On-Line Auto-Tuning Robust Nonlinear Neural PID Controller Design for Double-Pipe Heat Exchanger Based on Particle Swarm Optimization

Abstract

The heat exchanger systems have highly nonlinear features between its variables and time delay. In addition, the parameters of them change constantly during operation. Therefore, the heat exchangers need nonlinear robust controller for obtaining required outlet temperature under all operating conditions.In this paper, a robust nonlinear neural PID (NLNPID) controller is proposed for temperature control of double-pipe heat exchanger. Particle swarm optimization (PSO) technique is used to determine on line auto tuning the optimal parameters of proportional-integral-derivative (PID) controller. Mathematical model of heat exchanger takes into account the changes in the density, thermal conductivity, viscosity, specific heat and convection heat transfer coefficient of the fluid which are resulting from changing in temperatures and the velocities with classical PID control. Simulations results show that the performance of the NLNPID controller produces good responses features and give desired outlet temperature under all operating conditions without over shoot and fluctuating.