The Experts below are selected from a list of 2664 Experts worldwide ranked by ideXlab platform
Derong Liu - One of the best experts on this subject based on the ideXlab platform.
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numerical adaptive learning control scheme for discrete time non linear systems
Iet Control Theory and Applications, 2013Co-Authors: Qinglai Wei, Derong LiuAbstract: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.
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Finite-Approximation-Error-Based Optimal Control Approach for Discrete-Time Nonlinear Systems
IEEE transactions on cybernetics, 2013Co-Authors: Derong Liu, Qinglai WeiAbstract: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. The idea is to use an iterative ADP algorithm to obtain the iterative control law that makes the iterative performance index function reach the optimum. When the iterative control law and the iterative performance index function in each iteration cannot be accurately obtained, the convergence conditions of the iterative ADP algorithm are obtained. When convergence conditions are satisfied, it is shown that the iterative performance index functions can converge to a finite neighborhood of the Greatest Lower Bound of all performance index functions under some mild assumptions. Neural networks are used to approximate the performance index function and compute the optimal control policy, respectively, for facilitating the implementation of the iterative ADP algorithm. Finally, two simulation examples are given to illustrate the performance of the present method.
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2012 special issue an iterative e optimal control scheme for a class of discrete time nonlinear systems with unfixed initial state
Neural Networks, 2012Co-Authors: Qinglai Wei, Derong LiuAbstract: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 @e-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 @e 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 @e-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 @e-optimal control algorithm. Finally, a simulation example is given to show the effectiveness of the proposed method.
Qinglai Wei - One of the best experts on this subject based on the ideXlab platform.
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numerical adaptive learning control scheme for discrete time non linear systems
Iet Control Theory and Applications, 2013Co-Authors: Qinglai Wei, Derong LiuAbstract: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.
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Finite-Approximation-Error-Based Optimal Control Approach for Discrete-Time Nonlinear Systems
IEEE transactions on cybernetics, 2013Co-Authors: Derong Liu, Qinglai WeiAbstract: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. The idea is to use an iterative ADP algorithm to obtain the iterative control law that makes the iterative performance index function reach the optimum. When the iterative control law and the iterative performance index function in each iteration cannot be accurately obtained, the convergence conditions of the iterative ADP algorithm are obtained. When convergence conditions are satisfied, it is shown that the iterative performance index functions can converge to a finite neighborhood of the Greatest Lower Bound of all performance index functions under some mild assumptions. Neural networks are used to approximate the performance index function and compute the optimal control policy, respectively, for facilitating the implementation of the iterative ADP algorithm. Finally, two simulation examples are given to illustrate the performance of the present method.
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2012 special issue an iterative e optimal control scheme for a class of discrete time nonlinear systems with unfixed initial state
Neural Networks, 2012Co-Authors: Qinglai Wei, Derong LiuAbstract: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 @e-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 @e 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 @e-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 @e-optimal control algorithm. Finally, a simulation example is given to show the effectiveness of the proposed method.
Feiyue Wang - One of the best experts on this subject based on the ideXlab platform.
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adaptive dynamic programming for finite horizon optimal control of discrete time nonlinear systems with varepsilon error Bound
IEEE Transactions on Neural Networks, 2011Co-Authors: Feiyue WangAbstract:In this paper, we study the finite-horizon optimal control problem for discrete-time nonlinear systems using the adaptive dynamic programming (ADP) approach. The idea is to use an iterative ADP algorithm to obtain the optimal control law which makes the performance index function close to the Greatest Lower Bound of all performance indices within an -error Bound. The optimal number of control steps can also be obtained by the proposed ADP algorithms. A convergence analysis of the proposed ADP algorithms in terms of performance index function and control policy is made. In order to facilitate the implementation of the iterative ADP algorithms, neural networks are used for approximating the performance index function, computing the optimal control policy, and modeling the nonlinear system. Finally, two simulation examples are employed to illustrate the applicability of the proposed method.
Ping Wang - One of the best experts on this subject based on the ideXlab platform.
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Encoding multiple inheritance hierarchies for lattice operations
Data & Knowledge Engineering, 2004Co-Authors: M F Van Bommel, Ping WangAbstract:Incremental updates to multiple inheritance hierarchies are becoming more prevalent with the increasing number of persistent applications supporting complex objects. Efficient computation of the lattice operations Greatest Lower Bound (GLB), least upper Bound (LUB), and subsumption is critical. Techniques for the compact encoding of a hierarchy are required that support the operations. One method is to plunge the given ordering into a Boolean lattice of binary words, and perform lattic operations via Boolean operators. An overview of the approach is given and several methods are examined and compared. A new method is proposed, based on the top-down encoding of Caseau but without the lattice completion requirement, which permits incremental updates to the hierarchy to add nodes at the leaves. The algorithm requires polynomial time and space for encoding, and supports efficient lattice computations in applications where the classes of objects are stored as codes. Experimental results illustrate its effectiveness, and an analysis is provided on the effect of the order of insertion on the encoding.
Wei Li - One of the best experts on this subject based on the ideXlab platform.
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the constrained rayleigh quotient with a general orthogonality constraint and an eigen balanced laplacian matrix the Greatest Lower Bound and applications in cooperative control problems
IEEE Transactions on Automatic Control, 2018Co-Authors: Wei LiAbstract:Mathematically, the Rayleigh quotient is defined as the quadratic function of a symmetric matrix and a nonzero (usually unconstrained) variable vector. In this paper, we consider the constrained Rayleigh quotient, in which the variable vector has the orthogonality constraint, i.e., it is constrained to be orthogonal to a nonzero vector, this nonzero vector is called the orthogonality-constraint vector (or abbreviated as the OC-vector) for the variable vector. The matrix for the Rayleigh quotient is an eigen-balanced (EB) Laplacian matrix. A tighter Lower Bound of the constrained Rayleigh quotient has many implications in mathematics as well as in cooperative control problems. The main contributions in this paper are as follows: First, we provide the Greatest Lower Bound (or the infimum) of the constrained Rayleigh quotient with respect to a general OC-vector and the EB Laplacian matrix, whose results are novel and better than the existing results. Then, we interpret the physical meaning of our results in an insightful geometric form and characterize the properties of the results. Finally, as an example to illustrate the merit of the tighter Lower Bound of the constrained Rayleigh quotient, we consider the scale of the agents driving by a fundamental consensus protocol, and show that the convergence rate of the agents’ scale is characterized by the Greatest Lower Bound of the constrained Rayleigh quotient.
