摘要：AbstractIn this paper the problem of robust output-feedback Model Predictive Control (MPC) is considered. Uncertainty in the state estimates obtained from noisy measurements is bounded using set-membership techniques as we consider the noise to be bounded. Robustness of the MPC controller is achieved in a min-max sense. We use parallelotopic bounding for the state estimates. We propose enhancements to a well-known Recursive Optimal Parallelotopic Outbounding (ROPO) algorithm such that the resulting closed-loop cost is improved. All methodologies are tested using a simple linear case study. The results obtained show the benefits of the developed methods.
