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Fuzzy iterative learning control and numerical simulation of tall building seismic response control

Wang Quan,Wang Jianguo,Zhang Mingxiang

Strategic Study of CAE 2011, Volume 13, Issue 4,   Pages 81-86

Abstract: logic and iterative learning control (ILC), this paper provides a new type of fuzzy iterative learningcontrol strategy to reduce the seismic response of tall building.It improves the robustness of the iterative learning control.The result of simulation shows that fuzzy iterative learning control strategy can control the seismicresponse of the building effectively, and has advantages of simple and practical learning control law

Keywords： tall building     seismic response     iterative learning control     fuzzy control    

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Disturbance rejection via iterative learning controlwith a disturbance observer for active magnetic bearing None

Ze-zhi TANG, Yuan-jin YU, Zhen-hong LI, Zheng-tao DING

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 1,   Pages 131-140 doi: 10.1631/FITEE.1800558

Abstract:

Although standard iterative learning control (ILC) approaches can achieve perfect tracking for activeIn contrast to existing approaches, we address the tracking control problem of AMB systems under iteration-variantdisturbances that are in different channels from the control inputs.Using only output feedback, the proposed control approach estimates and attenuates the disturbances inSimulation and comparison studies demonstrate the superior tracking performance of the proposed control

Keywords： Active magnetic bearings (AMBs)     Iterative learning control (ILC)     Disturbance observer    

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Iterative HOEO fusion strategy: a promising tool for enhancing bearing fault feature

Frontiers of Mechanical Engineering 2023, Volume 18, Issue 1, doi: 10.1007/s11465-022-0725-z

Abstract: Second, an enhanced manifold learning algorithm is performed on the normalized MDIM to extract the intrinsic

Keywords： higher order energy operator     fault diagnosis     manifold learning     rolling element bearing     information    

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An integrated approach for machine-learning-based system identification of dynamical systems under control: application towards the model predictive control of a highly nonlinear reactor system

Frontiers of Chemical Science and Engineering 2022, Volume 16, Issue 2,   Pages 237-250 doi: 10.1007/s11705-021-2058-6

Abstract: Advanced model-based control strategies, e.g., model predictive control, can offer superior control ofnonlinear continuous-time multiple-input multiple-output system models for nonlinear model predictive controlThis procedure successfully identified system models that enabled effective control in both servo andThis demonstration of how such system models could be identified for nonlinear model predictive controlwhich, in the face of process uncertainties or modelling limitations, allow rapid and stable control

Keywords： nonlinear model predictive control     black-box modeling     continuous-time system identification     machinelearning     industrial applications of process control    

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INTERACTIVE KNOWLEDGE LEARNING BY ARTIFICIAL INTELLIGENCE FOR SMALLHOLDERS

Frontiers of Agricultural Science and Engineering 2023, Volume 10, Issue 4,   Pages 648-653 doi: 10.15302/J-FASE-2023505

Abstract: In the past, both the direct learning approach and the personnel extension system for improving fertilizationTherefore, this article proposes an interactive knowledge learning approach using artificial intelligenceThe interactive knowledge learning approach aims to identify and rectify incorrect practices in the knowledge-basedInvestigations show that the interactive knowledge learning approach can make a strong contribution to

Keywords： artificial intelligence     extension system     non-point source pollution control     smallholders     fertilization    

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Wavelet-based iterative data enhancement for implementation in purification of modal frequency for extremely

Hassan YOUSEFI, Alireza TAGHAVI KANI, Iradj MAHMOUDZADEH KANI, Soheil MOHAMMADI

Frontiers of Structural and Civil Engineering 2020, Volume 14, Issue 2,   Pages 446-472 doi: 10.1007/s11709-019-0605-8

Abstract: The main purpose of the present study is to enhance high-level noisy data by a wavelet-based iterativeFor this iterative method, a simple computational approach is proposed to estimate the dynamic threshold

Keywords： ambient vibration test     high level noise     iterative signal enhancement     wavelet     cross and autocorrelation    

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Sliding window games for cooperative building temperature control using a distributed learning method

Zhaohui ZHANG, Ruilong DENG, Tao YUAN, S. Joe QIN

Frontiers of Engineering Management 2017, Volume 4, Issue 3,   Pages 304-314 doi: 10.15302/J-FEM-2017045

Abstract: During the games, a distributed learning algorithm based on game theory is proposed such that each building

Keywords： game theory     demand response     HVAC control     multi-building system    

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Iterative finite element model of nonlinear viscoplastic analyses for blended granular porous media

WU Yuching, ZHU Cimian

Frontiers of Structural and Civil Engineering 2007, Volume 1, Issue 4,   Pages 464-473 doi: 10.1007/s11709-007-0063-6

Abstract: The iterative finite element model, in which an element is used to represent a single particle, is generated

Keywords： nonlinear viscoplastic     iterative     behavior     multiple-material    

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Improving prodeoxyviolacein production via multiplex SCRaMbLE iterative cycles

Juan Wang, Bin Jia, Zexiong Xie, Yunxiang Li, Yingjin Yuan

Frontiers of Chemical Science and Engineering 2018, Volume 12, Issue 4,   Pages 806-814 doi: 10.1007/s11705-018-1739-2

Abstract: and continuously generate genome diversification with the desired phenotype, the multiplex SCRaMbLE iterative

Keywords： synthetic biology     genome rearrangement     prodeoxyviolacein     SCRaMbLE     Saccharomyces cerevisiae    

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A fast compound direct iterative algorithm for solving transient line contact elastohydrodynamic lubrication

Jian LIU,Yuxue CHEN,Zhenzhi HE,Shunian YANG

Frontiers of Mechanical Engineering 2014, Volume 9, Issue 2,   Pages 156-167 doi: 10.1007/s11465-014-0297-7

Abstract:

A fast compound direct iterative algorithm for solving transient line contact elastohydrodynamicFirst, by introducing a special matrix splitting iteration method into the traditional compound direct iterativethe new algorithm increases computing speed several times more than the traditional compound direct iterative

Keywords： elastohydrodynamic lubrication     transient     line contact     matrix splitting iteration method     the Thomas method    

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Machine Learning and Data-Driven Techniques for the Control of Smart Power Generation Systems: An Uncertainty Review

Li Sun, Fengqi You

Engineering 2021, Volume 7, Issue 9,   Pages 1239-1247 doi: 10.1016/j.eng.2021.04.020

Abstract: The burgeoning era of machine learning (ML) and data-driven control (DDC) techniques promises an improvedThis paper reviews typical applications of ML and DDC at the level of monitoring, control, optimizationA holistic view is provided on the control techniques of smart power generation, from the regulation

Keywords： Smart power generation     Machine learning     Data-driven control     Systems engineering    

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A modified neural learning algorithm for online rotor resistance estimation in vector controlled induction

A. CHITRA,S. HIMAVATHI

Frontiers in Energy 2015, Volume 9, Issue 1,   Pages 22-30 doi: 10.1007/s11708-014-0339-1

Abstract: The training algorithm of the neural network determines its learning speed, stability, weight convergenceIn this paper, the neural estimator has been studied with conventional and proposed learning algorithmsThe proposed learning algorithm is found to exhibit good estimation and tracking capabilities.

Keywords： neural networks     back propagation (BP)     rotor resistance estimators     vector control     induction motor    

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Minimax Q-learning design for H∞ control of linear discrete-time systems Research Articles

Xinxing LI, Lele XI, Wenzhong ZHA, Zhihong PENG,lixinxing_1006@163.com,xilele.bit@gmail.com,zhawenzhong@126.com,peng@bit.edu.cn

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 3,   Pages 438-451 doi: 10.1631/FITEE.2000446

Abstract: The proposed method, which employs off-policy , learns the optimal control policies for the controllerDifferent from existing -learning methods, a novel gradient-based policy improvement scheme is proposedWe prove that the method converges to the saddle solution under initially admissible control policiesand an appropriate positive learning rate, provided that certain persistence of excitation (PE) conditions

Keywords： H∞ control     Zero-sum dynamic game     Reinforcement learning     Adaptive dynamic programming     Minimax Q-learning    

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Design and Implementation of Intelligent Risk Control Platform Based on Big Data

Zhang Ming, Liu Pei

Strategic Study of CAE 2020, Volume 22, Issue 6,   Pages 111-120 doi: 10.15302/J-SSCAE-2020.06.015

Abstract: To help banks accelerate the establishment of risk control platforms in the era of digital economy, thisstudy proposes an overall framework of an intelligent risk control platform with “five layersWhile ensuring the efficient and stable operation of the risk control platform, it can also provide sufficientsupport for risk control experts in risk control operation, data analysis, model design, and rule adjustmentFinally, using the intelligent risk control platform deployed by a financial institution as an example

Keywords： risk control，big data，machine learning，real-time computation，financial industry    

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Non-iterative parameter estimation of the 2R-1Cmodel suitable for low-cost embedded hardware Article

Mitar SIMIĆ, Zdenka BABIĆ, Vladimir RISOJEVIĆ, Goran M. STOJANOVIĆ

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 3,   Pages 476-490 doi: 10.1631/FITEE.1900112

Abstract: Parameter estimation of the 2R-1C model is usually performed using iterative methods that require high-performanceIn this study, we propose the quadratic interpolation non-iterative parameter estimation (QINIPE) methodreduces the number of required measurement points by 80% in comparison with our previously reported non-iterativeBoth non-iterative methods are implemented on a microcontroller-based device; the estimation accuracy

Keywords： 2R-1C model     Embedded systems     Parameter estimation     Non-iterative methods     Quadratic interpolation    

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Title Author Date Type Operation

Fuzzy iterative learning control and numerical simulation of tall building seismic response control

Wang Quan,Wang Jianguo,Zhang Mingxiang

Journal Article

Disturbance rejection via iterative learning controlwith a disturbance observer for active magnetic bearing

Ze-zhi TANG, Yuan-jin YU, Zhen-hong LI, Zheng-tao DING

Journal Article

Iterative HOEO fusion strategy: a promising tool for enhancing bearing fault feature

Journal Article

An integrated approach for machine-learning-based system identification of dynamical systems under control: application towards the model predictive control of a highly nonlinear reactor system

Journal Article

INTERACTIVE KNOWLEDGE LEARNING BY ARTIFICIAL INTELLIGENCE FOR SMALLHOLDERS

Journal Article

Wavelet-based iterative data enhancement for implementation in purification of modal frequency for extremely

Hassan YOUSEFI, Alireza TAGHAVI KANI, Iradj MAHMOUDZADEH KANI, Soheil MOHAMMADI

Journal Article

Sliding window games for cooperative building temperature control using a distributed learning method

Zhaohui ZHANG, Ruilong DENG, Tao YUAN, S. Joe QIN

Journal Article

Iterative finite element model of nonlinear viscoplastic analyses for blended granular porous media

WU Yuching, ZHU Cimian

Journal Article

Improving prodeoxyviolacein production via multiplex SCRaMbLE iterative cycles

Juan Wang, Bin Jia, Zexiong Xie, Yunxiang Li, Yingjin Yuan

Journal Article

A fast compound direct iterative algorithm for solving transient line contact elastohydrodynamic lubrication

Jian LIU,Yuxue CHEN,Zhenzhi HE,Shunian YANG

Journal Article

Machine Learning and Data-Driven Techniques for the Control of Smart Power Generation Systems: An Uncertainty

Li Sun, Fengqi You

Journal Article

A modified neural learning algorithm for online rotor resistance estimation in vector controlled induction

A. CHITRA,S. HIMAVATHI

Journal Article

Minimax Q-learning design for H∞ control of linear discrete-time systems

Xinxing LI, Lele XI, Wenzhong ZHA, Zhihong PENG,lixinxing_1006@163.com,xilele.bit@gmail.com,zhawenzhong@126.com,peng@bit.edu.cn

Journal Article

Design and Implementation of Intelligent Risk Control Platform Based on Big Data

Zhang Ming, Liu Pei

Journal Article

Non-iterative parameter estimation of the 2R-1Cmodel suitable for low-cost embedded hardware

Mitar SIMIĆ, Zdenka BABIĆ, Vladimir RISOJEVIĆ, Goran M. STOJANOVIĆ

Journal Article