Recurrent Neural Networks in Linear Systems Controlling

Abstract

Abstract. This paper presents an application of an ANN (Artificial Neural Network) of a RNRF type (Recurrent Network with Radial basis Function) in controlling a linear system. The performance of ANN-based control solution is compared with a classic controller and the results show that ANN behaves better than the classic controller. MATLAB simulation performed show that the coupling between the ANN and a proportional controller gives the best performance.

Speed Control of an Asynchronous Motor Using PID Neural Network

“…The values calculated by multiplying the output values of the neurons in the input layer and the connection weights between the input and hidden layers come to the neurons in this layer, and they are processed in the neurons that conduct the PID algorithm. Inputs formed in the neurons of the hidden layer are calculated as shown in Equation (2).…”

“…In classical control methods, the selection of the controller to be used in the control of systems and for the detection of The parameters that are determined in this way cannot always provide the desired system stability due to various factors, such as modelling mistakes, changes in the parameters of the controlled system, and disruptive effects. Due to all of these problems in classical control methods, practitioners began to use artificial neural networks (ANNs) in the control field because they have the ability to learn and generalize, and the derivation of a mathematical equation is not required [1,2]. Today, most of the systems used in industry exhibit non-linear, time-delay behaviour.…”

Identification and Control of pH using Optimal Piecewise Linear Wiener Model

“…This has led to development of non linear model predictive control (NMPC) in which a more accurate model is used for process prediction and optimization. Some papers have reported controllers incorporating nonlinear models such as neural networks, as Patic et al (2010) and Saindonat (1998); Volterra series model, Genceli and Nikolaou (1995). Wiener models are useful in representing the nonlinearities of a process without complications associated with general non linear operators.…”