This paper is concerned with stability analysis and H ∞ decentralized control of discrete-time nonlinear large-scale systems via fuzzy control approach. Each of them consists J interconnected discrete-time nonlinear...
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This paper is concerned with stability analysis and H ∞ decentralized control of discrete-time nonlinear large-scale systems via fuzzy control approach. Each of them consists J interconnected discrete-time nonlinear subsystems. First, the discrete-time nonlinear large-scale system is represented by an equivalent Takagi-Sugeno discrete-time fuzzy large scale model. Next, based on this fuzzy large-scale model, a sufficient condition on stability of the discrete-time nonlinear large-scale system is proposed. Furthermore, H ∞ disturbance attenuation performance is analyzed and a state feedback decentralized fuzzy control scheme is developed to override the external disturbances such that the H ∞ disturbance attenuation performance is achieved and the stability of the discrete-time nonlinear large-scale systems is also guaranteed. All these results are characterized in terms of a linear matrix inequalities (LMIs), which can be solved efficiently in practice by convex programming technique. The design method is illustrated by application to the problem of balancing double-inverted pendulums system.
One important issue in the motion planning of kinematic redundant manipulators is the online obstacle-avoidance. For such purposes, we proposed and unified the scheme formulation based on general quadratic-programs (Q...
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One important issue in the motion planning of kinematic redundant manipulators is the online obstacle-avoidance. For such purposes, we proposed and unified the scheme formulation based on general quadratic-programs (QP), which incorporates physical constraints such as joint physical limits and collision-avoidance inequality. In this paper, a simplified primal-dual neural network based on linear variationalinequalities (LVI) is presented for the real-time solution of such a collision-free inverse-kinematic planning scheme. The neural network solves the strictly-convex QP in an inverse-free manner, in addition to the simple piecewise-linear dynamics and global exponential convergence to optimal solutions. Further computer-simulations based on PA10 redundant robot manipulator substantiate the efficacy of the scheme formulation and its neural-network solver on window-shaped obstacle avoidance.
In this paper, we introduce and study a relaxed extragradient method for finding solutions of a general system of variationalinequalities with inverse-strongly monotone mappings in a real Hilbert space. First, this s...
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In this paper, we introduce and study a relaxed extragradient method for finding solutions of a general system of variationalinequalities with inverse-strongly monotone mappings in a real Hilbert space. First, this system of variationalinequalities is proven to be equivalent to a fixed point problem of nonexpansive mapping. Second, by using the demi-closedness principle for nonexpansive mappings, we prove that under quite mild conditions the iterative sequence defined by the relaxed extragradient method converges strongly to a solution of this system of variationalinequalities. In addition, utilizing this result, we provide some applications of the considered problem not just giving a pure extension of existing mathematical problems.
Four step (sequential) procedures are traditionally used in forecasting travel on an urban transportation network. A typical transportation network consists of different trip purposes, user classes, and transportation...
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ISBN:
(纸本)9789889884734
Four step (sequential) procedures are traditionally used in forecasting travel on an urban transportation network. A typical transportation network consists of different trip purposes, user classes, and transportation modes. Due to the inconsistency results from the four step procedures, some research works proposed to combine these four step procedures. The resulting transportation model becomes a quite general supply-demand equilibrium problem which is a class of variationalinequalities with a small number of asymmetric functions (also called combined model). Hence, we propose that Dantzig-Wolfe decomposition method should be used to divide the model into a small-scale equilibrium problem (variationalinequalities) with the asymmetric functions and a large-scale symmetric transportation model (a nonlinearprogramming) with all of the details of network structure and trip demand.
This paper describes a modification of FETI-DP for the corner nodes used on the contact interface for both coercive and semicoercive variationalinequalities through the additional condition that preserves the nonpene...
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In this paper, multiplicative and additive generalized Schwarz algorithms for solving obstacle problems with elliptic operators are developed and analyzed. Compared with the classical Schwarz algorithms, in which the ...
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In this paper, multiplicative and additive generalized Schwarz algorithms for solving obstacle problems with elliptic operators are developed and analyzed. Compared with the classical Schwarz algorithms, in which the subproblems are coupled by the Dirichlet boundary conditions, the generalized Schwarz algorithms use Robin conditions with parameters as the transmission conditions on the interface boundaries. As a result, the convergence rate can be speeded up by choosing Robin parameters properly. Convergence of the algorithms is established.
A recurrent neural network is proposed to deal with the nonlinearvariationalinequalities with linear equality and nonlinear inequality constraints. By exploiting the equality constraints, the original variational in...
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ISBN:
(纸本)9781424418206
A recurrent neural network is proposed to deal with the nonlinearvariationalinequalities with linear equality and nonlinear inequality constraints. By exploiting the equality constraints, the original variational inequality problem can be transformed into a simplified one with only inequality constraints. Therefore, by solving this simplified problem, the neural network architecture complexity is reduced dramatically. In addition, the proposed neural network can also be applied to the constrained optimization problems, and it is proved that the convex condition on the objective function of the optimization problem can be relaxed. Finally, the satisfactory performance of the proposed approach is demonstrated by simulation examples.
In this paper, which is an extension of [4], we first show the existence of solutions to a class of Min Sup problems with linked constraints, which satisfy a certain property. Then, we apply our result to a class of w...
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In this paper, which is an extension of [4], we first show the existence of solutions to a class of Min Sup problems with linked constraints, which satisfy a certain property. Then, we apply our result to a class of weak nonlinear bilevel problems. Furthermore, for such a class of bilevel problems, we give a relationship with appropriate d.c. problems concerning the existence of solutions.
In this paper, we study Levitin-Polyak type well-posedness for generalized vector variational inequality problems with abstract and functional constraints. Various criteria and characterizations for these types of wel...
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In this paper, we study Levitin-Polyak type well-posedness for generalized vector variational inequality problems with abstract and functional constraints. Various criteria and characterizations for these types of well-posednesses are given.
In this paper, we first give a vector-valued version of Brezis and Browder's scalar general principle. We then apply the vector-valued general principle to study a vector Ekeland's variational principle in a F...
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In this paper, we first give a vector-valued version of Brezis and Browder's scalar general principle. We then apply the vector-valued general principle to study a vector Ekeland's variational principle in a F-type topological space, which unifies and improves the corresponding vector-valued Ekeland's variational results in complete metric space.
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