Adaptive control over non­linear objects using the robust neural network FCMAC

Oleg Rudenko,Oleh Lebediev,Oleksandr Bezsonov

doi:10.15587/1729-4061.2018.128270

Abstract

The paper explores issues related to the application of artificial neural networks (ANN) when solving the problems on identification and control of nonlinear dynamic systems. We have investigated characteristics of the network, which is a result of the application of the apparatus of fuzzy logic in a classical СМАС neural network, which is titled FCMAC ‒ Fuzzy Cerebral Model Arithmetic Computer. We studied influence of the form of receptive fields of associative neurons on the accuracy of identification and control; various information hashing algorithms that make it possible to reduce the amount of memory required for the implementation of a network; robust learning algorithms are proposed allowing the use of a network in systems with strong perturbations. It is shown that the FСМАС network, when selecting appropriate membership functions, can be applied in order to synthesize indirect control systems with and without a reference model; it is more efficient to use it in control systems with the reference model. This sharply reduces the quantity of training pairs and simplifies the coding due to the narrower range of the applied values of input signals. The results obtained are confirmed by simulation modeling of the processes of identification of and control over nonlinear dynamical systems

Highlights

Artificial neural networks (ANNs) that are increasingly common at present are effective when solving problems on the identification and control of non-linear dynamic objects in real time
Where y is the output signal; x=(x1, x2,..., xN)T is the vector of input signals; f is an unknown nonlinear function; ξ is the disturbance with zero mathematical expectation; T is the symbol of transposition
We have proposed a structure of the robust FСМАС neural network, which is the result of combining a traditional СМАС with the apparatus of fuzzy logic and the robust M-estimation

Summary

Introduction

Artificial neural networks (ANNs) that are increasingly common at present are effective when solving problems on the identification and control of non-linear dynamic objects in real time. Among the existing large number of network structures, solving the specified problems mainly involve a multilayer perceptron (MP), radial-basis (RBN) and neural fuzzy (NFN) networks. All of these ANNs are based on the approximation of the examined function by a certain system of basis functions fi(x). In this case, the approximated function is represented as a neural network, containing, in addition to the input and output layers, one or more hidden layers.

Literature review and problem statement

Architecture of the robust neural network FCMAC

Robust algorithm for training a FСМАС neural network

Findings

Conclusions