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首页> 外文期刊>International Journal of Adaptive Control and Signal Processing >Fault detection and identification for a class of continuous piecewise affine systems with unknown subsystems and partitions
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Fault detection and identification for a class of continuous piecewise affine systems with unknown subsystems and partitions

机译：一类子系统和分区未知的连续分段仿射系统的故障检测与识别

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摘要

This paper establishes a novel online fault detection and identification strategy for a class of continuous piecewise affine (PWA) systems, namely, bimodal and trimodal PWA systems. The main contributions with respect to the state-of-the-art are the recursive nature of the proposed scheme and the consideration of parametric uncertainties in both partitions and in subsystems parameters. In order to handle this situation, we recast the continuous PWA into its max-form representation and we exploit the recursive Newton-Gauss algorithm on a suitable cost function to derive the adaptive laws to estimate online the unknown subsystem parameters, the partitions, and the loss in control authority for the PWA model. The effectiveness of the proposed methodology is verified via simulations applied to the benchmark example of a wheeled mobile robot.
机译：针对一类连续分段仿射（PWA）系统，即双峰和三峰PWA系统，建立了一种新颖的在线故障检测与识别策略。关于最新技术的主要贡献是所提出方案的递归性质，以及对分区和子系统参数中的参数不确定性的考虑。为了处理这种情况，我们将连续的PWA重铸为其最大形式的表示形式，并在合适的成本函数上利用递归的Newton-Gauss算法来推导自适应定律，以在线估计未知子系统参数，分区和参数。 PWA模型的控制权丢失。通过将模拟应用于轮式移动机器人的基准示例，验证了所提出方法的有效性。

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