Identification of Damavand tokamak using fractional order dynamic neural network

Abstract

The Damavand tokamak is a small size research machine for fusion-related studies. This paper is motivated by the need to create an accurate nonlinear subspace model that may be used for controller design. The system is identified based on a newly introduced Fractional Order Dynamic Neural Network (FODNN) optimized by evolutionary computation. The proposed method, owing to its rich structure, is appropriate for modeling of the complicated behavior of the plasma and its instability. In the proposed method, a Lyapunov-like analysis is used to derive a stable new learning rule for updating the proposed FODNN weights. To achieve optimal value for fractional order of the proposed FODNN, a Particle Swarm Optimization (PSO) is employed. The performance of the proposed identifier is verified by using experimental data and the results are also compared with the integer order dynamic neural network identifier. The results show that there is a bound for the “identification error” that vanishes to zero as time tends to infinity. Furthermore, the comparison of the results achieved by the proposed method and those of the integer order dynamic neural network depicts higher accuracy of the proposed FODNN.