Optimal nonlinear model predictive control based on Bernstein polynomial approach
Accepted version
Peer-reviewed
Repository URI
Repository DOI
Change log
Authors
Patil, BV
Ling, KV
Maciejowski, JM
Abstract
© 2017 IEEE. In this paper, we compare the performance of Bernstein global optimization algorithm based nonlinear model predictive control (NMPC) with a power system stabilizer and linear model predictive control (MPC) for the excitation control of a single machine infinite bus power system. The control simulation studies with Bernstein algorithm based NMPC show improvement in the system damping and settling time when compared with respect to a power system stabilizer and linear MPC scheme. Further, the efficacy of the Bernstein algorithm is also compared with global optimization solver BMIBNB from YALMIP toolbox in terms of NMPC scheme and results are found to be satisfactory.
Description
Keywords
4007 Control Engineering, Mechatronics and Robotics, 40 Engineering, 4010 Engineering Practice and Education
Journal Title
2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017
Conference Name
2017 IEEE 56th Annual Conference on Decision and Control (CDC)
Journal ISSN
0743-1546
Volume Title
2018-January
Publisher
IEEE
Publisher DOI
Sponsorship
National Research Foundation Singapore (via Cambridge Centre for Advanced Research and Education in Singapore (CARES)) (unknown)
National Research Foundation, Singapore.