Recursive And Iteraive Learning Identification Algorithms With Applications

System identification plays an important role in high performance automation technologies such as adaptive control and learning control. More high quality of products is required, in nowadays plants, to meet the needs of people's production and living demands. It should be challenged for system identification when high control performance is pursued. The parameters in dynamics of many real systems are time-dependent, which requires that identification techniques are of the ability of tracking, and can estimate time-varying parameters. We also need robust identification algorithms owing to the presence of abrupt disturbances. Take into account the issues above mentioned, this thesis focuses on the following aspects:1. The properties and deterministic convergence of recursive identification algorithm are considered, in which both a forgetting factor and weights have been incorporated.2. Iterative learning identification algorithms are presented for estimating time-varying parameters of a class of discrete time-varying systems over a finite time interval. The theoretic properties of iterative learning projection and least squares algorithms are further discussed, and corresponding numerical simulations are presented. Two prototype algorithms of iterative learning identification, iterative learning Bayes and stochastic Newton algorithms, are proposed with detail. Different from the bounded convergence performance obtained by conventional tracking algorithms, perfect estimation for the time-varying unknowns is achieved through iterative learning, and the parameter estimation error is guaranteed to converge to zero over the entire time interval.3. The approximate least l₁-norm recursive algorithm is presented for adaptive acoustic echo cancellation. The convergence performance of the algorithm is analyzed, by which the algorithm exhibits robustness with respect to bounded disturbances at steady-state. It is in turn ? useful for performance improvement of the acoustic echo cancellation. During duplex talk, the proposed algorithm works well without requiring Double-Talk Detector and without requiring more hardware in the system. 4. Based on the nonlinear PCA criterion, an iterative weighted least squares algorithm is presented for blind source separation. The iterative algorithm is for the purpose to approximate the l_p-norm index, and the theoretical properties of the algorithm are derived. In comparison with the conventional RLS algorithm, the proposed algorithm has comparable computational complexity, and is robust with respect to non-Gaussian disturbances or those with unknown statistical distribution.5. An ice storage air condition system is designed and implemented. The real system is modeled as a time-varying system, and the energy is predicted by the identification algorithm with both forgetting and weighted factors. For the implementation, KingView is adopted for data acquisition, with friendly human machine interface design, and MATLAB is used to carry out the on-line prediction computing. In the ice storage system, DDE technique is applied for the real time data exchange between the configuration software and MATLAB.