A novel dual-error approach to system identification

Abstract

Single error system identification techniques are widely used to estimate the parameters of dynamic mathematical models that are needed in a range of industrial applications. A novel Dual-Error system identification technique is proposed. It is based on a modification of the traditional single-error methods and shown to offer better accuracy for the estimation of model parameters. The benefits of the proposed method are demonstrated by a comparison with traditional methods when applied to both a simulated system and a DC motor.