Reentry guidance by accelerated fractional-order particle swarm optimization method
| Autor: | Weiduo Hu, Khurram Shahzad Sana |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Atmosphere (unit) Computer science Phase (waves) Control variable Aerospace Engineering Particle swarm optimization 02 engineering and technology Reentry 01 natural sciences 010305 fluids & plasmas 020901 industrial engineering & automation Control theory 0103 physical sciences Convergence (routing) Path (graph theory) Trajectory |
| Zdroj: | Aircraft Engineering and Aerospace Technology. 92:1281-1293 |
| ISSN: | 1748-8842 |
| DOI: | 10.1108/aeat-11-2019-0221 |
| Popis: | Purpose The aim of this study is to design a guidance method to generate a smoother and feasible gliding reentry trajectory, a highly constrained problem by formalizing the control variables profile. Design/methodology/approach A novel accelerated fractional-order particle swarm optimization (FAPSO) method is proposed for velocity updates to design the guidance method for gliding reentry flight vehicles with fixed final energy. Findings By using the common aero vehicle as a test case for the simulation purpose, it is found that during the initial phase of the longitudinal guidance, there are oscillations in the state parameters which cause to violate the path constraints. For the glide phase of the longitudinal guidance, the path constraints have higher values because of the increase in the atmosphere density. Research limitations/implications The violation in the path constraints may compromise the flight vehicle safety, whereas the enforcement assures the flight safety by flying it within the reentry corridor. Originality/value An oscillation suppression scheme is proposed by using the FAPSO method during the initial phase of the reentry flight, which smooths the trajectory and enforces the path constraints partially. To enforce the path constraints strictly in the glide phase, ultimately, another scheme by using the FAPSO method is proposed. The simulation results show that the proposed algorithm is efficient to achieve better convergence and accuracy for nominal as well as dispersed conditions. |
| Databáze: | OpenAIRE |
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