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The trajectory optimization design of interceptor is very important in the defense combat against a class of high-speed and strong-maneuvering aircraft. At present, an important idea is to use hp pseudospectral method to offline optimizes the trajectory of interceptor, but the solving efficiency of this method needs to be further improved. Aiming at this issue, an improved adaptive hp pseudospectral method is proposed in this paper. In order to shorten the solving time of the algorithm, the proposed method has two main improvements on the basis of the traditional hp pseudospectral method: on one hand, by judging the positions of the control sudden change points, prerefine the mesh around them according to certain rules. On the other hand, the curvature of the system state curve is used as the criterion to segment the original mesh nonuniformly so that more mesh points can be allocated where the curvature is large. These two points together ensure that the collocation point resources can be used more efficiently in the mesh refinement process. The simulation results show that the proposed method can solve the optimized trajectory of interceptor effectively, and it also proves that this method has higher solving efficiency than the traditional adaptive hp method.

For traditional predictor-corrector guidance algorithm for reentry glide vehicle, it cost a lot of time to obtain predicted flight range with a slow speed to iterate. In this paper, according to residual network (ResNet)’s block and dynamic model of vehicle, through analyzing the characteristics of predicted flight range with constraints, the flight range prediction block and flight range prediction neural network are designed, which can obtain the predicted range accurately and quickly; then aiming at the separation between guidance logic and no-fly zone avoidance logic, which may lead to guidance failure and increasing of the sign variation number of the bank angle, the no-fly zone crossrange and the no-fly zone mapping crossrange are proposed in this paper. According the repulsion force of artificial potential field, an adaptive crossrange corridor combining guidance logic and no-fly zone avoidance logic is proposed, and the convergence of the corridor is analyzed theoretically. Through simulation, the block number of flight range prediction network is determined firstly. By this method, the efficiency of lateral guidance can be improved. Then, through the simulation with the different no-fly zones under different disturbed conditions, the stability and validity of the guidance method are verified. Finally, compared with other predictor-corrector algorithms, the proposed method can realize guidance with less sign variation number of bank angle and better avoidance for no-fly zones.

This paper presents a noncertainty equivalent adaptive backstepping control scheme for advanced fighter attitude tracking, in which unsteady effects, parameter uncertainties, and input constraints are all considered which increase the design difficulty to a large extent. Based on unsteady attitude dynamics and the noncertainty equivalent principle, a new observer is first developed to reconstruct the immeasurable and time-varying unsteady states. Afterwards, the unsteady aerodynamics is compensated in the backstepping controller where the command filter is introduced to impose physical constraints on actuators. In order to further enhance the robustness, the noncertainty equivalent adaptive approach is again used to estimate the uncertain constant parameters. Moreover, stability of the closed-loop system that includes the state observer, parameter estimator, and backstepping controller is proven by the Lyapunov theorem in a unified architecture. Finally, simulation results show that performance of the deterministic control system can be captured when attractive manifolds are achieved. The effectiveness and robustness of the proposed control scheme are verified by the Herbst maneuver.

An adaptive neural control method is proposed in this paper for the flexible air-breathing hypersonic vehicle (AHV) with constraints on actuators. This scheme firstly converts the original control problem with input constraints into a new control problem without input constraints based on the control input saturation function. Secondly, on the basis of the implicit function theorem, the radial basis function neural network (RBFNN) is introduced to approximate the uncertain items of the model. And the minimal-learning-parameter (MLP) technique is adopted to design the adaptive law for the norm of network weight vector, which significantly reduces calculations. Meanwhile, the finite-time convergence differentiator (FD) is introduced, through which the model state variables and their derivatives are accurately estimated to ensure the control effect. Finally, it is theoretically proved that the closed-loop control system is stable. And the effectiveness of the designed controller is verified by simulation.