Ascendancy of level in nonlinear tank system by neuro controller

Abstract

The main motive of this paper is to control a nonlinear process tank system using an intelligent neuro-controller-based Artificial Neural Network (ANN). To reduce the mean squared error Proportional–Integral–Derivative (PID) and fuzzy controllers were evaluated with Bayesian Regularisation. Moreover, Bayesian regularization can also disclose potentially complex relationships, where it finds its application in quantitative research resulting in robust structure, thereby justifying its usage in the proposed system. Here the network training was based on the Levenberg–Marquardt algorithm. Two neural controllers were employed to emphasize the plant’s output response during step and nonlinear responses.The output of the neuro-controller was found to have a decreased overshoot time and undershoot time. The results of the proposed method’s settling time, overshoot, and undershoot were compared. The comparative results indicate a better performance of the proposed model. The MATLAB platform was used to complete the entire project.

Keywords

• ANN
• Neuro-controller
• Bayesian Regularisation
• Levenberg–Marquardt​ algorithm
• Mean squared error