A Quantum-Inspired Evolutionary Approach to Supplementing Dynamic Trim Control in Rockets
By Taran
The proposed research will examine the application of a Quantum-Inspired Evolutionary Optimization (QIEO) algorithm in solving a sample rocket trim state problem, a non-linear, complex aerospace engineering problem. This contribution extends numerical optimization toolkits like CasADi and csdl_alpha with quantum-inspired numerical computation. The project shall proceed by first developing an operational QIEO algorithm in Python to prove feasibility and then creating the actual rocket trim model. A QIEO approach may offer a more efficient alternative to traditional gradient-based solvers.
The intellectual value of this research lies in the integration of quantum-inspired algorithms and computation with aerospace vehicle dynamics. Though it has been demonstrated to have promise in other optimization areas, combining QIEOs with design libraries like CasADi or csdl_alpha to rocket flight dynamics is an uncharted area. This research borrows from ideas in evolutionary algorithms and quantum physics. This project will establish the feasibilty of applying quantum-inspired approaches to practical problems.
The broader implications of this study are important for the aerospace and defense industries. A successful deployment would pave the way for more efficient methods of studying vehicle stability and control. This might potentially be employed to optimize the design of autonomous flight systems, and enhance aerospace vehicle safety and performance. Furthermore, this project serves as a theoretical bridge, between quantum concepts and conventional design frameworks.