Fuzzy Control for Near-Field Optical Disk Drives Based on Reinforcement Learning
|Keywords:||模糊控制;增強性學習;近場光碟機;Fuzzy Control;Reinforcement Learning;Near-Field|
The flying head design in near-field optical disk drives applies the near-field optics theory to overcome the limit of conventional optical disk drive techniques, thereby substantially increasing data storage capacity. To that end, enhancing control performance to improve tracking speed and accuracy is required. This study aims to develop fuzzy control based reinforcement learning, which incorporates characteristics of reinforcement learning control into fuzzy control. Fuzzy control has excellent characteristics of dealing with model uncertainty, nonlinearity and easy implementation. Reinforcement learning is specially suitable for rough feedback signals; however, the measured feedback signals for an optical head is also rough in focusing, seeking and following. To carry out the present controller, it is not necessary to obtain beforehand the mathematical model of the pickup head. This work constructs fuzzy rules based model based on input-output data of the pickup head and tune fuzzy membership functions by reinforcement learning. During voltage excitation, a bimorph PZT simultaneously undergoes two axes deformation, which can be used for both tracking control and flying height control. To validate the proposed bimorph PZT method, this work conducts measurements.
|Appears in Collections:||Thesis|