Robust flight control system using neural networks: Adaptive dynamic surface design approach

Ju Won Lee, Jin Bae Park, Yoon Ho Choi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we propose an adaptive control method for micro aerial vehicle(MAV) flight system with model uncertainties. The proposed control system is constructed by the combination of the adaptive dynamic surface control(ADSC) technique and the self recurrent wavelet neural network(SRWNN). The ADSC technique which make the virtual controller using the first order filter provides us with the ability to overcome the explosion of complexity problems of the backstepping controller. The SRWNNs are used to observe the arbitrary model uncertainties of MAV flight system, and all their weights are trained on-line. From the Lyapunov stability theory, we derive the on-line tuning algorithms for all weights of SRWNNs and prove that all signals of a closed-loop system are uniformly ultimately bounded(UUB). Finally, we perform simulations to demonstrate the tracking performance and robustness of the proposed MAV control system during the pursuit guidance landing.

Original languageEnglish
Title of host publicationICCAS 2010 - International Conference on Control, Automation and Systems
Pages657-662
Number of pages6
Publication statusPublished - 2010 Dec 1
EventInternational Conference on Control, Automation and Systems, ICCAS 2010 - Gyeonggi-do, Korea, Republic of
Duration: 2010 Oct 272010 Oct 30

Publication series

NameICCAS 2010 - International Conference on Control, Automation and Systems

Other

OtherInternational Conference on Control, Automation and Systems, ICCAS 2010
CountryKorea, Republic of
CityGyeonggi-do
Period10/10/2710/10/30

Fingerprint

Flight control systems
Control surfaces
Antennas
Neural networks
Control systems
Controllers
Electronic guidance systems
Backstepping
Landing
Robustness (control systems)
Closed loop systems
Explosions
Tuning
Uncertainty

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Control and Systems Engineering

Cite this

Lee, J. W., Park, J. B., & Choi, Y. H. (2010). Robust flight control system using neural networks: Adaptive dynamic surface design approach. In ICCAS 2010 - International Conference on Control, Automation and Systems (pp. 657-662). [5669861] (ICCAS 2010 - International Conference on Control, Automation and Systems).
Lee, Ju Won ; Park, Jin Bae ; Choi, Yoon Ho. / Robust flight control system using neural networks : Adaptive dynamic surface design approach. ICCAS 2010 - International Conference on Control, Automation and Systems. 2010. pp. 657-662 (ICCAS 2010 - International Conference on Control, Automation and Systems).
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abstract = "In this paper, we propose an adaptive control method for micro aerial vehicle(MAV) flight system with model uncertainties. The proposed control system is constructed by the combination of the adaptive dynamic surface control(ADSC) technique and the self recurrent wavelet neural network(SRWNN). The ADSC technique which make the virtual controller using the first order filter provides us with the ability to overcome the explosion of complexity problems of the backstepping controller. The SRWNNs are used to observe the arbitrary model uncertainties of MAV flight system, and all their weights are trained on-line. From the Lyapunov stability theory, we derive the on-line tuning algorithms for all weights of SRWNNs and prove that all signals of a closed-loop system are uniformly ultimately bounded(UUB). Finally, we perform simulations to demonstrate the tracking performance and robustness of the proposed MAV control system during the pursuit guidance landing.",
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Lee, JW, Park, JB & Choi, YH 2010, Robust flight control system using neural networks: Adaptive dynamic surface design approach. in ICCAS 2010 - International Conference on Control, Automation and Systems., 5669861, ICCAS 2010 - International Conference on Control, Automation and Systems, pp. 657-662, International Conference on Control, Automation and Systems, ICCAS 2010, Gyeonggi-do, Korea, Republic of, 10/10/27.

Robust flight control system using neural networks : Adaptive dynamic surface design approach. / Lee, Ju Won; Park, Jin Bae; Choi, Yoon Ho.

ICCAS 2010 - International Conference on Control, Automation and Systems. 2010. p. 657-662 5669861 (ICCAS 2010 - International Conference on Control, Automation and Systems).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Lee JW, Park JB, Choi YH. Robust flight control system using neural networks: Adaptive dynamic surface design approach. In ICCAS 2010 - International Conference on Control, Automation and Systems. 2010. p. 657-662. 5669861. (ICCAS 2010 - International Conference on Control, Automation and Systems).