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Distributed Full Order Sliding Mode control for Finite-time Attitude Synchronization and Tracking of Spacecraft

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Distributed Full Order Sliding Mode Control for Finite-time ...

第37届中国控制会议

作者： Nsubuga Latifu Zhuoyue Song Chao Duan Zhen Li Housheng Su Qinghe Wu Xiangdong Liu State Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of Technology Key Laboratory of Drive and Control of Servo Motion System Ministry of Industry and Information Technology School of Automation Huazhong University of Science and Technology

This paper investigates a distributed finite-time attitude synchronization and tracking problem of multiple spacecraft when a time varying reference signal is availab.e to only a subset of group *** Rodrigues parameters are used for attitude representation.A two layer sliding mode surface with fast non-singular terminal sliding mode surface（FNTSMS） as the inner layer and terminal sliding mode surface（TSMS） as the outer layer is used to create a full order sliding mode *** proposed distributed attitude coordination control law guarantees attitude synchronization and tracking as long as their exists atleast one spacecraft that has a direct access to the virtual leader’s time varying ***-time stability of the closed loop system is guaranteed by Lyapunov-based stability *** results are presented to validate the performance of the proposed control strategy.

关键词： Attitude synchronization Attitude tracking Finite-time Full-order sliding mode（FOSM） Distributed control

An efficient decision and planning method for high speed autonomous driving in dynamic environment 28

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An efficient decision and planning method for high speed aut...

28th IEEE intelligent Vehicles Symposium, IV 2017

作者： Zhang, Kai Fu, Mengyin Yang, Yi Shang, Songtian Wang, Meiling State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institution of Technology Beijing 100081 China

ISBN: (纸本)9781509048052

This paper describes an improved decision and planning algorithm based on our previously proposed methods for unmanned ground vehicle (UGV). The new method can be applied to UGV driving both in structured environment and unstructured environment. In the improved method, the prospect of planning is extended from 40m to 100m for safe driving at high speed and some piecewise linear speed functions are designed for the new prospect. After this improvement our UGV now can drive at a maximum speed of 60km/h rather than 40km/h while avoiding obstacles safely. Besides, a velocity feedforward control is added to make the UGV overtake other cars driving at about 25km/h on the road. At last, the collision detection algorithm is improved to make the lane changing maneuver safer. The proposed decision and planning algorithm is implemented both on our old Polaris all terrain vehicle (ATV) and new FAW-H7 car, which exhibited good performance on Across Dangers & Obstacles 2016, Tahe, China and Future Challenge 2016, Changshu, China, respectively. © 2017 IEEE.

关键词： Planning Automobiles Laser radar Roads Feedforward neural networks Collision avoidance Cameras

Distributed Full Order Sliding Mode control for Finite-Time Attitude Synchronization and Tracking of Spacecraft

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Distributed Full Order Sliding Mode Control for Finite-Time ...

Chinese control Conference (CCC)

作者： Nsubuga Latifu Zhuoyue Song Chao Duan Zhen Li Housheng Su Qinghe Wu Xiangdong Liu State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing P. R. China School of Automation Huazhong University of Science and Technology Wuhan People's Republic of China

This paper investigates a distributed finite-time attitude synchronization and tracking problem of multiple spacecraft when a time varying reference signal is availab.e to only a subset of group members. Modified Rodrigues parameters are used for attitude representation. A two layer sliding mode surface with fast non-singular terminal sliding mode surface (FNTSMS) as the inner layer and terminal sliding mode surface (TSMS) as the outer layer is used to create a full order sliding mode surface. The proposed distributed attitude coordination control law guarantees attitude synchronization and tracking as long as their exists atleast one spacecraft that has a direct access to the virtual leader's time varying attitude. Finite-time stability of the closed loop system is guaranteed by Lyapunov-based stability method. Simulation results are presented to validate the performance of the proposed control strategy.

关键词： Space vehicles Attitude control Synchronization Sliding mode control Decentralized control Symmetric matrices Stability analysis

Crowdsensing game with demand uncertainties: A deep reinforcement learning approach

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arXiv 2018年

作者： Zhan, Yufeng Xia, Yuanqing Zhang, Jiang Li, Ting Wang, Yu School of Automation Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing100081 China Lab Department of Computer Science University of North Carolina at Charlotte CharlotteNC28223 United States

Currently, explosive increase of smartphones with powerful built-in sensors such as GPS, accelerometers, gyroscopes and cameras has made the design of crowdsensing applications possible, which create a new interface between human beings and life environment. Until now, various mobile crowdsensing applications have been designed, where the crowdsourcers can employ mobile users (MUs) to complete the required sensing tasks. In this paper, emerging learning-based techniques are leveraged to address crowdsensing game with demand uncertainties and private information protection of MUs. Firstly, a novel economic model for mobile crowdsensing is designed, which takes MUs' resources constraints and demand uncertainties into consideration. Secondly, an incentive mechanism based on Stackelberg game is provided, where the sensing-platform (SP) is the leader and the MUs are the followers. Then, the existence and uniqueness of the Stackelberg Equilibrium (SE) is proven and the procedure for computing the SE is given. Furthermore, a dynamic incentive mechanism (DIM) based on deep reinforcement learning (DRL) approach is investigated without knowing the private information of the MUs. It enables the SP to learn the optimal pricing strategy directly from game experience without any prior knowledge about MUs' information. Finally, numerical simulations are implemented to evaluate the performance and theoretical properties of the proposed mechanism and approach. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep learning

Aggregation in Intersection Region for Distributed Heterogeneous Mobile Sensor Networks

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Journal of systems Science and complexity 2017年第4期30卷 932-949页

作者： Zheng Zhi Peng Zhihong Chen Jie School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing 100081 School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing 100081

This paper presents two aggregation strategies in convex intersection region for the distributed mobile sensor network (MSN) with heterogeneous dynamics. First, the authors analyze individual local perception model and dynamics model, set the intersection of all the local perceptions as the region of interest (ROI). The MSN consists of sensors with first-order dynamics and second-order dynamics. Then, the authors design a control strategy to ensure that individuals aggregate at a point in the ROI relying on their local perceptions and the locations of neighbors within their communication scope. The authors describe this situation of aggregation as rendezvous. In addition, the authors introduce artificial potential field to make sensors deploy dispersedly in a bounded range near the ROI, which the authors call dispersed deployment. Finally, the authors prove the stability of the proposed strategies and validate the theoretical results by simulations. This research is applied for the cooperative deployment and data collection of mobile platforms with different dynamics under the condition of inaccurate perception.

关键词： Aggregation convex intersection heterogeneous dynamics mobile sensor

Mathematical model of brain tumour growth with drug resistance

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arXiv 2020年

作者： Trobia, José Tian, Kun Batista, Antonio M. Grebogi, Celso Ren, Hai-Peng Santos, Moises S. Protachevicz, Paulo R. Borges, Fernando S. Szezech, José D. Viana, Ricardo L. Caldas, Iberê L. Iarosz, Kelly C. Department of Mathematics and Statistics State University of Ponta Grossa Ponta GrossaPR84030-900 Brazil Graduate in Science Program - Physics State University of Ponta Grossa Ponta GrossaPR84030-900 Brazil Shaanxi Key Lab of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’anPR710048 China Institute of Physics University of São Paulo São PauloSP05508-900 Brazil Institute for Complex Systems and Mathematical Biology University of Aberdeen AberdeenAB24 3UE United Kingdom Xi’an Technological University Xi’anPR710021 China Center for Mathematics Computation and Cognition Federal University of ABC São Bernardo do CampoSP09606-045 Brazil Department of Physics Federal University of Paraná CuritibaPR80060-000 Brazil Faculdade de Telêmaco Borba FATEB Telêmaco BorbaPR84266-010 Brazil

Brain tumours are masses of abnormal cells that can grow in an uncontrolled way in the brain. There are different types of malignant brain tumours. Gliomas are malignant brain tumours that grow from glial cells and are identified as astrocytoma, oligodendroglioma, and ependymoma. We study a mathematical model that describes glia-neuron interaction, glioma, and chemotherapeutic agent. In this work, we consider drug sensitive and resistant glioma cells. We show that continuous and pulsed chemotherapy can kill glioma cells with a minimal loss of neurons. © 2020, CC BY.

关键词： Tumors

Learning ReLU networks on linearly separable data: Algorithm, optimality, and generalization

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arXiv 2018年

作者： Wang, Gang Giannakis, Georgios B. Chen, Jie Digital Technology Center Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States State Key Lab of Intelligent Control Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China Tongji University Shanghai200092 China

Neural networks with REctified Linear Unit (ReLU) activation functions (a.k.a. ReLU networks) have achieved great empirical success in various domains. Nonetheless, existing results for learning ReLU networks either pose assumptions on the underlying data distribution being e.g. Gaussian, or require the network size and/or training size to be sufficiently large. In this context, the problem of learning a two-layer ReLU network is approached in a binary classification setting, where the data are linearly separable and a hinge loss criterion is adopted. Leveraging the power of random noise perturbation, this paper presents a novel stochastic gradient descent (SGD) algorithm, which can provably train any single-hidden-layer ReLU network to attain global optimality, despite the presence of infinitely many bad local minima, maxima, and saddle points in general. This result is the first of its kind, requiring no assumptions on the data distribution, training/network size, or initialization. Convergence of the resultant iterative algorithm to a global minimum is analyzed by establishing both an upper bound and a lower bound on the number of non-zero updates to be performed. Moreover, generalization guarantees are developed for ReLU networks trained with the novel SGD leveraging classic compression bounds. These guarantees highlight a key difference (at least in the worst case) between reliably learning a ReLU network as well as a leaky ReLU network in terms of sample complexity. Numerical tests using both synthetic data and real images validate the effectiveness of the algorithm and the practical merits of the theory. Copyright © 2018, The Authors. All rights reserved.

关键词： Stochastic systems

Research on Social Marketing Strategies with An Agent-based Propagation Model

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IFAC-PapersOnLine 2017年第1期50卷 13581-13586页

作者： Zeng, Shuai Li, Juanjuan Ni, Xiaochun Yuan, Yong Wang, Fei-Yue The State Key Lab of Management Control for Complex Systems Institute of Automation Chinese Academy of Sciences China Qingdao Academy of Intelligent Industries BeijingShandong China Research Center of Military Computational Experiments and Parallel Systems National University of Defense Technology Changsha China

Considering the increasing complexity of social networks and user behaviors, it is very challenging for advertisers to formulate their strategies of selecting proper initial seed users in their social marketing efforts. In this paper, we tackle this challenge by proposing an agent-based propagation model and injecting it into typical social networks with three types of structures, i.e., Erds-Rényi random graph, Watt-Strogatz small world graph, and Barabási-Albert scale-free graph. We instantiate this agent-based model with demographic characteristics extracted from real-world census data collected in China. By investigating the diffusion process of advertising information in these social networks, we can analyze and compare the performance of advertisers’ targeting and influencer strategies. Our experimental results indicate that advertisers adopting influencer strategies should manipulate the initial well-connected seeds to deliver information only to the potential customers instead of a wide range of generic users. © 2017

关键词： Marketing Autonomous agents Behavioral research Commerce Computational methods Population statistics Agent based model Demographic characteristics Information diffusion Potential customers Propagation modeling Seeding strategies Small world graphs Social marketings

Effects of drug resistance in the tumour-immune system with chemotherapy treatment

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arXiv 2020年

作者： Trobia, José Gabrick, Enrique C. Seifert, Evandro G. Borges, Fernando S. Protachevicz, Paulo R. Szezech, José D. Iarosz, Kelly C. Santos, Moises S. Caldas, Iberê L. Tian, Kun Ren, Hai-Peng Grebogi, Celso Batista, Antonio M. Graduate Program in Science - Physics State University of Ponta Grossa Ponta GrossaPR84030-900 Brazil Department of Mathematics and Statistics State University of Ponta Grossa Ponta GrossaPR84030-900 Brazil Department of Physics State University of Ponta Grossa Ponta GrossaPR84030-900 Brazil Center for Mathematics Computation and Cognition Federal University of ABC São Bernardo do CampoSP09606-045 Brazil Institute of Physics University of São Paulo São PauloSP05508-900 Brazil Faculty of Telêmaco Borba FATEB Telêmaco BorbaPR84266-010 Brazil Graduate Program in Chemical Engineering Federal Technological University of Paraná Ponta Grossa Paraná84016-210 Brazil Shaanxi Key Lab of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’an710048 China Xi’an Technological University Xi’an710021 China Institute for Complex Systems and Mathematical Biology University of Aberdeen AberdeenAB24 3UE United Kingdom

Cancer is a term used to refer to a large set of diseases. The cancerous cells grow and divide and, as a result, they form tumours that grow in size. The immune system recognise the cancerous cells and attack them, though, it can be weakened by the cancer. One type of cancer treatment is chemotherapy, which uses drugs to kill cancer cells. Clinical, experimental, and theoretical research has been developed to understand the dynamics of cancerous cells with chemotherapy treatment, as well as the interaction between tumour growth and immune system. We study a mathematical model that describes the cancer growth, immune system response, and chemotherapeutic agents. The immune system is composed of resting cells that are converted to hunting cells to combat the cancer. In this work, we consider drug sensitive and resistant cancer cells. We show that the tumour growth can be controlled not only by means of different chemotherapy protocols, but also by the immune system that attacks both sensitive and resistant cancer cells. Furthermore, for all considered protocols, we demonstrate that the time delay from resting to hunting cells plays a crucial role in the combat against cancer cells. Copyright © 2020, The Authors. All rights reserved.

关键词： Chemotherapy