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第35届中国控制会议

作者： Liping Yan Jun Liu Lu Jiang Yuanqing Xia Bo Xiao Yang Liu Mengyin Fu Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of Technology School of Information and Communication Engineering Beijing University of Posts and Telecommunications

ISBN: (纸本)9781509009107

In the field of target tracking and navigation,multi-sensor data fusion has been widely *** of the data fusion algorithms are built on the premise that the sensor observation information is ***,in practical problems,due to the limitation of communication and sensor fault,etc.,data missing or unreliable measurements will happen *** addition,at present a lot of research is aimed at the situation where measurement noise between various sensors is not relevant,and process noise and measurement noise is *** correlation is more *** this paper,a multi-rate multi-sensor data fusion state estimation algorithm with unreliable observations under correlated noises is presented.A numerical example is given to show the feasibility and effectiveness of the presented algorithm.

关键词： state estimation sequential fusion correlated noise unreliable measurements

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Data-Based Self-Learning Optimal control for Continuous-Time Unknown Nonlinear systems With Disturbance

Data-Based Self-Learning Optimal Control for Continuous-Time...

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第28届中国控制与决策会议

作者： Qinglai Wei Derong Liu Ruizhuo Song Pengfei Yan The State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences School of Automation and Electrical Engineering University of Science and Technology Beijing

ISBN: (纸本)9781467397155

In this paper,a new data-based self-learning control scheme is developed to solve infinite horizon optimal control problems for continuous-time nonlinear *** developed optimal control scheme can be implement without knowing the mathematical model of the *** to the input-output data of the nonlinear systems,a recurrent neural network(RNN) is employed to reconstruct the dynamics of the nonlinear *** to the RNN model of the system,a new two-person zero-sum adaptive dynamic programming(ADP) algorithm is developed to obtain the optimal control,where the reconstruction error and the system disturbance are considered the control input of the ***-layer neural networks are used to construct the critic and action networks,which are presented to approximate the performance index function and the control law,***,simulation results will show the effectiveness of the developed data-based ADP methods.

关键词： Adaptive critic designs Adaptive dynamic programming Approximate dynamic programming Neurodynamic programming Recurrent neural network Data-based control Optimal control

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Robust Scene Matching Method Based on Sparse Representation

Robust Scene Matching Method Based on Sparse Representation

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第35届中国控制会议

作者： Sai Yang Bo Xiao Liping Yan Yuanqing Xia Yang Liu Mengyin Fu Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of Technology School of Information and Communication Engineering Beijing University of Posts and Telecommunications

ISBN: (纸本)9781509009107

This article presents two efficient scene matching methods based on sparse representation,which are robust to noise and *** process of searching for the correct matching position in the reference image can be considered as finding a certain atom in a dictionary to represent the vectorized sensed *** atoms that form the dictionary are vectorized blocks which represent all the possible matching *** also use two steps matching to speed up the running time,and further improve the performance to some *** demonstrate that our scene matching methods perform better than traditional methods due to the inherent robustness to corruption of sparse representation.

关键词： scene matching sparse representation visual navigation

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An Adaptive Dynamic Programming Based Method for Optimization of Electricity Consumption in Office Buildings

An Adaptive Dynamic Programming Based Method for Optimizatio...

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International Joint Conference on Neural Networks

作者： Guang Shi Qinglai Wei Derong Liu The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences School of Automation and Electrical Engineering University of Science and Technology Beijing

ISBN: (纸本)9781509006212

In this paper, an adaptive dynamic programming (ADP) based method is developed to optimize electricity consumption of rooms in office buildings through optimal battery management. Rooms in office buildings are generally divided into office room, computer room, storage room, meeting room, etc., each of which has different characteristics of electricity consumption, as divided into electricity consumption from sockets, lights and air-conditioners in this paper. The developed method based on ADP is elaborated, and different optimization strategies of electricity consumption in different categories of rooms are proposed in accordance with the developed method. Finally, a detailed case study on an office building is given to show the practical effect of the developed method.

关键词： electricity consumption office buildings Rooms office Air conditioners built structure Buildings Automatic data processing Adenosine Diphosphate dynamic programming

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Off-policy integral reinforcement learning optimal tracking control for continuous-time chaotic systems

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Chinese Physics B 2015年第9期24卷 147-152页

作者：魏庆来宋睿卓孙秋野肖文栋 The State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences School of Automation and Electrical Engineering University of Science and TechnologyBeijing School of Information Science and Engineering Northeastern University

This paper estimates an off-policy integral reinforcement learning（IRL） algorithm to obtain the optimal tracking control of unknown chaotic systems. Off-policy IRL can learn the solution of the HJB equation from the system data generated by an arbitrary control. Moreover, off-policy IRL can be regarded as a direct learning method, which avoids the identification of system dynamics. In this paper, the performance index function is first given based on the system tracking error and control error. For solving the Hamilton–Jacobi–Bellman（HJB） equation, an off-policy IRL algorithm is *** is proven that the iterative control makes the tracking error system asymptotically stable, and the iterative performance index function is convergent. Simulation study demonstrates the effectiveness of the developed tracking control method.

关键词： adaptive dynamic programming approximate dynamic programming chaotic system optimal tracking control

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Extended state observer based control for ground-coupled heat pump system 27

Extended state observer based control for ground-coupled hea...

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27th Chinese control and Decision Conference, CCDC 2015

作者： Yu, Zhang Wang, Junxiao Li, Shihua School of Automation Southeast University Nanjing Jiangsu Province China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education China

ISBN: (纸本)9781479970179

This paper aims to investigate an extended state observer (ESO) based control approach for the ground-coupled heat pump system (GCHP). As GCHP system has strong disturbances, a feasible approach of disturbance rejection should be proposed necessarily. ESO is employed to estimate the disturbance and compensate it to the controller to eliminate effects of the disturbance. By using ESO-based control and comparing with traditional control method, a better control performance is obtained through simulation verification. © 2015 IEEE.

关键词： disturbance rejection ESO GCHP simulation

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Adaptive Dynamic Programming Based Fault Compensation control for Nonlinear systems with Actuator Failures

Adaptive Dynamic Programming Based Fault Compensation Contro...

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International Joint Conference on Neural Networks

作者： Bo Zhao Derong Liu Yuanchun Li Guang Shi State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences School of Automation and Electrical Engineering University of Science and Technology Beijing Department of Control Science and Engineering Changchun University of Technology

ISBN: (纸本)9781509006212

This paper develops a novel fault compensation control scheme based on adaptive dynamic programming for nonlinear systems with actuator failures. The control scheme consists of a policy iteration algorithm and a fault compensation. For fault-free dynamic models, the Hamilton-Jacobi-Bellman equation is solved by policy iteration algorithm via constructing a critic neural network, and then the approximate optimal control policy can be derived directly. On the other hand, the online fault compensation is achieved without the fault detection and isolation mechanism by reconstructing the actuator failure. The closed-loop system is guaranteed to be asymptotically stable based on Lyapunov stability theorem. Two simulation examples are given to demonstrate the effectiveness of the present fault compensation control scheme.

关键词： Iterative algorithms Nonlinear systems compensator control dynamic programming Defect detection faults actuators system failures

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An automatic landmark localization method for 2D and 3D face 8th

An automatic landmark localization method for 2D and 3D face

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8th International Conference on Image and Graphics, ICIG 2015

作者： Liu, Junquan Da, Feipeng Deng, Xing Yu, Yi Zhang, Pu School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China

ISBN: (纸本)9783319219776

We propose an automatically and accurately facial landmark localization algorithm based on Active Shape Model (ASM) and Gabor Wavelets Transformation (GWT), which can be applied to both 2D and 3D facial data. First, ASM is implemented to acquire landmarks’ coarse areas. Then similarity maps are obtained by calculating the similarity between sets of Gabor jets at initial coarse positions and sets of Gabor bunches modeled by its corresponding manually marked landmarks in the training set. The point with the maximum value in each similarity map is extracted as its final facial landmark location. It is showed in our laboratory databases and FRGC v2.0 that the algorithm could achieve accurate localization of facial landmarks with state-of-the-art accuracy and robustness. © Springer International Publishing Switzerland 2015.

关键词： Metadata

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Nonlinear time-varying systems identification based on multi-dimensional taylor network and variable forgetting factor recursive least squares algorithm

Nonlinear time-varying systems identification based on multi...

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1st IFAC Conference on Modelling, Identification and control of Nonlinear systems, MICNON 2015

作者： Zhang, Jiao-Jun Yan, Hong-Sen School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China

A novel method for nonlinear time-varying systems identification based on multi-dimensional Taylor network and variable forgetting factor recursive least squares algorithm is proposed. In this paper, the connection weight coefficients of multi-dimensional Taylor network are regarded as time-varying parameters, which are trained by the variable forgetting factor recursive least squares algorithm, to reflect the input-output change of nonlinear time-varying systems. Simulation results show that the method proposed in this paper is valid.

关键词： Time varying networks

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Iterative Q-learning-based nonlinear optimal tracking control

Iterative Q-learning-based nonlinear optimal tracking contro...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Qinglai Wei Ruizhuo Song Yancai Xu Derong Liu The State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing China School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China

ISBN: (纸本)9781509042418

A new Q-learning algorithm is developed for a class of discrete-time nonlinear systems in this paper to solve the infinite horizon optimal tracking problems. Using system transformations, the optimal tracking problem is transformed to be an optimal regulation problem. Thereafter, for the regulation system, the new Q-learning algorithm is developed in order to obtain the optimal control law. Convergence of the iterative Q functions and the admissibility of the iterative control law are analyzed. In the end, two corresponding simulation examples are presented to illustrate the performance of the newly developed algorithm.

关键词： optimal control convergence discrete time systems iterative learning control nonlinear control systems

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