Browsing by Author "Lee, Tsu-Tian"

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A circuit simulation technique for network assignment problem
(2005)
Cho, Hsun-Jung
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Lee, Tsu-Tian
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Huang, Heng
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運輸與物流管理系註：原交通所+運管所
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Department of Transportation and Logistics Management
In this paper, under the network user equilibrium principle (Wardrop, 1952), a method to use the analogous electric circuit to simulate network traffic assignment, and to measure the current flow and voltage of the electric circuit to present the result of traffic assignment is proposed. The basic ideas are using the resistance and constant voltage source combines the analogous electric circuit and using the current source presents the net traffic flow. Also, the nonlinear link cost function is approximated by the piecewise function. Finally, an example shows that an electric circuit simulating the network traffic assignment is workable.
A Dynamic Neural Network Model for Nonlinear System Identification
(2008)
Wang, Chi-Hsu
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Chen, Pin-Cheng
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Lin, Ping-Zong
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Lee, Tsu-Tian
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電控工程研究所
In this paper, a new dynamic neural network based on the Hopfield neural network is proposed to perform the nonlinear system identification. Convergent analysis is performed by the Lyapunov-like criterion to guarantee the error convergence during identification. Simulation results demonstrate that the proposed dynamic neural network trained by the Lyapunov approach can obtain good identifted performance.
Adaptive asymmetric fuzzy neural network controller design via network structuring adaptation
(2008-10-16)
Hsu, Chun-Fei
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Lin, Ping-Zong
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Lee, Tsu-Tian
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Wang, Chi-Hsu
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電控工程研究所
This paper proposes a self-structuring fuzzy neural network (SFNN) using asymmetric Gaussian membership functions in the structure and parameter learning phases. An adaptive self-structuring asymmetric fuzzy neural-network control (ASAFNC) system which consists of an SFNN controller and a robust controller is proposed. The SFNN controller uses an SFNN with structure and parameter learning phases to online mimic an ideal controller, simultaneously. The structure learning phase consists of the growing-and-pruning algorithms of fuzzy rules to achieve an optimal network structure, and the parameter learning phase adjusts the interconnection weights of neural network to achieve favorable approximation performance. The robust controller is designed to compensate for the modeling error between the SFNN controller and the ideal controller. An online training methodology is developed in the Lyapunov sense, and thus the stability of the closed-loop control system can be guaranteed. Finally, the proposed ASAFNC system is applied to a second-order chaotic dynamics system. The simulation results show that the proposed ASAFNC can achieve favorable tracking performance. (C) 2008 Elsevier B.V. All rights reserved.
Adaptive fuzzy control using PID-type learning algorithm
(2007-01-01)
Lee, Bore-Kuen
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Hsu, Chen-Fei
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Chen, Guan-Ming
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Lee, Tsu-Tian
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電控工程研究所
This paper proposes a proportional-integral-derivative (PID)-learning-type adaptive fuzzy controller (AFC) for chaotic Duffing dynamic systems. The proposed PID-learning-type AFC 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 dispel the effect of the approximation error between the fuzzy controller and the ideal controller. All the control parameters are on-line tuned in the sense of Lyapunov theorem, thus the stability of the system can be guaranteed. Finally, a comparison between a conventional AFC and the proposed PID-learning-type AFC is presented. Simulation results verify that the proposed PID-learning-type AFC can achieve better tracking performance and faster tracking error convergence than the conventional AFC for chaotic Duffing dynamic systems.
Adaptive synchronization for unknown chaotic systems with fuzzy-neural network observer
(2006)
Wu, Bing-Fei
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Ma, Li-Shan
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Perng, Jau-Woei
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Chin, Hung-I
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Lee, Tsu-Tian
This investigation applies the adaptive fuzzy-neural observer (AFNO) to synchronize a class of unknown chaotic systems via scalar transmitting signal only. The proposed method can be used in synchronization if nonlinear chaotic systems can be transformed into the canonical form of Lur'e system type by the differential geometric method. In proposed approach, the receiver states can be reconstructed from one transmitted state using AFNO design. The adaptive fuzzy-niural network (INN) in AFNO is adopted to model the nonlinear term in the transmitter. Additionally, an observer is designed to estimate the other states of the master. Synchronization is achieved when all states are observed. The proposed scheme can adaptively estimated the transmitter states using AFNO, even if the transmitter changes into another chaotic system. Simulation results confirm that the proposed AFNO design is valid.
An improved auto-tuning scheme for PI controllers
(2008-01-01)
Mudi, Rajani K.
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Dey, Chanchal
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Lee, Tsu-Tian
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電控工程研究所
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Institute of Electrical and Control Engineering
Ziegler-Nichols tuned PI and PID controllers are usually found to provide poor performances for high-order and nonlinear systems. In this study, an improved auto-tuning scheme is presented for Ziegler-Nichols tuned PI controllers (ZNPICs). With a view to improving the transient response, the proportional and integral gains of the proposed controller are continuously modified based on the current process trend. The proposed controller is tested for a number of high-order linear and nonlinear dead-time processes under both set-point change and load disturbance. It exhibits significantly improved performance compared to ZNPIC, and Refined Ziegler-Nichols tuned PI controller (RZNPIC). Robustness of the proposed scheme is established by varying the controller parameters as well as the dead-time of the process under control. (c) 2007, ISA. Published by Elsevier Ltd. All rights reserved.
Computation intelligent foir eukaryotic cell-cycle gene network
(2006)
Wu, Shinq-Jen
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Wu, Cheng-Tao
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Lee, Tsu-Tian
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電控工程研究所
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Institute of Electrical and Control Engineering
Computational intelligent approaches is adopted to construct the S-system of eukaryotic cell cycle for further analysis of genetic regulatory networks. A highly nonlinear power-law differential equation is constructed to describe the transcriptional regulation of gene network from the time-courses dataset. Global artificial algorithm, based on hybrid differential evolution, can achieve global optimization for the highly nonlinear differential gene network modeling. The constructed gene regulatory networks will be a reference for researchers to realize the inhibitory and activatory operator for genes synthesis and decomposition in Eukaryotic cell cycle.
DC-DC intelligent power regulator design using wavelet neural network
(2007)
Hsu, Chun-Fei
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Lee, Tsu-Tian
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Hung, Kun-Neng
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電機學院
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College of Electrical and Computer Engineering
DC-DC power converters are the! devices which can convert a certain electrical voltage to another level of electrical voltage by switching action. This paper proposes a wavelet neural network control (WNNC) system for the DC-DC power converters. The WNNC system is comprised of a neural controller and a robust controller. The neural controller is used to mimic an ideal controller and the robust controller is designed to achieve L-2 tracking performance with desired attenuation level. Finally, some experimental results are provided to demonstrate the proposed WNNC system can track the output reference voltage variations and cope with the load resistance variations to ensure the stability while providing fast transient response.
Degraded License Plate Recognition System for Town Buses on Highway
(2013)
Wu, Bing-Fei
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Huang, Hao-Yu
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Chen, Yen-Lin
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Lin, Hsin-Yu
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Lee, Tsu-Tian
This study presented a Degraded License Plate Recognition (DLPR) to identify town buses on highway. Since the License Plates (LPs) of town buses are influenced by the external conditions and become hardly distinguished by the Optical Character Recognition (OCR) method. The proposed DLPR system adopts Histogram of Oriented Gradient (HOG) and Support Vector Machine (SVM)-for classification-based solution. The proposed system implements a two-level LPR, including LP localization and character recognition. For the recognition efficiency of the proposed system, two trained databases for LP localization and character recognition are reduced by the mathematical and feature-based approaches, respectively, and thus the searching time can be saved significantly. To certify the performance of recognizing the degraded LPs, the proposed system is demonstrated by physical degraded LP images, and thus the results reveal that the proposed system overcomes several degraded LP images with reasonable recognition accuracy.
Elucidating vehicle lateral dynamics using a bifurcation analysis
(2007-06-01)
Liaw, Der-Cherng
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Chiang, Hsin-Han
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Lee, Tsu-Tian
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電控工程研究所
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Institute of Electrical and Control Engineering
Issues of stability and bifurcation phenomena in vehicle lateral dynamics are presented. Based on the assumption of constant driving speed, a second-order nonlinear lateral dynamics model is obtained. Local stability and existence conditions for saddle-node bifurcation appearing in vehicle dynamics with respect to the variations in front wheel steering angle are then derived via system linearization and local bifurcation analysis. Bifurcation phenomena occurring in vehicle lateral dynamics might result in spin and/or system instability. A perturbation method is employed to solve for an approximation of system equilibrium near the zero value of the front wheel steering angle, which reveals the relationship between sideslip angle and the applied front wheel angle. Numerical simulations from an example model demonstrate the theoretical results.
Embedded Driver-Assistance System Using Multiple Sensors for Safe Overtaking Maneuver
(2014-09-01)
Chiang, Hsin-Han
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Chen, Yen-Lin
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Wu, Bing-Fei
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Lee, Tsu-Tian
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交大名義發表
Advanced driver-assistance systems have recently become one of the most active topics related to intelligent vehicles. Such assistance facilitates vehicular operation by allowing drivers increased control and an enhanced driving experience, and it even comes in the form of automated assistance for autonomous vehicle operation. This paper presents a driver-assistance system that uses a low-cost embedded digital signal processor, with the overall system installed in a commercial vehicle. Based on driving information supplied by multiple sensors, such as a real-time vision system, a vehicle-to-vehicle communication system, and in-vehicle sensors, the proposed system can facilitate decision making and the performing of driving tasks while executing overtaking maneuvers. This paper developed a data fusion stage based on a collision warning algorithm in which the overtaken vehicle and other vehicles in the neighboring lane are accounted for to avoid collisions. The system employs fuzzy control in the steering and speed automation to emulate the driving tasks performed by humans. The applicability of the proposed system was examined in a real-road environment, and a set of experimental results demonstrate the feasibility of using the involved coordination strategies while conducting various driving maneuvers.
Exponentially stabilizing output tracking of fully autonomous passenger cars with an observer
(2006)
Kumarawadu, Sisil
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Perng, Jau-Woei
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Lee, Tsu-Tian
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電控工程研究所
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Institute of Electrical and Control Engineering
This paper, inspired by resolved acceleration control techniques popular in robotics, presents an automated vehicle control algorithm for combined longitudinal and lateral motion control of highway vehicles with special emphasis on passenger cars. Key design features are: 1) inherent coupling effects will be taken into account as a result of combining of two control issues, viz., lateral and longitudinal control 2) rather ad hoc numerical approximations of lateral velocity will be avoided via a combined controller-observer design 3) closed-loop exponential stability issues of the overall system will be established. The algorithm is validated via formative mathematical analyzes and numerical simulations.
Fuzzy Control Using Intuitive Image Analysis
(2008)
Chen, Guan-Ming
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Lin, Ping-Zong
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Wang, Wei-Yen
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Lee, Tsu-Tian
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Wang, Chi-Hsu
In this paper, a novel fuzzy control scheme using intuitive image analysis is developed to imitate the intuitive human control behavior determined through human eyes. A CCD camera is used to gather the images of the controlled plant, and a simple algorithm is proposed to analyze the images. Unlike that in the visual servo control systems, the image information is utilized in a more intuitive way via the proposed image analysis algorithm. The difference between a reference image and the current image is numerically expressed and directly used by a fuzzy control system using a human-like control law. To investigate the effectiveness of the proposed fuzzy control scheme, it is applied to an inverted pendulum system. Simulation results show that the proposed scheme can achieve favorable tracking performance without prior knowledge of the controlled plant.
Fuzzy-identification-based adaptive backstepping control using a self-organizing fuzzy system
(2009-05-01)
Chen, Pin-Cheng
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Hsu, Chun-Fei
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Lee, Tsu-Tian
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Wang, Chi-Hsu
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電控工程研究所
In this paper, a fuzzy-identification-based adaptive backstepping control (FABC) scheme is proposed. The FABC system is composed of a backstepping controller and a robust controller. The backstepping controller, which uses a self-organizing fuzzy system (SFS) with the structure and parameter learning phases to on-line estimate the controlled system dynamics, is the principal controller, and the robust controller is designed to dispel the effect of approximation error introduced by the SFS. The developed SFS automatically generates and prunes the fuzzy rules by the proposed structure adaptation algorithm and the parameters of the fuzzy rules and membership functions tunes on-line in the Lyapunov sense. Thus, the overall closed-loop FABC system can guarantee that the tracking error and parameter estimation error are uniformly ultimately bounded; and the tracking error converges to a desired small neighborhood around zero. Finally, the proposed FABC system is applied to a chaotic dynamic system to show its effectiveness. The simulation results verify that the proposed FABC system can achieve favorable tracking performance even with unknown controlled system dynamics.
GPS navigation based autonomous driving system design for intelligent vehicles
(2007)
Wu, Bing-Fei
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Lee, Tsu-Tian
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Chang, Hsin-Han
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Jiang, Jhong-Jie
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Lien, Cheng-Nan
The main objective of this paper is to design and implement a real-time autonomous navigation system for intelligent vehicles by integrating a real-time kinematics differential global position system (RTK-DGPS) and a laser range finder. Firstly, a fuzzy logic algorithm for target position tracking is proposed to achieve the human driving concept. In addition, an actuator is equipped in the experimental vehicle TAIWAN iTS-1 so that the steering wheel can be controlled to let the vehicle follows a desired trajectory in digital maps. A laser range finder is used to detect obstacles in front of the vehicle for warning announcement and collision avoidance. A human machine interface is also built up to provide information from sensors. The performance and accuracy of the proposed system is verified by real-time experimental results.
Indirect Adaptive Control Using Hopfield-Based Dynamic Neural Network for SISO Nonlinear Systems
(2009)
Chen, Ping-Cheng
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Wang, Chi-Hsu
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Lee, Tsu-Tian
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電子工程學系及電子研究所
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Department of Electronics Engineering and Institute of Electronics
In this paper, we propose an indirect adaptive control scheme using Hopfield-based dynamic neural network for SISO nonlinear systems with external disturbances. Hopfield-based dynamic neural networks are used to obtain uncertain function estimations in an indirect adaptive controller, and a compensation controller is used to suppress the effect of approximation error and disturbance. The weights of Hopfield-based dynamic neural network are on-line tuned by the adaptive laws derived in the sense of Lyapunov, so that the stability of the closed-loop system can be guaranteed. In addition. the tracking error can be attenuated to a desired level by selecting some parameters adequately. Simulation results illustrate the applicability of the proposed control scheme. The designed parsimonious structure of the Hopfield-based dynamic neural network makes the practical implementation of the work in this paper much easier.
Inference of genetic network of Xenopus frog egg: Improved genetic algorithm
(2006)
Wu, Shinq-Jen
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Chou, Chia-Hsien
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Wu, Cheng-Tao
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Lee, Tsu-Tian
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電控工程研究所
An improved genetic algorithm (IGA) is proposed to acheive S-system gene network modeling of Xenopus frog egg. Via the time-courses training datasets from Michaelis-Menten model, the optimal parameters are learned. The S-system can clearly describe activative and inhibitory interacation between genes as generating and consuming process. We concern the mitotic control in cell-cycle of Xenopus frog egg to realize cyclin-Cdc2 and Cdc25 for MPF activity. The proposed IGA can acheive global search with migration and keep the best chromosome with elitism operation. The generated gene regulatory networks can provide biological researchers for further experiments in Xenopus frog egg cell cycle control.
Integrated headway adaptation with collision avoidance system for intelligent vehicles
(2007)
Chiang, Hsin-Han
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Wu, Bing-Fei
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Lee, Tsu-Tian
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Perng, Jau-Woei
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電控工程研究所
In this paper, an integrated headway adaptation (HA) with collision avoidance (CA) system is proposed to be associated with a human factor aspect for driving assistance. The maneuver in the CA strategy is developed based on the idea that warning or potential crash information generally can be graded such that the driver need not react to a discrete on/off warning. The control law for the HA strategy is designed by using a modified sliding surface to achieve driving comfort requirement and also stability in car-following. To simulate the speed controlling of human drivers, the throttle and brake actuation is directed by the regulation control which is designed based on a human reasoning approach. A description of an experimental vehicle and real traffic testing in highways and urban-like tracks shows the potential and validness of the integrated system.
Intelligent control for DC-DC power converter with recurrent fuzzy neural network approach
(2006)
Hsu, Chun-Fei
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Lee, Tsu-Tian
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Wen, Yao-Wei
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Ding, Fu-Shan
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電控工程研究所
For many years, the control approaches for the dc-dc power converters are limited to PI controller structures. However, it gives the overshoot in output voltage as the rise time of response is reduced. To tackle this problem, an adaptive recurrent fuzzy neural network (ARFNN) control system is developed in this paper. The on-line adaptive laws of the ARFNN control scheme are derived based on the Lyapunov stability theorem, so that the stability of the system can be guaranteed. Experimental results show that the proposed ARFNN control scheme can achieve good regulation performances.
Intelligent tracking control for Duffing oscillator using a self-organizing fuzzy neural network
(2006)
Chen, Te-Yu
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Hsu, Chun-Fei
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Lee, Tsu-Tian
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Ding, Fu-Shan
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Chiu, Chih-Hui
This paper proposes an intelligent tracking control for the chaotic systems via backstepping approach. The intelligent tracking control system is comprised of a neural controller and a robust controller. The neural controller containing a self-organizing fuzzy neural network (SOFNN) identifier is the principal controller, and the robust controller is designed to dispel the effect of minimum approximation error introduced by the SOFNN identifier. The SOFNN identifier is used to online estimate the chaotic dynamic function with structure and parameter learning phases of fuzzy neural network. Finally, simulation results verify that the proposed intelligent tracking control can achieve favorable tracking performance.