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${rm H}_{infty}$ Output Tracking Control of Discrete-Time Nonlinear Systems via Standard Neural Network Models

机译: $ {rm H} _ {infty} $ 离散非线性系统通过标准神经网络进行输出跟踪控制楷模

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摘要

This brief proposes an output tracking control for a class of discrete-time nonlinear systems with disturbances. A standard neural network model is used to represent discrete-time nonlinear systems whose nonlinearity satisfies the sector conditions. ${rm H}_{infty}$ control performance for the closed-loop system including the standard neural network model, the reference model, and state feedback controller is analyzed using Lyapunov–Krasovskii stability theorem and linear matrix inequality (LMI) approach. The ${rm H}_{infty}$ controller, of which the parameters are obtained by solving LMIs, guarantees that the output of the closed-loop system closely tracks the output of a given reference model well, and reduces the influence of disturbances on the tracking error. Three numerical examples are provided to show the effectiveness of the proposed ${rm H}_{infty}$ output tracking design approach.
机译:该摘要为一类具有干扰的离散时间非线性系统提出了输出跟踪控制。使用标准神经网络模型来表示离散时间非线性系统,该系统的非线性度满足扇区条件。 $ {rm H} _ {infty} $ 包括标准神经网络的闭环系统的控制性能使用Lyapunov–Krasovskii稳定性定理和线性矩阵不等式(LMI)方法分析了模型,参考模型和状态反馈控制器。 $ {rm H} _ {infty} $ 控制器,其参数是通过求解LMI获得的,确保闭环系统的输出很好地跟踪给定参考模型的输出,并减少干扰对跟踪误差的影响。提供了三个数值示例,以显示建议的 $ {rm H} _ {infty} $ 输出的有效性跟踪设计方法。

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