Research On Dual-Stage Hard Disk Drive Head Positioning

Hard disk drive (HDD), acted as requisite storage equipment in current information age, plays a more and more vital role in people's daily life, and it becomes a roll booster in rapid development of science and technology. However, with the increase of information capacity, we put forward a severe request for HDD data storage capacity. Unfortunately, due to the low bandwidth, low positioning accuracy in conventional HDD, magnetic head is hard to be positioned onto the destination track center, thus it limits the continuing increase in storage capacity. Therefore, how to design reasonable servo system structure and advanced algorithms so as to improve head positioning accuracy becomes a challenge in HDD storage technique research, this dissertation is mainly developed based on the above background.As far as servo system structure is concerned, conventional HDD only applies with voice coil motor (VCM) as a single actuator to drive the magnetic head to realize data reading and writing. Applying this single-stage head positioning structure, due to mechanical resonances and high frequency uncertainties existed in VCM, it is hard to improve the servo bandwidth, and further increase track density. In order to satisfy the requirements of the head positioning speed, accuracy and rapid frequency response, this dissertation develops dual-stage actuator structure, that is, coarse-fine dual-stage servo positioning system. This system helps to obtain high servo bandwidth and positioning accuracy. In this configuration, VCM, acted as the first actuator, is to control the read/write (R/W) head to realize coarse positioning, and its movement is mainly limited in low frequency range. While micro-actuator (MA), acted as the secondary actuator, is to control the head to realize accurate positioning in data track, and its movement is mainly limited in high frequency range.As far as advanced algorithms are concerned, first of all, there are noises existing in HDD servo signal. The existence of noises reduces the head positioning precision, affects the improvement of disk density, and to be more serious results in track misregistration. Therefore, we need to apply filtering technique in advance. Since there are overlaps between frequency spectrum of noise sources and that of useful signals, it is hard to obtain good filtering effect by using general digital filter. This dissertation puts forward a novel weighted recursive least square (WRLS) algorithm under this requirement.In order to expand dual-stage head positioning research work, this dissertation simply presents the situation of single-stage head positioning, and then introduces fractional order PI~λD~μcontroller into HDD servo control field. This controller is the generalization of conventional integer order PID controller. Besides the usual proportional,integral and derivative parameters, the orders of integrator and differentiator can also be tunable, which make the design of fractional order PI~λD~μcontroller be more flexible. In order to verify the superior performance of the fractional order PI~λD~μcontroller, simulation experiment, using hard disk drive servo control as an example, is done. Simulation results show that this controller can make the whole system be more robust and excellent.To realize more rapid and accurate head positioning, and simultaneously avoid actuator saturation, this dissertation proposes a novel control scheme----composite nonlinear feedback control combined with tracking differentiator which is based on modern control theory. The design philosophy of the former is that the damping ratio of the overall system is set very little in the seeking stage such that the system has rapid response. When the magnetic head gradually approaches the destination track, the control scheme dynamically increases the system damping ratio so as to reduce the overshoot caused by the initial little damping ratio. This idea can not only obtain fast rising time but also ensure less overshoot. The latter is injected into the servo system using a feedforward way, and the tracking differentiator is mainly used to reduce the control voltage to the actuators and hence avoid their saturation. Furthermore, this dissertation verifies the stability of the closed-loop system under this control scheme by constructing an appropriate Lyapunov function.Considering constant disturbances caused by data flex cable and other uncertain runouts which make the servo system be steady-state error, this dissertation presents an H_∞almost disturbance decoupling controller. The parameters of this controller can be arbitrarily adjusted. This is especially suitable for those occasions which have uncertain disturbances. According to the model of the HDD, this dissertation designs this robust controller in detail, and verifies the effectiveness of this controller in time domain and frequency domain.In track following mode, there inevitably exists repeatable runout (RRO) which is caused by spindle motor, and this RRO makes the head be hard to be positioned onto the desired track center. Therefore, this dissertation presents an adaptive feedforward compensation (AFC) controller. This adaptive controller needs to solve three types of parameters, that is, phase advance parameters, adaptive gains and feedthrough coefficients. In order to obtain the optimal parameters, this time-varying compensator should be converted into a linear time invariant (LTI) model, and then quantitively acquire these optimal parameters by using loop-shaping approach, Nyquist criteria and root locus analysis. Simulation results show that this adaptive feedforward compensator nearly eliminates the repeatable runout signal, and hence ensures the servo system achieve satisfactory tracking effect in the track following mode.With the improvement of HDD track density and the reduction of HDD dimensions, the accurate model construction becomes more and more difficult. This dissertation proposes an advanced control method which is independent of plant model, to be more specific, this method is tunable activation function-multilayer forward neural network (TAF-MFNN), this neural network can be used to train proportional, integral and derivative coefficients of the fractional order PI~λD~μcontroller. This control strategy can not only have weak dependence on plant model, but also have good characteristics such as high precision and robustness.Finally, a summary has been done for all discussions in the dissertation. The research works in further study are presented.