A velocity and position measurement system based on the method of convolution integral with spatial weighting functions is proposed. The proposed method is very useful to give many kinds of spatial filtering characteristics to a TV camera in parallel, because arbitrary spatial filtering characteristics can be realized by simple electronic circuits. In the proposed measuring system, one TV camera works as three pairs of sinusoidal spatial filters, the periods of which are different from each other. From the geometric relation between two outputs of one pair of sinusoidal spatial filters, a velocity is obtained by a simple argorithm, and a position is obtained from the difference between the outputs of two pairs of spatial filters. All of the spatial filters are made of the digital circuits which include six DSPs, while the two signal processing circuits to calculate the velocity and position are made by the analogue circuits. In order to realize the real time measurement, all of the signal processing which must be done in spatial domain are substituted by the signal processing done in time domain. As the results of some experimental confirmations, it became clear that the proposed measuring system is accurate enough for practice and has wide applications.

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This paper is concerned with a method for separately estimating line and continuous spectra of signals which are composed of multiple sinusoids and an autoregressive (AR) noise. The whitening filter approach is proposed in which the frequency estimates are obtained by applying the Toeplitz approximation method to the output of the whitening filter. The filter coefficients which correspond to the AR parameters are iteratively calculated so as to minimize the squared prediction error criterion, however, the solution tends to be a local minimum. By employing the genetic algorithm in the choice of initial values of the iterative AR parameter estimation, we can attain a globally optimal solution. The decision rule for deciding the number of sinusoids and the order of the AR model is also investigated. The validity of the proposed algorithm is examined in numerical simulations including a benchmark example. Finally we give an extension to a case where the continuous spectrum is expressed by an ARMA model.

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In general, since voltage in transmission lines is so high that the wave form need to be measured with static induction type of voltage sensor, which comprises a pair of electrodes, there should exist a problem on the discrepancy between the actual voltage wave form and the measured one in three phase power distribution systems owing to the mutual electric induction between each other phase.
We shall present the numerical electric field analysis of the sensors in such a power distribution system using two dimension finite element method taking account of the unknown equi-potentials and the complex permittivity to clarify the difference between the impressed voltage wave form across the electrodes of such a sensor and the actual voltage wave form.

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This paper describes the development of an anthropomorphic visual sensor with retina-like structure to perform the polar mapping. The sensor consists of a dove prism for image rotation and a linear CCD sensor with 512 pixel/line resolution, and holds approximately 45 Kbytes of image data. The retina-like sensor has variable resolution with increasing density towards the center of the visual field, and performs the polar transforming directly. The motion equation that relates the egomotion and/or the motion of the object in the scene to the optical flow is considerably simplified if the velocity is represented in a polar coordinate system, as opposed to a cartesian representation. Development of this sensor holds promise in application to high speed tracking systems, such as the eyes of navigation robots, because it has a data reduction characteristics and mapping function onto the polar plane.

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Robust stability and performance analysis are reduced to the computation of a structured singular value problem. However its computation is a difficult task and forms a bottleneck of the analysis because it is based on the numerical optimization. We show that this computational burden can be reduced in the case where the matrix under consideration is symmetric. In this case, the computation is equivalent to the maximization of spectral radius over (S+w-1)-parameters, where S is the number of the repeated scalar blocks and w is the total size of full blocks.

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For linear time-invariant systems, Falb and Wolovich first formulated the diagonal decoupling problem via output feedback. Since then, this problem has been extensively studied and many solvability conditions have been obtained.
Recently, for linear periodic discrete time systems, the authors formulated the blockk decoupling problem via periodic state feedback and gave a necessary and sufficient solvability condition by using “Geometric Approach”
This paper extends this result to an incomplete state feedback case, that is, when the measurement does not coincide with the controlled output.
Necessary and sufficient conditions such that the systems can be block decoupled via incomplete state feedback are derived.
Furthermore for the output feedback case, the structure of the block decoupled system and stabilization by sampled output periodic control are also considerd.

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Many stability proofs have been already presented both for continuous and discrete time model reference adaptive control (MRAC) systems. However, these are all for the direct MRAC scheme where the controller-parameters are directly adjutsted by the adaptive law, except for the one by Sastry and Bodson. This paper presents a simple stability proof for the continuous time indirect MRAC scheme where the controller-parameters are determined from the plant-parameter estimates via design calculation. In this method, the system is analyzed by using the relation between control error and identification error and the properties guaranteed by the adaptive law, and then the stability is proved by applying Gronwall's lemma as in the ones by Morse and Goodwin et al.. The proposed method is easier to understand than the one by Sastry and Bodson based on the regularity of the signals.

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In this paper, we propose a robust nonlinear control scheme for the tracking problem of rigid robot manipulator. In the design of this control scheme, Lyapunov stability theory and some important properties of rigid manipulator are used. The closed-loop system with the presented control scheme is globally exponentially stable, and the output tracking error that is shaped by a formula asymptotically converges to zero. Based on this formula, a desired transient error performance may be achieved by choosing appropriate control parameters. Some simulation results are provided for illustrating the performance of this contorl scheme.

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The authors proposed the application of fuzzy reasoning to the steering control of automated guided vehicles (AGVs) and achieved the stable control results. Furthermore, we proposed the steering control of AGVs by neural network using the teaching signal of the fuzzy control results. Since it is necessary in this control method to obtain the stable control results by other control methods in advance, it is not practical.
In this paper, a steering control strategy of AGVs is proposed with the successive learning neural network using the auto-tuning function (AT function). The coefficients of the proposed AT function and the initial values of the teaching signal are discussed by the computer simulation. The right and left turning experiments using the AGV built as a trial are implemented and the validity of the AT function is discussed. The excellent traveling control of the AGV is obtained in some trials. Then, the learning of the neural network is almost terminated and after then the excellent traveling lasts. Thus, the proposed AT function is proved to be very available for the successive learning of neural network.

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In order to control a nonlinear dynamic system with unknown characteristics, the use of learning elements, i. e. multi-layer neural networks, has been studied.
A method that uses an acquired inverse model of the target system by learning is used by many researchers. However the conventional methods for learning an inverse model of the target system have many drawbacks. A method that uses an acquired forward model of the target system and calculates the input of the target system by using iterative methods, i. e. Newton's method, was proposed. However since iterative methods use only local information, they sometimes converge to local optimal solutions and can not calculate the precise solution for controlling the target system.
In order to solve a static system concerning inverse problems, we proposed a hybrid system that consists of an inverse model and the feedback system extended for the nonlinearity of the target system. The extended feedback system conducts a kind of iterative method from an enough number of initial values generated by random searches until a correct solution is obtained. Usually one of the correct solutions can be obtained by this procedure. However this configuration of the extended feedback system can not be applied for a dynamic system.
By using the acquired forward model of the target system instead of the target system, the proposed extended feedback system can control a nonlinear dynamic system.
The extended feedback system usually takes computation time. As the inverse model becomes accurate by learning, the required number of iterations for calculating the control value decreases and the computation time also decreases. The performance of the proposed system gets better by learning.
The performance of the proposed method is shown by numerical simulations.

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In this paper we propose a discrete version of tunneling algorithm and apply it to a placement problem of VLSI layout. Although tunneling algorithm is powerful to obain a near global minimum of multimodal function optimization, it is applicable only to continuous optimization problems. We incorporate a heuristic search method into conventional tunneling algorithm so that tunneling algorithm can deal with discrete optimization problems such as VLSI placement and routing problems. We also introduce a hierarchical placement model in order to improve the search efficiency of tunneling algorithm and to reduce its computation time. Computational experiments show that proposed hierarchical method obtains better placement results with shorter running time than non-hierarchical method.

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This paper reports the initial experiments with an image recognition system for large scale drawings. The recognition process is based on interactions between two levels of modules, that is, when the lower modules extract local features, it informs the upper module as a hypothesis, the upper module then controls the lower modules in order to extract evidential features. The upper module also integrates the features into a geometric data, computes more global features, and identifies objects drawn in images. This style of processing avoids some problems created by sequential style of processing e. g. mistakes at any part of sequence cause a fatal mischief to the result. The lower module is an application of neural networks. It requires no pre-processing and therefore it is applicable to large scale drawing, in addition it extracts various types of features by the same architecture. The upper module consists of production system with truth maintenance mechanism by which misunderstandings are revised, in a top-down manner, by global consistency. This paper describes the techniques used, and reports the performance of an experimental system.

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A comfortable man-machine interfaces are required for current advanced systems. Keyboard switches as one of the most frequently used man-machine interfaces should provide comfortable feeling for operation. This paper aims at investigating the relation between reaction force of keyboard switches and human switch operation feeling.
We investigate how each reaction force of switches effects to the touch feeling of keyboard switches via sensory test. We made a switch that is assisted by a servo mechanism and can generate arbitrary reaction force characteristics for the sensory test.
The results of factor analysis for the questionnaire data obtained in the sensory test show that the most substantial factor is the feeling expressed by the words “Clear”, “Smooth”, “Stiff”, and “Clicking”.
Further quantitative relations between the reaction force and feelings are obtained by regression line for the questionnaire data. This quantitative relation provides a sensory guideline for keyboard switch design.
Finally, each feeling described by each word is directly evaluated by reaction forces or vice versa on dual scales. The dual scale for reaction forces and degree of feeling can be directly used for designing comfortable keyboard switch.

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The status selection planning system, which has been proposed by our group, is one of planning expert systems. The system uses two kinds of knowledge to solve a problem, that is, dispatching rules and a set of status selection rules. Dispatching rules mean fragmentary and convenient assignment algorithms. In this system, the most promising status from tentative statuses generated by applying the dispatching rules is selected by a set of status selection rules.
As the dispatching rules and the status selection knowledge are independent each other, it is easy to change the knowledge. Although the quality of the solution depends on the knowledge-base, it is usually difficult to acquire useful knowledge from human experts.
In this paper, we present a method of learning the status selection knowledge for flow shop problems. Using C 4.5 algorithm, which has its origin in ID 3, the status selection knowledge is created and formed into a decision tree. Training data are made from the path to the optimal solution. From the result of the simulation of the proposed method, we have confirmed that the learning method is so effective.

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In this paper, we propose a new elevator group control method during morning rush-hours. This method determines the number of cars which are sent to the main-floor during this period using a new logic and indexes. These logic and indexes are acquired through some simulations, in which cage movements under the rush-hour control are analyzed minutely. The effectiveness of the proposed method is verified by simulations using data of a real building.

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This paper presents a model matching method for finite Volterra series systems. The proposed method is an extension of the exact model matching (EMM) for linear systems to that for finite Volterra series systems. The effectiveness of this method is illustrated through a numerical example.

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In this paper, a simple method to design a full-order observer for linear descriptor systems with unknown inputs. The sufficient conditions for the existence of the observer are given under the assumption of observability in the sense of Rosenbrock. The realizabity conditions are nonrestrictive and checked practically for the system's parameters.

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For the production system of cars, the usefulness of a laser displacement meter is verified with experiment results. The obtained results show the posibilities to measure distance to an iron object and detect its edge.

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