A Reference-Model-Based Neural Network Control Method for Multi-Input Multi-Output Temperature Control System

Abstract

Neural networks (NNs), which have excellent ability of self-learning and parameter adjusting, has been widely applied to solve highly nonlinear control problems in industrial processes. This paper presents a reference-model-based neural network control method for multi-input multi-output (MIMO) temperature system. In order to improve the learning efficiency of the NN control, a reference model is introduced to provide the teaching signal for the NN controller. The control inputs for the MIMO system are given by the sum of the output of the conventional integral-proportional-derivative (I-PD) controller and the outputs of the neural network controller.The proposed NN control method can not only improve the transient response of the system, but can also realize temperature uniformity in MIMO temperature systems. To verify the proposed method, simulations are carried out in MATLAB/SIMULINK environment and experiments are carried out on the DSP (Digital Signal Processor)-based experimental platform, respectively. Both results are quantitatively compared to those obtained from the conventional I-PD control systems. The effectiveness of the proposed method has been successfully verified.

Deep Learning Control for Digital Feedback Systems: Improved Performance with Robustness against Parameter Change

“…904939). The second-order system with the plant transfer function given by Equation ( 5) is considered in this study, with analog controller given by Equation (8). The digital counterparts of this plant and its control sub-system (as shown in Figure 2) have been implemented using Equations ( 7) and ( 9) in the first round of the simulation, while the second round used control data from this system to train a DL network that eventually replaced the electronic control sub-system of Equation ( 9).…”

“…Relevant recent works in the literature include [8] where a NN controller with one hidden layer is trained for use with multi-input, multi-output (MIMO) systems resulting in an improvement in transient response regarding overshoot and settling time, but without resorting to DL. In [9], the speed of a DC motor is controlled in a feedback control loop with a proportional-integral-derivative (PID) controller, the most commonly used in industry.…”