Adaptive Control of Nonlinear Discrete-Time Systems by Using OS-ELM Neural Networks

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.

Performance comparison of learning algorithms for system identification and control

“…A comparison of (8) and (9) reveals that the former is obtained from the latter by setting Y 1,0 to be zero. This is equivalent to beginning the training of the network at some k = −N 0 , and assuming that x −N0 , .…”

“…Some attempts have appeared in the literature (e.g., [7] and [9]). The goal of this paper is to compare in the context of system identification and control the performance of the sequential version of the back propagation algorithm with the sequential variants of the ELM.…”

Adaptive Control Using Neural Networks and Approximate Models for Nonlinear Dynamic Systems

“…In the field of control engineering for a class of nonlinear discrete-time systems, a neural network has recently emerged as adjustable approximators capable of reproducing the complex behavior of nonlinear systems. Artificial neural networks have been effectively used as tracking controllers for unknown linear and nonlinear dynamic plants [6,7]. ANNs have been employed in various fields, like time series prediction, system identification and control, and function approx-imation [8].…”

Data-Driven Model-Free Adaptive Control Based on Error Minimized Regularized Online Sequential Extreme Learning Machine

“…Online sequential extreme learning machine (OS-ELM) [27] is an online learning algorithm, it adjusts the output weights online, besides the input weights and hidden biases are randomly chose. In recent years OS-ELM has gained a large amount of interests and been uesed to estimate unknown parameters of systems [28,29]. Some improved OS-ELM algorithms were introduced by some scholars, such as regularized online sequential extreme learning machine (REOS-ELM) [30], initial-training-free online extreme learning machine (ITF-OELM) [31], etc.…”