An analytical fault diagnosis method for yaw estimation of quadrotors

Lyu, Pin; Liu, Shichao; Lai, Jizhou; Liu, Jianye

doi:10.1016/j.conengprac.2019.03.010

Cited by 21 publications

(9 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Table 2 establishes the ESL between sensors (see Figure 5) and also from upstream to each valve. By substituting those pipeline parameters shown in Table 1 into the matrices A, ϕ(x), and φ(u), ξ(y) in (13), one can see that the observability matrix in (12) has full rank if z f ∈ (0, L r ), Torres et al [35].…”

Section: Methodsmentioning

confidence: 99%

“…Here, the additive output nonlinearity can be built from direct measurements and thus compensated in the observer design (as it was originally proposed by the authors in Krener and Isidori [28], J. Krener and Respondek [29], for instance). The representation (13) admits an observer of the form:˙x…”

Section: Extended Luenberger Observer For Mimo Systemsmentioning

confidence: 99%

“…On the other hand, analytical redundancy methods (the technique of several model-based methods) have demonstrated to be useful to improve the precision, reliability, and performance of a system. Notably, in the field of fault detection and isolation, the attention on this class of methods has increased lately in several topics, such as robotics Lyu et al [13], control theory Chouchane et al [14], diagnosis system Lunze [15], and the application of evolutionary algorithms and neural networks to fault diagnosis Witczak [16]. In particular, several works dealing the leak diagnosis problem in Water Distribution Networks (WDN) have also been proposed on the basis of genetic algorithms.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Evolutionary Observer Ensemble for Leak Diagnosis in Water Pipelines

et al. 2019

View full text Add to dashboard Cite

This work deals with the Leak Detection and Isolation (LDI) problem in water pipelines based on some heuristic method and assuming only flow rate and pressure head measurements at both ends of the duct. By considering the single leak case at an interior node of the pipeline, it has been shown that observability is indeed satisfied in this case, which allows designing an observer for the unmeasurable state variables, i.e., the pressure head at leak position. Relying on the fact that the origin of the observation error is exponentially stable if all parameters (including the leak coefficients) are known and uniformly ultimately bounded otherwise, the authors propose a bank of observers as follows: taking into account that the physical pipeline parameters are well-known, and there is only uncertainty about leak coefficients (position and magnitude), a pair of such coefficients is taken from a search space and is assigned to an observer. Then, a Genetic Algorithm (GA) is exploited to minimize the integration of the square observation error. The minimum integral observation error will be reached in the observer where the estimated leak parameters match the real ones. Finally, some results are presented by using real-noisy databases coming from a test bed plant built at Cinvestav-Guadalajara, aiming to show the potentiality of this method.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Extended Luenberger Observer For Mimo Systemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Evolutionary Observer Ensemble for Leak Diagnosis in Water Pipelines

et al. 2019

View full text Add to dashboard Cite

show abstract

“…The measurement equations of the two sub-filters are expressed as {z1=hc1false(xcfalse)+VboldnormalC1z2=hc2false(xcfalse)+VboldnormalC2, where VboldnormalC1 and VboldnormalC2 are the measurement noise matrices of C1 and C2. The updating progress of the FKF can be referred to in [30] and is not described here.…”

Section: Drag Model-lidar-imu Fusion Schemementioning

confidence: 99%

“…When there is no fault, λC1false(kfalse) follows a chi-square distribution [30]. The fault detection function can be constructed as TDfalse(kfalse)={1λC1false(kfalse)>τD 0λC1false(kfalse)<τD, where τD is the threshold.…”

Section: Drag Model-lidar-imu Fusion Schemementioning

confidence: 99%

A Drag Model-LIDAR-IMU Fault-Tolerance Fusion Method for Quadrotors

Lyu

Wang

Lai

et al. 2019

Sensors

Self Cite

View full text Add to dashboard Cite

In this paper, a drag model-aided fault-tolerant state estimation method is presented for quadrotors. Firstly, the drag model accuracy was improved by modeling an angular rate related item and an angular acceleration related item, which are related with flight maneuver. Then the drag model, light detection and ranging (LIDAR), and inertial measurement unit (IMU) were fused based on the Federal Kalman filter frame. In the filter, the LIDAR estimation fault was detected and isolated, and the disturbance to the drag model was estimated and compensated. Some experiments were carried out, showing that the velocity and position estimation were improved compared with the traditional LIDAR/IMU fusion scheme.

show abstract