Online Identification of Aircraft Dynamics in the Presence of Actuator Faults

Emami, Seyyed Ali; Banazadeh, Afshin

doi:10.1007/s10846-019-00998-z

Cited by 13 publications

(18 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This goal can be achieved simply by proper selection of the fault scenarios and flight conditions to be learned by local NNs. The high accuracy of the basic OS‐ELM‐based learning algorithm for online identification of aircraft dynamics in the presence of actuator faults has been previously demonstrated by the authors in the work of Emami and Banazadeh …”

Section: Trajectory Tracking Controlmentioning

confidence: 80%

“…Finally, it should be noted that the introduced disturbance observer can effectively capture the effects of unmodeled dynamics and sudden external disturbances on the system outputs. More specifically, the authors have demonstrated in the work of Emami and Banazadeh that the online identification of aircraft dynamics in the presence of different actuator faults using the basic OS‐ELM‐based approach can be performed with the prediction error of less than 0.3% for the prediction horizon of 10 steps, which is a very accurate prediction. It is notable that no disturbance observer has been used in the identification process in the work of Emami and Banazadeh .…”

Section: Trajectory Tracking Controlmentioning

confidence: 99%

“…More specifically, the authors have demonstrated in the work of Emami and Banazadeh that the online identification of aircraft dynamics in the presence of different actuator faults using the basic OS‐ELM‐based approach can be performed with the prediction error of less than 0.3% for the prediction horizon of 10 steps, which is a very accurate prediction. It is notable that no disturbance observer has been used in the identification process in the work of Emami and Banazadeh . Accordingly, the proposed disturbance observer in the current study can satisfactorily compensate for the minor prediction error of the introduced OS‐ELM‐based identification approach.…”

Section: Trajectory Tracking Controlmentioning

confidence: 99%

“…Accordingly, the proposed disturbance observer in the current study can satisfactorily compensate for the minor prediction error of the introduced OS‐ELM‐based identification approach. Moreover, the identification process in the work of Emami and Banazadeh has employed only a constant forgetting factor, not the directional forgetting factor, which can significantly improve the identification accuracy. As a result, the following assumption is considered true in the rest of this paper.…”

Section: Trajectory Tracking Controlmentioning

confidence: 99%

“…Multimodel structures can be also utilized satisfactorily in the case of faulty systems to identify the time‐varying dynamics of the system in the presence of different actuator faults or structural damages. As an instance, the authors have proposed a novel multiple model‐based identification approach for a conventional aircraft in the presence of actuator faults in the work of Emami and Banazadeh . A set of recurrent NNs have been trained first under different fault conditions, where an online learning approach has been employed to gather local submodels into a single model.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Intelligent trajectory tracking of an aircraft in the presence of internal and external disturbances

Emami

Banazadeh

2019

Intl J Robust & Nonlinear

Self Cite

View full text Add to dashboard Cite

Summary This research deals with developing an intelligent trajectory tracking control approach for an aircraft in the presence of internal and external disturbances. Internal disturbances including actuators faults, unmodeled dynamics, and model uncertainties as well as the external disturbances such as wind turbulence significantly affect the performance of the common trajectory tracking control approaches. There are several fault‐tolerant control approaches in the literature to overcome the effects of specific actuator or sensor faults during the flight. However, trajectory tracking control of an air vehicle in the presence of unexpected faults and simultaneous presence of wind turbulence is still a challenging problem. In this paper, an intelligent neural network‐based model predictive control structure is proposed, where the prediction model is updated in each iteration based on a novel proposed online sequential multimodel structure. A hybrid offline‐online learning algorithm is adopted in the introduced online sequential multimodel structure to identify the time‐varying dynamics of the system. The proposed control structure can satisfactorily deal with unexpected actuator faults and structural damages as well as unmodeled dynamics and wind turbulence. The stability of the closed‐loop system is proved under some realistic assumptions. The simulation results demonstrate the high capability of the proposed approach for trajectory tracking control of a conventional aircraft in the simultaneous presence of system faults and external disturbances.

show abstract

Section: Trajectory Tracking Controlmentioning

confidence: 80%

Section: Trajectory Tracking Controlmentioning

confidence: 99%

Section: Trajectory Tracking Controlmentioning

confidence: 99%

Section: Trajectory Tracking Controlmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Intelligent trajectory tracking of an aircraft in the presence of internal and external disturbances

Emami

Banazadeh

2019

Intl J Robust & Nonlinear

Self Cite

View full text Add to dashboard Cite

show abstract

Identification of nonlinear time‐varying systems using wavelet neural networks

Emami

Roudbari

2020

Adv Control Appl

View full text Add to dashboard Cite

The dynamic model of an aircraft changes significantly by altering the flight speed and the vehicle altitude. Thus, a conventional aircraft has a nonlinear time‐varying dynamic model in different regions of the flight envelope, and a dynamic model developed for a specific operating point is not valid in the entire flight envelope. This paper presents a novel identification approach that can deal with nonlinear and time‐varying characteristics of complex dynamic systems, especially an aerial vehicle in the entire flight envelope. In this regard, a set of local submodels are first developed at different operating points of the system, and subsequently, a multimodel structure is introduced to aggregate the outputs of the local models as a single model. Wavelet Neural Networks (WNNs), which combine both the universal approximation property of neural networks and the wavelet decomposition capability, are used as the local models of the proposed scheme. Also, three different approaches for determining the validity functions of the local models are introduced to allow for identifying the time‐varying dynamics of the system. The simulation results obtained for the Generic Transport Model aircraft suggest that the proposed WNN‐based multimodel structure can be used satisfactorily as the prediction model of model‐based flight control systems for long prediction horizons.

show abstract

Improving Control Performance of Unmanned Aerial Vehicles through Shared Experience

Jardine

Givigi

2021

J Intell Robot Syst

View full text Add to dashboard Cite

Online Identification of Aircraft Dynamics in the Presence of Actuator Faults

Cited by 13 publications

References 30 publications

Intelligent trajectory tracking of an aircraft in the presence of internal and external disturbances

Intelligent trajectory tracking of an aircraft in the presence of internal and external disturbances

Identification of nonlinear time‐varying systems using wavelet neural networks

Improving Control Performance of Unmanned Aerial Vehicles through Shared Experience

Contact Info

Product

Resources

About