The development of a neural network system for tuning proportional and integral (PI) feedback controllers is presented. The tuning process includes an initial gain setting procedure and a fine tuning procedure. The initial gain settings are obtained by using the standard Ziegler-Nichols tuning rules based on the openloop step response of the process. The fine tuning procedure is performed iteratively by using a neural network. The neural network suggests adjustments to the proportional gain and integrator time based on the closed-loop controlled system response. Four parameters are defined to describe the response characteristics. They are the normalised peak rise time, normalised overshoot, normalised peak to peak height, and normalised final error. These four parameters are used as inputs to the neural network. The tuning knowledge of the neural network is extracted from the tuning of a representative process. Finally, examples covering a wide range of process dynamics are tested to demonstrate the excellent performance of the tuner.
