Trajectory optimization problem for hypersonic vehicles has received wide attention as its high speed and large flight range. The strong nonlinear characteristic of the ascent phase aerodynamics makes the trajectory optimization problem difficult to be solved by the optimal control theory. In this paper, an improved chicken swarm optimization (ICSO) algorithm is proposed to optimize the hypersonic vehicle ascent trajectory. To overcome the obstacle of premature convergence, three improvement strategies are put forward. To be specific, the updating laws of roosters are modified by the average position of roosters, and the difference of the optimal solution between two adjacent iterations is used to calculate the mutated particle instead of the gradient. Meanwhile, the uniform mutation operator is used to get rid of the local minimum. The convergence analysis of the proposed ICSO is provided subsequently. To handle constraints, an improved adaptive penalty method is put forward. The comparison results show that the proposed ICSO outperforms CSO and PSO on benchmark functions of CEC2014. Finally, the trajectory optimization results for a generic hypersonic vehicle, in compare with the open-source optimization software PSOPT, are put forward to demonstrate the feasibility and effectiveness of the proposed method. The results of 50 independent runs show that the improved adaptive penalty function method is effective in constraints handling.

In this paper, an adaptive dynamic surface control (DSC) method based on neural network for the flight path angle of an aircraft is investigated in view of the parameters uncertainty, multi-disturbance and nonlinearity of the aircraft. First, a traditional backstepping controller is derived as a base. To enhance the adaptability and robustness, radial basis function (RBF) neural networks are introduced to estimate the unknown parameters of the model online and overcome the external disturbance. In addition, two first-order low-pass filters in the dynamic surface control, which can eliminate the expansion of the differential terms and simplify the design of controller's parameters, are devised to compute the derivative of the virtual controller. Then the parameters range of the dynamic surface controller is obtained by stability analysis, which is convenient for us to opt for and regulate these parameters independently. Eventually, the semiglobal stability of closed-loop system is rigorously proved by Lyapunov method. And the simulation results of dynamic surface control and backstepping control under multiple groups of different disturbances also manifest that the derived dynamic surface controller possesses faster and more precise tracking performance, stronger adaptive ability and robustness to external disturbances than backstepping controller. Generally speaking, the adaptive dynamic surface controller engineered in this paper has considerable reference significance for the control of practical aircraft. INDEX TERMS Adaptive neural network, dynamic surface control, disturbances, backstepping, aircraft.

Summary The solid rocket booster is widely used in the launch mission for hypersonic vehicles nowadays, whose ascent energy management is still a challenging problem due to the inherent dynamic nonlinearity. This paper casts the problem into an equivalent ascent guidance one and proposes a model predictive static programming (MPSP) based solution. The MPSP‐based method is a computationally efficient solver that adapts to different energy demands while guaranteeing the satisfaction of terminal constraints. To further improve the performance of MPSP in this problem, a Bézier spline guidance method is proposed to generate high‐quality reference trajectories such that the MPSP can converge with high accuracy while satisfying the real‐time requirement. Additionally, an online parameter estimation based on the extended Kalman filter is employed to avoid large terminal state deviations incurred by parametric uncertainties during the Bézier curve computation and linearization in MPSP. Numerical simulations with different hard terminal constraints and uncertainties are conducted to demonstrate the effectiveness and robustness of the proposed algorithm. The simulation results show that our proposed algorithm can guide the hypersonic vehicle to a given terminal state under uncertainties and outperforms several existing ascent energy management methods.

This paper proposes an improved Generalized Quasi-Spectral Model Predictive Static Programming (GS-MPSP) algorithm for the ascent trajectory optimization for hypersonic vehicles in a complex flight environment. The proposed method guarantees the satisfaction of constraints related to the state and control vector while retaining its high computational efficiency. The spectral representation technique is used to describe the control variables, which reduces the number of decision variables and makes the control input smooth enough. Through Taylor expansion, the constraints are transformed into an inequality containing only decision variables, such that it can be added into GS-MPSP framework. By Gauss quadrature collocation method, only a few collocation points are needed to solve the sensitivity matrix, which greatly accelerates the calculation. Subsequently, the analytical expression is obtained by combining the static optimization with the penalty function method. Finally, the simulation results demonstrate that the proposed improved GS-MPSP algorithm can achieve both high computational efficiency and high terminal precision under the constraints.

Aiming at the temperature control demand of the near space solar unmanned aerial vehicle (UAV) payload cabin, this paper establishes the thermodynamic model of the payload cabin through thermal characteristic analysis, and proposes a system design method of structural thermal control integration. The thermal design of the payload cabin is carried out by means of thermal control such as uniform temperature, heat insulation, coating and active temperature control. The design weight of the whole payload cabin is only 7kg. The results of thermal analysis show that the temperature of the equipment in the cabin meets the requirement of the target during the whole UAV mission period, which verifies the correctness of the thermal design method. Compared with the traditional active temperature control scheme, the low temperature level increases by 3°C under the same heating power. The heat dissipation of the payload cabin is mainly convective heat transfer, but with the increase of altitude, the proportion of radiation heat dissipation is gradually increased. The research results can provide reference for the thermal design of similar aircraft.