Aiming at the problem of synchronous control of multi-axis motor, a synchronous controller based on fuzzy-single neuron PID control strategy is studied. The synchronization control method is compared with the conventional PID algorithm. The results show that, compared with the multi-axis motor synchronous motion controller based on the conventional PID algorithm, the synchronous error of each motor is smaller under the action of the multi-axis motor synchronous motion controller based on the fuzzy? Single neuron PID algorithm. When the second motor has a sudden load change, the controller can effectively restrain the sudden load change to the whole multi-axis motor. The influence of machine system. The multi-axis motor synchronous motion controller based on fuzzy-single neuron PID algorithm can effectively improve the dynamic characteristics and stability of the multi-axis motor synchronous motion system. In order to effectively deal with the internal coordination of multi-axis motion, it is necessary to use multi-motor to achieve effective management. There is a proportional relationship between the rotational speeds of several motors in operation, and the rotational speeds may be the same. This kind of multi-motor is used in a variety of manufacturing equipment, such as textile equipment, paper-making equipment and lifting equipment. In addition, in the process of ship testing and aircraft control system development, several large motors are usually used to provide driving force. CNC machine tools, robots and other high-end heavy equipment, their accuracy is high, in the manufacturing process, the control system used in the internal motor speed must be coordinated, keep the phase equal, and control the error within the standard range. Only in this way can the accuracy of the equipment be effectively improved and the stability and safety of the equipment in operation be guaranteed [1?2]. In reference [3?4], the control method of multi-axis motor based on master-slave control is studied. In the master-slave control system, it includes the main motor and different slave motors. The speed of the latter is based on the speed of the former, and the speed of the slave motor depends on the main motor. The results of this type of control system are simple and effective, and can meet the needs. In this control system, the relevancy between the slave motors is not good and the information transmission is not smooth, which leads to the decline of the accuracy of the motor operation and the limited anti-interference ability to the outside world. The control method of multi-axis motor based on cross-coupling control is studied in reference [5].
The cross-coupling control strategy is significantly different from other control methods. In order to ensure that the control process is accurate and effective.
It will transfer the speed error information of the motor to other motors and compensate their rotational speed. When there are only two motors in the control system, the control method can be used, but when the number of motors increases, the control process will become more complex, and more reasonable control strategy is needed. In reference [7], a ring coupling synchronization control method is proposed. Combining the principle of coupling compensation with the thinking of control management will form a ring coupling control method. On the one hand, the control method is based on the error of internal motor speed and standard speed, on the other hand, it also synthesizes the speed error with the adjacent motor. The structure of the ring coupling control system is shown in Fig. 2. In order to make the control system have better performance in both open and termination states, it is required that the control system keep ring coupling and the internal motor signals are the same when it is running. Through the analysis of Figure 2, it can be seen that the error compensation of the motor in the vicinity of the system is synchronous. If there is an abnormal speed of the motor, it will lead to errors in the speed of the adjacent motor. By using the loop coupling synchronization control method, the speed error will be transmitted because of the existence of compensation module. Usually, the synchronous driver of multi-motor receives the same signal.
Using the speed coupling compensation module, the stability and consistency of the system will be improved effectively, thus avoiding the interference of external factors to the greatest extent.
For both nonlinear and linear systems, artificial neural networks can approximate arbitrarily, so uncertainties and effective control of nonlinear systems can be achieved. Quantitative or qualitative information is stored in the neurons of the neural network, which has certain fault tolerance and robustness. Neurons in neural networks can be approximated to any function. In addition, they have the advantages of easy implementation, short weight learning time, small computation and simple structure. They are suitable for application in some single output and multiple input non-linear processing units [9?11]. Therefore, the principle of the fuzzy? Single neuron PID controller used in this paper is shown in Fig.
3 [12]. With the above controller, the single neuron controller contains four adjustable parameters, including one output gain K and three weight learning rates. The output gain K has great influence on the convergence and stability of single neuron PID controller. When the K value increases, the output regulation of single neuron controller will be increased; if the adjustment time is reduced, the convergence speed will be accelerated; however, when the K value is large, the overshoot will occur, which will cause the system to oscillate [15]. In this paper, a self-tuning parameter fuzzy controller is used to adjust the gain K. Fuzzy control can play a better role in the process of controlling objects which can not establish precise mathematical models and some complex objects. It is suitable to be used in some control processes with serious cross-coupling, large variation of parameters with working point and high nonlinearity. Its simple design process is less dependent on the accuracy of the controlled model. The control rules established according to the designer’s engineering experience and theory can adopt better control ideas. The input of the fuzzy controller used to adjust the gain K is the synchronization error and the error change rate of the motor.
In order to facilitate computer calculation, the synchronization error and error change rate are normalized to the basic domain of [-1,1] by using the quantization factors of 0.1 and 0.05, respectively. Using the membership function of triangular distribution as input/output variable can improve the efficiency of the algorithm.
When using this function, the step size of the fuzzy universe is equal to the magnitude of membership. According to the following principles, choose the fuzzy rules: if the system has large deviation, in order to reduce the adjustment time and speed up the corresponding speed, the K value can be increased; if there is a small error, thermostatic element the K value can be used to improve the steady-state performance. Fuzzy rules are shown in Table 1 [16]. The synchronous motion controller of multi-axis motor studied in this paper is analyzed by simulation. The parameters of DC servo motor are as follows: armature resistance [Ra] is 1.8; armature inductance [La] is 0.003 65H; electromotive force factor [Ke] is 0.354 9V/rad; motor torque factor [Kt] is 0.000 163 51 N s/rad; rotor inertia [17] [J] is 0.
000 184 62 kg m2. The simulation model of multi-axis motor synchronous motion control based on MATLAB is shown in Fig.
4. In the simulation model, random loads ranging from 150 to 900 N m are applied to the second motor and constant loads of 600 N m are applied to the first and third motors, so as to study the synchronization control performance of the controller under non-uniform loads. The random load applied to the second motor is shown in Fig. 5. In this paper, the conventional PID algorithm is compared with the method used in this paper. Through simulation, the synchronization errors of motor 1 and motor 2 and motor 2 and motor 3 under the action of two controllers can be obtained. The synchronization errors of motor 2 and motor 3 are shown in figs. 6 and 7. From the simulation results of synchronization errors of motor 1 and motor 2 and motor 2 and motor 3, it can be seen that the synchronization errors before each motor are more than those of the multi-axis motor synchronization motion controller based on the conventional PID algorithm under the action of the multi-axis motor synchronization motion controller based on the fuzzy? Single neuron PID algorithm studied in this paper. Small. When the second motor has a sudden load change, the controller can effectively suppress the impact of the sudden load change on the whole multi-axis motor system. The multi-axis motor synchronous motion controller based on fuzzy-single neuron PID algorithm studied in this paper can effectively improve the dynamic characteristics and stability of multi-axis motor synchronous motion system. Aiming at the problem of synchronous control of multi-axis motor, a synchronous controller based on fuzzy-single neuron PID algorithm and ring coupling strategy is studied in this paper.
The synchronization control method studied in this paper is compared with the conventional PID algorithm. The results show that, compared with the multi-axis motor synchronous motion controller based on the conventional PID algorithm, the synchronous error before each motor is smaller under the multi-axis motor synchronous motion controller based on the fuzzy? Single neuron PID algorithm studied in this paper. When the second motor has a sudden load change, the controller can effectively suppress the impact of the sudden load change on the whole multi-axis motor system.