Adaptive model predictive control‐based attitude and trajectory tracking of a VTOL aircraft

Emami, Seyyed Ali; Řežaeizadeh, Amin

doi:10.1049/iet-cta.2017.1048

Cited by 38 publications

(34 citation statements)

References 37 publications

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“…The simulation parameters are chosen as follows: N p =6, N u =4, d =55 steps, and T s =20 ms, where T s is the sampling time of the simulations. Moreover, the suggested functions for the elements of the matrix Q in have been given in the work of Emami and Rezaeizadeh . Regarding the significant difference of the prediction horizon ( N p ) and the parameter d , it should be noted that the guidance law in the current study is developed based on the indirect formulation, where the guidance loop attempts to convert the desired trajectory to the desired attitude.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…The closed‐loop stability can be proved using an extended version of Theorem in the work of Emami and Rezaeizadeh . The following theorem proves the asymptotical stability of the proposed closed‐loop structure.…”

Section: Trajectory Tracking Controlmentioning

confidence: 92%

“…An effective modification in the MPC structure was introduced in the work of Emami and Rezaeizadeh for determining the proper values of the elements of matrix Q in each iteration. Indeed, the instantaneous value of each element of the matrix is proportional to the difference between the corresponding output of each element and the desired value of that output.…”

Section: Trajectory Tracking Controlmentioning

confidence: 99%

“…The prediction model of the air vehicle is generally a nonlinear function of the system inputs, ie,

boldY false(k N_{p} false) = boldS ((), boldy false(k false), normalΔ boldU false(k N_{u} false)),

where S is a nonlinear function . Using the introduced prediction model, the proposed control design becomes a nonlinear MPC structure.…”

Section: Trajectory Tracking Controlmentioning

confidence: 99%

“…where S is a nonlinear function. 22 Using the introduced prediction model, the proposed control design becomes a nonlinear MPC structure. However, solving a nonlinear optimization equation in each iteration is a challenging problem.…”

Section: Model Predictive Control With Nonlinear Prediction and Lineamentioning

confidence: 99%

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

Emami

Banazadeh

2019

Intl J Robust & Nonlinear

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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.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Trajectory Tracking Controlmentioning

confidence: 92%

Section: Trajectory Tracking Controlmentioning

confidence: 99%

“…The prediction model of the air vehicle is generally a nonlinear function of the system inputs, ie,

boldY false(k N_{p} false) = boldS ((), boldy false(k false), normalΔ boldU false(k N_{u} false)),

where S is a nonlinear function . Using the introduced prediction model, the proposed control design becomes a nonlinear MPC structure.…”

Section: Trajectory Tracking Controlmentioning

confidence: 99%

Section: Model Predictive Control With Nonlinear Prediction and Lineamentioning

confidence: 99%

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

Emami

Banazadeh

2019

Intl J Robust & Nonlinear

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Identification of nonlinear time‐varying systems using wavelet neural networks

Emami

Roudbari

2020

Adv Control Appl

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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.

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Data‐based nonlinear learning control for aircraft trajectory tracking via Gaussian process regression

Wei,

Meng,

Zhang

et al. 2024

Intl J Robust & Nonlinear

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In this article, a new data‐based iterative learning control (ILC) algorithm is proposed via Gaussian process regression (GPR) to accomplish the trajectory tracking objective of aircraft subject to completely unknown dynamics and strong nonlinearities. The nonlinear system input–output relationship of the unknown aircraft is formulated through GPR by leveraging historical data, based on which an optimal ILC framework is established. The monotonic convergence analysis of the GPR‐based ILC is explored such that high‐precision tracking tasks can be accomplished without prior model knowledge. Simulation tests are further conducted on a commercial aircraft performing a continuous climb operation to illustrate the effectiveness of the GPR‐based ILC approach.

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Adaptive model predictive control‐based attitude and trajectory tracking of a VTOL aircraft

Cited by 38 publications

References 37 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

Data‐based nonlinear learning control for aircraft trajectory tracking via Gaussian process regression

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