Informatica

The aim is to develop simple for industrial use neuro-fuzzy (NF) predictive controllers (NFPCs) that improve the system performance and stability compensating the nonlinear plant inertia and time delay. A NF plant predictor is trained from real time plant control data and validated to supply a main model-free fuzzy logic controller with predicted plant information. A proper prediction horizon is determined via simulation investigations. The NFPC closed loop system stability is validated based on a parallel distributed compensation (PDC) approximation of the NFPC. The PDC can easily be embedded in industrial controllers. The proposed approach is applied for the real time air temperature control in a laboratory dryer. The improvements are reduced overshoot and settling time.

This paper analyses coupled control of a nonlinear, time‐varying plant. Uncoupled and coupled direct adaptive controllers, based on fuzzy logics, are synthesized to control the water level and the air pressure in a closed tank. The satisfactory efficiency of the controllers is experimentally demonstrated running the plant under the different working conditions. Coupled fuzzy controllers are compared with the uncoupled fuzzy controllers.

This paper analyses the control of nonlinear plant with the changing dynamics. Adaptive controllers, based on fuzzy logics, are synthesized for the control of air pressure and water level. Their satisfactory efficiency is experimentally demonstrated under different working conditions. Fuzzy controllers are compared to conventional PI and PID controllers.