Title: Design of neural network based MRAC

Authors: Dixit Sethi; Swati Sharma; Jagdish Kumar; Rintu Khanna

Addresses: Department of Electrical Engineering, PEC University of Technology, Chandigarh, 160012, India ' Department of Electrical Engineering, PEC University of Technology, Chandigarh, 160012, India ' Department of Electrical Engineering, PEC University of Technology, Chandigarh, 160012, India ' Department of Electrical Engineering, PEC University of Technology, Chandigarh, 160012, India

Abstract: This paper presents the comparison of conventional-MRAC (model reference adaptive controller), Advanced-MRAC and neural network based MRAC (NN-MRAC) scheme. The MIT Rule and Lyapunov Rule is used for the design of controller parameter adaptation laws. The main focus of research is on how to adapt the control actions more effectively to solve the problem of disturbances and non-linearities. Conventional-MRAC alone is unable to handle nonlinearities and disturbances of the plant and to provide a stable and controlled output, we augment an NN controller, in parallel with MRAC controller, to compensate the nonlinearities and disturbances present in the plant and this scheme is called as NN-MRAC scheme. All methods are applied with analytical detail to a chosen single-input/singleoutput (SISO) second order inherently unstable system named Inverted Pendulum with the application of some uncertainties and disturbances. It is clearly seen from the computer simulation results that NN-MRAC system improves the performance of the system effectively.

Keywords: MRAC; model reference adaptive controller; AMRAC; advanced-MRAC; NN-MRAC; neural network based MRAC; MIT rule; Lyapunov Rule; PID; MBPNN; multilayer backpropagation neural network.

DOI: 10.1504/IJSCIP.2018.097138

International Journal of System Control and Information Processing, 2018 Vol.2 No.4, pp.288 - 304

Received: 17 May 2017
Accepted: 21 Feb 2018

Published online: 23 Dec 2018 *

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