Construction method for ant colony algorithm fractional order PID controller of axial mixing magnetic bearing

Abstract

The invention discloses a construction method for an ant colony algorithm fractional order PID controller of an axial mixing magnetic bearing. The initial information amount of nodes in crawl regions of five control parameters of a proportionality coefficient Kp, an integration coefficient Ki, and a differentiation coefficient Kd, a differential order coefficient lambda and an integration order coefficient mu are set, all ants are placed on starting points and begin to crawl, optimizing is conducted on the five control parameters respectively, the information amount of each node is updated, and optimum control parameters are output. The five control parameters of the fractional order PID controller are optimized through global searching features of an ant colony algorithm to ensure that the parameter optimizing process has good local optimizing performance and high converging speed, and therefore calculation precision and calculation efficiency are improved, and system stability can be better guaranteed when model parameters of the axial mixing magnetic bearing change.

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...

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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

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