Structural network modeling and control of rigid body robots

In this paper, two new parametric network models of robots are presented based on the systems' own functions. The complete dynamics of robots are given in a ready-to-used format which is constructed by finite dimensional static parametric networks for the inertia matrix and the potential energy (or the gravitational forces). As a result, dynamic models of robots can be automatically generated by software once given the number of degrees of freedom (DOF) and the sequence of the joint types, without knowing other parameters such as the lengths and the twist angles of the links. An existing adaptive controller is used as an example to show that some of the controllers can be easily modified such that adaptive controllers can be automatically generated. It is shown that all the closed-loop signals are bounded and tracking error goes to zero.