Tendon driven robots can contact with environment softly at any time by grace of the intrinsic elasticity of actuators. Meanwhile it is difficult to manipulate them accurately due to their own flexibilities. In the biologic systems, the muscles are controlled by the spinal cord and cerebellum at the lowest level. Due to those control systems, we can manipulate our limbs as intended. Thus, this paper attempts to develop a new trajectory tracking controller for the tendon driven robot by integrating the computational models of the spinal cord and cerebellum in the same manner as the biological system. The proposed controller consists of the reflex control inputs composed of the delayed force feedback and PD position error feedback, and the adaptive controller which estimates and eliminates the excess reflex component of reflexes caused by the inertial force. The asymptotic stability of the controlled system is proven along the Liapunov-Krasovskii stability theorem. The effectiveness of the proposed controller and the dependence of the control performance on delays are examined through some computer simulations.
In this paper, we propose a generation-based funding scheme of the public pension system in order to cope with the intergenerational inequity and financial failure. We simulate the variation of contribution-benefit ratio and government contribution in order to analyze the intergenerational equity and financial sustainability. It is shown that the generation-based funding scheme could keep the fixed contribution-benefit ratio and the same government contribution as today or less.
This paper proposes an independent component analysis method using state-space models. Firstly, a learning algorithm to estimate the parameters of the output function of the model is derived based on the property that the probability density function of the output of the model only depends on that of the input and the direct feedthrough term of the model. Secondly, the parameters of the dynamic equation are estimated by the information backpropagation method. Thirdly, since many systems such as mechanical systems do not have any direct feedthrough term, we extend the proposed algorithm to systems without direct feedthrough terms. Furthermore, a numerical simulation demonstrates the effectiveness of the proposed method.