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IEEE International Conference on Industrial Technology (ICIT)

作者： Xiao Liu Luefeng Chen Min Wu Weihua Cao Chengda Lu Witold Pedrycz 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 and the Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland Department of Computer Engineering Istinye University Sariyer/Istanbul Turkey

ISBN: (数字)9798350340266

ISBN: (纸本)9798350340273

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.

关键词： Drilling Robust control Uncertainty Robust stability Simulation Coal Resonance

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Optimization of the Landslide Susceptibility Model based on Deep Forest and Twice-Sample

Optimization of the Landslide Susceptibility Model based on ...

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Chinese automation Congress (CAC)

作者： Yuelin Yang Wanke Yu Zepeng Liu Lingran Zhao Chengda Lu Min Wu Ruiqing Niu 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 Institute of Geophysics and Geomatics China University of Geosciences Wuhan China

In the analysis of regional landslide susceptibility, the geological data used usually have the characteristics of multiplicity and nonlinearity. At the same time, in the past researches, the selection of non-landslide units in the training set is usually random or subjective, and there is no guarantee sample accuracy. In this paper, the deep forest algorithm combined with the twice-sample method is used to evaluate the landslide susceptibility in the Zigui Badong area of the Three Gorges Reservoir area. First, 11 landslide impact factors were selected according to the geological data to initially construct a sample set, and the cleaned data set was obtained by twice-sample. After dividing the training set and the test set, the deep forest model was used to predict the landslide susceptibility and evaluate the accuracy. The results show that the AUC of the susceptibility prediction model built using the deep forest is 1%-10% higher than that of GBDT, MLP and other methods, while the Twice-Sample method improves the AUC by 2%.

关键词： Training Geology Predictive models Feature extraction Reservoirs Prediction algorithms Terrain factors

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Landslide Sensitivity Mapping Based on One-Dimensional Residual Convolutional Neural Networks

Landslide Sensitivity Mapping Based on One-Dimensional Resid...

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Chinese automation Congress (CAC)

作者： Yougang Ke Chengda Lu Min Wu Wanke Yu Ruiqing Niu 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 Institute of Geophysics and Geomatics China University of Geosciences Wuhan China

Landslide is a common geological disaster. Landslide sensitivity mapping (LSM) is the key technology for landslide monitoring, early warning and risk assessment. Deep learning shows good performance in feature extraction. This paper proposes a one-dimensional residual convolution neural network (1DRCNN), which takes Yunyang County, Chongqing City, china, located in the Three Gorges Reservoir area as the research area. Extracting 12 evaluation factors from multi-source remote sensing data and building a training set. The spatial probability of landslide occurrence is quantitatively predicted by the proposed model, and finally, the landslide sensitivity mapping is generated. Compared with the common machine learning models SVM and logistic regression, the results show that the AUC (area under curve) and accuracy of 1D-RCNN are 0.9860 and 93.38%, respectively, which proving that this method is effective and can provide a reference for disaster prevention and reduction.

关键词： Support vector machines Training Sensitivity Urban areas Feature extraction Reservoirs Terrain factors

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Takeaway Method Based on the Crowdsensing Path Planning system: A Study on the Use of Idle Bicycle Resources for Delivery

SSRN

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SSRN 2023年

作者： Xiong, Yonghua Zeng, Hongrui Yu, Anjun Hiroaki, Kurokawa She, Jinhua School of Automation China University of Geosciences Wuhan430074 China Hubei key laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China Jiangxi Ganyue Expressway Co. Ltd. Jiangxi330200 China School of Engineering Tokyo University of Technology Tokyo192-0982 Japan

Due to the surge in takeaway demand, full-time delivery riders are facing challenges in completing their tasks on time. To address this issue, this study proposed a takeaway delivery method based on Crowdsensing. This strategy uses idle bicycle riders as resources to complete delivery tasks at a lower cost. To decrease the total delivery cost of merchants, the idle bicycle path planning model was established. The path planning problem of the proposed model is characterized by multiple riders, random locations, open deliveries, and pickup and delivery crossovers. Further, it belongs to the NP-hard problem. To solve this path planning model, a takeaway delivery-based genetic algorithm was designed. Order-preserving initialization was utilized to construct the initial population. The order crossover algorithm and adaptive mutation algorithm were introduced to overcome slow convergence and prevent the probability of falling into the local optimal solution of the traditional algorithm. The experimental results showed that the proposed takeaway delivery-based genetic algorithm had a fast convergence and high accuracy. Moreover, it can effectively solve the path planning model, thereby reducing merchant delivery cost and improving delivery timeliness. The method proposed in this paper has a significant application value. © 2023, The Authors. All rights reserved.

关键词： Genetic algorithms

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Load Prediction Model of Gas Boiler Generator Set Based on CNN-LSTM 11th

Load Prediction Model of Gas Boiler Generator Set Based on...

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11th International Symposium on Computational Intelligence and Industrial Applications, ISCIIA 2024

作者： Xu, Yan Wu, Min Hu, Jie Peng, Fu-Sheng Zhang, Wen Song, Wen-Shuo Li, Hui-Hang School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

ISBN: (纸本)9789819647521

With the proposal of carbon peak and carbon neutrality targets and the construction of a new power system, improving energy efficiency and reducing carbon emissions have become the key to the sustainable development of industrial boiler generator sets. However, the current load scheduling of gas boiler power plants is mainly based on manual experience, which limits achieving more efficient and stable operation. Accurate short-term load forecasting can help dispatchers to make reasonable production schedules. Therefore, this paper proposes a hybrid load forecasting model for generator sets based on convolutional neural networks combined with long short-term memory networks. First, the Isolation Forest algorithm eliminates anomalies in the historical data set. Then, variables related to unit load are selected by combining mechanistic analysis with the Spearman algorithm. Considering the multiple operating conditions and high complexity of unit loads, the CNN-LSTM network is used to fully extract the spatial and temporal characteristics to build the load prediction model. Finally, experiments are conducted using actual production data, showing that the proposed method is effectiveness. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electric load dispatching

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Nonlinear Cooperative Output Regulation with Input Delay Compensation

arXiv

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arXiv 2024年

作者： Zheng, Shiqi Ahn, Choon Ki Jiang, Xiaowei Yan, Huaicheng Shi, Peng The School of Automation China University of Geosciences Wuhan430079 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China The Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China The Shaoyang Institute of Advanced Manufacturing Technology Shaoyang422000 China The School of Electrical Engineering Korea University Seoul136-701 Korea Republic of The Key Laboratory of Smart Manufacturing in Energy Chemical Process of Ministry of Education East China University of Science and Technology Shanghai200237 China The School of Electrical and Mechanical Engineering The University of Adelaide AdelaideSA5005 Australia

This paper investigates the cooperative output regulation (COR) of nonlinear multi-agent systems (MASs) with long input delay based on periodic event-triggered mechanism. Compared with other mechanisms, periodic event-triggered control can automatically guarantee a Zeno-free behavior and avoid the continuous monitoring of triggered conditions. First, a new periodic event-triggered distributed observer, which is based on the fully asynchronous communication data, is proposed to estimate the leader information. Second, a new distributed predictor feedback control method is proposed for the considered nonlinear MASs with input delay. By coordinate transformation, the MASs are mapped into new coupled ODE-PDE target systems with some disturbance-like terms. Then, we show that the COR problem is solvable. At last, to further save the communication resource, a periodic event-triggered mechanism is considered in the sensor-to-controller transmission in every agent. A new periodic event-triggered filter is proposed to deal with the periodic event-triggered feedback data. The MASs with input delay are mapped into coupled ODE-PDE target systems with sampled data information. Then, Lyapunov-Krasovskii functions are constructed to demonstrate the exponential stability of the MASs. Simulations verify the validity of the proposed results. © 2024, CC BY-NC-ND.

关键词： Feedback control

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Hierarchical Adaptive Tracking control for Mobile Robots With Multiple Constraints

Hierarchical Adaptive Tracking Control for Mobile Robots Wit...

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Chinese automation Congress (CAC)

作者： Chenghao Zhao Shiqi Zheng Haiming Li Haitao Jian Shihao Wang School of Automation China University of Geosciences Wuhan Campus Hubei Key Laboratory of Advanced Control and Intelligent Automation fo Complex Systems Wuhan China

ISBN: (纸本)9781665426480

This paper studies the mobile robots with multiple constraints based on path planning of A-star algorithm. A hierarchical adaptive control method is presented to handle multiple constaints. On the upper layer, a Astar path planning algorithm is designed. On the lower layer, a new controller is provided. Then, we design barrier Lyapunov function and adaptive RBF neural network control method to make sure that tracking error and velocity error converges to the specified set within a finite amount of time. Compared with other existing works, this system considers multiple consraints include tracking error, constraints velocity constraints and output saturation constraint, provides guarantee for the safe operation of the mobile robot. In addition, because of the unknown conditions on the ground, system also considers the impact of slipping. Computer simulation result demonstrates the effectiveness of control method. In the experimental verification, we use A-star algorithm to generate the appropriate path as the desired trajectory of the mobile robot. The experiment proved the stability of control method.

关键词： automation Computer simulation Neural networks Approximation algorithms Stability analysis Trajectory Planning

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Development of a 3-RRS Micromanipulator Based on Origami-Inspired Spherical Joint

Development of a 3-RRS Micromanipulator Based on Origami-Ins...

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IEEE International Conference on Robotics and automation (ICRA)

作者： Haoqi Han Xiaoming Liu Yan Chen Hao Pang Xiaoqing Tang Dan Liu Qiang Huang Tatsuo Arai Beijing Advanced Innovation Center for Intelligent Robots and Systems and School of Mechatronical Engineering Key Laboratory of Biomimetic Robots and Systems Ministry of Education State Key Laboratory of Intelligent Control and Decision of Complex System Beijing Institute of Technology Beijing China Department of Micro-Nano Electronics Shanghai Jiao Tong University China Center for Neuroscience and Biomedical Engineering The University of Electro-Communications Tokyo Japan

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

In recent years, micromanipulation technology has achieved extensive applications in industry and life science. Improving the precision and bandwidth of the micromanipulator and simultaneously reducing size, weight, and cost pose significant challenges to the existing micromanipulator design and fabrication methods. Here, we propose a 3-RRS micromanipulator with an origami-inspired spherical joint based on the PC-MEMS process, aiming for miniaturization and cost-effectiveness. The spherical joint allows rotations of 140° around the x-axis approximately, 140° around the y-axis approximately, and 20° around the z-axis approximately. The micromanipulator has weights of 0.8 g, dimensions of 16 mm × 16 mm × 22 mm, and workspace of 0.7 mm 3 . The end platform of the micromanipulator can be equipped with various effectors to accomplish different kinds of tasks. Experimental results validated its high precision and bandwidth, exhibiting its potential to perform intricate micromanipulation tasks.

关键词： Industries Fabrication Costs Micromanipulators Design methodology Bandwidth Life sciences

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Coarray Tensor Train Decomposition for Bistatic MIMO Radar with Uniform Planar Array

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IEEE Transactions on Antennas and Propagation 2025年

作者： Xie, Qianpeng Wang, Zhanling Wen, Fangqing He, Jin Truong, Trieu-Kien National University of Defense Technology State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System Changsha410073 China Hubei University of Automotive Technology Institute of Vehicle Information Control and Network Technology Shiyan442002 China China Three Gorges University Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering Yichang443002 China Shanghai Jiao Tong University Shanghai Key Laboratory of Navigation and Location Based Services School of Electronic Information and Electrical Engineering Shanghai200240 China

Recently, tensor network decomposition has attracted increasing attention due to its high efficacy in modeling multi-dimensional correlations in high-order tensors. Considering this advantage, this study employs tensor train decomposition (TTD) to achieve joint two-dimensional direction-of-departure (2D-DOD) and direction-of-arrival (2D-DOA) estimation in bistatic multiple-input multiple-output (MIMO) radar systems with uniform planar array (UPA) configurations. First, a five-dimensional (5D) tensor is defined to incorporate the two-dimensional (2D) spatial information from transmit and receive UPAs, which is then combined with temporal information. Next, a low-rank four-dimensional (4D) tensor is generated by performing an autocorrelation operation along the temporal dimension of the 5D tensor, forming a structure that encapsulates the difference coarrays of the transmit and receive UPAs. Then, a TTD framework is designed to decompose the 4D tensor into interconnected lower-order TT-cores. Furthermore, a precise bidirectional mapping is performed between the TT-cores and the original Vandermonde factor matrices. Finally, leveraging distinct combinations of TT-cores, two innovative methods are developed to recover the Vandermonde factor matrices. The proposed methods conduct automatic pairing of 2D-DOD and obtain 2D-DOA estimates by enforcing consistent permutation orders across all factor matrices. The proposed methods are verified by simulation experiments. The results confirm that the proposed methods can surpass the existing tensor-based methods in terms of estimation accuracy. © 1963-2012 IEEE.

关键词： Matrix algebra

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Data-Driven Learning and control with Event-Triggered Measurements

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IEEE Transactions on Automatic control 2025年

作者： Feng, Shilun Shi, Dawei Chen, Tongwen Shi, Ling Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems MIIT Key Laboratory of Servo Motion System Drive and Control School of Automation Beijing100081 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Clear Water Bay Kowloon Hong Kong

Event-triggered control has attracted considerable attention for its effectiveness in resource-restricted applications. To make event-triggered control as an end-to-end solution, a key issue is how to effectively learn unknown system dynamics from event-triggered measurements and consequently, develop a learning-based event-triggered controller. Existing works learn system dynamics based on periodic time-triggered measurements, and it is yet to know how to learn a controller with performance guarantee based on event-triggered measurements. To address this issue, we consider the problem of learning an event-triggered state feedback controller for an unknown linear system based on event-triggered state measurements in this work. In particular, we first analyze the event-triggered measurements within a set-membership framework. We prove that the estimation error belongs to a bounded ellipsoid determined by the historical measurements and the event-triggering condition. Subsequently, we demonstrate that all admissible systems compatible with the collected data samples can be explicitly represented in the form of quadratic matrix inequalities using the state estimates. With the acquired set of admissible systems, a co-design problem for the data-driven controller and event-triggering condition is solved using the linear matrix inequality technique, with guaranteed closed-loop stability and L2-gain performance. Finally, numerical examples and comparisons are provided to illustrate the effectiveness of the proposed event-triggered learning and control approach. © 1963-2012 IEEE.

关键词： Networked control systems

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