Near Optimal Hamiltonian-Control and Learning via Chattering
This addresses control problems in enterprise scheduling, but appears incremental as it builds on existing chattering and optimization methods.
The paper tackles the problem of solving non-linear control problems that are classically not well behaved by developing a chattering algorithm that learns near optimal decision policies through open-loop feedback, reducing the optimal control problem to a series of linear optimization programs. It was implemented on a real-time enterprise scheduling and control process.
Many applications require solving non-linear control problems that are classically not well behaved. This paper develops a simple and efficient chattering algorithm that learns near optimal decision policies through an open-loop feedback strategy. The optimal control problem reduces to a series of linear optimization programs that can be easily solved to recover a relaxed optimal trajectory. This algorithm is implemented on a real-time enterprise scheduling and control process.