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Tuning function-based adaptive backstepping fault-tolerant control for nonlinear systems with actuator faults and multiple disturbances

机译：用于执行器故障的非线性系统的调整基于功能的自适应BackStepping容错控制和多种干扰

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摘要

The aim of this paper is to deal with the tracking control problem of strict-feedback nonlinear systems subject to unknown actuator faults and multiple disturbances simultaneously. To tackle the problem, a novel adaptive backstepping fault-tolerant control algorithm which is based on the tanh function, the tuning function, and neural networks is proposed. During the procedure of controller design, the effects of unknown actuator faults modeled as loss of effectiveness are eliminated without a fault detection and isolation mechanism. The multiple disturbances composed of the matched disturbance and the unmatched disturbance are approximated by neural networks and compensated by the tuning function-based adaptive technique, respectively. Moreover, the over-parametrization phenomenon resulting from unknown parameters is avoided via the tuning function. The sufficient condition for the closed-loop system to be asymptotically stable is gained through constructing an appropriate Lyapunov function. The presented scheme is reduced to two numerical examples, in which the practical system for high-speed trains is considered. Simulation results prove that the chattering phenomenon is eliminated via tanh function and the presented fault-tolerant strategy is feasible and effective.
机译：本文的目的是处理经过未知的执行器故障和多次干扰的严格反馈非线性系统的跟踪控制问题。为了解决问题，提出了一种基于Tanh函数，调谐功能和神经网络的新型自适应BackStepping容错控制算法。在控制器设计的过程中，消除了未知的执行器故障为有效性损失的效果而无故障检测和隔离机制。由匹配干扰和无与伦比的干扰组成的多个干扰由神经网络近似，并分别通过调谐功能的自适应技术来补偿。此外，通过调谐函数避免了由未知参数产生的过度参数化现象。通过构造适当的Lyapunov函数来获得闭环系统的足够条件。所提出的方案减少到两个数值例子，其中考虑了高速列车的实际系统。仿真结果证明，通过Tanh函数消除了喋喋不休的现象，并且呈现的容错策略是可行和有效的。

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