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2018 IEEE intelligent Vehicles Symposium, IV 2018

作者： Li, Xuan Wang, Yutong Wang, Kunfeng Yan, Lan Wang, Fei-Yue School of Automation Beijing Institute of Technology Beijing100081 China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Innovation Center for Parallel Vision Qingdao Academy of Intelligent Industries Qingdao266000 China Research Center for Computational Experiments and Parallel Systems Technology National University of Defense Technology Changsha410073 China

ISBN: (纸本)9781538644522

Offline training and testing are playing an essential role in design and evaluation of intelligent vehicle vision algorithms. Nevertheless, long-term inconvenience concerning traditional image datasets is that manually collecting and annotating datasets from real scenes lack testing tasks and diverse environmental conditions. For that virtual datasets can make up for these regrets. In this paper, we propose to construct artificial scenes for evaluating the visual intelligence of intelligent vehicles and generate a new virtual dataset called 'ParallelEye-CS'. First of all, the actual track map data is used to build 3D scene model of Chinese Flagship intelligent Vehicle Proving Center Area, Changshu. Then, the computer graphics and virtual reality technologies are utilized to simulate the virtual testing tasks according to the Chinese intelligent Vehicles Future Challenge (IVFC) tasks. Furthermore, the Unity3D platform is used to generate accurate ground-truth labels and change environmental conditions. As a result, we present a viable implementation method for constructing artificial scenes for traffic vision research. The experimental results show that our method is able to generate photorealistic virtual datasets with diverse testing tasks. © 2018 IEEE.

关键词： intelligent vehicle highway systems

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Short-term Bus Passenger Flow Forecast Based On Deep Learning

Short-term Bus Passenger Flow Forecast Based On Deep Learnin...

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International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)

作者： Xiaoshuang Li Ziyang Chen Fenghua Zhu Wei Chang Chang Tan Gang Xiong The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Traffic police detachment of Hefei Public Security Bureau Hefei China iFLYTEK CO.LTD Hefei China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781728105529

The public transportation system is an essential part of the life of the citizens and it's the basis of intelligent transportation system(ITS). This paper tries to predict shortterm bus passenger flow by using deep learning approach that called SAE model and DBN model. The model training and evaluation were carried out using the credit card records of the Suzhou bus IC card. The experimental results show that the SAE and DBN models can reduce the prediction error by 9.51% and 10.48%, respectively, compared with the traditional method. The methods of deep learning show a good application prospect in the short-term bus passenger flow forecasting.

关键词： intelligent transportation Short-term bus passenger flow forecasting Deep learning SAE model DBN model intelligent Transportation passenger flow Motor buses Machine Learning intelligent transportation systems Error of prediction life span of machinery parts transport system

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Trajectory Tracking based on Time-Varying Sliding Mode controller for Hypersonic Vehicle with Aileron Stuck 36

Trajectory Tracking based on Time-Varying Sliding Mode Contr...

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第36届中国控制会议

作者： Haibo Ji Lei Wang Zhuoyue Song Xiangdong Liu Zhirong Lei State Key Laboratory of Intelligent Control and Decision of Complex Systems and School of Automation Beijing Institute of Technology School of Automation Northwestern Polytechnical University

ISBN: (纸本)9781538629185

This paper presents nonlinear robust trajectory tracking strategies for the hypersonic vehicle whose ailerons are stuck at an unknown angular position. Firstly, a time-varying sliding mode control strategy for general second-order systems is introduced, and the chattering of sliding mode controller is alleviated. Secondly, the dynamics of hypersonic vehicle are transformed into two second-order systems. Then, the corresponding guidance law and attitude controller are designed using time-varying sliding mode controller. By the proposed strategies, the hypersonic vehicle can fly along the reference ***, the proposed strategies are applied to a hypersonic platform to verify its validity.

关键词： Trajectory Tracking Stuck Aileron Time-Varying Sliding Mode

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Design and 3D Motion Modeling of a 300-m Gliding Robotic Dolphin

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IFAC-PapersOnLine 2017年第1期50卷 12685-12690页

作者： Yuan, Jun Wu, Zhengxing Yu, Junzhi Zhou, Chao Tan, Min University of Chinese Academy of Sciences Beijing100049 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

This paper presents the mechatronic design and three-dimensional (3D) motion modeling of a gliding robotic dolphin capable of diving as deep as 300 m. Propulsion modes of bio-inspired robotic dolphin and traditional underwater gliders are integrated into the robot in order to endow it with both high maneuverability and long endurance simultaneously. It is realized by introducing a buoyancy adjusting mechanism on the basis of a bio-inspired robotic dolphin. Particular design on mechanical seal and compressive strength is implemented to achieve large diving depth. Further, a full-state dynamic model for 3D gliding motion is established for motion analysis. In comparison with traditional underwater gliders, the robot's flippers and fluke are controllable and thereby enrich the state-regulating modes of gliding motion, which is considered specially in the dynamic model. Simulations are conducted to analyze the robot's 3D gliding motion. Experiments are also carried out to validate the robot's gliding performance and the effectiveness of the formulated dynamic model. © 2017

关键词： 3D modeling Biomimetics Compressive strength Dolphins (structures) Dynamic models Dynamics Machine design Motion control Robotics Robots Adjusting mechanism Bio inspired robotics Mechatronic design Robotic dolphin state regulating Three dimensional (3D) motion Underwater gliders Underwater robotics

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A review of engineering education in China: History, present and future

A review of engineering education in China: History, present...

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2017 ASEE International Forum

作者： Dong, Xisong Liu, Xiwei State Key Laboratory of Management and Control for Complex Systems Institution of Automation Chinese Academy of Sciences China Institute of Smart Education Systems Qingdao Academy of Intelligent Industries Canada

In this globalization and information era, the level of engineering technology represents a country's core competitiveness. With the rapid development of China's industrialization, the high requirement for engineering talents become increasingly urgent. Then, engineering education is facing a great challenge. The history, current situation and problems of engineering education in China are systematically summed up and analyzed in this paper. The challenges and opportunities have also been deeply studied, which constitute the theoretical basis of the sustainable development of engineering education in China. © 2017 American Society for Engineering Education.

关键词： Engineering education

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Starcraft micromanagement with reinforcement learning and curriculum transfer learning

arXiv

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arXiv 2018年

作者： Shao, Kun Zhu, Yuanheng Zhao, Dongbin State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 University of Chinese Academy of Sciences Beijing

Real-time strategy games have been an important field of game artificial intelligence in recent years. This paper presents a reinforcement learning and curriculum transfer learning method to control multiple units in StarCraft micromanagement. We define an efficient state representation, which breaks down the complexity caused by the large state space in the game environment. Then a parameter sharing multi-agent gradientdescent Sarsa(a;) (PS-MAGDS) algorithm is proposed to train the units. The learning policy is shared among our units to encourage cooperative behaviors. We use a neural network as a function approximator to estimate the action-value function, and propose a reward function to help units balance their move and attack. In addition, a transfer learning method is used to extend our model to more difficult scenarios, which accelerates the training process and improves the learning performance. In small scale scenarios, our units successfully learn to combat and defeat the built-in AI with 100% win rates. In large scale scenarios, curriculum transfer learning method is used to progressively train a group of units, and shows superior performance over some baseline methods in target scenarios. With reinforcement learning and curriculum transfer learning, our units are able to learn appropriate strategies in StarCraft micromanagement scenarios. Copyright © 2018, The Authors. All rights reserved.

关键词： Reinforcement learning

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Optimal Tracking control for Reconfigurable Manipulators Based on Critic-only Policy Iteration Algorithm 36

Optimal Tracking Control for Reconfigurable Manipulators Bas...

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第36届中国控制会议

作者： Hongbing Xia Bo Zhao Yuanchun Li Department of Control Science and Engineering Changchun University of Technology The State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences

ISBN: (纸本)9781538629185

This paper tackles the optimal tracking control problem for reconfigurable manipulators based on critic-only policy iteration（Co PI） algorithm. By system transformation, the optimal tracking control problem is transformed into an optimal regulation problem. The optimal tracking controller is composed of the desired controller and the approximate optimal feedback one. The desired controller is developed to maintain the desired tracking performance at the steady-state, while the approximate optimal feedback controller is designed to stabilize the tracking error dynamics in an optimal manner. Then, a critic neural network is used to estimate the optimal performance index function, and the optimal feedback control is obtained by the Co PI algorithm. The convergence of the proposed method is analyzed and it is shown that the closed-loop system based on Co PI is uniformly ultimately bounded by using the Lyapunov approach. Finally, simulation studies are given to show the effectiveness of the developed method.

关键词： Adaptive dynamic programming Reconfigurable manipulators Optimal tracking control Policy iteration Neural networks

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Decision-Making in Driver-Automation Shared control: A Review and Perspectives

arXiv

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arXiv 2020年

作者： Wang, Wenshuo Na, Xiaoxiang Cao, Dongpu Gong, Jianwei Xi, Junqiang Xing, Yang Wang, Fei-Yue CA94720 United States Department of Engineering University of Cambridge Trumpington Street CambridgeCB2 1PZ United Kingdom Mechanical and Mechatronics Engineering University of Waterloo 200 University Ave. West Waterloo ONN2L3G1 Canada Department of Mechanical Engineering Beijing Institute of Technology Beijing100081 China School of Mechanical and Aerospace Engineering Nanyang Technological University Singapore639798 Singapore State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Research Center for Military Computational Experiments and Parallel Systems Technology National University of Defense Technology Changsha410073 China

Shared control schemes allow a human driver to work with an automated driving agent in driver-vehicle systems while retaining the driver’s abilities to control. The human driver, as an essential agent in the driver-vehicle shared control systems, should be precisely modeled regarding their cognitive processes, control strategies, and decision-making processes. The interactive strategy design between drivers and automated driving agents brings an excellent challenge for human-centric driver assistance systems due to the inherent characteristics of humans. Many open-ended questions arise, such as what proper role of human drivers should act in a shared control scheme? How to make an intelligent decision capable of balancing the benefits of agents in shared control systems? Due to the advent of these attentions and questions, it is desirable to present a survey on the decision-making between human drivers and highly automated vehicles, to understand their architectures, human driver modeling, and interaction strategies under the driver-vehicle shared schemes. Finally, we give a further discussion on the key future challenges and opportunities. They are likely to shape new potential research directions. Copyright © 2020, The Authors. All rights reserved.

关键词： Decision making

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Decentralized optimal control for modular and reconfigurable robots based on adaptive dynamic programming 36

Decentralized optimal control for modular and reconfigurable...

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第36届中国控制会议

作者： Zixu Wang Bo Dong Hongbing Xia Yuanchun Li Department of Control Science and Engineering Changchun University of Technology State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences

ISBN: (纸本)9781538629185

This paper presents a decentralized optimal control method for modular and reconflgurable robots（MRRs） based on adaptive dynamic ***,the dynamic model of MRRs is formulated by using the Newton-Euler iterative algorithm,and then the state space description is ***,the optimal control policy of the MRRs system is obtained based on the policy iteration algorithm,which is used to solve the Hamilton-Jacobi-Bellman（HJB） equation via the critic neural ***,the stability of the closed-loop system is proved by using the Lyapunov ***,simulations are conducted to illustrate the effectiveness for the 2-DOF MRRs.

关键词： Modular and reconfigurable robot Decentralized control strategy Adaptive dynamic programming Neural network

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Decentralized Adaptive Super-Twisting control for Modular and Reconfigurable Robots with Uncertain Environment Contact 36

Decentralized Adaptive Super-Twisting Control for Modular an...

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第36届中国控制会议

作者： Bo Dong Yan Li Keping Liu Yuanchun Li Department of Control Science and Engineering Changchun University of Technology State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences

ISBN: (纸本)9781538629185

This paper presents a decentralized adaptive super-twisting control method for modular and reconflgurable robots（MRRs） with uncertain environment *** conventional methods that rely on robot-environment contact model or force/torque sensing,this paper addresses the problem of controlling MRRs in contact with uncertain environment that using only local dynamic information of each joint *** dynamic model of MRR is formulated as a synthesis of interconnected *** on the integral sliding mode control（ISMC） technique and the adaptive super-twisting algorithm（ASTA）,the decentralized controller is designed to compensate the model uncertainty in which the up-bound is *** stability of the MRR system is proved by using the Lyapunov *** last,simulations are conducted for 2-DOF MRRs with different configurations under the situations of dynamic contact and collision to investigate the advantage of the proposed approach.

关键词： Modular and reconfigurable robot Decentralized control strategy Uncertain environment contact Adaptive supertwisting algorithm Interconnected couplings

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