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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Dealing With the Issues Crucially Related to the Functionality and Reliability of NN-Associated Control for Nonlinear Uncertain Systems
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Dealing With the Issues Crucially Related to the Functionality and Reliability of NN-Associated Control for Nonlinear Uncertain Systems

机译：处理与非线性不确定系统的神经网络相关控制的功能和可靠性息息相关的问题

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摘要

The “universal” approximating/learning feature of neural network (NN), widely and extensively used for control design, is contingent upon some critical conditions, either of which, if not satisfied, would render such feature vanished. In this paper, we show that these conditions are literally linked with several fundamental issues that have been overlooked in most existing NN-based control designs, either unconsciously or deliberately. We further propose a collective approach to explicitly address these issues, establishing a strategy enabling the NN unit to be fully functional in the control loop during the entire process of system operation and ensuring the more reliable and more effective NN-associated control performance. This is achieved by incorporating the control with a new structural NN unit, consisting of a group of diversified neurons with self-adjusting subneurons, each being driven/stimulated by input signals confined within a compact set. Meanwhile, the continuity of the control signal and the boundedness of all the closed-loop signals are ensured. Both the theoretical analysis and numerical simulation validate the effectiveness of the proposed method.
机译：神经网络（NN）的“通用”逼近/学习功能已广泛用于控制设计，它取决于某些关键条件，如果不满足，则其中任何一个都会使这种功能消失。在本文中，我们表明这些条件实际上与无意识地或有意地在大多数现有的基于NN的控制设计中被忽略的几个基本问​​题有关。我们进一步提出一种集体方法来明确解决这些问题，建立一种策略，使NN单元在系统运行的整个过程中都能在控制回路中完全发挥作用，并确保更可靠，更有效的NN相关控制性能。这是通过将控制与一个新的结构化NN单元合并而实现的，该单元由一组具有自调整子神经元的多样化神经元组成，每个子神经元都由限定在紧凑集中的输入信号驱动/刺激。同时，确保了控制信号的连续性和所有闭环信号的有界性。理论分析和数值模拟都验证了该方法的有效性。

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