Abstract: A controller based on a neural network whose output is responsive to input signals that represent user or designer defined control system parameters which may include process parameters, control parameters and/or disturbance parameters. The neural network can be "trained" to mimic an existing controller which may or not receive inputs of control system parameters. The trained neural network controller may have advantages of faster execution and reduced code size. The neural network can also be trained to result in a nonlinear controller that is more powerful than an existing controller.

Abstract: PID controllers form a large proportion of controllers in use in many controlled systems today. This application describes how to use a neural network which receives PID inputs to be a controller and operate as a PID controller to save on retraining and provide other efficiencies in control. Also shown is the user selectability between PID conventional controllers and Neural Network controllers.