Dirk Thiele

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.

Device Based Process Control in Foundation Fieldbus

Devices based on the Fieldbus Foundation’s specification will, as an integral part of their design, allow process inputs and outputs to be precisely sampled by the device without aliasing or skewing. Fieldbus Foundation also standardizes a device description language (DDL) in which device manufacturers can present a description of their field devices. Given these standards it became possible to distribute function block control technology to fieldbus devices of different vendors. An overview of monitor, control, and diagnosis techniques used in Foundation Fieldbus is presented. Also, the manner in which fieldbus may influence advanced control implementations, such as process identification for controller self-tuning is reviewed.