Diagnosis and Fault-Tolerant Control of Nonlinear Systems

Abstract

The demand for reliable, autonomous operation in complex, safety-critical non- linear systems such as Unmanned Aerial Vehicles (UAVs) necessitates advanced Fault-Tolerant Control (FTC) capabilities. Conventional FTC methods struggle with system non-linearity and lack the adaptive performance required for robust operation. Conversely, purely AI-based approaches, while highly adaptive, suffer from a lack of formal safety guarantees and increased computational complexity. This dissertation bridges this critical gap by proposing and validating a novel Hybrid Fault-Tolerant Control (HFTC) strategy that strategically integrates the verifiable rigor of conventional control with the optimizing power of metaheuristic techniques. The core of this framework is a robust Sliding Mode Control (SMC) architecture enhanced by a proposed Enhanced Triple Power Reaching Law (ET- PRL), which provides superior chattering attenuation and accelerated finite-time

convergence. The HFTC architecture employs a unique hybrid compensation strat- egy that includes an Online Diagnosis component, where a conventional extended kalman filter (EKF-based) fault detection and identification (FDI) unit provides real-time fault estimates, and an Offline Optimization component, where the par- ticle swarm optimization (PSO) algorithm is utilized to pre-compute and store a "Bank of Parameters" for numerous actuator fault scenarios. The efficacy of this framework is rigorously validated through a comprehensive case study on a UAV quadrotor subjected to severe Loss of Effectiveness (LOE) actuator faults. MAT- LAB/Simulink simulations, including challenging helical and square trajectory maneuvers, quantitatively demonstrate the superior performance of the HFTC- ETPRL system, confirming its ability to maintain stability and achieve minimal tracking error (e.g., up to a 50% reduction in Peak Deviation compared to bench- mark controllers), underscoring the benefits of non-iterative, pre-optimized recon- figuration. In synthesis, this research advances the state-of-the-art by validating an efficient and high-performance hybrid solution that addresses the fundamental trade-off between robustness and adaptability.