5,448 research outputs found
Neuro-Controller Design by Using the Multifeedback Layer Neural Network and the Particle Swarm Optimization
In the present study, a novel neuro-controller is suggested for hard disk drive (HDD) systems in addition to nonlinear dynamic systems using the Multifeedback-Layer Neural Network (MFLNN) proposed in recent years. In neuro-controller design problems, since the derivative based train methods such as the back-propagation and Levenberg-Marquart (LM) methods necessitate the reference values of the neural network’s output or Jacobian of the dynamic system for the duration of the train, the connection weights of the MFLNN employed in the present work are updated using the Particle Swarm Optimization (PSO) algorithm that does not need such information. The PSO method is improved by some alterations to augment the performance of the standard PSO. First of all, this MFLNN-PSO controller is applied to different nonlinear dynamical systems. Afterwards, the proposed method is applied to a HDD as a real system. Simulation results demonstrate the effectiveness of the proposed controller on the control of dynamic and HDD systems
Sequential Randomized Algorithms for Convex Optimization in the Presence of Uncertainty
In this paper, we propose new sequential randomized algorithms for convex
optimization problems in the presence of uncertainty. A rigorous analysis of
the theoretical properties of the solutions obtained by these algorithms, for
full constraint satisfaction and partial constraint satisfaction, respectively,
is given. The proposed methods allow to enlarge the applicability of the
existing randomized methods to real-world applications involving a large number
of design variables. Since the proposed approach does not provide a priori
bounds on the sample complexity, extensive numerical simulations, dealing with
an application to hard-disk drive servo design, are provided. These simulations
testify the goodness of the proposed solution.Comment: 18 pages, Submitted for publication to IEEE Transactions on Automatic
Contro
Self-organizing fuzzy sliding-mode control for a voice coil motor
[[abstract]]Voice coil motor (VCM) is widely known as its topquality
of free friction, low noise, fast transient response and well
repeatability. Yet the dynamic characteristic of a VCM is
nonlinear and time-varying, thus the model-based conventional
controller is difficult to achieve high-precision control
performance for a VCM. To attack this problem, a selforganizing
fuzzy sliding-mode control (SFSC) system is proposed
in this paper. All of the fuzzy rules are online grown and pruned
by the structure learning phase and the parameter learning
phase is designed to tune the controller parameter in the
gradient-descent-learning algorithm. From the experiment
results, it shows that the proposed SFSC system can successfully
control a VCM with favorable control response with enhanced
disturbance rejection performance.[[notice]]補正完畢[[conferencetype]]國際[[conferencedate]]April 9-11[[booktype]]紙本[[iscallforpapers]]Y[[conferencelocation]]Taipei, Taiwa
Randomized algorithms for control of uncertain systems with application to hand disk drives
Ph.DDOCTOR OF PHILOSOPH
Design of a hybrid controller for voice coil motors with simple self-learning fuzzy control
[[notice]]補正完畢[[conferencetype]]國際[[conferencedate]]Nov. 26-28[[booktype]]紙本[[iscallforpapers]]Y[[conferencelocation]]Kaohsiung, Taiwa
CHALLENGES OF CONTROL DESIGN FOR PRECISION SERVO SYSTEM WITH APPLICATION ON HARD DISK DRIVE
Ph.DDOCTOR OF PHILOSOPH
Disturbance attenuation with multi-sensing servo systems for high density storage devices
Ph.DDOCTOR OF PHILOSOPH
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