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Volume 33, Issue 11
Displaying 1-12 of 12 articles from this issue
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Naohiro UENO, Makoto KANEKO1997 Volume 33 Issue 11 Pages 1053-1058
Published: November 30, 1997
Released on J-STAGE: March 27, 2009
JOURNAL FREE ACCESSThis paper proposes the Self-excited Dynamic Active Antenna (SDAA) that can detect a contact location between an insensitive flexible beam and an object through observation of the fundamental natural frequency of the beam in contact with the object. The main part of SDAA is composed of a flexible beam, one permanent magnet and two electromagnets. The permanent magnet is attached to a part of the beam and two electro magnets are positioned at both side of the permanent magnet with a small gap. These magnets contribute to not only exciting the motion but also changing the boundary condition of the beam. We analyze the rerationship between the displacement of the permanent magnet and the induced force, and show the linear spring-like behavior of the magnetic force. We also show that the contact position is uniquely determined by utilizing two fundamental natural frequencies before and after changing the boundary condition, whereas both the fundamental and the second natural frequencies are necessary for localizing the contact point under the conventional type of Dynamic Active Antenna. We presents the basic working principle of SDAA and show some experimental result to verify the idea.View full abstractDownload PDF (2198K) -
Haruhisa KAWASAKI, Toshimi SHIMIZU1997 Volume 33 Issue 11 Pages 1059-1065
Published: November 30, 1997
Released on J-STAGE: March 27, 2009
JOURNAL FREE ACCESSThis paper presents a symbolic analysis of the base parameters for robots performing kinematic constrained task and/or having closed-chain mechanism by means of the completion procedure in polynomial ideal theory. The regresser of the robot dynamics is represented as a matrix of multivariate polynomials and reduced to the “normal form” besed on Buchbergers' algorithm by constructing reduced Grobner bases from kinematic constrained equations. The linear independence of column vectors of the reduced regresser is examined by the Gauss-Jordan elimination method. The original dynamic parameters are regrouped and some of them are eliminated based on the results. The feature of this method is that kinematic constrained equations are not needed to be solved explicitly. This method derives all the base parameters systematically in theory. Two examples are presented to illustrate the merits of the new method.View full abstractDownload PDF (1269K) -
Kentaro HIRATA, Yutaka YAMAMOTO, Allen TANNENBAUM, Tohru KATAYAMA1997 Volume 33 Issue 11 Pages 1066-1071
Published: November 30, 1997
Released on J-STAGE: March 27, 2009
JOURNAL FREE ACCESSThis paper derives a rank type formula for the computation of the singular value equation arising in the H∞ problem with infinite-dimensional unstable plants. It is based on an extension of the skew Toeplitz approach for the stable plant case. Similarly to the stable plant case, parametric representations of the singular value equation plays an essential role. An example is given to illustrate the result.View full abstractDownload PDF (1438K) -
Shigenori OKUBO1997 Volume 33 Issue 11 Pages 1072-1080
Published: November 30, 1997
Released on J-STAGE: March 27, 2009
JOURNAL FREE ACCESSIn the past time several designs of nonlinear regulator were proposed. Infinite expansion of state series was used in these methods, so convergence of the infinite series was difficult problem. This paper shows the rigorous design of nonlinear regulator using genetic algorithms. When the controlled object has (2N-1) order of state, and peformance function has quadratic form of power state vector of which polynomial order is (2N-1), the optimal control law can be obtained as the feedback of (2N-1) order power state vector. The coefficent matrix of feedback is constracted by a solution of an extended Riccati equation. An extended Riccati equation can not be solved mathematically because the number of variable is less than the number of equation. Genetic algorithms are used in searching method of the solution of an extended Riccati equation. Using the solution of an extended Riccati equatiuon, a Lyapunov function of quadratic form of N order power state vector can be constracted, so a nonlinear regulator which has global stability can be obtained.View full abstractDownload PDF (1460K) -
Shigeyuki TAKAHARA, Sadaaki MIYAMOTO1997 Volume 33 Issue 11 Pages 1081-1086
Published: November 30, 1997
Released on J-STAGE: March 27, 2009
JOURNAL FREE ACCESSA problem of optimizing locations of many rectangular pieces for efficiently utilizing a sheet is considered. The object is to minimize the amount of waste. This problem which is called the nesting problem is important in industrial applications, but it's NP hard. This problem is considered in two stages of the ordering the pieces and the selecting locations of the pieces using the order. In this paper, we propose a new greedy algorithm, called ridgeline method for selecting the optimal locations in the second stage. The order of the pieces strongly affects the performance of the method. Therefore, methods of meta-heuristics, i.e., local search, simulated annealing, tabu search and genetic algorithms, to optimize the order in the first stage are considered. Two options in genetic algorithms are used. These methods of meta heuristics as well as random ordering are compared using simulations experiments. The result shows that the simulated annealing is better than the other meta-heuristics.View full abstractDownload PDF (1663K) -
Masaki ARISAWA, Junzo WATADA1997 Volume 33 Issue 11 Pages 1087-1092
Published: November 30, 1997
Released on J-STAGE: March 27, 2009
JOURNAL FREE ACCESSIt is necessary to determine the size of a hierarchical neural network before we make the neural network learn some data. Moreover, the structure of a network has a large influence on the basic abilities to realize mapping between input and output and produce the generalization power. Generally, a network structure is determined by trial and error and on the basis of the experience and knowledge of the designers.
In this paper, we propose a Structural Learning Algorithm for a network which begins and carries out learning from a bigger network, which reduces redundant links according to fuzzy reasoning, and removes units based on their contribution. Stress is placed on the following points: It should not be necessary to determine the detailed structure of the network in advance, the proposed algorithm can reduce redundant links and units in the learning process, it enables us to build as compact a structure as possible, and also enables mapping between input and output. Furthermore, the proposed method can reduce the learning time which has been considered an unavoidable cost in the construction of a neural network, and sufficiently decrease its average of squared errors.View full abstractDownload PDF (1692K) -
Ken KITAGAWA, Noriyasu HONMA, Kenichi ABE1997 Volume 33 Issue 11 Pages 1093-1098
Published: November 30, 1997
Released on J-STAGE: March 27, 2009
JOURNAL FREE ACCESSWe propose a learning method for recurrent neural networks with dynamics. The core of this method is to keep a complexity of the network dynamics in the vicinity of the edge of chaos. To investigate the properties of the dynamics effectively and explicitly, novel stochastic parameters defined by combinations of the standard parameters such as individual connection strengths and thresholds are introduced, and then relations between complexities of the dynamics and the stochastic parameters are revealed. The standard parameters are changed by the core part based on the relations and also according to the global error measure. Some examples suggest that the method is practical one for temporal supervised learning tasks and therefore the dynamics of the edge of chaos are effectual for learning of the recurrent networks.View full abstractDownload PDF (1603K) -
Eiichi HORIUCHI1997 Volume 33 Issue 11 Pages 1099-1104
Published: November 30, 1997
Released on J-STAGE: March 27, 2009
JOURNAL FREE ACCESSThe introduction of robots to innovative tasks requires discovering skills, or knowledge which leads us to the success of the target tasks. This report proposes a general framework for supporting the skill discovery of robotic tasks by human operators; the presented framework expects human operators to complement where computers fail. Diminishing the role of human operator in the current framework provides a perspective toward complete automatization of the skill discovery process.
We define skill as a continuous function from input space with sensory information and time variables to output space with actuator control information. We approximate skill functions with polynomial expressions and reduce the skill discovery to a combinatorial optimization problem among the coefficients of monomials in the expressions. A genetic algorithm is applied to the optimization because of its all-around applicability and its robustness against local optima. The current framework relies on human experts for the strategies of making efficient search, such as partitioning search space into smaller ones and designing better fitness for GA. The procedure to extract basic mechanism explicitly out of the acquired robot behavior is called skill understanding. Our framework enables this procedure because the skill represented by polynomials is partitioned into monomials and which monomials dominate control information can be analyzed by human operators. In order to verify the validity of the proposed framework, two examples are examined; a visual servo problem of catching a flying ball by a robot baseball player and a problem to find open-loop control of a mechanical system with nonholonomic constraints. The first example illustrates the skill understanding procedure and the second one exhibits a unified manner to solve robotic problems with nonholonomic constraints.View full abstractDownload PDF (1668K) -
Tielong SHEN, Katsutoshi TAMURA1997 Volume 33 Issue 11 Pages 1105-1107
Published: November 30, 1997
Released on J-STAGE: March 27, 2009
JOURNAL FREE ACCESSAn uncertain nonlinear system has robust H∞ performance if there exists a positive function satisfying Hamilton-Jacobi inequality with the uncertain perturbated functions. It has been shown that a sufficient condition for the existenc of such positive function is as follows: an Hamilton-Jacobi inequality with a scaling function has positive definite solution. This paper shows the necessity of the condition.View full abstractDownload PDF (473K) -
Jiro MORIMOTO, Yoshikazu YAMAMOTO, Ikunori KOBAYASHI, Nanayo FURUMOTO, ...1997 Volume 33 Issue 11 Pages 1108-1110
Published: November 30, 1997
Released on J-STAGE: March 27, 2009
JOURNAL FREE ACCESSKalman filter based parameter tracking algorithm for linear regression models is developed. Main subject is to present a selection method of design parameter in the algorithm. It is shown that the algorithm proposed is asymptotically optimal in some sense.View full abstractDownload PDF (416K) -
Convex Analysis Approach Using Linear Matrix InequalitiesSeigo SASAKI, Kenko UCHIDA1997 Volume 33 Issue 11 Pages 1111-1113
Published: November 30, 1997
Released on J-STAGE: March 27, 2009
JOURNAL FREE ACCESSWe show a synthesis method of nonlinear state feedback control for input-affine polynomial-type nonlinear systems. The method consists of having this problem result in convex programming and solving linear matrix inequalities. To demonstrate efficiency of this method, we also show acomputer simulation for abilinear model of a continuous stirred tank reactor.View full abstractDownload PDF (465K) -
Deyu LI, Hiroshi SHIBATA, Toru FUJINAKA1997 Volume 33 Issue 11 Pages 1114-1116
Published: November 30, 1997
Released on J-STAGE: March 27, 2009
JOURNAL FREE ACCESSThis paper proposes a method which makes a discrete-time system ASPR by the insertion of supplementary dynamics (i) in feedback with the plant; (ii) in parallel with the plant; (iii) in cascade with the plant, respectively.View full abstractDownload PDF (286K)
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