Design and Performance Analysis of Level Control Strategies in a Nonlinear Spherical Tank

This work seeks to contribute the study of techniques for level control considering a nonlinear plant model. To achieve this goal, different approaches are applied classical and their results analyzed. Fuzzy Logic Control (FLC), Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), Model Predictive (MPC) Nonlinear Auto-Regressive Moving Average (NARMA-L2) controllers designed spherical tank. Subsequently, several tests scenarios similar those present in industrial processes established, while transient response controllers, performance indices monitoring reference value, rejection disturbances, presence parameter uncertainties effects noise The show good tracking, with settling time approximately 5 s cm rise less than 4 s. No evidence steady-state error or overshoot was found behave positively diverse assessed. FLC ANN showed greatest limitations, ANFIS, MPC NARMA-L2 exhibited competitive calculated.