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Power and Electrical Technology Conference (APET), Asia

作者： Jiahui Wang Zhili Tan School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

ISBN: (数字)9798350367690

ISBN: (纸本)9798350367706

The multitude of advantages offered by distributed power generation units are recognized as crucial factors in enhancing the security of distribution networks. Ensuring the optimal size and placement of distributed generation (DG) units not only enhances system security but also increases profitability. Haphazard placement of DG units may lead to adverse impacts on the power system, resulting in increased power losses and diminished voltage distribution. Therefore, to ensure the optimal size and placement of DG units, this paper proposes a method to optimize DG scheduling and operation, thereby improving voltage distribution, reducing power losses, and enhancing the overall reliability of the entire grid. To reduce power loss (PL) and improve voltage distribution (VP), an adaptive chaotic particle swarm optimization (ACPSO) algorithm is employed to determine the optimal locations and sizes of DG units. By comparing this algorithm with traditional approaches, its efficacy in enhancing performance can be discerned.

关键词： Profitability Voltage Distributed power generation Power system reliability Planning Security Reliability Particle swarm optimization Optimization Testing

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Feature-Prescribed Iterative Learning Control of Waggle Dance Movement for Social Motor Coordination in Joint Actions

arXiv

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arXiv 2024年

作者： Guo, Bowen Zhai, Chao School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-exploration Ministry of Education Wuhan430074 China

Extensive experiments suggest that motor coordination among human participants may contribute to social affinity and emotional attachment, which has great potential in the clinical treatment of social disorders or schizophrenia. Mirror game provides an effective experimental paradigm for studying social motor coordination. Nevertheless, the lack of movement richness prevents the emergence of high-level coordination in the existing one-dimensional experiments. To tackle this problem, this work develops a two-dimensional experimental paradigm of mirror game by playing waggle dance between two participants. In particular, an online control architecture of customized virtual player is created to coordinate with human player. Therein, an iterative learning control algorithm is proposed by integrating position tracking and behavior imitation with prescribed kinematic feature. Moreover, convergence analysis of control algorithm is conducted to guarantee the online performance of virtual player. Finally, the proposed control strategy is validated by matching experimental data and compared with other control methods using a set of performance indexes. Copyright © 2024, The Authors. All rights reserved.

关键词： Mirrors

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Robust Control of Feeding Speed for Coal Mine Tunnel Drilling Machines 25

Robust Control of Feeding Speed for Coal Mine Tunnel Drillin...

引用

25th IEEE International Conference on Industrial Technology, ICIT 2024

作者： Liu, Xiao Chen, Luefeng Wu, Min Cao, Weihua Lu, Chengda Pedrycz, Witold School of Automation China University of Geosciences Wuhan China School of Automation The Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems The Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Istinye University Department of Computer Engineering Sariyer Istanbul34396 Turkey

Changes in coal seam hardness cause fluctuations in the feed resistance at the drill bit during the drilling process, leading to unstable feeding speed. This paper proposes a robust dynamic output feedback controller to suppress disturbances caused by the variations in coal seam hardness in the feed system. Firstly, an unknown parameter measuring coal seam hardness is introduced, and an uncertain model of the feeding system is established based on the finite element model of the drill string. By designing weighted functions based on industrial field requirements and constructing a generalized plant, the controller achieves loop shaping, reducing the low-frequency impact of coal seam hardness variations on the feed system and suppressing the systems resonance peak. Simulation results demonstrate that the controller effectively suppresses parameter variations and external disturbances caused by changes in coal seam hardness, achieving stable control of the drilling speed. © 2024 IEEE.

关键词： Robust control

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TAO-Based Data Governance in intelligent Transportation systems

TAO-Based Data Governance in Intelligent Transportation Syst...

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International Conference on intelligent Transportation

作者： Xiaolong Liang Juanjuan Li Rui Qin Wenwen Ding Fei-Yue Wang Faculty of Innovation Engineering Macau University of Science and Technology Macao China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China The State Key Laboratory for Management and Control of Complex Systems and State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences and Collaborative Laboratory for Intelligent Science and Systems Beijing China

intelligent Transportation systems (ITS) are heavily dependent on private user data. However, most ITS fails to harness the potential of this invaluable data due to the absence of effective data governance mechanisms promoting users' contributions. Moreover, data contributors face security risks such as privacy preservation, data leakage, etc., as well as high costs in data sharing, while benefits are disproportionately reaped by system operators and interaction. This systemic imbalance could indirectly incentivize a surge in inactions and even malicious actions. In response to these challenges, we propose the design of a True Autonomous Organization (TAO) for ITS, namely ITS TAO. Utilizing the newly designed decision models with decentralized organization structures and the three-power structure, ITS TAO aims to realize the fair distribution of rights and benefits for ITS data contributors. Furthermore, we design a real-time evaluation system based on parallel intelligence capable of identifying potential hazards.

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Decision-making and Planning Framework with Prediction-Guided Strategy Tree Search Algorithm for Uncontrolled Intersections 25

Decision-making and Planning Framework with Prediction-Guide...

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25th IEEE International Conference on intelligent Transportation systems, ITSC 2022

作者： Zhang, Ting Fu, Mengyin Song, Wenjie Yang, Yi Wang, Meiling Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing 100081 China Nanjing University of Science and Technology Nanjing 210014 China

ISBN: (数字)9781665468800

ISBN: (纸本)9781665468817

Uncontrolled intersections are important and challenging traffic scenarios for autonomous vehicles. Vehicles not only need to avoid collisions with dynamic vehicles instantaneously but also predict their behavior then make long-term decisions in reaction. To solve this problem, we propose a cooperative framework composed of a Primary Driver (PD) for motion planning and a Subordinate Driver (SD) for decision-making. SD is essentially the combination of a prediction module and a high-level behavior planner, which develops a prediction-guided strategy tree to determine the optimal action sequence. Especially, under the guidance of the prediction results, the tree branches are evaluated in security metrics, then get trimmed in action and observation space to reduce the dimensional complexity. With the assistance of SD, PD works as a collision checker and a low-level motion planner to generate a safe and smooth trajectory. We use the INTERACTION dataset to validate our method and achieve more than 90% success rate with efficiency improvement in various situations. © 2022 IEEE.

关键词： Uncertainty Decision making Prediction algorithms Planning Trajectory complexity theory Behavioral sciences

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ToolEyes: Fine-Grained Evaluation for Tool Learning Capabilities of Large Language Models in Real-world Scenarios

arXiv

引用

arXiv 2024年

作者： Ye, Junjie Li, Guanyu Gao, Songyang Huang, Caishuang Wu, Yilong Li, Sixian Fan, Xiaoran Dou, Shihan Ji, Tao Zhang, Qi Gui, Tao Huang, Xuanjing School of Computer Science Fudan University China Institute of Modern Languages and Linguistics Fudan University China Key Laboratory of Intelligent Information Processing Fudan University Shanghai China Research Institute of Intelligent Complex Systems Fudan University China Pengcheng Laboratory China

Existing evaluations of tool learning primarily focus on validating the alignment of selected tools (e.g., various APIs) for large language models (LLMs) with expected outcomes. However, these approaches rely on a limited set of scenarios where answers can be pre-determined. Furthermore, a sole emphasis on outcomes disregards the complex capabilities required for LLMs to effectively use tools. To tackle this issue, we propose ToolEyes, a fine-grained system tailored for the evaluation of the LLMs’ tool learning capabilities in authentic scenarios. The system meticulously examines seven real-world scenarios, analyzing five dimensions crucial to LLMs in tool learning: format alignment, intent comprehension, behavior planning, tool selection, and answer organization. Additionally, ToolEyes incorporates a tool library boasting approximately 600 tools, serving as an intermediary between LLMs and the physical world. Evaluations involving ten LLMs across three categories reveal a preference for specific scenarios and limited cognitive abilities in tool learning. Intriguingly, expanding the model size even exacerbates the hindrance to tool learning. The code and data are available at https://***/Junjie-Ye/ToolEyes. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Fault-Tolerant Model Predictive Load Frequency Control for Power systems with Electrolytic Aluminum Load Under New Event-triggered Mechanism

Fault-Tolerant Model Predictive Load Frequency Control for P...

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Fully Actuated System Theory and Applications (CFASTA), Conference on

作者： Shisen Zhou Ziqian Wang Xiongbo Wan School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

ISBN: (数字)9798350373691

ISBN: (纸本)9798350373707

In this paper, the problem of event-based fault-tolerant model predictive load frequency control is investigated for the power system where the electrolytic aluminum load participates in frequency modulation and actuator faults. A dynamic event-triggered mechanism containing an internal dynamic variable (IDV) and an adjustable variable is designed to reduce the data transmission burden. The fault-tolerant model predictive control (FTMPC) problem is expressed as a "min-max" optimization problem (OP). According to the Lyapunov-like function that depends on the IDV, an auxiliary OP with constrained linear matrix inequalities is constructed. By solving the auxiliary OP, the FTMPC controller gains that ensure the input-to-state stability of the closed-loop system can be obtained. The effectiveness of the proposed algorithm is verified through a simulation example.

关键词： Fault tolerance Actuators Aluminum Fault tolerant systems Power system dynamics Power system stability Predictive models

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Fuel Ratio Prediction Model Based on Attention Mechanism

Fuel Ratio Prediction Model Based on Attention Mechanism

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Asian Control Conference

作者： Yihan Li Weihua Cao Wenkai Hu Yan Yuan School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan China

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

One of the key concerns in today's steel business is how to effectively save energy and reduce emissions, since the concepts of green environmental preservation, energy conservation, and emission reduction are deeply ingrained in people's hearts. Fuel ratio is a key parameter in blast furnace steelmaking. Identifying the factors that influence the fuel ratio and being able to model and predict it can greatly help in guiding the stable operation of the blast furnace, reducing fuel ratio, saving energy, and cutting emissions. In this paper, the fuel ratio mechanism of blast furnace is analyzed, and the parameters affecting the fuel ratio are initially selected. Subsequently, the parameters with the highest correlation to the fuel ratio are identified using the maximum correlation coefficient method. A CNN-LSTM-Attention-based fuel ratio prediction model was developed, and its accuracy and efficacy were demonstrated through comparison with three other models.

关键词： Attention mechanisms Accuracy Energy conservation Steel industry Predictive models Blast furnaces Data models

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Integrated Prediction of Unfavorable Geology in Main Tunnel Considering the Geological Conditions of Parallel Tunnel

Integrated Prediction of Unfavorable Geology in Main Tunnel ...

引用

Chinese Automation Congress (CAC)

作者： Meng Zhang Chao Gan Weihua Cao School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Currently, the advanced geological prediction for the main tunnel from the parallel tunnel often employs parallel deduction of detection conclusions, which faces issues such as a lack of diverse geological exploration information, a lack of analysis of geological structures and poor accuracy. In this paper, an integrated prediction method for unfavorable geology in main tunnel considering the geological conditions of parallel tunnel is proposed, which consists of three stages. In the first stage, based on the forecast mileage range and tunnel excavation conditions, the process is divided into four specific cases. In the second stage, it deduces the geology of the main tunnel along the orientation of rock strata occurrence or joint to parallel tunnel and selects the results of the rock strata occurrence or joint deduction according to the distance of the deduction. In the last stage, it deduces the geological conditions of parallel tunnel face and the integration of geophysical and drilling information for predictive conclusions based on the four specific cases. The proposed method offers higher prediction accuracy for rock mass integrity and surrounding rock grades compared to parallel deduction method.

关键词： Drilling Accuracy Automation Prediction methods Rocks Excavation Faces

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Electric Dual Variable Pump Controlled Hydraulic Motor

Electric Dual Variable Pump Controlled Hydraulic Motor

引用

Chinese Control Conference (CCC)

作者： Jianmin Yang Junzheng Wang Shanshuai Niu State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing P. R. China

The combined control of variable speed and variable displacement is a new type of volume control with high efficiency and fast response. However, due to the inherent nonlinearity of multiplication, it brings certain difficulties to the control. The electric double-variable pump[1] is a dual-input single-output system, and it is a nonlinear system[2]. It is necessary to linearize the system or use a nonlinear control method to control and solve the control problem of the system. In this paper, an intelligent control rule is proposed for the nonlinear problem of double input and single output. Through backstepping design[3], the nonlinear system is transformed into multiple linear subsystems. Then the original system is turned into two independent subsystems with single input and single output, which are controlled separately. The co-simulation platform based on AMESIM and Simulink[4] has been verified and compared with a single PID control algorithm to simulate the step response and sinusoidal tracking performance of the system. The results show that the response speed of the system has been greatly improved.

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