Numerical simulations and experimental results of a two-link robot manipulator actuated by dc servomotors are given to verify the effectiveness and robustness of the proposed methodologies. In addition, the superiority of the proposed control schemes is indicated in comparison with proportional-differential control, fuzzy-model-based control, T-S-type FNN control, and robust neural fuzzy network control systems. Index Terms--Adaptive control, fuzzy neural network (FNN), Lyapunov stability analyses, robot manipulator, Takagi-Sugeno (T-S) fuzzy model.

This study addresses a grid-connected photovoltaic (PV) generation system. In order to make the PV generation system more flexible and expandable, the backstage power circuit is composed of a high step-up converter and a pulsewidth-modulation (PWM) inverter. In the dc--dc power conversion, the high step-up converter is introduced to improve the conversion efficiency of conventional boost converters and to allow the parallel operation of low-voltage PV modules. Moreover, an adaptive total sliding-mode control system is designed for the current control of the PWM inverter to maintain the output current with a higher power factor and less variation under load changes. In addition, an adaptive step-perturbation method is proposed to achieve the objective of maximum power point tracking, and an active sun tracking scheme without any light sensors is investigated to make PV plates face the sun directly in order to capture maximum irradiation and enhance system efficiency. Experimental results are given to verify the validity of the high step-up converter, the PWM inverter control, the ASP method, and the active sun tracker for a grid-connected PV generation system. Index Terms--Active sun tracking scheme, adaptive step-perturbation (ASP) method, high step-up converter, photovoltaic (PV) generation system, pulsewidth-modulation (PWM) inverter, total sliding-mode control (TSMC).

This paper presents and analyzes a cascade direct adaptive fuzzy control (DAFC) scheme for a two-axis inverted-pendulum servomechanism. Because the dynamic characteristic of the two-axis inverted-pendulum servomechanism is a nonlinear unstable nonminimum-phase underactuated system, it is difficult to design a suitable control scheme that simultaneously realizes real-time stabilization and accurate tracking control, and it is not easy to directly apply conventional computed torque strategies to this underactuated system. Therefore, the cascade DAFC scheme including inner and outer control loops is investigated for the stabilizing and tracking control of a nonlinear two-axis inverted-pendulum servomechanism. The goal of the inner control loop is to design a DAFC law so that the stick angle vector can fit the stick angle command vector derived from the stick angle reference model. In the outer loop, the reference signal vector is designed via an adaptive path planner so that the cart position vector tracks the cart position command vector. Moreover, all adaptive algorithms in the cascade DAFC system are derived using the Lyapunov stability analysis, so that system stability can be guaranteed in the entire closed-loop system. Relying on this cascade structure, the stick angle and cart position tracking-error vectors will simultaneously converge to zero. Numerical simulations and experimental results are given to verify that the proposed cascade DAFC system can achieve favorable stabilizing and tracking performance and is robust with regard to system uncertainties. Index Terms--Adaptive fuzzy control, cascade structure, computed torque control (CTC), inverted pendulum, two-axis servomechanism.

Linear piezoelectric actuators are innovative manipulators that have shown a high potential in applications requiring operation within the submicrometer or even the nanometer range. The driving principle of a linear piezoelectric ceramic motor (LPCM) is based on the ultrasonic vibration force of piezoelectric ceramic elements and mechanical frictional force. Therefore, its dynamic model is complex, and the motor parameters are time varying due to increasing temperature and changing motor drive operating conditions, so that it is difficult to design a suitable driving strategy for the LPCM. This paper presents a comparative study of three voltage-source resonant driving schemes, including an inductance-capacitance (LC) resonant inverter, an LLCC-resonant inverter, and an LC-resonant inverter with energy feedback for the LPCM. The effectiveness of the reported driving strategies is verified by numerical analyses and experimental results under the variation of loads. By way of experimental validation, the newly designed LC-resonant inverter with energy feedback for driving the LPCM possesses good performances of high voltage gain and invariant output characteristic under different operating conditions. Index Terms--Energy feedback, inductance-capacitance (LC) resonant inverter, linear piezoelectric ceramic motor (LPCM), LLCC-resonant inverter, voltage-source inverter.

A magnetic-levitation (maglev) transportation system including levitation and propulsion control is a subject of considerable scientific interest because of highly nonlinear and unstable behaviors. In this paper, the dynamic model of a maglev transportation system including levitated electromagnets and a propulsive linear induction motor (LIM) based on the concepts of mechanical geometry and motion dynamics is developed first. Then, a model-based sliding-mode control (SMC) strategy is introduced. In order to alleviate chattering phenomena caused by the inappropriate selection of uncertainty bound, a simple bound estimation algorithm is embedded in the SMC strategy to form an adaptive sliding-mode control (ASMC) scheme. However, this estimation algorithm is always a positive value so that tracking errors introduced by any uncertainty will cause the estimated bound increase even to infinity with time. Therefore, it further designs an adaptive fuzzy-neural-network control (AFNNC) scheme by imitating the SMC strategy for the maglev transportation system. In the model-free AFNNC, online learning algorithms are designed to cope with the problem of chattering phenomena caused by the sign action in SMC design, and to ensure the stability of the controlled system without the requirement of auxiliary compensated controllers despite the existence of uncertainties. The outputs of the AFNNC scheme can be directly supplied to the electromagnets and LIM without complicated control transformations for relaxing strict constrains in conventional model-based control methodologies. The effectiveness of the proposed control schemes for the maglev transportation system is verified by numerical simulations, and the superiority of the AFNNC scheme is indicated in comparison with the SMC and ASMC strategies. Index Terms--Fuzzy neural network (FNN), linear induction motor (LIM), maglev transportation system, magnetic levitation (maglev), sliding-mode control (SMC).

This study develops a high-performance stand-alone photovoltaic (PV) generation system. To make the PV generation system more flexible and expandable, the backstage power circuit is composed of a high step-up converter and a pulsewidth-modulation (PWM) inverter. In the dc-dc power conversion, the high step-up converter is introduced to improve the conversion efficiency in conventional boost converters to allow the parallel operation of low-voltage PV arrays, and to decouple and simplify the control design of the PWM inverter. Moreover, an adaptive total sliding-mode control system is designed for the voltage control of the PWM inverter to maintain a sinusoidal output voltage with lower total harmonic distortion and less variation under various output loads. In addition, an active sun tracking scheme without any light sensors is investigated to make the PV modules face the sun directly for capturing the maximum irradiation and promoting system efficiency. Experimental results are given to verify the validity and reliability of the high step-up converter, the PWM inverter control, and the active sun tracker for the high-performance stand-alone PV generation system. Index Terms--Active sun tracking scheme, adaptive total sliding-mode control (ATSMC), high step-up converter, photovoltaic (PV) generation system, pulsewidth-modulation (PWM) inverter.

In general, the role of genetic algorithm (GA) is operated offline as a minor compensator or tuner in the control engineering because the systematic design and the latent stability problem of a GA-based control scheme are required to be solved. This paper originally designs a Lyapunov-based GA control (LGAC) scheme, and it applies for a practical control engineering example of the online motion control of a linear piezoelectric ceramic motor driven by a hybrid resonant inverter. In this control scheme, a GA control system via backstepping design technique is utilized to be the major controller, and adaptation laws derived from Lyapunov stability analyses are manipulated to adjust appropriate evolutionary steps. As a result, the system stability can be guaranteed directly without strict constraint conditions and detailed system knowledge. The effectiveness of the proposed drive and control system is verified by experimental results in the presence of uncertainties. From the measured results, the LGAC system performs superior high-precision motion control under wide operation range than conventional backstepping control system. Index Terms--Backstepping control (BSC), genetic algorithm (GA), hybrid resonant inverter, linear piezoelectric ceramic motor (LPCM), Lyapunov stability analysis.

This study focuses on the development of a high-efficiency bidirectional converter for power sources with large voltage diversity. In conventional bidirectional converters, transformer-based circuit topologies are commonly employed and soft-switching techniques including zero-voltage-switching (ZVS) or zero-current-switching (ZCS) are frequently applied to mitigate corresponding switching losses. Unfortunately, switches of four and upward in these transformer-based schemes increase production costs and reduce conversion efficiency. In this study, a coupled-inductor bidirectional converter scheme utilizes only three power switches to fulfill the objective of bidirectional current control. The high step-up and step-down ratios enable a battery module with a low voltage to be injected into a high-voltage dc bus for subsequent utilization. Some experimental results are given to verify the effectiveness of the proposed bidirectional converter. Since the techniques of voltage clamping, synchronous rectification and soft switching are exploited in this circuit topology, and the corresponding device specifications are adequately fulfilled, it can provide high-efficiency bidirectional power conversion for power sources with large voltage diversity. Index Terms--Bidirectional converter, coupled inductor, soft switching, synchronous rectification, voltage clamping.

This study presents a total sliding-mode-based genetic algorithm control (TSGAC) system for a linear piezoelectric ceramic motor (LPCM) driven by a newly designed hybrid resonant inverter. First, the motor configuration and driving circuit of an LPCM are introduced, and its hypothetical dynamic model is represented by a nonlinear function with unknown system parameters. In the hybrid resonant drive system, it has the merits of the high voltage gain from a parallel-resonant current source, and the invariant output characteristic from a two-inductance two-capacitance resonant driving circuit. Since the dynamic characteristics and motor parameters of the LPCM are highly nonlinear and time varying, a TSGAC system is therefore investigated based on direction-based genetic algorithm with the spirit of total sliding-mode control (TSC) and fuzzy-based evolutionary procedure to achieve high-precision position control under a wide operation range. In this control scheme, a GAC system is utilized to be the major controller, and the stability can be indirectly ensured by the concept of TSC without strict constraints and detailed system knowledge. In addition, the effectiveness of the proposed drive and control system is verified by numerical simulations and experimental results in the presence of uncertainties. Index Terms--Fuzzy-based evolution, genetic algorithm (GA), linear piezoelectric ceramic motor (LPCM), parallel-resonant current source, sliding-mode control, two-inductance two-capacitance (LLCC)-resonant driving circuit.

This study mainly deals with the key problem of chattering phenomena on the conventional sliding-mode control (SMC) and investigates an adaptive fuzzy sliding-mode control (AFSMC) system for an indirect field-oriented induction motor (IM) drive to track periodic commands. First, an indirect field-orientation method for an IM drive is introduced briefly. Moreover, a fuzzy logic inference mechanism is utilized for implementing a fuzzy hitting control law to remove completely the chattering phenomena on the conventional SMC. In addition, to confront the uncertainties existed in practical applications, an adaptive algorithm, which is derived in the sense of Lyapunov stability theorem, is utilized to adjust the fuzzy parameter for further assuring robust and optimal control performance. The indirect field-oriented IM drive with the AFSMC scheme possesses the salient advantages of simple control framework, free from chattering, stable tracking control performance, and robust to uncertainties. Furthermore, numerical simulation and experimental results due to periodic sinusoidal commands are provided to verify the effectiveness of the proposed control strategy, and its advantages are indicated in comparison with the conventional SMC system and the SMC system with a boundary layer. Index Terms--Fuzzy inference mechanism, indirect field-orientation method, induction motor (IM) drive, Lyapunov stability, sliding-mode control (SMC).

In this paper, a high-efficiency dc-dc converter with high voltage gain and reduced switch stress is proposed. Generally speaking, the utilization of a coupled inductor is useful for raising the step-up ratio of the conventional boost converter. However, the switch surge voltage may be caused by the leakage inductor so that it will result in the requirement of high-voltage-rated devices. In the proposed topology, a three-winding coupled inductor is used for providing a high voltage gain without extreme switch dutycycle and enhancing the utility rate of magnetic core. Moreover, the energy in the leakage inductor is released directly to the output terminal for avoiding the phenomenon of circulating current and the production of switch surge voltage. In addition, the delay time formed with the cross of primary and secondary currents of the coupled inductor is manipulated to alleviate the reverse-recovery current of the output diode. It can achieve the aim of high-efficiency power conversion. Furthermore, the closed-loop control methodology is utilized in the proposed scheme to overcome the voltage drift problem of the power source under the variation of loads. Some experimental results via an example of a proton exchange membrane fuel cell power source with 250-W nominal rating are given to demonstrate the effectiveness of the proposed power conversion strategy. Index Terms--DC-DC converter, coupled inductor, fuel cell, proton exchange membrane, reverse recovery.

This paper focuses on the development of a Petri-fuzzy-neural-network (PFNN) control for an indirect field-oriented linear-induction-motor (LIM) drive. First, an indirect field-oriented mechanism for a LIM drive is derived to preserve the decoupling control characteristic. Then, the concept of a Petri net (PN) is incorporated into a traditional FNN (TFNN) to form a new type of PFNN framework for alleviating the computation burden. Moreover, the supervised gradient descent method is used to develop the online training algorithm for the PFNN. In order to guarantee the convergence of tracking error, analytical methods based on a discrete-type Lyapunov function are proposed to determine the varied learning rates of the PFNN. With the proposed PFNN control system, the mover position of the controlled LIM drive possesses the advantages of good transient control performance and robustness to uncertainties for the tracking of periodic reference trajectories. In addition, the effectiveness of the proposed control scheme is verified by both numerical simulations and experimental results. Furthermore, the superiority of the proposed PFNN control system is indicated in comparison with the TFNN control system. Index Terms--Fuzzy neural network (FNN), indirect field-oriented mechanism, linear induction motor (LIM), Petri net (PN), varied learning rates.

This study focuses on the development of a robust Petri-fuzzy-neural-network (PFNN) control strategy applied to a linear induction motor (LIM) drive for periodic motion. Based on the concept of the nonlinear state feedback theory, a feedback linearization control (FLC) system is first adopted in order to decouple the thrust force and the flux amplitude of the LIM. However, particular system information is required in the FLC system so that the corresponding control performance is influenced seriously by system uncertainties. Hence, to increase the robustness of the L1M drive for high-performance applications, a robust PFNN control system is investigated based on the model-free control design to retain the decoupled control characteristic of the FLC system. The adaptive tuning algorithms for network parameters are derived in the sense of the Lyapunov stability theorem, such that the stability of the control system can be guaranteed under the occurrence of system uncertainties. The effectiveness of the proposed control scheme is verified by both numerical simulations and experimental results, and the salient merits are indicated in comparison with the FLC system. Index Terms--Decoupled control, feedback linearization, fuzzy neural network (FNN), linear induction motor (LIM), Lyapunov stability theorem, Petri net (PN).

This paper addresses the design and analysis of an intelligent control system for an n-link robot manipulator to achieve the high-precision position tracking. According to the concepts of mechanical geometry and motion dynamics, the dynamic model of an n-link robot manipulator including actuator dynamics is introduced initially. However, it is difficult to design a suitable model-based control scheme due to the uncertainties in practical applications, such as friction forces, external disturbances, and parameter variations. In order to deal with the mentioned difficulties, a robust neural-fuzzy-network control (RNFNC) system is investigated to the joint position control of an n-link robot manipulator for periodic motion. In this control scheme, a four-layer neural fuzzy network (NFN) is utilized for the major control role, and the adaptive tuning laws of network parameters are derived in the sense of a projection algorithm and the Lyapunov stability theorem to ensure network convergence as well as stable control performance. The merits of this model-free control scheme are that not only can the stable position tracking performance be guaranteed but also no prior system information and auxiliary control design are required in the control process. In addition, numerical simulations and experimental results of a two-link robot manipulator actuated by dc servo motors are provided to verify the effectiveness and robustness of the proposed RNFNC methodology. Index Terms--Adaptive tuning algorithm, dc servo motors, Lyapunov stability theorem, neural fuzzy network, robot manipulator.

Since the system behaviors of a dual-axis inverted-pendulum mechanism including actuator dynamics are highly nonlinear, it is difficult to design a suitable control system that realizes real-time stabilization and accurate tracking control at all times. In this paper, an adaptive sliding-mode control system is implemented to control a dual-axis inverted-pendulum mechanism that is driven by permanent magnet synchronous motors. First, the energy conservation principle is adopted to build a mathematical model of the motor-mechanism-coupled system. Moreover, an adaptive sliding-mode control system is developed for stabilizing and tracking control of the dual-axis inverted-pendulum system, where an adaptive algorithm is investigated to relax the requirement of the bound of lumped uncertainty in the traditional sliding-mode control. In addition, numerical simulation and experimental results show that the proposed control scheme provides high-performance dynamic characteristics and is robust with regard to parametric variations, various reference trajectories, and different initial states. Index Terms--Adaptive control, energy conservation principle, inverted pendulum, permanent-magnet (PM) synchronous motor, sliding-mode control.

This paper presents a supervisory genetic algorithm (SGA) control system for a linear piezoelectric ceramic motor (LPCM) driven by a two-inductance two-capacitance resonant driving circuit. First, the motor configuration and driving circuit of an LPCM are introduced, and its hypothetical dynamic model is described briefly. Since the dynamic characteristics and motor parameters of the LPCM are highly nonlinear and time varying, an SGA control system is therefore investigated to achieve high-precision position control. The proposed SGA control system is composed of two parts. One is a GA control that is utilized to search an optimum control effort online via gradient descent training process, and the other is a supervisory control to stabilize the system states around a predefined bound region. Compared with conventional GA control systems, the proposed control scheme possesses the salient advantages of simple structure, fewer executing time, and good self-organizing properties. The effectiveness of the proposed driving circuit and control system is verified with numerical simulations and hardware experiments under the occurrence of uncertainties. In addition, the advantages of the proposed control scheme are indicated in comparison with a traditional integral-proportional position-control system. Index Terms--Genetic algorithm (GA), linear piezoelectric ceramic motor (LPCM), supervisory control, two-inductance two-capacitance (LLCC) resonant driving circuit.

This paper presents an adaptive grey control (AGC) system for a linear piezoelectric ceramic motor (LPCM) driven by a newly designed hybrid resonant inverter. First, the motor configuration and the driving circuit of an LPCM are introduced, and its hypothetical dynamic model is described briefly. The hybrid resonant drive system has the merits of high voltage gain from a parallel resonant current source and the invariant output characteristic from a two-inductance two-capacitance (LLCC) resonant driving circuit. Since the dynamic characteristics and motor parameters of the LPCM are highly nonlinear and time varying, an AGC system is therefore investigated based on the principle of computed torque control and grey method to achieve high-precision position control under wide operation range. In this control system, a grey uncertainty predictor is utilized to estimate the lumped uncertainty on line to relax the requirement of the unknown uncertainty in the design of a computed torque position controller. In addition, the effectiveness of the proposed drive and control system is verified by numerical simulations and experimental results in the presence of uncertainties. Index Terms--Computed torque control, grey method, linear piezoelectric ceramic motor, LLCC resonant driving circuit, parallel resonant current source, uncertainty predictor.

The design and properties of an adaptive enhanced fuzzy sliding-mode control (AEFSMC) system for an indirect field-oriented induction motor (IM) drive to track periodic commands are addressed in this study. A newly designed EFSMC system, in which a translation-width idea is embedded into the FSMC, is introduced initially. Moreover, to confront the uncertainties existed in practical applications, an adaptive tuner, which is derived in the sense of the Lyapunov stability theorem, is utilized to adjust the EFSMC parameter for further assuring robust and optimal control performance. The indirect field-oriented IM drive with the AEFSMC scheme possesses the salient advantages of simple control framework, free from chattering, stable tracking control performance, and robust to uncertainties. In addition, numerical simulation and experimental results due to periodic sinusoidal commands are provided to verify the effectiveness of the proposed control strategy, and its advantages are indicated in comparison with FSMC and EFSMC systems. Index Terms--Adaptive control, electrical servo drive, fuzzy sliding-mode control (FSMC), indirect field-oriented mechanism, induction motor (IM).

This paper investigates a high-efficiency clamped-voltage dc--dc converter with reduced reverse-recovery current and switch-voltage stress. In the circuit topology, it is designed by way of the combination of inductor and transformer to increase the corresponding voltage gain. Moreover, one additional inductor provides the reverse-current path of the transformer to enhance the utility rate of magnetic core. In addition, the voltage-clamped technology is used to reduce the switch-voltage stress so that it can select the Schottky diode in the output terminal for alleviating the reverse-recovery current and decreasing the switching and conduction losses. Furthermore, the closed-loop control methodology is utilized in the proposed scheme to overcome the voltage-drift problem of power source under the variation of loads. Thus, the proposed converter topology has a favorable voltage-clamped effect and superior conversion efficiency. Some experimental results via an example of a proton-exchange-membrane fuel cell (PEMFC) power source with a 250-W nominal rating are given to demonstrate the effectiveness of the proposed power-conversion strategy. Index Terms--Converter, fuel cell, proton-exchange membrane (PEM), reverse recovery, voltage clamped.