Optimal nonlinear model predictive control based on Bernstein polynomial approach
2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017
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Patil, B., Ling, K., & Maciejowski, J. (2018). Optimal nonlinear model predictive control based on Bernstein polynomial approach. 2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017, 2018-January 4363-4369. https://doi.org/10.1109/CDC.2017.8264303
© 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.
National Research Foundation, Singapore.
National Research Foundation Singapore (via Cambridge Centre for Advanced Research and Education in Singapore (CARES)) (unknown)
External DOI: https://doi.org/10.1109/CDC.2017.8264303
This record's URL: https://www.repository.cam.ac.uk/handle/1810/279143