Multiple recurrent neural networks for stable adaptive control

It is difficult to realize adaptive control for some complex nonlinear processes which are operated in different environments and when 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 select the best model. The adaptive controller also has a multi-model structure. We introduced three different multi-model neuro controllers. The convergence of the neuro identifier, switching property and the stability of neuro control are proved. Numerical simulations are given to illustrate the performances of multiple neural identifiers and neural adaptive control on a pH neutralization process.