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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121-130 订阅

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Adaptive Iterative learning Backstepping control for Nonlinear Strict-feedback systems 9

Adaptive Iterative Learning Backstepping Control for Nonline...

引用

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

作者： Chen, Jianyong Wenzhou Vocat Coll Sci & Technol Wenzhou 325014 Peoples R China Zhejiang Univ Technol Hangzhou 310023 Peoples R China

ISBN: (纸本)9781728159225

In this paper, an adaptive iterative learning control strategy is proposed for a class of nonlinear strict-feedback systems in the presence of arbitrary initial state error. the presented control algorithm utilizes an expected error trajectory to resolve the initial condition problem. the controller is designed based on the backstepping technique, and the unknown constant parameters are estimated via differential-difference learning law. A typical series is introduced to solve the influence of external disturbance on tracking performance. theoretical analysis shows that all signals of the closed-loop system are bounded, and the system output perfectly follows the reference signal over the pre-specified interval. Finally, a simulation example is provided to demonstrate the effectiveness of the approach.

关键词： Iterative learning control Differential-difference learning law Initial condition problem

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Observer-based Optimal Adaptive control for Multi-motor Driving Servo System 9

Observer-based Optimal Adaptive Control for Multi-motor Driv...

引用

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

作者： Hu, Shuangyi Ren, Xuemei Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China

ISBN: (纸本)9781728159225

In this paper, an improved optimal sliding mode control strategy is proposed for multi-motor driving servo system. Some states of multi-motor drive system are not measurable and there exists unknown nonlinearity. To solve this problem, the disturbance observer and extended state observer are both applied to estimate the unknown states and nonlinearity. Based on optimal control theory, the optimal sliding surface is selected to guarantee the optimal dynamic performance of the sliding mode of the system. the effectiveness of designed control methods is illustrated by simulation results.

关键词： Disturbance observer Extended state observer Optimal sliding surface Multi-motor driving servo system

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Robust Adaptive control for DC-DC Buck Converters with Load Fluctuation 9

Robust Adaptive Control for DC-DC Buck Converters with Load ...

引用

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

作者： Yang, Cheng-Wei Jin, Xiao-Zheng HeFei Univ Technol Sch Elect Engn & Automat Hefei 230000 Peoples R China Qilu Univ Technol Sch Comp Sci & Technol Shandong Acad Sci Jinan 250353 Peoples R China

ISBN: (纸本)9781728159225

this paper mainly develops a robust adaptive tracking control scheme to counter effects caused by load fluctuation and external disturbances. An adaptive controller based on appropriate control strategy and adaptive law is presented based on Lyapunov stability theory. the output voltage asymptotic tracking of the buck converters is obtained even under the influence of load fluctuation and external disturbances. Simulation results also reveal the availability of the proposed adaptive control scheme.

关键词： DC-DC buck converter adaptive control load fluctuation

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data-driven Modelling of Freshwater Ecosystems: A Multiscale Framework Based on Global Geospatial data 9

Data-Driven Modelling of Freshwater Ecosystems: A Multiscale...

引用

9th International conference on Geographical Information systems theory, Applications and Management, GISTAM 2023

作者： Almeida, Bruna Cabral, Pedro Universidade Nova de Lisboa Campus de Campolide Lisbon1070-312 Portugal

ISBN: (纸本)9789897586491

Freshwater ecosystems are primarily impacted by climate, land use and land cover changes, and over-abstraction. Satellite Earth observation (SEO) data and technologies are key in environmental modelling and support decisions. these technologies combined with machine learning (ML) are a powerful approach for modelling freshwater ecosystems at a multiscale level. the goal of this study is to present a set of reference data and guidelines that can be used to estimate the water and wetness probability index (WWPI) in different spatial and temporal scales. To find the best model’s predictors, sensitivity analyses were carried out in a predictive ML model implemented in a transnational river basin district (Portugal – Spain), the Tagus Basin. Satellite imagery, satellite-derived data, biophysical variables, and landscape characteristics were the explanatory variables evaluated in the sensitivity analyses, and some of them were included in the framework as a reference source of spatial data. Copyright © 2023 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Machine learning

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Collaborative Output Tracking of Nonlinear Multi-Agent systems via Observer-Based learning control Approach 9

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

引用

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

作者： Li, Xuefang Huang, Deqing Sun Yat Sen Univ Sch Intelligent Syst Engn Guangzhou 510275 Peoples R China Southwest Jiaotong Univ Sch Elect Engn Chengdu 610031 Peoples R China

ISBN: (纸本)9781728159225

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： learning control collaborative tracking multi-agent system composite energy function

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Iteration-dependent High-order Internal Model based Iterative learning control for Continuous-time Nonlinear systems 9

Iteration-dependent High-order Internal Model based Iterativ...

引用

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

作者： Yu, Miao Chai, Sheng Zhejiang Univ Coll Elect Engn Hangzhou 310027 Peoples R China

ISBN: (纸本)9781728159225

In this paper, an adaptive iterative learning control (AILC) scheme based on high-order internal model (HOIM) is presented for a class of nonlinear continuous-time systems with unknown time-iteration- varying parameter. the time-iteration-varying parameter is generated by a general iteration-dependent HOIM with iteration-varying order and coefficients. Compared with the existing works based on iteration-invariant HOIM with fixed order and coefficients, our work significantly expands the application scope of HOIM-based ILC. Using the designed HOIM-based iterative learning controller, the learning convergence along the iteration axis is guaranteed through rigorous theoretical analysis under Lyapunov theory. Furthermore, the effectiveness of the proposed method is demonstrated according to the simulation results.

关键词： Iterative learning control iteration-varying high-order internal model non-repetitiveness time-iteration-varying parameter

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Adaptive NN Finite-Time Tracking control for PMSM with Full State Constraints 9

Adaptive NN Finite-Time Tracking Control for PMSM with Full ...

引用

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

作者： Yang, Jin-Zi Li, Yuan-Xin Liaoning Univ Technol Coll Informat Coll Sci Jinzhou 121001 Peoples R China

ISBN: (纸本)9781728159225

this paper proposes a finite-time adaptive tracking control scheme for permanent magnet synchronous motors (PMSM) with full state constraints. In order to deal with full state constraints, the nonlinear transformation function is first introduced to transform the constraint problem into a non-constraint problem. then, we will provide a tracking differentiator that get the differentiation of the virtual control laws. At the same time, the neural networks (NN) are employed to approximate unknown nonlinearities. Finally, simulation results are given to validate the proposed control scheme.

关键词： Backstepping design adaptive neural control PMSM full-state constraints finite-time

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A Deep Reinforcement learning Approach to the Flexible Flowshop Scheduling Problem with Makespan Minimization 9

A Deep Reinforcement Learning Approach to the Flexible Flows...

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

作者： Zhu, Jialin Wang, Huangang Zhang, Tao Tsinghua Univ Dept Automat Beijing 100036 Peoples R China

ISBN: (纸本)9781728159225

Recent work has demonstrated the efficiency of deep reinforcement learning (DRL) in making optimization decisions in complex systems. Compared with other DRL algorithms, the proximal policy optimization (PPO) has higher stability and lower complexity. the typical flexible flowshop scheduling problem (FFSP) with identical parallel machines is an NP-hard problem. this paper is the first case to utilize PPO to solve the problem with makespan minimization. the particular state, action and reward function are designed for the FFSP to follow the Markov property. the efficiency of PPO is evaluated on the wafer pickling instance and random instances with different scales. the results show that PPO can always provide satisfactory solutions within a reasonable computational time.

关键词： Deep Reinforcement learning Proximal Policy Optimization Flexible Flowshop Scheduling Makespan

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Iterative learning control for Multiple Time-Delays Discrete systems in Finite Frequency Domain 9

Iterative Learning Control for Multiple Time-Delays Discrete...

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

作者： Li, Xiaohui Shen, Jianqiang Tao, Hongfeng Hao, Shoulin Jiangnan Univ Minist Educ Key Lab Adv Proc Control Light Ind Wuxi 214122 Jiangsu Peoples R China

ISBN: (纸本)9781728159225

this paper developes iterative learning control scheme and the stability conditions for multiple time-delays discrete system. By formulating the problem over repetitive process form using 2D theory, sufficient stability conditions for multiple timedelays discrete system are developed along the trial, which guarantees the trial-to-trial error monotonic convergence. Moreover, the generalized Kalman-Yakubovich-Popov (KYP) lemma allows the iterative learning control scheme to develope stability conditions with LMI constraints and analyze in the finite frequency domain. A numerical simulation for multiple time-delays discrete system is given to verify the proposed method.

关键词： Multiple Time-Delays Repetitive Process KYP Lemma Iterative learning control

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data driven control for a Class of Nonlinear systems with Stochastic Fading Channels 9

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

引用

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

作者： Yu, Wei Bu, Xuhui Yin, Yanling Liang, Jiaqi Henan Polytech Univ Sch Elect Engn Jiaozuo 454000 Henan Peoples R China

ISBN: (纸本)9781728159225

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： data driven model free adaptive control fading channels stability analysis

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