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The return multi-flight-phase trajectory optimization of reusable launch vehicle is the key problem of vertical takeoff and vertical landing (VTVL) technologies. In order to solve the problem of ac-curate pinpoint soft landing in the high-dimensional parameter space composed of complex multi-variables and multi-constraints, this paper proposes a strategy of automatic parameter identification and classification and adaptive allocation of optimization algorithms based on clustering analysis. Firstly, state transition matrix of the terminal constraint variables relative to the design variables in the high-dimensional parameter space is obtained, which describes the sensitivity of the terminal constraint variables to the perturbation of the design variables. Then, K-means clustering algorithm is used to classify the sensitivity of the data elements in the state transition matrix, and the linear and nonlinear constraint variables and design variables together with linear state transition matrix are obtained according to the given linearity criterion. Finally, different optimization algorithms are allocated adaptively according to the linear and nonlinear characteristics of different parameter variables. Numerical simulation shows that the proposed algorithm can effectively deal with complex landing constraints, which has well accuracy, convergence and computational efficiency, and reduces the dimension and complexity of parameter space for the return trajectory optimization problem. Besides, the proposed algorithm is suitable for different return missions and has good adaptability and high expansibility. • A new VTVL reusable launch vehicle is studied. • A novel trajectory optimization strategy is presented for return multi-flight-phase. • A new method for parameter characteristics analysis is proposed to reduce dimension. • An adaptive allocation strategy is proposed to enhance adaptability and robustness. • The proposed algorithm has sufficient adaptability to handle VTVL return missions. [ABSTRACT FROM AUTHOR]

The uncertainties during the return trajectory of vertical takeoff and vertical landing reusable launch vehicle weaken the ability of precision landing and make the return process more challenging. This paper is devoted to quantifying the probability uncertainty of return trajectory with uncertain parameters. The uncertainty model of return multi-flight-phase under the uncertainties of initial flight path angle, axial aerodynamic coefficient, and atmospheric density is established using the generalized polynomial chaos expansion method. By parameterizing random uncertainties and introducing random parameters into the uncertainty model, the uncertainty analysis problem of return trajectory is transformed into stochastic trajectory approximation problem. The coefficients of the polynomial basis function are solved by the stochastic collocation method. Then state solutions, statistical properties, and global sensitivity with Sobol index are established based on coefficients. The simulation results show the efficiency and accuracy of this method compared with the Monte Carlo method, the evolution process of main output parameters under random parameters, and relative importance for random parameters. Through the uncertainty analysis of the return trajectory, the robustness of return trajectory can be quantified, which is contributed to improving the safety, reliability, and robustness of recovery and landing mission. [ABSTRACT FROM AUTHOR]