Design of a multivariable neural-net based PID controller

It is well known that most industrial processes are multivariate in nature, and yet PID controllers are being widely used in a multiloop framework for the control of such interacting systems. In this paper, a design scheme for a neural net-based controller with a PID structure is proposed for the control of such multivariable systems. The proposed controller consists of a pre-compensator designed with a static gain matrix which compensates for the low-frequency interaction, and PID controllers placed diagonally, whose gains are tuned by a neural network.