Rotor failure induces characteristics in quadrotor UAVs such as strong nonlinearity, rapid time-variance, and severe under-actuation. Consequently, traditional control methods often struggle to maintain flight stability, frequently resulting in loss of control and crashes. To address these challenges, this paper proposes a multi-mode fault-tolerant control method based on a hierarchical adaptive framework. This method innovatively constructs a control architecture comprising Dynamic Weighting Nonlinear Model Predictive Control (DWNMPC) and Adaptive Incremental Nonlinear Dynamic Inversion (AINDI). The upper-level DWNMPC controller employs a state-dependent weight adaptive mechanism. By dynamically adjusting the weights of various states in the cost function based on attitude errors, it prioritizes attitude stability to suppress tumbling at the instant of failure, subsequently transitioning smoothly to precise trajectory tracking once the system stabilizes. To cope with model uncertainties and strong aerodynamic disturbances under severe failure conditions, the lower-level AINDI controller is designed to provide online robust adaptive correction to DWNMPC commands. This controller utilizes sensor measurements to compensate for unmodeled moments in real-time and adopts the Recursive Least Squares (RLS) method with a forgetting factor to identify key parameters, such as the moment of inertia, thereby significantly enhancing system robustness. Experimental results demonstrate that the proposed method exhibits excellent trajectory tracking capabilities under fault-free, partial rotor failure, and complete rotor failure conditions. Furthermore, the control process relies solely on onboard sensors for state estimation, reflecting its high applicability in actual physical environments. In the extreme scenario of complete single-rotor failure resulting in a high-speed spin of approximately −10.5 rad/s, the Root Mean Square Error (RMSE) of trajectory tracking increased by only 0.047 6 m, 0.054 5 m, and 0.083 m on the x, y, and z axes, respectively, compared to the fault-free condition, significantly improving the reliability and safety of the UAV.

To address the requirements of modern aircraft for high maneuverability and strong countermeasure capability in complex tactical environments, and resolve the drawbacks of low accuracy and slow response in conventional indirect thrust control of engines, this paper proposes a direct thrust control method for multi-companion vectoring engines. First, a nonlinear component-level model consisting of one main engine and two companion engines is established, with refined modeling conducted for key components such as the bleed air system. To achieve accurate estimation of engine thrust, an on-board adaptive model based on the Unscented Kalman Filter (UKF) is designed. This model incorporates a steady/dynamic discrimination logic to on-line identify the engine’ s performance degradation parameters, thereby effectively suppressing the interference of flight dynamics on health assessment. On this basis, a data-driven Model-Free Adaptive Control (MFAC) strategy is further proposed, and a Multi-Input Multi-Output (MIMO) direct thrust controller is constructed to realize decoupled, rapid, and precise control of the thrust of the main and companion engines. Simulation results demonstrate that the designed on-board adaptive model can accurately track the actual state of the engine, and its thrust estimation results are highly consistent with the true values; the direct thrust controller responds rapidly, achieving stable and precise tracking of the commanded thrust under the conditions that the settling time of the main engine is less than 1 s and the overshoot is below 4%. This study provides an effective solution for the control system design of novel combined vectored-thrust engines and verifies the feasibility and potential of this scheme in improving engine control performance.

To address the challenge of efficiently constructing environment maps and achieving long-distance global planning for UAVs in complex scenarios, this paper proposes a probabilistic update-based pruning visibility map construction method and a hierarchical planning strategy. The approach generates a grid map through probabilistic updates, extracts obstacle boundaries via hierarchical mapping, and constructs a visibility map with collision detection. A pruning strategy for the visibility map is introduced to reduce the search space and accelerate pathfinding. The hierarchical planning framework is based on search and optimization, where the outer planning layer employs an improved A* algorithm based on exploration degree. By incorporating path exploration degree into the cost function, global planning performance in complex environments is significantly enhanced. The inner planning layer uses trajectory optimization based on Minimum Control Effort (MINCO) trajectory representation to generate smooth flight paths that satisfy UAV speed and acceleration constraints. Experimental simulations and real-flight validations show that compared to the traditional A* algorithm, the proposed pruning visibility map-based improved A* algorithm reduces flight distance by 12.92% and flight time by 16.43%, demonstrating the algorithm's ability to improve planning efficiency and optimality in complex scenarios.

The High Flow Dual Variable Cycle Engine (HFDVCE) achieves high performance by the aerodynamic and thermodynamic schemes such as synchronous combination of high flow rate, high throttle ratio, bypass ratio, and boost ratio regulation in the full speed range, as well as high turbine front temperature, meeting the requirements of low-speed economy and high-speed high thrust. Compared to conventional engines, HFDVCE has more prominent aerodynamic stability issues within the full range and mode switching. This article conducts research on the aerodynamic stability of HFDVCE, and proposes an HFDVCE model with instability simulation to address the lack of overall instability simulation and parameter mismatch between sub-models under multiple operating conditions. Firstly, a high-precision nonlinear model of the engine was established based on the component method to obtain the aerodynamic parameters of compression components. Then, a dynamic modeling method for aerodynamic instability of compression components was proposed. By introducing a torque term to characterize the influence of rotational speed on the characteristics of the instability model, an instability model with two types of unstable conditions, stall and surge, was established to avoid the assumption of constant rotational speed and solve the problem of coefficient mismatch between multiple instability sub-models. Furthermore, the coupling relationship between the instability model of compression components and the nonlinear model of the engine was clarified. The impact of aerodynamic instability of the compression system on the overall performance of the engine was characterized by the instability coefficient, and an HFDVCE model with instability simulation was established to achieve dynamic simulation of overall instability. Finally, simulation experiments were conducted on four typical instability modes of HFDVCE, verifying the effectiveness of the model and providing a simulation platform for active stability control design.

Increasing attention has been attracted to the dynamic performance and safety of advanced performance predictive control systems of the next-generation aeroengine. The latest research demonstrates that Subspace-based Improved Model Predictive Control (SIMPC) can overcome the difficulty in solving the predictive model in MPC/NMPC applications. However, applying constant design parameters cannot maintain consistent control effects in all states. Meanwhile, the designed system relies too much on sensor-measured data, and thus it is difficult to thoroughly validate the safety of the system because of its high complexity. This means that any potential hardware/software faults will endanger the engine. Therefore, this paper first presents a novel nonlinear mapping relationship to adaptively tune the tracking weight online with the change of Power Lever Angle (PLA) and real-time relative tracking error. Thus, without introducing additional design parameters, an Adaptive Tracking Weight-based SIMPC (ATW-SIMPC) controller is designed to improve the control performance in all operating states effectively. Then, a Primary/Backup Hybrid Control (PBHC) strategy with the ATW-SIMPC controller as the primary system and the traditional speed (N_f) controller as the backup system is proposed to ensure safety. The designed affiliated switching controller and the real-time monitor therein can be used to realize reasonable and smooth switching between primary/backup systems, so as to avoid bump transition. The PBHC system switches to the N_f controller when the ATW-SIMPC controller is wrong because of potential hardware/software faults; otherwise, the ATW-SIMPC controller keeps acting on the engine. The main results prove that the ATW-SIMPC controller with the optimal nonlinear mapping relationship, compared with the existing SIMPC controller, uplifts the dynamic control performance by 32% and reduces overshoots to an allowable limit, resulting in a better control effect in full state. The comparison results consistently indicate that the PBHC can guarantee engine safety in occurrence of hardware/software faults, such as sensor/onboard adaptive model faults. The approach proposed is applicable to the design of a model-based engine intelligent control system.

The onboard adaptive model can achieve the online real-time estimation of performance parameters that are difficult to measure in a real aero-engine, which is the key to realizing model-based performance control. It must possess satisfactory numerical stability and estimation accuracy. However, the positive definiteness of the state covariance matrix may be destroyed in filter estimation because of the existence of some uncertain factors, such as the accumulated measurement error, noise, and disturbance in the strongly nonlinear engine system, inevitably causing divergence of estimates of Cholesky decomposition-based Spherical Unscented Kalman Filter (SUKF). Therefore, this paper proposes an improved SUKF algorithm (iSUKF) and applies it to the performance degradation estimation of the engine. Compared to SUKF, the iSUKF mainly replaces the Cholesky decomposition with the Singular Value Decomposition (SVD), which is numerically stable without any strict requirement for the state covariance matrix. Meanwhile, a correction factor is designed to assess the measurement deviation between the real engine and the nonlinear onboard model to correct the state covariance matrix, thus maintaining better numerical stability of parameters estimated by the filter. Then, an offline correction strategy is also proposed to eliminate the influence of the degradation of unestimated health parameters or the filter’s inadequate estimation of the coupled health parameters. This action effectively promotes the onboard adaptive model’s estimation accuracy concerning the degradation of the engine’ real health parameters and its performance parameters. Finally, the simulation results show that the iSUKF can maintain the numerical stability of the filter’s estimation of health parameters. Compared with the existing methods, the offline correction strategy improves the estimation accuracy of the iSUKF-based nonlinear onboard adaptive model for the performance parameters of the real engine by more than 50%. The proposed method will provide feasible technical support for model-based aero-engine performance control.

Active control of aero-engine turbine tip clearance is one of the best chances for engine performance uplift currently. To do that, the first requirement is real-time measurement of tip clearance in aero-engine working environment. However, turbine complexity makes it unlikely for tip clearance sensors to be loaded. In recognition of that, this paper proposed a model-based method for tip clearance measurement. Firstly, by considering previously wrongly neglected factors such as load deformation, a mathematical model to monitor dynamic tip clearance changes is built to improve calculation accuracy. Then, after clarifying the coupling relationship between engine models and tip clearance models, this paper builds a component-level mathematical model integrating dynamic characteristics of turbine tip clearance, which helps realize accurate measurement of tip clearance in working environment. How tip clearance affects turbine efficiency is studied afterwards and reported to aero-engine model, so as to mitigate performance difference between aero-engine model and real engines caused by turbine tip clearance. Lastly, by hardware-in-the-loop simulation, tip clearance model demonstrates 15.9% better accuracy than previously built models in terms of turbine centrifugal deformation calculation. As tip clearance measurement model takes averagely 0.34 ms in calculation, meeting the operation requirement, it proves to be an effective new way.

Surge active control can expand the stable operating range of the compressor. However, the difficulty of flow measurement, dynamic uncertainty disturbance, actuator delay characteristics, hard constraints of control variable, and system security measures have not been fully considered in the existing active control system, which significantly hinders its engineering application. Therefore, a nonlinear model predictive surge active control method is first presented based on flow estimator designed by using a continuous-time Kalman filter for dealing with the hard constraint of control variable and the impact of actuator delay of compression system with dynamic uncertainty. Then, a high-safety active/surge passive hybrid control strategy is designed, dominated by the surge active control and supplemented by the surge passive control, to ensure the compression system’s safe and stable operation. Lastly, the simulation results suggest that the flow estimator accurately estimates the compressor flow. When considering the delay impact of the actuators and sensors and measurement noise on the system, the proposed method exhibits stronger robustness than the existing methods. The active/surge passive hybrid control strategy can successfully ensure the compression system’s safe and stable operation. This paper is of high practical significance for the engineering application of future compressor surge active control technologies.