Summaries - Research
Back Speeding Up Model Predictive Control and Moving Horizon Estimation
|Division||Graduate School of Engineering & Applied Science|
|Investigator(s)||Krener, Arthur J.|
|Sponsor||Air Force Office of Scientific Research (Air Force)|
Because of the difficulty of solving the Hamilton Jacobi Bellman or the Dynamic Programming Equations, Model Predictive Control (MPC) has become an increasing popular way of stabilizing a plant to an operating point. MPC was originally developed in the chemical processing industries where time constants are long and the processes are nearly stable. The goal of this research proposal is to develop techniques to speed up MPC so that it can be used to control faster processes such as planes and helicopters. MPC is heavily dependent on the nonlinear program solvers that it utilizes to find the stabilizing control in real time. Much effort has gone in to speeding up these solvers.
But the solvers are unaware that the nonlinear programs that they are asked to solve come from a dynamic control problem. We present several analytic techniques that take advantage of the dynamic nature of the problem. We believe these will simplify the task of the solvers thereby enabling MPC to be control faster processes.
The state estimation problem is dual to the state stabilization problem. Moving Horizon Estimation (MHE) use MPC techniques to estimate the state from partial and noisy measurements. Its roots go back to Minimum Energy Estimation (MEE). We propose to develop a discrete time MEE method that will allow us to simplify MHE by making the horizon as short as possible.
|Publications||Publications, theses (not shown) and data repositories will be added to the portal record when information is available in FAIRS and brought back to the portal|
|Data||Publications, theses (not shown) and data repositories will be added to the portal record when information is available in FAIRS and brought back to the portal|