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检索条件"任意字段=2024 International Conference on Intelligent Robotics and Automatic Control"

共 5237 条记录，以下是61-70 订阅

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An intelligent Collision Avoidance Design for Unmanned Surface Vessels 9

An Intelligent Collision Avoidance Design for Unmanned Surfa...

引用

9th IEEE international conference on Advanced robotics and Mechatronics (ICARM)

作者： Chen, Yung-Yue Sun, Cheng-Yen Hung, I-Ching Natl Cheng Kung Univ Dept Syst & Naval Mechatron Engn Tainan 701401 Taiwan

ISBN: (纸本)9798350385731;9798350385724

An intelligent collision avoidance design which possesses abilities of a precise trajectory tracking and a collision free property for unmanned surface vessels is investigated in this paper. This proposed control system provides an effective mean for letting controlled unmanned surface vessels be able to execute given tasks by following collision free trajectories under influences of modelling uncertainties and ocean environmental disturbances. For eliminating effects of modelling uncertainties and ocean environmental disturbances, a compensator is developed. Furthermore, the required robust control commands are perfectly performed by a pair of rotatable propellers. Besides, for the purpose of rapidly generating an optimal collision free trajectory, a rapid image searching method is developed to cooperate with a continuous trajectory generator. One demonstration is examined for validating the control performance of this proposed control system.

关键词： Unmanned surface vessel Rapid trajectory generator Nonlinear control design control allocation

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Research and Application of Liquid Level control Algorithm for Oil Electric Drive Sand Mixing Truck

Research and Application of Liquid Level Control Algorithm f...

引用

2024 international conference on intelligent Computing and robotics, ICICR 2024

作者： Zhang, Guangquan Zhao, Dong Zhou, Bohua Yin, Jianli CRRC Qingdao Sifang Rolling Stock Research Institute Co. Ltd Qingdao China

ISBN: (纸本)9798350372274

The automatic control system of the sand mixing truck is one of the core equipment in oil fracturing operations. The liquid level control of the sand mixing tank is a key link in the control system of the sand mixing truck. Its control effect not only affects the efficiency of fracturing operations, but also affects the safety of equipment and operators. This article takes the liquid level control system of a 130 barrel electric drive sand mixing truck as the research platform, establishes the transfer function of the water tank liquid level, and proposes a new control strategy by combining traditional PID control with fuzzy control algorithm. The simulation and experimental results show that compared with traditional PID control strategies, this method can solve the problems of multiple time delays and nonlinearity in liquid level control systems, and has advantages such as fast response, small overshoot, and small steady-state deviation. It improves the efficiency and stability of sand mixing operations. ©2024 IEEE.

关键词： Fuzzy control

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Inductance and Core Loss Modeling and Estimation of Permanent Magnet Synchronous Motor with Subspace Learning-Based Flux Linkage Denoising

Inductance and Core Loss Modeling and Estimation of Permanen...

引用

2024 international conference on intelligent robotics and automatic control

作者： Zhu, Zupeng He, Bigan Zhang, Zhisheng Fang, Kunhong Feng, Guodong Huang, Kaide Foshan Univ Sch Math Foshan Peoples R China Guangdong Inst Special Equipment Inspect & Res Fo Foshan Peoples R China Sun Yat Sen Univ Sch Intelligent Syst Engn Shenzhen Peoples R China

ISBN: (纸本)9798350389814;9798350389807

In the field of control and optimization for permanent magnet synchronous motors (PMSM), it is challenging to accurately estimate the inductances along the motor's d-axis and q-axis, as well as to quantify the complex effects of core loss. This study proposes a inductance and core loss estimation method for PMSM based on flux linkage denoising and neural network modeling. Specifically, an equivalent circuit model of the PMSM considering the core loss effect is established. A noise removal technique based on subspace learning is introduced to denoise the flux linkages data. Furthermore, the flux linkage equations considering the core loss effect are integrated into the neural network to estimate the motor's inductances and parameters related to core loss. Experimental results demonstrate that the proposed neural network can accurately estimate the inductances and core loss-related parameters of PMSM, successfully addressing the issue of quantifying core loss effects, which validate effectiveness and robustness of the proposed approach.

关键词： Permanent magnet synchronous motor core loss model subspace learning neural network parameter estimation

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Driver's Perception of Autonomous Cornering control Based on a Sense of Circular Vision Model 13

Driver's Perception of Autonomous Cornering Control Based on...

引用

Joint 13th international conference on Soft Computing and intelligent Systems and 25th international Symposium on Advanced intelligent Systems, SCIS and ISIS 2024

作者： Degoshi, Ren Masuta, Hiroyuki Fuse, Yotaro Sawai, Kei Koyanagi, Ken'ichi Almassri, Ahmed Toyama Prefectural University Dept. of Intelligent Robotics Toyama Japan

ISBN: (纸本)9798350373332

Recently, Research and Development of autonomous driving technology are rapidly conducted in the automobile industry. Expectations for automated driving technology are growing. However, some people have concerns about comfort and safety of driving systems. One reason is that the difference between perception of automated driving and the perception of human driving. Current automated driving technology is considered human perception. However, current automated cornering control doesn't consider human perception. Previous studies have defined and modeled Sense of Circular Vision (SoCV) as the sensation perceived during cornering behavior. Moreover, automatic turning control has been developed using model predictive control based on a SoCV model. In this paper, we evaluate how people feel by the autonomous cornering control based on the SoCV model using a VR simulator. © 2024 IEEE.

关键词： Automotive industry

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A Lightweight Meta-Learning for Few-Shot Fault Diagnosis of control Valve

A Lightweight Meta-Learning for Few-Shot Fault Diagnosis of ...

引用

2024 international conference on intelligent robotics and automatic control

作者： Fan, Ruipeng Nan, Jing Dai, Wei China Univ Min & Technol Sch Informat & Control Engn Xuzhou Jiangsu Peoples R China

ISBN: (纸本)9798350389814;9798350389807

Due to the distribution of control valves across different control loops within industrial systems, posing a significant challenge in collecting adequate fault data that covers various working conditions. Furthermore, industrial equipment often faces resource constraints when deploying data-driven models. To this end, a lightweight meta-learning fault diagnosis method is proposed to address the problem of few-shot fault diagnosis under varying operating conditions. First, to effectively address the unsteady time-varying fault signals, the two-dimensional (2-D) cloud model (CM) is employed to reconstruct the fault signals, thereby characterizing the states and features of the fault signals more comprehensively. Subsequently, a lightweight meta-learning model is constructed, the initial network parameters are randomly generated, and the parameters are updated by gradient descent. Finally, the parameters of the meta-learner are improved on the new task, and a few-shot data can be used to achieve rapid adaptation to new tasks. The experimental results show that the proposed method has significant advantages compared with other traditional meta-learning methods.

关键词： Few-shot fault diagnosis various operating conditions cloud model meta-learning control valve

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Reinforcement Learning-tuned Active Disturbance Rejection controller for Tracking control of a 4-DoF Robot Manipulator 12

Reinforcement Learning-tuned Active Disturbance Rejection Co...

引用

12th international conference on control Mechatronics and Automation

作者： Yousef, Hanna Siddique, Tanjulee Fareh, Raouf Choutri, Kheireddine Dylov, Dmitry Khadraoui, Sofiane Univ Sharjah Dept Mech Engn Sharjah U Arab Emirates Univ Sharjah Dept Elect Engn Sharjah U Arab Emirates Blida 1 Univ Aeronaut & Space Studies Inst Blida Algeria Skolkovo Inst Sci & Technol Moscow Russia

ISBN: (纸本)9798331517519;9798331517526

In recent years, the use of robotics has significantly advanced numerous fields, offering enhanced precision and efficiency in complex tasks without requiring human intervention. One field of robotics that has hence attained a great deal of attention and technological advancements is the control of robots. The Active Disturbance Rejection control (ADRC) method, which can be considered as advanced iteration of the PID controller, stands out for its exceptional disturbance handling capabilities, garnering recognition in various industries. However, the manual adjustment of ADRC parameters proves time-consuming, prompting exploration into intelligent techniques for automatic parameter tuning. In this paper, we investigate Reinforcement Learning (RL) as a promising approach for optimizing parameters, specifically focusing on the tracking control of a 4 Degree of Freedom (DOF) robot manipulator. The obtained simulation results demonstrate the successful training of the system, showcasing its proficiency in tracking control tasks. By leveraging RL, we achieve efficient parameter optimization, reducing the need for manual intervention. This research not only highlights the potential of RL in enhancing ADRC performance but also underscores its broader applicability in advancing robotics integration with various fields for enhanced precision and automation.

关键词： active disturbance rejection control reinforcement learning tracking control DDPG

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An Improved Informed-RRT* algorithm for Mobile Robot Path Planning

An Improved Informed-RRT* algorithm for Mobile Robot Path Pl...

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2024 international conference on intelligent robotics and automatic control

作者： Li, Chuang Yuan, Yuhao Tan, Zhongwan Chen, Yilin Nanjing Tech Univ Coll Elect Engn & Control Sci Nanjing Peoples R China

ISBN: (纸本)9798350389814;9798350389807

Aiming at the limitations of the Informed Rapidly-exploring Random Tree (Informed-RRT*) algorithm in mobile robot path planning-specifically, its lack of goal orientation and directionality, inefficient searching, and lengthy planning times-this paper proposes an enhanced path planning algorithm that integrates the Artificial Potential Field (APF) method with the Informed-RRT* approach. Firstly, a probabilistic adaptive goal-biasing strategy is introduced to reduce search randomness. Secondly, the artificial potential field method is incorporated into the path extension process to reduce the required number of iterations. Additionally, a dynamic growth step size, based on obstacle density, is applied during the path generation phase to prevent local optima. Lastly, a bisection method is employed to create parent nodes, thereby enhancing path quality. Simulation results indicate that the proposed algorithm significantly improves both efficiency and path quality over the standard Informed-RRT* algorithm.

关键词： Informed-RRT APF path planning dynamic step size

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Bi-ACT: Bilateral control-Based Imitation Learning via Action Chunking with Transformer

Bi-ACT: Bilateral Control-Based Imitation Learning via Actio...

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IEEE/ASME international conference on Advanced intelligent Mechatronics (AIM)

作者： Buamanee, Thanpimon Kobayashi, Masato Uranishi, Yuki Takemura, Haruo Osaka Univ Grad Sch Informat Sci & Technol Toyonaka Osaka 5600043 Japan Osaka Univ Cybermedia Ctr Toyonaka Osaka 5600043 Japan

ISBN: (纸本)9798350355376;9798350355369

Autonomous manipulation in robot arms is a complex and evolving field of study in robotics. This paper proposes work stands at the intersection of two innovative approaches in the field of robotics and machine learning. Inspired by the Action Chunking with Transformer (ACT) model, which employs joint angle and image data to predict future movements, our work integrates principles of Bilateral control-Based Imitation Learning to enhance robotic control. Our objective is to synergize these techniques, thereby creating a more robust and efficient control mechanism. In our approach, the data collected from the environment are images from the gripper and overhead cameras, along with the joint angles, angular velocities, and torques of the follower robot using bilateral control. The model is designed to predict the subsequent steps for the joint angles, angular velocities, and torques of the leader robot. This predictive capability is crucial for implementing effective bilateral control in the follower robot, allowing for more nuanced and responsive maneuvering.

关键词： Robotic arms

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Harnessing Symmetry Breaking in Soft robotics: A Novel Approach for Underactuated Fingers

Harnessing Symmetry Breaking in Soft Robotics: A Novel Appro...

引用

2024 international conference on intelligent Robots and Systems

作者： Hashem, Ryman Howison, Toby Stilli, Agostino Stoyanov, Danail Xu, Weiliang Iida, Fumiya Univ Cambridge Bioinspired Robot Lab Dept Engn Cambridge England UCL Wellcome EPSRC Ctr Intervent & Surg Sci London England Univ Auckland Dept Mech & Mechatron Engn Auckland New Zealand

ISBN: (纸本)9798350377712;9798350377705

Soft robotics, an emerging domain in modern robotics, introduces innovative possibilities alongside challenges in controllability, particularly with multi-degree inflatable actuators. We present a novel manipulation method using underactuated soft fingers that addresses these challenges by harnessing symmetry breaking. Central to our approach is the mechanism of self-organization within a ring actuator equipped with five fingers. Typically considered a drawback, we exploit the actuator's buckling behavior to facilitate inhand manipulation. This strategic utilization enables object motion in both clockwise and counterclockwise directions via system perturbations and adjustments in frequency and duty cycle parameters. Employing the self-organizing properties of our actuator, our method is empirically validated through simulations and real actuator experiments, demonstrating the system's ability in manipulating objects by leveraging the inherent flexibility and morphological advantages. The design enables two degrees of freedom with minimal input, allowing objects to rotate due to the actuator's self-organizing actions. This simplification of control mechanisms is essential for soft robotics manipulation. Our findings indicate that control systems in soft robotics can be significantly simplified, harnessing the adaptable behavior inherent in its morphology.

关键词： Degrees of freedom (mechanics)

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Attitude control Design of Quadrotor Aircraft Based on Cascade LADRC with Feedforward Compensation

Attitude Control Design of Quadrotor Aircraft Based on Casca...

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IEEE international conference on automatic control and intelligent Systems (I2CACIS)

作者： Yu, Wensheng Wu, Yibo Yan, Lirong Wang, Jun Li, Hang Wuhan Univ Technol Sch Automot Engn Wuhan Peoples R China Wuhan Leishen Special Equipment Co Wuhan Peoples R China

ISBN: (纸本)9798350372113;9798350372106

Quadrotor aircraft is a highly coupled and underactuated controlled object, and it faces challenges such as external disturbances and model uncertainties during flight. Therefore, high precision attitude estimation and flight controllers are required. This paper proposes a cascaded linear active disturbances rejection control (CLADRC) method for attitude control of quadrotor aircraft, based on feedforward compensation. The control method effectively constrains the attitude angles of the quadrotor aircraft. Simulation results demonstrate that compared to traditional linear active disturbances rejection control methods, the proposed control method can effectively suppress system oscillations and achieve faster response, thereby improving the stability of the quadrotor aircraft.

关键词： Quadrotor Aircraft Cascade LADRC Attitude control

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