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A Unified Framework Integrating decision making and Trajectory Planning Based on Spatio-Temporal Voxels for Highway Autonomous Driving

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IEEE Transactions on intelligent Transportation Systems 2022年第8期23卷 10365-10379页

作者： Zhang, Ting Song, Wenjie Fu, Mengyin Yang, Yi Tian, Xiaohui Wang, Meiling Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing100081 China Nanjing University of Science and Technology School of Automation Nanjing210014 China

intelligent decision making and efficient trajectory planning are closely related in autonomous driving technology, especially in highway environment full of dynamic interactive traffic participants. This work integrates them into a unified hierarchical framework with long-term behavior planning (LTBP) and short-term dynamic planning (STDP) running in two parallel threads with different horizon, consequently forming a closed-loop maneuver and trajectory planning system that can react to the dynamic environment effectively and efficiently. In LTBP, a novel voxel structure and the 'voxel expansion' algorithm are proposed for the generation of driving corridors in 3D configuration, which involves the prediction states of surrounding vehicles. By using Dijkstra search, the maneuver with minimal cost is determined in form of voxel sequences, then a quadratic programming (QP) problem is constructed for solving the optimal trajectory. And in STDP, another small-scaled QP problem is performed to track or adjust the reference trajectory from LTBP in response to the dynamic obstacles. Meanwhile, a Responsibility-Sensitive Safety (RSS) Checker keeps running at high frequency for real-time feedback to ensure security. Experiments on real data collected in different highway scenarios demonstrate the effectiveness and efficiency of our work. © 2000-2011 IEEE.

关键词： decision making

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Deep Convolutional Correlation Filter Learning Toward Robust Visual Object Tracking

Deep Convolutional Correlation Filter Learning Toward Robust...

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第34届中国控制与决策会议

作者： Tayssir Bouraffa Zihang Feng Yuxuan Wang Liping Yan Yuanqing Xia Bo Xiao Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of Technology School of Artificial Intelligence Beijing University of Posts and Telecommunications

Recently,convolutional neural network has been pervasively adopted in visual object tracking for its potential in discriminating the target from the surrounding *** of the visual object trackers extract deep features from a specific layer,generally from the last convolutional ***,these trackers are less effective,especially when the target undergoes drastic appearance variations caused by the presence of different challenging situations,such as occlusion,illumination change,background clutter and so *** this research paper,a novel tracking algorithm is developed by introducing an elastic net constraint and a contextual information into the convolutional network to successfully track the desired target throughout a video *** features are extracted from the shallow and the deep convolutional layers to further improve the tracking accuracy and *** the deep convolutional layers capture important semantic information,they are more robust to the target appearance *** for the shallow convolutional layers,they encode significant spatial details,which are more accurate to precisely localize the desired ***,Peak-Strength Context-Aware correlation filters are embedded to each convolutional layer output that produce multi-level convolutional response maps to collaboratively identify the estimated position of the target in a coarse-to-fine *** and qualitative experiments are performed on the widely used benchmark,the OTB-2015 dataset that shows impressive results compared to the state-of-the-art trackers.

关键词： Visual tracking convolutional neural network correlation filters hierarchical features

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Adaptive control of Discrete-time Nonlinear Systems Using ITF-ORVFL

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IEEE/CAA Journal of Automatica Sinica 2022年第3期9卷 556-563页

作者： Xiaofei Zhang Hongbin Ma Wenchao Zuo Man Luo School of Automation Beijing Institute of TechnologyBeijing 100081 School of Vehicle and Mobility Tsinghua UniversityBeijing 100084China School of Automation Beijing Institute of Technologyand also with the State Key Laboratory of Intelligent Control and Decision of Complex Systems(Beijing Institute of Technology)Beijing 100081China School of Automation Beijing Institute of TechnologyBeijing 100081and he is with Beijing Institute of Electronic System EngineeringBeijing 100854China School of Automation Beijing Institute of TechnologyBeijing 100081and she is with Ant GroupBeijing 310013China

Random vector functional ink(RVFL)networks belong to a class of single hidden layer neural networks in which some parameters are randomly *** network structure in which contains the direct links between inputs and outputs is unique,and stability analysis and real-time performance are two difficulties of the control systems based on neural *** this paper,combining the advantages of RVFL and the ideas of online sequential extreme learning machine(OS-ELM)and initial-training-free online extreme learning machine(ITFOELM),a novel online learning algorithm which is named as initial-training-free online random vector functional link algo rithm(ITF-ORVFL)is investigated for training *** link vector of RVFL network can be analytically determined based on sequentially arriving data by ITF-ORVFL with a high learning speed,and the stability for nonlinear systems based on this learning algorithm is *** experiment results indicate that the proposed ITF-ORVFL is effective in coping with nonparametric uncertainty.

关键词： Adaptive control initial-training-free online learning algorithm random vector functional link networks

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Dynamic collision avoidance for cooperative fixed-wing UAV swarm based on normalized artificial potential field optimization

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Journal of Central South University 2021年第10期28卷 3159-3172页

作者： LIU Wei-heng ZHENG Xin DENG Zhi-hong State Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of TechnologyBeijing 100081China China Aerospace Science and Industrial Corporation Beijing 100048China

Cooperative path planning is an important area in fixed-wing UAV ***,avoiding multiple timevarying obstacles and avoiding local optimum are two challenges for existing approaches in a dynamic ***,a normalized artificial potential field optimization is proposed by reconstructing a novel function with anisotropy in each dimension,which can make the flight speed of a fixed UAV swarm independent of the repulsive/attractive gain coefficient and avoid trapping into local optimization and local ***,taking into account minimum velocity and turning angular velocity of fixed-wing UAV swarm,a strategy of decomposing target vector to avoid moving obstacles and pop-up threats is ***,several simulations are carried out to illustrate superiority and effectiveness.

关键词： fixed-wing UAV swarm cooperative path planning normalized artificial potential field dynamic obstacle avoidance local optimization

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Dynamic Event-Triggered control for Nonlinear Systems:A Small-Gain Approach

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系统科学与复杂性学报（英文版） 2020年第4期33卷 930-943页

作者： ZHAO Qingtao SUN Jian BAI Yongqiang School of Automation Beijing Institute of Technology Beijing 100081 China Key Laboratory of Intelligent Control and Decision of Complex System Beijing Institute of Technology Beijing 100081 China

An event-triggered control (ETC) system transmits data packages and updates control inputs only when the predefined criterion is *** this way,network communication and computing resources are scheduled more reasonably in contrast to the traditional periodic sampling ***-gain approach proposed in recent literatures is a new modeling method to deal with nonlinear ETC *** from traditional ETC models,stability criteria are proposed in the form of input to state stability (ISS) gain to design the triggering *** paper introduces additional dynamic variables in this model and proposes a small-gain based dynamic event-triggered *** conditions to guarantee the stability of the system are derived with the help of cyclic-small-gain theorem and Zeno behaviors are avoided to ensure the feasibility of this method in practical *** simulations are given to demonstrate the effectiveness of our approach.

关键词： Cyclic-small-gain theorem event-triggered control input-to-state stability (ISS) nonlinear systems

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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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Data-Driven Model for Detection, Isolation and Accommodation of Faulty Angle of Attack Sensor Measurements in Fixed Winged Aircraft

Data-Driven Model for Detection, Isolation and Accommodation...

引用

Chinese control and decision Conference, CCDC

作者： Bemnet Wondimagegnehu Mersha Hongbin Ma School of Automation Beijing Institute of Technology Beijing China State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing China

ISBN: (纸本)9781665478977

Ethiopian Airlines’ Boeing 737-8 MAX nosedived and crashed shortly after takeoff on March 10, 2019, at Ejere Town, south of Addis Ababa. A faulty angle of attack (AOA) sensor was the cause of the crash. Many airplane accidents have been linked to faulty AOA sensors in the past. The majority of the AOA sensor fault detection, isolation, and accommodation (SFDIA) literature relied on linear model-driven techniques, which are not suitable when the system’s model is uncertain, complex, or nonlinear. Traditional multilayer perceptron (MLP) models have been employed in data-driven models in the literature and the effectiveness of deep learning-based data-driven models has not been investigated. In this work, a data collection and processing method that ensures the collected data is not monotonous and a data-driven model for AOA SFDIA is proposed. The proposed model uses a deep learning-based recurrent neural network (RNN) to accommodate for faulty AOA measurement under flight conditions with faulty AOA measurement, faulty total velocity measurement, and faulty pitch rate measurement. Conventional residual analysis with a fixed threshold is used to detect and isolate faulty AOA sensors. The proposed and benchmark models are trained with the adaptive momentum estimation (Adam) algorithm. We show that the proposed model effectively detects, isolates, and accommodates faulty AOA measurements when compared to other data-driven benchmark models. The method is able to detect and isolate faulty AOA sensors with a detection delay of 0.5 seconds for ramp failure and 0.1 seconds for step failure.

关键词： Adaptation models Recurrent neural networks Atmospheric modeling Fault detection Transportation Benchmark testing Velocity measurement

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Torque Compensation Model and Touchdown Detection Model of the Hydraulic Quadruped Robot controlled by VMC

Torque Compensation Model and Touchdown Detection Model of t...

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第34届中国控制与决策会议

作者： Ranjiang Wang Jiangbo Zhao Junzheng Wang Key Laboratory of Complex System Intelligent Control and Decision School of AutomationBeijing Institute of Technology Key Laboratory of Servo Motion System Drive and Control Ministry of Industry and Information TechnologySchool of AutomationBeijing Institute of Technology

In this paper,according to the different dynamic models of the support phase and the swing phase,the joint torque compensation of the support phase and the swing phase are given to compensate the joint torque of the hydraulic quadruped robot controlled by the virtual model control(VMC).This compensation can increase the tracking effect of the foot end on the swing trajectory,reduce the elasticity and damping coefficient of the virtual model method,and increase the flexibility of the *** this paper establishes the touchdown detection function based on the joint torque obtained by the Lagrange dynamics equation and the joint torque obtained by the force sensor to determine the touch state of the foot of the quadruped robot to switch the phase of the *** the end of this paper,simulation and experiment prove that the compensation model and touchdown detection function model have feasibility and correctness.

关键词： Quadruped robot Torque compensation Touchdown detection Virtual model control

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Analysis of OODA Loop based on Adversarial for complex Game Environments

arXiv

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arXiv 2022年

作者： Lu, Xiangri Ma, Hongbin Wang, Zhanqing State Key Laboratory of Intelligent Decision Control for Complex Systems Beijing Institute of Technology Beijing China Automated Institute Beijing Institute of Technology Beijing China

To address the problem of imperfect confrontation strategy caused by the lack of information of game environment in the simulation of non-complete information dynamic countermeasure modeling for intelligent game, the hierarchical analysis game strategy of confrontation model based on OODA ring (Observation, Orientation, decision, Action) theory is proposed. At the same time, taking into account the trend of unmanned future warfare, NetLogo software simulation is used to construct a dynamic derivation of the confrontation between two tanks. In the validation process, the OODA loop theory is used to describe the operation process of the complex system between red and blue sides, and the four-step cycle of observation, judgment, decision and execution is carried out according to the number of armor of both sides, and then the OODA loop system adjusts the judgment and decision time coefficients for the next confrontation cycle according to the results of the first cycle. Compared with traditional simulation methods that consider objective factors such as loss rate and support rate, the OODA-loop-based hierarchical game analysis can analyze the confrontation situation more comprehensively. © 2022, CC BY.

关键词： decision theory

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Valuing Equity-Linked Guaranteed Minimum Death Benefits with European-Style Asian Payoffs Under the Regime-Switching Diffusion Process with Jump

SSRN

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SSRN 2022年

作者： Wang, Yayun Liu, Shengda Key Laboratory of Intelligent Analysis and Decision on Complex Systems Chongqing University of Posts and Telecommunications Chongqing400065 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

In this paper, we consider a novel and efficient method to price equity-linked guaranteed minimumdeath benefits (GMDB) with European-style geometric Asian and arithmetic Asian payoffs. In thesituation of continuous transition of market economy, we assume that the underlying asset price processfollows regime-switching Ĺevy model and use complex Fourier series (CFS) expansion method to derivethe approximate value of GMDB products. The error analysis shows an exponential convergence rateunder geometric Asian and arithmetic Asian payoffs. Furthermore, the CFS approach is comparedwith other Fourier transform methods and Monte Carlo simulation, the numerical experiment resultsillustrate our method has a distinct advantage over other methods in both efficiency and accuracy. © 2022, The Authors. All rights reserved.

关键词： Monte Carlo methods

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