Aircraft reconfiguration using neural generalized predictive control

Soloway, Don; Haley, P.

doi:10.1109/acc.2001.946348

Cited by 12 publications

(9 citation statements)

References 6 publications

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“…where in (17), x and y are treated like fix parameters and´. 2 / evolves on its stretched time scale ( 2 ).…”

Section: Intuitive Description Of the Three-time Scale Decompositionmentioning

confidence: 99%

“…Note that G.x/ represents the quasi-steady-state of the boundary layer † F -subsystem, (19), when " 1 D 0, that is, 0 D g.x, y,´/ ! y D G.x/, and H.x, G.x// represents the quasi-steady-state of the boundary layer † U -subsystem, (17), when evolving on the manifold of the † F -subsystem.…”

Section: Intuitive Description Of the Three-time Scale Decompositionmentioning

confidence: 99%

“…Calise and Kim used feedback linearization along with NNs, as an alternative to gain scheduling, extending their approach to a wide range of aerospace systems . Haley and Soloway proposed a Neural Generalized Predictive Control algorithm capable of real‐time control law reconfiguration, model adaptation, and the ability to identify failures in control effectiveness for aircrafts. Bull, Kaneshige, and Totah used an innovative generic neural flight control and autopilot system.…”

Section: Introductionmentioning

confidence: 99%

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Three‐time scale singular perturbation control and stability analysis for an autonomous helicopter on a platform

Esteban

Gordillo

Aracil

2012

Intl J Robust & Nonlinear

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SUMMARYA three‐time scale singular perturbation control law is designed for a nonlinear helicopter model in vertical flight. The proposed control law is based on time scale decomposition and is able to achieve the desired altitude by selecting a desired angular velocity and the associated collective pitch angle of the blades. The stability of the system is performed by presenting a stability analysis for generic three‐time scale singularly perturbed systems, which allows to construct a composite Lyapunov function for the resultant closed‐loop system by using time scale separation and also providing mathematical expressions for the upper bounds of the singularly perturbed parameters that define the three‐time scale. Numerical results on both, the singular perturbation control strategy and the stability analysis, are also presented for the studied nonlinear highly coupled helicopter model. Copyright © 2012 John Wiley & Sons, Ltd.

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“…where in (17), x and y are treated like fix parameters and´. 2 / evolves on its stretched time scale ( 2 ).…”

Section: Intuitive Description Of the Three-time Scale Decompositionmentioning

confidence: 99%

Section: Intuitive Description Of the Three-time Scale Decompositionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Three‐time scale singular perturbation control and stability analysis for an autonomous helicopter on a platform

Esteban

Gordillo

Aracil

2012

Intl J Robust & Nonlinear

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“…An artificial neural network is a collection of nodes that are simple processors. [2][3][4] Each node takes a set of inputs and generates a set of outputs which are minimized to match the target outputs supplied during training. Through randomized iterative processing, the nodes converge to modeling the behavior of the input and output data.…”

Section: A Backgroundmentioning

confidence: 99%

Health Monitoring via Neural Networks

Selig

Neogi

2010

AIAA Infotech@Aerospace 2010

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Monitoring of semi-autonomous systems increases situational awareness and is highly applicable to small remotely piloted aircraft. The complex dynamics of aircraft flight were abstracted by modeling the system with a neural network. The neural network accurately modeled the non-faulty dynamics for comparison to the actual system. Since, the system included unmodeled lag, the residual calculation needed to account for unmodeled lag. The method presented here decreased the effects of the lag on the residual. A ratio between the predicted and measured system outputs was used to understand the faults and understand how the future performance will be affected by the fault. A state machine abstraction offered a flexible fault-detection framework that can be adapted and designed to detect different faults without requiring a full dynamics model of each fault. Through using abstractions, the complex dynamics models required for fault detection were simplified and a state machine offered the flexibility to detect different suites of faults.

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“…With the availability of fast computational algorithms and high-speed processors, fast online numerical solution of algebraic equations is not considered to be an infeasible task. For example, the viability of the Newton-Raphson technique for online applications is discussed in [26,27] where the authors have used the technique for complex real-life problems. Note that a good initial guess solution can be provided at any time step k as ( )…”

Section: (Ii)mentioning

confidence: 99%

Model-following neuro-adaptive control design for non-square, non-affine nonlinear systems

Padhi

Unnikrishnan

Balakrishnan

2007

IET Control Theory Appl.

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This paper proposes a new model-following adaptive control design technique for a class of non-affine and nonsquare nonlinear systems using neural networks. An appropriate stabilizing controller is assumed available for a nominal system model. This nominal controller may not be able to guarantee stability/satisfactory performance in the presence of unmodeled dynamics (neglected algebraic terms in the mathematical model) and/or parameter uncertainties present in the system model. In order to ensure stable behavior, an online control adaptation procedure is proposed in this paper. The controller design is carried out in two steps: (i) synthesis of a set of neural networks which capture matched unmodeled (neglected) dynamics or model uncertainties due to parametric variations and (ii) synthesis of a controller that drives the state of the actual plant to that of a desired nominal model. The neural network weight update rule is derived using Lyapunov theory, which guarantees both stability of the error dynamics (in a practical stability sense) and boundedness of the weights of the neural networks. A desirable characteristic of the adaptation procedure presented in this paper is that it is independent of the technique used to design the nominal controller; and hence can be used in conjunction with any known control design technique. Numerical results for two challenging illustrative problems are presented in this paper which demonstrate these features and clearly bring out the potential of the proposed approach.

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Aircraft reconfiguration using neural generalized predictive control

Cited by 12 publications

References 6 publications

Three‐time scale singular perturbation control and stability analysis for an autonomous helicopter on a platform

Three‐time scale singular perturbation control and stability analysis for an autonomous helicopter on a platform

Health Monitoring via Neural Networks

Model-following neuro-adaptive control design for non-square, non-affine nonlinear systems

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