Robust Model Predictive Control with Least Favorable Measurements

Abstract — Closed-loop model predictive control of nonlinear systems, whose internal states are not completely accessible, incorporates the impact of possible future measurements into the planning process. When planning ahead in time, those measurements are not known, so the closed-loop controller accounts for the expected impact of all potential measurements. We propose a novel conservative closed-loop control approach that does not calculate the expected impact of all measurements, but solely considers the single future measurement that has the worst impact on the control objective. In doing so, the model predictive controller guarantees robustness even in the face of high disturbances acting upon the system. Moreover, by con-sidering only a single dedicated measurement, the complexity of closed-loop control is reduced significantly. The capabilities of our approach are evaluated by means of a path planning problem for a mobile robot. I