Abstract
This paper presents a neural net controller for a multi-input, multi-output (MIMO) process. The controller is based on the PENN (Policy- and Experience-driven Neural Network) method. The PENN uses two types of knowledge for learning: local experiences obtained from the control results; and global policies based on rule-based control strategy. The structured PENN that is proposed for a MIMO process in this study has a structure in which small controllers for SISO processes are assembled. By use of such a structured assembly, both learning and control for a MIMO process can be accomplished. To prevent the effect of noises observed in the controlled variables, a noise reduction mechanism modifying the error function in the learning algorithm is proposed. The simulation results for a crystal growth process indicate that the proposed controller has the ability to learn the interactions between control variables and to get satisfactory control results.
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