Fault-Tolerant Control of Quadrotor UAVs Based on Back-Stepping Integral Sliding Mode Approach and Iterative Learning Algorithm

Abstract

In this paper, a fault-tolerant control system based on back-stepping integral sliding mode controller (BISMC) is designed and analyzed for both nonlinear translational and rotational subsystems of the quadrotor unmanned aerial vehicles (UAVs). The novelty of this paper is about combination of a classic controller with a repetitive algorithm to reduce the response time to actuator faults and have better tracking performance. The actuator fault is defined based on the loss of effectiveness and bias fault. Next, the iterative learning control algorithm (ILCA) is used to compensate for the unknown fault input according to previous recorded experiences. In the normal condition (without actuators fault), BISMC can force the actual trajectories toward the desired commands and reduce chattering about control signals, and in the presence of the actuators fault or external disturbances, the mentioned learning algorithm can incline the accuracy of the tracking performance and compensate for the occurred error. The Lyapunov theory illustrates that the proposed control strategy can stabilize the system despite the actuators’ fault and external disturbances. The simulation results show the effectiveness of the proposed scheme in comparison with another method.

Rotor Failure Compensation in a Biplane Quadrotor Based on Virtual Deflection

“…The fault recoverable measure for a nonlinear system like a UAV is presented [23], which is customized to ensure an adequate redundancy level for the achievement of FTC, and a guide to increase the redundancy level while the FTC is developed [24] to reconfigure the trust system based on the optimal control during the failure in the multi-rotor UAVs. A complete active FTC system for quadrotor UAVs has been developed [25], while the BSMC approach and iterative learning algorithm-based FTC are developed in [26]. A meta-learning-based scheme is developed to improve the trajectory tracking performance of UAVs in the presence of the failure in the system and external disturbance [27], and a model-free deep reinforcement learning scheme is applied for a quadrotor with signal rotor failure in [28].…”