Multiple model adaptive control using second level adaptation for a delta-wing aircraft

Ahmadian, Narjes; Khosravi, Alireza; Sarhadi, Pouria

doi:10.1108/ijius-08-2014-0006

Cited by 7 publications

(5 citation statements)

References 27 publications

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“…The numerical values of the coefficients matrices A p and B p are selected according to the three operating points given in equations ( 37)- (39). Furthermore, the optimal negative feedback coefficients (K) obtained from the LQR method and the state coefficient matrix of the reference model system (A ref ) are defined according to these three operating points.…”

Section: Simulation Of the Gas Turbine Enginementioning

confidence: 99%

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Control of a jet engine using predictor-based adaptive strategy in different flight modes

Ranjbar

Khosravi

Ahmadian

2023

Proceedings of the Institution of Mechanical Engineers, Part I:

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In this article, a predictor-based model reference adaptive control method is proposed for a JetCat SPT5 turboshaft engine in full thrust, cruise, and idle modes. In the predictor-based model reference adaptive control method, in addition to the tracking error, the predictor error is also utilized in adaptive laws to achieve the desired control objectives, such as improving the tracking performance and control signals, and reducing the tracking error. The proposed method is implemented on a turboshaft engine system with actual dynamic values. First, three properly separated equilibrium points are selected on the nominal plant equilibrium manifold to linearize the plant model at the equilibrium points. Then, the predictor-based model reference adaptive control controller is designed and investigated for the three equilibrium points generating three operating modes of the engine, that is, full thrust, cruise, and idle. The stability of the control system is proved by the Lyapunov method. To evaluate the efficiency of the state predictor method in the simulation scenarios, the proposed method is compared with the classic model reference adaptive control method. The simulation results illustrate the superiority of the predictor-based model reference method due to the reduction of unwanted fluctuations in tracking performance, faster convergence of the tracking error to zero, and smoother control signals for all the equilibrium points.

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Section: Simulation Of the Gas Turbine Enginementioning

confidence: 99%

“…The PMRAC design method can be applied to the original system in equation (12) without adding integrated output tracking errors. As a result, the control input will be as follows 39…”

Section: Model Description and Controller Designmentioning

confidence: 99%

Control of a jet engine using predictor-based adaptive strategy in different flight modes

Ranjbar

Khosravi

Ahmadian

2023

Proceedings of the Institution of Mechanical Engineers, Part I:

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“…18 Another problem is related to the maneuverings and traversing the curved paths at high speeds in which the goal is to achieve the system output to track a desired trajectory. 20…”

Section: Introductionmentioning

confidence: 99%

“…18 Another problem is related to the maneuverings and traversing the curved paths at high speeds in which the goal is to achieve the system output to track a desired trajectory. 20 Considering the input delay caused by slow dynamics of input actuators will create a serious problem for the system. The control input always lags from the system as much as the delay time.…”

Section: Introductionmentioning

confidence: 99%

Composite adaptive posicast controller for the wing rock phenomenon in a delta-wing aircraft

Yousefimanesh

Khosravi

Sarhadi

2019

Proceedings of the Institution of Mechanical Engineers, Part G:

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The nonlinear dynamic phenomenon like wing rock is one of the important issues in the high performance aircraft autopilot design. This phenomenon occurs in the form of constant amplitude oscillations in the roll dynamics, during the flight at high angles of attack (AOAs) and endangers carrying out the mission of an aircraft. In this paper, a composite adaptive posicast controller is designed for the wing rock phenomenon in a delta-wing aircraft with known input delay. The existence of the input delay besides the parametric uncertainties of the system dynamics adds to the complexity of the problem and can cause undesirable troubles in regulation and tracking performance or instability in the control system. Consequently, there is a need for a controller that can provide the stability and desirable regulation and tracking for the system. The proposed control method uses the system state forecasting and the composite model reference adaptive controller in an integrated control structure based on linear quadratic regulator (LQR). Combining the tracking error and the prediction error to form the adaptive laws in the composite model reference adaptive controller improves the characteristics of the system response and provides a better performance compared to the model reference adaptive controller in which the adaptive laws are formed only with the tracking error. Simulation results show the efficiency of the composite adaptive posicast controller in counteracting the system uncertainties in the presence of considerably large input delay cases.

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“…Lately, autopilot design for airplanes, multirotors and missiles have received substantial research attention. There exists a variety of scientific studies making use of robust and adaptive dynamic inversion (Lungu and Lungu, 2012, 2016), reconfigurable flight control laws (Calise et al, 2001), robust non-linear control (Wang and Stengel, 2000), Lyapunov vector fields (Lawrence et al, 2008), command-filtered backstepping (Du et al, 2015), sliding mode control (Liu et al, 2015), multiple model adaptive control (Ahmadian et al, 2015) and invariant manifolds (Karagiannis and Astolfi, 2010).…”

Section: Introductionmentioning

confidence: 99%

Scheduled smooth MIMO robust control of aircraft verified through blade element SIL testing

Kasnakoğlu

2016

Transactions of the Institute of Measurement and Control

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This paper demonstrates a multi-input multi-output (MIMO) robust control approach where multiple scheduled designs are merged to produce a smooth control law. The design is verified using software-in-the-loop (SIL) testing based on blade element theory (BET) for highly realistic flight simulations. An inner-loop attitude controller balances performance and robustness, achieving a fast response time, low overshoot, good noise rejection and minimal lateral–longitudinal coupling. The controllers are formed at several predetermined grid points so the design covers a wide flight envelope. Blade element SIL testing shows that the flight control system preserves stable flight and follows the references well, even under tough weather conditions. The proposed strategy is also compared with a classical autopilot design procedure and is seen to be superior.

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Multiple model adaptive control using second level adaptation for a delta-wing aircraft

Cited by 7 publications

References 27 publications

Control of a jet engine using predictor-based adaptive strategy in different flight modes

Control of a jet engine using predictor-based adaptive strategy in different flight modes

Composite adaptive posicast controller for the wing rock phenomenon in a delta-wing aircraft

Scheduled smooth MIMO robust control of aircraft verified through blade element SIL testing

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