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检索条件"任意字段=13th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2024"

共 716 条记录，以下是21-30 订阅

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Markov Parameters Estimation and data-driven Iterative learning control for LDTV systems 13

Markov Parameters Estimation and Data-Driven Iterative Learn...

引用

13th ieee data driven control and learning systems conference, ddcls 2024

作者： Liu, Chuyang Ruan, Xiaoe School of Science Chang'an University Xi'an710064 China School of Mathematics and Statistics Xi'an Jiaotong University Xi'an710049 China

ISBN: (纸本)9798350361674

In this paper, a kind of Markov parameters estimation and data-driven iterative learning control scheme is constructed for a class of linear discrete-time-varying systems. Firstly, a linear combination of the predicted system output error energy and estimation compensation energy is designed as the performance index function. through algebraic operations, the estimation formula for the Markov matrix that minimizes the performance index function is explicitly solved. Simultaneously, within the framework of norm-optimal iterative learning control where the controller is regarded as an optimization variable, the updating rule for control input is formulated. Substituting the Markov matrix with its estimation in the control input updating rule, a data-driven iterative learning control scheme is established. Numerical experiments demonstrate that both the estimation error of the system Markov parameters and the tracking error of the system output monotonically decrease along the iteration axis. Moreover, a smaller estimation ratio leads to a faster decrease in the estimation error, and a smaller learning ratio results in a faster decrease in the tracking error. © 2024 ieee.

关键词： Markov processes

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Gain-varying P-type ILC for Nonlinear Multi-agent systems against FDI Attacks 13

Gain-varying P-type ILC for Nonlinear Multi-agent Systems ag...

引用

13th ieee data driven control and learning systems conference, ddcls 2024

作者： Peng, Huiming Lin, Na Chi, Ronghu School of Automation & Electronic Engineering Qingdao University of Science & Technology Qingdao266061 China

ISBN: (纸本)9798350361674

this work studies the consensus learning control problem of nonlinear multi-agent systems (MASs) under false data injection (FDI) attacks. Applying the attacked output and consensus error information to the control law and the parameter updating algorithm effectively reduces the impact of FDI attacks. the provided control scheme, which is entirely data-driven and more suited for real-world industrial applications, makes no use of any model knowledge from the controlled MASs. In addition, compared with the P-type ILC method, the variable gain effectively improves the robustness of the presented GV-PILC scheme to the external uncertainties. the simulation results verify the effectiveness of the proposed method by comparing with fixed gain P-type ILC. © 2024 ieee.

关键词： Multi agent systems

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Gaussian Reinforcement learning: Optimal Tracking control for Uncertain Linear systems 13

Gaussian Reinforcement Learning: Optimal Tracking Control fo...

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13th ieee data driven control and learning systems conference, ddcls 2024

作者： Gang, Yiqing Li, Jinna School of Information and Control Engineering Liaoning Petrochemical University Fushun113001 China

ISBN: (纸本)9798350361674

In this paper, a design method for PID control based on Gaussian reinforcement learning is proposed to find the optimal policy for linear quadratic tracking of an unknown system. Firstly, the mathematical model of the linear quadratic tracking problem with random disturbance is presented, and an analysis of the PID control structure is carried out. Secondly, a Gaussian model predictive control for the unknown model with random disturbances is provided. Finally, a Gaussian Q-learning method is given for the system based on this predictive model. the effectiveness of the proposed method is demonstrated through a numerical example. © 2024 ieee.

关键词： Optimal detection

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Event-Triggered Model-Free Adaptive Sliding Mode control for a Class of Nonlinear systems 13

Event-Triggered Model-Free Adaptive Sliding Mode Control for...

引用

13th ieee data driven control and learning systems conference, ddcls 2024

作者： Zhang, Ning Niu, Yugang Lv, Xinyu Chen, Bei Ministry of Education Shanghai200237 China The School of Electric and Electronic Engineering Shanghai University of Engineering Science Shanghai201620 China

ISBN: (纸本)9798350361674

this work proposes an event-triggered model-free adaptive sliding mode control (SMC) strategy for a class of discrete-time nonlinear networked control systems. Under the data-driven framework, a nonlinear dynamic linearization model is obtained via the time-varying pseudo-partial derivative (PPD). then, an event-triggered mechanism is designed to reduce the transmission of system signal without relying on any model information. In addition, based on the tracking error, an event-triggered sliding mode controller is designed to drive systems state into a specified sliding surface and achieve the output track objective. Finally, the availability of the proposed data-driven event-triggered SMC scheme is verified by a numerical simulation. © 2024 ieee.

关键词： Sliding mode control

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Containment control via Iterative learning of Fractional-order Singular Multi-agent systems with Initial State learning 13

Containment Control via Iterative Learning of Fractional-ord...

引用

13th ieee data driven control and learning systems conference, ddcls 2024

作者： Wang, Guangxu Zhou, Xingyu Dai, Xisheng Nantong University School of Electrical and Automation Nantong226019 China School of Zhang Jian Nantong University Nantong226019 China School of Automation Guangxi University of Science and Technology Liuzhou545006 China

ISBN: (纸本)9798350361674

In a category of nonlinear fractional-order singular multi-agent systems (MASs) with initial state learning scheme, the closed-loop Dα -type iterative learning control protocol is introduced to address the containment control problem for the aforementioned MASs. Subsequently, in terms of the fractional-order calculus and the generalized Gronwall inequality, sufficient conditions are derived for the learning convergence of containment errors and initial containment errors in such MASs. Lastly, a numerical illustration is given to validate the efficacy of the closed-loop Dα -type iterative learning containment control protocol. © 2024 ieee.

关键词： Multi agent systems

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Iterative learning Fault Estimation for Stochastic Time-varying systems 13

Iterative Learning Fault Estimation for Stochastic Time-vary...

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13th ieee data driven control and learning systems conference, ddcls 2024

作者： Tang, Jiawen Zhang, Wenli Liu, Anning Shi, Jiantao Nanjing Tech University College of Electrical Engineering and Control Science Nanjing211816 China

ISBN: (纸本)9798350361674

the article investigates the fault estimation problem for a class of stochastic time-varying systems based on iterative learning methods. Unlike most traditional works, the system investigated in this paper is affected by disturbances, noises and initial state deviations. A type of iterative learning fault estimator is designed utilizing the historical output estimation errors for receiving the fault information of the system. Furthermore, the existence condition is analyzed in order to meet boundedness with respect to the A-norm of fault estimation errors based on the proposed iterative learning scheme. At last, a case study is applied for verify the effectiveness of the studied scheme. © 2024 ieee.

关键词： Contrastive learning

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Adaptive Iterative learning control for High-Order Fully Actuated Nonlinear systems with Parametric Uncertainties 13

Adaptive Iterative Learning Control for High-Order Fully Act...

引用

13th ieee data driven control and learning systems conference, ddcls 2024

作者： Wang, Junkai Li, Xiao-Dong Li, Xuefang School of Intelligent Systems Engineering Shenzhen Campus of Sun Yat-Sen University Shenzhen518000 China

ISBN: (纸本)9798350361674

In this paper, a novel adaptive iterative learning control (AILC) scheme is proposed for high-order fully actuated systems (HOFASs) with parametric uncertainties, where the convergence of the tracking error is analyzed under the framework of the composite energy function method. Moreover, by applying the backstepping, the proposed AILC method is extended to strict-feedback HOFASs (SFHOFASs), which widens the application scope of AILC significantly. Finally, two illustrative examples are presented to verify the effectiveness of the proposed AILC scheme. © 2024 ieee.

关键词： Backstepping

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Event-Triggered Iterative learning control for First-Order MIMO Discrete Nonlinear Hyperbolic Distributed Parameter systems 13

Event-Triggered Iterative Learning Control for First-Order M...

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13th ieee data driven control and learning systems conference, ddcls 2024

作者： Wang, Xuhui Dai, Xisheng School of Automation Guangxi University of Science and Technology Liuzhou545006 China Institute of Intelligent Systems and Control Guangxi University of Science and Technology Liuzhou545006 China

ISBN: (纸本)9798350361674

this paper studies the iterative learning control issue for a class of MIMO discrete nonlinear hyperbolic distributed parameter system with event-triggered strategy. the purpose of the event-triggered mechanism is to reduce the number of input updates in the iterative learning process. the event-triggered condition is about the error information of two previous adjacent iterative batches. then an event-triggered iterative learning control algorithm is proposed. Rigorous mathematical analysis is shown, tracking error can converge to zero along the direction of iterative axis. Simulation results illustrates the effectiveness of the proposed event-triggered iterative learning control algorithm. © 2024 ieee.

关键词： Adversarial machine learning

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An Improved YOLO Model for Automatic Detection of Heliostats in a Concentrated Solar Power Plant 13

An Improved YOLO Model for Automatic Detection of Heliostats...

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13th ieee data driven control and learning systems conference, ddcls 2024

作者： Xu, Fen Li, Changhao North China University of Technology Beijing100144 China

ISBN: (纸本)9798350361674

In the scenario where cameras are set up on the tower to capture the whole image of heliostat field, traditional image processing algorithms often fail to correctly identify the heliostats, due to issues such as over exposure, illumination change, occlusion, and scale variations. To solve these problems, a deep learning method based on YOLOv5 and No-Parameter Attention mechanism is proposed for in-situ automatic detection and recognition of heliostats. A SimAM module is added before each Convolution module in the head section of YOLOv5. the modified model achieves a recognition accuracy of 99.7% for the test set, much better than that of traditional method. Experimental results demonstrate the effectiveness of deep learning method on heliostat detection. © 2024 ieee.

关键词： Deep learning

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Research on the Teaching Method of Automatic control Principle Based on Project-Based learning (PBL) 13

Research on the Teaching Method of Automatic Control Princip...

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13th ieee data driven control and learning systems conference, ddcls 2024

作者： Liu, Tao Lu, Hongqian School of Information and Automation Qilu University of Technology JiNan250353 China

ISBN: (纸本)9798350361674

this study discusses the teaching method of control principle based on problem-oriented learning (PBL), and verifies its feasibility and effectiveness through teaching practice and effect evaluation. Research has found that the PBL teaching method can stimulate students 'interest, improve their learning effect, and cultivate students' problem-solving ability, teamwork and innovation ability. Reasonable teaching process design and selection of teaching resources and tools are crucial to the successful implementation of PBL teaching. In teaching, the in-depth exploration of teaching design is conducted to further expand the sample size, combined with the specific course content, improve the evaluation system, and explore the combination with other teaching methods and introduce more advanced technologies to promote the innovation and development of control principle teaching. © 2024 ieee.

关键词： Students

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