In this paper, two different adaptive strategies are presented for continuous time uncertain nonlinear systems with unknown disturbances and faults. In first strategy, a sliding mode control based adaptive neural observer approach is anticipated for estimation of unknown disturbances and faults by using the multi-layer perceptron, the weight parameters are updated by using the sliding mode online learning strategy. Conventionally, gradient descent back-propagation adaptation methods are used for neural networks training, within these adaptation methods a new theory of sliding mode control is added to conventional gradient descent back-propagation procedure. In this nonlinear control concept, the Sliding Mode Control is employed as a learning strategy, in which the neural network is considered as a control process and computes the stable and dynamic learning rates of neural network. By considering the unknown faults approximation and reconstruction, this online learning strategy shows a rapid sensor fault detection, approximation, and reconstruction with high preciseness and rapidness compared to conventional strategy and algorithms presented in literature. Approaches used in literature do not have much higher preciseness and fast response to fault occurrence compared to the strategy proposed in this study. In second strategy, the neural network controller strategy is proposed with concept of filtered error scheme. Online weight updating strategy comprise of additional term to back-propagation, plus an additional robustifying term, assures the stability and rapid convergence of the faulty system. The stability analysis of the proposed fault tolerance control is also provided. While considering stability of system, this robust online adaptive fault tolerance control shows a fast convergence in the presence of unknown disturbances and faults. The robust adaptive neural controller is compared with the conventional gradient descent based controller in the existence of various sensor faults and failures. The proposed strategies are validated on Boeing 747 100/200 aircraft, results show the efficiency, preciseness and robustness of strategies compared to the algorithm presented in literature.

本文针对具有未知扰动和故障的连续时间不确定非线性系统，提出了两种不同的自适应策略。在第一种策略中，期望使用基于滑模控制的自适应神经观测器方法，通过使用多层感知器来估计未知的干扰和故障，并使用滑模在线学习策略来更新权重参数。常规地，梯度下降反向传播自适应方法用于神经网络训练，在这些自适应方法内，将滑模控制的新理论添加到常规的梯度下降反向传播程序中。在这种非线性控制概念中，滑模控制被用作学习策略，其中将神经网络视为控制过程，并计算神经网络的稳定和动态学习率。通过考虑未知故障的近似和重构，与文献中介绍的常规策略和算法相比，这种在线学习策略显示了一种快速的传感器故障检测，逼近和重构，具有很高的精度和速度。与本研究中提出的策略相比，文献中使用的方法没有更高的精确度和对故障发生的快速响应。在第二种策略中，提出了神经网络控制器策略，并提出了过滤错误方案的概念。在线权重更新策略包括反向传播的附加条件，以及附加的稳定化条件，确保故障系统的稳定性和快速收敛性。还提供了所提出的容错控制的稳定性分析。在考虑系统稳定性的同时，这种强大的在线自适应容错控制在存在未知干扰和故障的情况下显示出快速收敛。在存在各种传感器故障和故障的情况下，将鲁棒的自适应神经控制器与常规的基于梯度下降的控制器进行了比较。所提出的策略在波音747 100/200飞机上得到了验证，与文献中提出的算法相比，结果表明了该策略的效率，准确性和鲁棒性。这种强大的在线自适应容错控制在出现未知干扰和故障时显示出快速收敛。在存在各种传感器故障和故障的情况下，将鲁棒的自适应神经控制器与常规的基于梯度下降的控制器进行了比较。所提出的策略在波音747 100/200飞机上得到了验证，与文献中提出的算法相比，结果表明了该策略的效率，准确性和鲁棒性。这种强大的在线自适应容错控制显示了在未知干扰和故障存在下的快速收敛。在存在各种传感器故障和故障的情况下，将鲁棒的自适应神经控制器与常规的基于梯度下降的控制器进行了比较。所提出的策略在波音747 100/200飞机上得到了验证，与文献中提出的算法相比，结果表明了该策略的效率，准确性和鲁棒性。