Abstract

This paper represents a receding horizon predictive control algorithm for constrained nonlinear systems which, unlike earlier works, can be solved by linear programming methods. Use is made of a terminal inequality constraint in conjunction with a cost penalizing an upper bound on the tracking error over a finite control horizon. The optimization procedure is based on predictions made by linearized incremental models at points of a given seed trajectory and the effects of linearization error are taken into account to give a bound on the predicted tracking error. The proposed algorithm is posed in the form of LP and its asymptotic stability can be guaranteed by proper selection of the terminal weights of the cost index.

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 call

Disclaimer: 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.