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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
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
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
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.
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.
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.

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