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

Abstract

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.

Fault‐tolerant predictive trajectory tracking of an air vehicle based on acceleration control

“…Thus, the convergence of the proposed approach can be guaranteed using the standard persistent excitation (PE) condition. As discussed in [26], this condition can be satisfied even in the closed‐loop control by injecting some useful excitations to the closed‐loop system [27]. Also, a useful modification has been introduced in [28] to update ELMs in the closed‐loop structure.…”

Identification of nonlinear time‐varying systems using wavelet neural networks

“…Regarding the convergence of the proposed multimodel structure, it should be noted that the accuracy and convergence of a similar multimodel structure have been studied in detail in References 27,42. Briefly, concerning the accuracy of local models, it is notable that the employed NARX neural networks are trained offline under the corresponding flight condition of each local model.…”

“…Thus, the introduced model can be an optimal choice for the prediction model of Model-based Predictive Controllers (MPCs) in the case of uncertain and faulty systems. 27,28 3. The introduced approach has a general formulation, which can be effectively employed in the case of different complex dynamic systems.…”

“…This, in turn, leads to the accurate prediction of the system outputs for long prediction horizons, where it can also alleviate the effects of sudden disturbances on the system outputs through the online tuning of the validity functions. Thus, the introduced model can be an optimal choice for the prediction model of Model‐based Predictive Controllers (MPCs) in the case of uncertain and faulty systems 27,28 The introduced approach has a general formulation, which can be effectively employed in the case of different complex dynamic systems. A multimodel structure is developed in this research which leads to more efficient training of each NN.…”

A self-organizing multi-model ensemble for identification of nonlinear time-varying dynamics of aerial vehicles

“…Therefore, the proposed identification algorithm can be satisfactorily employed to generate a cost-effective prediction model in the framework of model predictive controllers (MPCs), especially in the case of complex time-varying dynamic systems. 23 More specifically, an intelligent trajectory tracking control scheme has been proposed by Emami and Banazadeh 24 for a fixed-wing air vehicle in the presence of actuator faults and external disturbances. A multi-model MPC-based trajectory tracking approach was introduced in the article where each local model corresponds to a specific fault and flight condition.…”