教師著作

Permanent URI for this collectionhttp://rportal.lib.ntnu.edu.tw/handle/20.500.12235/31268

Browse

Now showing 1 - 19 of 19

Approximationransform using higher order integrators and its applications in sampled-data control systems
(Taylor & Francis, 1998-01-01) C.-H. Wang; C.-C. Hsu; W.-Y. Wang
In this paper, we first clarify the difference between the approximate z transform and the discrete equivalent of a continuous system using higher-order integrators. It is shown that a 1/ ts factor needs to be included for the approximate z transform but not for the discrete equivalent. We further apply the approximate z transform to facilitate the stability analysis of sampled-data control systems, with or without uncertain parameters, ft is shown in this paper that the approximate z transform greatly simplifies the stability analysis of a sampled-data control system, which is regarded as rather difficult ( if not impossible) to handle because of its transcendental nature. The results can be easily obtained and show reasonably good approximations with this approach. Several examples are used to illustrate the effectiveness of this new method.
A composite controller for unknown nonlinear dynamical systems using robust adaptive fuzzy-neural control schemes
(2000-09-27) W.-Y. Wang; C.-C. Hsu; Y.-G. Leu
A robust adaptive fuzzy-neural control scheme for nonlinear dynamical systems is proposed to attenuate the effects caused by unmodeled dynamics, disturbance and modeling errors. A composite update law, which has a generalized form combining the projection algorithm modification and the switching-σ adaptive law, is used to tune the adjustable parameters for preventing parameter drift and confining states of the system into the specified regions. Moreover, a fuzzy variable structure control method is incorporated into the control law so that the derived controller is robust with respect to unmodeled dynamics, disturbances and modeling errors. Compared with previous control schemes for nonlinear systems, the magnitude of the control input by using the proposed approach is much smaller, which is a significant advantage in designing controllers for practical applications. To demonstrate the effectiveness and applicability of the proposed method, several examples are illustrated in the paper
Discrete modeling of continuous interval using high-order integrators
(1999-06-04) C.-C. Hsu; W.-Y. Wang
A higher-order integrator approach is proposed to obtain an approximate discrete-time transfer function for uncertain continuous systems having interval uncertainties. Thanks to simple algebraic operations of this approach, the resulting discrete model is a rational function of the uncertain parameters. The problem of non-linearly coupled coefficients of exponential nature in the exact discrete-time transfer function is therefore circumvented. Furthermore, interval structure of the uncertain continuous-time system is preserved in the resulting discrete model by using this approach. Formulas to obtain the lower and upper bounds for the discrete interval system are derived, so that existing robust results in the discrete-time domain can be easily applied to the discretized system. Digital simulation and design for the continuous-time interval plant can then be performed based on the obtained discrete-time interval model
Discrete modeling of uncertain continuous systems having an interval structure using higher-order integrators
(Taylor & Francis, 2000-01-01) C.-C. Hsu; W.-Y. Wang
Discrete-Time Model Reduction of Sampled Systems Using an Enhanced Multiresolutional Dynamic Genetic Algorithm
(2001-10-10) C.-C. Hsu; K.-M. Tse; W.-Y. Wang
A framework to automatically generate a reduced-order discrete-time model for the sampled system of a continuous plant preceded by a zero-order hold (ZOH) using an enhanced multi-resolution dynamic genetic algorithm (EMDGA) is proposed in this paper. Chromosomes consisting of the denominator and the numerator parameters of the reduced-order model are coded as a vector with floating-point-type components and searched by the genetic algorithm. Therefore, a stable optimal reduced-order model satisfying the error range specified can be evolutionarily obtained. Because of the use of the multi-resolution dynamic adaptation algorithm and the genetic operators, the convergence rate of the evolution process to search for an optimal reduced-order model can be expedited. Another advantage of this approach is that the reduced discrete-time model evolves based on samples taken directly from the continuous plant, instead of the exact discrete-time model, so that computation time is saved
Distance measurement based on pixel variation of CCD images
(ISA, 2009-10-01) C.-C. Hsu; M.-C. Lu; W.-Y. Wang; Y.-Y. Lu
This paper presents a distance measurement method based on pixel number variation ofimages for digital cameras by referencing to two arbitrarily designated points in image frames. Based on an established relationship between the displacement of the camera movement along the photographing direction and the difference in pixel counts between reference points in the images, distance from an object can be calculated via the proposed method. To integrate the measuring functions into digital cameras, circuit design implementing the proposed measuring system in selecting reference points, measuring distance, and displaying measurement results on CCD panel of the digital camera is proposed in this paper. In comparison to pattern recognition or image analysis methods, the proposed measuring approach is simple and straightforward for practical implementation into digital cameras. Experiment results have demonstrated that the proposed method is capable of yielding satisfactory measurement resultsin a very responsive way.
DSP-based fuzzy neural networks and its application in speech recognition
(1999-10-15) S.-C. Chen; C.-C. Hsu; W.-Y. Wang
A fuzzy-neural network needs to be trained through a learning process, so that suitable membership functions and weightings can be obtained. However, most neural networks are only simulated by computer software, which are not practical for real applications. It is therefore our objective to design an integrated circuit system based on a DSP processor with powerful arithmetical capabilities and fast data processing, and relevant peripheral devices to implement the fuzzy neural network. In terms of implementation cost and feasibility for practical applications, this DSP-based fuzzy neural network will be more practical and usable. Finally, a prospective application of the DSP processor-based fuzzy neural network to recognize speech from a non-designated person is proposed
Fuzzy-neural function approximation using a vector evaluation genetic algorithm
(2003-01-01) W.-Y. Wang; C.-C. Hsu; C.-W. Tao; Y.-H. Li
A GA-based indirect adaptive fuzzy-neural controller for uncertain nonlinear systems
(2002-12-06) W.-Y. Wang; C.-C. Hsu; C.-W. Tao; Y.-H. Li
In this paper, a novel approach to adjust both the control points of B-spline membership functions (BMFs) and the weightings of fuzzy-neural networks using a reduced-form genetic algorithm (RGA) is proposed. Chromosomes consisting of both the control points of BMFs and the weightings of fuzzy-neural networks are coded as an adjustable vector with real number components and searched by the RGA. Moreover, we propose an application of the RGA in designing an RGA-based indirect adaptive fuzzy-neural controller (RIAFC) for uncertain nonlinear dynamical systems. The free parameters of the indirect adaptive fuzzy-neural controller can successfully be tuned on-line via the RGA approach. A supervisory controller is incorporated into the RIAFC to stabilize the closed-loop nonlinear system. An example of a nonlinear system controlled by RIAFC are demonstrated to show the effectiveness of the proposed method.
GA-based learning of BMF fuzzy-neural network
(2002-05-17) W.-Y. Wang; T.-T. Lee; C.-C. Hsu; Y.-H. Li
An approach to adjust both control points of B-spline membership functions (BMFs) and weightings of fuzzy-neural networks using a simplified genetic algorithm (SGA) is proposed. The SGA is proposed by using a sequential-search-based crossover point (SSCP) method in which a better crossover point is determined and only the gene at the specified crossover point is crossed as a single point crossover operation. Chromosomes consisting of both the control points of BMFs and the weightings of fuzzy-neural networks are coded as an adjustable vector with real number components and searched by the SGA. Because of the use of the SGA, faster convergence of the evolution process to search for an optimal fuzzy-neural network can be achieved. Nonlinear functions approximated by using the fuzzy-neural networks via the SGA are demonstrated to illustrate the applicability of the proposed method
Genetic algorithms-derived digital integrators and their applications in discretization of continuous systems
(2002-01-01) C.-C. Hsu; W.-Y. Wang; C.-Y. Yu
A set of enhanced digital integrators (EDI) with improved accuracy via genetic algorithms are proposed in this paper. By specifying a desired power for the integrator to be sought and the interval for comparison, chromosomes consisting of parameters of the enhanced digital integrator are then searched by the genetic algorithm based on root mean squared (RMS) error between the original integrator and candidates of the enhanced digital integrator. Thus, all the best parameters of an optimal enhanced digital integrator can be evolutionarily obtained. To demonstrate the effectiveness of the proposed approach, the derived enhanced digital integrators are used to obtain the discrete approximation for continuous systems.
Impact of sampling time on tustin digitization
(ACTA Press, 1996-01-01) C.-H. Wang; W.-Y. Wang; C.-C. Hsu
This paper investigates the impact of sampling time on Tustin digitization. A Q-matrix representation for the digitized system via Tustin transformation is first proposed. It is shown that Tustin transformation is a special case of the higher-order integrator approaches to digitize a continuous system. Pole-variation loci is then introduced to describe the trajectories of poles of the digitized system using Tustin transformation when sampling time is varied from zero to infinity. With new theorems derived in this paper, the pole-variation loci can be easily sketched. Sampling time of any point on the pole-variation loci of the digitized system can be determined by the angle of the vector drawn from the origin to the designated pole location. System dynamics of the digitized system can then be estimated from the sampling time, which determines the pole locations.
A method of distance measurement by digital camera
(2006-11-11) T.-H. Wang; C.-C. Hsu; C.-P. Tsai; M.-C. Lu; W.-Y. Wang; C.-C. Chen
Minimum-phase criteria for sampled systems via symbolic approach
(1996-12-13) C.-H. Wang; W.-Y. Wang; C.-C. Hsu
In this paper, we propose a symbolic approach to determine the sampling-time range which guarantees minimum-phase behaviours for a sampled system with a zero-order hold. By using Maple, a symbolic manipulation package, the symbolic transfer function of the sampled system, which contains sampling time T as an independent variable, can be easily obtained. We then adopt the critical stability constraints to determine the sampling-time range which ensures that the sampled system has only stable zeros. In comparison with existing methods, the approach proposed in this paper has less restrictions on the continuous plant and is very easy to implement in any symbolic manipulation packages. Several examples are illustrated to show the effectiveness of this approach
Minimum-phase criteria for sampled systems via symbolic approach
(Taylor & Francis, 1997-01-01) C.-H. Wang; W.-Y. Wang; C.-C. Hsu
In this paper, we propose a symbolic approach to determine the sampling-time range which guarantees minimum-phase behaviours for a sampled system with a zero-order hold. By using Maple, a symbolic manipulation package, the symbolic transfer function of the sampled system, which contains sampling time T as an independent variable, can be easily obtained. We then adopt the critical stability constraints to determine the sampling-time range which ensures that the sampled system has only stable zeros. In comparison with existing methods, the proposed approach in this note has less restrictions on the continuous plant and is very easy to implement in any symbolic manipulation package. Several examples are illustrated to show the effectiveness of this approach.
Model reduction of discrete interval systems using genetic algorithms
(World Scientific and Engineering Academy and Society (WSEAS), 2005-11-01) C.-C. Hsu; T.-C. Lu; W.-Y. Wang
In this paper, an evolutionary approach is proposed to derive a reduced-order model for discretetime interval systems based on resemblance of discrete sequence energy between the original and reduced systems. With the use of the recursive algebraic algorithm and interval arithmetic manipulations, the problem to identify boundaries of the uncertain coefficients of the reduced-order model can be formulated as an optimization problem, which is subsequently solved by a proposed genetic algorithm. To demonstrate the effectiveness of the proposed approach, system performance of the reduced-order discrete interval model is validated based on time responses in comparison to existing approaches. Because of the time-consuming process that genetic algorithms generally exhibit, particularly the problem nature which demands heavy calculation of the fitness function, a parallel computation scheme is also presented to accelerate the evolution process to derive the reduced-order model.
A Practical Nighttime Vehicle Distance Alarm System
(2008-10-15) M.-C. Lu; C.-P. Tsai; W.-Y. Wang; M.-C. Chen; C.-C. Hsu; Y. Y. Lu
This paper presents a practical nighttime vehicle distance measuring method based on a single CCD image. The method combines an image-based distance measuring system. To solve the nighttime feature extraction problem, the proposed method uses two taillights as the feature. Based on the proportionality of similar triangles, distance between a CCD camera and the taillights of the vehicle ahead can be measured. The method focuses on detecting the taillights and differentiating the targeted vehicle from others on the basis of partial image analysis instead of whole image processing. The system is both fast and simple. The accuracy of the proposed method is demonstrated in this paper through experiences.
Robust adaptive fuzzy-neural control of nonlinear dynamical systems using generalized projection update law and variable structure controller
(IEEE Systems, Man, and Cybernetics Society, 2001-02-01) W.-Y. Wang; Y.-G. Leu; C.-C. Hsu
In this paper, a robust adaptive fuzzy-neural control scheme for nonlinear dynamical systems is proposed to attenuate the effects caused by unmodeled dynamics, disturbance, and modeling errors. A generalized projection update law, which generalizes the projection algorithm modification and the switching-σ adaptive law, is used to tune the adjustable parameters for preventing parameter drift and confining states of the system to the specified regions. Moreover, a variable structure control method is incorporated into the control law so that the derived controller is robust with respect to unmodeled dynamics, disturbances, and modeling errors. To demonstrate the effectiveness of the proposed method, several examples are illustrated in this paper
Three Dimensional Measurement of Distant Objects Based on Laser-Projected CCD Images
(Institution of Engineering and Technology, 2009-05-01) C.-C. Hsu; M.-C. Lu; W.-Y. Wang; Y.-Y. Lu
A novel measuring system based on a single CCD camera and two laser projectors to record images and perform three-dimensional measurement of a distant object is proposed here. Because of the alignment of the laser beams which form in parallel with the optical axis of the CCD camera, projected spots will appear on the same scan line in a CCD image. As a result processing of a single scan line rather than the whole image is only required to identify the projected spots in the CCD image. Complex computation of video signals of the whole image via either pattern recognition or image analysis methods is therefore circumvented. On the basic of an established relationship between the distance and pixel counts between the projected spots in the CCD image, the proposed system not only measures the distance from a distant object but also the length of two arbitrarily designated points on the object. To provide better accuracy, intrinsic parameters of the CCD camera are taken into consideration in the measurement. Furthermore, the effect of laser diffusion is also proved to be irrelevant to the measuring accuracy here. Experimental results have demonstrated that the proposed measuring method is capable of yielding accurate results of three-dimensional measurement for a distant object in a very responsive way.