Coupled human balancing tasks are investigated based on both pscud ( ト neural controllers of time −delεし yed feedback controllers with random gaill and natural human balancing tasks ． It is shown numericany that being compared with single b 副 ancing tasks ， the bala皿 cing tasks coupled by mcchanical structures increasc stability of bala」 lcing crrors both in amplitudes and vGlocities and also improves tracking ability of the contrQllers ． We then perfc ) rm 跏 experimcnt 呈 n whlch the numerical pscudGneural control ｝ ers are replaced wlth natural human balancing tasks through the use of computer mouse devices ． The result shows that thG coupling structure yields asymmQtric tracking abilities betwcen the subj ts whose trad(illg abilities are nearly symmctric in their single balancing tasks．

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In this paper, we experimentally study cooperative human balancing tasks performed by a pair of a human subject and an artificial controller, based on the coupled inverted pendula (CIP) model. In order to examine what kind of influence does the feedback gain of the controller on dynamic stabilities of the cooperative balancing tasks, we experimentally estimate Lyapunov exponents of balancing errors of the system of human subject and artificial controller, in which the human subject is in cooperation with the artificial controller having several different feedback gains. The result implies that the human subject seems to try to make the artificial controller minimally or neutrally stable.

In this paper ， we investigate the relationship between sense and manipulation in coupled hurnan balar ： cing tasks． For this purpose ， we proPOse a measurem 臼 nt syStem to obtain the balancing error product by two

In this paper, we investigate human generated bang-bang inputs that can switch equilibriums of nonlinear dynamical systems. For this purpose, we propose an experimental system in which the human can generate the bang-bang input to switch the four equilibriums of a coupled inverted pendulum model. We numerically obtain reachable sets of given equilibriums to characterize the switching time of the bang-bang input to reach the equilibrium. Then, we perform an experiment in which human subjects manually optimize the bang-bang control inputs. The result shows that the motion strategies generated by the subjects become improved as the number of trial increases and that such optimization is not unique and can be classified into certain categories. 1.( )Smeesters (6)Inoue ( ( 321-8585 7-1-2)