Least-squares parameter estimation algorithm for a class of input nonlinear systems

This paper studies least-squares parameter estimation algorithms for input nonlinear systems,including the input nonlinear controlled autoregressive (in-car) model and the input nonlinear controlled autoregressive autoregressive moving average (in-cararma) model. the basic idea is to obtain linear-in-parameters models by overparameterizing such nonlinear systems and to use the least-squares algorithm to estimate the unknown parameter vectors. it is proved that the parameter estimates consistently converge to their true values under the persistent excitation condition. a simulation example is provided. © 2012 weili xiong et al.