Abstract
The challenges encountered in the design of nonlinear control systems are very different from those faced by the equivalent linear systems. Recent work on two different types of nonlinear control systems like microbial fuel cells [1] and artificial pancreas [2] with challenging problems related to parametric uncertainties have evoked keen interest in related areas. Especially with SJA parametric uncertainties, the challenge is more as the uncertainty compensation has to be carried forth across all the control loops. When we extend the study of SJA compensation to nonlinear aircraft dynamical systems, we must consider that the number of available inputs is not sufficient to control all the states. Therefore, we utilize the available control signals to control the intermediate states, and those are then useful in the control of the other remaining state variables. We design the corresponding control signals, and an inverse compensator to account for the unknown actuator nonlinearities.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Patel, R., Deb, D., Dey, R., Balas, V.E.: Adaptive control of single chamber two-population MFC. Intelligent Systems Reference Library, pp. 81–89 (2019). https://doi.org/10.1007/978-3-030-18068-3_7
Nath, A., Deb, D., Dey, R.: Robust observer based adaptive control of blood glucose in diabetic patients. Int. J. Control 1–14 (2020). https://doi.org/10.1080/00207179.2020.1750705
Franklin, G., Powell, J., Emami-Naeini, A.: Feedback Control of Dynamic Systems. Prentice-Hall, Upper Saddle River (2002)
Deb, D., Tao, G., Burkholder, J., Smith, D.: An adaptive inverse control scheme for a synthetic jet actuator model. In: Proceedings of the 2005, American Control Conference (2005). https://doi.org/10.1109/acc.2005.1470367
Multivariable control design guidelines: Technical Report WL-TR-96-3099. Honeywell and Lockheed Martin, Wright Patterson AFB, OH (1996)
Stevens, B.L., Lewis, F.L.: Aircraft Control and Simulation. Wiley, New York (1992)
Deb, D., Tao, G., Burkholder, J., Smith, D.: Adaptive compensation control of synthetic jet actuator arrays for airfoil virtual shaping. J. Aircraft 44(2), 616–626 (2007). https://doi.org/10.2514/1.24910
Bryson, A.E., Jr.: Control of Spacecraft and Aircraft. Princeton University Press, Princeton (1994)
Deb, D., Tao, G., Burkholder, J., Smith, D.: Adaptive synthetic jet actuator compensation for a nonlinear tailless aircraft model at low angles of attack. In: 2006 American Control Conference. https://doi.org/10.1109/acc.2006.1655376
Deb, D., Tao, G., Burkholder, J.: High-order design of adaptive inverses for signal-dependent actuator nonlinearities. In: 2008 American Control Conference (2008). https://doi.org/10.1109/acc.2008.4587029
Deb, D., Tao, G., Burkholder, J., Smith, D.: An adaptive inverse control scheme for synthetic jet actuator arrays (2005). Infotech@Aerospace. https://doi.org/10.2514/6.2005-7170
Deb, D., Tao, G., Burkholder, J.: An adaptive inverse compensation scheme for signal-dependent actuator nonlinearities. In: 2007 46th IEEE Conference On Decision And Control (2007). https://doi.org/10.1109/cdc.2007.4434743
Deb, D., Sonowal, S.: Synthetic jet actuator based adaptive neural network control of nonlinear fixed pitch wind turbine blades. In: 2013 IEEE International Conference On Control Applications (CCA) (2013). https://doi.org/10.1109/cca.2013.6662759
Tao, G.: Adaptive Control Design and Analysis. Wiley, New York (2003)
Patel, R., Deb, D., Dey, R., Balas, V.E.: Introduction to adaptive control. Intelligent Systems Reference Library, pp. 53–65 (2019). https://doi.org/10.1007/978-3-030-18068-3_5
Nath, A., Deb, D., Dey, R., Das, S.: Blood glucose regulation in type 1 diabetic patients: an adaptive parametric compensation control-based approach. IET Syst. Biol. 12(5), 219–225 (2018). https://doi.org/10.1049/iet-syb.2017.0093
Liu, Y., Deb, D., Tao, G.: Modeling and multivariable adaptive control of aircraft with synthetic jet actuators. In: 2008 7th World Congress On Intelligent Control And Automation (2008). https://doi.org/10.1109/wcica.2008.4593263
Patel, R., Deb, D., Dey, R., Balas, V.E.: Model reference adaptive control of microbial fuel cells. Intelligent Systems Reference Library, pp. 109–121 (2019). https://doi.org/10.1007/978-3-030-18068-3_10
Chatlynne, E., Rumigny, N., Amitay, M., Glezer, A.: Virtual aero-shaping of a Clark-Y airfoil using synthetic jet actuators. In: 39th Aerospace Sciences Meeting And Exhibit (2001). https://doi.org/10.2514/6.2001-732
Deb, D., Tao, G., Burkholder, J., Smith, D.: Adaptive synthetic jet actuator compensation for a nonlinear aircraft model at low angles of attack. IEEE Trans. Control Syst. Technol. 16(5), 983–995 (2008). https://doi.org/10.1109/tcst.2007.912124
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2022 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Deb, D., Burkholder, J., Tao, G. (2022). Adaptive Compensation at Low Angles of Attack: Nonlinear Aircraft Model. In: Adaptive Compensation of Nonlinear Actuators for Flight Control Applications. Studies in Systems, Decision and Control, vol 386. Springer, Singapore. https://doi.org/10.1007/978-981-16-4161-9_3
Download citation
DOI: https://doi.org/10.1007/978-981-16-4161-9_3
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-4160-2
Online ISBN: 978-981-16-4161-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)