Neural network assisted control loop tuner

Explores the application of nonlinear tuning rules estimators to a known relay-oscillation tuner. Two approaches were tested. One uses nonlinear functions to approximate the desirable controller parameters. The other incorporates a neural network for computing the process model and controller parameters. As a basis for computation, the ultimate gain, ultimate period, and process dead time are defined during the tuning experiment. The neural network is trained in simulation using these process parameters as inputs and known process model parameters and desired PID controller tuning parameters as outputs. The PID tuning parameters are defined from the simulation process model using IMC or lambda tuning rules. This concept was implemented in a scalable industrial control system. Simulation test results show a vast improvement in model identification and control loop performance as compared to previous relay-oscillation based tuning approaches.