Kazumasa Ohsaka

Evaluation of an in-motion vehicle weighing method via Grey estimation model

A novel weighing method for in-motion vehicles is set up in this paper. The measurement principle is described and an estimation of measurement uncertainty using Grey Error Theory is presented. Theoretical analysis and experimental research are given to show that the method could be used to solve the weighing problem for in-motion vehicles with higher accuracy.

Identification of the Nonlinearity of Robot Manipulators by a Neural Network.

In order to ensure the high-speed and high-precision control of trajectory tracking for robot manipulators, it is necessary to construct a precise dynamic model by which the manipulator is controlled. In the motion equation of the manipulator, the driving force is a very important term from the standpoint of control. We assume the nonlinearities of the manipulator and driving system are expressed as nonlinear functions between the driving force and the control input of the driving motors. This paper proposes an identification method for the nonlinearities of a robot manipulator by means of a neural network. It was experimentally confirmed that the proposed method is effective in the case of trajectory tracking control for a 2-link manipulator.