Application Of The Identification Method Based On Model Reference Adaptive System In Motion Control System Of Placement Machine

Placement machine is a complex system consisting of multiple techniques in the fieldsof mechanism, photology, electronics and computer control. It is the most high-techequipment among the whole SMT product line. And it has great influence on assemblycapacity and product quality. Speed and accuracy of motion control system are theimportant indicators used to measure the whole performance of the placement machine. So,the research on motion control system of placement machine is an important topic thatneeds to be addressed urgently.Control quality of the system depends on its control strategy. However, the choice ofthe control strategy is based on the mathematical model of the controlled object. Given thata motion control system with specific performance adopts appropriate control algorithm, itwill achieve good control effects. Therefore, modeling of placement machine’s motioncontrol system is a prerequisite for the design of the control system.Based on the Quad QSP-2placement machine in SMT laboratory, this paper makes anexploratory study on system identification modeling for its motion control system.Firstly, the basic principles of system identification are introduced in this paper. Basedon the analysis of the primary equipments and workflow of the Quad QSP-2placementmachine, the idea of input and output data synchronous acquisition without additionalhardware equipments is put forward. And the program is designed in VC++with relevantfunctions provided by GT motion controller. Secondly, we determine the structure of thecontrolled object. On this basis, methods of parameter identification based on least squareand model reference adaptive system are used for its parameter identification respectively.Then, the results obtained by the identification methods above is estimated by loss function.In addition, the merits and demerits of the methods are discussed in the paper. Amathematical model relatively close to the ideal model of the controlled object is obtained. And it will lay a good theoretical foundation for the research of a more effective andtargeted control strategy. At last, the summary of this paper and future picture of ourresearch work is outlined.