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

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING systems 2014年第3期25卷 621-634页

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

This paper is concerned with a new discrete-time policy iteration adaptive dynamic programming (ADP) method for solving the infinite horizon optimal control problem of nonlinear systems. The idea is to use an iterative ADP technique to obtain the iterative control law, which optimizes the iterative performance index function. The main contribution of this paper is to analyze the convergence and stability properties of policy iteration method for discrete-time nonlinear systems for the first time. It shows that the iterative performance index function is nonincreasingly convergent to the optimal solution of the Hamilton-Jacobi-Bellman equation. It is also proven that any of the iterative control laws can stabilize the nonlinear systems. Neural networks are used to approximate the performance index function and compute the optimal control law, respectively, for facilitating the implementation of the iterative ADP algorithm, where the convergence of the weight matrices is analyzed. Finally, the numerical results and analysis are presented to illustrate the performance of the developed method.

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

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Direct adaptive control for a class of discrete-time unknown nonaffine nonlinear systems using neural networks

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR control 2015年第12期25卷 1844-1861页

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

In this paper, a direct adaptive state-feedback control approach is developed for a class of nonlinear systems in discrete-time (DT) domain. We study MIMO unknown nonaffine nonlinear DT systems and employ a two-layer NN to design the controller. By using the presented method, the NN approximation is able to cancel the nonlinearity of the unknown DT plant. Meanwhile, pretraining is not required, and the weights of NNs used in adaptive control are directly updated online. Moreover, unlike standard NN adaptive controllers yielding uniform ultimate boundedness results, the tracking error is guaranteed to be uniformly asymptotically stable by utilizing Lyapunov's direct method. Two illustrative examples are provided to demonstrate the effectiveness and the applicability of the theoretical results. Copyright (c) 2014 John Wiley & Sons, Ltd.

关键词： adaptive control discrete-time nonaffine system NN feedback control online learning MIMO Lyapunov method

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A neural-network-based online optimal control approach for nonlinear robust decentralized stabilization

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SOFT COMPUTING 2016年第2期20卷 707-716页

作者： Wang, Ding Liu, Derong Li, Hongliang Ma, Hongwen Li, Chao Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

In this paper, the robust decentralized stabilization of continuous-time uncertain nonlinear systems with multi control stations is developed using a neural network based online optimal control approach. The novelty lies in that the well-known adaptive dynamic programming method is extended to deal with the nonlinear feedback control problem under uncertain and large-scale environment. Through introducing an appropriate bounded function and defining a modified cost function, it can be observed that the decentralized optimal controller of the nominal system can achieve robust decentralized stabilization of original uncertain system. Then, a critic neural network is constructed for solving the modified Hamilton-Jacobi-Bellman equation corresponding to the nominal system in an online fashion. The weights of the critic network are tuned based on the standard steepest descent algorithm with an additional term provided to guarantee the boundedness of system states. The stability analysis of the closed-loop system is carried out via the Lyapunov approach. At last, two simulation examples are given to verify the effectiveness of the present control approach.

关键词： Adaptive dynamic programming Approximate dynamic programming Neural networks Online optimal control Robust decentralized stabilization Uncertain nonlinear systems

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A Novel Iterative θ-Adaptive Dynamic Programming for Discrete-Time Nonlinear systems

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2014年第4期11卷 1176-1190页

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

This paper is concerned with a new iterative theta-adaptive dynamic programming (ADP) technique to solve optimal control problems of infinite horizon discrete-time nonlinear systems. The idea is to use an iterative ADP algorithm to obtain the iterative control law which optimizes the iterative performance index function. In the present iterative theta-ADP algorithm, the condition of initial admissible control in policy iteration algorithm is avoided. It is proved that all the iterative controls obtained in the iterative theta-ADP algorithm can stabilize the nonlinear system which means that the iterative theta-ADP algorithm is feasible for implementations both online and offline. Convergence analysis of the performance index function is presented to guarantee that the iterative performance index function will converge to the optimum monotonically. Neural networks are used to approximate the performance index function and compute the optimal control policy, respectively, for facilitating the implementation of the iterative theta-ADP algorithm. Finally, two simulation examples are given to illustrate the performance of the established method.

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

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COMPUTER VISION-BASED DETECTION AND state RECOGNITION FOR DISCONNECTING SWITCH IN SUBSTATION AUTOMATION

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INTERNATIONAL JOURNAL OF ROBOTICS & AUTOMATION 2017年第1期32卷 1-12页

作者： Chen, Hongkai Zhao, Xiaoguang Tan, Min Sun, Shiying Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

state recognition in disconnecting switches is important during substation automation. Here, an effective computer vision-based automatic detection and state recognition method for disconnecting switches is proposed. Taking advantage of some important prior knowledge about a disconnecting switch, the method is designed using two important features of the fixed-contact facet of such disconnecting switches. First, the Histograms of Oriented Gradients (HOG) of the fixed-contact are used to design a Linear Discriminant Analysis (LDA) target detector to position the disconnecting switches and distinguish their loci against a usual cluttered background. Then a discriminative Norm Gradient Field (NGF) feature is used to train the Support Vector Machine (SVM) state classifier to discriminate disconnecting switch states. Finally, experimental results, compared with other methods, demonstrate that the proposed method is effective and achieves a low miss rate while delivering high performance in both precision and recall rate. In addition, the adopted approach is efficient and has the potential to work in practical substation automation scenarios.

关键词： Computer vision substation automation disconnecting switch state recognition histograms of oriented gradients norm gradient field

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End-to-end scene text recognition using tree-structured models

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PATTERN RECOGNITION 2014年第9期47卷 2853-2866页

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

Detecting and recognizing text in natural images are quite challenging and have received much attention from the computer vision community in recent years. In this paper, we propose a robust end-to-end scene text recognition method, which utilizes tree-structured character models and normalized pictorial structured word models. For each category of characters, we build a part-based tree-structured model (TSM) so as to make use of the character-specific structure information as well as the local appearance information. The TSM could detect each part of the character and recognize the unique structure as well, seamlessly combining character detection and recognition together. As the TSMs could accurately detect characters from complex background, for text localization, we apply TSMs for all the characters on the coarse text detection regions to eliminate the false positives and search the possible missing characters as well. While for word recognition, we propose a normalized pictorial structure (PS) framework to deal with the bias caused by words of different lengths. Experimental results on a range of challenging public datasets (ICDAR 2003, ICDAR 2011, SVT) demonstrate that the proposed method outperforms state-of-the-art methods both for text localization and word recognition. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： End-to-end Scene text recognition Part-based tree-structured models (TSMs) Normalized pictorial structure

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Reinforcement learning for adaptive optimal control of unknown continuous-time nonlinear systems with input constraints

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INTERNATIONAL JOURNAL OF control 2014年第3期87卷 553-566页

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

In this paper, an adaptive reinforcement learning-based solution is developed for the infinite-horizon optimal control problem of constrained-input continuous-time nonlinear systems in the presence of nonlinearities with unknown structures. Two different types of neural networks (NNs) are employed to approximate the Hamilton-Jacobi-Bellman equation. That is, an recurrent NN is constructed to identify the unknown dynamical system, and two feedforward NNs are used as the actor and the critic to approximate the optimal control and the optimal cost, respectively. Based on this framework, the action NN and the critic NN are tuned simultaneously, without the requirement for the knowledge of system drift dynamics. Moreover, by using Lyapunov's direct method, the weights of the action NN and the critic NN are guaranteed to be uniformly ultimately bounded, while keeping the closed-loop system stable. To demonstrate the effectiveness of the present approach, simulation results are illustrated.

关键词： adaptive control input constraints neural networks optimal control reinforcement learning

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A practical and intuitive calibration technique for cross-line structured light

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OPTIK 2016年第20期127卷 9582-9602页

作者： Kiddee, Prasarn Fang, Zaojun Tan, Min Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Structured light plays an important role in visual sensing system of various applications. At present, many types of structured light are available throughout the market. Among different types of structured light, cross-line structured light (CLSL) has been also employed in some visual sensing systems. However, it seems that the existing calibration methods are not practical and simple for users. In this paper, a calibration technique is proposed as an alternative for those who are seeking for a practical and simple calibration. A regular chessboard is modified in order to have intersections between light stripes and additional lines. Two views of the CLSL captured during camera calibration process give sufficient information for estimating the parameters of light planes. Simple image processing techniques are applied to the images yielding 3D coordinates of points on the light planes. Based on three non-collinear 3D points, the parameters of the light planes are computed. The experimental results prove that the accuracy of the calibration is acceptable and suitable for moderate precise measurement systems. (C) 2016 Elsevier GmbH. All rights reserved.

关键词： Practical and intuitive calibration Cross-line structured light Three non-collinear 3D points Hand-eye visual sensing system

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Optimal control for discrete-time systems with actuator saturation

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OPTIMAL control APPLICATIONS & METHODS 2017年第6期38卷 1071-1080页

作者： Lin, Qiao Wei, Qinglai Zhao, Bo Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

In this study, we use generalized policy iteration approximate dynamic programming (ADP) algorithm to design an optimal controller for a class of discrete-time systems with actuator saturation. A integral function is proposed to manage the saturation nonlinearity in actuators and then the generalized policy iteration ADP algorithm is developed to deal with the optimal control problem. Compared with other algorithm, the developed ADP algorithm includes 2 iteration procedures. In the present control scheme, 2 neural networks are introduced to approximate the control law and performance index function. Furthermore, numerical simulations illustrate the convergence and feasibility of the developed method.

关键词： approximate dynamic programming discrete time generalized policy iteration optimal control saturating actuators

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Multi-View Feature Selection for PolSAR Image Classification via l2,1 Sparsity Regularization and Manifold Regularization

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2021年 30卷 8607-8618页

作者： Huang, Xiayuan Nie, Xiangli Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Feature is a crucial element of polarimetric synthetic aperture radar (PolSAR) image classification. Multiple types of Features, such as polarimetric features (PF) generated from the PolSAR data and various polarimetric target decompositions, texture features (TF) of the Pauli color-coded PolSAR images are used as features for PolSAR image classification. The obtained PF and TF often form the high-dimensional data, which leads to high computational complexity. Moreover, some features are irrelative and do nothing to improve the classification performance. Therefore, it is fairly indispensable to select a subset of useful features for PolSAR image classification. This paper proposes a multi-view feature selection method for PolSAR image classification. Firstly, two types of features, PF and TF are generated separately. Then the optimization model is built to pursue the feature selection matrices. Specifically, in order to maintain the consistency of different types of features, we search for the common representation of multiple types of features in the optimization problem. The l(2,1) norm sparsity regularization is imposed on the feature selection matrices to achieve feature selection. In addition, the manifold regularization on the common representation is utilized to preserve the structure information of the data. The effectiveness of the proposed method is evaluated on three real PolSAR data sets. Experimental results demonstrate the superiority of the proposed method.

关键词： Multi-view feature selection PolSAR image classification l(2,1) norm manifold regularization

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