Zhang Dehua, Meng Lei, Hao Yao, Xia Ruixue
School of Artificial Intelligence, Henan University, Zhengzhou, 450046, China.
Sci Rep. 2025 Jul 28;15(1):27504. doi: 10.1038/s41598-025-13659-z.
This paper investigates the performance decline in complex systems, such as Unmanned Aerial Vehicles (UAVs), caused by unanticipated faults and external perturbations. To improve system resilience and achieve swift recovery without depending on fault detection, a passive Fault-Tolerant Control (FTC) approach is developed, combining Sliding Mode Control (SMC) with Radial Basis Function (RBF) neural networks. The RBF network, utilizing its robust approximation abilities, is applied to dynamically estimate system uncertainties, thereby alleviating the chattering issue typical of traditional SMC and minimizing its negative effects on system reliability and operation. Notably, this work addresses the challenges of instability and slow convergence often encountered in conventional gradient descent techniques for adjusting RBF network parameters. Instead, an enhanced Particle Swarm Optimization (PSO) method, incorporating an adaptive mutation mechanism (MPSO), is employed to effectively fine-tune the RBF network's critical parameters (centers and widths), resulting in improved convergence rates, learning performance, and parameter stability. The stability of the closed-loop system is thoroughly established using Lyapunov theory, ensuring that all signals remain bounded. Lastly, extensive simulations on a quadrotor UAV model under diverse fault conditions and disturbances are conducted to confirm the efficacy and highlight the advantages of the proposed MPSO-RBF-based adaptive sliding mode FTC approach over both conventional and standard adaptive SMC benchmarks.
本文研究了由意外故障和外部扰动导致的复杂系统（如无人机）性能下降问题。为提高系统弹性并在不依赖故障检测的情况下实现快速恢复，开发了一种被动容错控制（FTC）方法，将滑模控制（SMC）与径向基函数（RBF）神经网络相结合。利用RBF网络强大的逼近能力来动态估计系统不确定性，从而缓解传统SMC典型的抖振问题，并将其对系统可靠性和运行的负面影响降至最低。值得注意的是，这项工作解决了传统梯度下降技术在调整RBF网络参数时经常遇到的不稳定和收敛缓慢的挑战。相反，采用了一种增强的粒子群优化（PSO）方法，即自适应变异粒子群优化（MPSO），来有效地微调RBF网络的关键参数（中心和宽度），从而提高收敛速度、学习性能和参数稳定性。利用李雅普诺夫理论全面建立了闭环系统的稳定性，确保所有信号保持有界。最后，在多种故障条件和干扰下对四旋翼无人机模型进行了广泛的仿真，以证实所提出的基于MPSO-RBF的自适应滑模FTC方法相对于传统和标准自适应SMC基准的有效性，并突出其优势。
