Sparse Approximate Hamilton-Jacobi Solutions for Optimal Feedback Control with Terminal Constraints

Abstract

A semi-analytic method is proposed to solve a class of optimal control problems while exploiting its underlying Hamiltonian structure. Optimal control problems with a fixed final state at a fixed terminal time are considered. The solution methodology proposed in this work solves the Hamilton-Jacobi equation over a predefined domain of states and co-states. The advantage over traditional methods is that an approximate generating function (analogous to the value function of HJB theory) is obtained as a function of time, which allows for the computation of co-states for any final time and final state specified. Numerical experiments are conducted to demonstrate the efficacy of developed method while considering benchmark problems including spin stabilization.

Publication
62nd IEEE Conference on Decision and Control, December 13-15, 2023, Singapore
Amit Jain
Amit Jain
Aerospace Engineer

My research interests encompass uncertainty propagation and optimal control, system identification, as well as the application of machine learning and artificial intelligence to astrodynamics challenges.

Related