Description

This project aims to explore the application of genetic programming (GP) enhanced with the adjoint state method for designing interpretable control laws in the aerospace transportation framework. The focus will be on developing control systems that are both optimal and interpretable, leveraging the GP approach to generate control laws and the adjoint state method for efficient gradient evaluation. The project will involve testing the developed methodology on specific aerospace transportation problems, such as aircraft trajectory optimisation or attitude control, to assess its effectiveness in real-world scenarios.

Key Objectives

• Develop a GP framework enhanced with the adjoint state method for control law design (in MATLAB).

• Apply the framework to aerospace transportation systems, focusing on trajectory optimisation or attitude control.

• Compare the performance of the developed control laws with traditional methods, analysing both their optimality and interpretability.

• Evaluate the computational efficiency and scalability of the approach in complex aerospace applications.

• Evaluate the feasibility of the concept for repurposing wind turbine blades.

• Propose potential scalable applications for the repurposed blades.