Adaptive sliding mode fault-tolerant control of UAV systems based on radial basis function neural networks.

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.