Iterative Learning Control-Based Optimal Linear Quadratic Digital Tracker for Sampled-Time Active Magnetic Bearing System

摘要

This paper presents an iterative learning control (ILC) methodology-based optimal linear quadratic digital tracker (LQDT) for the five degree of freedom (five-DOF) active magnetic bearing (AMB) system as a multi-input multi-output sampled-date system. The combination of ILC and an observer is given out the perfect tracking responses and steady-state. The paper is organized as: (i) System-state observer Kalman filter identification (OKID) is used to construct control system. (ii).