- North Carolina State University
Wednesday, May 2, 2018
The solution to a nonlinear optimal control problem is defined in terms of the solution to the Hamilton-Jacobi-Bellman equation, which has been solved for linear systems but is very difficult to solve for general nonlinear systems. An alternative approach is find a tabilizing feedback control first, then establish that it optimizes a specified cost functional. This is known as the inverse optimal control problem. Solving the inverse optimal control problem for discrete-time nonlinear systems requires the construction of a stabilizing feedback control law based on a control Lyapunov function (CLF). However, there are few systematic approaches available for defining appropriate CLFs. We propose an approach that employs Bayesian filtering methodology to parameterize a quadratic CLF. In particular, we use the ensemble Kalman filter (EnKF) to estimate parameters used in defining the CLF within the control loop of the inverse optimal control problem formulation. Using the EnKF in this setting provides a natural link between uncertainty quantification and optimal design and control, as well as a novel and intuitive way to find the one control out of an ensemble that stabilizes the system the fastest. Results are demonstrated on both a linear and nonlinear test problem.
Contact persons: Antonio Marigonda e Marco Caliari
- Programme Director
- Publication date
April 10, 2018