Ehsan Keikha

A price-based adaptive task allocation for Wireless Sensor Network

Applications for Wireless Sensor Networks may be decomposed into the deployment of tasks on different sensor nodes in the network. Task allocation algorithms assign these tasks to specific sensor nodes in the network for execution. Given the resource-constrained and distributed nature of Wireless Sensor Networks (WSNs), existing static (offline) task scheduling may not be practical. Therefore there is a need for an adaptive task allocation scheme that accounts for the characteristics of the WSN environment such as unexpected communication delay and node failure. In this paper, we focus on task allocation in WSNs which is performed with the aim of achieving a fair energy balance amongst the sensor nodes while minimizing delay using a market-based architecture. In this architecture, nodes are modeled as sellers communicating a deployment price for a task to the consumer. To address this task allocation problem, proposed price formulation is used as it continuously adapts to changes of the availabilities of resources. This scheme also accommodates for the node failure during task assignment. The Centralized and distributed message exchanged mechanisms between the nodes (sellers) and task allocator (consumer) are proposed to determine the winner among the sellers with the goal of reducing overhead and energy consumption. Simulation results show that, compared with a static scheduling scheme with an objective in energy balancing, the proposed scheme adapts to new environmental changes and uncertain network condition more dynamically and achieves a much better performance on energy balancing.

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.

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.

Multi-frequency Technique for Frequency Response Measurement and its Application to Servo System with Friction

Abstract In this paper, the effect of nonlinear distortion on system identification is studied. In particular, the effect of nonlinear friction on frequency response measurement is examined. Design of multi-frequency excitation signal that minimizes nonlinear effect on frequency response measurement has been explored and the best linear approximation model has been defined. An identification method using differential No Interharmonic Distortion (NID) excitation with optimized crest factor is proposed. The method is used to identify the best linear model of a hard disk drive actuator.

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.

Probabilistic Robust Approach for Discrete Multi-objective Control of Track-Following Servo Systems in Hard Disk Drives

Abstract This paper deals with the problem of different uncertainties in discrete time track following control of read/write head in hard disk drives (HDD). A multi-objective robust controller is designed which minimizes the worst case root mean square (RMS) value of the positioning error signal (PES) subject to the closed-loop stability in the presence of parametric and dynamic uncertainties. A sequential algorithm based on ellipsoid iteration is utilized to handle parametric uncertainty. Dynamic uncertainty is also represented as linear fractional transformation (LFT) and by the virtue of small gain theorem, the stability of closed-loop system is guaranteed. In this design, two sources of conservatism are avoided: embedding the original non-linear parametric uncertainty into affine structure (converting the original uncertain system into a polytopic uncertain system) and using a single Lyapunov matrix to test all the objectives. The resulting controller has much better track following performance compared to the classical robust approaches which tends to higher storage capacity of HDDs. Simulation as well as experimental results verify the effectiveness of the designed controller.