## Adaptive Model Predictive Control of Uncertain Constrained Dynamic Systems

- April 13, 2015
- 1:30 p.m.
- LeConte 312

## Abstract

This talk focuses on adaptive model predictive control (AMPC) of systems with time-varying and state-dependent uncertainties. In the AMPC algorithm, an estimation and prediction architecture is proposed within a min-max MPC framework. The adaptive estimator is used to estimate the set-valued measure of the uncertainty using piecewise constant adaptive law, where this measure can be arbitrarily accurate if the sampling period in adaptation is small enough. Based on this measurement, a prediction scheme is provided that predicts the time-varying feasible set of the uncertainty over the prediction horizon. The results indicate that the proposed approach can efficiently reduce the size of the feasible set for the uncertainty in min-max MPC setting, and therefore improve the control performance.