Moali Oumaïma, Mezghani Dhafer, Mami Abdelkader, Oussar Abdelatif, Nemra Abdelkrim
UR-LAPER, Faculty of Sciences of Tunis, University of Tunis El Manar, Tunis 2092, Tunisia.
School of Control and Automation, Ecole Militaire Polytechnique, EMP, Bordj El Bahri, Algiers 16111, Algeria.
Sensors (Basel). 2024 Oct 17;24(20):6678. doi: 10.3390/s24206678.
Unmanned Aerial Vehicle (UAV)-type Quadrotors are highly nonlinear systems that are difficult to control and stabilize outdoors, especially in a windy environment. Many algorithms have been proposed to solve the problem of trajectory tracking using UAVs. However, current control systems face significant hurdles, such as parameter uncertainties, modeling errors, and challenges in windy environments. Sensitivity to parameter variations may lead to performance degradation or instability. Modeling errors arise from simplifications, causing disparities between assumed and actual behavior. Classical controls may lack adaptability to dynamic changes, necessitating adaptive strategies. Limited robustness in handling uncertainties can result in suboptimal performance. Windy environments introduce disturbances, impacting system dynamics and precision. The complexity of wind modeling demands advanced estimation and compensation strategies. Tuning challenges may necessitate frequent adjustments, posing practical limitations. Researchers have explored advanced control paradigms, including robust, adaptive, and predictive control, aiming to enhance system performance amidst uncertainties in a scientifically rigorous manner. Our approach does not require knowledge of UAVs and noise models. Furthermore, the use of the Type-2 controller makes our approach robust in the face of uncertainties. The effectiveness of the proposed approach is clear from the obtained results. In this paper, robust and optimal controllers are proposed, validated, and compared on a quadrotor navigating an outdoor environment. First, a Type-2 Fuzzy Logic Controller (FLC) combined with a PID is compared to a Type-1 FLC and Backstepping controller. Second, a Genetic Algorithm (GA) is proposed to provide the optimal PID-Type-2 FLC tuning. The Backstepping, PID-Type-1 FLC, and PID-Type-2 FLC with GA optimization are validated and evaluated with real scenarios in a windy environment. Deep robustness analysis, including error modeling, parameter uncertainties, and actuator faults, is considered. The obtained results clearly show the robustness of the optimal PID-Type-2 FLC compared to the Backstepping and PID-Type-1 FLC controllers. These results are confirmed by the numerical index of each controller compared to the PID-type-2 FLC, with 12% for the Backstepping controller and 51% for the PID-Type-1 FLC.
无人机类型的四旋翼飞行器是高度非线性系统，在户外难以控制和稳定，尤其是在有风的环境中。已经提出了许多算法来解决使用无人机进行轨迹跟踪的问题。然而，当前的控制系统面临重大障碍，例如参数不确定性、建模误差以及有风环境中的挑战。对参数变化的敏感性可能导致性能下降或不稳定。建模误差源于简化，导致假设行为与实际行为之间存在差异。经典控制可能缺乏对动态变化的适应性，因此需要自适应策略。在处理不确定性方面的鲁棒性有限可能导致性能次优。有风的环境会引入干扰，影响系统动态和精度。风建模的复杂性需要先进的估计和补偿策略。调优挑战可能需要频繁调整，带来实际限制。研究人员探索了先进的控制范式，包括鲁棒、自适应和预测控制，旨在以科学严谨的方式在不确定性中提高系统性能。我们的方法不需要无人机和噪声模型的知识。此外，使用二阶控制器使我们的方法在面对不确定性时具有鲁棒性。从获得的结果可以清楚地看出所提出方法的有效性。在本文中，提出了鲁棒和最优控制器，并在导航户外环境的四旋翼飞行器上进行了验证和比较。首先，将二阶模糊逻辑控制器（FLC）与PID相结合，与一阶FLC和反步控制器进行比较。其次，提出了一种遗传算法（GA）来提供最优的PID - 二阶FLC调优。对反步、PID - 一阶FLC以及经过GA优化的PID - 二阶FLC在有风环境中的实际场景进行了验证和评估。考虑了包括误差建模、参数不确定性和执行器故障在内的深度鲁棒性分析。获得的结果清楚地表明，与反步和PID - 一阶FLC控制器相比，最优PID - 二阶FLC具有鲁棒性。与PID - 二阶FLC相比，每个控制器的数值指标证实了这些结果，反步控制器为12%，PID - 一阶FLC为51%。
