This paper presents an H2 robust fault tolerant controller design method for uncertain systems in the presence of unknown failures. The developed H2 controller with optimal index incorporates a neural network learning action and sliding mode control action. A radial basis function (RBF) neural network is utilized to approximate the unknown nonlinear dynamics. An updating rule is designed to estimate actuator failure. The sliding-mode control is used to eliminate the effect of neural network approximation error. Based on Lyapunov function, the sufficient condition for H2 optimal performance is developed in terms of nonlinear quadratic matrix inequality. In order to reduce computing cost, a simplification algorithm is developed, which avoids solving on-line nonlinear matrix inequality. A numerical example on a spacecraft system is presented to demonstrate the effectiveness of the proposed methods.%针对存在执行器故障的不确定系统,本文研究了一种H2鲁棒容错控制的设计.控制器包括三个功能:1)利用径向基函数(Radial basis function,RBF)神经网络估计得到的近似非线性函数构成闭环控制,抵消系统的非线性特征；2)能实现H2性能指标的最优控制；3)利用滑模控制抑制模型估计误差以提高控制精度,并且控制器具有指定稳定裕度的设计功能.文中提出了用于执行器故障估计的调整规则,故障估计信息用于控制律的设计.基于Lyapunov函数,推导了满足H2最优性能的充分条件:非线性二次矩阵不等式.为了降低计算成本,给出了不等式求解的简化算法,避免了在线求解非线性矩阵不等式.通过一个空间飞行器模型的仿真,验证了本文提出方法的有效性.
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