An Adaptive Liquid Level Controller Using Multi Sensor Data Fusion

Abstract

This paper describes a design of adaptive liquid level control system using the concept of Multi Sensor Data Fusion (MSDF). Purpose of the work is to design a controller for accurately controlling the level of liquid in a process tank with liquid temperature changes. The proposed objective is obtained by i) implementing a MSDF framework using Pau's framework for measuring liquid level and temperature, ii) analyzing the behavior of actuator output for variation in liquid temperature, and iii) designing a suitable adaptive controller which will produce desired control action for controlling liquid level accurately using neural network algorithms. Outputs from sensors are fused to obtain the fluid level output and also relation of level transmitter output for change in temperature. This information is used by controller to train the neural network so as to tune the controller parameters (proportional gain, integral constant, and differential constant), to drive the actuator. Results obtained show that the system is able to control liquid level within range of 1.915% of set point even with variations in liquid temperature.

A multistage successive approximation method for Riccati differential equations

“…Mathematical modelling and simulation have been applied extensively in the areas of telecommunication [1], [2], computing [3]- [5], electronics [6], [7] and control [8], [9]. One of important models in these areas is the Riccati differential equation.…”