用于mimo系统特征结构在线分配的单层感知器神经网络

Anais do 14º Simpósio Brasileiro de Automação Inteligente Pub Date : 1900-01-01 DOI:10.17648/sbai-2019-111153

B. Coelho, Daniel Trovao Simoes, J. V. F. Neto, Patricia H. Moraes Rego

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引用次数: 0

摘要

为了改善MIMO系统的稳定性能和响应形式，模态的控制与设计规范的实施有关。提出了一种多变量控制系统在线特征结构赋值的仿生方法。该方法基于人工神经网络来执行控制律，并通过状态反馈来训练控制器增益。针对满足所有设计规范的状态空间控制器增益矩阵的在线整定问题，提出了一种计算最符合MIMO动态系统给定工作范围的增益矩阵的方法。在一个具有两个输入电压和两个可控电压电平的四阶RLC电路的数学模型中对该方案进行了评估。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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SINGLE LAYER PERCEPTRONS NEURAL NETWORKS FOR ONLINE EIGENSTRUCTURE ASSIGNMENT IN MIMO SYSTEMS

: To improve the stability performance and the response form of MIMO systems, the control of the modes is relevant to impose the design speciﬁcations. A bioinspired method for online eigenstructure assignment for the design of multivariable control systems is presented in this paper. The method is based on artiﬁcial neural networks for the execution of the control law and for training the controller gains by state feedback. Aiming at online tuning of state space controllers, the gain matrix that satisﬁes all the design speciﬁcations, a proposal is presented to compute the gain matrices that best meet a given operating range of MIMO dynamic systems. The proposal is evaluated in a mathematical model that represents a fourth order RLC circuit with two input voltages and two controllable voltage levels.

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Anais do 14º Simpósio Brasileiro de Automação Inteligente

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