2 축 짐벌 시스템의 외란에 강인한 임베디드 신경회로망 제어기 구현에 대한 연구
A study on Implementation of a Robust Embedded Neural Network Controller to Disturbance in a 2 Axis Gimbal System
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Gimbal systems have been used for attitude control performances in many applications such as satellites, ships, motorcycles, and balancing systems. Two major roles of the attitude control of gimbal systems are for balancing control of the system by inducing gyroscopic torques and for image stabilization of optical systems. In this paper, the neural network controllers for a 2-axis gimbal system are implemented. To improve the disturbance rejection performance of the gimbal system, a neural network is used as an auxiliary controller. The realtime control performance of the neural network controller for disturbance rejection was validated by experimental results of 87% error reduction. The feasibility of using neural network controllers for the robust control performance is validated.
Abstract
Gimbal systems have been used for attitude control performances in many applications such as satellites, ships, motorcycles, and balancing systems. Two major roles of the attitude control of gimbal systems are for balancing control of the system by inducing gyroscopic torques and for image stabilization of optical systems. In this paper, the neural network controllers for a 2-axis gimbal system are implemented. To improve the disturbance rejection performance of the gimbal system, a neural network is used as an auxiliary controller. The realtime control performance of the neural network controller for disturbance rejection was validated by experimental results of 87% error reduction. The feasibility of using neural network controllers for the robust control performance is validated.