Model Predictive Control based on Real Time Particle Swarm Optimization (IPO)

A novel approach for the implementation of nonlinear model predictive control (NMPC) is proposed based on Individual particle optimizer (IPO1) while functional link neural network (FLNN) is introduced as a nonlinear model of the plant where individual particle optimization (IPO2) is applied for training of the neural network. The IPO algorithm is used as a real-time optimal tuning technique, which is applied to the neural network so that the proposed optimized FLNN can be used in nonlinear model predictive control scheme. Finally, the proposed NMPC applied to the Load frequency control (LFC) problem. Simulation results verify that the proposed IPO based technique possesses efficient performance in the sense of speed up and set point tracking.).