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An Adaptive NN-Based Approach for Fault-Tolerant Control of Nonlinear Time-Varying Delay Systems With Unmodeled Dynamics

机译:基于自适应神经网络的非线性时变时滞系统的容错控制

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This paper presents an adaptive neural network (NN)-based fault-tolerant control approach for the compensation of actuator failures in nonlinear systems with time-varying delay. The novelty of this paper lies in the fact that both the lock in place and loss of effectiveness faults, unmodeled dynamics, and dynamic disturbances are catered for simultaneously. Furthermore, this is achieved by the adaptation of only one parameter, which simplifies the computation of the control effort, and therefore extends its applicability. In the approach, the Razumikhin lemma and a dynamic signal are employed. It is shown that the output of the system converges to a neighborhood of the reference signal and the semiglobal boundedness of all signals is guaranteed. A simulation example is used to illustrate the validity and efficacy of the approach.
机译:本文提出了一种基于自适应神经网络的容错控制方法,用于补偿时变时滞非线性系统的执行器故障。本文的新颖之处在于,可以同时解决锁定错误和有效性故障,未建模的动力学以及动态扰动的问题。此外,这通过仅适配一个参数来实现,这简化了控制量的计算,因此扩展了其适用性。在该方法中,采用了Razumikhin引理和动态信号。结果表明,系统的输出收敛到参考信号的邻域,并保证了所有信号的半全局有界性。仿真例子说明了该方法的有效性和有效性。

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