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Memetic algorithms based real-time optimization for nonlinear model predictive control

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2 Author(s)
Peng Chen ; Department of Automation, Shanghai Jiaotong University, China ; Yong-Zai Lu

The system performances of nonlinear model predictive control (NMPC) are greatly dependent upon the efficiency of online optimization algorithm. This paper proposes a novel hybrid solution with the integration of bio-inspired computational intelligence extremal optimization (EO) and deterministic sequential quadratic programming (SQP) for numerical optimization. Inheriting the advantages of the two approaches, the proposed EO-SQP algorithm is able to solve nonlinear programming (NLP) problems effectively. Furthermore, the proposed algorithm is employed as the online solver of NMPC. The simulation results on a benchmark nonlinear continuous stirred tank reactor (CSTR) show considerable performance improvement over traditional quadratic programming (QP) method.

Published in:

Proceedings 2011 International Conference on System Science and Engineering

Date of Conference:

8-10 June 2011