Two DOF temperature control using RBFNN for stretch PET blow molding machines

Abstract

This paper presents a novel two degrees-of-freedom (DOF) digital controller using radial basis function neural network (RBFNN) for a stretch polyethyleneterephthalate (PET) blow molding machine, in order to achieve satisfactory temperature control of the PET bottle performs passing through both heating ovens. The proposed two-DOF controller is composed of a feedforward controller used to improve the transient performance and track quickly temperature setpoints, and an RBFNN self-tuning digital proportional-integral-derivative (PID) controller employed to eliminate remaining temperature errors and achieve disturbances rejection. Such a controller not only retains the practical expertise of the control practitioners working for PET blow molding machines, but also keeps automatic tuning ability of the PID controller parameters. Finally, the computer simulation and experimental result reveal disturbance rejection and good setpoint tracking performance of the proposed control method. The results clearly indicate effectiveness and merit of the proposed method.

Study on Setpoint Tracking Performance of the PID SISO and MIMO Under Noise and Disturbance for Nonlinear Time-Delay Dynamic Systems

“…Many researchers have introduced and applied advanced methods for tuning the PID gain parameter. The authors in [1], [2] introduce a self-tuning PID controller, and Tsai et al [3] present a PID parameter tuning using the Radial Basis Function Neural Network (RBFNN) method for stretch PET blow molding machines. The author in [4] presents a predictive PID controller using an Output Recurrent Fuzzy Wavelet Neural Network (ORFWNN) method, which is an improvement from the method proposed by the author in [5], which presents a Fuzzy Wavelet Neural Network (FWNN) method for tuning the PID gain parameter.…”