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Block Control Design Based on Multiplayer Neural Network

机译:基于多人神经网络的块控制设计

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摘要

For a class of linear systems with mismatched uncertainties of unknown bound, a robust control method which combines block control, neural network control and backstepping techniques is proposed based on block control principle. The bounds of the uncertainties are not required to be known. The mismatched uncertainties are overcome by using backstepping technique. The uncertainties are estimated by multiplayer neural network(MNN) approximators. The performance of the system is improved by using robust control. The stability of the closed-loop system is proved in the sense of Lyapunov stability theorem. Simulation examples have shown the Tightness and effectiveness of the proposed scheme.
机译:针对一类未知边界不确定性不匹配的线性系统,提出了一种基于块控制原理的结合了块控制,神经网络控制和后推技术的鲁棒控制方法。不确定性的界限不需要知道。通过使用反推技术可以克服不匹配的不确定性。不确定性由多人神经网络(MNN)逼近器估算。通过使用鲁棒控制来改善系统的性能。在Lyapunov稳定性定理的意义上证明了闭环系统的稳定性。仿真实例表明了该方案的紧密性和有效性。

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