We have already proposed a nonlinear feedback control of induction motor drives, where the induction motor is modeled as a bilinear system. This control method guarantees the robust stability in the large. Whereas it is not guaranteed by the so-called vector control method. This paper gives some experimental results obtained by applying the proposed method to a practical induction motor which is fed by a three-phase twelve-pulse bridge cycloconverter. The proposed method is implemented by a digital signal processor which is a special microprocessor designed for the high speed signal processing. The performance of the proposed method is verified by these experimental results. These results also show that the proposed method is superior, at least for robust stability against the secondary resistance variation from the nominal value, to the vector control method. At the last section, this paper also gives some remarks on applications of this control method to practical systems.
In this paper an approximation method for discrete-time linear systems with the internal structure is proposed. For this, the method of Mullis-Roberts is used. A reduced order model constructed through this method retains the same internal structure as the original one. It is also an optimal approximation in the sense that it minimizes the curvilinear integral of the squared norm of an approximation error along the unit circle of z-plane. It is shown that the Levinson-Wiggins-Robinson algorithm can be used as a fast recursive algorithm computing the reduced order model. A feature of this method is that we can compute reduced order ARMA-type models of each subsystem independently. Thus, subsystems to be reduced are arbitary chosen.This approximation method can also be used for identifying each subsystem composing the total system.
The modality constrained programming (MCP) problems were previously proposed by the authors. MCP problems are a sort of fuzzy mathematical programming (FMP) problems and have some analogy with the chance constrained programming (CCP) problems. In MCP problems, the probability in CCP problems is replaced by the possibility or the necessity. In this paper, we focus on the relationships between MCP problems and the other various FMP problems, i.e., flexible programming problem, fuzzy goal programming problem, FMP problem of Orlovsky, that of Tanaka et al., that of Inuiguchi et al. and that of Dubois. FMP problems discussed in this paper can be interpreted in the framework of MCP problems. MCP problems are the basic models for unifying FMP problems.
The performance of a personal computer has improved very much and also the price has become low. Accordingly, the opportunity that amateur or physically handicapped persons utilize computers is increasing. But the typing from a key board has so far been a main input method to a computer. This method is annoying for these people. We developed a voice input system to a computer. In this system we are using a voice recognition device on the market. The device is a word recognition system for a special speaker. There are many cases when voice recognition fails among similar utterance words. We propose the improvement method of recognition score that utilizes a simple learning system. This learning system is evaluated by voice recognition experiments. From experimental results, we have been able to confirm that this learning system has an improved effect in voice recognition.