Model Reference Learning Control for Rigid Robots

Abstract

Abstract The equations of motion of a rigid robot are often known only approximately, as some of the parameters are not known exactly and there are also unmodelled nonlinearities. Most adaptive control schemes can estimate the parameters if the structure of the equations is known, but are not very useful if structure itself is not known. In this paper we propose a model reference learning control scheme using Adaptive Network based Fuzzy Inference System (ANFIS) for control of rigid robots whose model may have parametric and structural uncertainties. The approximate model of a robot, which may differ very significantly from the actual robot in parametric values and structure, is used as a reference plant and a nonlinear model based controller is designed based on this model. The ANFIS corrector provides an additional correction to control input as a function of the present and desired states of the plant. The error between states of plant and that of reference plant is used to tune the ANFIS corrector. The proposed control scheme has been implemented for a two-degree-of-freedom serial rigid robot. The results of the simulation experiments carried out show that the proposed control scheme can learn to control the unmodelled dynamics. The ANFIS controller is shown to give improved performance for parameter as well as structural uncertainties.