Modular flight control reconfiguration design and simulation

Lombaerts, Thomas; Chu, Q.P.; Mulder, J.A.; Joosten, Diederick

doi:10.1016/j.conengprac.2010.12.008

Cited by 23 publications

(24 citation statements)

References 35 publications

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“…The use of dynamic inversion eliminates the need for a gain scheduled controller. Nonlinear dynamic inversion control as applied by Spaulding et al (2005), Steinberg (2001), Lombaerts et al (2011), Bajodah (2009 and Lee et al (2005) has been successful. However, NDI controllers are susceptible to errors arising from the inversion of the plant dynamics.…”

Section: Jo Pedro Et Almentioning

confidence: 99%

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A nonlinear dynamic inversion-based neurocontroller for unmanned combat aerial vehicles during aerial refuelling

Pedro¹,

Panday²,

Dala³

2013

International Journal of Applied Mathematics and Computer Science

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The paper presents the development of modelling and control strategies for a six-degree-of-freedom, unmanned combat aerial vehicle with the inclusion of the centre of gravity position travel during the straight-leg part of an in-flight refuelling manoeuvre. The centre of gravity position travel is found to have a parabolic variation with an increasing mass of aircraft. A nonlinear dynamic inversion-based neurocontroller is designed for the process under investigation. Three radial basis function neural networks are exploited in order to invert the dynamics of the system, one for each control channel. Modal and time-domain analysis results show that the dynamic properties of the aircraft are strongly influenced during aerial refuelling. The effectiveness of the proposed control law is demonstrated through the use of simulation results for an F-16 aircraft. The longitudinal neurocontroller provided interesting results, and performed better than a baseline nonlinear dynamic inversion controller without neural network. On the other hand, the lateral-directional nonlinear dynamic inversion-based neurocontroller did not perform well as the longitudinal controller. It was concluded that the nonlinear dynamic inversion-based neurocontroller could be applied to control an unmanned combat aerial vehicle during aerial refuelling.

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Section: Jo Pedro Et Almentioning

confidence: 99%

“…This work led to taking advantage of two control techniques. A reconfigurable flight control algorithm was also designed combining model identification and adaptive NDI (Lombaerts et al, 2011). Parametric uncertainty was considered along with dynamic inversion by MacKunis et al (2010).…”

mentioning

confidence: 99%

A nonlinear dynamic inversion-based neurocontroller for unmanned combat aerial vehicles during aerial refuelling

Pedro¹,

Panday²,

Dala³

2013

International Journal of Applied Mathematics and Computer Science

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“…In civil aviation, many developments focus on the improvement of safety levels and reducing the risks that critical faults occur. 1 According to the International Civil Aviation Organization, 2 loss of control in-flight is one of the three high-risk accident occurrence categories. It accounts for 33% of all fatal accidents and 60% of all fatalities in 2013.…”

Section: Introductionmentioning

confidence: 99%

Active Fault-Tolerant Control System using Incremental Backstepping Approach

Kampen

2015

AIAA Guidance, Navigation, and Control Conference

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This paper proposes an Active Fault-Tolerant Control System (AFTCS) using the Incremental Backstepping (IBS) approach. The AFTCS contains a sensor Fault Detection and Diagnosis (FDD) system and a Fault-Tolerant Control (FTC) system and it is able to cope with Inertial Measurement Unit (IMU) sensor and Air Data Sensors (ADS) faults, as well as actuator faults and process faults. IMU faults are detected and estimated by an Iterative Optimal Two-Stage Extended Kalman Filter whereas ADS faults are detected and diagnosed by designing an Iterative Three-Step Extended Kalman Filter. The FTC system uses an IBS controller in order to cope with actuator and process faults. The FDD system provides more reliable information for the FTC system which guarantees that the IBS can maintain the flight. The performance of the complete AFTCS is validated using a Cessna Citation II aircraft model with the objective of attitude control. The simulation results demonstrate that the AFTCS is able to maintain the flight in the presence of sensor and actuator faults.Nomenclature A x , A y , A z = specific forces along the body axis, m/s 2 p, q, r = roll, pitch and yaw rate along the body axis, rad/s u a , v a , w a = air speed components along the body axis, m/s u B GS , v B GS , w B GS = ground speed components along the body axis, m/s V t = true airspeed, m/s α, β = angle of attack, sideslip angle, rad φ, θ, ψ = roll, pitch and yaw angles along the body axis, rad δ e , δ a , δ r

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“…The topic of FTC has been widely developed in the last decade, [4][5][6][7][8][9] and many different paradigms have subsequently been applied to the problem of fault-tolerant flight control. [10][11][12] For instance, control allocation (CA), [13][14][15][16] modular or physical approaches, [17][18][19] model predictive control, 20,21 backstepping/nonlinear dynamic inversion, [22][23][24] set invariant methods, 25  ∞ approaches, 4,26 and extensions to linear parameter-varying (LPV) systems [27][28][29][30][31][32] have investigated. In recent years, adaptive controllers have been studied and particularly those in an l 1 framework have also seen a renaissance.…”

Section: Introductionmentioning

confidence: 99%

Development and evaluation of an integral sliding mode fault‐tolerant control scheme on the RECONFIGURE benchmark

Chen

Alwi

Edwards

2017

Intl J Robust & Nonlinear

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Summary This paper describes the development, application, and evaluation of a linear parameter‐varying integral sliding mode control allocation scheme to the Reconfiguration of Control in Flight for Integral Global Upset Recovery benchmark model to deal with an actuator failure/fault scenario. The proposed scheme has the capability to maintain close to nominal (fault free) load factor control performance in the face of elevator failures/faults by including a retrofitted integral sliding mode term and then rerouting (via control allocation) the augmented control signal to healthy elevators without reconfiguring the baseline controller. In order to mitigate any chattering appearing in the elevator demands, the retrofitted signal is based on a super‐twisting sliding mode structure. This produces a control signal that is continuous and does not have the discontinuous switching nature of traditional sliding mode schemes. The scheme is evaluated using an industrial Functional Engineering Simulator developed as part of the Reconfiguration of Control in Flight for Integral Global Upset Recovery project. Monte Carlo campaign results are shown to demonstrate the performance of the proposed scheme.

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Modular flight control reconfiguration design and simulation

Cited by 23 publications

References 35 publications

A nonlinear dynamic inversion-based neurocontroller for unmanned combat aerial vehicles during aerial refuelling

A nonlinear dynamic inversion-based neurocontroller for unmanned combat aerial vehicles during aerial refuelling

Active Fault-Tolerant Control System using Incremental Backstepping Approach

Development and evaluation of an integral sliding mode fault‐tolerant control scheme on the RECONFIGURE benchmark

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