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

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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Data-Driven Dynamic Modeling for a Swimming Robotic Fish

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2016年第9期63卷 5632-5640页

作者： Yu, Junzhi Yuan, Jun Wu, Zhengxing Tan, Min Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

This paper proposes a data-driven dynamic modeling method for multijoint robotic fish with irregular geometric profiles and numerous heterogeneous hydrodynamic parameters. The method is composed of two main components: dynamic modeling and hydrodynamic parameter identification. In dynamic modeling, fluid forces exerted on the robotic fish are analyzed by the Morrison equation and the strip method. A dynamic model with an explicit formulation is derived, in which all terms involved in the dynamic analysis are converted to the coordinate system attached to the head. Further, the parameter identification technique is integrated into dynamic modeling, which reshapes it with data-driven feature and thereby makes it be competent to model swimming robots with complex geometric profiles and numerous heterogeneous hydrodynamic parameters. Experimental data of the swimming robotic fish are collected to identify the parameters directly. The obtained dynamic model is validated by data captured under extensive motion modes like forward swimming, varying velocity, and turning. Comparisons of simulated and experimental results demonstrate the effectiveness of the method.

关键词： Hydrodynamic analysis modeling and control parameter identification robotic fish

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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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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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Reinforcement-Learning-Based Robust controller Design for Continuous-Time Uncertain Nonlinear systems Subject to Input Constraints

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IEEE TRANSACTIONS ON CYBERNETICS 2015年第7期45卷 1372-1385页

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

The design of stabilizing controller for uncertain nonlinear systems with control constraints is a challenging problem. The constrained-input coupled with the inability to identify accurately the uncertainties motivates the design of stabilizing controller based on reinforcement-learning (RL) methods. In this paper, a novel RL-based robust adaptive control algorithm is developed for a class of continuous-time uncertain nonlinear systems subject to input constraints. The robust control problem is converted to the constrained optimal control problem with appropriately selecting value functions for the nominal system. Distinct from typical action-critic dual networks employed in RL, only one critic neural network (NN) is constructed to derive the approximate optimal control. Meanwhile, unlike initial stabilizing control often indispensable in RL, there is no special requirement imposed on the initial control. By utilizing Lyapunov's direct method, the closed-loop optimal control system and the estimated weights of the critic NN are proved to be uniformly ultimately bounded. In addition, the derived approximate optimal control is verified to guarantee the uncertain nonlinear system to be stable in the sense of uniform ultimate boundedness. Two simulation examples are provided to illustrate the effectiveness and applicability of the present approach.

关键词： Approximate dynamic programming (ADP) neural networks (NNs) neuro-dynamic programming nonlinear systems optimal control reinforcement learning (RL) robust control

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An automated weld seam tracking system for thick plate using cross mark structured light

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2016年第9-12期87卷 3589-3603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

This paper presents a weld seam tracking system using cross mark structured light. The hardware of the proposed system consists of a two degrees of freedom (DOF) welding robot, a camera with cross mark structured light, and two computers. The system has two parts namely visual sensing and motion control. In the visual sensing part, the cross mark of the structured light is utilized to set a region of interest (ROI). In the ROI, an adapted line fitting algorithm is employed to estimate the lines. Then, intersections of the lines are computed and used as the pin points for templates creating. During the matching process, a modified template matching is used to detect the edges of V-groove weld seam. By using this technique, a huge computational cost in image processing can be reduced, and therefore the tracking can be made in real time. The position based visual servoing with proportional-derivative(PD) and velocity feedback controller is designed for seam tracking. The experimental results show that the proposed method performs the real-time tracking efficiently with sufficient accuracy.

关键词： Structured light Self-creating template Modified template matching Seam tracking control

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Neuro-optimal tracking control for a class of discrete-time nonlinear systems via generalized value iteration adaptive dynamic programming approach

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SOFT COMPUTING 2016年第2期20卷 697-706页

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

In this paper, a novel value iteration adaptive dynamic programming (ADP) algorithm, called "generalized value iteration ADP" algorithm, is developed to solve infinite horizon optimal tracking control problems for a class of discrete-time nonlinear systems. The developed generalized value iteration ADP algorithm permits an arbitrary positive semi-definite function to initialize it, which overcomes the disadvantage of traditional value iteration algorithms. Convergence property is developed to guarantee that the iterative performance index function will converge to the optimum. Neural networks are used to approximate the iterative performance index function and compute the iterative control policy, respectively, to implement the iterative ADP algorithm. Finally, a simulation example is given to illustrate the performance of the developed algorithm.

关键词： Adaptive dynamic programming Approximate dynamic programming Adaptive critic designs Optimal control Neural networks Nonlinear systems Reinforcement learning

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Deep sparse representation-based mid-level visual elements discovery in fine-grained classification

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SOFT COMPUTING 2019年第18期23卷 8711-8722页

作者： Lv, Le Zhao, Dongbin Shao, Kun Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

In this paper, we propose a new mid-level visual elements discovery method and apply it to the fine-grained classification. We present the duality between image patches and features extracted by the convolutional winner-take-all autoencoder (CONV-WTA-AE). The sparsity constraints used by CONV-WTA-AE make a group of objects sharing the same feature components. Hence, the image patches could be clustered by their sharing feature components and the feature components can be clustered by their co-occurrence in the image patches. We propose formulating the mid-level visual elements mining as a bipartite graph partitioning problem. The spectral partitioning algorithm is employed to co-cluster image patches and feature components. The CONV-WTA-AE is an unsupervised feature learning method. Hence, it avoids using expensive annotations. Our experiments demonstrate that the spectral partitioning method is very efficient but only the confident instances in a cluster are well discriminated. The similarity metric used by this algorithm is not accurate enough. Hence, we propose training a group of linear support vector machine (SVM) to refine the clustering results. These SVMs will be trained on the initial confident instances and provide a better discriminative similarity. Then we can re-assign instances to each clusters. To avoid overfitting, this process is iterated on many data subsets. We conduct a series of experiments on the MNIST dataset to verify our algorithm. The experimental results show that our method can discover meaningful image patch clusters. In the fine-grained classification task, visual elements are input into an ensemble of convolutional neural networks. The experiments on the CompCars dataset illustrate that our method can achieve the state-of-the-art performance.

关键词： Mid-level visual elements discovery Fine-grained classification Winner-take-all autoencoder Bipartite graph spectral partitioning

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Distributed exponential finite-time coordination of multi-agent systems: containment control and consensus

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INTERNATIONAL JOURNAL OF control 2015年第2期88卷 237-247页

作者： Liu, Huiyang Cheng, Long Tan, Min Hou, Zengguang Wang, Yunpeng Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100190 Peoples R China

In this paper, exponential finite-time coordination problems of multi-agent systems are investigated, including containment control and consensus. The theoretical basis is that a class of nonlinear systems has favourable finite-time convergence characteristic. For the objective of containment control, the proposed protocol ensures that the boundary agents in the same strong component exponentially reach a consensus and the internal agents exponentially converge to the convex hull spanned by the boundary agents in a finite time. For the objective of consensus, a pinning control strategy is designed for a fraction of agents such that all the agents exponentially reach a consensus with the leader in a finite time. The distinguished features of this paper lie in the following two points: (1) a smaller settling time of the Lyapunov function is obtained, which manifests in a faster convergence rate than the traditional one and (2) the weakly connected topology considered in this paper is more general than the ones (a spanning tree, a spanning forest, and so on) in other coordination problems. All the results are illustrated by some simulations.

关键词： consensus containment control exponential finite-time pinning control multi-agent systems

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Online approximate solution of HJI equation for unknown constrained-input nonlinear continuous-time systems

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INFORMATION SCIENCES 2016年 328卷 435-454页

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

This paper is concerned with the approximate solution of Hamilton-Jacobi-Isaacs (HJI) equation for constrained-input nonlinear continuous-time systems with unknown dynamics. We develop a novel online adaptive dynamic programming-based algorithm to learn the solution of the HJI equation. The present algorithm is implemented via an identifier-critic architecture, which consists of two neural networks (NNs): an identifier NN is applied to estimate the unknown system dynamics and a critic NN is constructed to obtain the approximate solution of the HJI equation. An advantage of the proposed architecture is that the identifier NN and the critic NN are tuned simultaneously. With introducing two additional terms, namely, the stabilizing term and the robustifying term to update the critic NN, the initial stabilizing control is no longer required. Meanwhile, the developed critic tuning rule not only ensures convergence of the critic to the optimal saddle point but also guarantees stability of the closed-loop system. Moreover, the uniform ultimate boundedness of the weights of the identifier NN and the critic NN are proved by using Lyapunov's direct method. Finally, to illustrate the effectiveness and applicability of the developed approach, two simulation examples are provided. (C) 2015 Elsevier Inc. All rights reserved.

关键词： Adaptive dynamic programming Hamilton-Jacobi-Isaacs equation Input constraint Neural network Optimal control Reinforcement learning

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