article · IEEE Access
This research proposes an Adaptive Neuro-Fuzzy Inference System-based Sliding Mode Controller (ANFIS-SMC) for robust trajectory tracking in fixed-wing unmanned aerial vehicles (FWUAVs). FWUAVs have complex, non-linear dynamics, making precise control challenging. The ANFIS-SMC integrates the adaptive capabilities of ANFIS with the robustness of Sliding Mode Control (SMC) to mitigate issues like the chattering effect and limitations of traditional fuzzy logic controllers. The ANFIS component is trained to approximate the discontinuous control action of SMC, maintaining smoothness and adaptability while preserving robustness. Lyapunov theory confirms the controller's finite-time convergence. Simulation results demonstrate that ANFIS-SMC significantly improves trajectory tracking performance, particularly during mass variations, outperforming both Fuzzy Sliding Mode Control (FSMC) and standard SMC.
Accurate and stable trajectory tracking is crucial for the safe and effective operation of unmanned aerial vehicles in various applications. This research offers a more robust and adaptive control solution, potentially leading to more reliable autonomous flight, even in challenging and dynamic environmental conditions, which is vital for many sectors.
This early-stage research, validated through simulations, could lead to more reliable control systems for fixed-wing unmanned aerial vehicles. Potential users include organisations involved in defence, surveillance, and logistics, where precise and stable autonomous flight is critical. The improved robustness and adaptability could enhance the operational capabilities of future UAV platforms.
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This paper proposes a robust Adaptive Neuro-Fuzzy Inference System-based Sliding Mode Controller (ANFIS-SMC) for trajectory tracking in fixed-wing unmanned aerial vehicles (FWUAVs). FWUAVs are autonomous and versatile, finding applications in many fields such as defense, surveillance, and logistics. However, their dynamic model is highly complex due to nonlinearity and coupling effects. In this paper the decoupling is held based on extracting dominant inputs, and all other inputs are considered uncertainties, for designing controllers. The proposed ANFIS-SMC integrates the adaptability of the ANFIS with the robustness of SMC to overcome issues such as the chattering effect and shortcomings of traditional fuzzy logic controllers. ANFIS is trained with the sliding surface as an input data and SMC’s discontinuous control effort as an output data. Through such training, ANFIS can effectively approximate the discontinuous control action without losing smoothness and adaptability of control while keeping the robustness of the traditional SMC strategy. The Lyapunov theory ensures the finite time convergence of both reaching and sliding phases of sliding mode controller. The proposed ANFIS-SMC controller is stronger and more adaptive compared to FSMC and SMC. During mass variation along the x-axis, ITAE increased from 10.94 to 63.52 using ANFIS-SMC, from 17.39 to 128.3 using FSMC, and from 38.28 to 830.1 using SMC. This is an improvement of 50% over FSMC and 90% over SMC, which demonstrates improved trajectory tracking performance for fixed-wing UAVs in dynamic environments. Simulations on MATLAB®/Simulink® confirm that the proposed ANFIS-SMC ensures stable flight and accurate trajectory tracking, even in challenging flight conditions.
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DOI: 10.1109/access.2025.3557472
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