A Verification-Driven Approach to Control Analysis and Tuning

Crespo, Luis G.; Kenny, Sean P.; Giesy, Daniel P.

doi:10.2514/6.2008-6340

Cited by 5 publications

(5 citation statements)

References 22 publications

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“…Most, if not all, proposed approaches formulate the design task as an optimization problem, in which the merit function evaluation requires numerical simulation of the augmented system's timeresponse. For instance, (Crespo et al, 2008) develops optimization-based strategies for control analysis and tuning at the control verification stage, which build upon numerical evaluation of controller's performance metrics that require simulation of the augmented model. Other authors (dos Santos Coelho, 2009) suggest using chaotic optimization algorithms for enhancing the computational efficiency of the numerical optimization problem.…”

Section: Introductionmentioning

confidence: 99%

Automatic Flight Control Systems

Sadraey

2020

Synthesis Lectures on Mechanical Engineering

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Dr Thomas Lombaerts was born in 1980 in Brussels, Belgium. He graduated cum laude in 2004 as an aerospace engineer, specializing in flight control at the Faculty of Aerospace Engineering, Delft University of Technology in the Netherlands. In May 2010, he successfully defended his PhD research on Fault Tolerant Flight Control using a physical model approach at the same university. Since September 2010, he has been a research fellow at the German Aerospace Center DLR in Munich. His research interests include aircraft state estimation and Kalman filtering, aerodynamic model identification, adaptive and nonlinear control, control allocation, handling qualities, and pilot workload analysis. In 2011, he won a Marie Curie International Outgoing Fellowship (IOF) from the European Union for the ADFLICO research project, involving the scientific partners NASA and DLR. Several conference and journal publications as well as book chapters about his research have been published in the past years. ContentsPreface XI Part Literature Review and Theoretical Developments 1Chapter Fundamentals of GNSS-Aided Inertial Navigation 3

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Section: Introductionmentioning

confidence: 99%

Automatic Flight Control Systems

Sadraey

2020

Synthesis Lectures on Mechanical Engineering

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“…The PSM has the same units as the uncertain parameters, and depends on the shape, but not the size, of the reference set. The sign convention enforced by γ in Equations (14,16) implies the following. If the PSM takes on a negative value, the controller does not even satisfy the requirements for nominal parameter point.…”

Section: Figures Of Merit For Deterministic Uncertainty Modelsmentioning

confidence: 99%

“…The need for assessing bad controllers results from the desire of searching for optimal ones in an automated fashion. 14 Formulations that enable the deformation of hyper-spherical and hyper-rectangular sets in the parameter space are presented in the next section. These formulations require solving an optimization problem.…”

Section: Set Deformationsmentioning

confidence: 99%

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Figures of Merit for Control Verification

Crespo

Kenny

2008

AIAA Guidance, Navigation and Control Conference and Exhibit

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This paper proposes a methodology for evaluating a controller's ability to satisfy a set of closed-loop specifications when the plant has an arbitrary functional dependency on uncertain parameters. Control verification metrics applicable to deterministic and probabilistic uncertainty models are proposed. These metrics, which result from sizing the largest uncertainty set of a given class for which the specifications are satisfied, enable systematic assessment of competing control alternatives regardless of the methods used to derive them. A particularly attractive feature of the tools derived is that their efficiency and accuracy do not depend on the robustness of the controller. This is in sharp contrast to Monte Carlo based methods where the number of simulations required to accurately approximate the failure probability grows exponentially with its closeness to zero. This framework allows for the integration of complex, high-fidelity simulations of the integrated system and only requires standard optimization algorithms for its implementation.

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“…Besides, the larger the value of ξ , the smaller the density of points over the surface of the approximated maximal set, and the greater the chance to converge to an overly large approximation of the true maximal set. Procedures to generate the p i points are available in reference [3].…”

Section: B Enlarging An Approximation To the Maximal Setmentioning

confidence: 99%

Verification and Tuning of an Adaptive Controller for an Unmanned Air Vehicle*

Crespo

Matsutani

Annaswamy

2010

AIAA Guidance, Navigation, and Control Conference

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This paper focuses on the analysis and tuning of a controller based on the "Adaptive Control Technology for Safe Flight (ACTS)" architecture. The ACTS architecture consists of a nominal, non-adaptive controller that provides satisfactory performance under nominal flying conditions, and an adaptive controller that provides robustness under off-nominal ones. A framework unifying control verification and gain tuning is used to make the controller's ability to satisfy the closed-loop requirements more robust to uncertainty. In this paper we tune the gains of both controllers using this approach. Some advantages and drawbacks of adaptation are identified by performing a global robustness assessment of both the adaptive controller and its non-adaptive counterpart. The analyses used to determine these characteristics are based on evaluating the degradation in closed-loop performance resulting from uncertainties having increasing levels of severity. The specific adverse conditions considered can be grouped into three categories: aerodynamic uncertainties, structural damage, and actuator failures. These failures include partial and total loss of control effectiveness, locked-in-place control surface deflections, and engine out conditions. The requirements considered are the peak structural loading, the ability of the controller to track pilot commands, the ability of the controller to keep the aircraft's state within the reliable flight envelope, and the handling/riding qualities of the aircraft. The nominal controller resulting from these tuning strategies was successfully validated using the NASA GTM Flight Test Vehicle.

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A Verification-Driven Approach to Control Analysis and Tuning

Cited by 5 publications

References 22 publications

Automatic Flight Control Systems

Automatic Flight Control Systems

Figures of Merit for Control Verification

Verification and Tuning of an Adaptive Controller for an Unmanned Air Vehicle*

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