| Title |
Design of an RBF Neural Network Supervisory Controller Based on a Sliding Mode Control Approach |
| Authors |
손영익(Young Ik Son) ; 임승철(Seungchul Lim) |
| DOI |
https://doi.org/10.5370/KIEE.2021.70.12.1984 |
| Keywords |
RBF Neural Network; Supervisory Control; System Uncertainty; Robust Control; Sliding Mode Control |
| Abstract |
This paper presents an RBF neural network supervisory controller by combining the sliding mode control approach as a method to improve the control performance in the presence of a model uncertainty and external disturbances. Remarkably, the proposed method does not require the exact system model equations. In order to verify the performance of the proposed algorithm, computer simulations using two practical systems have been performed. In the first example a motor controller for a parallel index mechanism has been designed to achieve the desired constant speed in spite of large disturbances caused by the intermittently-rotating load. The proposed controller can successfully solve the second angle tracking problem of a classical inverted pendulum system with a sinusoidal reference against a time-varying disturbance. |