Estimation of Liquid Level in a Harsh Environment Using Chaotic Observer

The increased demand for liquid level measurement has been a key factor in designing accurate and reliable control systems. Here, study was carried out to calculate the tank using pressure sensor changes inlet parameters like temperature, density velocity. Prediction of their variables long term is essential due randomness present input measurement. Hence, observer design state estimation non-linear dynamic system with uncertainties process becomes important. This work provides feedback solution multiple inputs single measurable output. A full model developed estimate system’s states placed at definite position from pipe’s point through which flows different densities temperatures. Using observability properties, Luenberger designed by various methods, verified MATLAB SIMULINK estimation. To incorporate noise noise, Kalman estimator integrated system. Chaotic systems are susceptible initial conditions, variations complex However, providing consistently precise measurements particular meters necessitates time-consuming computations that can be reduced employing machine learning approaches make use optimizers. results obtained compared prediction models Artificial Neural Networks validated readings experimental setup.