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

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.