Active Fault-Tolerant Control for Quadrotor UAV against Sensor Fault Diagnosed by the Auto Sequential Random Forest

Abstract

Active disturbance rejection control (ADRC) is a model-independent method widely used in passive fault-tolerant control of the quadrotor unmanned aerial vehicle. While ADRC’s effectiveness in actuator fault treatment has been proven, its tolerance to sensor faults requires improvements. In this paper, an ADRC-based active fault-tolerant control (AFTC) scheme is proposed to control the flying attitude against sensor fault for reliability enhancement. Specifically, a semi-model-dependent state tracker is raised to reduce the influence of slow tracking, and accentuate the sensor fault even in varying maneuvers. Derived from the random forest, an enhanced method named auto sequential random forest is designed and applied to isolate and identify faults in real time. Once the tolerance compensation is generated with the fault information, a high-performance AFTC is achieved. The simulation results show that the proposed method can effectively follow the residual when a sensor fault and a change of maneuver occur concurrently. Precise fault information is obtained within 0.04 s, even for small faults on the noise level. The diagnosis accuracy is greater than 86.05% (100% when small faults are excluded), and the identification precision exceeds 97.25%. The short settling time (0.176 s when the small fault is excluded) and modest steady-state error validate the advanced and robust tolerance performance of the proposed AFTC method.

Sliding Mode Control with Adaptive-Reaching-Law-Based Fault-Tolerant Control for AUV Sensors and Thrusters

“…The hyperbolic tangent function is one of the most effective methods for solving the chattering of sliding mode control inputs. The function is shown in Equation (19), where σ represents the weighting factor. The selection of parameters directly affects the robustness and stability of the system.…”

“…Therefore, in this paper, the weighting factor is appropriately selected according to the state of the system. tanh(σs) = e σx − e −σx e σx + e −σx (19) Combining Equations ( 17) and ( 19), the final sliding mode control law can be transformed into the following form:…”

“…For instance, in reference [18], an adaptive compensatory observer is designed to address the coupling problem between the fault tolerance control of actuators and sensors in nonlinear systems. In reference [19], an active fault-tolerant control method for aircraft, based on a disturbance observer, utilizes an extended state observer to estimate sensor information and incorporates the estimated sensor information into the closed-loop motion control of the aircraft, achieving sensor fault tolerance. Reference [20] proposes an adaptive fault-tolerant control method for AUV sensors based on interval observers, which can compensate for sensor faults and robot uncertainties, ensuring the stability of AUV trajectory tracking.…”

Fractional Gradient Descent RBFNN for Active Fault-Tolerant Control of Plant Protection UAVs

“…The active disturbance rejection control algorithm has the characteristic of not overly relying on the system model, and is a good solution for control systems with uncertain models. However, for systems where changes in system model parameters are detected, using machine learning algorithms is also a good solution [30]. Zhong et al introduced the model reference adaptive control into the RBFNN for online identification of the system model's changing parameters [16].…”

A Fuzzy Backstepping Attitude Control Based on an Extended State Observer for a Tilt-Rotor UAV

“…Active disturbance rejection control (ADRC) can effectively overcome the influence of internal and external disturbances [23][24][25]. The core of ADRC is to treat the internal and external disturbances of the system as the total disturbance, estimate the total disturbance through an extended-state observer (ESO), and use the ESO for real-time system compensation.…”