Construction Method of Axial Hybrid Magnetic Bearing Ant Colony Algorithm Fractional Order PID Controller

Abstract

The invention discloses a construction method of an axial hybrid magnetic bearing ant colony algorithm fractional order PID controller, setting a proportional coefficient Kp , integral coefficient Ki , differential coefficient k , Differential order coefficient lambda , Integral order coefficient mu The initial information of the nodes in the crawling area of ​​these five control parameters, all the ants are placed at the starting point to start crawling, the five control parameters are optimized respectively, the information of each node is updated, and the optimal control parameters are output; the present invention The five control parameters of the fractional-order PID controller are optimized by using the global search characteristics of the ant colony algorithm, so that the parameter optimization process has better global optimization performance and faster convergence speed, thereby improving calculation accuracy and efficiency. When the parameters of the axial hybrid magnetic bearing model change, the stability of the system can be better guaranteed.

Description

technical field [0001] The invention belongs to the technical field of control, and in particular relates to the design of a control system and a control method of a non-mechanical contact magnetic suspension bearing (magnetic bearing). Background technique [0002] Axial hybrid magnetic bearing is a kind of magnetic bearing in which the control magnetic flux is generated by the control coil and the bias magnetic flux is generated by the permanent magnet, so that the rotor is suspended in space. It has controllable suspension force, high precision, adjustable stiffness, low power consumption Low cost, low cost, good space heat dissipation and other advantages. At present, the control system of axial hybrid magnetic bearing mostly adopts the classic PID controller to control it, but this method relies too much on the model parameters of the control object, and the robustness is poor. And a precise control system is extremely needed, and it is difficult to meet the requiremen...

AI Technical Summary

Problems solved by technology

[0003] The control performance of the PID controller is directly related to its controller parameter setting (parameter setting and optimization). It is difficult to find the optimal parameters by using the conventional PID parameter setting method, and it is easy to generate oscillation and overshoot, and it is difficult to achieve a good control effect.

Although some intelligent tuning methods can find the optimal solution, there are problems such as the cumbersome algorithm and the difficulty in determining the parameters of the algorithm itself.

Especially for the fractional-order PID controller, due to the addition of two adjustable parameters, the process of parameter optimization will become longer and more difficult during the parameter tuning process, thereby increasing the complexity of the control system. The accuracy and real-time performance of the

Images