Direct measurement of engine thrust during aircraft flight remains challenging. Currently, engineering methods yield only coarse approximations of thrust during flight. This limitation significantly impedes aerodynamic parameter identification from powered flight data, particularly undermining the credibility and accuracy of the identification results of aerodynamic force coefficients. To address these challenges inherent in aerodynamic parameter identification from powered flight data, this study proposes a novel joint online estimation method capable of simultaneously estimating system states, unknown aerodynamic parameters, and engine thrust. The algorithm integrates Kalman filters with computationally efficient recursive least squares (RLS) estimators to perform sequential estimation of flight data. This structure provides real‐time access to unmeasurable engine thrust and enhancement of the estimation precision of aerodynamic parameters. The effectiveness of the proposed algorithm was rigorously validated and assessed using both simulation and flight test data from the CAE‐AVM benchmark aircraft model. The method successfully generated valid estimates of engine thrust and aerodynamic parameters from both datasets and exhibited superiority over the EKF and MMAE algorithms. Specifically, for flight test phases including climb, cruise, and descent, the maximum root mean square relative error (RMSE) for thrust estimates was found to be only 17.36%. These results demonstrate the high estimation accuracy of the proposed joint estimation method for both simulation and flight test data and validate its high effectiveness for the identification and processing of aircraft‐powered flight data.

As the “visual perception hub” of unmanned aerial vehicles (UAVs), electro-optical (EO) pods play an increasingly critical role in tasks such as intelligence gathering, situational awareness, target tracking, and localization. With the expanding scope and depth of UAV applications, higher demands are placed on the precision and adaptability of installation error calibration techniques for EO pods. Current mainstream calibration methods typically require specialized procedures under constrained conditions, while few approaches integrate existing UAV system capabilities and mission requirements, which leads to cumbersome, time-consuming processes and suboptimal alignment between calibration outcomes and task objectives. This paper proposes an online calibration method for UAV EO pod installation errors based on target tracking, which can rapidly compute the optimal closed-form solution for installation errors by leveraging UAV tracking missions. First, an observation equation for pod installation errors is established using tracking results. Second, multi-temporal observations are combined to model the calibration problem as an optimal rotation matrix estimation task, and then the optimal closed-form solution for installation errors is derived. Concurrently, a statistics-based approximate calibration method is introduced specifically for tracking missions. Furthermore, an online calibration system compatible with diverse UAV platforms is designed, along with different rapid calibration schemes for emergency response scenarios, fully incorporating existing system capabilities and mission needs. Finally, a fixed-wing UAV experimental platform is developed, with calibration tests conducted under various flight regimes. Experimental results validate the feasibility and robustness of the proposed methodology.

The continuous advancement of aeronautical engineering technologies, combined with increasingly application demands, has led to the development of aircraft with larger scale and higher value. This progress necessitates corresponding enhancements in reliability standards for aircraft systems. Redundant flight control systems are widely employed in aerospace engineering as an effective methodology for enhancing aircraft reliability. This study first employs integrated analytical simulation methodologies to reveal limitations in a commonly used redundancy management method for parallel redundant flight control systems. Furthermore, a system state evaluation method was established based on channel isolation frequency, enabling effective elimination of potential coordination conflicts and voting singularities during signal voting processes. Finally, the efficacy of the redundancy management method was validated through fault injection simulations and flight tests.

The research object of this paper is a novel morphing aircraft. The V‐tail is a tiltable heading stabilizer that can be deflected in the range of 0°~75° continuously. According to the mission requirements, the aircraft can be transformed into stealth flying wings and high mobility layout configuration; the “scorpion tail” at the rear of the fuselage is a pitching fluid thrust vectoring control surface based on the Coandă effect, which can effectively reduce the infrared characteristics of the engine jet and realize the short takeoff and landing of the aircraft. To be appropriate for the morphing, a new control method of “butterfly rudder” is proposed, which solves the problem of coordinated control of pitch, roll, and yaw in the flying wing configuration. Theoretical analyses and flight tests reveal that the stability and control characteristics of this morphing aircraft differ significantly from those of conventional fixed‐geometry aircraft. It exhibits multi‐axis static instability, along with multivariate, strongly coupled, and highly nonlinear dynamics. Guided by the Control‐Configured Vehicle (CCV) concept, the design prioritizes optimized aerodynamic performance. A multidisciplinary design analysis integrating aerodynamics, stealth, control systems, and materials science was conducted to achieve balanced performance across multiple metrics. The flight control algorithm designed with it achieves stable flight, with good command tracking characteristics and controllable variation process, which effectively proves the correctness of the design scheme.