Neuro‐Predictive Control of an Infrared Dryer with a Feedforward‐Feedback Approach

Abstract

In this research, a hybrid control system is proposed to address the temperature control of an infrared dryer. The control system includes a feedback-predictive controller and a neural network steady state control law. The feedback-predictive controller outputs the amplified value of the predicted error as the transient control command. The predictive model was employed to suppress the undesirable effect of the dead-time of the system. A multilayer perceptron was designed and tested based on a control equilibrium point and steady state control to be used as a feedforward controller. The stability of the control system in a continuous domain was proved with no limit on the amplification gain of the predictive-feedback controller. In other words, there is no concern about losing stability with accelerating convergence towards the reference. The entire control system was constructed in Simulink and compiled to a C code and applied on the experimental setup. Experimental results are outstanding in comparison with the results of an interactively tuned IMC-based PID controller.

Real-time monitoring system for quality monitoring of jujube slice during drying process

“…Ju et al [3] enhanced the drying rate, energy efficiency, and quality of fruits and vegetables under hot air drying by controlling the relative humidity during the drying process based on the Weibull model and the Bi-Di model. Mohammadzaheri et al [12] proposed a mixed temperature control system for infrared dryers based on temperature monitoring to address the temperature control of an infrared dryer.…”

Neural Network Predictive Controller Based Nonlinearity Identification Case Study: Nonlinear Process Reactor - CSTR

“…Nonlinearity behavior in the process control reactor occurs from various parameters such as temperature dependence of reaction rates [1] . It may also result from process limitations such as valve limits, leading to input saturation (i.e., flow rate manipulation) or from physical constraints on output variables (e.g., mole fractions of chemical species) [2], [3] . Optimization and control of process systems usually requires a precise process model [4] .…”