Direct Neural Controller Applied to DC Motor and Electro-Hydraulic Servo System

• Main Authors: Ming-Huei Chu, 朱明輝
• Other Authors: Yuan Kang
• Format: Others
• Language: zh-TW
• Published: 2002
• Online Access: http://ndltd.ncl.edu.tw/handle/86772659610172931050

博士 === 中原大學 === 機械工程學系 === 90 === A direct adaptive neural network controller with specialized learning architecture and its applications are studied in this research. The adaptation law is applied to the direct neural controller for approximating the term of the output layer so that the back propagation iteration can be executed. An arctangent function is applied to be the activation function so that the neural network controller output has negative or positive value. The proposed direct adaptive neural network controllers without reference model are applied to speed and position control of DC motors and position control of Electro-hydraulic servo systems. Simulation shows that a previous training of the neural controller can learn the approximate behavior of the plant and create better initial weights, then followed by on-line trained to fine-tune the network in the operating process. Experiment shows stable and fast responses can be achieved. The same controller with reference model is applied to control the swash plate angle of a variable displacement axial piston pump, which is nonlinear, time variant and with load disturbance. Mathematic simulation and experiment show that the direct adaptive neural network controllers enhance adaptability and robustness of the system and improve the pump performance.