Design of a Free Model Adaptive-Neural Controller for Level and Temperature Control of Liquid Storage Tanks

In this paper, an adaptive-neural free model scheme is proposed to control a widely-used nonlinear multivariable industrial system, a quadruple-tank process (QTP). The system consists of four tanks that are arranged in two upper and two lower formations. The main objective is defined as maintaining the level of the liquid in lower tanks via two pumps. Controlling this system is not an easy task since it has nonlinear dynamics, strong interaction between different channels, and highly interacted input and output variables. In the adaptive part of the proposed controller, the parameters and rules obtained from Lyapunov stability analysis, along with the estimation of nonlinear functions performed with the neural network, constitute the controller design steps. To highlight the controller's abilities, an additional object is defined, which is controlling the temperature of liquid of those two tanks by adding a heater to the QTP system as a modified system. Obviously, the interactions amongst the control loops are multiplied because the modified quadruple tank process (MQTP) system has four inputs and four outputs. One of the main contributions of this paper is the implementation of the closed-loop system. Regarding the importance of such a system in the industry and to test the controller practically, the closed-loop system is implemented in an industrial automation environment with the connection of Process Control System SIMATIC (PCS7) industrial software to MATLAB with Open Platform Communications (OPC) protocol. The effectiveness of the introduced scheme is verified by performing some experimental validation.