Particle filter-based adaptive super-twisting sliding mode fault-tolerant control for helicopter systems

Fault detection and control of nonlinear helicopter systems is crucial in ensuring safety and reliability. The effects of faults on system dynamics become more challenging to control due to the complexity of helicopter dynamics, which exhibit significant nonlinearity and cross-coupling as well as external disturbances like wind, icing, and air turbulence that affect the system. The goal of this work is to provide an active fault diagnosis and control approach for a nonlinear two-degrees-of-freedom helicopter system when it is exposed to different abnormalities, such as sensor, actuator, and component faults. An integrated design of fault diagnostic and fault-tolerant control is designed using adaptive sliding mode control. The fault diagnosis is carried out using particle filter, and based on the state feedback from the particle filter, the controller gain is adjusted to provide fault tolerance. System state and error covariance are both used by the controller as state feedback. Simulations are carried out to show the effectiveness of adaptive super-twisting SMC compared to traditional super-twisting SMC. Results indicate that the adaptive SMC tracks the system states perfectly. The effectiveness of SMC algorithms is evaluated using a variety of performance indicators, and the results demonstrate a marginal improvement in controller performance over super-twisting SMC. Also a stability analysis is carried out using Lyapunov approach in the face of faults and the stability of the controller is guaranteed.