A nonlinear model is proposed for effective adaptive control design. The model represents a natural way to describe input output nonlinear systems. A combined parameter off line estimation and structure detection al...A nonlinear model is proposed for effective adaptive control design. The model represents a natural way to describe input output nonlinear systems. A combined parameter off line estimation and structure detection algorithm is developed that can use an initial set of data. Then, an efficient model is obtained using orthogonal estimation with an error reduction test and other monitoring modifications. A recursive on line identification scheme is established based on the ELS algorithm to account for future time variations in the process of the parsimonious model.展开更多
An efficient model of nonlinear stochastic systems that can use on initial batch of data is developed using orthogonal estimation including the error reduction test and other monitoring modifications. A recursive iden...An efficient model of nonlinear stochastic systems that can use on initial batch of data is developed using orthogonal estimation including the error reduction test and other monitoring modifications. A recursive identification on line algorithm is implemented to track the nonlinear time variable process. The ELS algorithm is proposed for the parameters, and the nonlinear adaptive controller is designed by the ELS algorithm. The convergence rate of the parameter estimation and the adaptive tracking are established.展开更多
文摘A nonlinear model is proposed for effective adaptive control design. The model represents a natural way to describe input output nonlinear systems. A combined parameter off line estimation and structure detection algorithm is developed that can use an initial set of data. Then, an efficient model is obtained using orthogonal estimation with an error reduction test and other monitoring modifications. A recursive on line identification scheme is established based on the ELS algorithm to account for future time variations in the process of the parsimonious model.
文摘An efficient model of nonlinear stochastic systems that can use on initial batch of data is developed using orthogonal estimation including the error reduction test and other monitoring modifications. A recursive identification on line algorithm is implemented to track the nonlinear time variable process. The ELS algorithm is proposed for the parameters, and the nonlinear adaptive controller is designed by the ELS algorithm. The convergence rate of the parameter estimation and the adaptive tracking are established.
