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Neural-Network-Based Online HJB Solution for Optimal Robust Guaranteed Cost control of Continuous-Time Uncertain Nonlinear systems

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IEEE TRANSACTIONS ON CYBERNETICS 2014年第12期44卷 2834-2847页

作者： Liu, Derong Wang, Ding Wang, Fei-Yue Li, Hongliang Yang, Xiong Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

In this paper, the infinite horizon optimal robust guaranteed cost control of continuous-time uncertain nonlinear systems is investigated using neural-network-based online solution of Hamilton-Jacobi-Bellman (HJB) equation. By establishing an appropriate bounded function and defining a modified cost function, the optimal robust guaranteed cost control problem is transformed into an optimal control problem. It can be observed that the optimal cost function of the nominal system is nothing but the optimal guaranteed cost of the original uncertain system. A critic neural network is constructed to facilitate the solution of the modified HJB equation corresponding to the nominal system. More importantly, an additional stabilizing term is introduced for helping to verify the stability, which reinforces the updating process of the weight vector and reduces the requirement of an initial stabilizing control. The uniform ultimate boundedness of the closed-loop system is analyzed by using the Lyapunov approach as well. Two simulation examples are provided to verify the effectiveness of the present control approach.

关键词： Adaptive critic designs adaptive/approximate dynamic programming (ADP) Hamilton-Jacobi-Bellman (HJB) equation neural networks optimal robust guaranteed cost control uncertain nonlinear systems

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On the Optimality of Data-Aided Coarse Timing With Dirty Templates

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2014年第4期63卷 1759-1769页

作者： Zhang, Wenshu Yang, Liuqing Cheng, Xiang Zang, Wei Colorado State Univ Dept Elect & Comp Engn Ft Collins CO 80523 USA Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Peking Univ Sch Elect Engn & Comp Sci Beijing 100871 Peoples R China MathWorks Inc Natick MA 01760 USA

Rapid and accurate timing synchronization is a critical task in ultrawideband (UWB) systems. Yang and Giannakis introduced a promising algorithm, i.e., data-aided timing with dirty templates (TDT), which is known for low complexity and relaxed operation conditions in the presence of unknown time hopping and multipath channels. In this paper, we will explore the optimality of TDT. We develop a maximum-likelihood (ML) timing algorithm and obtain its optimum training sequence. It is shown that the optimum training sequence of the ML timing estimator coincides with that of the TDT algorithm. In addition, we prove that the ML algorithm can be simplified using this training sequence and that the simplified ML (SML) is equivalent to TDT.

关键词： Maximum-likelihood (ML) estimation timing synchronization ultrawideband (UWB) systems

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Finite-Approximation-Error-Based Discrete-Time Iterative Adaptive Dynamic Programming

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IEEE TRANSACTIONS ON CYBERNETICS 2014年第12期44卷 2820-2833页

作者： Wei, Qinglai Wang, Fei-Yue Liu, Derong Yang, Xiong Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

In this paper, a new iterative adaptive dynamic programming (ADP) algorithm is developed to solve optimal control problems for infinite horizon discrete-time nonlinear systems with finite approximation errors. First, a new generalized value iteration algorithm of ADP is developed to make the iterative performance index function converge to the solution of the Hamilton-Jacobi-Bellman equation. The generalized value iteration algorithm permits an arbitrary positive semi-definite function to initialize it, which overcomes the disadvantage of traditional value iteration algorithms. When the iterative control law and iterative performance index function in each iteration cannot accurately be obtained, for the first time a new "design method of the convergence criteria" for the finite-approximation-error-based generalized value iteration algorithm is established. A suitable approximation error can be designed adaptively to make the iterative performance index function converge to a finite neighborhood of the optimal performance index function. Neural networks are used to implement the iterative ADP algorithm. Finally, two simulation examples are given to illustrate the performance of the developed method.

关键词： Adaptive critic designs adaptive dynamic programming (ADP) approximate dynamic programming approximation error neural networks neuro-dynamic programming nonlinear systems optimal control reinforcement learning value iteration

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A Social Choice Force Model for Estimating Collective Action Manipulation

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IFAC Proceedings Volumes 2014年第3期47卷 9847-9852页

作者： Jie Zhang Shuai Zeng Wenli Liu Tao Wang Fei-Yue Wang C. L. Philip Chen The State Key Lab of Management and Control for Complex Systems CASIA Beijing 100190 China National University of Defense Technology Hunan 410073 China University of Macau Macau China

For e-commerce websites collective actions have significant influence on the behaviors and decisions of individual customers. In this work, we propose a dynamic utility model for customers in e-commerce by considering a “social choice force” (SCF) effect on utility functions of agents. We apply the Artificial societies, Computational experiments, and Parallel execution (ACP) approach to investigate the short-term efforts of collective action manipulation. Experimental results show that the proposed agent model and algorithm outperform the baseline prediction algorithm and illustrate the effect of collective action manipulation in a group buying directory website.

关键词： Cyber Space Collective Actions E-Commerce Agent Modeling Manipulation Neural Networks

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Traffic light Time adjustment based on Interval type-2 fuzzy sets

Traffic light Time adjustment based on Interval type-2 fuzzy...

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International Conference on Informative and Cybernetics for Computational Social systems (ICCSS)

作者： Gao, Zhen Mo, Hong Zhu, FengHua Changsha Univ Sci & Technol Coll Elect & Informat Engn Changsha Hunan Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

ISBN: (纸本)9781479947522

According to the actual circumstance of the intersection, reasonably adjusting traffic light time can help to ease traffic pressure and save transportation time. In this paper, Interval type - 2 fuzzy sets and matched-degree are applied to the time of intersection signal adjusting, and five words are used to cover the range of adjusting lights' time, finally according to the average number of stranded vehicles, a query table is built to inquire and control the signal time during a certain period.

关键词： Interval type-2 fuzzy sets footprint of uncertainty computing with words traffic light time

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Multiple Actor-Critic Structures for Continuous-Time Optimal control Using Input-Output Data

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING systems 2015年第4期26卷 851-865页

作者： Song, Ruizhuo Lewis, Frank Wei, Qinglai Zhang, Hua-Guang Jiang, Zhong-Ping Levine, Dan Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China Univ Texas Arlington UTA Res Inst Ft Worth TX USA Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110004 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Northeastern Univ Sch Informat Sci & Engn Shenyang 110004 Peoples R China NYU Polytech Sch Engn Dept Elect & Comp Engn Brooklyn NY 11201 USA Univ Texas Arlington Dept Psychol Arlington TX 76019 USA

In industrial process control, there may be multiple performance objectives, depending on salient features of the input-output data. Aiming at this situation, this paper proposes multiple actor-critic structures to obtain the optimal control via input-output data for unknown nonlinear systems. The shunting inhibitory artificial neural network (SIANN) is used to classify the input-output data into one of several categories. Different performance measure functions may be defined for disparate categories. The approximate dynamic programming algorithm, which contains model module, critic network, and action network, is used to establish the optimal control in each category. A recurrent neural network (RNN) model is used to reconstruct the unknown system dynamics using input-output data. NNs are used to approximate the critic and action networks, respectively. It is proven that the model error and the closed unknown system are uniformly ultimately bounded. Simulation results demonstrate the performance of the proposed optimal control scheme for the unknown nonlinear system.

关键词： Actor-critic approximate dynamic programming (ADP) category optimal control shunting inhibitory artificial neural network (SIANN)

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The Identifier-based Relative Position Estimation for Leader-follower Robotic System

The Identifier-based Relative Position Estimation for Leader...

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11th IEEE International Conference on Mechatronics and Automation (ICMA)

作者： Xu, Lingyi Cao, Zhiqiang Zhao, Peng Yin, Yixin Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China

ISBN: (纸本)9781479939794

A monocular vision measurement approach is presented for leader-follower robotic system to estimate the position of the leader robot relative to the follower robot. The color identifier with green and red blocks is attached on the back of the leader robot, and it may be captured by the camera mounted on the follower robot. A coarse-to-fine searching method is given to extract the center point of the identifier. The parameters of the camera with the combination of the intrinsic and extrinsic ones are calibrated via least square method with given relative positions and the corresponding image coordinates of the identifier's center. The parameters are further optimized with Levenberg-Marquardt method. Then the position of the leader robot relative to the follower robot can be estimated only with single feature point. The experimental results verify the validity of the given approach.

关键词： Visual measurement feature extraction camera calibration monocular vision leader-follower robotic system

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Budget Planning for Coupled Campaigns in Sponsored Search Auctions

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INTERNATIONAL JOURNAL OF ELECTRONIC COMMERCE 2014年第3期18卷 39-65页

作者： Yang, Yanwu Qin, Rui Jansen, Bernard J. Zhang, Jie Zeng, Daniel Huazhong Univ Sci & Technol Sch Management Internet Sci & Econ Comp Res Grp Wuhan Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Penn State Univ Coll Informat Sci & Technol University Pk PA 16802 USA Univ Arizona Dept Management Informat Syst Tucson AZ 85721 USA

Budget-related decisions in sponsored search auctions are recognized as a structured decision problem rather than a simple constraint. Budget planning over several coupled campaigns (e.g., substitution and complementarity) remains a challenging but important task for advertisers. In this paper, we propose a dynamic multicampaign budget planning approach using optimal control techniques, with consideration of the substitution relationship between advertising campaigns. A three-dimensional measure of substitution relationships between campaigns is presented, namely, the overlapping degree in terms of campaign contents, promotional periods, and target regions. We also study some desirable properties and possible solutions to our budget model. Computational simulations and experiments are conducted to evaluate our model using real-world data from practical campaigns in sponsored search auctions. Experimental results show that (1) our approach outperforms the baseline strategy that is commonly used in practice;(2) coupled campaigns with a higher overlapping degree in between reduce the optimal total budget level, then reduce the optimal payoff, and reach the budgeting cap earlier than those with a less overlapping degree;and (3) the advertising effort can be seriously weakened by ignoring the degree of overlapping between campaigns.

关键词： Advertising campaigns budget planning decision analysis online advertising operations research in marketing optimal control sponsored search sponsored search auctions

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Biologically Inspired Visual Model With Preliminary Cognition and Active Attention Adjustment

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IEEE TRANSACTIONS ON CYBERNETICS 2015年第11期45卷 2612-2624页

作者： Qiao, Hong Xi, Xuanyang Li, Yinlin Wu, Wei Li, Fengfu Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China Chinese Acad Sci Grad Sch Inst Automat Beijing 100190 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Sci & Technol China Hefei Natl Lab Phys Sci Microscale Hefei 230027 Peoples R China Chinese Acad Sci Inst Appl Math Acad Math & Syst Sci Beijing 100190 Peoples R China

Recently, many computational models have been proposed to simulate visual cognition process. For example, the hierarchical Max-Pooling (HMAX) model was proposed according to the hierarchical and bottom-up structure of V1 to V4 in the ventral pathway of primate visual cortex, which could achieve position-and scale-tolerant recognition. In our previous work, we have introduced memory and association into the HMAX model to simulate visual cognition process. In this paper, we improve our theoretical framework by mimicking a more elaborate structure and function of the primate visual cortex. We will mainly focus on the new formation of memory and association in visual processing under different circumstances as well as preliminary cognition and active adjustment in the inferior temporal cortex, which are absent in the HMAX model. The main contributions of this paper are: 1) in the memory and association part, we apply deep convolutional neural networks to extract various episodic features of the objects since people use different features for object recognition. Moreover, to achieve a fast and robust recognition in the retrieval and association process, different types of features are stored in separated clusters and the feature binding of the same object is stimulated in a loop discharge manner and 2) in the preliminary cognition and active adjustment part, we introduce preliminary cognition to classify different types of objects since distinct neural circuits in a human brain are used for identification of various types of objects. Furthermore, active cognition adjustment of occlusion and orientation is implemented to the model to mimic the top-down effect in human cognition process. Finally, our model is evaluated on two face databases CAS-PEAL-R1 and AR. The results demonstrate that our model exhibits its efficiency on visual recognition process with much lower memory storage requirement and a better performance compared with the traditional purely computational meth

关键词： Active attention adjustment association biologically inspired visual model memory object recognition

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A Probabilistic Spectral Graph Matching Algorithm for Robust Correspondence between Lunar Surface Images 11

A Probabilistic Spectral Graph Matching Algorithm for Robust...

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11th World Congress on Intelligent control and Automation

作者： Yang, Xu Liu, Chuan-Kai Liu, Zhi-Yong Qiao, Hong Wang, Bao-Feng Wang, Zi-Dong Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100864 Peoples R China Beijing Aerosp Flight Control Ctr Beijing Peoples R China Donghua Univ Sch Informat Sci & Technol Shanghai Peoples R China

ISBN: (纸本)9781479958252

The correspondence between key points is an important problem in lunar surface image processing, and further lays the foundation for the navigation of a rover and the terrain reconstruction of the lunar surface. However, the problem is still challenging due to the existence of large scale and rotation transformations, reflected view of the same scenery, and different illumination conditions between acquired images as the lunar rover moves forward. Traditional appearance matching algorithms, like SIFT, often fail in handling the above situations. By utilizing the structural cues between points, in this paper we propose a probabilistic spectral graph matching method to tackle the point correspondence problem in lunar surface images acquired by Yutu lunar rover which has been recently transmitted to the moon by China's Chang'e-3 lunar probe. Compared with traditional methods, the proposed method has three advantages. First, the incorporation of the structural information makes the matching more robust with respect to geometric transformations and illumination changes. Second, the assignment between points is interpreted in a probabilistic manner, and thus the best assignments can be easily figured out by ranking the probabilities. Third, the optimization problem can be efficiently approximately solved by spectral decomposition. Simulations on real lunar surface images witness the effectiveness of the proposed method.

关键词： graph matching lunar surface image spectral graph theory point correspondence

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