Ricerc@Sapienza

Recent advances in computation techniques and the latest progress in computing hardware hold the promise to transform aerospace guidance and control technologies and to dramatically increase the level of autonomy of future aerospace vehicles. Traditional designs, based on analytical solutions, can be replaced by algorithms that, by intensively relying on onboard computation, can accomplish the complex guidance and control tasks associated with autonomous operations. Model predictive control (MPC) falls into this class of methods and it is recognized by the aerospace community as one of the most promising ones, due to its systematic treatment of constraints, optimized performance, and robustness to uncertainty.

This research aims at designing novel guidance algorithms that embed convex optimization into the MPC framework to exploit the computation speed and deterministic guarantees of convergence of convex programming. Since most real-world aerospace problems are not naturally convex, state-of-the-art convexification methods are examined to make general optimization problems computationally tractable, thus enabling their real-time implementation. Furthermore, the research seeks to analyze the performance and accuracy properties of several discretization methods and evaluate their suitability for time-critical applications.