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International Journal of Electrical and Computer Engineering (IJECE)International Journal of Electrical and Computer Engineering (IJECE)

This article investigates the discount factor-based data-driven reinforcement learning control (DDRLC) algorithm for completely uncertain unmanned aerial vehicle (UAV) quadrotors. The proposed cascade control structure of UAV is categorized with two control loops of attitude and position sub-systems, which are established the proposed discount factor-based DDRLC algorithm. Through the analysis of the Bellman functions time derivative from two perspectives, a revised Hamilton-Jacobi-Bellman (HJB) equation including a discount factor is developed. Then, in the view of off-policy consideration, an equation is formulated to simultaneously solve the approximate Bellman function and approximate optimal control law in the proposed DDRLC algorithm with guaranteed convergence. According to the modified state variables vector, the development of the discount factor-based DDRLC algorithm in each control loop is indirectly implemented by transforming the time-varying tracking error model into the time invariant system. Finally, a simulation study on the proposed discount factor-based DDRLC algorithm is provided to validate its effectiveness.

The proposed data-driven reinforcement learning algorithm incorporating a discount factor was developed for the two subsystems of a UAV quadrotor to address performance challenges in fully uncertain UAV systems.Utilizing the off-policy approach, the model-free cascade control framework was constructed to simultaneously obtain the optimal control law and the corresponding Bellman function.The network weights were adjusted to approximate the solution of the modified Hamilton-Jacobi-Bellman (HJB) equation, with theoretical guarantees of both convergence and stability.

Berdasarkan penelitian ini, beberapa saran penelitian lanjutan dapat diajukan untuk memperluas pemahaman dan penerapan kontrol UAV yang lebih canggih. Pertama, penelitian dapat difokuskan pada pengembangan algoritma RL yang lebih adaptif terhadap perubahan lingkungan dan parameter UAV secara real-time, sehingga meningkatkan robustitas sistem kontrol. Kedua, eksplorasi integrasi sensor yang lebih beragam, seperti LiDAR atau kamera termal, dapat memberikan informasi tambahan untuk pengambilan keputusan kontrol yang lebih akurat dan aman, terutama dalam kondisi cuaca buruk atau lingkungan yang kompleks. Ketiga, penelitian dapat mengarah pada pengembangan arsitektur kontrol terdistribusi untuk swarm UAV, di mana setiap UAV dapat berkoordinasi secara otonom untuk mencapai tujuan bersama, membuka peluang aplikasi baru dalam bidang survei, pengiriman, dan pencarian dan penyelamatan.

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