Adaptive control of unstable systems based on Kautz expanded inverse model

In this work a method for discrete-time indirect adaptive control of linear unstable minimum phase systems using plant inverse modeling is presented. The resulting adaptive algorithm is proved to be asymptotic stabilizing. The main advantage of the method is that the adaptive control start-up phase does not require any main prior plant knowledge other than their Schur stability. The method can reach high degrees of control. Performance due to the controller design based on minimization of regulation error energy.