Nonlinear adaptive tracking and stabilization of a robot manipulator

Control of a robot manipulator requires solution of ordinary, coupled nonlinear differential equations. In Sinha, Kayalar, and Yurtseven (1990), a model reference system was introduced by ignoring the Coriolis terms where the system of nonlinear equations were almost decoupled by careful selection of nonlinear input torque expression. Varying a single parameter provided a stable solution about a known equilibrium point. In this work, the authors propose to extend the model reference system of Seraji (1988) to the case where there is parameter uncertainty and noise due to model imperfections. It is shown that a linear state feedback type of input torque provides a stable solution under uncertainty. A new theoretical method of stabilization of robot manipulator dynamics by nonlinear feedback is given. A partial linearization of robot arm control may yield a simpler nonlinear feedback control which has not yet been considered. The results obtained apply to a large class of nonlinear control systems