Simulation Analysis of Controlled Items of Numerical Control Equipment

System Identification Strategy Analysis In order to identify the controlled object, it is necessary to know the input and output information. The input information of the controlled object is an analog voltage. If the specific hardware tool is not used, it is difficult to directly obtain the information, and it is difficult to directly identify the controlled object. However, the closed-loop part of the system shown can be considered as a whole, which is transformed into a structural form as shown, represented by G1(z-1) as a PID regulator and G2(z-1) as a D/A conversion. G3 (z-1) is the controlled object, and r and y are the input and output information of the closed-loop system, respectively. Thus, by means of the Gather function provided by PMAC, the input information r and the output information y of the system can be conveniently collected, and then the model of the closed-loop system can be solved according to the least squares identification method.

Sampling of system parameters The PMAC motion controller selected by the system provides the user with a function-rich function library. Through the function library, the user can develop and expand the system. The Gather function in the function library can be used to input and output information to the system. Query and collect with system parameters. In this paper, a VC6.0 language tool is used to program a system information acquisition software module in the PWIN32 software environment. The module can sample the output information of the system and store the sampled information in the PMAC user register. The GetResponse function uploads the collected data to the IPC host computer in the required format for system identification.

When sampling system parameters, it should be noted that the sampling timer is selected. The VC6.0 programming language provides a timer, but since the IPC microcomputer is a multi-threaded working system, the timer task of the sampling module needs to be executed after the higher priority thread is completed, and the real-time performance is not guaranteed, and the sampling accuracy is affected. Great impact. In this paper, the PMAC internal timer is adopted. The PMAC servo cycle or integer multiple of the servo cycle is used as the sampling period of the system. It has high real-time performance and its sampling accuracy is guaranteed. The determination of the M-sequence and the excitation signal required to generate the system identification should best reflect the various frequency characteristics of the system. White noise is an ideal excitation signal, but it is difficult to implement in engineering. The two-bit pseudo-random M-sequence signal has the characteristics of approximate white noise and is easy to implement. Therefore, it is selected as the excitation signal for system identification.

There are two important parameters to be determined for the selection of the M sequence: one is the amplitude. If the amplitude is too large, it may exceed the linear region of the servo amplifier, causing saturation nonlinearity. If the amplitude is too small, the quantization error and friction The influence of hysteresis and other factors is large. Combined with the characteristics and convenience of the controlled object, the excitation amplitude selected in this paper is 180° servo motor rotation angle. The other parameter is the excitation time. If the excitation time is too long, the amount of sampled data is large, which may exceed the system storage capacity. If it is too short, the system excitation is insufficient, which affects the identification accuracy. This paper selects the 7th order M sequence, a total of 127 bits. The trigger period is 250ms, and the excitation time is 31750ms. Thus, when the system sampling time is set to 5ms, the sampled data is only 6250, which meets the limitation of system storage capacity.

Conclusion Through the query function of multi-axis motion controller PMAC, the output information of the system is collected, and the pseudo-random M sequence is used as the input information of the system. The model of the closed-loop system is identified by least squares method, and the model of the controlled object is finally obtained through mathematical derivation. The method is simple and effective, and does not need to add any hardware and modification to the system. This provides a practical and feasible path for the control algorithm research, parameter tuning and fault diagnosis of the numerical control system.

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