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Numerical adaptive learning control scheme for discrete-time non-linear systems

IET control THEORY AND APPLICATIONS 2013年第11期7卷 1472-1486页

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

In this study, a novel numerical adaptive learning control scheme based on adaptive dynamic programming (ADP) algorithm is developed to solve numerical optimal control problems for infinite horizon discrete-time non-linear systems. Using the numerical controller, the domain of definition is constrained to a discrete set that makes the approximation errors always exist between the numerical controls and the accurate ones. Convergence analysis of the numerical iterative ADP algorithm is developed to show that the numerical iterative controls can make the iterative performance index functions converge to the greatest lower bound of all performance indices within a finite error bound under some mild assumptions. The stability properties of the system under the numerical iterative controls are proved, which allow the present iterative ADP algorithm to be implemented both on-line and off-line. Finally, two simulation examples are given to illustrate the performance of the present method.

关键词： Domain of Definition Numerical infinite horizon Self tuning Numerical controls Iterative discrete set System stability Converge learning control Approximation error discrete-time performance indicators

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Model-Free Optimal control for Affine Nonlinear systems With Convergence Analysis

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2015年第4期12卷 1461-1468页

作者： Zhao, Dongbin Xia, Zhongpu Wang, Ding Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

In this paper, a self-learning control scheme is proposed for the infinite horizon optimal control of affine nonlinear systems based on the action dependent heuristic dynamic programming algorithm. The policy iteration technique is introduced to derive the optimal control policy with feasibility and convergence analysis. It shows that the "greedy" control action for each state is uniquely existent, the learned control policy after each policy iteration is admissible, and the optimal control policy is able to be obtained. Two three-layer perceptron neural networks are employed to implement the scheme. The critic network is trained by a novel rule to conform to the Bellman equation, and the action network is trained to yield a better control policy. Both training processes alternate until the optimal control policy is achieved. Two simulation examples are provided to validate the effectiveness of the approach.

关键词： Action dependent heuristic dynamic programming adaptive dynamic programming model-free optimal control neural networks policy iteration

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Neural-network-based online optimal control for uncertain non-linear continuous-time systems with control constraints

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IET control THEORY AND APPLICATIONS 2013年第17期7卷 2037-2047页

作者： Yang, Xiong Liu, Derong Huang, Yuzhu Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

In this study, an online adaptive optimal control scheme is developed for solving the infinite-horizon optimal control problem of uncertain non-linear continuous-time systems with the control policy having saturation constraints. A novel identifier-critic architecture is presented to approximate the Hamilton-Jacobi-Bellman equation using two neural networks (NNs): an identifier NN is used to estimate the uncertain system dynamics and a critic NN is utilised to derive the optimal control instead of typical action-critic dual networks employed in reinforcement learning. Based on the developed architecture, the identifier NN and the critic NN are tuned simultaneously. Meanwhile, unlike initial stabilising control indispensable in policy iteration, there is no special requirement imposed on the initial control. Moreover, by using Lyapunov's direct method, the weights of the identifier NN and the critic NN are guaranteed to be uniformly ultimately bounded, while keeping the closed-loop system stable. Finally, an example is provided to demonstrate the effectiveness of the present approach.

关键词： adaptive control approximation theory closed loop systems continuous time systems Lyapunov methods neurocontrollers nonlinear control systems optimal control robust control uncertain systems neural network-based online adaptive optimal control uncertain nonlinear continuous-time systems control constraints infinite-horizon optimal control problem control policy saturation constraints identifier-critic architecture Hamilton-Jacobi-Bellman equation approximation uncertain system dynamics critic NN action-critic dual networks reinforcement learning identifier NN policy iteration LyapunovaEuros direct method closed loop system stability

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A Recurrent Attention and Interaction Model for Pedestrian Trajectory Prediction

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IEEE/CAA Journal of Automatica Sinica 2020年第5期7卷 1361-1370页

作者： Xuesong Li Yating Liu Kunfeng Wang Fei-Yue Wang State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190and also with the University of Chinese Academy of SciencesBeijing 100049China College of Information Science and Technology Beijing University of Chemical TechnologyBeijing 100029China State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China IEEE

The movement of pedestrians involves temporal continuity,spatial interactivity,and random *** a result,pedestrian trajectory prediction is rather *** existing trajectory prediction methods tend to focus on just one aspect of these challenges,ignoring the temporal information of the trajectory and making too many *** this paper,we propose a recurrent attention and interaction(RAI)model to predict pedestrian *** RAI model consists of a temporal attention module,spatial pooling module,and randomness modeling *** temporal attention module is proposed to assign different weights to the input sequence of a target,and reduce the speed deviation of different *** spatial pooling module is proposed to model not only the social information of neighbors in historical frames,but also the intention of neighbors in the current *** randomness modeling module is proposed to model the uncertainty and diversity of trajectories by introducing random *** conduct extensive experiments on several public *** results demonstrate that our method outperforms many that are state-ofthe-art.

关键词： Deep learning long short-term memory(LSTM) recurrent attention and interaction(RAI)model trajectory prediction

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Scene text detection using graph model built upon maximally stable extremal regions

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PATTERN RECOGNITION LETTERS 2013年第2期34卷 107-116页

作者： Shi, Cunzhao Wang, Chunheng Xiao, Baihua Zhang, Yang Gao, Song Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100190 Peoples R China

Scene text detection could be formulated as a bi-label (text and non-text regions) segmentation problem. However, due to the high degree of intraclass variation of scene characters as well as the limited number of training samples, single information source or classifier is not enough to segment text from non-text background. Thus, in this paper, we propose a novel scene text detection approach using graph model built upon Maximally Stable Extremal Regions (MSERs) to incorporate various information sources into one framework. Concretely, after detecting MSERs in the original image, an irregular graph whose nodes are MSERs, is constructed to label MSERs as text regions or non-text ones. Carefully designed features contribute to the unary potential to assess the individual penalties for labeling a MSER node as text or non-text, and color and geometric features are used to define the pairwise potential to punish the likely discontinuities. By minimizing the cost function via graph cut algorithm, different information carried by the cost function could be optimally balanced to get the final MSERs labeling result. The proposed method is naturally context-relevant and scale-insensitive. Experimental results on the ICDAR 2011 competition dataset show that the proposed approach outperforms state-of-the-art methods both in recall and precision. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Scene text detection MSER Graph model Cost function Graph cut

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Generalized Policy Iteration Adaptive Dynamic Programming for Discrete-Time Nonlinear systems

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IEEE TRANSACTIONS ON systems MAN CYBERNETICS-systems 2015年第12期45卷 1577-1591页

作者： Liu, Derong Wei, Qinglai Yan, Pengfei Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

This paper is concerned with a novel generalized policy iteration algorithm for solving optimal control problems for discrete-time nonlinear systems. The idea is to use an iterative adaptive dynamic programming algorithm to obtain iterative control laws which make the iterative value functions converge to the optimum. Initialized by an admissible control law, it is shown that the iterative value functions are monotonically nonincreasing and converge to the optimal solution of Hamilton-Jacobi-Bellman equation, under the assumption that a perfect function approximation is employed. The admissibility property is analyzed, which shows that any of the iterative control laws can stabilize the nonlinear system. Neural networks are utilized to implement the generalized policy iteration algorithm, by approximating the iterative value function and computing the iterative control law, respectively, to achieve approximate optimal control. Finally, numerical examples are presented to verify the effectiveness of the present generalized policy iteration algorithm.

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

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Dictionary-based light field acquisition using sparse camera array

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OPTICS EXPRESS 2014年第20期22卷 24081-24095页

作者： Cao, Xuan Geng, Zheng Li, Tuotuo Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

We propose a dictionary-based dense light field acquisition technique. This technique captures light field successfully from a sparse camera array with no mask or any other optical modifications on cameras. Light rays in wider field are captured by our system to achieve larger disparity and higher angular resolution. We also accelerate the reconstruction of light field significantly by a local sliding window which applies median filter only in disaster areas and acquire satisfactory quality. In our experiments, light field with 7x7 views at resolution of 384x512 is restored from 5 cameras with PSNR of 33.0192dB with a computing time of 1.85 hours on a consumer-grade desktop computer. (C) 2014 Optical Society of America

关键词： Image processing Light fields Nonlinear spatial filtering Plenoptic imaging Reconstruction algorithms Spatial resolution

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An iterative ∈-optimal control scheme for a class of discrete-time nonlinear systems with unfixed initial state

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NEURAL NETWORKS 2012年 32卷 236-244页

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

In this paper, a finite horizon iterative adaptive dynamic programming (ADP) algorithm is proposed to solve the optimal control problem for a class of discrete-time nonlinear systems with unfixed initial state. A new is an element of-optimal control algorithm based on the iterative ADP approach is proposed that makes the performance index function iteratively converge to the greatest lower bound of all performance indices within an error is an element of in finite time. The convergence analysis of the proposed ADP algorithm in terms of performance index function and control policy is conducted. The optimal number of control steps can also be obtained by the proposed is an element of-optimal control algorithm for the unfixed initial state. Neural networks are used to approximate the performance index function, and compute the optimal control policy, respectively, for facilitating the implementation of the is an element of-optimal control algorithm. Finally, a simulation example is given to show the effectiveness of the proposed method. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Adaptive dynamic programming Approximate dynamic programming is an element of-optimal control Finite horizon Neural networks

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Parallel Intelligence: Belief and Prescription for Edge Emergence and Cloud Convergence in CPSS

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systems 2020年第5期7卷 1105-1110页

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

Welcome to the new issue of IEEE Transactions on Computational Social systems (TCSS). First of all, on behalf of the Board of Governors and Prof. Enrique Herrera Viedma, Vice President for Publication, of the IEEE SMCS, I would like to announce and introduce the new Editor-in-Chief of the IEEE TCSS, Prof. Bin Hu, our Associate Editor since 2017 and a member of BoG since 2018. Currently, Bin Hu is the Director of the Gansu Provincial key laboratory of Wearable Computing, Lanzhou University, Lanzhou, China, and an Adjunct Professor with the Computing Department, The Open University, Milton keynes, U.K. He is the Chair of the IEEE SMC Technical Committee on Computational Psychophysiology at IEEE SMC and the Vice- Chair of the China Committee of the International Society for Social Neuroscience. He is also serving as an Associate Editor for IEEE Transaction on Affective Computing. I am sure that IEEE TCSS will march to a new level of excellence under Prof. Bin Hu’s leadership. Congratulations to him and TCSS for the beginning of a new chapter!

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Approximate optimal solution of the DTHJB equation for a class of nonlinear affine systems with unknown dead-zone constraints

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SOFT COMPUTING 2014年第2期18卷 349-357页

作者： Zhang, Dehua Liu, Derong Wang, Ding Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

In this paper, an optimal control scheme of a class of unknown discrete-time nonlinear systems with dead-zone control constraints is developed using adaptive dynamic programming (ADP). First, the discrete-time Hamilton-Jacobi-Bellman (DTHJB) equation is derived. Then, an improved iterative ADP algorithm is constructed which can solve the DTHJB equation approximately. Combining with Riemann integral, detailed proofs of existence and uniqueness of the solution are also presented. It is emphasized that this algorithm allows the implementation of optimal control without knowing internal system dynamics. Moreover, the approach removes the requirements of precise parameters of the dead-zone. Finally, simulation studies are given to demonstrate the performance of the present approach using neural networks.

关键词： Nonlinear affine system Dead-zone DTHJB Neural networks Riemann integral

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