Robust control of intelligent robots by parametrized state feedback methods

Intelligent robots must be able to be faced to many new difficult tasks. This implies the use of many parameters in the control laws. We show that if these parameters are slowly varying before the dynamics of the grand motion axes, the classical decoupling methods remain valid with parametrized laws, but if parameters dynamic are non negligible before the decoupling dynamic, we are in the fast varying parameters case, and we show in this case that decoupling methods operators must be changed in order to include these dynamical effects, which lead by using the classical decoupling methods, to non-efficient control. The computation of the static and dynamic feedback laws are given in the case of nonlinear decoupling, linearization and rejection of perturbations. A robust example in robotics is given, and the contribution of the parameters dynamic is shown. We show also that this kind of robust robot model can be treated by differential flatness methods. An N link robot manipulator is considered in this way and the same operators appear. Finally, an application to the pseudo differential flatness of these models are presented by a closed loop approach.