Adaptive neural network control for a diaphragm-type pneumatic vibration isolator

• Main Authors: Yan-Teng Chou, 周彥騰
• Other Authors: Jin-Wei Liang
• Format: Others
• Language: zh-TW
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/sbc3ar

碩士 === 明志科技大學 === 機械工程系機械與機電工程碩士班 === 103 === It is well known that a pneumatic actuating system has nonlinear uncertainty and time-varying characteristics. It is difficult to establish an accurate process model for designing a model-based controller to monitor the pneumatic actuating force. An intelligent control strategy for a pneumatic vibration isolation system is developed in this research. In this paper, a model-free adaptive wavelet neural network (AWNN) controller and radial basis function neural network (ARBFNN) controller is proposed to control a diaphragm-type pneumatic vibration isolator. This approach has online learning ability and the advantage to achieve the controller design without knowledge of the system dynamic model. In order to validate the proposed method, a composite control scheme using pressure and velocity measurements as feedback signals is implemented. In addition, Taguchi method had been utilized to obtain the optimal control gain values for this control system. Experimental results are executed to show the control performance of the proposed intelligent controller.