Applications of Linear and Nonlinear Robustness Analysis Techniques to the F/A-18 Flight Control Laws

Chakraborty, Abhijit; Seiler, Peter; Balas, Gary J.

doi:10.2514/6.2009-5675

Cited by 16 publications

(13 citation statements)

References 21 publications

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“…The landing mathematic model of the F/A-18 carrier-based aircraft is described in Chakraborty 31 and Chakraborty et al, 32,33 and the flight test data of F/A-18 Hornet aircraft are introduced in detail. The nonlinear landing functions, aerodynamic parameter, and control surface and actuator configurations of F/A-18 are referred by this article.…”

Section: Longitudinal Landing Mathematic Modelmentioning

confidence: 99%

“…The MPC algorithm derived from the linear landing model is employed to control the nonlinear landing model. The aerodynamic parameter of the landing model can be seen by Chakraborty 31 and Chakraborty et al 32,33 The landing mathematic model is realized by the Visual Studio 2010, and the three-dimensional scene is realized by the Vega Prime 6.0, and MPC algorithm is realized by the MATLAB. The data interactions are completed through the network communication.…”

Section: Simulated Analysismentioning

confidence: 99%

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Automatic flight control design considering objective and subjective risks during carrier landing

Wang

Zhang

Zhu

2019

Proceedings of the Institution of Mechanical Engineers, Part I:

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In this article, a design scheme of automatic carrier landing system control law based on combination of the objective risk and the subjective risk is proposed, in order to improve the safety and flying quality of the landing. The nonlinear longitudinal mathematical model is constructed in the air wake turbulence condition during carrier landing, which is transformed into a linear perturbed model by the state-space equations with deviation state variables. The concepts of the objective risk and the subjective risk in the recovery of an aircraft aboard a carrier are addressed. A principle of predicting the future states based on the current ones is put forward so that a mathematic model for the objective risk is established, synthetically considering the current and future landing state deviations. For the other risk, the corresponding model is obtained by the subjective experiences of the pilots in the flight simulation tests. Furthermore, a novel model predictive control algorithm, which contains the additional subjective risk and the time-varying weights of the state terms, is proposed. Automatic carrier landing control law is built by introducing the objective risk, the subjective risk, and the effect of carrier air wake disturbance. In the rolling optimization progress, these time-varying weights are dynamically tuned according to the constantly changing objective risk to control the state deviations and suppress this risk, while the subjective risk is handled by the additional risk terms. Besides, the action of carrier air wake disturbance is considered and compensated in the derivation of the linear matrix inequalities. Test results based on a semi-physical simulation platform indicate that the new automatic carrier landing system control algorithm proposed by this article brings about an excellent carrier landing performance as well as an improved flying quality.

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Section: Longitudinal Landing Mathematic Modelmentioning

confidence: 99%

Section: Simulated Analysismentioning

confidence: 99%

Automatic flight control design considering objective and subjective risks during carrier landing

Wang

Zhang

Zhu

2019

Proceedings of the Institution of Mechanical Engineers, Part I:

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“…Many of these incidents have been described as a falling leaf motion of the aircraft [41]. Additional details on this analysis can be found in [42]. The complex dynamics of the falling leaf motion and lack of flight data from the departure events pose a challenge in studying this motion.…”

Section: Roa Estimation For An F/a-18mentioning

confidence: 99%

“…The computational burden of SOS optimization also restricts the model to cubic degree polynomials. Further details of this polynomial model approximation are provided in [42]. The polynomial model captures the key characteristics of the full 6 DOF model.…”

Section: Roa Estimation For An F/a-18mentioning

confidence: 99%

Optimization Based Clearance of Flight Control Laws

2012

Lecture Notes in Control and Information Sciences

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ForewordAs far as the laws of mathematics refer to reality, they are not certain; and as far as they are certain, they do not refer to reality.Albert EinsteinSince the early days of aviation, engineers with inventive talents had to overcome tremendous challenges. From building vehicles that can fly in a controllable manner designed and flown by pioneers like Lilienthal, Wright brothers, Sperry in the beginnings, to developing todays modern comfortable and reliable vehicles for daily routine all-weather operations (Airbus, Boeing and others), there were many creative efforts to improve performance (aircraft size, endurance, speed), minimize structural weight, provide necessary thrust, and guarantee safe flight operations. Nowadays all these inventions assure the high mobility of the modern human society in a global world. The aeronautical challenges were drivers for many new technologies and methodologies that are commonly used in other industries today. In the last three decades, the requirements for the design of high-performance flight control systems that enhance automation of flight, initiated a number of ingenious technological developments. Flight control law design is one of the areas where aeronautical engineers are pioneering new technologies.During development (design and test) of flight control laws, engineers rely on mathematical models. Inevitably such models cannot mimic all aspects of a highly complex, physical plant as a modern high-performance jet airplane and its environment (atmosphere, air traffic, etc.) with absolute fidelity. The above quotation from Einstein describes the fundamental difficulty that control engineers are facing, when striving the clearance of flight control laws on basis of mathematical models.However, engineers (named after the Latin word ingenium, meaning innate quality, especially mental power, hence a clever invention) often find solutions even to the most challenging problems. To prove that an aircraft is safe to fly VI Foreword they develop aircraft dynamics models with quantified uncertainties and apply adequate mathematical theories for their analysis. Those models can be used to verify that the control laws operate as specified even when deviations from the nominal conditions occur. In addition, test engineers consider all imaginable test conditions using experience from the past including knowledge about abnormal cases, incidents and accidents. This approach extends the uncertain parameter space with many additional test cases, with special care to investigate the critical ones. The huge amount of possible parameter combinations as well as the presence of model nonlinearities result in an incredibly high number of test cases to be checked for the clearance of flight control laws. The objective of the EC-sponsored project COFCLUO aims at mastering this Herculean task, by applying efficient optimization-based search techniques to discover hidden weaknesses of flight control laws and to determine worst-case parameter combinations to aid possible control law redesigns...

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“…Linear analysis tools did not detect the potential of the closed-loop system to exhibit the falling leaf mode. Thus there is a need for nonlinear analysis tools to fill this gap (Chakraborty et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Nonlinear region of attraction analysis for flight control verification and validation

Chakraborty

Seiler

Balas

2011

Control Engineering Practice

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Current practice for flight control validation relies heavily on linear analyses and nonlinear, high-fidelity simulations. This process would be enhanced by the addition of nonlinear analyses of the flight control system. This paper demonstrates the use of region of attraction estimation for studying nonlinear effects. A nonlinear polynomial model is constructed for the longitudinal dynamics of NASA's Generic Transport Model aircraft. A polynomial model for the short period dynamics is obtained by decoupling this mode from the nonlinear longitudinal model. Polynomial optimization techniques are applied to estimate region of attractions around trim conditions. CONTROL ENGINEERING PRACTICEPAPER CHECKLIST for the preparation of papers.Please ensure that your paper conforms to the following guidelines. PREFERRED FORMAT AND LAYOUT:

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Applications of Linear and Nonlinear Robustness Analysis Techniques to the F/A-18 Flight Control Laws

Cited by 16 publications

References 21 publications

Automatic flight control design considering objective and subjective risks during carrier landing

Automatic flight control design considering objective and subjective risks during carrier landing

Optimization Based Clearance of Flight Control Laws

Nonlinear region of attraction analysis for flight control verification and validation

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