Hybrid Adaptive Flight Control with Model Inversion Adaptation

Nguyen, Nhan T.

doi:10.5772/15208

Cited by 4 publications

(2 citation statements)

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“…7 Nguyen presented a hybrid adaptive control approach which combines a direct model-reference adaptive control to reduce the tracking error and an indirect recursive leastsquares parameter estimation to reduce the modeling error simultaneously. [9][10][11] Zou et al studied model-reference adaptive control using the Chebyshev orthogonal polynomials for attitude control of spacecraft. 12 Chowdhary and Johnson developed a least squares modification to adaptive control problems where the uncertainty can be linearly parametrized and the modified weight training law uses an estimate of the ideal weights formed online by solving a least squares problem using recorded and current data concurrently.…”

Section: Introductionmentioning

confidence: 99%

“…Least-squares adaptive control has been well-studied by numerous authors. [5][6][7][9][10][11][12][13][14][15] Lai and Wei established consistency of recursive extended least-squares and proposed simple modifications of the Åström-Wittenmark self-tuning regulator. 5 Artificial neural network based adaptive control using least-squares methods is presented by Suykens et al 6 Guo studied a recursive least-squares algorithm with slowly decreasing weights for linear stochastic systems which is found to have self-convergence property.…”

Section: Introductionmentioning

confidence: 99%

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Predictor-Model-Based Least-Squares Model-Reference Adaptive Control with Chebyshev Orthogonal Polynomial Approximation

Nguyen

2013

AIAA Infotech@Aerospace (I@A) Conference

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This paper presents a model-reference adaptive control approach for systems with unstructured uncertainty based on two least-squares parameter estimation methods: gradient-based method and recursive leastsquares method. The unstructured uncertainty is approximated by Chebyshev orthogonal polynomial basis functions. The use of orthogonal basis functions improves the function approximation significantly and enables better convergence of parameter estimates. The adaptation of least-squares adaptive control is driven by the plant modeling error which manifests itself as the tracking error, but not vice versa. A predictor model is introduced to estimate the plant modeling error without the need for differentiation of the state signals which can introduce noise. The least-squares gradient adaptive control achieves superior parameter convergence as compared to the standard model-reference adaptive control. Flight control simulations were conducted with four adaptive controllers: least-squares gradient adaptive control, recursive least-squares adaptive control, standard model-reference adaptive control, and neural-network adaptive control. The results show that the recursive least-squares adaptive control achieves better robustness as measured by a time-delay margin while the least-squares gradient adaptive control achieves better tracking performance than both the standard model-reference adaptive control and neural-network adaptive control.

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Section: Introductionmentioning

confidence: 99%