Gravity Compensation for Manipulator Control by Neural Networks with Partially Preorganized Structure
Toshio Tsuji, Masataka Nishida, Toshiaki Takahashi and Koji Ito
Faculty of Engineering Hiroshima University, Higashi-Hiroshima-shi, 724, Japan
The gravity torque of a manipulator can be compensated if the equation of motion can be correctly introduced, but in general industrial manipulators, there are many cases when the parameter values such as the position of center of mass are not clear, and these values largely change by the exchange of hand portions and the grasping of substances. Furthermore, in addition to unclear parameters, there are factors which occur by structural gravity compensation (spring and counter-balance) and which in many cases are difficult to express with the equation of motion. In this paper, compensation of the gravity torque of the manipulator is studied by, the use of neural networks. For this purpose, a model which makes the structure known to be contained in mapping as a unit with preorganized characteristics prepared in parallel with hidden unit of error back propagation-type neural network is proposed, by which the characteristics of the link system which is the object for learning can be imbedded into the network as preorganized knowledge beforehand. Finally, the results of experiments done with the use of industrial manipulators are given, and it is made clear that the compensation of gravity torque of manipulator and adaptive learning for end-point load are possible by the use of this model.