Neuro-Controller Design by Using the Multifeedback Layer Neural Network and the Particle Swarm Optimization

Abstract

Abstract:In the present study, a novel neuro-controller is suggested for hard disk drive (HDD) systems in addition to nonlinear dynamic systems using the MultifeedbackLayer Neural Network (MFLNN) proposed in recent years. In neuro-controller design problems, since the derivative based train methods such as the back-propagation and Levenberg-Marquart (LM) methods necessitate the reference values of the neural network's output or Jacobian of the dynamic system for the duration of the train, the connection weights of the MFLNN employed in the present work are updated using the Particle Swarm Optimization (PSO) algorithm that does not need such information. The PSO method is improved by some alterations to augment the performance of the standard PSO. First of all, this MFLNN-PSO controller is applied to different nonlinear dynamical systems. Afterwards, the proposed method is applied to a HDD as a real system. Simulation results demonstrate the effectiveness of the proposed controller on the control of dynamic and HDD systems.

Design and Control of a Three-Coil Permanent Magnet Spherical Motor

“…In particular, the positioning system is designed as a visual servo control system, allowing the gathering of input-output data pairs to build a very accurate artificial neural network architecture [34][35][36][37][38][39][40]. A comprehensive evaluation of the positioning control of the proposed model when engaged in the task of tracking a desired trajectory is reported.…”