Unmanned aerial vehicles (UAVs) have been widely applied over the past decades, especially in the military field. Due to the unpredictability of the flight environment and failures, higher requirements are placed on the design of the control system of the fixed-wing UAV. In this study, a sliding mode disturbance observer-based (SMDO) adaptive dynamic inversion fault-tolerant controller was designed, which includes an outer-loop sliding mode observer-based disturbance suppression dynamic inversion controller and an inner-loop real-time aerodynamic identification-based adaptive fault-tolerant dynamic inversion controller. The sliding mode disturbance observer in the outer-loop controller was designed based on the second-order super-twisting algorithm to alleviate chattering. The aerodynamic identification in the inner-loop controller adopts the recursive least squares algorithm to update the aerodynamic model of the UAV online, thereby realizing the fault-tolerant control for the control surface damage. The effectiveness of the proposed SMDO enhanced adaptive fault-tolerant control method was validated by mathematical simulation.

In this paper, a design approach for simultaneous cooperative interception is presented for a scenario where the successful handover cannot be guaranteed by a single interceptor due to the target maneuver and movement information errors at the handover moment. Firstly, the concepts of the reachable interception area and predicted interception area are introduced, a performance index function is constructed, and the probability of a successful handover is described by considering the coverage of the predicted interception area. Taking the probability of successful handover as a constraint, the simultaneous cooperative interception design problem is formulated based on area coverage. Then, an area coverage optimization algorithm is presented to design the spatial distributions of the interceptors. In order to enhance the handover probability, a simultaneous cooperative interception design approach is proposed to obtain the number of interceptors and the corresponding spatial distributions. Finally, simulation experiments are carried out to validate the effectiveness of the proposed approach.

In order to overcome the influence of complex environmental disturbance factors such as nonlinear time-varying characteristics on the dynamic control performance of small fixed-wing UAVs, the nonlinear expression relationship of neural networks (NNs) is combined with the recursive least squares (RLSs) identification algorithm. This paper proposes a hybrid aerodynamic parameter identification method based on NN-RLS offline network training and online learning correction. The simulation results show that compared with the real value of the identification value obtained by this algorithm, the residual error of the moment coefficient is reduced by 69%, and the residual error of the force coefficient is reduced by 89%. Under the same identification accuracy, the identification time is shortened from the original 0.1 s to 0.01 s. Compared with traditional identification algorithms, better estimation results can be obtained. By using this algorithm to continuously update the NN model and iterate repeatedly, iterative learning for complex dynamic models can be realized, providing support for the optimization of UAV control schemes.

There are serious among various components of the hypersonic vehicle at wide-range Mach numbers, and the coupling among aerodynamic shape, propulsion system, structure, and control system is remarkable. For excellent performance, its components and functions are highly integrated, which brings serious challenges to traditional design examples, methods, and tools. To solve the integrated control problem of flight-propulsion coupling, a controller based on pole assignment is designed in this paper, and the control parameters can be adjusted according to real-time aerodynamic data. The parametric model of hypersonic vehicle at wide-range Mach numbers with coupling characteristics is established and the flight data under various working conditions are obtained. The flight dynamical model is established with a coupling relationship between the airframe and engine. Combined with the characteristic parameters of the aircraft attitude control closed-loop system, the control gain is designed based on expected poles and adjusted in real-time. The comparative simulations are carried out, and the results show that the control gain adjustment strategy based on aerodynamic parameter identification can improve the attitude angle command tracking quality and the robustness of the system.

To improve the flexibility of the trajectory and the diversity of the drop point of the reentry vehicle, a flight capability assessment method based on a dynamics–informed neural network (DINN) is proposed. Firstly, the concept of a reachable domain is introduced to characterize the flight capability of the reentry vehicle and to estimate whether there are appropriate TAEM points in the area. Secondly, after the impact characteristic analysis, the reachable domains corresponding to different initial flight states are obtained through moderate dynamic simulations and reasonable mathematical expansion. The flight states and boundary point positions of the reachable domain are used as the training database of DINN, and the acquired DINN can realize the fast solution of reachable domains. Finally, the effectiveness of DINN in solving the reachable domain is verified using simulation. The simulation results show that DINN manifests the same accuracy as the existing solving methods and can meet the demand of determining whether the target point is located in the reachable domain. Additionally, the running time is shortened to one–800th of the existing methods, reaching the millisecond level, to support real–time assessment and decision–making. A predictor–corrector guidance algorithm with the piecewise objective function is also introduced. The simulation results illustrate that the proposed algorithm can stably guide the vehicle from the initial state points to the target points in the reachable domain.