Abstract
As a kind of novel feedforward neural network with single hidden layer, ELM (extreme learning machine) neural networks are studied for the identification and control of nonlinear dynamic systems. The property of simple structure and fast convergence of ELM can be shown clearly. In this paper, we are interested in adaptive control of nonlinear dynamic plants by using OS-ELM (online sequential extreme learning machine) neural networks. Based on data scope division, the problem that training process of ELM neural network is sensitive to the initial training data is also solved. According to the output range of the controlled plant, the data corresponding to this range will be used to initialize ELM. Furthermore, due to the drawback of conventional adaptive control, when the OS-ELM neural network is used for adaptive control of the system with jumping parameters, the topological structure of the neural network can be adjusted dynamically by using multiple model switching strategy, and an MMAC (multiple model adaptive control) will be used to improve the control performance. Simulation results are included to complement the theoretical results.
Highlights
Modeling and controlling of nonlinear systems are always the main research field in control theory and engineering [1,2,3]; great progresses in this field have been made in the past 30 years, but many problems still exist
We find that OS-extreme learning machine (ELM) neural networks have the capability of identification of and controling nonlinear systems
OS-ELM neural network can improve the accuracy of identification and the control quality dramatically
Summary
Modeling and controlling of nonlinear systems are always the main research field in control theory and engineering [1,2,3]; great progresses in this field have been made in the past 30 years, but many problems still exist. A new learning algorithm for single-hidden-layer feedforward neural networks (SLFNs) named extreme learning machine (ELM) has been proposed by Huang et al [6, 7]. Our interest will be kept in the identification and control of nonlinear dynamic plants by using OSELM neural networks. The adaptive controller based on OSELM neural network model can be constructed. On one hand, since the training process of OSELM neural network is sensitive to the initial training data, different regions of the initial data can cause large diversity of control result.
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