Identification And Predictive Control Of Multirate Sampled Systems

The theoretic research direction of multi-rate systems model identification focuses on lifted model identification and extracting fast-rate model. All of these are one of key points of this field. In this thesis, some theoretic problems in multi-rate sampled systems are studied. The study of model identification is analyzed based on the lifting technology and the traditional system identification algorithm. The final fast-rate single-rate model identification results are derived. According to the identification results, the multi-rate predictive control algorithm is investigated. This thesis is composed of follows:1. Controllability and observability of the lifted system are analyzed, which are essential to the identifiability issues. The time delay of the process from the liftd model is estimated, the accuracy of this estimation process is analyzed. This thesis discusses the causality issues involved in the lifed models and proposes a subspace-based least squares algorithm for identifying the state-space models of the lifted systems.2. For cases where states are unmeasured, based on the hierarchical identification principle and the least squares algorithm, the unknown parameters and states of the lifted model are estimated. Based on the results studied in the proceeding sections, the fast-rate single-rate model is extracted. In this section, time domain and frequency domain approaches to compute a fast-rate model are presented.3. By using the prediction error approach based on dual-rate systems, the lifted model is identified. The lifted model identification result is used for the design of dualrate predictive controller.4. In cascade control system, the sample rate of outer loop and inner loop are always different. So a single model predictive controller that could handle the multirate cascade control strategy in a straight forward manner is proposed.