摘要:

Identification of non-linear, dynamical systems described by the Hammerstein model are discussed. Such a system consists of a multi-input single-output nonlinear, memoryless subsystem followed by a dynamic, linear subsystem. Outputs of both subsystems are corrupted by random noise. The parameters of the linear subsystem are identified by a correlation technique. The main contribution lies in estimating the non-linear, memoryless subsystem. The identification algorithm is based on the recursive kernel regression estimate. No restrictions are imposed on the functional form of the non-linearity as well on its continuity. We prove global convergence of the algorithm regardless of the distribution of the random input and for outputs with bounded moment of order greater than 2. The rate of convergence is obtained for the Lipschitz non-linearities and all input distributions.

ISSN:0020-7721    

DOI:10.1080/00207729308949508

出版商:Taylor & Francis Group    

年代:1993

数据来源: Taylor

摘要:

A design criterion is developed to achieve the input-output decoupling of multivariable feedback systems and the robust stabilization of systems with time-varying nonlinear uncertainties. Moreover, an effective design algorithm is derived to achieve the robust optimization of multivariable feedback systems subjected to time-varying nonlnear uncertainties. The theory of minimumH∞-norm and the optimal interpolation technique are employed to solve this robust optimization problem. Since the requirements of internal stability are satisfied, this design algorithm performs appropriately, even if the plant is unstable and/or non-minimum phase. From the result of the robust optimization, we can predict the maximum sector bounds of nonlinear uncertainties that can be tolerated in the multivariable feedback system.

ISSN:0020-7721    

DOI:10.1080/00207729308949509

出版商:Taylor & Francis Group    

年代:1993

数据来源: Taylor