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An Adaptive Strategy via Reinforcement Learning for the Prisoner＇s Dilemma Game

IEEE/CAA Journal of Automatica Sinica 2018年第1期5卷 301-310页

作者： Lei Xue Changyin Sun Donald Wunsch Yingjiang Zhou Fang Yu Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University Nanjing 210096 China Department of Electrical and Computer Engineering Missouri University of Science and Technology Rolla MO 65409 USA College of Automation Nanjing University of Posts and Telecommunications Nanjing 210023 China Institute of Logistics Science and Engineering Shanghai Maritime University Shanghai 210096 China IEEE

The iterated prisoner's dilemma(IPD) is an ideal model for analyzing interactions between agents in complex networks. It has attracted wide interest in the development of novel strategies since the success of tit-for-tat in Axelrod's tournament. This paper studies a new adaptive strategy of IPD in different complex networks, where agents can learn and adapt their strategies through reinforcement learning method. A temporal difference learning method is applied for designing the adaptive strategy to optimize the decision making process of the agents. Previous studies indicated that mutual cooperation is hard to emerge in the IPD. Therefore, three examples which based on square lattice network and scale-free network are provided to show two features of the adaptive strategy. First, the mutual cooperation can be achieved by the group with adaptive agents under scale-free network, and once evolution has converged mutual cooperation, it is unlikely to shift. Secondly, the adaptive strategy can earn a better payoff compared with other strategies in the square network. The analytical properties are discussed for verifying evolutionary stability of the adaptive strategy.

关键词： complex network prisoner’s dilemma reinforcement learning temporal differences learning

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Disturbance rejection control for Raymond mill grinding system based on disturbance observer

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Journal of Central South University 2017年第9期24卷 2019-2027页

作者：牛丹陈夕松杨俊周杏鹏 Key Laboratory of Measurement and Control of Complex Systems of Engineering of Ministry of Education School of AutomationSoutheast UniversityNanjing 210096China

In the Raymond mill grinding processes,high-accuracy control for the current of Raymond mill is vital to enhance the product quality and production efficiency as well as cut down the consumption of spare ***,strong external disturbances,such as variations of ore hardness and ore size,always *** is not easy to make the current of Raymond mill constant due to these strong *** control strategies have been proposed to control the grinding ***,most of them(such as PID and MPC)reject disturbances merely through feedback regulation and do not deal with the disturbances directly,which may lead to poor control performance when strong disturbances *** improve disturbance rejection performance,a control scheme based on PI and disturbance observer is proposed in this *** scheme combines a feedforward compensation part based on disturbance observer and a feedback regulation part using *** test results illustrate that the proposed method can obtain remarkable superiority in disturbance rejection compared with PI method in the Raymond mill grinding processes.

关键词： disturbance observer proportional integral-disturbance observer （PI-DOB） disturbance rejection Raymond mill grinding process

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Wheel Hub Automatic Production System Based on Industrial Robots

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Journal of Physics: Conference Series 2021年第4期1885卷

作者： Bin Zhang Zongxue Lu Qiang Wang Shiyi Cui Guangxin Jia Jun Yang School of Automation Southeast University 210096 P.R. China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing 210096 P. R. China Lianyungang JARI Automation CO. LTD. Lianyungang 222006 China

In this thesis, a control system for a wheel hub machining line based on a Fanuc robot, which includes adopting Siemens 300PLC as the main control unit, performing wheel circumferential positioning and center correction based on vision technology, and controlling the machine tool to perform real-time tool compensation based on the measured data, is researched and designed to achieve fully automatic unmanned wheel processing.

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Driver Action Recognition Based on Attention Mechanism

Driver Action Recognition Based on Attention Mechanism

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International Conference on systems and Informatics (ICSAI)

作者： Wenhao Wang Xiaobo Lu Pengguo Zhang Huibin Xie Wenbin Zeng Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China School of Automation Southeast University Nanjing China Zhejiang Uniview Technologies Co. Ltd Hangzhou China

ISBN: (数字)9781728152561

ISBN: (纸本)9781728152578

According to the world health organization, millions of people are killed by traffic accidents worldwide every year, and more than 80 percent of accidents are caused by unsafe driving. This paper studies driver behavior recognition, aiming to standardize driver's driving behavior and reduce the probability of traffic accidents. However, the inter-class variance of drivers' different actions is small, making it difficult to identify. To improve fine-grained identification, an attention module is designed to be inserted into convolutional neural network, which consists of two parallel parts: channel level attention and space level attention. The introduction of attention mechanism can focus the weight of the network on the meaningful pixels and channels, promote the expression of effective features, and suppress the interference of noise. The experiments show that the recognition accuracy is improved after applying attention mechanism. The visualization results show that the introduction of attention mechanism can make the network focus on the prominent areas of the feature map.

关键词： Vehicles Convolutional neural networks Training Convolution Mathematical model Visualization Accidents

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Efficient Dense Spatial Pyramid Network for Lane Detection

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Journal of Physics: Conference Series 2020年第1期1575卷

作者： Yinggang Li Xiaobo Lu School of Automation Southeast University Nanjing 210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing 210096 China

Lane detection separating the roads and positioning each lane is a significant task in Autonomous-driving field. Most of existing methods take lane detection as a semantic segmentation problem, and the methods based on encoder-decoder networks achieve robust performances than traditional methods with the disadvantages of large amounts of parameters and high computational cost. To improve efficiency and better adapt to adverse environments, we propose an EDSPnet by compiling the efficient dense module of depthwise dilated separable convolution (EDD module) and dense spatial pyramid (DSP) module. The two proposed modules enable our network to make full use of the contextual information from different feature maps when compared to a chain of layers. Different from symmetrical segmentation networks, we concentrate on the encoder block and a simple decoder is designed to match the input resolution. The decoder is complemented by two methods of upsampling: bilinear interpolation and deconvolution, which guarantee the accuracy and efficiency simultaneously. Our method is evaluated on the TuSimple dataset, and the experimental results show that our network has less parameters and is still efficient even compared with the state-of-the-art semantic segmentation networks.

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Vehicle and Driver Detection on Highway Based on Cascade R-CNN

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Journal of Physics: Conference Series 2020年第1期1575卷

作者： Yifei Geng Xiaobo Lu School of Automation Southeast University Nanjing 210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing 210096 China

Vehicle and driver detection in the highway scene has been a research hotspot in the field of object detection in recent years, and it is still a challenging problem in the research of traffic order and road safety. In this paper, we propose a novel end-to-end vehicle and driver detection method named VDDNet which is based on Cascade R-CNN and SENet. By introducing FPN structure and SENet attention mechanism in the backbone, the ability of the model to learn effective features is enhanced. It can improve the accuracy of detection in difficult scenes such as weak light, partial occlusion, and low picture resolution. The test results based on the database of highway traffic vehicle and drivers constructed by the Jiangsu Provincial Public Security Department. It shows that the detection method has the AP rate of 91.3% and the Recall rate of 92.4%, which demonstrates the effectiveness of the proposed method in complex highway environments.

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Towards End-to-End Face Recognition Method through Spatial Transformer

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Journal of Physics: Conference Series 2020年第1期1575卷

作者： Yanfang Wu Xiaobo Lu Chen Qin School of Automation Southeast University Nanjing 210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing 210096 China

Face recognition technology is widely applied in daily life, but in most methods, similarity or affine transformation is employed to align face images according to five facial landmarks. The face alignment module is implemented independently, thus it's difficult in end-to-end training. In this paper, the main purpose is to design a towards end-to-end trainable face recognition method based on indoor scenes. Due to that spatial transformer can implement any parametrizable transformation, we joint it with recognition network, making end-to-end training possible. Simultaneously, any prior knowledge on facial landmarks isn't required. The model jointly with spatial transformer can achieve 0.3% higher accuracy than similarity transformation. Most downsampling methods ignore the sampling theorem, making convolutional networks not shift-invariant. We replace max-pooling by MaxBlurPool in spatial transformer network, and the accuracy is improved by 0.25%.

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Finite-time Consensus for Uncertain Nonlinear Second-order Multi-agent systems

Finite-time Consensus for Uncertain Nonlinear Second-order M...

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International Workshop on Variable Structure systems, VSS

作者： Xiangyu Wang Guipu Li Shihua Li Ministry of Education Key Laboratory of Measurement and Control of Complex Systems of Engineering Nanjing China

This paper studies the finite-time consensus problem of leader-follower second-order multi-agent systems with mismatched uncertain nonlinearities, which are assumed to satisfy Hölder condition. By integrating sliding-mode control, distributed adding a power integrator technology and feedback domination method together, a kind of nonlinear distributed control algorithms is proposed. Under the proposed distributed control algorithms, the followers states track the leader states accurately in finite time. The rigorous stability analysis of the closed-loop system is presented based on finite-time stability theory. Moreover, a simulation example is given, which shows the effectiveness of the proposed control scheme.

关键词： Multi-agent systems Uncertainty control design Sliding mode control Decentralized control Stability analysis

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Spatial-Temporal Fusion Convolutional Neural Network for Simulated Driving Behavior Recognition

Spatial-Temporal Fusion Convolutional Neural Network for Sim...

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International Conference on control, Automation, Robotics and Vision

作者： Yaocong Hu MingQi Lu Xiaobo Lu Ministry of Education Key Laboratory of Measurement and Control of Complex Systems of Engineering Nanjing China

Abnormal driving behaviour is one of the leading cause of terrible traffic accidents endangering human life. Therefore, study on driving behaviour surveillance has become essential to traffic security and public management. In this paper, we conduct this promising research and employ a two stream CNN framework for video-based driving behaviour recognition, in which spatial stream CNN captures appearance information from still frames, whilst temporal stream CNN captures motion information with pre-computed optical flow displacement between a few adjacent video frames. We investigate different spatial-temporal fusion strategies to combine the intra frame static clues and inter frame dynamic clues for final behaviour recognition. So as to validate the effectiveness of the designed spatial-temporal deep learning based model, we create a simulated driving behaviour dataset, containing 1237 videos with 6 different driving behavior for recognition. Experiment result shows that our proposed method obtains noticeable performance improvements compared to the existing methods.

关键词： driving behaviour convolutional neural networks spatial-temporal deep learning fusion

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Multi-Dimensional Taylor Network (MTN)-Based Adaptive Tracking control for a Class of Nonlinear systems with Input Constraints

Multi-Dimensional Taylor Network (MTN)-Based Adaptive Tracki...

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Chinese control Conference (CCC)

作者： Yu-Qun Han Hong-Sen Yan Ministry of Education Key Laboratory of Measurement and Control of Complex Systems of Engineering Nanjing China

This paper focuses on the problem of multi-dimensional Taylor network (MTN)-based adaptive tracking control for a class of nonlinear systems with input constraints. As preliminaries, the saturation is first presented by a smooth function, and then a novel MTN-backstepping-based adaptive control method is designed on the basis of the Lyapunov stability theory. It is shown that the proposed control method can guarantee that all the signals in the closed-loop system are bounded and the tracking error converges to an arbitrarily small neighborhood around the origin. Finally, one example is given to illustrate the effectiveness of the proposed design approach.

关键词： Nonlinear systems Adaptive control Lyapunov methods Backstepping Closed loop systems

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