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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371-380 订阅

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

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

引用

data driven control and learning systems (ddcls)

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

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

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.

关键词： learning systems Additives PI control Attitude control Simulation Variable speed drives Observers

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A License Plate Recognition Method Based on YOLOv8-Pose and E-LPRNet

A License Plate Recognition Method Based on YOLOv8-Pose and ...

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data driven control and learning systems (ddcls)

作者： Honghai Ji Qirui Shi Lingling Fan Li Wang Shuangshuang Xiong School of Electronics & Control Engineering North China University of Technology Beijing China School of Automation Beijing Information Science and Technology University Beijing China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

this paper proposes a license plate recognition method based on YOLOv8-Pose and E-LPRNet for complex scenarios such as urban roadside and road inspection. E-LPRNet is a character recognition network formed by modifying the activation function of the backbone network in LPRNet (License Plate Recognition Network). this method also includes a license plate correction feature, which accurately recognizes license plate characters even when the plates are at large angles, thereby increasing the recognition accuracy. A series of experiments using a large license plate dataset verified the effectiveness of this method.

关键词： learning systems Accuracy Roads Inspection control systems Reflection Character recognition

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Real-Time Modified Predictive control Model for Large Scale Wind Turbine Pitch System

Real-Time Modified Predictive Control Model for Large Scale ...

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data driven control and learning systems (ddcls)

作者： Qi-Wu Lin Zhao-Hua Liu School of Information and Electrical Engineering Hunan University of Science and Technology Xiangtan China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In order to effectively reduce the fluctuation of blade loads during independent pitch change of large wind turbines, a real-time correction model predictive control (MPC) control strategy for independent pitch control is proposed. the strategy adds a linear processor on the basis of the model predictive control, and by discretizing the pitch angle with multiple times and variable accelerations and sending them to the controller in sequence, it achieves the effect of real-time correction, reducing signal fluctuations and blade load fluctuations. the simulation results show that the proposed control strategy can effectively reduce the load fluctuation and has better performance than PI control and MPC control alone.

关键词： Fluctuations Blades Simulation Predictive models Real-time systems Mathematical models Wind turbines

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data-driven Stochastic control of Atomic Number Density in Spin-Exchange Relaxation-Free Co-Magnetometers

Data-Driven Stochastic Control of Atomic Number Density in S...

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data driven control and learning systems (ddcls)

作者： Bodong Qin Zhuo Wang Wenfeng Fan Feng Li Zehua Liu Jiahang Li School of Instrumentation and Optoelectronic Engineering Beihang University Beiing China Beihang University Institute of Large-scale Scientific Facility and Centre for Zero Magnetic Field Science Beiing China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

this paper presents an innovative application of stochastic control methodologies for optimizing the performance of spin-exchange relaxation-free co-magnetometers under conditions of internal heat source instability-induced atomic number density fluctuations. A data-driven modeling framework is adopted to develop an ARMAX model for the non-magnetoelectric heating system, followed by the implementation of a minimum variance control strategy to stabilize atomic number density within the gas cell. Comparative analysis against open-loop and ADRC approaches, based on simulation and experimental results, reveals a substantial reduction in atomic number density variance, showcasing superior control efficacy. this study emphasizes the integration of stochastic control techniques into advanced instrumentation, thereby offering avenues for enhancing the operational capabilities of thermal atomic instruments.

关键词： Heating systems learning systems Fluctuations Magnetometers Instruments Stochastic processes Process control

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Nominal Input Gain Estimation of ADRC for Lightly Damped System with Multiple Eigenmodes

Nominal Input Gain Estimation of ADRC for Lightly Damped Sys...

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data driven control and learning systems (ddcls)

作者： Jiedong Li Mingwei Sun Yongshuai Wang Zengqiang Chen College of Artificial Intellegence Nankai university Tianjin P. R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Active disturbance rejection control (ADRC) has been widely employed for governing a class of lightly damped systems with multiple eigenmodes. However, it has so far not investigated in detail how to estimate their nominal input gains, associated with the relative order, especially under different desirable control bandwidths for the same controlled object. this paper proposes an estimation rule regarding the nominal input gain of ADRC. the modal decomposition for a class of lightly damped systems with multiple eigenmodes is deduced in detail. then, the frequency response for an elementary example of a system with two eigenmodes is analyzed, and the ADRC design with the corresponding tuning guideline is developed. the validity and superiority of the proposed method are demonstrated through simulations.

关键词： Robust control learning systems Estimation Bandwidth control systems Frequency response Tuning

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Active Disturbance Rejection Gain Scheduling control of Hypersonic Vehicle Based on Guardian Maps

Active Disturbance Rejection Gain Scheduling Control of Hype...

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data driven control and learning systems (ddcls)

作者： Tingyu Zhang Yongshuai Wang Mingwei Sun Mingrui Hao Zengqiang Chen College of Artificial Intelligence Nankai University Tianjin China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

this paper presented an adaptive gain scheduling algorithm for hypersonic vehicles with wide envelopes. First, the linear parameter variable parameter (LPV) model of hypersonic vehicles is established in the flight envelope. then, the active disturbance rejection controller is considered to cope with disturbances and uncertainties, and the control gain is scheduled using the guardian maps method to adapt to the wide envelope of velocity and height. Finally, numerical simulation results show good control performance and meet the required performance indexes in all operating conditions, demonstrating the effectiveness and robustness of the proposed method.

关键词： Uncertainty PI control Scheduling algorithms Atmospheric modeling Numerical simulation Scheduling Robustness

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Distributed control Problems of Second-Order Directed Signed Networks Based on Sampled data

Distributed Control Problems of Second-Order Directed Signed...

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data driven control and learning systems (ddcls)

作者： Jinchao Lil Yi Zhang Mingjun Du Shaopeng Pang Peng Ji School of Information and Automation Engineering Qilu University of Technology (Shandong Academy of Science) Jinan Shandong Province P. R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In this study, the bipartite consensus problem of directed signed network based on sampled data is studied. A control protocol for second-order signed network is proposed, which is based on sampling data. By leveraging principles of the Laplacian matrix and graph theory, the protocol is derived, and the necessary and sufficient conditions for achieving bipartite consensus and state stability are identified. the research findings suggest that bipartite consensus can be reached in structurally balanced network topologies with the correct choice of sampling period and parameters, whereas state stability is achievable in structurally unbalanced configurations. In addition, simulation examples are used to verify the accuracy of the derived results.

关键词： learning systems Sufficient conditions Protocols Laplace equations Network topology Decentralized control Distributed databases

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An Enhanced GC-RANSAC Approach for Subway Indoor Ground Segmentation

An Enhanced GC-RANSAC Approach for Subway Indoor Ground Segm...

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data driven control and learning systems (ddcls)

作者： Ye Ren Wenxuan Fang Li Wang Shida Liu School of Electronics & Control Engineering North China University of Technology Beijing

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Addressing the challenges of traditional LiDAR ground segmentation algorithms in processing complex subway scenes captured, an improved GC-RANSAC ground segmentation algorithm is proposed. this method initially uses the RANSAC algorithm to extract several planes. then, it calculates the slope threshold for each plane to preserve non-ground areas. Finally, the min-cut algorithm is utilized to complete the ground segmentation. Based on the quadruped robots experimental platform, empirical data from subway scenarios are collected and tested. the processing results show the proposed algorithm demonstrates excellent performance compared with other selected methods. the False Negative Rates are 0.56%, 0.84%, and 1.67% for elevators, subway station corridors, and subway station halls respectively, which meets the effectiveness requirements for quadruped robots.

关键词： Point cloud compression learning systems Laser radar Radar detection Radar tracking control systems Elevators

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Model Free Adaptive control for Nonlinear Multi-Agent systems Under Actuator Faults and DoS Attacks

Model Free Adaptive Control for Nonlinear Multi-Agent System...

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data driven control and learning systems (ddcls)

作者： Ce Chuai Haibin Yin Fuyong Wang Zhongxin Liu College of Artificial Intelligence Nankai University Tianjin China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

A model free fault-tolerant and intrusion-tolerant control approach is developed for nonlinear multi-agent systems (MASs) characterized by unknown dynamics, facing denial-of-service (DoS) attacks and actuator faults. Initially, the equivalent dynamic linearization method based on pseudo-partial derivatives is employed to characterize actuator fault information, while models for DoS attacks on communication links follow the Bernoulli distribution. Compensation mechanisms for actuator faults and data packets loss are then introduced. Subsequently, a distributed model free adaptive control scheme is devised based on above mechanisms, and the bounded convergence of the tracking error is proven using a compressive mapping method. Numerical simulations showcase the efficacy of the control approach.

关键词： Actuators Adaptation models Protocols Packet loss Denial-of-service attack control systems Numerical simulation

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H∞ Tracking control of Tow Time Scale Linear System Based on Off-Policy Reinforcement learning

H∞ Tracking Control of Tow Time Scale Linear System Based o...

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data driven control and learning systems (ddcls)

作者： MinYin Shen Fei Liu Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education) Institute of Automation Jiangnan University Wuxi China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In this paper, by integrating singular perturbation method and off-policy reinforcement learning, a new approach to solving the H∞ tracking control problem is presented for linear time-invariant two time scales systems. A composite control scheme is designed through decomposing the two time scales system into fast and slow subsystems. For the slow subsystem, a model-free H∞ tracking controller is designed based on off-policy reinforcement learning to accomplish system tracking of the desired trajectory while effectively suppressing disturbances, without knowledge of the dynamics. Meanwhile, for the fast subsystem, a model-based offline controller is adopted. To better tracking performance, an exponential discount factor is introduced to weight the cost of input and tracking errors. By analysis and simulation, the effectiveness and superior tracking performance of proposed method has been verified.

关键词： Linear systems learning systems Costs Perturbation methods Design methodology Reinforcement learning control systems

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