Learning of Hybrid Fuzzy Controller for the Optical Data Storage Device

A hybrid track-seeking fuzzy controller for an optical disk drive (ODD) is proposed in this paper. The proposed hybrid fuzzy controller (HFC) smoothes the voltage applied to the sled motor and improves the track-seeking efficiency. The HFC consists of two subsystems including an intelligent time switch and a driving force controller. Both subsystems are designed based on fuzzy logic inferences. The main functions of the proposed HFC are to drive the optical head unit (OHU) to the target track neighborhood as fast as possible and smoothly park the OHU in the least time in the target track neighborhood. An automatic learning approach based on genetic algorithms (GAs) is proposed for learning the fuzzy rules for both the intelligent time switch and driving force controller. Modulated orthogonal membership functions are utilized in both fuzzy controllers to improve the GA learning efficiency. The number of parameters needed to parameterize the fuzzy rule base is greatly reduced with the modulated orthogonal membership functions. Compared to the conventional track-seeking controller currently utilized in most ODDs that employ a speed profile as the reference signal for the track-seeking feedback control system, the proposed HFC outperforms the conventional track-seeking control schemes. Experiments are performed to justify the performance comparison.