| Summary: | 碩士 === 國立臺灣科技大學 === 工程技術研究所 === 81 === The purpose of constant turning force control is to increase
metal removal rate (MRR) and to prevent tool breakage in the
turning process. Then the productivity could be increased. This
object can be reached by using feedrate manipulation to
maintain a specified cutting force. Classical control theory
(PID) which has good robustness is applied in constant turning
force control system with fixed cutting depth. However, this
system may became unstable when the cutting depth is changed
significantly. In this conditon, a pole assignment self-tuning
adaptive control theory with recursive least square parameters
estimator is proposed to solve this problem.Unfortunately, the
adaptability and robustness of self- tuning adaptive control
system can not be maintained in good condition simultaneously.
To address these problems, in this paper a method for self-
tuning adaptive control based on neural network is derived
which can improve both the adaptability and the robustness. In
order to verify this new method, we design a feedrate mechanism
to model the turning process. The experimental results of
implementing the control theory to this imitative mechanism are
satisfactory.
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