MPC based on NBPSO for nonlinear process with constraints

Predictive control of systems is very much related to the efficiency and cost of systems, as well as to the quality of systems outcomes. However, it is difficult to achieve optimal predictive control because most predictive controls for systems have characteristics of randomness, strong and complex constraints and nonlinearity. Conventional methods of solving constrained nonlinear optimization problems for predictive control are mainly based on quadratic programming, which is quite sensitive to initial values, easy to trap in local minimal points, and requires large computational effort. In order to overcome these problems, Discrete binary particle swarm optimization is used to perform model predictive controller for nonlinear process with constraints. The performances obtained are compared with those given by the MPC method. The simulation results show that the proposed algorithm outperforms MPC algorithm in terms of performance and robustness.