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システム/制御/情報 Vol. 23 (2010), No. 9

ISIJ International
belloff
オンライン版ISSN: 2185-811X
冊子版ISSN: 1342-5668
発行機関: THE INSTITUTE OF SYSTEMS, CONTROL AND INFORMATION ENGINEERS (ISCIE)

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システム/制御/情報 Vol. 23 (2010), No. 9

運動エネルギーの観点から見た2周期歩容の性能と最適性

浅野 文彦

pp. 197-206

抄録

This paper investigates the efficiency of a 2-period limit-cycle gait from the kinetic energy viewpoint. First, we formulate a steady 2-period gait by using simple recurrence formulas for the kinetic energy of an asymmetric rimless wheel. Second, we theoretically show that, in the case that the mean value of the hip angle is constant, the generated 2-period steady gait is less efficient than a 1-period symmetric one in terms of kinetic energy. Furthermore, we show that the symmetric gait is not always optimal from another viewpoint. Finally, we investigate the validity of the derived theory through numerical simulations of virtual passive dynamic walking using a compass-like biped robot.

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運動エネルギーの観点から見た2周期歩容の性能と最適性

アファイン拘束を受ける非ホロノミックハミルトニアンシステムに対する一般化正準変換と受動性に基づく制御

甲斐 健也

pp. 207-214

抄録

This paper is devoted to control of nonholonomic Hamiltonian systems with affine constraints (NHSAC). We first sum up some concepts on nonholonomic affine constraints and the HNSAC. Next, we derive two versions of generalized canonical transformations for the NHSAC; the one transforms the NHSAC into a simpler system that has the structure of the extended chained form, the other changes the Hamiltonian of the NHSAC in order to make the NHSAC satisfy passivity. We then design a control algorithm based on passivity for a physical example, a coin on a rotating table. Finally, some simulations are carried out to verify the proposed method.

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アファイン拘束を受ける非ホロノミックハミルトニアンシステムに対する一般化正準変換と受動性に基づく制御

一般化包絡分析法を用いた多目的PSO法

尹 禮分, 中山 弘隆

pp. 215-222

抄録

Several meta-heuristic methods such as genetic algorithms and particle swarm optimization (PSO) have been applied for solving multi-objective optimization problems, and have been observed to be useful for generating the whole Pareto optimal solutions. In this research, we propose a new method of multi-objective particle swarm optimization by using generalized data envelopment analysis (GDEA) in order to improve the convergence and the diversity when searching for the solutions as well as to decide easily parameters in PSO. In addition, we investigate the effectiveness of the proposed PSO method using GDEA through some numerical examples.

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一般化包絡分析法を用いた多目的PSO法

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