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检索条件"任意字段=9th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2020"

共 506 条记录，以下是81-90 订阅

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Discrete-time Adaptive ILC for Uncertain systems with Unknown Nonlinear Dead Zone Inputs and control Directions 9

Discrete-time Adaptive ILC for Uncertain Systems with Unknow...

引用

9th ieee data driven control and learning systems conference (ddcls)

作者： Xu, Qing-yuan Li, Ya Cheng, Jing Xiao, Tengfei Guangdong Polytech Normal Univ Sch Elect & Informat Guangzhou 510665 Peoples R China Sun Yat Sen Univ Sch Intelligent Syst Engn Guangzhou 510006 Peoples R China

ISBN: (纸本)9781728159225

A discrete-time adaptive iterative learning control (ILC) with Nussbaum function for systems with uncertain nonlinear dead zone inputs and control directions is considered in this work. the adaptive ILC is based on a fuzzy rules emulated network and an adaptive item, where the unknown nonlinear dead zone input as well as iteration-varying uncertainties can be well compensated. through rigorous analysis, the convergence of the proposed discrete-time adaptive ILC is achieved and the boundedness of system signals is guaranteed. In the end, the behaviors of the proposed ILC method are presented by a simulation example.

关键词： unknown nonlinear dead zone input unknown control direction fuzzy rules emulated network adaptive iterative learning control (ILC) Nussbaum function

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Closed-Loop Pitch Attitude control of Biomimetic Robotic Fish 9

Closed-Loop Pitch Attitude Control of Biomimetic Robotic Fis...

引用

9th ieee data driven control and learning systems conference (ddcls)

作者： Zhang, Yujie Li, Zonggang Du, Yajiang Lanzhou Jiaotong Univ Sch Mech Engn Lanzhou Peoples R China Lanzhou Jiaotong Univ Robot Inst Lanzhou Peoples R China

ISBN: (纸本)9781728159225

this paper considers the pitch control of a robotic fish with three degree-of-freedom(DOF) pectoral fins, wire-driven flexible body and a passive caudal fin. the linear model with undetermined parameters is first suggested to describe the dynamics of the pitch angles, which are the control parameters to adjust the posture of robotic fish. then, a control network based on Central Pattern Generators(CPGs) is proposed to separately adjust the state of each DOF. Finally, a compound controller is designed to adjust the amplitudes of pectoral Fins such that the desired pitch angle is tracked, in which an adaptive law combined with RBF neural network is proposed to estimate the undetermined parameters. the stability of the proposed algorithm is proved and then the validity is investigated by the simulation results.

关键词： Biomimetic robotic fish CPG pitch attitude control adaptive control RBF neural network

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A Novel Attracting-Law based Digital control Strategy with Improved Convergence Rate and Steady-State Error 9

A Novel Attracting-Law based Digital Control Strategy with I...

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9th ieee data driven control and learning systems conference (ddcls)

作者： Wu, Lingwei Sun, Mingxuan Taizhou Univ Coll Elect & Informat Engn Taizhou 318000 Peoples R China Zhejiang Univ Technol Coll Informat Engn Hangzhou 310023 Peoples R China

ISBN: (纸本)9781728159225

In this paper, a novel attracting law with improved convergence rate and steady-state error band is presented for uncertain discrete-time systems, which applies the tracking error itself for the control design. the attracting law is designed based on the monotone increasing continuous function taking its value between 0 and 1 for the absolute value of the tracking error. the disturbance compensation is introduced in the attracting law, by which the controller is designed to ensure faster convergence and better robust stability of the closed-loop system. For characterizing the tracking performance, detailed results of both the absolute attractive layer bound and the steady-state error band are given. the validity of the proposed approach is confirmed by simulation results.

关键词： Attracting law Steady-state error band Convergence Discrete-time system

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Model-Free Adaptive control Based on Neural Network Observer for the Chaotic Power Supply System 9

Model-Free Adaptive Control Based on Neural Network Observer...

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9th ieee data driven control and learning systems conference (ddcls)

作者： Bai, Ao Luo, Yanhong Zhang, Huaguang Northeastern Univ Sch Informat Sci & Engn Shenyang 110819 Peoples R China

ISBN: (纸本)9781728159225

In this paper, we considered a type of chaotic power supply system and presented a neural network adaptive method with Neural Network Observer (NNO). First, the mathematical model of the chaotic power supply system is summarized. then aiming for the unknown model of n-order nonlinear system, the controller is designed by the neural network adaptive method. there is no need to know the accurate mathematical model and state information of the controlled object. We estimate the state information and model information of the controlled object through the input and output data of the object first, and use the obtained estimation results to implement the controller, and give the corresponding theoretical analysis. Finally, the effectiveness of the designed controller is verified by simulation of a power system with chaotic motion.

关键词： Neural Network Adaptive data-driven Model-free Adaptive control Power Supply System Chaotic Oscillation

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Synchronization control of Position and Velocity for Heterogeneous Teleoperation System 9

Synchronization Control of Position and Velocity for Heterog...

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9th ieee data driven control and learning systems conference (ddcls)

作者： Liu, Xia Pan, Chengwei Chen, Yong Xihua Univ Sch Elect Engn & Elect Informat Chengdu 610039 Peoples R China Univ Elect Sci & Technol China Sch Automat Engn Chengdu 611731 Peoples R China

ISBN: (纸本)9781728159225

Due to different mechanical structures, communication methods and task spaces, the master robot and the slave robot in a heterogeneous teleoperation system are difficult to keep synchronization. this paper presents a synchronization control strategy of position and velocity for the heterogeneous teleoperation system. the interaction force between the operator and the master is utilized to make the master generate the position information and position variation. the position information of the master is used to control the position of the slave. Meanwhile, the position variation of the master is employed to indirectly control the velocity of the slave. In this way, the position and velocity of the slave can be in synchronization with those of the master. the experimental results demonstrate that with the proposed control strategy the slave can act on the target more accurately and quickly, enhancing the operator's operational flexibility.

关键词： heterogeneous teleoperation system master robot slave robot synchronization control position and velocity

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Iterative learning control (ILC) Guided Reinforcement learning control (RLC) Scheme for Batch Processes 9

Iterative Learning Control (ILC) Guided Reinforcement Learni...

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9th ieee data driven control and learning systems conference (ddcls)

作者： Xu, Xinghai Xie, Huimin Shi, Jia Xiamen Univ Dept Chem & Biochem Engn Sch Chem & Chem Engn Xiamen 361005 Fujian Peoples R China

ISBN: (纸本)9781728159225

Iterative learning control (ILC) is a kind of effective learning control scheme which is mainly designed to solve the problems in controlling a batch or repetitive process. Although the control performances of ILC systems can improved from batch to batch, it still strongly depends on the repeatability of the process and control target. Reinforcement learning (RL) is another learning based optimization algorithm which can be applied to many complicated decision-making scenarios. data-driven based RL algorithms have good robustness due to the generalization of the policy neural network, however, it is low-data efficiency in network training. In this paper, for batch process control we propose a new reinforcement learning control (RLC) scheme which is guided by classical iterative leaning control. On the one hand, this RLC scheme has capability to optimize the policy network faster than RL algorithm without guidance, on the other hand, the generalization of deep policy network improves the robustness of the control system. Based on the numerical simulations, the effectiveness of the proposed control scheme is demonstrated by comparing with the conventional reinforcement learning algorithm and the P-type iterative learning control scheme. this paper provides a new way for the application of reinforcement learning algorithm to batch process control.

关键词： Iterative learning control (ILC) Deep reinforcement learning Batch/repetitive processes

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Intelligent Measurement Modeling Using a Novel Multi-nonlinear Mapping Based Extreme learning Machine Integrated with Partial Least Square Regression 9

Intelligent Measurement Modeling Using a Novel Multi-nonline...

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9th ieee data driven control and learning systems conference (ddcls)

作者： Zhu, Qunxiong Zhang, Xiaohan Xu, Yuan He, Yanlin Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing 100029 Peoples R China Minist Educ China Engn Res Ctr Intelligent PSE Beijing 100029 Peoples R China

ISBN: (纸本)9781728159225

Accurate intelligent measurement modeling plays a key role in complex process industries. However, establishing an accurate and robust measurement model tends to be more and more difficult because of the increasing complexity in terms of nonlinearity and collinearity of data. To solve this problem, a novel multi-nonlinear mapping based extreme learning machine integrated with partial least square regression is proposed in this paper. In the proposed model, two problems of nonlinearity and collinearity are effectively dealt with by using multi-nonlinear mapping and partial least square regression, respectively. For evaluating performance, empirical studies on a commonly used bench mark problem and a real-world application confirm that the presented method can obtain high accuracy and high stability performance for intelligent measurement.

关键词： Extreme learning Machine Intelligent measurement Ensemble Modeling Partial least squares regression

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A Variable Parameter Model-Free Adaptive control Algorithm and Its Application in Distillation Tower System 9

A Variable Parameter Model-Free Adaptive Control Algorithm a...

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9th ieee data driven control and learning systems conference (ddcls)

作者： Feng, Yu Dong, Na Li, Yongzhou Lv, Wenjin Tianjin Univ Sch Elect & Informat Engn 92 Weijin Rd Tianjin Peoples R China

ISBN: (纸本)9781728159225

In order to achieve better control performance of chemical process, model free adaptive control (MFAC) scheme is improved by adding two new parameters L-1, L-2, furthermore to apply in distillation tower system. Compared with basic MFAC, the number of parameters in this novel method is reduced and variable. Firstly, nonlinear system with time-varying desired output is used to carry out numerical simulation for the sake of verifying the effectiveness of this algorithm. After that, the improved MFAC algorithm is applied to the control of the distillation tower system, and the result fully demonstrates the proposed algorithm has strong stability, fast tracking speed. At last, for many systems with time delay in chemical process, such as distillation tower system, a set of validated control method frameworks is proposed in this paper. It is expected to be universally popularized and applied to the control of chemical process.

关键词： Model-free adaptive control Parameter estimation control and Distillation tower system

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Co-occurrence Mining of "data driven control", "model free adaptive control" and "iterative learning control" in Chinese Dissertation data 9

Co-occurrence Mining of "data driven control", "model free a...

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9th ieee data driven control and learning systems conference (ddcls)

作者： Liu, Xiangpeng Ai, Yang Guo, Lantian Chi, Ronghu Qingdao Univ Sci & Technol Sch Math & Phys Qingdao 266061 Peoples R China Qingdao Univ Sci & Technol Sch Automat & Elect Engn Qingdao 266061 Peoples R China

ISBN: (纸本)9781728159225

this paper focuses on the master and doctoral dissertations on "data-driven control", "model-free adaptive control" and "iterative learning control" collected by Chinese National Knowledge Infrastructure (CNKI) database before October 31, 2019. Exploiting big data technology, three topics mentioned above are studied in the form of mapping knowledge graph by Python, ROSTCM and Ucinet social network analysis software. Our research mainly explores and analyzes the temporal evolution as well as development status of the subject words, the cultivation organizations, the fund support, the co-occurrence of key words, the co-occurrence of dissertations topics and the characteristics of supervisor -student relationship, etc., which can provide references for relevant research fields.

关键词： big data the co-occurrence mining knowledge graph CNKI

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Open-Closed-Loop Iterative learning control for Discrete-time systems with Vector Relative Degree under Iterative Varying Duration 9

Open-Closed-Loop Iterative Learning Control for Discrete-tim...

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9th ieee data driven control and learning systems conference (ddcls)

作者： Wei, Yun-Shan Weng, Zhi-Bin Xiao, Teng-Fei Guangzhou Univ Sch Mech & Elect Engn Guangzhou 510006 Peoples R China Ctr Intelligent Equipment & Internet Connected Sy Guangzhou 510006 Peoples R China Sun Yat Sen Univ Sch Intelligent Syst Engn Guangzhou Peoples R China

ISBN: (纸本)9781728159225

An open-closed-loop iterative learning control (ILC) scheme is presented for linear discrete-time multiple-input multiple-output (MIMO) systems with iterative varying duration in this article, where the vector relative degree of MIMO system is considered. To recompense the absent tracking information at former iterations caused by iterative varying duration, the feedback control strategy is adopted in modified tracking error based ILC law. It is proved that the convergent condition depends on the P-type control gain. By selecting the proper feedback control gain, the developed open-closed-loop ILC scheme can expedite the convergent speed. Finally, a numerical example is provided for validation.

关键词： Iterative learning control Vector relative degree Open-closed-loop Iterative varying duration

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