Flight control systems employ redundant actuators to enhance reliability and improve overall system performance. This redundancy becomes especially critical when one of the actuators experiences degraded performance or suffers a fault; under such circumstances, the system can continue operating by leveraging the remaining actuators, thereby avoiding serious malfunctions. However, due to the presence of multiple actuators with similar capabilities, optimal control allocation among them is of paramount importance. Control allocation refers to the process of optimally distributing the available control effort among the actuators, significantly contributing to the maintenance of the desired aircraft performance, particularly in the event of actuator faults. The primary objective of this research is to design and integrate an adaptive control allocation algorithm with a Model Reference Adaptive Controller (MRAC) for managing actuator faults, uncertainties, and modeling errors inherent in flight control systems. By utilizing the MRAC principles, the proposed method effectively addresses faults, uncertainties, and model inaccuracies, ensuring optimal performance under varying operational conditions. Furthermore, the approach is designed to account for actuator saturation and to perform adaptive estimation of unknown system parameters, thereby enhancing both the performance and reliability of the system. To eva‎luate the performance of the proposed algorithm, the adaptive controller was implemented on a linearized aircraft model. Specifically, a particular aircraft model (such as the ADMIRE) was linearized, and after designing a controller for this linear model, simulations were carried out to assess its performance on the ADMIRE model. The simulation results demonstrate that the proposed strategy effectively compensates for actuator faults and improves actuator performance.

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جواد عسگري مارناني , فريد شيخ الاسلام