Browsing by Author "Lee, TT"

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A bifurcation study of vehicle's steering dynamics
(2005)
Liaw, DC
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Chiang, HH
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Lee, TT
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電控工程研究所
;
Institute of Electrical and Control Engineering
Issues of stability and bifurcation phenomena in vehicle's steering dynamics are presented. Based on the assumption of constant driving speed, a second order nonlinear lateral dynamical model is obtained. Local stability and existence conditions for saddle node bifurcation appearing in vehicle dynamics with respect to the variation of the front wheel steering angle are then derived via system linearization and local bifurcation analysis. Bifurcation phenomena occurring in nonlinear vehicle lateral dynamics might result in spin and/or system instability. 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 study of a simple example verifies the theoretical analysis.
A new method for mixed H-2/H-infinity control with regional pole constraints
(2003-05-01)
Wu, JL
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Lee, TT
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電控工程研究所
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Institute of Electrical and Control Engineering
In this paper, the problem of state feedback mixed H-2/H-infinity control with regional pole constraints is studied. The constraint region is represented by several algebraic inequalities. This constrained optimization problem cannot be solved via the LMI approach. Based on the barrier method, we instead solve an auxiliary minimization problem to get an approximate solution. We shall show that the obtained minimal solution of the auxiliary minimization problem can be arbitrarily close to the infimal solution of the original problem. An example is provided to illustrate the benefits of the approach. Copyright (C) 2003 John Wiley Sons, Ltd.
Adaptive fuzzy control with PI learning algorithm for induction servomotor systems
(2005)
Chen, GM
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Hsu, CF
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Lee, TT
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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 controller for linear systems with mismatched time-varying uncertainties
(2003-04-01)
Tao, CW
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Chan, ML
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Lee, TT
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電控工程研究所
;
Institute of Electrical and Control Engineering
A new design approach of an adaptive fuzzy sliding mode controller (AFSMC) for linear systems with mismatched time-varying uncertainties is presented in this paper. The coefficient matrix of the sliding function can be designed to satisfy a sliding coefficient matching condition provided time-varying uncertainties are bounded. With the sliding coefficient matching condition satisfied, an AFSMC. is proposed to stabilize the uncertain system. The parameters of the output fuzzy sets in the fuzzy mechanism are on-line adapted to improve the performance of the fuzzy sliding mode control system. The bounds of the uncertainties are not required to be known in advance for the presented AFSMC. The stability of the fuzzy control system is guaranteed and the system is shown to be invariant on the sliding surface. Moreover, the chattering around the sliding surface in the sliding mode control can be reduced by the proposed design. approach. Simulation results are included to,illustrate the effectiveness of the proposed AFSMC.
Adaptive fuzzy sliding-mode control for linear piezoelectric ceramic motor
(2005)
Hsu, CF
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Lin, CM
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Lee, TT
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電控工程研究所
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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.
Adaptive hybrid intelligent control for uncertain nonlinear dynamical systems
(2002-10-01)
Wang, CH
;
Lin, TC
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Lee, TT
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Liu, HL
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電控工程研究所
A new hybrid direct/indirect adaptive fuzzy neural network (FNN) controller with state observer and supervisory controller for a class of uncertain nonlinear dynamic systems is developed in this paper. The hybrid adaptive FNN controller, the free parameters of which can be tuned on-line by observer-based output feedback control law and adaptive law, is a combination of direct and indirect adaptive FNN controllers. A weighting factor, which can be adjusted by the tradeoff between,plant knowledge and control knowledge, is adopted to sum together the control efforts from indirect adaptive FNN controller and direct adaptive FNN controller. Furthermore, a supervisory controller is appended into the FNN controller to force the state to be within the constraint set. Therefore, if the FNN controller cannot maintain the stability, the supervisory controller starts working to guarantee stability. On the other hand, if the FNN controller works well, the supervisory controller will be deactivated. The overall adaptive scheme guarantees the global stability of the resulting closed-loop system in the sense that all signals involved are uniformly bounded. Two nonlinear systems, namely, inverted pendulum system and Chua's chaotic circuit, are fully illustrated to track sinusoidal signals. The resulting hybrid direct/indirect FNN control systems show better performances, i.e., tracking error and control effort can be made smaller and it is more flexible during the design process.
An intelligent vision-based real-time integration system on autonomous vehicles
(2004)
Wu, BF
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Chiu, CC
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Chen, CJ
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Wu, WC
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Perng, JW
In this paper, an intelligent real-time system on autonomous vehicles is presented. A wireless-based remote control scheme is developed. A vision-based obstacle detection system and a radar sensor are integrated into the on-vehicle controller to automatically brake the vehicle under emergency. Finally, a successfully on-road demonstration is also completed with our experimental car. TAIWAN iTS-1.(1)
Application of fuzzy sets theory in process data filtering
(2003)
Chiang, HH
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Wu, SJ
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Lee, TT
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電控工程研究所
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Institute of Electrical and Control Engineering
introduced and applied to the problems of signal restoration. A fuzzy approach for system identification and state estimation is developed. The estimated values obtained from the fuzzy model are in the form of fuzzy numbers which present the possibility of the system structure. Both L-R and symmetric triangular type membership functions are employed to derive the fuzzy-data based estimator (FBE). Illustrative examples with white and color noise are provided to demonstrate the applicability and effectiveness of the developed FBE. Comparisons between the FBE and various modified Kalman filter are also included.
Application of neural-fuzzy modeling and optimal fuzzy controller for nonlinear magnetic bearing systems
(2003)
Yu, SS
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Wu, SJ
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Lee, TT
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電控工程研究所
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Institute of Electrical and Control Engineering
In this paper we apply a new approach, called optimal fizzy control based on linear TS type fuzzy model, to deal with nonlinear magnetic bearing systems. The linear TS type fuzzy model is used to represent the nonlinear plant To obtain the linear TS fuzzy model, we use linear self-constructing neural fuzzy inference network(linear SONFIN) to model the nonlinear system. Once the TS fuzzy model of the magnetic bearing system is obtained, the optimal fuzzy controller can be applied if the system is completely controllable and observable. Simulation results show that the proposed optimal fuzzy controller can operate in widely range of shaft position.
Combined controller-observer design with guaranteed closed-loop stability for automated vehicle operation
(2005)
Kumarawadu, S
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Lee, TT
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電控工程研究所
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Institute of Electrical and Control Engineering
Automated vehicle control systems is a key technology for Intelligent Vehicle Highway Systems (IVHSs). A model-based combined longitudinal and lateral vehicle control algorithm that encompasses a lateral velocity observer is presented in this paper for IVHSs applications. Closed-loop stability issues are analyzed using a Lyapunov-based method. The combined controller-observer scheme can guarantee uniform ultimate bounds (UUB) of controller errors and observer estimation error. Analysis, design, and simulation testing presented are based on 3 DOF complex dynamic model of a FWS/FWD passenger wagon.
Design of a multi-layer fuzzy logic controller using pole assignment for bipedal walking at varying speeds
(2004-01-01)
Tu, KY
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Lee, TT
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電控工程研究所
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Institute of Electrical and Control Engineering
When a biped robot detects obstacles along its path, it will reduce its walking speed to change direction for obstacle avoidance. It is essential for a biped robot to be able to vary its speed during walking. To control biped robots with variable walking speed capability, a Multi-Layer Fuzzy Logic Controller (MLFLC) is developed using the pole assignment technique in this paper. This method extends the previous result, that a switching surface, whose parameters are scaling factors, exists in a Multi-Layer Fuzzy Logic Controller (Tu et al., 2000). In this study, the poles of a closed-loop control system are derived relative to the switching surface parameters, i.e. the normalized scaling factors. Therefore, after choosing the appropriate poles, scaling factors are determined and then a Multi-Layer Fuzzy Logic Controller is designed. This design has two important features: (1) simplifying Fuzzy Logic Controller (FLC) design to select only the scaling factors; (2) pursuing the system performance requirement, based on the pole assignment. A three-link biped robot is used as the illustrative example. Simulation results are included.
Design of a new fuzzy suction controller using fuzzy modeling for nonlinear boundary layer
(2005-10-01)
Tu, KY
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Lee, TT
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Wang, CH
;
電控工程研究所
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Institute of Electrical and Control Engineering
There are two types of fuzzy modeling: 1) imitating an expert experiment or fulfilling an engineering knowledge, and 2) modeling a complex or unknown system. In this paper, based on the first type of fuzzy modeling, a new fuzzy suction controller (NFSC) is proposed using its linguistic rules to design nonlinear boundary layer. Two kinds of nonlinear boundary layers are discussed. The first kind is designed by three rules derived according to a new interpretation of the switching conditions for a suction controller such that the new controller reduces chattering and spends less energy than a suction controller does. A design procedure summarizes the NFSC design. The second kind of nonlinear boundary layer is the linguistic rules designed to have sliding sectors to control a mobile robot for trajectory tracking. The discussion emphasizes the advantage of nonlinear boundary layers, compared with traditional suction controllers usually using linear boundary. In addition, the proposed NFSC provides a flexible way to adjust the controller functions using linguistic rules based on the first type of fuzzy modeling.
Design of sampled-data systems with large plant uncertainty using quantitative feedback theory
(2001-03-01)
Lin, TC
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Wang, CH
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Teng, CC
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Lee, TT
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電控工程研究所
This paper proposes a new quantitative feedback theory (QFT) design framework for dealing,with sampled-data systems with large plant uncertainty. After the QFT-based design in the continuous-time domain is completed, the analogue controller can he transformed directly into a rational discrete-time transfer function via approximate Z transform with the sampling time as a free parameter. The sampling time can therefore be adjusted to make the uncertain sampled-data system robustly stable. In comparison with other approaches, our approach is much more systematic without the solvability problem and yet significant enough to guide the designer to realize the physical controller in which the plant transfer function has prescribed bounds on its parameters. Several examples are used to illustrate the proposed approach and excellent results are obtained.
Dynamical optimal training for interval type-2 fuzzy neural network (T2FNN)
(2003)
Wang, CH
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Cheng, CS
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Lee, TT
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電控工程研究所
;
Institute of Electrical and Control Engineering
Type-2 fuzzy logic system (FLS) cascaded with neural network called type-2 fuzzy neural network (T2FNN), is presented in this paper to handle uncertainty with dynamical optimal learning. A T2FNN consists of type-2 fuzzy linguistic process as the antecedent part and the two-layer interval neural network as the consequent part. The dynamical optimal training algorithm for the two-layer consequent part of interval T2FNN is first developed The stable and optimal left and right learning rates for the interval neural network, in the sense of maximum error reduction, can be derived for each iteration in the training process. It can also be shown both learning rates can not be both negative. Excellent results are obtained for the truck backing-up control, which yield more improved performance than those using type-1 FNN.
Dynamical optimal training for interval type-2 fuzzy neural network (T2FNN)
(2004-06-01)
Wang, CH
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Cheng, CS
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Lee, TT
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電控工程研究所
;
Institute of Electrical and Control Engineering
Type-2 fuzzy logic system (FLS) cascaded with neural network, type-2 fuzzy neural network (T2FNN), is presented in this paper to handle uncertainty with dynamical optimal learning. A T2FNN consists of a type-2 fuzzy linguistic process as the antecedent part, and the two-layer interval neural network as the consequent part. A general T2FNN is computational-intensive due to the complexity of type 2 to type 1 reduction. Therefore, the interval T2FNN is adopted in this paper to simplify the computational process. The dynamical optimal training algorithm for the two-layer consequent part of interval T2FNN is first developed. The stable and optimal left and right learning rates for the interval neural network, in the sense of maximum error reduction, can be derived for each iteration in the training process (back propagation). It can also be shown both learning rates cannot be both negative. Further, due to variation of the initial MF parameters, i.e., the spread level of uncertain means or deviations of interval Gaussian MFs, the performance of back propagation training process may be affected. To achieve better total performance, a genetic algorithm (GA) is designed to search optimal spread rate for uncertain means and optimal learning for the antecedent part. Several examples are fully illustrated. Excellent results are obtained for the truck backing-up control and the identification of nonlinear system, which yield more improved performance than those using type-1 FNN.
Fuzzy approach for disturbance reduction
(2003)
Chiang, HH
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Wu, SJ
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Lee, TT
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電控工程研究所
;
Institute of Electrical and Control Engineering
The basic principles of the fuzzy set theory are applied to the signal restoration problems. The estimated values obtained from the fuzzy model are in the form of fuzzy numbers which present the possibility of the system structure. Both L-R and symmetric triangular type membership functions are employed to derive the fuzzy disturbance reduction. The quarter-car semi-active suspension system with unknown disturbance caused by roughness of road is provided to demonstrate the applicability and effectiveness of the proposed fuzzy algorithm in disturbance reduction problem.
Fuzzy data mining and grey recurrent neural network forecasting for traffic information systems
(2005)
Wen, YH
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Lee, TT
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交大名義發表
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National Chiao Tung University
This study presents a systematic process combining trajfic forecasting and data mining models for traffic information systems. Fuzzy c-means clustering model was developed for mining traffic flow-speed-occupancy relationships, then to extrapolate traffic information. The hybrid grey-based recurrent neural network (G-RNN) was developed for traffic parameter forecasting. G-RNN integrates grey modeling into recurrent neural networks that is capable of dealing with both randomness and spatial-temporal properties in trajfic data implicitly. Field data from Taiwan national freeway was used as an example for testing the proposed models. Study results were shown that the G-RNN model is capable of predicting traffic parameters with a high degree of accuracy. The application presents three clusters built from data and recognized three types of traffic conditions. Study results also showed feasibility of the method for advanced traffic information systems.
Gain-phase margin analysis of dynamic fuzzy control systems
(2004-10-01)
Perng, JW
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Wu, BF
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Chin, HI
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Lee, TT
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電控工程研究所
In this paper, we apply some effective methods, including the gain-phase margin tester, describing function and parameter plane, to predict the limit cycles of dynamic fuzzy control systems with adjustable parameters. Both continuous-time and sampled-data fuzzy control systems are considered. In general, fuzzy control systems are nonlinear. By use of the classical method of describing functions, the dynamic fuzzy controller may be linearized first. According to the stability equations and parameter plane methods, the stability of the equivalent linearized system with adjustable parameters is then analyzed. In addition, a simple approach is also proposed to determine the gain margin and phase margin which limit cycles can occur for robustness. Two examples of continuous-time fuzzy control systems with and without nonlinearity are presented to demonstrate the design procedure. Finally, this approach is also extended to a sampled-data fuzzy control system.
Gain-phase margin analysis of fuzzy control systems
(2003)
Perng, JW
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Wu, BF
;
Lee, TT
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電控工程研究所
;
Institute of Electrical and Control Engineering
In this paper, we extend some effective methods, including the gain-phase margin tester method, the describing function method and the parameter plane method, to predict the limit cycles of fuzzy control systems. In general, fuzzy control systems are nonlinear. By use of the classical method of describing functions, a fizzy controller may be linearized first. The stability of the equivalent linearized system with adjustable parameters is then analyzed due to the stability equations and parameter plane methods. For the robust design, a novel way is proposed to determine the gain margin and phase margin which limit cycles can occur. Two examples are offered to illustrate the design procedure. First, the fuzzy control system with a third-order linear plant is considered. In addition, this approach is also extended to the fuzzy control system with nonlinearity. The results of computer simulation can verify its validity.
H(infinity) tracking-based sliding mode longitudinal control of a platoon of vehicles
(2004)
Wu, BF
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Ma, LS
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Perng, JW
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Lee, TT
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電控工程研究所
In this paper, the H, tracking-based sliding mode design is applied into the longitudinal control of a platoon of vehicles. Due to this approach, the robustness for the external disturbance and the suppression of the chattering control input are both provided. In provided scheme, the upper bound of the external disturbance should not be known previously and designed control law can still work to reduce the effect of the external disturbance.