Behrooz Shahsavari

Adaptive Repetitive Control Design With Online Secondary Path Modeling and Application to Bit-Patterned Media Recording

This paper presents an adaptive repetitive controller for active tracking (rejecting) of unknown periodic trajectories (disturbances). The proposed control law is based on a modified filtered-x least mean squares (MFX-LMS) algorithm with a novel variable step size that improves the convergence rate and fades the steady state excess error in a stochastic environment. A novel secondary path modeling scheme is also proposed to adaptively compensate for the dynamic mismatches between the internal model of the MFX-LMS and the real dynamic system in an online fashion. We further discuss the application of this adaptive controller in servo mechanisms for hard disk drives (HDDs) that use bit patterned media recording in which full spectrum tracking of a periodic trajectory is crucial. Finally, comprehensive numerical simulations and experimental implementations are presented for an HDD servo system that is subjected to periodic disturbances known as repeatable run-out.

Adaptive Repetitive Control Using a Modified Filtered-x lms Algorithm

In this paper we develop a modified filtered-x least mean squares (MFX-LMS) method to synthesis an adaptive repetitive controller for rejecting periodic disturbances at selective frequencies. We show how a MFX-LMS algorithm can be utilized when the reference signal is deterministic and periodic. A new adaptive step size is proposed with the motivation to improve the convergence rate of the MFX-LMS algorithm and fade the steady state excess error caused by the variation of estimated parameters in a stochastic environment. A novel secondary path modeling scheme is proposed to compensate for the modeling mismatches online. We further discuss the application of this adaptive controller in hard disk drives that use Bit Patterned Media Recording. Finally we present the results of comprehensive realistic numerical simulations and experimental implementations of the algorithms on a hard disk drive servo mechanism that is subjected to periodic disturbances known as repeatable runout.

Robust Track-Following Controller Design for Hard Disk Drives With Irregular Sampling

This paper considers robust controller design for track-following in hard disk drives (HDD) with irregular sampling of the position error signal (PES) but regular (clock-driven) control updates. This sampling and actuation behavior is modeled by applying a novel discretization method to a continuous-time model of an HDD, resulting in a discrete-time linear periodically time-varying model. Then, the controller design is performed using optimal H∞ control for periodic systems and uses a generalization of the disk margin to quantify the robustness of the closed-loop system. To show the effectiveness of the proposed method, the design methodology is applied to a hard disk drive model and the resulting controller is validated by examining its nominal performance in terms of the root mean square of the standard deviation of the PES and robustness in terms of disk margin. Since the proposed controller has too many parameters to be implementable on an HDD due to memory limitations, we use a vector quantization method to approximate the entire parameter set of the designed controller by a smaller set of parameters.

Stochastic model predictive control design for load management system of aircraft electrical power distribution

Aircraft Electric Power Systems (EPS) route power from generators to vital avionics loads by configuring a set of electronic control switches denoted as contactors. The external loads applied to an EPS, power requirement of the system, electrical component failure events, and the dynamics of the system are inherently uncertain. In this paper, we address the problem of designing a stochastic optimal control strategy for the EPS contactors. We first represent mathematical models of different components of an EPS, and formalize the performance metrics of the system as well as the constraints that should be satisfied in a stochastic modeling framework. We then formulate the optimization of the system performance as a stochastic model predictive control (SMPC) problem, and present two special cases of the proposed SMPC analysis to approximate the problem with linear mixed-integer optimization problems. Finally, we report simulation results to confirm the effectiveness of the proposed approach.

Adaptive mismatch compensation for vibratory gyroscopes

This paper presents an online adaptive controller to compensate damping and stiffness frequency mismatches in vibratory gyroscopes. This adaptive controller is running together with the method of averaging. The adaptive controller first estimates the effective damping and stiffness frequency mismatches using least square method and then compensates these estimated mismatches in an online fashion. Simulation results show that the proposed adaptive mismatch compensation controller can eliminate the effects of damping and stiffness frequency mismatches on quadrature oscillation, precession angle oscillation and precession angle drift very effectively.

Adaptive Mismatch Compensation for Rate Integrating Vibratory Gyroscopes With Improved Convergence Rate

This paper presents an online adaptive algorithm to compensate damping and stiffness frequency mismatches in rate integrating Coriolis Vibratory Gyroscopes (CVGs). The proposed adaptive compensator consists of a least square estimator that estimates the damping and frequency mismatches, and an online compensator that corrects the mismatches. In order to improve the adaptive compensator’s convergence rate, we introduce a calibration phase where we identify relations between the unknown parameters (i.e. mismatches, rotation rate and rotation angle). Calibration results show that the unknown parameters lie on a hyperplane. When the gyro is in operation, we project parameters estimated from the least square estimator onto the hyperplane. The projection will reduce the degrees of freedom in parameter estimates, thus guaranteeing persistence of excitation and improving convergence rate. Simulation results show that utilization of the projection method will drastically improve convergence rate of the least square estimator and improve gyro performance.Copyright

REPEATABLE RUNOUT FOLLOWING IN BIT PATTERNED MEDIA RECORDING

An adaptive feedforward controller design for tracking repeatable runout (RRO) in bit patterned media recording (BMPR) is proposed for single stage hard disk drives (HDD). The technique is based on modified filtered-x least mean squares (MFXLMS) algorithm with deterministic periodic input, and a novel variable step size that boosts both the convergence rate and the steady state error. Comprehensive simulations and comparisons are provided to show the effectiveness of the proposed method.

Adaptive rejection of periodic disturbances acting on linear systems with unknown dynamics

This paper proposes a novel direct adaptive control method for rejecting unknown deterministic disturbances and tracking unknown trajectories in systems with uncertain dynamics when the disturbances or trajectories are the summation of multiple sinusoids with known frequencies, such as periodic profiles or disturbances. The proposed algorithm does not require a model of the plant dynamics and does not use batches of measurements in the adaptation process. Moreover, it is applicable to both minimum and non-minimum phase plants. The algorithm is a “direct” adaptive method, in the sense that the identification of system parameters and the control design are performed simultaneously. In order to verify the effectiveness of the proposed method, an add-on controller is designed and implemented in the servo system of a hard disk drive to track unknown nano-scale periodic trajectories.

Limits of Performance in Systems with Periodic Irregular Sampling and Actuation Rates

Abstract This paper examines the limits of performance in systems with periodic irregular sampling rate when the actuation is not necessarily synchronized with the sampling. For such a system, three sampling and actuation schemes are considered: when the sampling and control rate are both regular, when they are both irregular, and when the sampling rate is irregular while the control rate is regular. To ascertain the limits of performance of this type of systems under each sampling and actuation scheme, the system is modeled as a linear periodically time-varying (LPTV) system; optimal LQG control design with a variance constraint is applied to find the smallest achievable mean variance of the performance signal subject to a constraint on the mean control effort variance. In addition, to deal with the computational delay of the controller, an innovative discretization method is proposed which does not introduce any extra states into the state space model. The proposed method is exploited to determine the performance of a hard disk drive (HDD) in track-following mode. A simulation study demonstrates that in the presence of 30% irregularity in sampling time, using the irregular sampling and regular control action scheme for the HDD achieves an RMS 3s value of the position error signal (PES) that is 40% smaller than the corresponding value achieved by using a controller provided by our industry partner, in which both the sampling and control rates are irregular.

Optimal mode-switching and control synthesis for floating offshore wind turbines

This paper proposes a multi-objective optimal control and switching strategy for floating offshore wind turbines when the wind speed can be approximately predicted. The system is modeled as a hybrid automaton with two modes corresponding to the turbine operation in low- and high-speed wind profiles. The main control objective in the low-speed wind mode is to maximize the total captured power in a finite time horizon, whereas in the high-speed mode, it is desired to regulate the generator torque and speed around predefined rated values even under gust loads. The problem is formulated as a constrained mixed-integer bilinear program in a model predictive control framework. The posed constraints correspond to the electrical/mechanical limitations of the blade actuators and generators. Various practical considerations, such as minimizing the number of switching occurrences and mechanical fatigue prevention, are explicitly considered in the optimization problem. The proposed control method is applied to the dynamical model of a real wind turbine and simulation results are presented.