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arXiv 2022年

作者： Wang, Xin Berberich, Julian Sun, Jian Wang, Gang Allgöwer, Frank Chen, Jie The Key Laboratory of Intelligent Control and Decision of Complex System Beijing Institute of Technology Beijing10081 China The Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China The University of Stuttgart Institute for Systems Theory and Automatic Control Stuttgart70550 Germany The Department of Control Science and Engineering Tongji University Shanghai201804 China The State Key Lab of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China

The present paper considers the model-based and data-driven control of unknown linear time-invariant discretetime systems under event-triggering and self-triggering transmission schemes. To this end, we begin by presenting a dynamic event-triggering scheme (ETS) based on periodic sampling, and a discrete-time looped-functional approach, through which a model-based stability condition is derived. Combining the model-based condition with a recent data-based system representation, a data-driven stability criterion in the form of linear matrix inequalities (LMIs) is established, which also offers a way of co-designing the ETS matrix and the controller. To further alleviate the sampling burden of ETS due to its continuous/periodic detection, a self-triggering scheme (STS) is developed. Leveraging pre-collected input-state data, an algorithm for predicting the next transmission instant is given, while achieving system stability. Finally, numerical simulations showcase the efficacy of ETS and STS in reducing data transmissions as well as of the proposed co-design methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Linear matrix inequalities

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Suppression of Actuator Nonlinearities Based on the Cascade Active Disturbance Rejection Approach

Suppression of Actuator Nonlinearities Based on the Cascade ...

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2019 IEEE International Conference on Unmanned systems, ICUS 2019

作者： Mingjun, Wei Cheng, Xu Wanhai, Hu Hua, Ji Feng, Gao Beijing Electro-Mechanical Engineering Institute Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China Aviation Institute of Technology of CASIC Beijing China

ISBN: (纸本)9781728137926

In this paper, influences of actuator nonlinearities and their suppression methods are studied. Firstly, the influences of the dead zone and backlash characteristics of the actuator on the control performance of statically unstable missiles are analyzed in detail. Then based on the cascade control method and the active disturbance rejection control (ADRC) theory, a nonlinear "double-layer compensation" suppression method for the actuator nonlinearities is proposed. Finally, the simulation verification is carried out. Simulation results show that the "double-layer compensation" suppression method based on the cascade ADRC approach can significantly suppress the adverse effects such as system self-sustaining oscillation caused by actuator nonlinearities, and effectively improve the control performance of the statically unstable missile. © 2019 IEEE.

关键词： Disturbance rejection

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S$^{\text{4}}$TP: Social-Suitable and Safety-Sensitive Trajectory Planning for Autonomous Vehicles

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on intelligent Vehicles 2023年第2期9卷 3220-3231页

作者： Xiao Wang Ke Tang Xingyuan Dai Jintao Xu Quancheng Du Rui Ai Yuxiao Wang Weihao Gu Engineering Research Center of Autonomous Unmanned System Technology Ministry of Education Anhui University Hefei China Haomo Technology Company Ltd. Beijing China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Qingdao Academy of Intelligent Industries Qingdao China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

In public roads, autonomous vehicles (AVs) face the challenge of frequent interactions with human-driven vehicles (HDVs), which render uncertain driving behavior due to varying social characteristics among humans. To effectively assess the risks prevailing in the vicinity of AVs in social interactive traffic scenarios and achieve safe autonomous driving, this article proposes a social-suitable and safety-sensitive trajectory planning (S $^{\text{4}}$ TP) framework. Specifically, S $^{\text{4}}$ TP integrates the Social-Aware Trajectory Prediction (SATP) and Social-Aware Driving Risk Field (SADRF) modules. SATP utilizes Transformers to effectively encode the driving scene and incorporates an AV's planned trajectory during the prediction decoding process. SADRF assesses the expected surrounding risk degrees during AVs-HDVs interactions, each with different social characteristics, visualized as two-dimensional heat maps centered on the AV. SADRF models the driving intentions of the surrounding HDVs and predicts trajectories based on the representation of vehicular interactions. S $^{\text{4}}$ TP employs an optimization-based approach for motion planning, utilizing the predicted HDVs' trajectories as input. With the integration of SADRF, S $^{\text{4}}$ TP executes real-time online optimization of the planned trajectory of AV within low-risk regions, thus improving the safety and the interpretability of the planned trajectory. We have conducted comprehensive tests of the proposed method using the SMARTS simulator. Experimental results in complex social scenarios, such as unprotected left-turn intersections, merging, cruising, and overtaking, validate the superiority of our proposed S $^{\text{4}}$ TP in terms of safety and rationality. S $^{\text{4}}$ TP achieves a pass rate of 100% across all scenarios, surpassing the current state-of-the-art methods Fanta of 98.25% and Predictive-Decision of 94.75%.

关键词： Trajectory Safety Vehicles Trajectory planning Autonomous vehicles Risk management Behavioral sciences

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An initial alignment approach for improving the accuracy of azimuth setting

An initial alignment approach for improving the accuracy of ...

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2019 IEEE International Conference on Unmanned systems, ICUS 2019

作者： Guan, Xujun Wang, Xing Guo, Jianzhong Fu, Wenxing Meng, Lingbao Beijing Electro-Mechanical Engineering Institute Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China China Aerospace Science and Industry Corporation Beijing China

ISBN: (纸本)9781728137926

Considering the azimuth discrepancy between the main and sub-inertial navigation systems (INS) for a platform-carried unmanned aerial vehicles(UAV) in case of tilted launching at a large pitch angle, an initial alignment method for improving the accuracy of the azimuth setting is proposed. First, an algorithm for computing the azimuth discrepancy is derived. Then, a mathematical model for initial alignment is established based on azimuth setting. The simulation results show that correcting the azimuth discrepancy between the main and sub-INS can greatly improve the accuracy of azimuth setting, and therefore the initial alignment accuracy of the sub-INS. Meanwhile, the drift for the gyros in three directions can be effectively estimated. © 2019 IEEE.

关键词： Inertial navigation systems

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Evaluation method of UAV cluster simulation models with different granularity based on sensitivity analysis

Evaluation method of UAV cluster simulation models with diff...

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2019 IEEE International Conference on Unmanned systems, ICUS 2019

作者： Zhang, Qing Li, Jing Shen, Chao Science and Technology on Omplex-System Control and Intelligent-Agent Cooperation Laboratory Beijing China Beijing Electromechanical-Engineering Institute Beijing China

ISBN: (纸本)9781728137926

Simulation test evaluation of the unmanned aerial vehicle (UAV) cluster operational effectiveness requires the support of simulation models with different granularity levels such as precise, medium, and coarse levels. In order to ensure the confidence of the evaluation, difficulties in evaluating the effectiveness of different granularity simulation models of the UAV cluster need to be solved. Aiming at the problem that the existing evaluation methods are not fully applicable due to the constraints of a large number of actual flight data, this paper proposes the evaluation method of different granularity simulation models, completes the rationality analysis of the granularity partitioning of UAV cluster at three levels: precise, medium and coarse, and the rationality analysis of the support relationship between different granularity models. Based on the special input method, capability meta-models of the UAV cluster with different granularity are evaluated. The evaluation method of different granularity simulation models of the UAV cluster based on sensitivity analysis is proposed. The sensitivity factor extraction and simulation analysis of precise granularity in the motion layer, medium granularity in the perception layer, and coarse granularity in the decision layer are completed. The validity evaluation of the different granularity simulation models has laid a solid foundation for the simulation test evaluation of the UAV cluster operational effectiveness. © 2019 IEEE.

关键词： Sensitivity analysis

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Mixed Motivation Driven Social Multi-agent Reinforcement Learning for Autonomous Driving

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IEEE/CAA Journal of Automatica Sinica 2025年第6期12卷 1272-1282页

作者： Long Chen Peng Deng Lingxi Li Xuemin Hu State Key Laboratory of Multimodal Artificial Intelligence Systems and the State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing WAYTOUS Inc. Beijing Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an China School of Computer Science and Information Engineering Hubei University Wuhan China Purdue School of Engineering and Technology Indiana University-Purdue University Indianapolis Indianapolis IN USA School of Artificial Intelligence Hubei University Wuhan Key Laboratory of Intelligent Sensing System and Security (Hubei University) Ministry of Education Wuhan China

Despite great achievement has been made in autonomous driving technologies, autonomous vehicles (AVs) still exhibit limitations in intelligence and lack social coordination, which is primarily attributed to their reliance on single-agent technologies, neglecting inter-AV interactions. Current research on multi-agent autonomous driving (MAAD) predominantly focuses on either distributed individual learning or centralized cooperative learning, ignoring the mixed-motive nature of MAAD systems, where each agent is not only self-interested in reaching its own destination but also needs to coordinate with other traffic participants to enhance efficiency and safety. Inspired by the mixed motivation of human driving behavior and their learning process, we propose a novel mixed motivation driven social multi-agent reinforcement learning method for autonomous driving. In our method, a multi-agent reinforcement learning (MARL) algorithm, called Social Learning Policy Optimization (SoLPO), which takes advantage of both the individual and social learning paradigms, is proposed to empower agents to rapidly acquire self-interested policies and effectively learn socially coordinated behavior. Based on the proposed SoLPO, we further develop a mixed-motive MARL method for autonomous driving combined with a social reward integration module that can model the mixed-motive nature of MAAD systems by integrating individual and neighbor rewards into a social learning objective for improved learning speed and effectiveness. Experiments conducted on the MetaDrive simulator show that our proposed method outperforms existing state-of-the-art MARL approaches in metrics including the success rate, safety, and efficiency. More-over, the AVs trained by our method form coordinated social norms and exhibit human-like driving behavior, demonstrating a high degree of social coordination.

关键词： Measurement Reinforcement learning Safety Driver behavior Autonomous vehicles Optimization

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Optimal Terrain Following Trajectory Regeneration Using Linear Gauss Pseudo-Spectral Method

Optimal Terrain Following Trajectory Regeneration Using Line...

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2019 IEEE International Conference on Unmanned systems, ICUS 2019

作者： Feng, Yijun Chen, Wanchun Yang, Liang Wei, Donghui Beihang University School of Astronautics Beijing China Beijing Electro-Mechanical Engineering Institute Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (纸本)9781728137926

A numerical method for two-dimensional terrain following optimal trajectory regeneration is proposed in this paper, which corrects the planned optimal trajectory to avoid collision with previously unknown obstacles. The neighboring extremal problem is established near the optimal terrain following trajectory, which can be regarded as a two-point boundary-value problem (TPBVP) and solved using Linear Gauss Pseudo-Spectral (LGP) method. Compared with the classical backward sweep method for solving TPBVPs, LGP method is much faster in obtaining approximate results. Numerical simulations are provided to validate the effectiveness and demonstrate the features of the problem. © 2019 IEEE.

关键词： Numerical methods

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Spirometer and sEMG respiratory patterns for clinical decision support system

Spirometer and sEMG respiratory patterns for clinical decisi...

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IEEE Instrumentation and Measurement technology Conference

作者： Amit Krishan Kumar Mansour H. Assaf Voicu Z. Groza Emil M. Petriu Faculty of Electrical-Electronic Engineering Duy Tan University Da Nang Vietnam Key Laboratory of Complex System Intelligent Control and Decision School of Automation Beijing Institute of Technology Beijing China School of Information Technology Engineering Mathematics and Physics The University of the South Pacific Suva Republic of Fiji School of Electrical Engineering and Computer Science University of Ottawa Ottawa Canada

To improve support system for subjects with respiratory ailments, this work analyses the respiratory patterns from spirometer and surface electromyography (sEMG). The patterns are integrated with risk and biochemical systems model to optimise the clinical decision support system (CDSS). The work considers terminal events for the spirometer and sEMG. The events and their associated patterns are correlated and projected on a hyperspace prior implementing it in decision support system. The proposed method shows that the different characteristics of breathing indicate some essential hidden information which when used as 4D scattered projection, optimises the decision making in CDSS. The analysis shows the effectiveness of bimodal respiratory patterns for different breathing capacities, which is promising for the modelled conditions.

关键词：

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Monocular visual based obstacle distance estimation method for ultra-low altitude flight

Monocular visual based obstacle distance estimation method f...

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2019 IEEE International Conference on Unmanned systems, ICUS 2019

作者： Cheng, Jin Wei, Donghui Chen, Wanchun Gao, Wang Beijing Electro-Mechanical Engineering Institute Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China Beihang University School of Astronautics Beijing China

ISBN: (纸本)9781728137926

This paper focuses on the problem of ultra-low-altitude flight obstacle distance estimation for monocular vision-assisted aircraft. First, the basic error model of distance estimation using sequence images is derived and the phenomenon of "epipole dead zone" is revealed. Then the distance estimation algorithm based on pairs of coplanar points is proposed as well as its error model. This algorithm is proved to be a solution of the "epipole dead zone" problem theoretically. Finally, numerical simulations show that the proposed distance estimation algorithm can effectively improve the accuracy of distance estimation in the "epipole dead zone". © 2019 IEEE.

关键词： Vision

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Point Cloud Preprocessing on 3D LiDAR data for Unmanned Surface Vehicle in Marine Environment

Point Cloud Preprocessing on 3D LiDAR data for Unmanned Surf...

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Information technology,Big Data and Artificial Intelligence (ICIBA), International Conference on IEEE International Conference on

作者： Xianzhi Qi Wenxing Fu Pei An Bingli Wu Jie Ma National Key Laboratory of Science and Technology on Multispectral Information Processing School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (数字)9781728152240

ISBN: (纸本)9781728152257

Point cloud preprocessing is still a challenging task in the marine environment, for it is difficult to filter out non-obstacle points while avoiding damage to the obstacle completeness. In this paper, we propose a novel data preprocessing method on 3D LiDAR data for the unmanned surface vehicle in the marine environment. It consists of two tasks: outlier removal and wake filtering. As the spatial resolution of LiDAR changes with distance, we exploit distance normalization on statistical outlier filter for robust outlier removal. Considering the gradient difference between wave wake and obstacle surface near the water, we define and calculate the vertical state of point cloud on the range image to obtain the pre-filtering point set, and then use the RANSAC method to filter out the wake points. Experiments on real data has demonstrated the effectiveness of the proposed algorithm both in terms of feasibility and accuracy.

关键词： Three-dimensional displays Laser radar Filtering Filtering algorithms Spatial resolution Task analysis Measurement by laser beam

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