R.A. de Callafon

Suboptimal Feedback Control by a Scheme of Iterative Identification and Control Design

In this paper a framework for an iterative procedure of identification and robust control design is introduced wherein the robust performance is monitored during the subsequent steps of the iterative scheme. By monitoring the performance via a model-based approach, the possibility to guarantee performance improvement in the iterative scheme is being employed. In order to monitor achieved performance (for a present controller) and to guarantee robust performance (for a future controller), an uncertainty set is used where the uncertainty structure is chosen in terms of model perturbations in the dual Youla parametrization. This uncertainty structure is shown to be particularly suitable for the control performance measure that is considered. The model uncertainty set can be identified by an uncertainty estimation procedure on the basis of closed-loop experimental data. To obtain performance robustness, robust control design tools are used to synthesise controllers on the basis of the identified uncertainty set.

Robust dynamic modeling and control of dual-stage actuators

In a hard disk drive with a dual-stage actuator the standard rotary actuation of the voice coil motor (VCM) is combined with an additional micro- or milli-actuation to accomplish high-bandwidth and highly accurate track following. For extremely high density recording, the servo control algorithm designed for a specific dual-stage actuator needs to maintain high performance track following in lieu of external disturbances and uncertainties in the dynamic response induced by product manufacturing tolerances. Essential in the robust design of a servo control algorithm is to bound these uncertainties and design a robust track following servos accordingly. In this paper, we present an experiment based methodology to characterize and bound the uncertainties in the dynamic response of a dual-stage actuator in the form of an uncertainty model. Subsequently, the uncertainty model is used for the analysis and synthesis of a robust dual-stage servo controller that is able to optimize position error variance in the presence of uncertainty information.

Multivariable closed-loop identification: from indirect identification to dual-Youla parametrization

Classical indirect methods of closed-loop identification can be applied on the basis of different closed-loop transfer functions. Here the multivariable situation is considered and conditions are formulated under which identified approximative plant models are guaranteed to be stabilized by the present controller. Additionally it is shown in which sense the classical indirect methods are generalized by a previously introduced identification method based on the dual-Youla parametrization. For stable controllers the two methods are shown to be basically equivalent to each other.Classical indirect methods of closed-loop identification can be applied on the basis of different closed-loop transfer functions. Here the multivariable situation is considered and conditions are formulated under which identified approximative plant models are guaranteed to be stabilized by the present controller. Additionally it is shown in which sense the classical indirect methods are generalized by a previously introduced identification method based on the dual-Youla parametrization. For stable controllers the two methods are shown to be basically equivalent to each other.

Multivariable least squares frequency domain identification using polynomial matrix fraction descriptions

In this paper an approach is presented to estimate a linear multivariable model on the basis of (noisy) frequency domain data via a curve fitting procedure. The multivariable model is parametrized in either a left or a right polynomial matrix fraction description and the parameters are computed by using a two-norm minimization of a multivariable output error. Additionally, input-output or element-wise based multivariable frequency weightings can be specified to tune the curve fitting error in a flexible way. The procedure is demonstrated on experimental data obtained from a 3 input 3 output wafer stepper system.In this paper an approach is presented to estimate a linear multivariable model on the basis of (noisy) frequency domain data via a curve fitting procedure. The multivariable model is parametrized in either a left or a right polynomial matrix fraction description and the parameters are computed by using a two-norm minimization of a multivariable output error. Additionally, input-output or element-wise based multivariable frequency weightings can be specified to tune the curve fitting error in a flexible way. The procedure is demonstrated on experimental data obtained from a 3 input 3 output wafer stepper system.

Multivariable feedback relevant system identification of a wafer stepper system

This paper discusses the approximation and feedback relevant parametric identification of a positioning mechanism present in a wafer stepper. The positioning mechanism in a wafer stepper is used in chip manufacturing processes for accurate positioning of the silicon wafer on which the chips are to be produced. The accurate positioning requires a robust and high-performance feedback controller that enables a fast throughput of silicon wafers. A control relevant set of multivariable finite dimensional linear time invariant discrete-time models is formulated and estimated on the basis of closed-loop experiments. The set of models is shown to be suitable for model-based robust control design of the positioning mechanism. This is illustrated by a successful design and implementation of a robust controller.

Identification of normalized coprime plant factors for iterative model and controller enhancement

Recently introduced methods of iterative identification and control design are directed towards the design of high performing and robust control systems. These methods show the necessity of identifying approximate models from closed loop plant experiments. In this paper a method is proposed to approximately identify normalized coprime plant factors from closed loop data. The fact that normalized plant factors are estimated gives specific advantages both from an identification and from a robust control design point of view. It will be shown that the proposed method leads to identified models that are specifically accurate around the bandwidth of the closed loop system. The identification procedure fits very naturally into the iterative identification/control design scheme as presented by Schrama (1992).<<ETX>>

Dynamic Modeling and Control of a Piezo-Electric Dual-Stage Tape Servo Actuator

We present a data-based approach for modeling and controller design of a dual-stage tape servo actuator. Our method uses step response measurements and a generalized realization algorithm to identify a multivariable discrete-time model of the actuator. The data acquisition and modeling can be implemented in the servo firmware of a tape drive. We have designed a dual-stage controller, based on the model, using loop shaping techniques adopted for multivariable control problems. We applied the procedure to the prototype of a dual-stage actuator tape head to reduce the effect of lateral tape motion. The prototype consists of a conventional voice coil motor for coarse positioning and a micro-actuator for fine positioning. The micro-actuator, which is mounted on the voice coil motor, uses a piezo crystal to follow high-frequency lateral tape motion (up to the kilohertz regime), while the voice coil motor follows only low-frequency lateral tape motion. Compared to a single-stage design, the dual-stage servo design provides a 25% bandwidth improvement and a voice coil motor control signal that is much smaller in magnitude.

Dynamic Flying Height Adjustment in Hard Disk Drives Through Feedforward Control

A dynamic model of the resistance heater element in a thermal flying height control (TFC) slider of a hard disk drive is identified and used for dynamic flying height control. Experimental data obtained on a spin stand and a generalized realization algorithm are used for identification of a discrete-time dynamic model of the thermal actuator. The flying height change is measured in two different ways: using servo burst information written onto the disk surface and using information written in the data sectors only. The resistance change of the thermal actuator based on the input power level is measured. Based on the identified discrete-time model of the heater and using convex optimization techniques, a computational scheme is proposed to obtain optimized feedforward input profiles to the heater element that minimize repeatable flying height variations and enable low flying heights.

An iterative learning design for repeatable runout cancellation in disk drives

In this paper, we consider the iterative learning control (ILC) framework to design a reference signal that directly cancels periodic disturbances in a feedback measurement. Cancellation of periodic disturbances is useful in reducing undesirable repeatable tracking errors in applications such as the two-stage servo track writing process for disk drives. A general problem description is given for a linear discrete-time periodic system and convergence results for the learning system are derived. A learning filter is designed with the use of a finite-impulse response model approximation for the inverse of the closed-loop sensitivity such that convergence is achieved in learning a reference signal that provides cancellation with periodic perturbations affecting the system measurement. The ILC algorithm is applied to a disk drive system where experimental results demonstrate the effectiveness of the method in reducing periodic measurement disturbances.

AN ADAPTIVE INTERNAL MODEL-BASED CONTROLLER FOR PERIODIC DISTURBANCE REJECTION

Abstract This paper details the design of an adaptive internal model-based controller to attenuate periodic disturbances in the presence of random noise. The internal model-based controller is designed in two steps, making use of the separation principle to design a controller with the desired properties that stabilizes the closed-loop system. Periodic disturbances appear as harmonics of a fundamental frequency. The fundamental frequency of the disturbance is estimated with a magnitude/phase-locked loop and is used to adjust the parameters of the controller, specifically the natural frequency of the internal model and the feedback gain. An active noise control study shows the ability of the proposed method to cancel time varying disturbances in an acoustic system.