Comparative study of self tuning, adaptive and multiplexing FTC strategies for successive failures in an Octorotor UAV

Abstract

This paper presents three fault-tolerant control (FTC) strategies for a coaxial octorotor unmanned aerial vehicle (UAV) regarding motor failures. The first FTC is based on a control mixing strategy which consists of a set of control laws designed offline, each one dedicated to a specific fault situation. The second FTC, a robust adaptive sliding mode control allocation is presented, where the control gains of the controller are adjusted online in order to redistribute the control signals among the healthy motors in order to stabilize the overall system. The third FTC strategy is a new strategy proposed in this article, which is based on a self-tuning sliding mode control (STSMC) where the control gains are readjusted based on the detected error to maintain the stability of the system. Multiple indoor experiments on an octorotor UAV are conducted to show and compare the effectiveness and the behavior of each FTC scheme after successive faults are injected. More specifically, we inject complete actuator's failures into the top four motors of our octorotor. Every strategies show good fault tolerance results, although the control mixing method performs slightly better overall while the adaptive method performs slightly worse. However, the control mixing method requires a huge design effort to take into account as much situations as possible, while the adaptive method and the STSMC only require to determine a few gains. The adaptive method do not need fault detection to operate, but it thus does not provide information on the system's health without an additional fault identification and diagnosis mechanism, while both the control mixing method and the STSMC provide such information.

Sliding Mode Control with Application to Fault-Tolerant Control: Assessment and Open Problems

“…The alternative consists of the use of more actuated UAVs, e.g., octorotor UAVs. For instance, two SMC-FTC solutions are investigated in [113]. The first solution relies on a robust adaptive sliding mode Control Allocation (CA) where the control gains of the controller are adjusted online in order to redistribute the control signals among the healthy motors.…”

Adaptive Nonsingular Fast-Reaching Terminal Sliding Mode Control Based on Observer for Aerial Robots

“…Paper [27] introduced a finite-time extended disturbance observer-based adaptive neural sliding mode control approach, which not only eliminates chattering but also improves the network's learning speed. A selftuning sliding mode control strategy was proposed in [28], which proved that the tracking error is able to converge faster compared to the adaptive method. In [29], a continuous nonsingular terminal sliding mode control strategy was proposed to tackle the singularity problem and alleviate the chattering phenomenon, which enhanced practicability.…”

Fault Tolerant Control for Autonomous Surface Vehicles via Model Reference Reinforcement Learning

“…Most FTC algorithms belong to model-based approaches. Passive FTC needs to know the "worst-case" system faults for the design of robust control [10]. Active FTC needs the degraded system model under faults for control reconfiguration [11].…”