Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/46399
標題: Neural-network-based self-tuning PI controller for precise motion control of PMAC motors
作者: Wang, G.J.
王國禎
Fong, C.T.
Chang, K.J.
關鍵字: neural network
permanent-magnet synchronous servo motor control
self-tuning control system
extract heuristic knowledge
induction-motors
dynamical-systems
incipient faults
fuzzy system
servo drive
identification
friction
期刊/報告no:: Ieee Transactions on Industrial Electronics, Volume 48, Issue 2, Page(s) 408-415.
摘要: In general, proportional plus integral (PI) controllers used in computer numerically controlled machines possess fixed gain. They may perform web under some operating conditions, but not ah, To increase the robustness of fixed-gain PI controllers, we propose a new neural-network-based self-tuning PI control system, In this new approach, a well-trained neural network supplies the PI controller with suitable gain according to each operating condition pair (torque, angular velocity, and position error) detected. To demonstrate the advantages of our proposed neural-network-based self-tuning PI control technique, both computer simulations and experiments were executed in this research. During the computer simulation, the Direct Experiment Method was adopted to better model the problem of hysteresis in the ac servo motor. In real experiments, a PC-based controller was used to carry out the control tasks. Results of both computer simulations and experiments show that the newly developed dynamic PI approach outperforms the fixed PI scheme in rise time, precise positioning, and robustness.
URI: http://hdl.handle.net/11455/46399
ISSN: 0278-0046
Appears in Collections:生醫工程研究所

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