Browsing by Author "Hsu, CF"

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A comprehensive study of hot carrier stress-induced drain leakage current degradation in thin-oxide n-MOSFET's
(1999-09-01)
Wang, TH
;
Chiang, LP
;
Zous, NK
;
Hsu, CF
;
Huang, LY
The mechanisms and characteristics of hot carrier stress-induced drain leakage current degradation in thin-oxide n-MOSFET's are investigated. Both interface trap and oxide charge effects are analyzed. Various drain leakage current components at zero V-gs such as drain-to-source subthreshold leakage, band-to-band tunneling current, and interface trap-induced leakage are taken into account, The trap-assisted drain leakage mechanisms include charge sequential tunneling current, thermionic-field emission current,:and Shockley-Read-Hall generation current. The dependence of drain leakage current on supply voltage, temperature, and oxide thickness is characterized. Our result shows that the trap-assisted leakage may become a dominant drain leakage mechanism as supply voltage is reduced. In addition, a strong oxide thickness dependence of drain leakage degradation is observed. In ultra-thin gate oxide (30 Angstrom) n-MOSFET's, drain leakage current degradation is attributed mostly to interface trap creation, while in thicker oxide (53 Angstrom) devices, the drain leakage current exhibits two-stage degradation, a power law degradation rate in the initial stage due to interface trap generation, followed by an accelerated degradation rate in the second stage caused by oxide charge creation.
Adaptive fuzzy control with PI learning algorithm for induction servomotor systems
(2005)
Chen, GM
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Hsu, CF
;
Lee, TT
;
電控工程研究所
;
Institute of Electrical and Control Engineering
This paper proposes an adaptive fuzzy controller (AFC) with a proportional-integral (PI) learning algorithm for an induction servomotor. The proposed AFC is comprised of a fuzzy controller and a robust controller. The fuzzy controller is to mimic an ideal controller and the robust controller is to dispel the effect of the approximation error between the fuzzy controller and the ideal controller. All the control parameters of the AFC are on-line tuned by a PI learning algorithm in the Lyapunov sense, thus the stability of the system can be guaranteed. Finally, a comparison between a fuzzy controller, an AFC with integral learning algorithm, and the proposed AFC with PI learning algorithm is presented. Simulation results verify that for the induction servomotor systems, the tracking performance of the AFC with PI learning algorithm is better than those of the fuzzy controller and the AFC with integral learning algorithm. Also, the convergence of the tracking error is speeded up.
Adaptive fuzzy sliding-mode control for linear piezoelectric ceramic motor
(2005)
Hsu, CF
;
Lin, CM
;
Lee, TT
;
電控工程研究所
;
Institute of Electrical and Control Engineering
This paper proposes an adaptive fuzzy sliding-mode controller with proportional-integral learning algorithm (PI-AFSMC) for the unknown nonlinear systems. All the controller parameters are on-line tuned by the derived learning algorithms in the Lyapunov stability theorem, thus the stability of the system can be guaranteed. Finally, the proposed PI-AFSMC is applied to control a linear piezoelectric ceramic motor. Experimental results demonstrate the effectiveness of the proposed PI-AFSMC can achieve favorable tracking performances with unknown the controlled system dynamics.
Excess low-frequency noise in ultrathin oxide n-MOSFETs arising from valence-band electron tunneling
(2005-09-01)
Wu, JW
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You, JW
;
Ma, HC
;
Cheng, CC
;
Hsu, CF
Low-frequency flicker noise in analog n-MOSFETs with 15-angstrom gate oxide is investigated. A new noise generation mechanism resulting from valence-band electron tunneling is proposed. In strong inversion conditions, valence-band electron tunneling from Si substrate to polysilicon gate takes place and results in the splitting of electron and hole quasi-Fermi-levels in the channel. The excess low-frequency noise is attributed to electron and hole recombination at interface traps between the two quasi-Fermi-levels. Random telegraph signals due to the capture of channel electrons and holes is characterized in a small area device to support our model.
Fuzzy neural network classification design using support vector machine
(2004)
Lin, CT
;
Yeh, CM
;
Hsu, CF
;
電控工程研究所
;
Institute of Electrical and Control Engineering
Fuzzy neural networks (FNNs) for pattern classification usually use the backpropagation or C-cluster type learning algorithms to learn the parameters of the fuzzy rules and membership functions from the training data. However, such kinds of learning algorithms usually cannot minimize the empirical risk (training error) and expected risk (testing error) simultaneously, and thus cannot reach a good classification performance in the testing phase. To tackle this drawback, a support-vector-based fuzzy neural network classification (SVFNNC) is proposed. The SVFNNC combines the superior classification power of support vector machine (SVM) in high dimensional data spaces and the efficient human-like reasoning of FNN in handling uncertainty information. The learning algorithm consists of two learning phases. In the phase 1, the fuzzy rules and membership functions are automatically determined by the clustering principle. In the phase 2, the parameters of FNN are calculated by the SVM with the proposed adaptive fuzzy kernel function. To investigate the effectiveness of the proposed SVFNNC, it is applied to the Iris, Vehicle and Dna datasets. Experimental results show that the proposed SVFNNC can achieve good classification performance with drastically reduced number of fuzzy kernel functions.
Fuzzy-identification-based adaptive controller design via backstepping approach
(2005-04-01)
Hsu, CF
;
Lin, CM
;
電控工程研究所
;
Institute of Electrical and Control Engineering
This paper proposes a fuzzy-identification-based adaptive control scheme for the chaotic dynamic systems using backstepping control approach, which is referenced as adaptive fuzzy backstepping control (AFBC). The proposed AFBC offers a design approach to drive the chaotic trajectory to track a desired trajectory, and it is comprised of a fuzzy backstepping controller and a robust controller. The fuzzy backstepping controller containing a fuzzy estimation system is the principal controller, and the robust controller is designed to dispel the effect of minimum approximation error introduced by the fuzzy estimation system. Moreover, the Taylor linearization technique is employed to derive the linearized model of the fuzzy estimation system so that all the parameters in the fuzzy system could be updated according. The adaptation laws of the control system are derived in the sense of Lyapunov function and Barbalat's lemma, thus the stability of the system can be guaranteed. For comparison, the partial- and full-tuned cases for the parameters in the fuzzy system are simulated. Finally, simulation results verify that the proposed AFBC system can achieve favorable tracking performance for the chaotic system with regard to parameter variations and unknown dynamic function. (C) 2004 Elsevier B.V. All rights reserved.
Hybrid adaptive fuzzy control wing rock motion system with H-infinity robust performance
(2003)
Lin, CT
;
Lee, TT
;
Hsu, CF
;
Lin, CM
;
電控工程研究所
In this paper, a hybrid adaptive fuzzy control (HAFC) system is developed for a wing rock motion system. The design of HAFC system contains three parts: one is an indirect controller, the other is a direct controller and the last is a robust controller. A weighting factor a, which can be adjusted by a tradeoff between plant knowledge and control knowledge, is adopted to sum together the control efforts from the indirect and direct controllers. The robust controller is designed to achieve favorable control performance with a desired robustness. Simulation results demonstrate that the HAFC system can achieve favorable desired tracking performances for unknown the wing rock motion dynamics.
Longitudinal control of vehicle platoon via wavelet neural network
(2004)
Hsu, CF
;
Wang, WJ
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Lee, TT
;
Lin, CM
;
電控工程研究所
Transportation technology is one of the most influential areas on the human life. There has been an interest in the development of an automated highway system in which high traffic flow rates may be safely achieved Upon entering the automated highway system, the longitudinal control of car-following collision prevention system will drive a vehicle along the fully automated highway. This paper proposes an intelligent wavelet neural network (IWNN) control system for the car-following collision prevention system based on the wavelet neural network (WNN) approach. The WNN combines the capability of artificial neural networks for learning from processes and the capability of wavelet decomposition for control dynamic systems. In the proposed IWNN system, a WNN controller is used to mimic an ideal controller and a robust controller is designed to compensate for the difference between the ideal controller and the WNN controller. The adaptation laws of the IWNN are derived in the sense of Lyapunov stability analysis, so that the stability of the control system can be guaranteed. Finally, simulation results show that the proposed IWNN control system can achieve favorable tracking performance for a safe car-following control.
Neural-network-identification-based adaptive control of wing rock motions
(2005-01-01)
Hsu, CF
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Lin, CM
;
Chen, TY
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電控工程研究所
;
Institute of Electrical and Control Engineering
The proposed control system comprises a computation controller and a tracking controller. The computation controller containing a recurrent neural network identifier is the principal controller, and the tracking controller is designed to achieve L-2 tracking performance with a desired attenuation level. To investigate the effectiveness of the proposed control system the design methodology is used to control a wing rock motion, manifested by a limit-cycle oscillation predominantly for an aircraft operating at subsonic speeds and high angles of attack. Simulation results demonstrate that the proposed control system can achieve favourable tracking performance for the wing rock motion.
Recurrent-neural-network-based adaptive-backstepping control for induction servomotors
(2005-12-01)
Lin, CM
;
Hsu, CF
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電控工程研究所
;
Institute of Electrical and Control Engineering
This study is concerned with the position control of an induction servomotor using a recurrent-neural-network (RNN)-based adaptive-backstepping control (RNABC) system. The adaptive-backstepping approach offers a choice of design tools for the accommodation of system uncertainties and nonlinearities. The RNABC system is comprised of a backstepping controller and a robust controller. The backstepping controller containing an RNN uncertainty observer is the principal controller, and the robust controller is designed to dispel the effect of approximation error introduced by the uncertainty observer. Since the RNN has superior capabilities compared to the feed-forward NN for dynamic system identification, it is utilized as the uncertainty observer. In addition, the Taylor linearization technique is employed to increase the learning ability of the RNN. Meanwhile, the adaptation laws of the adaptive-backstepping approach are derived in the sense of the Lyapunov function, thus, the stability of the system can be guaranteed. Finally, simulation and experimental results verify that the proposed RNABC can achieve favorable tracking performance for the induction-servomotor system, even with regard to parameter variations and input-command frequency variation.
Robust cerebellar model articulation controller design for unknown nonlinear systems
(2004-07-01)
Lin, CM
;
Peng, YF
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Hsu, CF
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電控工程研究所
;
Institute of Electrical and Control Engineering
In this study, a robust cerebellar model articulation controller (RCMAC) is designed for unknown nonlinear systems. The RCMAC is comprised of a cerebellar model articulation controller (CMAC) and a robust controller. The CMAC is utilized to approximate an ideal controller, and the weights of the CMAC are on-line tuned by the derived adaptive law based on the Lyapunov sense. The robust controller is designed to guarantee a specified H-infinity robust tracking performance. In the RCMAC design, the sliding-mode control method is utilized to derive the control law, so that the developed control scheme has more robustness against the uncertainty and approximation error. Finally, the proposed RCMAC is applied to control a chaotic circuit. Simulation results demonstrate that the proposed control scheme can achieve favorable tracking performance with unknown the controlled system dynamics.
Robust hybrid control for antilock braking systems
(2003)
Lee, TT
;
Hsu, CF
;
Lee, S
;
電控工程研究所
;
Institute of Electrical and Control Engineering
The antilock braking systems are designed to maximize wheel traction by preventing the wheels from locking during braking, while also maintaining adequate vehicle steerability; however, the performance is often degraded under harsh road conditions. In this study, a robust hybrid control system is developed for the antilock braking systems. In this design method, the robust hybrid control system is comprised of a hybrid controller and a robust controller to achieve a desired H-infinity control performance. The hybrid controller containing a fuzzy uncertainty observer is the principal controller, and the robust controller is designed to achieve the desired H-infinity tracking performance. Finally, simulations are performed to demonstrate the effectiveness of the proposed robust hybrid control system for antilock braking control under various road conditions.
Robust neuro-fuzzy controller design via sliding-mode approach
(2004)
Hsu, CF
;
Lee, TT
;
Lin, CM
;
Chen, LY
;
電控工程研究所
The computed torque or inverse dynamics control techniques are based on a good understanding of the system dynamics and even its environment. For the real-time applications, the system dynamics always is difficult to obtain. To tackle this drawback, the goal of this paper is to develop a model-free control method which is referred to as robust neuro-fuzzy sliding-mode control (RNFSMC) system. The proposed RNFSMC system is comprised of a fuzzy controller and a robust controller. The fuzzy controller is utilized to approximate an ideal controller by the developed tuning algorithms, and the robust controller is designed to achieve H-infinity tracking performance index. To investigate the effectiveness of the proposed RNFSMC system, it is applied to control a Chua's chaotic circuit system. Finally, simulation results demonstrate that the effect of the fuzzy approximation error on the tracking error can be attenuated efficiently by the proposed method without any knowledge of the controlled systems.
Self-learning fuzzy sliding-mode control for antilock braking systems
(2003-03-01)
Lin, CM
;
Hsu, CF
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電控工程研究所
;
Institute of Electrical and Control Engineering
The antilock braking system (ABS) is designed to optimize braking effectiveness and maintain steerability; however, the ABS performance will be degraded in the case of severe road conditions. In this study, a self-learning fuzzy sliding-mode control (SLFSMG) design method is proposed for ABS. The SLFSMC ABS will modulate the brake torque for optimum braking. The SLFSMC system is comprised of a fuzzy controller and a robust controller. The fuzzy controller is designed to mimic an ideal controller and the robust controller is designed to compensate for the approximation error between the ideal controller,and the fuzzy controller. The tuning algorithms of the controller are derived in the Lyapunov sense; thus, the stability of the system can be guaranteed. Also, the derivation of the proposed SLFSMC ABS does not need to use a vehicle-braking model. Simulations are performed to demonstrate the effectiveness of the proposed SLFSMC ABS in adapting to changes for various road conditions.
Self-organizing fuzzy learning CLOS guidance law design
(2003-10-01)
Lin, CM
;
Hsu, CF
;
Mon, YJ
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電控工程研究所
;
Institute of Electrical and Control Engineering
A new self-organizing fuzzy logic control (SOFLC) design method is proposed. The proposed method is applied to the command line-of-sight (CLOS) guidance law design. The SOFLC contains two sets of fuzzy inference logic. One is the fuzzy logic controller and the other is the rule modifier. The new learning method of the rule modifier is developed based on a fuzzy learning algorithm. The modification value of each rule is based on the fuzzy firing weight, so that learning of the rule bases is reasonable. Finally, two engagement scenarios are examined, and a comparison between a fuzzy logic control (FLC), an optimal learning FLC, and the proposed SOFLC CLOS guidance laws is made. Simulation results show that the proposed SOFLC guidance law can achieve better guidance performance than the other guidance laws.
Supervisory recurrent fuzzy neural network control of wing rock for slender delta wings
(2004-10-01)
Lin, CM
;
Hsu, CF
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電機資訊學士班
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Undergraduate Honors Program of Electrical Engineering and Computer Science
Wing rock is a highly nonlinear phenomenon in which an aircraft undergoes limit cycle roll oscillations at high angles of attack. In this paper, a supervisory recurrent fuzzy neural network control (SRFNNC) system is developed to control the wing rock system. This SRFNNC system is comprised of a recurrent fuzzy neural network (RFNN) controller and a supervisory controller. The RFNN controller is investigated to mimic an ideal controller and the supervisory controller is designed to compensate for the approximation error between the RFNN controller and the ideal controller. The RFNN is inherently a recurrent multilayered neural network for realizing fuzzy inference using dynamic fuzzy rules. Moreover, an on-line parameter training methodology, using the gradient descent method and the Lyapunov stability theorem, is proposed to increase the learning capability. Finally, a comparison between the sliding-mode control, the fuzzy sliding control and the proposed SRFNNC of a wing rock system is presented to illustrate the effectiveness of the SRFNNC system. Simulation results demonstrate that the proposed design method can achieve favorable control performance for the wing rock system without the knowledge of system dynamic functions.
Type-2 fuzzy controller design using a sliding-mode approach for application to DC-DC converters
(2005-11-01)
Lin, PZ
;
Lin, CM
;
Hsu, CF
;
Lee, TT
;
電機資訊學士班
Fuzzy controllers and fuzzy sliding-mode controllers have found extensive use in a variety of applications. Generally, type-1 fuzzy sets are used for the membership functions of these controllers. However, in real-time applications, uncertainty associated with the available information always occurs. A type-2 fuzzy controller and a type-2 fuzzy sliding-mode controller are proposed that are able to solve the problem that the words used in the inference rules can mean different things to different people. Since the membership functions use type-2 fuzzy sets then, the proposed control schemes can handle the rule uncertainties when the operation is extremely uncertain and/or the membership grades cannot be exactly determined. The proposed control systems are applied to control a buck DC-DC converter. A comparison between a PI controller, a type-2 fuzzy controller and a type-2 fuzzy sliding-mode controller is made. The experimental results show that the type-2 fuzzy sliding-mode controller achieves the best control performance.
Type-2 fuzzy logic controller design for buck DC-DC converters
(2005)
Lin, PZ
;
Hsu, CF
;
Lee, TT
;
電控工程研究所
;
Institute of Electrical and Control Engineering
Type-1 fuzzy logic controllers (T1FLCs) have been successfully developed and used in various applications. The experience and knowledge of human experts are needed to decide both the membership functions and the fuzzy rules. However, in the real-time applications, uncertainty associated with the available information always happens. This paper proposes a type-2 fuzzy logic control (T2FLC), which involves the fuzzifier, rule base, fuzzy inference engine, and output processor with type reduction and defuzzifier. Because the antecedent and/or consequent membership functions of the T2FLC are type-2 fuzzy sets, the T2FLC can handle rule uncertainties when the operation is extremely uncertain and/or the engineers cannot exactly determine the membership grades. Furthermore, the proposed T2FLC is applied to a buck DC-DC converter control. Experimental results show that the proposed T2FLC is robust against input voltage and load resistance variations for the converter control.
Voltage scaling and temperature effects on drain leakage current degradation in a hot carrier stressed n-MOSFET
(1998)
Wang, TH
;
Hsu, CF
;
Chiang, LP
;
Zous, NK
;
Chao, TS
Drain leakage current degradation at zero V-gs in a hot carrier stressed n-MOSFET is measured and modeled. The dependences of drain leakage current on supply voltage and temperature are characterized. In modeling, various drain leakage current mechanisms including drain-to-source subthreshold leakage current, band-to-band tunneling current and interface trap assisted leakage current are taken into account. Our result shows that interface trap induced leakage current appears to be a dominant drain leakage mechanism as the supply voltage is scaled below 3.0V. Drain leakage current degradation by orders of magnitude has been observed due to hot carrier stress.