Launch Vehicle Trajectory Optimization Based On Improved Quantum Particle Swarm Algorithm

Trajectory optimization design is one of the most important part in launchvehicle overall optimization design, the trajectory optimization results directly affectthe overall performance of the launch vehicle design. In the period of development,experiment and actual application, it is necessary to design reasonable launch vehicletrajectory parameters and select reasonable trajectory, so that the launch vehiclemoves according to scheduled rule. Only the reasonable trajectory can exactly assessthe flying results and authenticate the characters for launch vehicle and eachsubsystem. Trajectory optimization design for the launch vehicle, which caneffectively improve the most payload and the fuel’s using efficiency, ensure thesuccessful probability of launch vehicle in test period, finally, reduce the overalldevelopment cost and manpower cost for the launch vehicle. This paper’s mainresearch content and research results are followed.1. Information related to the launch vehicle trajectory optimization is researchedin this paper, and characteristic of the launch vehicle trajectory optimization problemis in-depth studied, moreover, several common algorithms of trajectory optimizationare summarized and discussed respectively, a new requirement and developmentorientation are brought up for launch vehicle trajectory optimization.2. The basic theoretic knowledge of trajectory optimization is studied andcommon way and process for trajectory optimization are analyzed. And then, themodel of launch vehicle trajectory optimization design problem is established，and amodeling idea that establishes the trajectory surrogate model through Kriging methodis put forward due to the high nonlinear and complexity of trajectory computing. TheKriging’s basic theory, correlative function and setting ways of parameters areresearched. The launch vehicle trajectory computing approximate model is establishedthrough Kriging method, and precision of the approximate model is verified so as totrajectory computing surrogate model is effective.3. The particle swarm optimization(PSO) and quantum-behavior particle swarmoptimization(QPSO) are deeply studied, and performance analysis is made for bothtwo optimization algorithms，which show that the QPSO has better performance thanPSO in global convergence ability, but when solve complex optimization problems,the QPSO is still likely to get into local optimal along with decrease of swarmdiversity. In the paper, a improved QPSO based on simulated annealing(SimQPSO) is put forward for the limitation of QPSO, then, a performance analysis is made for theSimQPSO and that illustrates the advantage of the SimQPSO. Subsequently，anexample analysis is made for PSO, QPSO and SimQPSO by using four classicalfunctions, that proves the SimQPSO has better global optimization ability than others.4The optimization simulations are made respectively using SimQPSO andQPSO for the launch vehicle trajectory based on Kriging surrogate model, the resultsshow that the SimQPSO gets better trajectory performance than QPSO, theSimQPSO’s control parameters need to be set is few, and the method can be easilyrealized. The simulation results demonstrate the SimQPSO algorithm is feasible andeffective in solving trajectory optimization problem, and can be extended to otheroptimization problems.