Novel Decentralized Iterative Learning Trackers for the Unknown Sampled-data Interconnected Large-scale State-delay System with Closed-loop Decoupling Property

• Main Authors: Tze-YuYu, 余澤佑
• Other Authors: Jason Sheng-Hong Tsai
• Format: Others
• Language: en_US
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/53401450619542839353

碩士 === 國立成功大學 === 電機工程學系碩博士班 === 98 === The decentralized iterative learning trackers for the unknown sampled-data interconnected large-scale state-delay system consisting of multi-input multi-output subsystems with the closed-loop decoupling property is proposed in this thesis. The off-line observer/Kalman filter identification (OKID) method is used to obtain the decentralized linear models for subsystems in the interconnected large-scale system. In order to get over the effect of modeling error on the identified linear model of each subsystem, an improved observer with the high-gain property based on the digital redesign approach is developed to replace the observer identified by OKID. Then, iterative learning control (ILC) scheme is embedded to the decentralized models. Notice that the convergence of ILC is directly influenced by the initial control input. To accelerate the convergence of ILC, the digital-redesign linear quadratic tracker with the high-gain property is proposed as the initial control input of ILC. The high-gain property controllers can suppress the uncertain errors such as modeling errors, nonlinear perturbations, and external disturbances. Thus, the system output can quickly and accurately track the desired reference in a short time interval with the closed-loop decoupling property.