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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71-80 订阅

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Improved quantized predictive iterative learning control for systems with variable interval lengths and data dropouts 13

Improved quantized predictive iterative learning control for...

引用

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

作者： Shao, Zhen Xue, Songyi Wang, Ronghao Chen, Yu Duan, Zhaoxia Kong, Fanrong College of Artificial Intelligence and Automation Hohai University Nanjing211100 China College of National Defense Engineering Army Engineering University of Pla Nanjing210007 China

ISBN: (纸本)9798350361674

In this paper, the problem of complete tracking control is investigated for single-input single-output unknown nonlinear discrete systems with variable interval lengths, data quantization and data dropouts. First, a quantized model-free adaptive predictive iterative learning control scheme is proposed based on the dynamic linearization model of the system. the scheme employs the encoding-decoding mechanism and an intermittent update principle to eliminate the inherent errors caused by data quantization and data dropouts, and designs compensation and prediction mechanisms to deal with the missing data problem caused by variable interval lengths. then, it is demonstrated that convergence of the tracking control error can be achieved by utilizing only a small amount of input and output data. Finally, the effectiveness of the proposed method is verified by simulation. © 2024 ieee.

关键词： Contrastive learning

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Iterative learning control for Two-Sensors System with User's Preference 13

Iterative Learning Control for Two-Sensors System with User'...

引用

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

作者： Zhang, Zhenfa Shen, Dong School of Mathematics Renmin University of China Beijing100872 China Engineering Research Center of Finance Computation and Digital Engineering Ministry of Education China

ISBN: (纸本)9798350361674

this study proposes an iterative learning control (ILC) scheme for a two-sensor system with user's preference. ILC updates the system input using error information from previous iterations to sequentially enhance tracking performance. Typ-ically, it is challenging to minimize the tracking performances of both sensors simultaneously. therefore, we aim to track the Pareto optimal inputs that correspond to different trade-offs in tracking performances. In certain applications, there may be a requirement for the tracking performances of both sensors to satisfy a specific proportionality relationship, i.e., user's preference. In this study, we introduce an algorithm that addresses the tracking problem in a two-sensor system while considering user's preference. this algorithm not only tracks a Pareto optimal input but also ensures that the tracked performances meet user's preference. © 2024 ieee.

关键词： Contrastive learning

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Adaptive Neural Network-Based Terminal Sliding Mode control for AUV 13

Adaptive Neural Network-Based Terminal Sliding Mode Control ...

引用

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

作者： Jia, Wenjie Wang, Shubo Gao, Qingyi College of Automation Qingdao Unversity Qingdao266071 China

ISBN: (纸本)9798350361674

this paper proposes a fast terminal sliding mode control based on adaptive neural network to address the problem of external interference and internal uncertainty in trajectory tracking control for a six degree of freedom autonomous underwater vehicle. A kind of radial basis neural network is employed to estimate external disturbances and internal uncertainties. In constructing the sliding mode surface, an improved funnel function is defined and integrated into the sliding mode surface. A rapid terminal sliding mode controller based on adaptive neural networks is designed to ensure the output error remains within a predefined range and can rapidly converge to zero. Simulation results validate the effectiveness of the proposed control approach. © 2024 ieee.

关键词： Sliding mode control

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Model-Free H2/H∞ Predictive control for Discrete-Time System via Q-learning 13

Model-Free H2/H∞ Predictive Control for Discrete-Time Syste...

引用

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

作者： Lin, Yihong He, Peng Wan, Haiying Liu, Zhuangyu Luan, Xiaoli Liu, Fei Institute of Automation Jiangnan University Key Laboratory of Advanced Process Control for Light Industry Ministry of Education Wuxi214122 China

ISBN: (纸本)9798350361674

this paper presents a model-free H2/H∞ Q-learning predictive control strategy for linear discrete-time systems. To design predictive controller with the system measured states, a policy iteration solution algorithm is employed to approximate the control inputs. Specifically, the developed algorithm is formulated in the form of linear matrix inequalities, designed to stabilize the system with mixed H2/H∞ control performance. Additionally, to improve robust performance under system disturbance variations, we introduce the receding horizon optimization into the Q-learning based predictive control. Finally, simulation results demonstrate the effectiveness of the proposed approach. © 2024 ieee.

关键词： H2/H∞ performance index Model-free predictive control Q-learning Receding horizon optimization Unknown discrete-time systems

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Consensus Tracking data-driven control of Multi-Agent systems based on Matrix S-Lemma under Noisy data 13

Consensus Tracking Data-Driven Control of Multi-Agent System...

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

作者： Tan, Shuli Zhang, Xiufeng Yang, Chunxi Kunming University of Science and Technology Faculty of Mechanical and Electrical Engineering Kunming China Yunnan Key Laboratory of Intelligent Control and Application Yunnan Kunming650504 China

ISBN: (纸本)9798350361674

In this paper, the problem of consensus tracking for multi-agent systems in the presence of noise inter-ference is investigated. Unlike the traditional model-based approach, this paper assumes that the dynamics of all agents are unknown and only a portion of the input-state data is available. the offline data contaminated with noise interference is utilized. By introducing the matrix S-lemma, a necessary and sufficient conditions for directly designing consensus tracking controllers from bounded noisy input-state data is given. this method can be applied to large datasets and effectively addresses computational challenges associated with extensive data. Simulation result illustrates the effectiveness of the theoretical analysis. © 2024 ieee.

关键词： Multi agent systems

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Real-Time Conflict-Free Path Planning for Multi-AGVs Based on Deep Reinforcement learning 13

Real-Time Conflict-Free Path Planning for Multi-AGVs Based o...

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

作者： Yang, Guodong Zhong, Renxin School of Intelligent Systems Engineering Shenzhen Campus of Sun Yat-sen University Guangdong Provincial Key Laboratory of Intelligent Transportation System Shenzhen518107 China

ISBN: (纸本)9798350361674

Automated guided vehicles (AGVs) are crucial transportation equipment that connects quay cranes (QCs) with yard cranes (YCs) in automated container terminals (ACTs). Ensuring real-time path planning without conflicts is vital for improving the operational efficiency and safety of ACTs. this paper studies the conflict-free AGV routing problem in ACTs with uncertainties. To improve the efficiency and safety of the ACT operations, we propose a deep reinforcement learning (DRL) method based on invalid action masking and artificial potential fields. In this method, we mask out invalid actions to resolve conflicts between AGVs. the reward-shaping strategy is based on the artificial potential field to handle the slow iteration of DRL algorithms due to sparse rewards. Simulation experiments verify the effectiveness of the proposed method. © 2024 ieee.

关键词： Deep reinforcement learning

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A Deep learning Method for the Endpoint Carbon Prediction in BOF Steelmaking Process 13

A Deep Learning Method for the Endpoint Carbon Prediction in...

引用

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

作者： Liang, Bingkun Wang, Kang Li, Xiaoli Beijing University of Technology Faculty of Information Technology Beijing100124 China

ISBN: (纸本)9798350361674

In many basic oxygen furnace (BOF) steelmaking processes, if the furnace endpoint carbon can be monitored in real time, it is a breakthrough for BOF steelmaking intelligence. this paper presents a deep learning model used to predict the endpoint carbon content in BOF steelmaking process. A convolution long short-term memory network based on attention mechanism (CNN-LSTM-AM) model is proposed for time-series data in BOF process to extract spatial-temporal characteristics of time sequence features and a back propagation (BP) model is proposed for pre-furnace data to auxiliary increase the accuracy of the model. the BOF steelmaking data from an actual process were used for the testing, the result shown that 84.34% of prediction result were within the ±0.02 range, which is better than use those two types of data and model individually. © 2024 ieee.

关键词： Long short-term memory

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Tail-Sitter UAV Attitude Disturbance Rejection control with Compensating Function Observer Based Additive State Decomposition controller 13

Tail-Sitter UAV Attitude Disturbance Rejection Control with ...

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

作者： Qin, Yulong Qi, Guoyuan Li, Kuo School of Control Science and Engineering Tiangong University Tianjin300387 China

ISBN: (纸本)9798350361674

this paper, a control method for suppressing attitude disturbance of a tail-sitter UAV is proposed. Due to the special structure of the UAV and the change of pitch angle during the transition, nonlinear terms and unwanted disturbance are introduced, which bring additional challenges to the controller design. the nonlinear and disturbance terms are placed in the primary system according to the principle of additive decomposition controller (ASD), then the matching and non-matching disturbance in the primary system are estimated using compensating function observer (CFO), and then the controllers of the primary and secondary systems can be obtained from the pole configurations, and the original system controllers are obtained by summing up these two controllers. Simulation results demonstrate that this control method has superior tracking effect in the face of large matching and non-matching disturbances compared with the traditional PID control algorithm. © 2024 ieee.

关键词： Disturbance rejection

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Distributed Adaptive Formation control of Connected Vehicles with Actuator Saturation and Time-Varying Spacing 13

Distributed Adaptive Formation Control of Connected Vehicles...

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

作者： Ji, Honghai La, Xiaoyan Fan, Lingling Wang, Li Liu, Shida Ren, Ye School of Electronics & Control Engineering North China University of Technology Beijing100144 China School of Automation Beijing Information Science and Technology University Beijing100096 China

ISBN: (纸本)9798350361674

this study investigates the problem of distributed adaptive formation control of connected vehicles with actuator saturation and time-varying spacing. Firstly, optimization performance metrics are defined based on the requirements of vehicle formation control to ensure communication coordination among the following vehicles while enabling them to track the leader vehicle's trajectory. Secondly, time-varying spacing is introduced to meet the formation requirements in different scenarios. Furthermore, actuator saturation are imposed to ensure passenger comfort during the ride. Finally, the effectiveness of the proposed algorithm is validated through three numerical simulation cases. © 2024 ieee.

关键词： Adaptive control systems

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Particle Swarm Optimization Tuned Compensation Function Observer Based control Method for Ship Motion 13

Particle Swarm Optimization Tuned Compensation Function Obse...

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

作者： Ren, Jia Wang, Zhaoxia Chen, Zengqiang Sun, Mingwei Sun, Qinglin College of Artificial Intelligence Nankai University Tianjin300350 China School of Computing and Information Systems Singapore Management University Singapore178902 Singapore Key Lab of Intelligent Robotics of Tianjin Tianjin300350 China

ISBN: (纸本)9798350361674

To achieve more stable and rapid control of ship motion, we proposed the Compensation Function Observer (CFO)-based control algorithm and used the Particle Swarm Optimization (PSO) to adjust its parameters. the performance of the proposed method is compared with PSO based Active Disturbance Rejection control (ADRC) and traditional ADRC. the simulation results show that the ship motion system controlled by our proposed method has smaller deviation and jitter. Even under different interference, it still has better performance. © 2024 ieee.

关键词： Particle swarm optimization (PSO)

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