Closed Loop Nonlinear Process Identification Using Internally Recurrent Nets
The feasibility of using an internally recurrent network (IRN) as a nonlinear dynamic model for a plant and directly identifying the model using the plant input and output data from the controlled plant is discussed in this paper. It is shown that if the setpoint signal is employed as the excitation to the plant, an open loop model can be identified by the direct identification method from closed loop data. Simulations show that the IRN structure is a satisfactory nonlinear dynamic model structure for identifying nonlinear plants under closed loop control, and the long term prediction performance of an identified IRN model is generally good. In our investigation, we used nonlinear programming for IRN training, and it proved to be a good method for off-line network training.  Elsevier Science Ltd. All Rights Reserved.
closed loop control internally recurrent neural networks nonlinear dynamic plant identification