检索结果-南通市图书馆

同方期刊数据库

改进无模型自适应迭代学习的直线电机滑模控制

在线全文

学校读者我要写书评

暂无评论

电机与控制学报 2024年第7期28卷 120-130页

作者：郑鑫鑫张大海曹荣敏侯忠生北京交通大学电气工程学院北京100044 北京信息科技大学自动化学院北京100192 青岛大学自动化学院山东青岛266071

二维直线电机具有非线性、多变量及强耦合等特点,其精确模型无法获得,同时,在实际过程中由于参数摄动及扰动而造成的运行不稳定现象,致使对其跟踪控制十分困难。针对以上问题,根据无模型自适应迭代学习控制不依赖被控系统精确数学模型... 详细信息

二维直线电机具有非线性、多变量及强耦合等特点,其精确模型无法获得,同时,在实际过程中由于参数摄动及扰动而造成的运行不稳定现象,致使对其跟踪控制十分困难。针对以上问题,根据无模型自适应迭代学习控制不依赖被控系统精确数学模型的特点,及滑模控制具有可设计且与对象参数和扰动无关的滑动模态,提出一种改进无模型自适应迭代学习的滑模结构复合控制策略。在无模型自适应迭代学习控制方案的准则函数中加入误差变化率,并对其收敛性进行分析论证;然后在紧格式动态线性化基础上,设计指数趋近律滑模控制,使改进无模型自适应迭代学习的滑模控制复合策略能够克服不稳定现象并具有很强的鲁棒性,从而进一步提高系统响应速度和控制精度。最后通过仿真和实物验证,控制精度稳定在1μm范围内,并与其他控制方案相比,验证了所提方法的准确性和有效性。

关键词：改进无模型自适应迭代学习控制误差变化率准则函数滑模控制二维直线电机

A NEW NEURAL NETWORK-BASED ADAPTIVE ILC FOR NONLINEAR DISCRETE-TIME SYSTEMS WITH DEAD ZONE SCHEME

同方期刊数据库博看期刊评论

在线全文

学校读者我要写书评

暂无评论

Journal of Systems Science & Complexity 2009年第3期22卷 435-445页

作者： Ronghu CHI zhongsheng hou Institute of Autonomous Navigation and Intelligent Control School of Automation and Electrical EngineeringQingdao University of Science and Technology Qingdao 266042 China. Advanced Control Systems Laboratory School of Electronics and Information Engineering Beijing JiaotongUniversity Beijing 100044 China.

By introducing a deadwzone scheme, a new neural network based adaptive iterative learning control （ILC）（NN-AILC） scheme is presented for nonlinear discrete-time systems, where the NN weights are time-varying. The most distinct contribution of the proposed NN-AILC is the relaxation of the identical conditions of initial state and reference trajectory, which are common requirements in traditional ILC problems. Convergence analysis indicates that the tracking error converges to a bounded ball, whose size is determined by the dead-zone nonlinearity. Computer simulations verify the theoretical results.

关键词： Adaptive control iterative learning control neural network non-identical initial condition,non-identical trajectory.

Robust iterative learning control for nonlinear systems with measurement disturbances

在线全文

学校读者我要写书评

暂无评论

Journal of Systems Engineering and Electronics 2012年第6期23卷 906-913页

作者： Xuhui BuI FashanYu zhongsheng hou Haizhu Yang School of Electrical Engineering & Automation Henan Polytechnic University Jiaozuo 454003 E R. China Henan Provincial Open Laboratory for Control Engineering Key Discipline Henan Polytechnic University Jiaozuo 454003 P. R. China Advanced Control Systems Laboratory Beijing Jiaotong University Beijing 100044 P. R. China

The iterative learning control （ILC） has been demon-strated to be capable of considerably improving the tracking perfor-mance of systems which are affected by the iteration-independent disturbance. However, the achievable performance is greatly degraded when iteration-dependent, stochastic disturbances are pre-sented. This paper considers the robustness of the ILC algorithm for the nonlinear system in presence of stochastic measurement disturbances. The robust convergence of the P-type ILC algorithm is firstly addressed, and then an improved ILC algorithm with a decreasing gain is proposed. Theoretical analyses show that the proposed algorithm can guarantee that the tracking error of the nonlinear system tends to zero in presence of measurement dis-turbances. The analysis is also supported by a numerical example.

关键词： iterative learning control (ILC) nonlinear system mea-surement disturbance iteration-varying disturbance.

Event-Triggered Asymmetric Bipartite Consensus Tracking for Nonlinear Multi-Agent Systems Based on Model-Free Adaptive Control

在线全文

学校读者我要写书评

暂无评论

IEEE/CAA Journal of Automatica Sinica 2023年第3期10卷 662-672页

作者： Jiaqi Liang Xuhui Bu Lizhi Cui zhongsheng hou School of Electrical Engineering and Automation Henan Polytechnic UniversityJiaozuo 454003China School of Automation Qingdao UniversityQingdao 266071China IEEE

In this paper,an asymmetric bipartite consensus problem for the nonlinear multi-agent systems with cooperative and antagonistic interactions is studied under the event-triggered *** the agents described by a structurally balanced signed digraph,the asymmetric bipartite consensus objective is firstly defined,assigning the agents'output to different signs and module *** with the completely unknown dynamics of the agents,a novel event-triggered model-free adaptive bipartite control protocol is designed based on the agents'triggered outputs and an equivalent compact form data *** utilizing the Lyapunov analysis method,the threshold of the triggering condition is ***,the asymptotic convergence of the tracking error is deduced and a sufficient condition is obtained based on the contraction mapping ***,the simulation example further demonstrates the effectiveness of the protocol.

关键词： Asymmetric bipartite consensus tracking eventtriggered model-free adaptive control(MFAC) nonlinear systems signed digraph

Quasi-Newton method based control design for unknown nonlinear systems with input constraints

在线全文

学校读者我要写书评

暂无评论

Science China(Information Sciences) 2020年第5期63卷 224-226页

作者： Shuangshuang XIONG zhongsheng hou Chenkun YIN Advanced Control Systems Lab School of Electronic and Information EngineeringBeijing Jiaotong University School of Automation Qingdao University

Dear editor,With the development in digital computers, various and huge data of controlled plants are obtained and stored, which has led to a rapid growth in the application of numerical methods in controller design. In recent years, the quasi-Newton method [1, 2], as one of the powerful numerical methods which has superlinear convergence speed

关键词： Newton Quasi-Newton method based control design for unknown nonlinear systems with input constraints

Composite Model-free Adaptive Predictive Control for Wind Power Generation Based on Full Wind Speed

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

CSEE Journal of Power and Energy Systems 2022年第6期8卷 1659-1669页

作者： Shuangxin Wang Jianshen Li zhongsheng hou Qingye Meng Meng Li School of Mechanical Electronic and Control EngineeringBeijing Jiaotong UniversityBeijing 100044China.Z.S.Hou is with the School of Electronic and Information EngineeringBeijing Jiaotong UniversityBeijing 100044China

Aiming at the problem that the existing model-based control strategy cannot fully reflect stochastic fluctuations of wind power,this paper presents a model-free adaptive predictive controller(MFAPC)for variable pitch systems with speed disturbance ***,an improved small-world neural network with topology optimization is used for 15-second-ahead forecasting of wind speed,whose rolling time is 1s,and the predicted value serves as a feedforward to obtain the early compensation variation of the pitch ***,a function of the multi-objective optimization at full wind speed with optimal power point tracking and minimum control variation is constructed,and an advanced one-step adaptive predictive control algorithm for wind power is proposed based on the online estimation and prediction of the time-varying pseudo partial derivative(PPD).In addition,the compound MFAPC framework is synthetically obtained,whose closed-loop effectiveness is verified by a BP-built pitch system based on the SCADA data with all working *** of the schemes has been analyzed in terms of parametric uncertainties and different operating conditions,and a detailed comparison is finally *** results show that the proposed MFAPC can not only effectively suppress the random disturbance of wind speed,but also meet the stability of wind power and the security of grid-connections for all operating conditions.

关键词： Feedforward correction full wind speed model-free adaptive predictive control(MFAPC) wind power wind speed prediction

On iterative learning control design for tracking iteration-varying trajectories with high-order internal model

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

控制理论与应用（英文版） 2010年第3期8卷 309-316页

作者： Chenkun YIN Jianxin XU zhongsheng hou Advanced Control Systems Laboratory School of Electronics and Information Engineering Beijing Jiaotong University Beijing 100044 China Department of Electrical and Computer Engineering National University of Singapore Singapore 117576 State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing 100044 China

In this paper, iterative learning control （ILC） design is studied for an iteration-varying tracking problem in which reference trajectories are generated by high-order internal models （HOLM）. An HOlM formulated as a polynomial operator between consecutive iterations describes the changes of desired trajectories in the iteration domain and makes the iterative learning problem become iteration varying. The classical ILC for tracking iteration-invariant reference trajectories, on the other hand, is a special case of HOlM where the polynomial renders to a unity coefficient or a special first-order internal model. By inserting the HOlM into P-type ILC, the tracking performance along the iteration axis is investigated for a class of continuous-time nonlinear systems. Time-weighted norm method is utilized to guarantee validity of proposed algorithm in a sense of data-driven control.

关键词： ILC High-order intemal model Iteration-varying Nonlinear systems Continuous-time

Iterative Learning Control for Discrete-Time Nonlinear Systems Based on Adaptive Tuning of 2D Learning Gain

在线全文

学校读者我要写书评

暂无评论

Iterative Learning Control for Discrete-Time Nonlinear Syste...

Effect Analysis of Data Dropout on Iterative learning control

第36届中国控制会议

作者： Xian Yu zhongsheng hou Chenkun Yin Advanced Control Systems Laboratory School of Electronics and Information EngineeringBeijing Jiaotong University

ISBN: (纸本)9781538629185

A novel data-driven iterative learning control（ILC） based on the adaptive tuning of the 2D learning gain,is proposed in this paper for a class of general discrete-time nonlinear SISO *** on an equivalent compact form dynamic linearization data model of the controlled nonlinear system in the iteration domain,an iterative learning law is formulated by using a recursive search algorithm to adaptively tune the 2D learning gain with only the requirement of the measured I/O data of the controlled nonlinear *** theoretical analysis shows that the proposed ILC guarantees convergence of the tracking *** effectiveness of the proposed ILC is validated by simulations on a complex unknown nonlinear system with time-varying structure,order and parameters.

关键词： Iterative Learning Control Compact Form Dynamic Linearization Recursive Search Algorithm Discrete-Time Nonlinear System

来源： cnki会议评论

在线全文

cnki会议

学校读者我要写书评

暂无评论

Effect Analysis of Data Dropout on Iterative learning contro...