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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301-310 订阅

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RBFNN-Based Event-Triggered data-driven Formation control for a Connected Heterogeneous Vehicle Platoon

RBFNN-Based Event-Triggered Data-Driven Formation Control fo...

引用

data driven control and learning systems (ddcls)

作者： Panpan Zhu Shangtai Jin Chenkun Yin School of Electronic and Information Engineering Beijing Jiaotong University Beijing China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

this paper presents a novel distributed data-driven control scheme for the longitudinal formation control of a connected heterogeneous vehicle (CHV) platoon. Initially, an event-triggered mechanism is devised to alleviate the computational burden on the distributed controller. Subsequently, the future pseudo-gradient (PG) vector is predicted using the radial basis function neural network (RBFNN) method, and a data-driven control scheme is developed for the coordinated formation control of the CHV platoon. Ultimately, the simulation results demonstrate that the proposed distributed RBFNN-based event-triggered data-driven control (RBFNN-ETDDC) approach effectively achieves the objective of maintaining low-spacing error formation control for the CHV platoon.

关键词： learning systems Simulation Estimation Radial basis function networks Prediction algorithms control systems Formation control

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Robust Adaptive control for Robotic Manipulator with Output Constraints

Robust Adaptive Control for Robotic Manipulator with Output ...

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data driven control and learning systems (ddcls)

作者： Lei Chen Xiangbin Liu Lang Zou Min Zhang School of Automation and Intelligence Beijing Jiaotong University Beijing China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

this paper investigates the control problem of robotic manipulator subjected to uncertain dynamics and output constraints. the robotic manipulator system is described as a Lagrangian system with parameterized uncertainties and external disturbances. To deal with these uncertain terms, a robust adaptive control scheme is proposed to ensure the system with desired performance. Furthermore, the Barrier Lyapunov Function is introduced into the controller design to guarantee the output of the robotic manipulators satisfy the constraints. Finally, the effectiveness of the proposed control scheme is demonstrated by a numerical simulation.

关键词： learning systems Uncertainty Numerical simulation Stability analysis Closed loop systems Adaptive control Manipulator dynamics

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Smart Soil Monitoring System With Crop and Fertilizer Recommendation Features 13

Smart Soil Monitoring System With Crop and Fertilizer Recomm...

引用

13th ieee International conference on Communication systems and Network Technologies, CSNT 2024

作者： Saha, Gourab Suvo, Shabbir Hoshen Tonmoy, Farhan Tanjim Asad, Jiad Bin Imran, Mohammed thushar Azad, Akm Abdul Malek Brac University Department of Eee Dhaka Bangladesh Brac University Control & Applications Research Center Department of Eee Dhaka Bangladesh

ISBN: (纸本)9798350305463

this paper represents the design of a portable and IoT based device for monitoring nitrogen, phosphorus, potassium, pH, and moisture of soil, along with surrounding temperature and humidity, for crops and fertilizer recommendation. the idea and execution of a wireless, portable system for assessing soil quality, recommending the optimal amount of fertilizer, and suggesting appropriate crops depending on the soil conditions are the main goals of this research project. the system is designed to have a user-friendly interface. It uses a cloud-based platform for analyzing data and implementation of machine learning models. Multiple classification-based machine learning algorithms are compared to gain better accuracy of the system. the results of the system are validated using different samples of both indoor and outdoor environments. this cost-effective design, which includes monitoring of seven parameters necessary for crop cultivation along with crop recommendation feature and fertilizer suggestion, makes it desirable for agroindustry. © 2024 ieee.

关键词： Machine learning

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data-driven Deadbeat control for Energy Storage systems

Data-Driven Deadbeat Control for Energy Storage Systems

引用

data driven control and learning systems (ddcls)

作者： Yanyu Zhang Pengpeng Li Shun Zhou Xibeng Zhang Yi Zhou School of Artificial Intelligence Henan University Zhengzhou P. R. China Longzihu New energy Laboratory Zhengzhou P. R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In recent years, Deadbeat control (DBC) has gained recognition as an effective method for controlling Energy Storage systems (ESS). However, traditional DBC often exhibits substantial steady-state errors attributed to inaccurate system modeling and losses. this paper introduces a novel approach employing Deep Reinforcement learning (DRL) for model-based control to accurately generate current references by compensating for power losses. the proposed method is evaluated through simulation results, highlighting its effectiveness in addressing the limitations of traditional DBC.

关键词： learning systems Adaptation models Adaptive systems Software packages Simulation systems modeling Deep reinforcement learning

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IoT Network Security with PCA and Deep learning for Unmasking Anomalies 13

IoT Network Security with PCA and Deep Learning for Unmaskin...

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13th ieee International conference on Communication systems and Network Technologies, CSNT 2024

作者： Geo Francis, E. Sheeja, S. Antony John, E.F. Joseph, Jismy Karpagam Academy of Higher Education Dept. of Computer Science Tamil Nadu Coimbatore India Dept. of Mathematics Kerala Thrissur India Dept. of Mathematics Kerala Mala India

ISBN: (纸本)9798350305463

the rapid proliferation of Internet of things (IoT) devices has revolutionized various domains, introducing unprecedented convenience and efficiency. However, this expansion has concurrently intensified the obstacles associated with network security. Detecting and mitigating anomalies in IoT networks are of utmost importance to protect critical systems and sensitive data. this paper presents an innovative approach to IoT network security that combines Principal Component Analysis (PCA) with advanced deep learning approaches, encompassing Generative Adversarial Networks (GANs) and Transformers. In this research, we investigate the potential of PCA for dimensionality reduction and feature selection within IoT network data. By simplifying data complexity, PCA aids in uncovering critical features and patterns contributing to network anomalies. Advanced deep learning models, such as GANs for generating synthetic data and Transformers for sequence modeling, are harnessed to construct anomaly detection systems capable of identifying irregularities in real-time IoT data streams. Our approach is rigorously evaluated on a comprehensive dataset collected from diverse IoT devices and networks. the results underscore the efficacy of the PCA and deep learning-based anomaly detection model, leveraging GANs and Transformers, in accurately and efficiently unmasking anomalies in IoT network traffic. through extensive experiments and evaluations, we underscore substantial advancements over traditional security methods. this research significantly enhances IoT network security by presenting a sophisticated, data-driven anomaly detection system adaptable to the dynamic and evolving nature of IoT environments. As the IoT landscape continues to expand, our approach offers a crucial solution to safeguard against the ever-evolving threat of network anomalies. © 2024 ieee.

关键词： Internet of things

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Dissipative Consensus via ILC of Singular Multiagent systems

Dissipative Consensus via ILC of Singular Multiagent Systems

引用

data driven control and learning systems (ddcls)

作者： Meiyu Zhang Senping Tian Panpan Gu Xiangyang Li School of Automation Science and Engineering South China University of Technology Guangzhou China School of Electrical Engineering and Automation Hefei University of Technology Hefei China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In this paper, the problem of dissipative consensus iterative learning control (ILC) is studied for singular multiagent systems (MASs). Firstly, a novel ILC algorithm is designed for such singular MASs. then, under a connected communication graph, a sufficient condition is presented to make dissipative singular MASs abtain precise consensus tracking within a certain time interval. Finally, a simulation is conducted to validate the effectiveness of the proposed method.

关键词： learning systems Sufficient conditions Protocols control systems Multi-agent systems Iterative learning control Convergence

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Vehicle Formation control Based on Model-Free Adaptive Iterative learning

Vehicle Formation Control Based on Model-Free Adaptive Itera...

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data driven control and learning systems (ddcls)

作者： Ye Ren Yongqiang Xie Li Wang Honghai Ji School of Electronics & Control Engineering North China University of Technology Beijing

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In this paper, a vehicle formation control method based on model-free adaptive iterative learning is proposed for vehicles with the same turn at a road intersection. At road intersections, inconsistencies in speed and spacing between vehicles with the same turn can lead to traffic congestion. therefore, this study investigates the path planning and speed planning for vehicles with the same turn. In the case of vehicular networking, the formation control of intelligent networked vehicles with the same turn makes the vehicles travel in formation in a fast and orderly manner, thus improving the efficiency and enhancing the safety of road intersections.

关键词： learning systems Adaptation models Roads Formation control Path planning Safety Planning

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Gaussian Reinforcement learning: Optimal Tracking control for Uncertain Linear systems

Gaussian Reinforcement Learning: Optimal Tracking Control fo...

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data driven control and learning systems (ddcls)

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

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

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.

关键词： Linear systems learning systems PI control Q-learning Design methodology Predictive models Mathematical models

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Optimal control of Stochastic systems with Wiener and Poisson Noise based on data-driven Approach

Optimal Control of Stochastic Systems with Wiener and Poisso...

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data driven control and learning systems (ddcls)

作者： Zhiguo Yan Guocui Chen Guolin Hu School of Information and Automation Qilu University of Technology (Shandong Academy of Sciences) Jinan P. R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In this paper, we investigate the optimal control problem pertaining to a specific class of continuous-time stochastic systems that incorporate both Wiener and Poisson noise. Firstly, a data-driven strategy iteration algorithm is designed, which iteratively solves the Riccati equation through online state and collected data without knowing the system coefficient matrix; Secondly, an algorithm is proposed to calculate the maximum value of Poisson intensity under a given convergence accuracy; Finally, a numerical example is employed to demonstrate the efficacy of the proposed algorithm.

关键词： learning systems Accuracy System dynamics Heuristic algorithms Stochastic systems Noise Riccati equations

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A Novel Hyperbolic-Secant Reaching Law of Discrete-Time Sliding Mode control

A Novel Hyperbolic-Secant Reaching Law of Discrete-Time Slid...

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data driven control and learning systems (ddcls)

作者： Guoliang Zhou Lingwei Wu School of Intelligent Manufacture Lishui Vocational and Technical College Lishui China School of Intelligent Manufacture Taizhou University Taizhou China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In this paper, a hyperbolic-secant reaching law-based sliding mode control method is presented for uncertain discrete-time controlled systems. the hyperbolic-secant reaching law based on the hyperbolic-secant function and the saturation function is designed to realize the increasing of the reaching speed and the reduction of the chattering. To characterize the tracking performance of the discrete controlled system, the detailed results about the absolute attractive layer bound and the quasi-sliding mode band are given and analyzed. the validity and usefulness of the proposed control approach can be verified by numerical simulation results.

关键词： learning systems Design methodology Switches Numerical simulation Sliding mode control Convergence

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