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Study On Model Identification And Force/Position Control Of X-Y Numerical Table

Posted on:2010-12-19Degree:MasterType:Thesis
Country:ChinaCandidate:X J TianFull Text:PDF
GTID:2178360302959410Subject:Control theory and control engineering
Abstract/Summary:PDF Full Text Request
Number control (NC) System is the core of advanced manufacturing engineering. Numerical control technique and number control machine tool's general level symbolizes a country's industrial modernization level and overall strength, and it concerns the science technology development of a country. Because X-Y NC table is typical NC equipment in this dissertation, it is treated as a key research object, it is treated as a key research object. The main research work is divided into some aspects as follows:This article first aims at the X-Y NC table dynamics model identification to conduct the research, proposed one improvement fuzzy system identification method. Firstly, using the subtraction cluster initialization, then further optimizes using the improvement FCM algorithm, then the use least squares method obtains the model the conclusion parameter, through optimizes the T-S model structure, realizes the platform system model identification.The NC table system often requests the precise localization and the fine motion control, to enhance the X-Y NC table the force/position control precision, in view of NC table's dynamics and external environment's uncertainty, proposed two kinds of control policies to conduct the research.First uses to adaptive fuzzy with the slip form control method as the algorithm which the position control part uses, simultaneously uses the cerebellum model neural network to apply in the additional force outer ring's fuzzy controller, takes the position control part through the force controller's output the corrected value, through increases the position control precision to achieve the controlling force the goal.Then also proposes one force/position mixture control method which expert PID and adaptive fuzzy control are unifies. The entire control system uses to the position and the force carries on the control separately, the controller of position uses the expert PID control, the force controller uses the fuzzy logic system to approach in system's indefinite item, uses the robust control to carry on the compensation again, increased control system's control precision. The simulation results indicated that these two kinds of control policy made the force/ position track effect to achieve the ideal level.
Keywords/Search Tags:Number control (NC) System, Model identification, Force/position control, Adaptive fuzzy, Cerebellum model neural network, Expert PID
PDF Full Text Request
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