Multi-axis cooperative control method based on time synchronization

Abstract

The invention discloses a multi-axis cooperative control method based on time synchronization. The multi-axis cooperative control method comprises the steps: 1, enabling N axes to start to operate; 2, collecting a sampling period, an actual position point and an initial planning position point; 3, calculating the independent delay time; 4, if the precision requirement is met, performing ending, otherwise, entering step 5; 5, calculating the difference value delay time; 6, calculating a compensation planning position point; and 7, generating a new planning track according to the compensation planning position point and running, and returning to the step 2. The method has the advantages that under the condition that hardware is not changed, planning position points are improved, points with large delay time are synchronized to points with small delay time, the accuracy of multi-axis cooperation is high, portability is good, the application range is wide, the multi-axis coupling relation is achieved, the control structure is simple, anti-interference performance is good, the problem of multi-axis cooperation is well solved, and control precision is improved.

Description

technical field [0001] The invention relates to the technical field of multi-axis control, in particular to a multi-axis coordinated control method based on time synchronization. Background technique [0002] In recent years, with the continuous improvement of product process requirements in the industrial field and the surge in production scale, in order to obtain higher machining accuracy, the importance of synergy between subsystems in the manufacturing stage has become particularly prominent. In multi-axis motion control , people have been working to obtain higher motion control accuracy, so that the actual motion of each axis can track the input signal more accurately. However, due to the delay of the servo system itself, the inevitable friction of the mechanical structure and the backlash of the transmission system There are still problems to be solved in accurately tracking the planned path to obtain a high-precision trajectory. It has been widely used in multi-varia...

AI Technical Summary

Problems solved by technology

[0004] PID algorithm, that is, proportional-integral-derivative control, is the most common and mature control method in the field of industrial control so far. This algorithm is mainly suitable for linear non-time-varying systems. Steady-state error; the intelligent control algorithm can provide solutions for the uncertainty and complexity of the environment, goals and tasks of the control object, has good adaptive ability, good robustness, and is suitable for time-varying and linear systems, but Its self-adjustment ability is poor, and the control effect on nonlinear and strongly coupled systems is not ideal; neural network algorithms are mainly used in multivariable, nonlinear and various complex uncertain systems, especially in multi-input and output systems. It is one of the hottest intelligent control algorithms at present. The advantage of the neural network is that it has strong self-learning and nonlinear approximation capabilities. The disadvantage is that the acquisition of network weights requires training on a large amount of data. In the case of high performance requirements, the neural network algorithm has relatively large limitations; the fuzzy algorithm is an intelligent control method based on fuzzy set theory, fuzzy language variables and fuzzy logic reasoning, but it uses a When the system is determined, the accuracy obtained will be relatively low, and the adjustment time will be relatively long; sliding mode control has been widely used in motor speed control systems due to its insensitivity to parameter changes and disturbances, strong robustness, and fast response speed. However, while its switching characteristics ensure the robustness of the system, it also leads to output chattering

[0005] The adjustment effect of the PID algorithm depends on the mathematical model of the controlled object. Only when the model is accurate and the model parameters are constant can the ideal control effect be obtained, and the control effect on the nonlinear strong coupling system is not good, and the multi-linear motor servo The system is a nonlinear and time-varying complex system, and it is very difficult to establish an accurate mathematical model, and when the system state changes during operation, the PID controller parameters cannot be adjusted accordingly, so the traditional PID control is adopted The effect will become worse; the general shortcoming of the intelligent cooperative control algorithm is that the calculation time of the intelligent algorithm will increase with the increase of the number of cooperative motion axes. The exploration of intelligent algorithms and their practical engineering applications still need more in-depth research. In addition, the above two types of control algorithms need to design independent controller hardware. When the hardware conditions of the equipment cannot be changed, the realization of the control algorithm is difficult.

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