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Study of Direction Probability and Algorithm of Improved Marriage in Honey Bees Optimization for Weapon Network System

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Defence Technology（防务技术） 2009年第2期5卷 152-157页

作者：杨晨光涂序彦陈杰 Systems Engineering Research Institute China State Shipbuilding Corporation Department of Automation School of Information Science and Technology Beijing Institute of Technology Ministry of Education of Key Laboratory of Complex System Intelligent Control and Decision Beijing Institute of Technology

To solve the weapon network system optimization problem against small raid objects with low attitude,the concept of direction probability and a new evaluation index system are *** calculating the whole damaging probability that changes with the defending angle,the efficiency of the whole weapon network system can be subtly *** such method,we can avoid the inconformity of the description obtained from the traditional index *** new indexes are also proposed,*** index,overlap index and cover index,which help manage the relationship among several *** normalizing the computation results with the Sigmoid function,the matching problem between the optimization algorithm and indexes is well ***,the algorithm of improved marriage in honey bees optimization that proposed in our previous work is applied to optimize the embattlement *** is carried out to show the efficiency of the proposed indexes and the optimization algorithm.

关键词：网络系统优化问题破坏概率算法改进核武器蜜蜂婚姻 Sigmoid函数

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A Novel LMI Approach to Global Impulsive Exponential Synchronization of Chaotic Delayed Neural Networks

A Novel LMI Approach to Global Impulsive Exponential Synchro...

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Joint 48th IEEE Conference on Decision and control and 28th Chinese control Conference

作者： Tiedong Ma Huaguang Zhang Derong Liu Zhiliang Wang School of Information Science and Engineering Northeastern University Key Laboratory of Complex Systems and Intelligence Science Institute of AutomationChinese Academy of Sciences

In this paper, the exponential synchronization problem of a class of chaotic delayed neural networks(DNNs)via impulsive control method is *** on the theory of impulsive functional differential equations(FDEs), some new synchronization criteria expressed in the form of linear matrix inequalities(LMIs) are *** designed impulsive controller not only can globally exponentially stabilize the error dynamics, but also can control the exponential synchronization rate of the error ***, to estimate the stable region, a novel optimization control algorithm is developed,which can deal with the minimum problem with two nonlinear terms coexisting in LMIs ***, simulation results demonstrate the effectiveness of the proposed method.

关键词： A Novel LMI Approach to Global Impulsive Exponential Synchronization of Chaotic Delayed Neural Networks

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Mixed model based SLAM approach for mobile robot

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Nanjing Li Gong Daxue Xuebao/Journal of Nanjing University of Science and Technology 2009年第SUPPL. 1期33卷 98-103页

作者： Fang, Fang Ma, Xu-Dong Dai, Xian-Zhong School of Automation Southeast University Nanjing 210096 China Key Laboratory of Measurement and Control of CSE Complex Systems of Engineering Nanjing 210096 China

A new simultaneous localization and mapping approach based on mixed map model using laser data and odometry information is presented in this paper. Mixed model composed of occupancy grids and line maps is utilized to represent environment maps. At the same time Hough transform is introduced to extract line features. Then robot localization and map building task is accomplished using line features matching and extended Kalman filter. Experimental results indicate the feasibility and validity of this approach.

关键词： Extended Kalman filters

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Nonlinear Observer Design for Discrete MIMO systems with Unknown Time Delay

Nonlinear Observer Design for Discrete MIMO Systems with Unk...

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Joint 48th IEEE Conference on Decision and control and 28th Chinese control Conference

作者： Jing Na Guido Herrmann Xuemei Ren Phil Barber Education Ministry Key Laboratory of Complex System Intelligent Control and Decision School of Automation Beijing Institute of Technology IEEE Department of Mechanical Engineering University of Bristol

This paper proposes an adaptive observer for discrete MIMO systems with unknown dynamics and *** case that the system output equation has time-delay and nonlinearities is also *** delayed states in the system are with a nonlinear form and only the estimated delay is *** using a high-order neural network(HONN),the precise system model,and the Lipschitz or norm-boundedness assumption of unknown nonlinearities are not required.A novel converse Lyapunov Lemma is also developed to prove the uniform ultimate boundedness(UUB) of the proposed *** of the presented scheme is verified by simulation.

关键词： Lyapunov Nonlinear Observer Design for Discrete MIMO systems with Unknown Time Delay Observer

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LS-SVM based neural controller as optimized by particle swarm algorithm using dual heuristic dynamic programming

LS-SVM based neural controller as optimized by particle swar...

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International Joint Conference on Neural Networks (IJCNN)

作者： Si-Yao Fu Guo-Sheng Yang Zeng-Guang Hou School of Information and Engineering Central University for Nationalities Beijing China Key laboratory of Complex Systems and Intelligence Science Institute of Automation Chinese Academy and Sciences Beijing China

The dual heuristic programming (DHP) approach has a superior ability for solving approximate dynamic programming problems in adaptive critic designs (ACD). The common approaches applied in the DHP are design the multilayer feedforward neural networks (MLFNN) as the differential model of the plant for training the critic and action networks. However, the problems of overfitting and premature convergence to local optima usually pose great challenges in the practice of MLFNNs during the training procedure. In this paper a least squares support vector machine (LS-SVM) regressor optimized by particle swarm algorithm (PSO) is proposed for generating the control actions and the learning rules for the critic and action networks. PSO is introduced to select the LS-SVM's hyper-parameters. The introduction of the SVM based training mechanism imparts the developed algorithm with inherent capacity for combating the overfitting problem as well as showing relatively high efficiency in converging to the optima. Simulation on the balancing of a cart pole plant shows that the proposed learning strategy is verified as faster convergence and higher efficiency as compared to traditional BP based adaptive dynamic programming approaches.

关键词： Particle swarm optimization Heuristic algorithms Dynamic programming Multi-layer neural network Convergence Support vector machines Least squares approximation Neural networks Feedforward neural networks Least squares methods

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Nonsingular terminal sliding mode guidance law

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Dongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Southeast University (Natural Science Edition) 2009年第SUPPL. 1期39卷 87-90页

作者： Wang, Zhao Li, Shihua Fei, Shumin School of Automation Southeast University Nanjing 210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Southeast University Nanjing 210096 China Dongying 257061 China

In order to intercept maneuvering targets, a guidance law based on nonsingular terminal sliding mode (NTSM) technique is presented. The proposed NTSM contains the line-of-sight (LOS) angular rate and a desired LOS angular. Through introducing the nonlinear sliding mode surface to improve the convergent characteristics of the system, the states of the closed-loop system can be converged to the origin in finite time and the system performance is improved greatly. The LOS angular rate is nullified in finite time to guarantee the zero miss-distance between the missile and the target;the LOS angular reaches the desired value in finite time to guarantee the missile attitude of hitting the target. With the maneuverability of acceleration of the target regarded as an unknown bounded disturbance, the proposed guidance law is robust to target maneuvering by using the invariance of the variable structure control. The guidance law has a simple structure and is easy to be implemented. Two cases of target maneuvering are simulated to validate the effectiveness of the method. The simulation results show that the algorithm has strong robustness and high guidance accuracy.

关键词： Variable structure control

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Data-based Optimal control for Discrete-time Zero-sum Games of 2-D systems Using Adaptive Critic Designs

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Acta Automatica Sinica 2009年第6期35卷 682-692页

作者： Qing-Lai WEI Hua-Guang ZHANG Li-Li CUI The Key Laboratory of Complex Systems and Intelligence Science Institute of Automation Chinese Academy of Sciences Beijing 100190 P.R. China The School of Information Science and Engineering Northeastern University Shenyang 110004 P.R. China

In this paper, an iterative adaptive critic design (ACD) algorithm is proposed to solve a class of discrete-time two-person zero-sum games for Roesser type 2-D system. The idea is to use adaptive critic technique to obtain the optimal control pair iteratively to make the performance index function reach the saddle point of the zero-sum games. The proposed iterative ACD algorithm can be implemented based on the input and state data without the system model. Stability analysis of the 2-D system is presented and the convergence property of the performance index function is also proved. Neural networks are used to approximate the performance index function and compute the optimal control policies, respectively, for facilitating the implementation of the iterative ACD algorithm. The optimal control scheme of the air drying process is given to illustrate the performance of the proposed method.

关键词： Adaptive critic designs (ACD) optimal control zero-sum game 2-D system neural networks

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A new approach for estimating the domain of attraction for linear systems with time-varying delay and saturating actuators

A new approach for estimating the domain of attraction for l...

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Asian control Conference

作者： Zhiqiang Zuo Daniel W. C. Ho Yijing Wang Cuili Yang Department of Mathematics City University of Hong Kong Hong Kong China Tianjin Key Laboratory of Process Measurement and Control School of Electrical Engineering and Automation Tianjin University TJU Tianjin China

This paper is concerned with the estimation of the domain of attraction for a class of linear continuous-time systems subject to both interval time-varying delay and actuator saturation. A new type of delay-range-dependent condition is firstly proposed using the free-weighting matrix technique to derive a tighter upper bound on the derivative of a Lyapunov-Krasovskii functional. Based on it, a state feedback controller is then designed with the solution of a set of linear matrix inequalities. An optimization problem with LMI constraints is formulated in order to find an initial set as large as possible to obtain a less conservative estimate of the domain of attraction for such systems. An example is carried out to illustrate the theoretical results.

关键词： Delay estimation Linear systems Time varying systems Delay systems Hydraulic actuators Linear matrix inequalities Upper bound State feedback Linear feedback control systems Constraint optimization

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An FES cycling control system based on CPG

An FES cycling control system based on CPG

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Peng-Feng Li Zeng-Guang Hou Feng Zhang Min Tan Hong-Bo Wang Yi Hong Jun-Wei Zhang Graduated School of Chinese Academy of Science Beijing China Key Laboratory of Complex Systems and Intelligence Science Institute of Automation Chinese Academy and Sciences Beijing China Key Laboratory of Complex Systems and Intelligence Science Chinese Academy of Sciences Beijing China School of Mechanical Engineering Yanshan University Hebei China Department of SCI Surgery China Rehabilitation Research Center Beijing China

This paper presents a scientific strategy for cycling induced by the functional electrical stimulation. In order to simulate the FES-cycling movement produced by human body, a neuro-musculo-skeletal model containing 16 segments and 186 muscles is developed, which can simulate human movements precisely. This model contains mathematical model of electrically stimulated skeletal muscles. Having known the kinematics and dynamics of the model, we design an FES-cycling control system based on the central pattern generator (CPG), which can produce rhythm stimulus to produce desired torque and generate rhythm cycling movements. And an approach to control multiple muscles is proposed. In the end of this paper, the simulation results are provided.

关键词： control systems Biological system modeling Muscles Mathematical model Rhythm Neuromuscular stimulation Humans Kinematics control system synthesis Centralized control

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Solving convex optimization problems using recurrent neural networks in finite time

Solving convex optimization problems using recurrent neural ...

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International Joint Conference on Neural Networks (IJCNN)

作者： Long Cheng Zeng-Guang Hou Noriyasu Homma Min Tan Madam M. Gupta The Key Laboratory of Complex Systems and Intelligence Science Institute of Automation Chinese Academy and Sciences Beijing China School of Health Sciences Faculty of Medicine Chinese Academy and Sciences Sendai Japan Intelligent Systems Research Laboratory College of Engineering University of Saskatchewan Saskatoon SAS Canada

A recurrent neural network is proposed to deal with the convex optimization problem. By employing a specific nonlinear unit, the proposed neural network is proved to be convergent to the optimal solution in finite time, which increases the computation efficiency dramatically. Compared with most of existing stability conditions, i.e., asymptotical stability and exponential stability, the obtained finite-time stability result is more attractive, and therefore could be considered as a useful supplement to the current literature. In addition, a switching structure is suggested to further speed up the neural network convergence. Moreover, by using the penalty function method, the proposed neural network can be extended straightforwardly to solving the constrained optimization problem. Finally, the satisfactory performance of the proposed approach is illustrated by two simulation examples.

关键词： Recurrent neural networks Neural networks Constraint optimization Asymptotic stability Current supplies Convergence Robot control Design optimization Lagrangian functions Quadratic programming

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