Abstract: A novel fuzzy sliding mode control (FSMC) strategy is proposed to enhance the robustness and stability of position control for underactuated quadrotor unmanned aerial vehicles (UAVs) in the presence of external disturbances and model uncertainties. To realize the adaptive ability and robustness of the system in complex dynamic environments, an intelligent two-dimensional fuzzy controller is designed based on traditional sliding mode control (SMC) to adjust SMC parameters in real time, thereby adapting to the variable structure parameters of the system. First, based on the designed filter variables regarding errors, traditional SMC is used to reduce tracking errors. Then, the fuzzy logic module (FLM) combined with SMC, i.e., the self-learning module (FLM+SMC), is developed based on the filter variables and their rate of change to adjust the two parameters of the above SMC. Subsequently, the output signals of the FLM are fed back into the SMC module, and then a closed-loop tuning system using FSMC is developed for the UAVs. Moreover, the stability of the FSMC is rigorously verified using the Lyapunov theory. Finally, comprehensive simulations demonstrate that the designed FSMC not only offers accurate trajectory precision but also has robustness and disturbance rejection, and comparative simulations using SMC and adaptive radial basis function neural network control (RBFNNC) are used to validate the result.

Key words: Sliding mode control; Fuzzy logic theory; Underactuated system; Unmanned aerial vehicle; Self-learning strategy

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