Direct Mnn Control of Continuous Stirred Tank Reactor Based on Input-Output Model

Abstract

In this paper, a direct multi-layer neural network control scheme is investigated for a class of continuous stirred tank reactors (CSTR). The CSTR plant under study is discretized to an input-output based τ-step ahead discrete-time model. By implicit function theorem, the existence of the implicit desired feedback control (IDFC) is proved. Multi-layer neural networks are used as the emulator of the desired feedback control. Projection algorithms are used to guarantee the boundness of the multi-layer neural network weights. Simulation results show the effectiveness of the proposed controller.