Optimal Control of Missile Attitude using Particle Swarm Optimization

The attitude control of a rocket during flight is crucial for ensuring precise trajectory adjustments, maneuverability, and mission success. In recent years, thrust vectoring has emerged as a key technology in rocket propulsion systems, offering the capability to manipulate the direction of thrust and thereby control the attitude of the rocket. This thesis focuses specifically on the design of a control system that makes use of thrust vectoring vanes to achieve attitude control for a rocket under development at T4i S.P.A. (where the undersigned student carried out a curricular internship). The control technique that is implemented for this work is the Linear Quadratic Regulator (LQR), which consists of minimizing a quadratic cost function that captures the trade-off between control effort and desired system performance. In this thesis, the Particle Swarm Optimization (PSO), a population-based optimization algorithm inspired by the social behavior of bird flocking or fish schooling, is applied to tune the LQR control parameters in order to achieve the desired system response. Finally, to validate the effectiveness of the designed control system, Monte Carlo simulations are run in order to evaluate the performance of the control system in terms of attitude tracking accuracy, response time, energy consumption, and stability under different flight scenarios and perturbations.