AICSA-SQP Hybrid Optimization Method For The Trajectory Of Launch Vehicle’s Ascending Phase

At present,the concept of "Operationally Responsive Space" proposed by the United States has been paid more and more attention by the large spaceflight countries.This concept indicates the direction of the rapid launch,maneuver and low cost of the launch vehicle to enter the space.At the same time,a higher requirement for the trajectory optimization ability of the launch vehicle is put forward.Among them,the multivariable,multi-constrained,high-precision trajectory optimization method is of great significance to the design of a launch vehicle.It is also an important issue throughout the entire life cycle of a launch vehicle.At the same time,the complex and varied flight environment together with many other uncertainties makes the trajectory optimization design of a launch vehicle very difficult.In this context,this paper studies the Adaptive Immune Clonal Selection Algorithm-Sequential Quadratic Programming(AICSA-SQP)hybrid optimization method for launching trajectory of launch vehicle.In this paper,the main research contents and innovations include:(1)In the launching inertial reference frame,the mathematical model of the ascending phase of the launch vehicle is established.The large difference in magnitude between variables in the motion equation is a very unstable factor for the numerical method.Therefore,to ensure the stability of the numerical calculation and to improve the efficiency of the trajectory optimization algorithm,this paper further transforms the dimensional model into a dimensionless model.(2)The hp-adaptive pseudospectral method,which can adaptively adjust the number and distribution of the collocations,is faster and more accurate than the global pseudospectral method.In this paper,the Radau pseudospectral method is selected as the discrete scheme of hp-adaptive pseudospectral method,and the discrete parameterization process of Radau pseudospectral method and the hp-RPM adaptive strategy are introduced in detail.(3)Based on the basic immune clonal selection algorithm(ICSA),an adaptive immune clonal selection algorithm(AICSA)is proposed.The number of genes that may be mutated in antibodies and the probability of their mutation are combined with evolution algebra so as to improve the diversity in the early stages of evolution and local search capabilities at the end of evolution.And the overall convergence rate of the algorithm also becomes faster.(4)In order to improve the accuracy of the AICSA and reduce the sensitivity of the SQP to the initial value,this paper combines the AICSA and the SQP to design the AICSA-SQP hierarchical serial optimization algorithm.For the ascent problem of a launch vehicle,the optimization result shows that the AICSA can improve the convergence rate and the AICSA-SQP has low sensitivity to initial values and high precision.The AICSA-SQP can solve the trajectory optimization problem of the ascent for a launch vehicle effectively.