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Control barrier function based terrain and path following control of unmanned aerial vehicle considering attitude constraint

Published online by Cambridge University Press:  09 April 2025

H.-g. Kang Open the ORCID record for H.-g. Kang [Opens in a new window]  and
Y. Kim Open the ORCID record for Y. Kim [Opens in a new window]
H.-g. Kang
Affiliation:
Department of Aerospace Engineering, Institute of Advanced Aerospace Technology, Seoul National University, Seoul, Republic of Korea
Y. Kim*
Affiliation:
Department of Aerospace Engineering, Institute of Advanced Aerospace Technology, Seoul National University, Seoul, Republic of Korea
*
Corresponding author: Y. Kim; Email: ydkim@snu.ac.kr
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Abstract

A terrain and path following control scheme is designed for ground detection mission of a fixed-wing unmanned aerial vehicle (UAV) considering the attitude constraint. The attitude of the UAV should be maintained for efficient exploration, leading to the degradation of mission performance. The proposed controller makes the attitude of the UAV remain in a desired range, which alleviates the mission performance degradation. The proposed algorithm consists of the guidance law and the nonlinear flight path controller. The guidance law is designed by combining a terrain-following altitude controller and a horizontal path following controller based on the Lyapunov control scheme. The generated command by the guidance law is used as a reference input to be followed in the flight path controller. The flight path controller is designed considering the attitude constraint. Especially, the roll and pitch angles of the UAV are considered as attitude constraints so that the angles remain within the desired range. To design a flight path controller satisfying the attitude constraint, the control system is decomposed into three feedback loops. State-feedback controllers are designed using the sliding mode control scheme for flight path control in the outermost loop as well as for angular rate control in the inner loop. In the second-outer loop, a quadratic programming (QP)-based controller is designed to control the sideslip angle while satisfying the attitude constraint. The control Lyapunov function is adopted to determine the QP constraint for the sideslip angle control, and the control barrier function is used to obtain the QP constraint for the attitude constraint. Numerical simulation is performed to demonstrate the effectiveness of the proposed algorithm.

Keywords

Control barrier function (CBF)attitude constraintterrain-following controlpath following control
Type
Research Article
Information
The Aeronautical Journal , First View , pp. 1 - 22
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Copyright
© The Author(s), 2025. Published by Cambridge University Press on behalf of Royal Aeronautical Society

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References

Totoki, H., Ochi, Y., Sato, M. and Muraoka, K. Design and testing of a low-order flight control system for quad-tilt-wing UAV, J. Guid. Control Dyn., October 2016, 39, (10), pp 2423–2430.Google Scholar
Panza, S., Lovera, M., Sato, M. and Muraoka, K. Structured ${\rm{\mu }}$ μ-synthesis of robust attitude control laws for quad-tilt-wing unmanned aerial vehicle, J. Guid. Control Dyn., December 2020, 43, (12), pp 2258–2274.Google Scholar
Wang, Y.C., Chen, W.S., Zhang, S.X., Zhu, J.W. and Cao, L.J. Command-filtered incremental backstepping controller for small unmanned aerial vehicles, J. Guid. Control Dyn., April 2018, 41, (4), pp 952–965.Google Scholar
Sedlmair, M., Theis, J. and Thielecke, F. Flight testing automatic landing control for unmanned aircraft including curved approaches, J. Guid. Control Dyn, April 2022, 45, (4), pp 726–739.Google Scholar
Prajna, S., Jadbabaie, A. and Pappas, G.J. A framework for worst-case and stochastic safety verification using barrier certificates, IEEE Trans. Autom. Control, August 2007, 52, (8), pp 1415–1428.CrossRefGoogle Scholar
Wieland, P. and Allgöwer, F. Constructive safety using control barrier functions, IFAC Proc. Vol., August 2007, 40, (12), pp 462–467.Google Scholar
Ames, A.D., Xu, X., Grizzle, J.W. and Tabuada, P. Control barrier function based quadratic programs with application to adaptive cruise control, in 53rd IEEE Conference on Decision and Control (CDC), Los Angeles, CA, IEEE, December 2014, pp 6271–6278.CrossRefGoogle Scholar
Xu, X., Grizzle, J.W., Tabuada, P. and Ames, A.D. Correctness guarantees for the composition of lane keeping and adaptive cruise control, IEEE Trans. Automat. Sci. Eng., July 2018, 15, (3), pp 1216–1229.CrossRefGoogle Scholar
Wang, L., Ames, A.D. and Egerstedt, M. Safety barrier certificates for collisions-free multirobot systems, IEEE Trans. Robot., June 2017, 33, (3), pp 661–674.Google Scholar
Wu, G. and Sreenath, K. Safety-critical control of a planar quadrotor, in 2016 American Control Conference (ACC), Boston, MA, IEEE, July 2016, pp 2252–2258.CrossRefGoogle Scholar
Wang, L., Ames, A.D. and Egerstedt, M. Safe certificate-based maneuvers for teams of quadrotors using differential flatness, in 2017 IEEE International Conference on Robotics and Automation (ICRA), Marina Bay Sands, Singapore, IEEE, May 2017, pp 3293–3298.CrossRefGoogle Scholar
Cortez, W.S, Oetomo, D., Manzie, C. and Choong, P. Control barrer functions for mechanical systems: Theory and application to robotic grasping, IEEE Trans. Control Syst. Technol., March 2021, 29, (2), pp 530–545.CrossRefGoogle Scholar
Romdlony, M.Z. and Jayawardhana, B. Uniting control Lyapunov and control barrier functions, in 53rd IEEE Conference on Decision and Control (CDC), Los Angeles, CA, IEEE, December 2014, pp 2293–2298.CrossRefGoogle Scholar
Romdlony, M.Z. and Jayawardhana, B. Stabilization with guaranteed safety using control Lyapunov-barrier function, Automatica, April 2016, 66, pp 39–47.CrossRefGoogle Scholar
Ames, A.D., Xu, X., Grizzle, J.W. and Tabuada, P. Control barrier function based quadratic programs for safety critical systems, IEEE Trans. Automat. Control, August 2017, 62, (8), pp 3861–3876.CrossRefGoogle Scholar
Jeaong, H., Park, O., Kim, S. and Suk, J. Adaptive transition control of a tiltrotor UAV using dynamic inversion with neural network, in 2021 Asia-Pacific International Symposium on Aerospace Technology (APISAT), Jeju, Korea, Springer, November 2021, pp 783–795.CrossRefGoogle Scholar
Stevens, B.L., Lewis, F.L. and Johnson, E.N. Aircraft Control and Simulation, 3rd Edition. Wiley, Hoboken, NJ, 2016.Google Scholar
Ames, A.D., Coogan, S., Egerstedt, M., Notomista, G., Sreenath, K. and Tabuada, P. Control barrier functions: Theory and applications, in 18th European Control Conference (ECC), Napoli, Italy, IEEE, June 2014, pp 3420–3431.CrossRefGoogle Scholar
Ames, A.D., Galloway, K., Sreenath, K. and Grizzle, J.W. Rapidly exponentially stabilizing control Lyapunov functions and hybrid zero dynamics, IEEE Trans. Automat. Control, April 2014, 59, (4), pp 876–891.CrossRefGoogle Scholar