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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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Distance- And velocity-based collision avoidance for time-varying formation control of second-order multi-agent systems

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IEEE Transactions on Circuits and systems II: Express Briefs 2021年第4期68卷 1253-1257页

作者： Pang, Zhong-Hua Zheng, Chang-Bing Sun, Jian Han, Qing-Long Liu, Guo-Ping Key Laboratory of Fieldbus Technology and Automation of Beijing North China University of Technology Beijing100144 China State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China School of Software and Electrical Engineering Swinburne University of Technology MelbourneVIC3122 Australia Department of Artificial Intelligence and Automation Wuhan University Wuhan430072 China

This brief addresses the time-varying formation control problem with collision avoidance for second-order multi-agent systems. By taking both distances and velocities between agents into account, a novel collision avoidance scheme is proposed on the basis of conventional distance-based artificial potential field methods. Compared with the latter, the proposed scheme can more accurately determine possible collision situations and thus significantly reduce possible collision areas, which consequently not only can reduce energy consumption, but also has as few adverse effects on formation maintenance as possible. Then a time-varying formation control protocol is designed so as to achieve zero steady-state position and velocity formation tracking errors without collision. Finally, theoretical analysis, numerical demonstration, as well as comparative simulation are provided to verify the effectiveness of the proposed method. © 2004-2012 IEEE.

关键词： Multi agent systems

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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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High Precision Waveform Reproduction of Shaking Table Based on Linear Active Disturbance Rejection control 39

High Precision Waveform Reproduction of Shaking Table Based ...

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39th Chinese control Conference, CCC 2020

作者： Wang, Sheng Lei, Qi Central South University School of Automation Changsha410083 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China

ISBN: (数字)9789881563903

ISBN: (纸本)9789881563903

Seismic simulation shaking table plays a key role in many industrial structural dynamic experiments. High-precision waveform repetition has a great impact on the experimental accuracy of structural dynamic experiments. The equipment of seismic simulation shaker table has obvious uncertainties in the modeling process, so it is difficult to describe the seismic simulation shaker table with a more accurate mathematical model. Therefore, this paper adopt the Linear Active Disturbance Rejection control (LADRC) to achieve high-precision reproduction of the waveform of the seismic simulation shaker. This method will all the model uncertainty and external disturbance as total disturbance, using the extended state observer (ESO) to estimate the total disturbance, and by using state feedback control law of generalized disturbance compensation, to get better performance of resistance to interference. This method does not rely on the model of the specimen and does not need to obtain prior knowledge of the specimen. In this paper, the inertial and single-degree-of-freedom elastic specimens are simulated for vibration test, and the control effect of this strategy is fully verified by comparison with the traditional PID control method. © 2020 Technical Committee on control Theory, Chinese Association of automation.

关键词： Structural dynamics

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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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A Reinforcement Learning Approach for Robust Restoration of Generators in Power System

A Reinforcement Learning Approach for Robust Restoration of ...

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Chinese automation Congress (CAC)

作者： Huimiao Li Xiaozhou Yang Chao Zhai School of Automation China University of Geosciences Wuhan Campus Hubei Key Laboratory of Advanced Control and Intelligent Automation fo Complex Systems Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan China Singapore-ETH Centre CREATE Campus Future Resilient Systems Singapore

In the past decades, cascading blackouts have caused serious damages to power systems and affected the normal operation of society, so it is crucial to quickly restore the damaged power system to normal state. In this paper, a reinforcement learning (RL) approach is developed to achieve the robust restoration of generators in power systems. Firstly, the performance recovery of damaged power system is modeled as markov decision process, and constraints such as line voltage and active power output of generators are considered. Secondly, a RL algorithm is proposed to search the optimal control strategy for generator units recovery. Based on the proposed restoration approach, Q-learning algorithm is employed to obtain the optimal strategy for power supply of generaors in power grids. Finally, numerical simulations are carried out on IEEE 9-bus system under different scenarios of external disturbances and certainties. The simulation results demonstrate the effectiveness and feasibility of the proposed approach.

关键词： Q-learning Power supplies Simulation Power system protection Generators Explosions Power systems

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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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Adaptive Finite-Time Almost Sure Tracking of Drill-Bits Driven by a Lévy Process

Adaptive Finite-Time Almost Sure Tracking of Drill-Bits Driv...

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第40届中国控制会议

作者： Liyan Chen Xiaofeng Zong School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

This paper studies the finite-time tracking control problem for the stochastic drill-bits system driven by a Lévy process with the bit-rock interaction. The finite-time tracking control problem of the stochastic drill-bits system driven by a Lévy process can be regarded as the finite-time stability analysis for the stochastic nonlinear equations driven by a Lévy process. So the Lyapunov-type finite-time stability theorem is firstly developed to obtain the finite-time almost sure stability for n-dimensional stochastic nonlinear equations driven by a Lévy process. Then based on finite-time stability theorem, the adaptive finite-time almost sure tracking of drill-b its is achieved. A drill-bit simulation is given to demonstrate the control effect.

关键词： Finite-time almost sure tracking Lévy process Drill-bits

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An Improved Distributed Secondary control to Attain Concomitant Accurate Current Sharing and Voltage Restoration in DC Microgrids

An Improved Distributed Secondary Control to Attain Concomit...

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第40届中国控制会议

作者： Danyun Li Dashuang Zhao School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

This article proposes a distributed secondary control strategy for accurate current allocation and voltage restoration in DC microgrids. This method consists of a high coefficient droop controller and a voltage shifting controller which needs to obtain the voltage information of the adjacent converters through a low bandwidth communication link, and then calculates the voltage shifting required for the reference voltage. The system small-signal model considering the specific converter object is established to analyze the regulation rules of the parameters of the secondary controller. Moreover, the proposed method does not require complex control structure and a large amount of information of converter variables. A DC microgrid environment was built in MATLAB/Simulink, and the effectiveness of the proposed control strategy was verified.

关键词： Distributed control Voltage restoration Droop control DC microgrid

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A Multiple Ant Colony System for the Electric Vehicle Routing Problem with Time Windows

A Multiple Ant Colony System for the Electric Vehicle Routin...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Michalis Mavrovouniotis Georgios Ellinas Changhe Li Marios Polycarpou Department of Electrical and Computer Engineering KIOS Research and Innovation Center of Excellence University of Cyprus Nicosia Cyprus School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China

ISBN: (纸本)9781665487696

Ant colony optimization (ACO) has been found to be useful on several vehicle routing problem variations. In this work, ACO is applied to the electric vehicle routing problem with time windows (E-VRPTW). The E-VRPTW has a hierarchical multiple objective function, which is to minimize the number of electric vehicles and the total distance traveled. A multiple ACO is applied to E-VRPTW in which two colonies cooperate to minimize the objectives in parallel. A local search is embedded in ACO to improve the quality of the output. The experimental results on a set of benchmark instances show that the multiple ACO is competitive with existing methods.

关键词： Ant colony optimization Vehicle routing Benchmark testing Electric vehicles Routing Search problems Linear programming

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