A bacterial foraging strategy-based recurrent neural network for identifying and controlling nonlinear systems

Abstract

Identification and control of nonlinear dynamic system plays an important role in many applications. In this paper, a novel bacterial foraging strategy-based Elman neural network is proposed for identifying and controlling nonlinear systems. We first present a learning algorithm for dynamic recurrent networks based on a bacterial foraging strategy oriented by quorum sensing and communication. The proposed algorithm computes concurrently both the weights, initial inputs of the context units and self-feedback coefficient of the Elman network. Thereafter, we introduce and discuss a novel control method based on the proposed algorithm. More specifically, a dynamic identifier is constructed to perform speed identification and a controller is designed to perform speed control for Ultrasonic Motors (USM). Numerical experiments show that the identifier and controller can both achieve higher convergence precision and speed. Besides, a preliminary examination on a random perturbation also shows the robust characteristics of the proposed models.