Adaptive output recurrent cerebellar model articulation controller for nonlinear system control

Abstract

In this study, an adaptive output recurrent cerebellar model articulation controller (AORCMAC) is investigated for a nonlinear system. The proposed AORCMAC has superior capability to the conventional cerebellar model articulation controller in efficient learning mechanism and dynamic response. The dynamic gradient descent method is adopted to online adjust the AORCMAC parameters. Moreover, the analytical method based on a Lyapunov function is proposed to determine the learningrates of AORCMAC so that the stability of the system can be guaranteed. Furthermore, the variable optimal learningrates are derived to achieve the best convergence of tracking error. Finally, the effectiveness of the proposed control system is verified by the several simulation and experimental results. Those results show that the favorable performance can be obtained by using the proposed AORCMAC.

Design and Development of Mamdani-Like Fuzzy Control Algorithm for a Wheeled Human-Conveyance Vehicle Control

“…The main topic of this research is how to balance a transporter while the vehicle moves. In fact, a WHCV can be considered an inverted pendulum [6]- [15] on two coaxial wheels. By moving the two coaxial wheels back and forth, the pendulum can be maintained in the upright position.…”