Abstract
In this paper, a new hybrid supervised learning control scheme is presented for continuous stirred tank reactor (CSTR) systems. The control system works at distal supervised learning control mode or extreme control mode, respectively. Neural networks controller is trained by using extreme control signal as supervised signal when system works at extreme control mode and there is no supervised signal for training neural networks controller when system works at distal control mode. The precise mathematical model of the CSTR is not necessary for designing hybrid supervised control system. Simulation results show the presented control system with satisfactory dynamic and stationary performance and strong adaptability.
