Fast Computable Recoverable Sets and Their Use for Aircraft Loss-of-Control Handling

McDonough, Kevin; Kolmanovsky, Ilya

doi:10.2514/1.g001747

Cited by 20 publications

(6 citation statements)

References 28 publications

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“…The commonly used methods for calculating the stability region of nonlinear systems include the traditional Lyapunov energy function method [28], [29], the Sum of Squares method [30], the manifold method [31], [32], the normal form method [33], the reachable set method [34], [35] and so on. The theory and method of determining the stability region mentioned above have different advantages and disadvantages, and are applied in the aviation field.…”

Section: Calculating Methods Of Stability Region In Monte Carlo Smentioning

confidence: 99%

On Flight Risk Analysis Method Based on Stability Region of Dynamic System Under Icing Conditions

Duan

et al. 2020

IEEE Access

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The occurrence of loss of control in flight is often accompanied by deep coupling of flight parameters. In order to explore the mechanism of coupling induced flight risk, a method of flight risk assessment based on the stability region of a nonlinear dynamic system was proposed. The Monte Carlo method was improved to identify the boundary of the stability region, and the key parameters that can represent the flight safety were set. The risk quantification method and the colorized flight risk characterization method were proposed. Combined with the case of aircraft encountering icing, the variation trend of flight stability region and flight risk under different icing degrees was calculated. The results showed that with the aggravation of icing, the flight stability region was significantly reduced and the coupling of flight parameters was aggravated. And compared with the traditional angle of attack protection method, this method can find the potential flight risk earlier, and characterize the way of parameters' coupling and the flight risk evolution process. The proposed method can improve the pilot's situational awareness, and also provide theoretical support for the prevention of flight risk under adverse conditions, and provide reference for the further development of boundary protection control law. INDEX TERMS Flight risk, quantitative assessment, stability region, ice encountering, risk visualization.

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Section: Calculating Methods Of Stability Region In Monte Carlo Smentioning

confidence: 99%

On Flight Risk Analysis Method Based on Stability Region of Dynamic System Under Icing Conditions

Duan

et al. 2020

IEEE Access

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“…The reason that the considered nonlinear regression can be implemented more effectively by training neural networks with the Levenberg-Marquardt algorithm is twofold. First, a modification of the backpropagation algorithm is incorporated into the Levenberg-Marquardt algorithm for computation of the Jacobian matrix in (41).…”

Section: Fig 14 Two-layer Feedforward Neural Network Architecturementioning

confidence: 99%

Investigating Impaired Aircraft's Flight Envelope Variation Predictability Using Least-Squares Regression Analysis

Norouzi

Kosari

Sabour

2020

Journal of Aerospace Information Systems

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Aircraft failures alter the aircraft dynamics and cause maneuvering flight envelope to change. Such envelope variations are nonlinear and generally unpredictable by the pilot as they are governed by the aircraft complex dynamics. Hence, in order to prevent in-flight Loss of Control it is crucial to practically predict the impaired aircraft's flight envelope variation due to any a-priori unknown failure degree. This paper investigates the predictability of the number of trim points within the maneuvering flight envelope and its centroid using both linear and nonlinear least-squares estimation methods. To do so, various polynomial models and nonlinear models based on hyperbolic tangent function are developed and compared which incorporate the influencing factors on the envelope variations as the inputs and estimate the centroid and the number of trim points of the maneuvering flight envelope at any intended failure degree. Results indicate that both the polynomial and hyperbolic tangent function-based models are capable of predicting the impaired fight envelope variation with good precision. Furthermore, it is shown that the regression equation of the best polynomial fit enables direct assessment of the impaired aircraft's flight envelope contraction and displacement sensitivity to the specific parameters characterizing aircraft failure and flight condition.Nomenclature V = total airspeed, knot ,  = angle of attack, sideslip angle, respectively, deg p, q, r = angular velocity components (roll rate, pitch rate, yaw rate, respectively), deg/s 1 Ph.D. Candidate, Faculty of New Sciences and Technologies; ramin.norouzi@ut.ac.ir. Student Member AIAA. 2 Associate Professor, Faculty of New Sciences and Technologies; kosari_a@ut.ac.ir. 3 Assistant Professor, Faculty of New Sciences and Technologies; sabourmh@ut.ac.ir. , , ,  = Euler angles (roll, pitch, yaw, respectively), flight path angle, deg , = state vector, control vector, respectively , , , ℎ = deflection angles (elevator, aileron, rudder, respectively), deg, throttle setting (%) ∈ [0, 1] , �, = fitted model's residual, outcome, parameters (coefficients) vectors, respectively , = predictor vector, model parameters (coefficients) vector, respectively = total degree of polynomial , ℍ, , = gradient, Hessian, Jacobian of the objective function , identity matrix, respectively = Newton's method search direction = Marquardt parameter = hyperbolic tangent function 1 , 2 = total number of neurons in the first (hidden) layer, second (last) layer, respectively = backpropagation training iteration ℒ , ℒ , ℒ , ℒ, = (net output, net input, bias) of ℒ ℎ neuron in layer , respectively ℒ,= weight's element of ℒ ℎ neuron in layer due to ℎ neuron of the previous layer .̂ = standard backpropagation sensitivity, Marquardt sensitivity, respectively ℰ, = contribution of the ℰ ℎ neuron of the last (second) layer to the error of the ℎ training sample = statistical expected value of the ℎ input factor Subscripts , = total number of training samples, total number of model parameters, res...

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“…Conventional approaches to developing flight control algorithms rely on first identifying aircraft linear time‐invariant (LTI) models corresponding to different aircraft internal states and external operating conditions (off‐line), designing feedback control laws at these states (off‐line), and then implementing a control corresponding to the current state based on gain scheduling/gain interpolation (online) . Our iterative approach, in contrast, optimizes the control corresponding to the current state without relying on pre‐identified LTI models.…”

Section: Pitch Attitude Flight Controlmentioning

confidence: 99%

LQ control of unknown discrete‐time linear systems—A novel approach and a comparison study

Kolmanovsky

Girard

2018

Optim Control Appl Methods

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In this paper, we propose a novel approach to the linear quadratic (LQ) optimal control of unknown discrete-time linear systems. We first describe an iterative procedure for minimizing a partially unknown static function. The procedure is based on simultaneous updates in the estimation of unknown parameters and in the optimization of controllable inputs. We then use the procedure for control optimization in unknown discrete-time dynamic systems-we consider applications to the finite-horizon and the infinite-horizon LQ control of linear systems in detail. To illustrate the approach, an example of the pitch attitude control of an aircraft is considered. We also compare our proposed approach to several other approaches to finite/infinite-horizon LQ control problems with unknown dynamics from the literature, including extremum seeking and adaptive dynamic programming/reinforcement learning. Our proposed approach is competitive with these approaches in speed of convergence and in implementation and computational complexity. KEYWORDSLQ control, optimal control, unknown system Optim Control Appl Meth. 2019;40:265-291. wileyonlinelibrary.com/journal/oca © 2018 John Wiley & Sons, Ltd. 265 266 LI ET AL.To cope with the lack of accurate models or the changes in dynamics, control techniques such as adaptive control 3 and robust optimal control 4 can be applied. The goal of adaptive control is, in principle, different from that of optimal control. On the one hand, adaptive control usually concerns itself with the adaptation of a fixed-form controller to initially uncertain system parameters to achieve typical control requirements such as stabilization and reference tracking. On the other hand, the goal of optimal control is to find a control that minimizes a specified cost function, while stabilization and reference tracking may be achieved through the cost function design. Robust optimal control pursues such a goal for systems with uncertain parameters: it optimizes the control to minimize the worst cost (in a min-max formulation) 5,6 or the cost expectation (in a probability-weighted-average formulation) 7 over an uncertainty set.An alternative technique is optimal control for unknown systems, which is also referred to as model-free optimal control, ie, optimal control without the need for prior knowledge of the system dynamics. Differently from the problem setting of robust optimal control, model-free optimal control typically pursues control optimization to minimize the true cost associated with a given but unknown system.Model-free optimal control approaches relying on learning and dynamic programming have been developed, for example, in the works of Dierks et al, 8 Lewis and Vamvoudakis, 9 and Wang et al. 10 In the work of Dierks et al, 8 a neural network is used to learn the plant dynamics online; then, an adaptive dynamic programming (ADP) algorithm is exploited to obtain an optimal control law off-line based on the learned neural network model. In the work of Lewis and Vamvoudakis, 9 policy iteration and value itera...

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Fast Computable Recoverable Sets and Their Use for Aircraft Loss-of-Control Handling

Cited by 20 publications

References 28 publications

On Flight Risk Analysis Method Based on Stability Region of Dynamic System Under Icing Conditions

On Flight Risk Analysis Method Based on Stability Region of Dynamic System Under Icing Conditions

Investigating Impaired Aircraft's Flight Envelope Variation Predictability Using Least-Squares Regression Analysis

LQ control of unknown discrete‐time linear systems—A novel approach and a comparison study

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