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第42届中国控制会议

作者： Chuanshang Luo Yifeng Tang Zhihui Huang Yifan Xu Bin Liu Hai-Tao Zhang School of Artificial Intelligence and Automation the Engineering Research Center of Autonomous Intelligent Unmanned Systems the Key Laboratory of Image Processing and Intelligent Control and the State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology

This paper solves USV path planning problem constrained by multiple factors via ant-colony optimization ***, this paper uses the ways of multi-objective optimization to model the USV path planning problem. An improved ant colony algorithm named ACO-SA is put forward afterwards to effectively solve the problem. The algorithm is a combination of ACO algorithm(ant colony algorithm) and SA algorithm(simulated annealing algorithm), which has three improments: change the initial distribution of pheromone to guide the search when the algorithm has just started running;change the heuristic function and state transition probability taking three factors into consideration;change the pheromone update rule and make the ants compete for the right to update pheromone by simulated annealing algorithm, and update the best solution by the same ***, simulation experiment and field experiment are conducted to check the validity of ACO-SA algorithm.

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Q -learning based model-free input-output feedback linearization control method ⋆

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IFAC-PapersOnLine 2023年第2期56卷 9534-9539页

作者： Yipu Sun Xin Chen Wenpeng He Ziying Zhang Edwardo F. Fukushima Jinhua She School of Automation China University of Geosciences Wuhan 430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan 430074 China School of Engineering Tokyo University of Technology Hachioji Tokyo 192-0982 Japan School of Arts and Communication China University of Geosciences Wuhan 430074 China

Input-output feedback linearization is a nonlinear control method that relies on a precise dynamical model. Combining Q -learning techniques, an input-output feedback linearization correction framework is presented to accomplish model-free feedback linearization of affine nonlinear systems in order to tackle the problem caused by the unknown dynamics model. This framework formulates a model reference tracking control problem that guides the input-output relationship of the nonlinear system into a linear relationship. Due to the two Lie derivative terms present in the feedback linearized controller, the controller is designed as a dual network structure. To overcome the issue of coupling in the dual-network controller, a model-free Q -learning method is presented to solve the unknown controller network weights. The proposed method is experimentally validated on a single-link flexible joint manipulator system, and the resultant linearized system exhibits dynamics similar to the desired linear system in a new tracking task, proving the effectiveness of the proposed method.

关键词： Input-output feedback linearization unknown nonlinear systems Q-learning model-free nonlinear control method

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Linear Encryption Techniques for Counteracting Information-based Stealthy Attacks

Linear Encryption Techniques for Counteracting Information-b...

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International Conference on control, automation, Robotics and Vision (ICARCV)

作者： Jun Shang Hanwen Zhang Weixiong Rao Yiguang Hong Department of Control Science and Engineering National Key Laboratory of Autonomous Intelligent Unmanned Systems Shanghai Institute of Intelligent Sci-ence and Technology Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai China Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China School of Software Engineering Tongji University Shanghai China

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

This study explores linear encryption techniques to protect against information-based stealthy attacks on re-mote state estimation. Utilizing smart sensors equipped with local Kalman filters, the system transmits innovations rather than raw measurements via wireless networks. However, this transmission is susceptible to malicious data interception and manipulation by attackers. To safeguard against these stealthy threats, encryption and decryption modules are integrated into the system. This research aims to assess the effectiveness of the encryption strategy when faced with information-based stealthy attacks. A key contribution of this paper is the adoption of the most comprehensive attack models, moving away from the conventional reliance on innovation-based linear attack models. Our results demonstrate that the proposed linear encryption approach effectively mitigates stealthy attacks under certain mild conditions. The efficacy of the encryption is further validated through numerical examples, corroborating the theoretical advancements presented in this paper.

关键词： Technological innovation automation Wireless networks Encryption Numerical models Kalman filters State estimation Robots intelligent sensors

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Multi-Home Energy Coordination Scheduling Based on Adaptive Dynamic Programming

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The International Journal of intelligent control and systems 2024年第1期29卷 30-36页

作者： Kang Xiong Qinglai Wei Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China.

Implementing cooperative scheduling of multi-home microgrid energy and reducing the dependence on the main grid have become the focus of microgrid energy management *** paper proposes a new multi-agent adaptive dynamic programming(MAADP)method for the cooperative control of distributed home *** home is defined as a learning agent that needs to reasonably schedule the energy storage system to meet the respective load demand while accomplishing cooperative scheduling among the individual *** addition,an energy clearing center(ECC)is introduced to complete the energy exchange between each microgrid to protect the benefits of all *** proposed method adopts the learning strategy of“centralized learning and decentralized execution”to avoid the leakage of private *** experimental comparison with the benchmark method verifies that the method can realize the cooperative scheduling of each home and reduce the dependence on the main grid.

关键词： Multi-home scenarios adaptive dynamic programming(ADP) optimal home energy management smart grid

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A Vehicle-to-Vehicle Cooperative control Method for Intersections with Cav- Dedicated Lane

SSRN

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SSRN 2024年

作者： Zhang, Xiqiao Cui, Leqi Yang, Longhai Lv, Yisheng School of Transportation Science and Engineering Harbin Institute of Technology Harbin150090 China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

With the development of intelligent connected technology and autonomous driving technology, connected autonomous vehicles (CAV) and connected human-driven vehicles (CHV) will coexist for a long time in the future. The popularization of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication enables the improvement of CAV platooning efficiency and the joint control of CAV trajectory and signal timing. Therefore, this paper proposes a CAV-dedicated lane-based vehicle-to-vehicle interactive intersection control method, which is suitable for managing mixed traffic flow of CHV and CAV at a single intersection. In the proposed method, the road capacity is enhanced by separating the mixed traffic flow with a CAV-dedicated lane. Through vehicle-to-vehicle interaction and CHV induction, the traffic flow spatiotemporal distribution is regulated by a limited number of controlled vehicles. Furthermore, by jointly controlling CAV trajectory and signal timing, vehicles can efficiently pass through the intersection in the CHV phase without the dedicated CAV phase. The optimization of signal timing and vehicle trajectory at the intersection aims to improve intersection capacity and smooth vehicle trajectories. In addition, the car-following and lane-changing models of CHV are revised based on the prospect theory to validate the performance of platooning strategies under cooperative induction information in practical traffic operations. Simulation results demonstrate that compared to control methods without considering vehicle-to-vehicle interaction, the proposed method in this paper has significant advantages in reducing average vehicle delay and improving intersection efficiency. © 2024, The Authors. All rights reserved.

关键词： Autonomous vehicles

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Data-Driven control of Distributed Event-Triggered Network systems

arXiv

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arXiv 2022年

作者： Wang, Xin Sun, Jian Wang, Gang Allgöwer, Frank Chen, Jie State Key Lab of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China Institute for Systems Theory and Automatic Control University of Stuttgart Stuttgart70550 Germany Department of Control Science and Engineering Tongji University Shanghai201804 China State Key Lab of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China

The present paper deals with data-driven event-triggered control of a class of unknown discrete-time interconnected systems (a.k.a. network systems). To this end, we start by putting forth a novel distributed event-triggering transmission strategy based on periodic sampling, under which a model-based stability criterion for the closed-loop network system is derived, by leveraging a discrete-time looped-functional approach. Marrying the model-based criterion with a data-driven system representation recently developed in the literature, a purely data-driven stability criterion expressed in the form of linear matrix inequalities (LMIs) is established. Meanwhile, the data-driven stability criterion suggests a means for co-designing the event-triggering coefficient matrix and the feedback control gain matrix using only some offline collected state-input data. Finally, numerical results corroborate the efficacy of the proposed distributed data-driven ETS in cutting off data transmissions and the co-design procedure. Copyright © 2022, The Authors. All rights reserved.

关键词： Linear matrix inequalities

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Design of Recursive Filtering Scheme via An Improved Weighted Try-Once Discard Protocol

Design of Recursive Filtering Scheme via An Improved Weighte...

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International Conference on Industrial Artificial Intelligence (IAI)

作者： Kaiwen Zheng Jun Hu Junhua Du Hui Yu Department of Applied Mathematics Harbin University of Science and Technology Harbin China Heilongjiang Provincial Key Laboratory of Optimization Control and Intelligent Analysis for Complex Systems Harbin University of Science and Technology Harbin China School of Automation Harbin University of Science and Technology Harbin China

ISBN: (数字)9798350356618

ISBN: (纸本)9798350356625

This paper investigates the recursive filtering (RF) problem for stochastic multi-rate (MR) systems, where the information transmission is regulated by an improved weighted try-once-discard protocol (IWTODP). In order to reduce communication overhead and mitigate network congestion, the IWTODP is firstly proposed and designed to schedule the order of data transmission from the sensors. The main objective of this paper is to design a new RF scheme to minimize the upper bound (UB) on the filtering error (FE) covariance in each iteration step in the presence of IWTODP and MR sampling. In particular, an iterative way is given to provide the expression form of the gain of RF. Finally, the effectiveness of the proposed RF method is illustrated through simulation example.

关键词： Radio frequency Schedules Protocols Upper bound Filtering Filtering algorithms Iron Sensor systems Data communication State estimation

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Data-Driven Interval Robust Optimization Method of Vpp Bidding Strategy in Spot Market Under Multiple Uncertainties

SSRN

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SSRN 2024年

作者： Ma, Ying Li, Zhen Liu, Ruyi Liu, Bin Yu, Samson S. Liao, Xiaozhong Shi, Peng School of Automation Key Laboratory for Intelligent Control & Decision on Complex Systems Beijing Institute of Technology Beijing100081 China School of Mathematics and Statistics University of Sydney NSW2006 Australia Beijing102249 China School of Engineering Deakin University VIC3216 Australia School of Electrical and Electronic Engineering Univerty of Adelaide SA5005 Australia Research and Innovation Center Obuda University Budapest1034 Hungary

Virtual power plant (VPP) participating in the spot market can improve the flexibility of new-type power systems with the increasing penetration of renewable energy. However, multiple uncertainties on the market side, load side and generation side affect the bidding plans and operation efficiency of a VPP. In this paper, a two-stage interval robust optimization model is established to optimize the bidding plans of a VPP including gas turbine, energy storage, photovoltaic (PV) and electric vehicles (EVs). Interval numbers generated by the data-driven model are used to describe the uncertainty and correlation of electricity price in the spot market. Uncertainty sets are employed to describe the uncertainties in the number of EVs and PV power generation. The objective of the model is to jointly optimize the cost of VPP participating in the spot market considering arbitrage opportunity. The objective function containing interval numbers is transformed into a solvable form based on a ranking method considering pessimistic decisions. To solve this model, an improved column-and-constraint generation (C&CG) algorithm is developed based on the combination method of genetic algorithm (GA) and solver Gurobi. The results show that the interval numbers of electricity price generated by the data-driven model proposed in this paper can reduce the cost fluctuation of VPP by 3.2%. The two-stage interval robust model proposed in this paper can reduce the cost of VPP by 1.4% compared with the conventional single-stage robust method, and lessen the cost by 35.2% compared with the robust method that does not consider arbitrage opportunities in day-ahead and real-time markets. The improved C&CG algorithm presents superior performance in convergence and accuracy. Compared to the stochastic optimization method that generates n scenarios, the computational time of the model with the interval optimization method introduced in this paper can be reduced to 1∕n. Results from case studies illust

关键词： Digital storage

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Evaluation of Landslide Susceptibility Using Machine Learning Based on Information Value Sampling Method

Evaluation of Landslide Susceptibility Using Machine Learnin...

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Chinese automation Congress (CAC)

作者： Lang Liu Xin Chen Lingran Zhao 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 Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan China Exploration Institute of Geological Engineering of Xiangyang Xiangyang China

In landslide susceptibility prediction using machine learning (ML) models, the selection of appropriate negative samples significantly impacts the accuracy of the predictions. Many existing studies tend to randomly choose negative samples from the entire research area or specific attribute areas such as low slopes, leading to inaccuracies and biases that decrease the reliability of susceptibility mapping. To address this issue, this paper proposes an ML model based on the coupled information value (IV) method for susceptibility modeling. In this approach, the attribute values of environmental factors are transformed into information values, representing their contribution to landslides, using the information quantity method. Very low and low prone areas are then identified based on these information values, and random points are selected from these areas as negative samples for training and verifying subsequent machine learning models, which include the Random Forest (RF) model and Support Vector Machine (SVM) *** study focuses on the Three Gorges reservoir area in the Zigui-Badong area as an example. The comparative analysis of models trained using traditional sampling methods reveals that the RF model and SVM model, which were sampled using the information value method, achieve accuracies of 95.33% and 91.13%, respectively. In contrast, models trained using traditional sampling methods achieve accuracies of 90.43% and 88.69%, respectively.

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Data-Driven Interval Robust Optimization Method of Vpp Bidding Strategy in Spot Market Under Multiple Uncertainties

SSRN

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SSRN 2024年

Virtual power plant (VPP) participating in the spot market can improve the flexibility of new-type power systems with the increasing penetration of renewable energy. However, multiple uncertainties on the market side, load side and generation side affect the bidding plans and operation efficiency of a VPP. In this paper, a two-stage interval robust optimization model is established to optimize the bidding plans of a VPP including gas turbine, energy storage, photovoltaic (PV) and electric vehicles (EVs). Interval numbers generated by the data-driven model are used to describe the uncertainty and correlation of electricity price in the spot market. Uncertainty sets are employed to describe the uncertainties in the number of EVs and PV power generation. The objective of the model is to jointly optimize the cost of VPP participating in the spot market considering arbitrage opportunity. The objective function containing interval numbers is transformed into a solvable form based on a ranking method considering pessimistic decisions. To solve this model, an improved column-and-constraint generation (C&CG) algorithm is developed based on the combination method of genetic algorithm (GA) and solver Gurobi. The results show that the interval numbers of electricity price generated by the data-driven model proposed in this paper can reduce the cost fluctuation of VPP by 3.2%. The two-stage interval robust model proposed in this paper can reduce the cost by 1.4% compared with the single-stage robust method, and lessen the cost by 35.2% compared with the robust method that does not consider arbitrage opportunities in day-ahead and real-time markets. The improved C&CG algorithm presents superior performance in convergence and accuracy. Compared to the stochastic optimization method that generates n scenarios, the computational time of the model with the interval optimization method introduced in this paper can be reduced to 1∕n. Results from case studies illustrate the effectivene

关键词： Digital storage

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