Developments in learning control enhancements for nonlinear servomechanisms

Abstract

Two learning control enhancement schemes applicable to nonlinear dynamical systems of the type commonly encountered in many practical servomechanisms are developed. The key difference between the two schemes lies in the fact that the first scheme utilizes previous cycle errors in the learning strategy while the second utilizes present cycle errors. The uniqueness of these learning controllers is that, in contrast to existing learning methods, they make full use of available a priori information on nominal models of the system and, as a result, overcome the problem of large swings in the control effort during the learning process, which is a major problem with conventional learning controllers. Properties of the schemes are discussed, and it is shown that the strategies developed assure iterative improvement in repetitive operation and asymptotic tracking of the reference signal. The performance and effectiveness of the proposed schemes are then investigated and compared through simulation. © 1995.