다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

Time-delay systems: an overview of some recent advances and open problems

An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

This book, written by two major figures in adaptive control, provides a wealth of material for researchers, practitioners, and students. While some researchers in adaptive control may note the absence of a particular topic, the book‘s scope represents a high-gain instrument. It can be used by designers of control systems to enhance their work through the information on many new theoretical developments, and can be used by mathematical control theory specialists to adapt their research to practical needs. The book is strongly recommended to anyone interested in adaptive control.

Adaptive Control

This paper describes a robust adaptive compensation method for friction and force ripple present in the dynamics of permanent-magnet linear motors The method is used in ultraprecise positioning applications The compensation algorithm consists of a PID component and an adaptive component for estimating friction and force ripple The adaptive component is continuously refined on the basis of just prevailing input and output signals Computer simulations and real-time experimental results verify the effectiveness of the proposed scheme for high-precision motion trajectory tracking

Robust adaptive numerical compensation for friction and force ripple in permanent-magnet linear motors

We propose a decentralized neural network (NN) controller for a class of large-scale nonlinear systems with the strong interconnections. The NNs are used to approximate the unknown subsystems and interconnections. Due to the functional approximation capabilities of NNs, the additional precautions are not required to be made for avoiding the possible control singularity problems. Semiglobal asymptotic stability results are obtained and the tracking error converges to zero. Furthermore, the issue of transient performance of the subsystems is also addressed under an analytical framework.

Decentralized control design for large-scale systems with strong interconnections using neural networks

In this paper, we address the problem of precision motion control of permanent-magnet linear motors (PMLMs) under the influence of significant disturbances. We establish a mathematical model of a PMLM driven by a sinusoidal pulsewidth-modulated (PWM) amplifier, obtaining it from a describing function analysis of the essentially nonlinear characteristics. The overall model (PWM+PMLM) inevitably inherits uncertainties in the face of load changes, system parameter perturbation, noise, and inherent system nonlinearities, etc., all of which constitute disturbances to the control system that will adversely affect the precision and accuracy. We propose a robust control scheme employing a disturbance observer to address the sensitivity of the control performance to the disturbances. Real-time experimental results are provided to verify and confirm the practical effectiveness of the proposed approach.

Precision motion control with disturbance observer for pulsewidth-modulated-driven permanent-magnet linear motors

Recent developments in nuclear reprogramming and intracytoplasmic sperm injection reflect an increasing need for more advanced and automatic micromanipulation technologies. In this paper, we present an automatic cell injection system, which is capable of visually monitoring the injecting process and controlling the microactuators. Traditionally, cell injection was manually operated, and it was laborious, time consuming, of low accuracy, and prone to contamination due to the handling requirements. An automatic and efficient strategy is required to eliminate these drawbacks. In this paper, a system is developed where the injection process is monitored and controlled automatically via integration of a vision system to an injector manipulation system. The cell is located, and the pipette is positioned and driven by the algorithm to achieve effective penetration. The precision achieved is physically proven to be within a good tolerance range.

Vision-Servo System for Automated Cell Injection

This paper studies the multistability of a class of discrete-time recurrent neural networks with unsaturating piecewise linear activation functions. It addresses the nondivergence, global attractivity, and complete stability of the networks. Using the local inhibition, conditions for nondivergence are derived, which not only guarantee nondivergence, but also allow for the existence of multiequilibrium points. Under these nondivergence conditions, global attractive compact sets are obtained. Complete stability is studied via constructing novel energy functions and using the well-known Cauchy Convergence Principle. Examples and simulation results are used to illustrate the theory.

Kok Kiong Tan

Papers

Robust adaptive numerical compensation for friction and force ripple in permanent-magnet linear motors

Decentralized control design for large-scale systems with strong interconnections using neural networks

Precision motion control with disturbance observer for pulsewidth-modulated-driven permanent-magnet linear motors

Vision-Servo System for Automated Cell Injection

Multistability of discrete-time recurrent neural networks with unsaturating piecewise linear activation functions