Hardware Implementation of the Neural Network Predictive Controller for Coupled Tank System

Abstract

In this paper, a neural network based predictive controller is designed for controlling the liquid level of the coupled tank system. The controlled process is a nonlinear system; therefore, a nonlinear prediction method can be a better match in a predictive control strategy. The neural network predictive controller that is discussed in this paper uses a neural network model of a nonlinear plant to predict future plant performance. The simulation results are compared with PID control. The results show that the effectiveness of using the neural predictive controller for the coupled tank system. The Simulink Toolbox in MATLAB has been used to simulate the controlled system with the proposed controller. The VHDL has been used to describe the implementation of neural controller. Xilinx ISE Project Navigator Version 10.1 is used to obtain the compilation and timing test results as well as the synthesized design. The hardware implementation of the neural network predictive controller using FPGA board is proposed. To make sure that the FPGA board works like the simulated neural predictive controller, MATLAB programme is used to compare between the set of the data that are obtained from the ModelSim program and the set of the data that are obtained from the MATLAB Simulink model. Simulation results show that the FPGA board can be used as neural predictive controller for controlling the liquid level of the coupled tank system.

Modeling and Simulation of a New Duty-Cycle Modulation Scheme for Signal Transmission Systems

“…In engineering, the use of Matlab/Simulink as a suitable framework for rapid simulation of dynamic systems ( [14,15,16]). Following this common practice, the complete Matlab/Simulink model of the DCM-based transmission system is presented in Figure 2, where the subsystems are numbered according to the same order used in the general DCM architecture shown earlier in Figure 1.…”