In order to solve the difficulty in complicated system control, a new direct inverse model control strategy is proposed based on a new improved CMAC (Cerebellar Model Articulation Controller) neural network to control a kind of nonlinear system with strong hysteresis i.e. continuous-stirred tank reactor (CSTR). The idea of credit is introduced to help design a new Improved Credit Assigned CMAC (ICA-CMAC) with fast learning speed, which is helpful in real time control of CSTR. Simulation results show that the ICA-CMAC based method performs faster than conventional CMAC, and is strong in self-learning and helpful for improving the nonlinear control performance.
['Yingqi Ge', 'Shanshan Ma', 'Xiaoping Luo']
Direct inverse model control based on a new improved CMAC neural network