Displaying similar documents to “Neural network optimal control for nonlinear system based on zero-sum differential game”

Robust neural network control of robotic manipulators via switching strategy

Lei Yu, Shumin Fei, Jun Huang, Yongmin Li, Gang Yang, Lining Sun (2015)

Kybernetika

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In this paper, a robust neural network control scheme for the switching dynamical model of the robotic manipulators has been addressed. Radial basis function (RBF) neural networks are employed to approximate unknown functions of robotic manipulators and a compensation controller is designed to enhance system robustness. The weight update law of the robotic manipulator is based on switched multiple Lyapunov function method and the periodically switching law which is suitable for practical...

Neural network-based MRAC control of dynamic nonlinear systems

Ghania Debbache, Abdelhak Bennia, Noureddine Golea (2006)

International Journal of Applied Mathematics and Computer Science

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This paper presents direct model reference adaptive control for a class of nonlinear systems with unknown nonlinearities. The model following conditions are assured by using adaptive neural networks as the nonlinear state feedback controller. Both full state information and observer-based schemes are investigated. All the signals in the closed loop are guaranteed to be bounded and the system state is proven to converge to a small neighborhood of the reference model state. It is also...

Neural networks learning as a multiobjective optimal control problem.

Maciej Krawczak (1997)

Mathware and Soft Computing

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The supervised learning process of multilayer feedforward neural networks can be considered as a class of multi-objective, multi-stage optimal control problem. An iterative parametric minimax method is proposed in which the original optimization problem is embedded into a weighted minimax formulation. The resulting auxiliary parametric optimization problems at the lower level have simple structures that are readily tackled by efficient solution methods, such as the dynamic programming...

Design of a multivariable neural controller for control of a nonlinear MIMO plant

Stanisław Bańka, Paweł Dworak, Krzysztof Jaroszewski (2014)

International Journal of Applied Mathematics and Computer Science

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The paper presents the training problem of a set of neural nets to obtain a (gain-scheduling, adaptive) multivariable neural controller for control of a nonlinear MIMO dynamic process represented by a mathematical model of Low-Frequency (LF) motions of a drillship over the drilling point at the sea bottom. The designed neural controller contains a set of neural nets that determine values of its parameters chosen on the basis of two measured auxiliary signals. These are the ship's current...

Sliding-mode pinning control of complex networks

Oscar J. Suarez, Carlos J. Vega, Santiago Elvira-Ceja, Edgar N. Sanchez, David I. Rodriguez (2018)

Kybernetika

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In this paper, a novel approach for controlling complex networks is proposed; it applies sliding-mode pinning control for a complex network to achieve trajectory tracking. This control strategy does not require the network to have the same coupling strength on all edges; and for pinned nodes, the ones with the highest degree are selected. The illustrative example is composed of a network of 50 nodes; each node dynamics is a Chen chaotic attractor. Two cases are presented. For the first...