Realization of Petri Fuzzy-neural-network Position Control for Linear Induction Motor via Digital Signal Processor

• Main Authors: Chia-Chin Chu, 朱嘉慶
• Other Authors: Rong-Jong Wai
• Format: Others
• Language: en_US
• Published: 2005
• Online Access: http://ndltd.ncl.edu.tw/handle/73062005585378956602

碩士 === 元智大學 === 電機工程學系 === 94 === This thesis focuses on the development of an indirect field-oriented mechanism and a feedback linearization decoupled technique based on a Petri fuzzy-neural-network (PFNN) for the position control of a linear induction motor (LIM) drive via a digital signal processor (DSP). First, an indirect field-oriented mechanism for a LIM drive is derived to preserve the ideal decoupling control characteristic. Then, the concept of a Petri net (PN) is incorporated into a traditional fuzzy-neural-network (TFNN) to form a newly-type PFNN framework for alleviating the computation burden so that it is convenient to implement by DSP. Moreover, the supervised gradient-descent method is used to develop online-training algorithms for the PFNN. In order to guarantee the convergence of tracking error, analytical methods based on a discrete-type Lyapunov function are proposed to determine varied learning-rates of the PFNN. In this control system, the mover position of the controlled LIM drive possesses the advantages of good transient control performance and robustness to uncertainties for the tracking of periodic reference trajectories. Unfortunately, nonlinear transient responses are ignored in the indirect field-oriented mechanism. In order to solve this problem, the feedback linearization decoupled technique is further investigated in this thesis. First, a feedback linearization control (FLC) system is designed to deal with the decoupling relation of the thrust force and flux amplitude of the LIM. However, prior system information is required in the FLC system so that the control performance is influenced easily by system uncertainties. Thus, a robust PFNN control system is designed in this thesis to reform this problem. In this control system, the PFNN is utilized to mimic the FLC system, and adaptive tuning algorithms for network parameters are derived in the sense of Lyapunov stability theorem so that system stability can be guaranteed. In addition, an adaptive flux observer is adopted in the robust PFNN control system to acquire the information of the secondary flux of the LIM. Furthermore, the effectiveness and robustness of the proposed control schemes is verified by both numerical simulations and experimental results.