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International Conference on intelligent Transportation

作者： Mengyin Fu Wenjie Song Yang Yi Meiling Wang State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institution of Technology Beijing P. R. China State Key Lab. of Intell. Control & Decision of Complex Syst. Beijing Instn. of Technol. China

ISBN: (纸本)9781467365970

In this paper, we focus on the need for autonomous vehicles' path planning and decision making in the urban environment. In our modified method, traffic lanes, road edge and geographic information system(GIS) are integrated to generate three structured road models, which can be used to reduce car's lateral positioning error and guarantee car's driving stably without a precise pre-computed map for localization. Based on these models, car's position, attitude and speed are taken into account to find an optimal guided path and a suitable behavior strategy to keep our car obeying traffic rules. Thus, in the whole architecture, the instability of some modules' detection results and low positioning accuracy of GPS receiver is resolved. Applied to our autonomous vehicle - IN 2 BOT, which has taken part in "Chinese intelligent Vehicle Challenge" for many times, this method is proved more effective than our previous method after experiments under various conditions.

关键词： Path planning Roads Vehicles decision making Planning Mobile robots Image edge detection

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Modelling multivariate extreme value distributions via Markov trees∗

arXiv

引用

arXiv 2022年

作者： Hu, Shuang Peng, Zuoxiang Segers, Johan School of Science Key Laboratory of Intelligent Analysis and Decision on Complex Systems Chongqing University of Posts and Telecommunications Chongqing China School of Mathematics and Statistics Southwest University Chongqing China LIDAM/ISBA UCLouvain Belgium

Multivariate extreme value distributions are a common choice for modelling multivariate extremes. In high dimensions, however, the construction of flexible and parsimonious models is challenging. We propose to combine bivariate max-stable distributions into a Markov random field with respect to a tree. Although in general not max-stable itself, this Markov tree is attracted by a multivariate max-stable distribution. The latter serves as a tree-based approximation to an unknown max-stable distribution with the given bivariate distributions as margins. Given data, we learn an appropriate tree structure by Prim’s algorithm with estimated pairwise upper tail dependence coefficients as edge weights. The distributions of pairs of connected variables can be fitted in various ways. The resulting tree-structured max-stable distribution allows for inference on rare event probabilities, as illustrated on river discharge data from the upper Danube *** Codes 62G32, 62H22 Copyright © 2022, The Authors. All rights reserved.

关键词： Markov processes

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Adaptive Based Fixed-time Formation Tracking Control for MASs with External Disturbance and Region Constraints

Adaptive Based Fixed-time Formation Tracking Control for MAS...

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Chinese Control and decision Conference, CCDC

作者： Fenglan Sun Zhonghua Xu Xuemei Yu Key Lab of Intelligent Analysis and Decision on Complex Systems School of Science Chongqing University of Posts and Telecommunications Chongqing P. R. China Key Lab of Intelligent Air-Ground Cooperative Control for Universities in Chongqing College of Automation Chongqing University of Posts and Telecommunications Chongqing P. R. China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

In this paper, a fixed-time time-varying formation tracking controller is proposed for second-order multi-agent systems (MASs) with region constraints based on adaptive observation. A practical finite-time disturbance observer (DO) with an adaptive update law is proposed to eliminate the negative effects of disturbance. Using the sliding mode control technique, a novel fixed-time time-varying region constraints formation tracking controller is designed and the upper bound of convergence time is given. The simulation results show that the disturbance is estimated accurately and quickly by the designed DO, the desired time-varying regular hexagonal formation is realized within the upper bound of convergence time, and all agents remain within the constraint region, which verifies the effectiveness and feasibility of the theoretical results.

关键词： Upper bound Simulation Numerical simulation Disturbance observers Safety Time-varying systems Sliding mode control

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Consensus in multi-agent systems with multiple disturbances and time delay via proportional-integral control

Consensus in multi-agent systems with multiple disturbances ...

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第40届中国控制会议

作者： Fenglan Sun Yunhao Shen Key Laboratory of Intelligent Analysis and Decision on Complex Systems School of ScienceChongqing University of Posts and Telecommunications Key Laboratory of Intelligent Air-Ground Cooperative Control for Universities in Chongqing College of AutomationChongqing University of Posts and Telecommunications Potsdam Institute for Climate Impact Research

This paper studies the consensus in multi-agent systems with multiple disturbances and time delay over fixed and switching topology via proportional-integral control. The controller that includes proportional and integral actions is *** adopting matrix theory, algebraic graph theory and Lyapunov stability theory, sufficient conditions for consensus are obtained. Several numerical simulations are given to verify the effectiveness of the algorithms.

关键词： consensus multiple disturbances switching topology proportional-integral control

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Data-Driven Discovery of Nonlinear Dynamical systems from Noisy and Sparse Observations

Data-Driven Discovery of Nonlinear Dynamical Systems from No...

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New Trends in Computational Intelligence (NTCI), International Conference on

作者： Wei Zhu Chao Pei Yulan Liang Zhang Chen Jingsui Li School of Science Chongqing University of Posts and Telecommunications Chongqing China Key Laboratory of Intelligent Analysis and Decision on Complex Systems Chongqing China College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9798331517021

ISBN: (纸本)9798331517038

Sparse Identification of Nonlinear Dynamics (SINDy) is a data-driven algorithm for interpretability mod-eling. SINDy has a strong ability to learn dynamical systems from time series data. Sparse Identification of Nonlinear Dynamics (SINDy) can extract equations from data, but its performance will degrade when data has noise or sparse. In order to deal with this case, the idea of Monte Carlo Markov Chain (MCMC) is introduced, which is used to optimally select models. The SINDy with MCMC (MCMC-SINDy) can comprehensively discover model structures by employing random samples in data space. Simulation results show that MCMC-SINDy is more robust than SINDy with 1 % -15 % levels of noise or sparse 0.5. These results are promising for the discovery of governing equations for complex systems in neuroscience, power grids, epidemiology, finance or ecology, where governing equations have remained elusive. mportantly, compared to existing machine learning methods, the accuracy of modeling using MCMC SINDy is higher.

关键词： Neuroscience Biological system modeling Simulation Noise Time series analysis Mathematical models Power grids Nonlinear dynamical systems Noise measurement Periodic structures

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Data-driven resilient predictive control under denial-of-service

arXiv

引用

arXiv 2021年

作者： Liu, Wenjie Sun, Jian Wang, Gang Bullo, Francesco Chen, Jie State Key Lab of Intelligent Control and Decision of Complex Systems The School of Automation Beijing Institute of Technology Beijing100081 China State Key Lab of Intelligent Control and Decision of Complex Systems The School of Automation Beijing Institute of Technology Beijing100081 China The Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China Mechanical Engineering Department The Center of Control Dynamical Systems and Computation UC Santa Barbara CA93106-5070 United States Department of Control Science and Engineering Tongji University Shanghai201804 China State Key Lab of Intelligent Control and Decision of Complex Systems The School of Automation Beijing Institute of Technology Beijing100081 China

The study of resilient control of linear time-invariant (LTI) systems against denial-of-service (DoS) attacks is gaining popularity in emerging cyber-physical applications. In previous works, explicit system models are required to design a predictor-based resilient controller. These models can be either given a priori or obtained through a prior system identification step. Recent research efforts have focused on data-driven control based on pre-collected input-output trajectories (i.e., without explicit system models). In this paper, we take an initial step toward data-driven stabilization of stochastic LTI systems under DoS attacks, and develop a resilient model predictive control (MPC) scheme driven purely by data-dependent conditions. The proposed data-driven control method achieves the same level of resilience as the model-based control method. For example, local input-to-state stability (ISS) is achieved under mild assumptions on the noise and the DoS attacks. To recover global ISS, two modifications are further suggested at the price of reduced resilience against DoS attacks or increased computational complexity. Finally, a numerical example is given to validate the effectiveness of the proposed control method. © 2021, CC BY.

关键词： Model predictive control

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Dynamics of Small World Effect and Random Fuzzy Graph

Research Square

引用

Research Square 2021年

作者： Zhang, Yijin Huang, Jie Lin, Zongbing Key Lab of Intelligent Analysis and Decision on Complex Systems Chongqing Municipal Key Laboratory of Industrial Internet of Things & Networked Control Ministry of Education Chongqing University of Posts and Telecommunications Chongqing400065 China School of Mathematics and Computer Science Panzhihua University Panzhihua617000 China

Small World Effect (or Six Degrees of Separation theory) has generated significant influence in the world. Many researchers and institutes have done lots of work on the study of it. We model Small World Effect by random process and Graph, calculate the probability that any two people i and j in the world can contact each other after n steps (forwarding messages by intermediaries) based on different R (average number of acquaintances everyone has in the world).When R=50 or 80, if, the probability is 0.848 and the search is very likely to happen. When R=150, n=4, this probability is 0.99994, that is, after 4 steps, any two people in the world will establish connection almost surely. In the sense of Dunbar's number (R=150), six degrees of separation becomes four degrees of separation. We propose the concepts of (directed) Random Fuzzy Graph for the very first time which can describe the fact of recognition among people best, because the relation among persons is random and fuzzy. © 2021, CC BY.

关键词： Random processes

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Image Classification Based on Convolution and Lite Transformer

Image Classification Based on Convolution and Lite Transform...

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International Conference on Applied Machine Learning (ICAML)

作者： Xiaoliang Han Kaihe Wang Shengxia Tu Weisong Zhou Key Laboratory of Intelligent Analysis and Decision on Complex Systems Chongqing University of Posts and Telecommunications Chongqing China School of Science Chongqing University of Posts and Telecommunications Chongqing China Huawei Technologies Co. Ltd. Shenzhen China

Utilizing the self-attention mechanism, Vision transformer (ViT) can tap long-distance dependencies and get excellent performance in vision tasks. However, it is difficult for ViT to learn rich characteristics with limited processing resources or data. As a result, based on the ViT model, we use a lightweight module that combines the information intersection of window self-attention and the inductive bias of convolutional neural network (CNN) to build a backbone network Convolution Lite Transformer (CLT). Further, experimental results show that CLT achieves 79.21 percent accuracy on the ImageNet with fewer parameters, which is more accurate than the traditional CNN model and more suitable for lightweight deployment compared with the ViT model.

关键词： Computer vision Image coding Convolution Computational modeling Machine learning Transformers Convolutional neural networks

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Trapezoidal Pythagorean Fuzzy Linguistic Bonferroni Mean Operator based on the Preference Relation: A Case Study for Targeted Poverty Alleviation

Trapezoidal Pythagorean Fuzzy Linguistic Bonferroni Mean Ope...

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International Conference on Fuzzy systems and Knowledge Discovery (FSKD)

作者： Yue Xiao Sidong Xian School of Automation Chongqing University of Posts and Telecommunications Chongqing P. R. China Key Laboratory of Intelligent Analysis and Decision on Complex Systems Chongqing University of Posts and Telecommunications Chongqing P. R. China

ISBN: (纸本)9781538680988;9781538680971

The 19th session of national congress of the communist party of China reaffirmed its determination to win the fight against poverty. Therefore, it is of great importance to determine how to help people in different poverty-stricken areas. Bonferroni mean (BM), which can capture interrelationship among the individual arguments. And the intuitionistic preference relation is a powerful mean to express decision makers' information of intuitionistic preference over criteria in the process of decision making. Hence, in this paper, we present weighted trapezoidal Pythagorean fuzzy linguistic preference relation (TrPFLPR), afterwards, we propose the BM into trapezoidal Pythagorean fuzzy linguistic preference environment, present the trapezoidal Pythagorean fuzzy linguistic Bonferroni mean (TrPFLBM) operator and the weight TrPFLBM operator. In conclusion, a numerical instance about targeted poverty alleviation selection is given to demonstrate the method's effectiveness.

关键词：

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Design of Simultaneous State and Unknown Input Filtering Algorithm for a Class of Nonlinear Stochastic systems with Multiple Sensors 8

Design of Simultaneous State and Unknown Input Filtering Alg...

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8th IEEE International Conference on Cloud Computing and Intelligence systems, CCIS 2022

作者： Hu, Zhibin Hu, Jun Chen, Cai Du, Junhua Harbin University of Science and Technology Department of Applied Mathematics Harbin150080 China Harbin University of Science and Technology School of Automation Harbin150080 China Harbin University of Science and Technology Heilongjiang Prov. Key Lab. of Optimization Control and Intelligent Analysis for Complex Systems Harbin150080 China Qiqihar University College of Science Qiqihar161006 China

ISBN: (纸本)9781665477352

In this paper, we investigate the simultaneous state and unknown input (SUI) filtering issue for a class of multi-sensor networked systems (MSNSs). The unknown input with no prior knowledge is introduced in the system state and output. The focus of this paper is to design the local filters with regard to the SUI, which can yield that the local upper bounds of the filtering error covariance for the SUI are derived at each instant. Moreover, the local filter gains of the SUI are designed such that the obtained upper bounds can be minimized. Finally, the proposed joint SUI algorithm is verified by using the simulation example. © 2022 IEEE.

关键词： Stochastic systems

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