Linearizing Control of Induction Motor Based on Networked Control Systems

A new approach to speed control of induction motors is developed by introducing networked control systems （NCSs） into the induction motor driving system. The control strategy is to stabilize and track the rotor speed of the induction motor when the network time delay occurs in the transport medium of network data. First, a feedback linearization method is used to achieve input-output linearization and decoupling control of the induction motor driving system based on rotor flux model, and then the characteristic of network data is analyzed in terms of the inherent network time delay. A networked control model of an induction motor is established. The sufficient condition of asymptotic stability for the networked induction motor driving system is given, and the state feedback controller is obtained by solving the linear matrix inequalities （LMIs）. Simulation results verify the efficiency of the proposed scheme.

Time delay recursive neural network-based direct adaptive control for a piezo-actuated stage

Piezo-actuated stage is a core component in micro-nano manufacturing field.However,the inherent nonlinearity,such as rate-dependent hysteresis,in the piezo-actuated stage severely impacts its tracking accuracy.This study proposes a direct adaptive control(DAC)method to realize high precision tracking.The proposed controller is designed by a time delay recursive neural network.Compared with those existing DAC methods designed under the general Lipschitz condition,the proposed control method can be easily generalized to the actual systems,which have hysteresis behavior.Then,a hopfield neural network(HNN)estimator is proposed to adjust the parameters of the proposed controller online.Meanwhile,a modular model consisting of linear submodel,hysteresis submodel,and lumped uncertainties is established based on the HNN estimator to describe the piezoactuated stage in this study.Thus,the performance of the HNN estimator can be exhibited visually through the modeling results.The proposed control method eradicates the adverse effects on the control performance arising from the inaccuracy in establishing the offline model and improves the capability to suppress the influence of hysteresis on the tracking accuracy of piezo-actuated stage in comparison with the conventional DAC methods.The stability of the control system is studied.Finally,a series of comparison experiments with a dual neural networks-based data driven adaptive controller are carried out to demonstrate the superiority of the proposed controller.

Global Asymptotic Synchronization of a Class of BAM Neural Networks with Time Delays via Integrating Inequality Techniques

In this paper,the authors are concerned with global asymptotic synchronization for a class of BAM neural networks with time delays.Instead of using Lyapunov functional method,LMI method and matrix measure method which are recently widely applied to investigating global exponential/asymptotic synchronization for neural networks,two novel sufficient conditions on global asymptotic synchronization of above BAM neural networks are established by using a kind of new study method of global synchronization:Integrating inequality techniques.The method and results extend the study of global synchronization of neural networks.

Finite-Time Stability for Fractional-Order Bidirectional Associative Memory Neural Networks with Time Delays

This paper is concerned with fractional-order bidirectional associative memory(BAM) neural networks with time delays. Applying Laplace transform, the generalized Gronwall inequality and estimates of Mittag–Leffler functions, some sufficient conditions which ensure the finite-time stability of fractional-order bidirectional associative memory neural networks with time delays are obtained. Two examples with their simulations are given to illustrate the theoretical findings. Our results are new and complement previously known results.

50 GHz optical true time delay beamforming in hybrid optical/mm-wave access networks with multi-core optical fiber distribution

We propose and experimentally validate an optical true time delay beamforming scheme with straightforward integration into hybrid optical/millimeter（mm）-wave access networks. In the proposed approach, the most complex functions, including the beamforming network, are implemented in a central office, reducing the complexity and cost of remote antenna units. Different cores in a multi-core fiber are used to distribute the modulated signals to high-speed photodetectors acting as heterodyne mixers. The mm-wave carrier frequency is fixed to 50 GHz（VBand）, thereby imposing a progressive delay between antenna elements of a few picoseconds. That true time delay is achieved with an accuracy lower than 1 ps and low phase noise.

Real-Time Control for CPS of Digital Airplane Assembly with Robust H-Infinity Theory

This study introduces a real-time controller design method under the effects of network time delay and external disturbance. The study first introduces the digital, virtual, intelligent trend of airplane assembly and reveals the status and problems of digital airplane assembly studies. The Cyber-Physical System （CPS） structure is then proposed for digital airplane assembly, and the real-time control issues are discussed. Then, the question of real- time control undertaken by a parallel robot is simplified to a control question with bounded time delay and complex interference, and a mathematical description is presented. Next, a robust Hoo controller with a disturbance degree of decay 7 is designed according to the mathematical description. Finally, a simulation is conducted. All of the experiment results show the feasibility of the above proposed methods.

Performance Comparison of Artificial Neural Network Models for Daily Rainfall Prediction

With an aim to predict rainfall one-day in advance, this paper adopted different neural network models such as feed forward back propagation neural network （BPN）, cascade-forward back propagation neural network （CBPN）, distributed time delay neural network （DTDNN） and nonlinear autoregressive exogenous network （NARX）, and compared their forecasting capabilities. The study deals with two data sets, one containing daily rainfall, temperature and humidity data of Nilgiris and the other containing only daily rainfall data from 14 rain gauge stations located in and around Coonoor （a taluk of Nilgiris）. Based on the performance analysis, NARX network outperformed all the other networks. Though there is no major difference in the performances of BPN, CBPN and DTDNN, yet BPN performed considerably well confirming its prediction capabilities. Levenberg Marquardt proved to be the most effective weight updating technique when compared to different gradient descent approaches. Sensitivity analysis was instrumental in identifying the key predictors.