Abstract
In this paper an approach is studied to guaranteed (set-membership) state estimation for robust output-feedback model predictive control (MPC) with hard input and state constraints. Uncertainties are assumed to arise in a dynamic system from unknown initial conditions of state variables and due to unknown-but-bounded measurement noise. The uncertainty in the state variables is represented as a parallelotopic set. The employed state-estimation algorithm recursively outbounds the feasible set that is given by an intersection of model predictions with obtained measurement information. Along with the well-known minimum-volume criterion for parallelotopic outbounding, three alternative criteria are proposed and studied. The aim is to identify the best outbounding approach for improving performance of the robust MPC.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
