A population extremal optimization based modified constrained generalized predictive control method

As one of the most popular and successful methods in industrial applications, model predictive control (MPC) has attracted increasing interest in the past two decades. However, one of open issues in this research filed is how to solve the constrained nonlinear optimization problems in MPC. From the perspective of evolutionary algorithm, this paper presents a novel population extremal optimization (PEO) based modified constrained generalized predictive control (CGPC) method called CGPC-PEO. The key idea behind the proposed CGPC-PEO is using PEO for rolling optimization to minimize the weighted objective function subjecting to a set of constraints. Its superiority to other evolutionary algorithms such as genetic algorithm and particle swarm optimization based CGPC is demonstrated by the simulation results on an industrial process plant.