Adaptive control of nonlinear system using online error minimum neural networks. (November 2016) - Jia, Chao; Li, Xiaoli; Wang, Kang; Ding, Dawei
Abstract: In this paper, a new learning algorithm named OEM-ELM (Online Error Minimized-ELM) is proposed based on ELM (Extreme Learning Machine) neural network algorithm and the spreading of its main structure. The core idea of this OEM-ELM algorithm is: online learning, evaluation of network performance, and increasing of the number of hidden nodes. It combines the advantages of OS-ELM and EM-ELM, which can improve the capability of identification and avoid the redundancy of networks. The adaptive control based on the proposed algorithm OEM-ELM is set up which has stronger adaptive capability to the change of environment. The adaptive control of chemical process Continuous Stirred Tank Reactor (CSTR) is also given for application. The simulation results show that the proposed algorithm with respect to the traditional ELM algorithm can avoid network redundancy and improve the control performance greatly. Abstract : Highlights: A nova network learning structure algorithm named OEM-ELM (Online Error Minimum Extreme Learning Machine) is proposed. OEM-ELM algorithm combines the advantages of OS-ELM (Online sequence Extreme Learning Machine) and EM-ELM (Error Minimum Extreme Learning Machine) algorithm. This algorithm can adjust the neural network structure online by increasing the hidden nodes as need as possible. We put forward a nova adaptive method based on OEM-ELM neural networks and apply this method in chemical process Continuous Stirred Tank Reactor (CSTR). The simulation results for a chemical process CSTR show that the adaptive control based on OEM-ELM neural network has a strong reliability and can improve control property greatly.