Fuzzy PID Controller for UAV Based on Reinforcement Learning

Zhang, Benyi; Zhang, Weiping; Mou, Jiawang; Yang, Runmin; Zhang, Yichen

doi:10.1007/978-981-99-0479-2_160

Cited by 2 publications

(1 citation statement)

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“…Because the PID is designed on linear assumptions, it has difficulties controlling nonlinear systems. The elimination of steady-state errors is also a limitation that reduces the control performance [12]. For this reason, more robust nonlinear control techniques are considered in this work to deal with such issues.…”

Section: Single-rotor Uavmentioning

confidence: 99%

Robust Nonlinear Trajectory Controllers for a Single-Rotor UAV with Particle Swarm Optimization Tuning

Portillo,

Garza-Castañón,

Minchala-Avila

et al. 2023

Machines

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This paper presents the utilization of robust nonlinear control schemes for a single-rotor unmanned aerial vehicle (SR-UAV) mathematical model. The nonlinear dynamics of the vehicle are modeled according to the translational and rotational motions. The general structure is based on a translation controller connected in cascade with a P-PI attitude controller. Three different control approaches (classical PID, Super Twisting, and Adaptive Sliding Mode) are compared for the translation control. The parameters of such controllers are hard to tune by using a trial-and-error procedure, so we use an automated tuning procedure based on the Particle Swarm Optimization (PSO) method. The controllers were simulated in scenarios with wind gust disturbances, and a performance comparison was made between the different controllers with and without optimized gains. The results show a significant improvement in the performance of the PSO-tuned controllers.

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Section: Single-rotor Uavmentioning

confidence: 99%