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Coalition formation based on a task-oriented collaborative ability vector

Frontiers of Information Technology & Electronic Engineering 2017年第1期18卷 139-148页

作者： Hao FANG Shao-lei LU Jie CHEN Wen-jie CHEN School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems

Coalition formation is an important coordination problem in multi-agent systems, and a proper description of collaborative abilities for agents is the basic and key precondition in handling this problem. In this paper, a model of task-oriented collaborative abilities is established, where five task-oriented abilities are extracted to form a collaborative ability vector. A task demand vector is also described. In addition, a method of coalition formation with stochastic mechanism is proposed to reduce excessive competitions. An artificial intelligent algorithm is proposed to compensate for the difference between the expected and actual task requirements, which could improve the cognitive capabilities of agents for human commands. Simulations show the effectiveness of the proposed model and the distributed artificial intelligent algorithm.

关键词： Collaborative vector Task allocation Multi-agent system Coalition formation Artificial intelligence

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A Multi-model Estimation of Distribution Algorithm for Agent Routing Problem in Multi-point Dynamic Task

A Multi-model Estimation of Distribution Algorithm for Agent...

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第37届中国控制会议

作者： Sai Lu Bin Xin Lihua Dou Ling Wang School of Automation Beijing Institute of Technology Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Advanced Innovation Center for Intelligent Robots and Systems Department of Automation Tsinghua University

The agent routing problem in multi-point dynamic task（ARP-MPDT） is a multi-task routing problem of a mobile agent. In this problem, there are multiple tasks to be carried out in different locations. As time goes on, the state of each task will change nonlinearly. The agent must go to the task points in turn to perform the tasks, and the execution time of each task is related to the state of the task point when the agent arrives at the point. ARP-MPDT is a typical NP-hard optimization problem. In this paper, we establish the nonlinear ARP-MPDT model. A multi-model estimation of distribution algorithm（EDA） employing node histogram models（NHM） and edge histogram models（EHM） in probability modeling is used to solve the ARP-MPDT. The selection ratio of NHM and EHM probability models is adjusted adaptively. Finally, performance of the algorithm for solving the ARP-MPDT problem is verified by the computational experiments.

关键词： Estimation of distribution algorithm Multi-model Routing Multi-point dynamic task

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ICB-MOEA/D: An interactive classification-based multi-objective optimization algorithm

ICB-MOEA/D: An interactive classification-based multi-object...

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第37届中国控制会议

作者： Bin Xin Hepeng Li Ling Wang School of Automation Beijing Institute of Technology Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Advanced Innovation Center for Intelligent Robots and Systems Department of Automation Tsinghua University

Interactive multi-objective optimization algorithms have developed rapidly in recent years. In this paper, we propose a new classification-based interactive multi-objective optimization algorithm named ICB-MOEA/D to solve the formulated multiobjective optimization problem. ICB-MOEA/D provides several solutions for the decision maker to choose. The decision maker chooses his/her most preferred solution from these solutions and the historical solutions which have been chosen as the current most preferred solution. ICB-MOEA/D records this solution and classifies the objectives according to the updated preference information into four categories: 1) objectives which are expected to be improved;2) objectives which can be sacrificed;3) objectives which are expected to remain basically unchanged;4) objectives which do not matter currently. Accoding to the number of the objectives in the first category, a new single-objective opitimization model or multi-objective optimization model will be built. The single-objective optimization model will be optimized by a classic variant of differential evolution DE/rand/1/bin, and the multi-objective optimization model will be optimized by a popular docomposition-based multi-objective optimizer MOEA/*** the classifications are done automatically by the algorithm, reducing the burden of the decision maker. ICB-MOEA/D was tested on the two-objective instance ZDT1, and the experiment results show the effectiveness of ICB-MOEA/D.

关键词： interactive multi-objective optimization decomposition classification virtual decision maker

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Event-Trigger Extended Kalman Filter Design for Distributed Generation Network with Limited Communication 36

Event-Trigger Extended Kalman Filter Design for Distributed ...

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第36届中国控制会议

作者： Xing Xin Luyu Li Sen Li Zhen Li Jian Li Zhen Chen Xiangdong Liu Beijing Institute of Electronic System Engineering School of Automation & Key Laboratory for Intelligent Control & Decision on Complex Systems Beijing Institute of Technology

ISBN: (纸本)9781538629185

For distributed generation(DG) network, it is important to estimate the real-time states. The information-centric networking(ICN) is established to take charge of the communication of DG network. However, the assumption of ideal communication between sensors and the estimation center cannot be guaranteed due to the communication constraints of ICN with the increasing DG network. A conventional algorithm, which reduces the communication burden in ICN, is to drop the observation of each smart grid in a random way. However, the accuracy of this algorithm recession decays rapidly with the increasing drop rate. To guarantee an appropriate estimation accuracy when the drop rate increases, this paper introduces the event-trigger strategy into the estimation algorithm. An event-trigger extended kalman filter(ET-EKF) is established in this paper to adapt the nonlinearity of DG system. ET-EKF reduces the communication burden and achieves an appropriate estimation accuracy at the same time. Finally, its feasibility and performance are demonstrated using the standard IEEE 39-bus system with phasor measurement units(PMUs).

关键词： Event-trigger extended Kalman filter(ET-EKF) real-time estimation distributed generation network smart grids phasor measurement unit(PMU)

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Power system state estimation via feasible point pursuit: Algorithms and Cramér-rao bound

Power system state estimation via feasible point pursuit: Al...

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作者： Wang, Gang Zamzam, Ahmed S. Giannakis, Georgios B. Sidiropoulos, Nicholas D. State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing100081 China Digital Technology Center Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States Department of Electrical and Computer Engineering University of Virginia CharlottesvilleVA22904 United States

Accurately monitoring the system's operating point is central to the reliable and economic operation of an autonomous energy grid. Power system state estimation (PSSE) aims to obtain complete voltage magnitude and angle information at each bus given a number of system variables at selected buses and lines. Power flow analysis amounts to solving a set of noise-free power flow equations, and is cast as a special case of PSSE. Physical laws dictate quadratic relationships between available quantities and unknown voltages, rendering general instances of power flow and PSSE nonconvex and NP-hard. Past approaches are largely based on gradient-type iterative procedures or semidefinite relaxation (SDR). Due to nonconvexity, the solution obtained via gradient-type schemes depends on initialization, while SDR methods do not perform as desired in challenging scenarios. This paper puts forth novel feasible point pursuit (FPP)-based solvers for power flow analysis and PSSE, which iteratively seek feasible solutions for a nonconvex quadratically constrained quadratic programming reformulation of the weighted least-squares (WLS). Relative to the prior art, the developed solvers offer superior numerical performance at the cost of higher computational complexity. Furthermore, they converge to a stationary point of the WLS problem. As a baseline for comparing different estimators, the Cramér-Rao lower bound is derived for the fundamental PSSE problem in this paper. Judicious numerical tests on several IEEE benchmark systems showcase markedly improved performance of our FPP-based solvers for both power flow and PSSE tasks over popular WLS-based Gauss-Newton iterations and SDR approaches. © 1991-2012 IEEE.

关键词： State estimation

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An algorithm for Quadruped Robot’s Inverse Kinematic Problems Based on PSO

An algorithm for Quadruped Robot’s Inverse Kinematic Proble...

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第37届中国控制会议

作者： Wei Shen Min Li Junzheng Wang Changkun Chai Wen Zheng Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of Technology Institute of Software Chinese Academy of Sciences Siemens China LTD

The inverse problem is an essential problem in robot *** order to deal with the inverse kinematic problems of hydraulic quadruped robot,the motion process of a single leg is modeled,simulated and analyzed.A new method based on particle swarm optimization（PSO） is proposed in this paper to overcome low computational efficiency,long calculation time and accumulative *** proposed algorithm is attributed to:1) limit the range of particles initialization;2) select suitable compressibility factors;3) employ linear interpolation method to improve the efficiency of the inverse *** simulation results demonstrate that the developed method can provide enhanced computational efficiency and significantly reduce the calculation time while ensuring the precision requirement,and can also eliminate the accumulative error.

关键词： Inverse kinematics Motion optimization Particle swarm optimization Quadruped robot

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State Estimation and Fault Detection for Nonlinear Dynamic Systems

State Estimation and Fault Detection for Nonlinear Dynamic S...

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第37届中国控制会议

作者： Baohua Lu Liping Yan Hongxue Chen Yuanqing Xia Mengyin Fu Bo Xiao Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology School of Information and Communication Engineering Beijing University of Posts and Telecommunications

In practical problems, the dynamic systems are usually nonlinear. In this case, the traditional Kalman filter cannot be used for state estimation or fault detection. The two typical extension based on Kalman filtering framework is the Extended Kalman Filter(EKF) and the Unscented Kalman Filter(UKF). Theoretically speaking, UKF is better than EKF when estimation accuracy is concerned, especially for high degree nonlinear cases. This paper is concerned with the state estimation and fault detection problem for a class of nonlinear dynamic systems. A novel fault detection and analyse method is presented based on the period residual of EKF and UKF. For different kind of faults, mainly, the system parameter error, the sensor/data error, EKF and UKF are used, and the estimation and fault detection effects are compared and analyzed.

关键词： nonlinear system state estimation fault detection parameter error sensor error

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Convolutional Neural Network Features Comparison Between Back-Propagation and Extreme Learning Machine

Convolutional Neural Network Features Comparison Between Bac...

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Chinese control Conference

作者： Atmane Khellal Hongbin Ma Qing Fei 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

Recently deep learning based architectures have been widely deployed in many problems of artificial intelligence. Among deep learning models, Convolutional Neural Networks (CNN) have been reported in numerous successful applications such as object recognition, and natural language processing. The convolutional neural networks are trained by back-propagating the classification error using the Back-Propagation (BP) algorithm, which requires a large amount of data and slows the training process. To overcome these difficulties, a new fast and accurate approach based on Extreme Learning Machine (ELM) to train any convolutional neural network has been proposed. The developed framework (ELM-CNN) is based on the concept of auto-encoding to learn the convolutional filters with biases, by reconstructing the normalized input and the intercept term. In this paper, systematic comparison with traditional back-propagation based training method (BP-CNN) has been made with respect to two aspects qualitative and quantitative. The experimental results on the popular MNIST dataset show that the ELM-CNN algorithm achieves competitive results in terms of generalization performance and up to 16 times faster than the back-propagation based training of CNN.

关键词： Training Convolutional neural networks Feature extraction Computer architecture Convolution Machine learning Encoding

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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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Approach and landing guidance design for reusable launch vehicle using multiple sliding surfaces technique

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Chinese Journal of Aeronautics 2017年第4期30卷 1582-1591页

作者： Xiangdong LIU Fengdi ZHANG Zhen LI Yao ZHAO School of Automation Beijing Institute of Technology Beijing 100081 China National Key Laboratory of Complex System Intelligent Control and Decision Beijing Institute of Technology Beijing100081 China China Academy of Launch Vehicle Technology Beijing 100076 China

An autonomous approach and landing（A＆L） guidance law is presented in this paper for landing an unpowered reusable launch vehicle（RLV） at the designated runway touchdown. Considering the full nonlinear point-mass dynamics, a guidance scheme is developed in threedimensional space. In order to guarantee a successful A＆L movement, the multiple sliding surfaces guidance（MSSG） technique is applied to derive the closed-loop guidance law, which stems from higher order sliding mode control theory and has advantage in the finite time reaching *** global stability of the proposed guidance approach is proved by the Lyapunov-based *** designed guidance law can generate new trajectories on-line without any specific requirement on off-line analysis except for the information on the boundary conditions of the A＆L phase and instantaneous states of the RLV. Therefore, the designed guidance law is flexible enough to target different touchdown points on the runway and is capable of dealing with large initial condition errors resulted from the previous flight phase. Finally, simulation results show the effectiveness of the proposed guidance law in different scenarios.

关键词： Finite time control Landing guidance Lyapunov stability Reusable launch vehicle Sliding mode control

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