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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311-320 订阅

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Consensus learning Tracking of Two-dimensional Discrete Networks Based on Sliding Mode Method

Consensus Learning Tracking of Two-dimensional Discrete Netw...

引用

data driven control and learning systems (ddcls)

作者： Zijian Luo Wenjun Xiong School of Economic Information Engineering Southwestern University of Finance and Economics Chengdu P. R. China

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

In this paper, the consensus tracking of two-dimensional discrete networks is studied by designing a kind of control strategies. the designed control strategies combine the advantages of sliding mode control and iterative learning control, which can be applied in general situations. And it is proven that the consensus tracking can be achieved by running repeatedly in a finite time interval. Some sufficient conditions are presented to guarantee the asymptotical convergence of the tracking error. Finally, the effectiveness is illustrated by numerical simulations.

关键词： Manganese Technological innovation Economics Convergence Traffic control Sliding mode control Robustness

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Iterative learning Reliable control Strategy for a Class of SISO systems with Time-Delay and Actuator Faults

Iterative Learning Reliable Control Strategy for a Class of ...

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data driven control and learning systems (ddcls)

作者： Heng Liu Yanjie Wang Ruikun Zhang School of Mathematics and Physics Qingdao University of Science and Technology Qingdao China Research Institute for Mathematics and Interdisciplinary Sciences Qingdao University of Science and Technology

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

this manuscript presents an iterative learning control strategy for a class of single-input single-output (SISO) nonlinearly parameterized systems with unknown time-varying state delays and actuator faults. Based on some basic assumptions and the property of the state delays and actuator faults of the SISO nonlinear system, we design the P-type iterative learning reliable controller to deal with the nonlinearity caused by the time-delays term and actuator faults. And then, a composite energy function(CEF) is used to show the convergence property of the state tracking error. Finally, a numerical simulation is used to verify the correctness and effectiveness of control scheme.

关键词： Actuators Delays Iterative learning control Convergence control systems Nonlinear systems Uncertainty

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data-driven Backstepping control of Chemical Process

Data-Driven Backstepping Control of Chemical Process

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data driven control and learning systems (ddcls)

作者： Jiawen Gao Jingwen Huang Beijing University of Chemical Technology Beijing China

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

Modeling and control design of complex chemical processes are challenge tasks because of their multi-variable, timedelay and non-linear features. On the other hand, the plant dynamics are hard to characterize precisely on line when facing uncertain disturbance. In the light of this, this paper presents a data-driven backstepping control scheme for the nonlinear chemical process. Compared with other regular chemical process control schemes, the proposed scheme is independent of specific mathematical models, and free of decoupling operation, linearization, or off-line recognition and modeling. By constructing Lyapunov function and feedback control rate based on real-time data, the integral stability is guaranteed. Williams-Otto reactor example is provided to demonstrate the effectiveness and applicability of the scheme.

关键词： Mathematical model Backstepping Inductors Chemical processes data models Process control control design

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On the Equivalence between PID-like controller and LADRC for second-order systems

On the Equivalence between PID-like controller and LADRC for...

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data driven control and learning systems (ddcls)

作者： Xiangyang Li Zhiqiang Gao Wei Ai Senping Tian School of Automation Science and Engineering South China University of Technology Guangzhou China Cleveland State University Cleveland OH USA

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

In this paper, the equivalence is established between the linear active disturbance rejection control (LADRC) and the two-degree of freedom (2-DOF) proportional-integral-derivative (PID) with lead-lag compensators. this allows advantage of LADRC, particularly its bandwidth-parameterization strategy, to be incorporated into the run-of-mill PID controllers. Specifically, the competing design objectives in disturbance rejection, tracking, and noise sensitivities can now be handled with ease.

关键词： Tuning Transient analysis Generators PD control Observers Lead Transfer functions

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Iterative learning control for networked nonlinear systems with fading communication

Iterative learning control for networked nonlinear systems w...

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data driven control and learning systems (ddcls)

作者： Ganggui Qu Dong Shen Beijing University of Chemical Technology Beijing P. R. China Renmin University of China Beijing P. R. China

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

In this paper, an accurate tracking problem for discrete-time nonlinear systems with output fading is investigated. It is assumed that the output signal occurs random fading during the network transmission. Random fading is mainly a multiplier disturbance to the signal, which can be modeled by multiplying a stochastic variable with the original signal. the modified P-type learning algorithm is proposed to solve accurate tracking problem. then, the convergence of the proposed algorithm in an almost sure sense is analyzed in detail. Finally, the effectiveness of our learning algorithm is verified by numerical simulation.

关键词： Fading channels Convergence Nonlinear systems Kalman filters Iterative learning control Stochastic processes Wireless communication

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

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

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data driven control and learning systems (ddcls)

作者： Qing-yuan Xu Ya Li Jing Cheng Tengfei Xiao School of Electronic and Information Guangdong Polytechnic Normal University Guangzhou China School of Intelligent Systems Engineering Sun Yat-sen University Guangzhou China

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

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： Adaptive systems Adaptation models Uncertain systems Trajectory Iterative learning control Uncertainty Nonlinear systems

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data driven control for a Class of Nonlinear systems with Stochastic Fading Channels

Data Driven Control for a Class of Nonlinear Systems with St...

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data driven control and learning systems (ddcls)

作者： Wei Yu Xuhui Bu Yanling Yin Jiaqi Liang School of Electrical Engineering Henan Polytechnic University Jiaozuo

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

this paper investigates the data driven model free adaptive control (MFAC) problem for a class of non-affine nonlinear systems with stochastic fading channels. Firstly, the phenomenon of signal fading is regarded as an independent stochastic process occurring at the output side, which has known mathematical expectations. Using an innovative linearization method, the considered non-affine system is converted into a linear model with a time-varying parameter called PPD and the MFAC controller is redesigned by utilizing the faded outputs. the stability of the system is analyzed rigorously and the influence of incomplete signal transmission on system convergence is explored. Finally, a numerical example shows the validity of the presented strategies.

关键词： Fading channels data models Mathematical model Stochastic processes Stability analysis Nonlinear systems Adaptation models

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A learning Mechanism for BRBES Using Enhanced Belief Rule-Based Adaptive Differential Evolution 9

A Learning Mechanism for BRBES Using Enhanced Belief Rule-Ba...

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Joint 9th International conference on Informatics, Electronics and Vision (ICIEV) / 4th International conference on Imaging, Vision and Pattern Recognition (ICIVPR)

作者： Ul Islam, Raihan Hossain, Mohammad Shahadat Andersson, Karl Lulea Univ Technol Pervas & Mobile Comp Lab Lulea Sweden Univ Chittagong Dept Comp Sci & Engn Chittagong Bangladesh

ISBN: (纸本)9781728193311

Nowadays, belief rule-based expert systems (BRBESs) are widely used in various domains which provides a framework to handle qualitative and quantitative data by addressing several kinds of uncertainty. learning plays an important role in BRBES to upgrade its knowledge base and parameters values, necessary for the improvement of the prediction accuracy. Different optimal training procedures such as Particle Swarm Optimisation (PSO), Differential Evolution (DE), and Genetic Algorithm (GA) have been used as learning mechanisms. Among these procedures, DE performs comparatively better than others. However, DE's performance depends significantly in assigning near optimal values to its control parameters including cross over and mutation factors. therefore, the objective of this article is to present a novel optimal training procedure by integrating DE with BRBES. this is named as enhanced belief rule-based adaptive differential evolution (eBRBaDE) algorithm because it has the ability to determine the near-optimal values of both the control parameters while ensuring the balanced exploitation and exploration in the search space. In addition, a new joint optimization learning mechanism by using eBRBaDE is presented where both parameter and structure of BRBES are considered. the reliability of the eBRBaDE has been compared with evolutionary optimization algorithms such as GA, PSO, BAT, DE and L-SHADE. this comparison has been carried out by taking account of both conjunctive and disjunctive BRBESs while predicting the Power Usage Effectiveness (PUE) of a datacentre. the comparison demonstrates that the eBRBaDE provides higher prediction accuracy of PUE than from other evolutionary optimization algorithms. Contribution-An enhanced differential evolution algorithm has been proposed in this paper, which is later used as a novel optimal training procedure for BRBES.

关键词： Optimization learning Evolutionary Algorithm Differential evolution Belief Rule-based Expert systems

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Collaborative Output Tracking of Nonlinear Multi-Agent systems via Observer-Based learning control Approach

Collaborative Output Tracking of Nonlinear Multi-Agent Syste...

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data driven control and learning systems (ddcls)

作者： Xuefang Li Deqing Huang School of Intelligent Systems Engineering Sun Yat-Sen University Guangzhou China School of Electrical Engineering Southwest Jiaotong University Chengdu China

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

In this work, the collaborative output tracking control of a class of nonlinear multi-agent systems is investigated under the framework of adaptive learning control, where a state observer is utilized to estimate the unmeasurable system states. By updating the individual input of each subsystem simultaneously based on the collaborative tracking error, the convergence of collaborative learning (co-learning) can be guaranteed. It is shown that with an appropriate unity partition on the derivative of desired output, the convergence of individual learning for each agent implies the convergence of co-learning. the rigorous convergence analysis of the co-learning is provided based on the composite energy function (CEF) methodology. In the end, an numerical example is illustrated to present the effectiveness of the proposed controller.

关键词： Collaboration Convergence Task analysis Observers Collaborative work Production Multi-agent systems

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Formation tracking for discrete-time multi-agent system with unknown dynamics by using adaptive finite rational orthogonal basis functions

Formation tracking for discrete-time multi-agent system with...

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data driven control and learning systems (ddcls)

作者： Yu Ge Yong Fang Zhichao Sheng Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai University Shanghai China

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

this paper considers iterative learning control (ILC) for formation tracking problem of discrete-time multi-agent system with unknown system dynamics. It is assumed that each agent has its own system dynamics. thus, the unknown system dynamics and information interaction between multi-agent make formation tracking difficult to achieve. In this paper, we utilize the adaptive Fourier decomposition (AFD) algorithm to approximate the system parameters only with input and output data. then a learning control scheme is proposed with estimated system dynamics. Simulations show trajectories of multi-agent are tracked precisely after a certain number of iterations.

关键词： Multi-agent systems System dynamics Trajectory Convergence Transfer functions control systems Mathematical model

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