Recently the demand of air transportation has been remarkably increased. However it is impossible to expand their facilities, because most of airports in Japan are located in the centers of great cities. This situation necessitates a new approach for air traffic control and the evaluation of a plan for the airport facilities, so that air traffic flow at the airport may be streamlined and maintained in order.
From this standpoint, a prototype simulation system for air traffic flow at airports with several runways is developed, using an object-oriented approach (Smalltalk-80). This paper presents a method of air traffic control which gives instructions so as to satisfy the safety constraints on distance and time separations between arriving and/or departing aircraft, taking account of various probabilistic fluctuations. The validity of the proposed system is demonstrated through examples.

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This paper considers the problem of estimating the parameters, of the process transfer function G (z^-1) by using the estimates of the noise autocorrelation function φ_v (l), from input data and output data containing colored measurement noise in the single-input/single-output linear discrete system. Based on the correlation analysis (instrumental variable (IV) method), a three-step estimation procedure is proposed, i. e.1st-step : an initial estimation of a transfer function model using N given by input, 2nd-step : an estimation of φ_v (l), and 3rd-step : a final estimation of parameters from the standpoint of the optimal IV estimation method. In the 2nd-step, the estimation for φ_v (l) is derived from solving simultaneous equations with relation between input/output correlation functions, G (z^-1) and φ_v (l). Extending the input/output correlation information by selecting a model with higher order than the true order in the 1st-step, better estimates of the parameters of G (z^-1) are obtained in the final step. The validity of our estimation procedure is demonstrated by computer simulations.

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While most researches study sampled-data systems from the discrete-time system viewpoint, their actual responses are continuous-time functions so that their performance must be evaluated not only at sampled instants but also during intersample periods. This paper studies a method of ripple-free deadbeat control design for sampled-data systems taking into account the continuous-time transient response. The indicial response is obtained so as to minimize the mean square error performance index.

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This paper aims to construct a framework of evidence theory by normal possibility distributions defined by exponential functions. Since possibility distributions are obtained by an expert knowledge or can be identified by given data, a possibility distribution is regarded as an evidence in this paper. A rule of combination of evidences is given with the same concept as. Dempster's rule. Also, measures of ignorance and fuzziness of an evidence are defined by a normality factor and the area of a possibility distribution, respectively. These definitions are similar to those given by G. Shafer. Next, marginal and conditional possibilities are defined from a joint possibility distribution and it is shown that these definitions are well matched to each other. Thus, the posterior possibility is derived from the prior possibility in the same form as Bayes' formula. This shows the possibility that an information-decision theory can be reconstructed from the viewpoint of possibility distributions. Furthermore, linear systems whose variables are defined by possibility distributions are discussed. Operations of fuzzy vectors by multi-dimensional possibility distributions are well formulated, using the extension principle of L. A. Zadeh. Last, some comment on an application of possibility distributions is given in a discriminant analysis using fuzzy if-then rules.

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In this paper, we consider a flexible beam having a rigid tip body, of which the mass center lies on the centroidal axis of the beam. In this case the bending vibration and the torsional vibration of the beam are decoupled. Therefore we need two control motors to suppress the vibrations. Each set of dynamic equations is derived in the form of evolution equation in an appropriate Hilbert space. A stabilizing feedback control law of each rotation motor will be established on the basis of modal analysis. Experimental method is discussed in detail, and the results demonstrate the validity of the dynamic model and the proposed control law.

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