KEYWORDS: Control systems, Data modeling, Systems modeling, Device simulation, Numerical simulations, Vibration control, Motion models, Computer simulations, Digital signal processing, Servomechanisms
This paper demonstrates that filtered inverse system can be used effectively for the feed forward control of a
flexible stacker crane. A numeric model for the crane is developed from the experimental input and output data. From this
model, a feedfoward controller based on an inverse system was developed and simulated. We applied the
feedforward control to an actual stacker crane. Validity of the proposed control method was verified from the
numerical simulation and the experiment. The numerical simulation and experimental results show the good control
performance.
The present paper describes a two-degree-of-freedom control of a self-sensing micro-actuator for a dual-stage hard disk
drive. The two-degree-of-freedom control system is comprised of a feedforward controller and a feedback controller.
Two controllers are designed for the two-degree-of-freedom control system, one for the inverse dynamic model for the
feedforward controller and one for the feedback controller using self-sensing signal. The feedback controller can realize
the self-sensing signal. The micro-actuator uses a PZT actuator pair, installed on the assembly of the suspension. The
self-sensing micro-actuator can be used to form a combined actuation and sensing mechanism. Experimental results
show that the two-degree-of-freedom control approach can be used effectively for the control of the self-sensing microactuator
system.
KEYWORDS: Neural networks, Control systems, Systems modeling, Neurons, Device simulation, Dynamical systems, Digital signal processing, Vibration control, System identification, Adaptive control
In this paper, control of a two-link flexible manipulator using neural networks is presented. The neural networks are
trained so as to make the error between the root strain and the desired root strain and the error between the joint angles
and the desired joint angles almost zero. In the process, the neural networks learn the inverse dynamics of the system.
Also we compared the learning algorithms between conventional back propagation method and adaptive learning method.
The numerical results and experimental results show that this NN control system can suppress the vibrations of the
flexible manipulator within a short time in comparison with no feedback. Numerical and experimental results for the
vibration control of a two-link flexible manipulator are presented and verify that the proposed NN control system is
effective at controlling flexible dynamical systems.
This paper presents a passivity based control combined with velocity estimation by the extended Kalman filter for a
magnetically levitated flexible beam with both ends free. Passivity analyses result in that the system can be decomposed
into two passive subsystems: a mechanical subsystem that consists of the flexible beam with both ends free and an
electrical subsystem that is comprised of electromagnets. An output feedback controller combined with velocity
estimation by the extended Kalman filter for the mechanical subsystem computes desired force required to achieve
position convergence and vibration suppression of the flexible beam. In a practical point of view, economic
considerations restrict accurate measurements of both position and velocity of the supporting object while the control
strategy requires accurate measurements of them. Thus, we employ the extended Kalman filtering technique in order to
reject noise in the velocity signals at high sampling frequencies and/or micro-displacement vibrations. A feed forward
controller that generates the desired force is designed for the electrical subsystem. Effectiveness of the proposed
controller and velocity estimator is demonstrated by a numerical simulation.
This paper presents a system identification process and control system design of an artificial neural network based suspension assembly with self-sensing micro-actuator for dual-stage hard disk drive. Artificial neural networks can be used effectively for the identification and control of nonlinear dynamical systems such as a flexible micro-actuator and self-sensing system. Three neural networks are developed for the self-sensing micro-actuator, the first for system identification, the second for inverse model for control using laser sensor signal, and the third for inverse model for control using only self-sensing piezoelectric signal. And we use a neural network inverse model to control the suspension assembly which includes the micro-actuator pair. Simulation and experimental results show that good control performance can be achieved by using artificial neural networks approach.
In this paper an active random noise control using adaptive learning rate neural networks with an immune feedback law is presented. The adaptive learning rate strategy increases the learning rate by a small constant if the current partial derivative of the objective function with respect to the weight and the exponential average of the previous derivatives have the same sign, otherwise the learning rate is decreased by a proportion of its value. The use of an adaptive learning rate attempts to keep the learning step size as large as possible without leading to oscillation. In the proposed method, because of the immune feedback law change a learning rate of the neural networks individually and adaptively, it is expected that a cost function minimize rapidly and training time is decreased. Numerical simulations and experiments of active random noise control with the transfer function of the error path will be performed, to validate the convergence properties of the adaptive learning rate Neural Networks with the immune feedback law. Control results show that adaptive learning rate Neural Networks control structure can outperform linear controllers and conventional neural network controller for the active random noise control.
KEYWORDS: Digital signal processing, Sensors, Digital filtering, Amplifiers, Control systems, Solids, Motion estimation, Beam analyzers, Motion measurement, Actuators
This paper dealt with a problem of vibration suppression of a piezoelectric beam using a self-sensing algorithm. Two methods, which are PPF(positive position feedback) and SRF(strain rate feedback), were considered to suppress a residual vibration of a piezoelectric beam developed during the step positioning of a beam end point. A self-sensing algorithm treated here is basically a strain rate estimator of a beam movement and is to be used for the closed loop
control. The efficacy of the proposed idea was evaluated through experiments.
KEYWORDS: Bridges, System identification, Ferroelectric materials, Sensors, Smart structures, Vibration control, Systems modeling, Amplifiers, Feedback control, Control systems
This paper addresses system identification and vibration control of a cantilever fabricated from piezoelectric materials (PZT), and shows how system identification and state estimation can be used to achieve self-maintenance of a self-sensing system. Currently, self-sensing systems that have concurrent actuation and sensing can be made by using a bridge circuit. However, hardware tuning is still needed due to the unstable nature of an imbalanced bridge circuit. This problem becomes serious in the space environment where human beings may not be available to perform the maintenance. A method of achieving self-sensing without a bridge circuit is proposed in this paper. Analysis of the system dynamics indicates that the subsystem corresponding to the bridge circuit for a self-sensing cantilever with PZT can be described as a direct transmission component in the state space expression of the system. This means that the problem of balancing the bridge circuit is equivalent to the system identification and state estimation problem. By performing a simple experiment, a model of the system was identified using the 4SID (SubSpace State Space Identification method). Observer theory can be used to estimate state vectors which include information about the mechanical dynamics. Thus, system stability depends on the estimated value of the state vectors. The system can be stabilized using a state feedback controller such as a LQ controller. The proposed method was verified with experimental results, demonstrating that smart structures can achieve self-maintenance.
A novel alignment technology for electron-beam lithography is proposed for hybrid use with i-line steppers. This alignment technology was developed based on the evaluation of alignment characteristics and on the investigation of alignment errors in electron-beam lithography systems used in the mix-and-match process. In this alignment method, global alignment using representative chips on a wafer effectively achieves accurate overlay and high throughput. Overlay measurements showed that the deviation in the alignment error is smaller than 70 nm within 3 sigma.
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