Adaptive reconfigurable learning algorithm for robust optimal longitudinal motion control of UAVs

Saleem, Omer; Tanveer, Aliha; Iqbal, Jamshed

Abstract

This study presents the formulation and verification of a novel online adaptive reconfigu-rable learning control algorithm (RLCA) for improved longitudinal motion control and disturbance compensation in unmanned aerial vehicles (UAVs). The proposed algorithm is formulated to track the optimal trajectory yielded by the baseline Linear Quadratic Inte-gral (LQI) controller. However, it also leverages reconfigurable dissipative and an-ti-dissipative actions to enhance adaptability under varying system dynamics. The an-ti-dissipative actor delivers aggressive control effort to compensate for large errors, while the dissipative actor minimizes control energy expenditure under low error conditions to improve the control economy. The dissipative and anti-dissipative actors are augmented with state-error-driven hyperbolic scaling functions, which autonomously reconfigure the associated learning gains to mitigate disturbances and uncertainties, ensuring superior performance metrics such as tracking precision and disturbance rejection. By integrating the reconfigurable dissipative and anti-dissipative actions in its formulation, the proposed RLCA adaptively steers the control trajectory as the state conditions vary. The enhanced performance of the proposed RLCA in controlling the longitudinal motion of a small UAV model is validated via customized MATLAB simulations. The simulation results demon-strate the proposed control algorithm’s efficacy in achieving rapid error convergence, dis-turbance rejection, and seamless adaptation to dynamic variations, as compared to the baseline LQI controller.

Citation

Saleem, O., Tanveer, A., & Iqbal, J. (2025). Adaptive reconfigurable learning algorithm for robust optimal longitudinal motion control of UAVs. Algorithms, 18(4), Article 180. https://doi.org/10.3390/a18040180

• Journal Article Type: Article
• Acceptance Date: Mar 17, 2025
• Online Publication Date: Mar 21, 2025
• Publication Date: Apr 1, 2025
• Deposit Date: Mar 17, 2025
• Publicly Available Date: Mar 24, 2025
• Print ISSN: 1999-4893
• Electronic ISSN: 1999-4893
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 18
• Issue: 4
• Article Number: 180
• DOI: https://doi.org/10.3390/a18040180
• Keywords: Unmanned aerial vehicle; Adaptive control; Reconfigurable learning algorithm; Longitudinal motion; Disturbance rejection
• Public URL: https://hull-repository.worktribe.com/output/5084220
• Publisher URL: https://www.mdpi.com/1999-4893/18/4/180