Robust fault diagnosis and fault-tolerant control for nonlinear quadrotor unmanned aerial vehicle system with unknown actuator faults

Jinjin Guo,Juntong Qi,Chong Wu

doi:10.1177/17298814211002734

Jinjin Guo, Juntong Qi + Show 1 more

Open Access

https://doi.org/10.1177/17298814211002734

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Abstract

This article addresses the problem that quadrotor unmanned aerial vehicle (UAV) actuator faults, including small-amplitude bias faults and gain degradation, cannot be detected in time. A hybrid observer, which combines the fast convergence from adaptive observer and the strong robustness from sliding mode observer, is proposed to detect and estimate UAV actuator faults accurately with model uncertainties and disturbances. A nonlinear quadrotor UAV model with model uncertainties and disturbances is considered and a more precise unified expression for actuator faults that do not require knowing where the upper or lower bound is provided. The original system is decomposed into two subsystems by coordinate transformation to improve detection accuracy for small amplitude bias faults and avoid external influences. The hybrid observer is then designed to estimate subsystem states and faults with good stability by selecting a Lyapunov function. A fault-tolerant controller is obtained depending on fault estimation by compensating the normal controller (proportion integral differential [PID] controller). Several numerical simulations confirmed that unknown actuator faults can be accurately detected, estimated, and compensated for even under disturbance conditions.

Highlights

With the development of autonomous control technology, various unmanned aerial vehicles (UAVs) have been extensively used in logistics transportation, agricultural plant protection, and so on.[1,2,3,4] Quadrotor UAVs have several advantages compared with UAV types, such as flight flexibility and fewer site requirements, and have attracted considerable research attention.[5,6] quadrotor UAV is a nonlinear control system with multiple and significantly coupled inputs and outputs, and control system complexity increases as performance improves.[7]
The article proposes a hybrid observer that can deal with quadrotor UAV actuator faults leveraging advantages from adaptive and sliding mode observers and providing better balance between speed and robustness
The expression of quadrotor UAV with bias fault or gain fault is shown in the above equation (5), as long as the designed normal controller can stabilize the system, the actuator faults can be compensated in time following the fault-tolerant control (FTC) strategy (equation (37)) after accurate fault estimation is obtained, and the system state tracking error asymptotically converges to zero

Summary

Introduction

With the development of autonomous control technology, various unmanned aerial vehicles (UAVs) have been extensively used in logistics transportation, agricultural plant protection, and so on.[1,2,3,4] Quadrotor UAVs have several advantages compared with UAV types, such as flight flexibility and fewer site requirements, and have attracted considerable research attention.[5,6] quadrotor UAV is a nonlinear control system with multiple and significantly coupled inputs and outputs, and control system complexity increases as performance improves.[7]. The article proposes a hybrid observer that can deal with quadrotor UAV actuator faults leveraging advantages from adaptive and sliding mode observers and providing better balance between speed and robustness. The main contributions of this article are summarized as follows: (1) The considered quadrotor model is nonlinear and the exterior uncertainties and disturbances are fully considered; (2) a more precise unified expression for actuator bias fault and gain fault is provided, considering small or potential faults where knowing upper and lower bounds of faults are not required; and (3) the external disturbance and uncertainties are compensated by designing the discontinuity term of sliding mode observer to achieve the robustness of fault diagnosis, and adaptive laws of adaptive observer are designed to quickly estimate the actuator bias and gain faults. From Assumptions 1 and 2, the system is detectable, and there exist matrices PT1 1⁄4 P1 2 RðnÀrÞÂðnÀrÞ > 0, PT2 1⁄4 P2 2 RrÂr > 0, L1 2 RðnÀrÞÂðnÀrÞ, L2 2 RrÂr, and positive scalars 1 and 2, such that

AT11P1 þ P1A11 À H T11RT1 À R1H 11 þ sI nÀr

Findings

Conclusion