| Abstract: |
| This talk presents an output feedback robust model predictive control (MPC) method for stabilizing nonlinear systems with additive disturbances and noisy output measurements. A robustly stable observer updates the estimation error set magnitude online, which is then used in the controller design for a predictive control inverse problem. By leveraging the sublevel set defined by the Riemannian distance, a set-valued system is constructed around the predicted trajectory. Reachability analysis parameterizes this set-valued system to obtain tube dynamics, forming the basis for robust constraint conditions. The proposed framework removes the need for explicit geodesics, avoiding online synthesis of auxiliary controllers. Additionally, two exponentially convergent observers are developed through contraction analysis, and a method for improving output feedback via online set-valued estimation is introduced. Numerical simulations show the effectiveness of the proposed approach in stabilizing the system while satisfying state and control input constraints. |
|