Adaptive control with multiple neural networks

It is difficult to realize adaptive control for some complex nonlinear processes which are operated in different environments and the operation conditions are changed frequently. In this paper we propose an identifier-based adaptive control (or indirect adaptive control). The identifier uses two effective tools: multiple models and neural networks. A hysteresis switching algorithm is applied to the new identification approach and the convergence of the identifier is proved. Adaptive controller also has a multi-model structure. We consider three different architectures of the multi-model neuro control. The simulation results show that the multiple neuro controllers have better performances for the pH neutralization process.