WO2025201574A1 - 一种用于磁悬浮轴承系统的pd参数自整定方法及系统 - Google Patents

一种用于磁悬浮轴承系统的pd参数自整定方法及系统

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Publication number
WO2025201574A1
WO2025201574A1 PCT/CN2025/096432 CN2025096432W WO2025201574A1 WO 2025201574 A1 WO2025201574 A1 WO 2025201574A1 CN 2025096432 W CN2025096432 W CN 2025096432W WO 2025201574 A1 WO2025201574 A1 WO 2025201574A1
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Prior art keywords
parameter
magnetic bearing
parameters
bearing system
per unit
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English (en)
French (fr)
Inventor
周鸣曲
蒋栋
丁建夫
刘自程
李闻一
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Hubei Shunyi Technology Co Ltd
Huazhong University of Science and Technology
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Hubei Shunyi Technology Co Ltd
Huazhong University of Science and Technology
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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B11/00—Automatic controllers
    • G05B11/01—Automatic controllers electric
    • G05B11/36—Automatic controllers electric with provision for obtaining particular characteristics, e.g. proportional, integral, differential
    • G05B11/42—Automatic controllers electric with provision for obtaining particular characteristics, e.g. proportional, integral, differential for obtaining a characteristic which is both proportional and time-dependent, e.g. P. I., P. I. D.

Definitions

  • the present invention belongs to the field of magnetic bearing control, and more specifically, relates to a proportional-differential (PD) parameter self-tuning method and system for a magnetic bearing system.
  • PD proportional-differential
  • Magnetic bearings utilize electromagnetic force to levitate the rotor, enabling contactless operation between the rotor and stator.
  • Magnetic bearings are frictionless, pollution-free, and have a long lifespan. They are suitable for high-speed and ultra-high-speed applications, as well as high-performance transmissions requiring contactless, lubricant-free, and pollution-free operation.
  • Magnetic bearings usually use proportional-integral-differential (PID) controllers for their displacement control.
  • PID proportional-integral-differential
  • the parameters of the PID controller are difficult to select, and experienced engineers need to spend a lot of time to adjust them, which limits the use scenarios and usage thresholds of magnetic bearings.
  • the purpose of the present invention is to propose a proportional-differential (PD) parameter self-tuning method for a magnetic bearing system, aiming to accelerate the on-site parameter adjustment process of a magnetic bearing system that actually uses proportional-integral-differential (PID) displacement control, lower the usage threshold of the magnetic bearing system, and solve the problem of position closed-loop control parameter design under open-loop instability conditions of the magnetic bearing.
  • PD proportional-differential
  • the present invention provides a PD parameter self-tuning method for a magnetic bearing system, which determines the suspension performance evaluation index, optimization target and optimization object of the magnetic bearing system with PID displacement control; based on the optimization target and optimization object, a parameter self-tuning algorithm is established in which an empirical compensator and a heuristic algorithm are executed in parallel; a method for determining the starting point of the parameters at the beginning of self-tuning is provided; and a system protection method during the self-tuning process is provided, and the protection method does not affect the self-tuning process.
  • the present invention provides a PD parameter self-tuning method for a magnetic bearing system, wherein the magnetic bearing system uses PID displacement control, comprising:
  • a parameter self-tuning algorithm is established in which the empirical compensator and the heuristic algorithm are executed in parallel to self-tune the P and D parameters of the magnetic bearing system; wherein, both the empirical compensator and the heuristic algorithm take the optimization objective as input and the correction amount of the P parameter and the D parameter as output.
  • the system During the tuning process, if a set of parameters causes the magnetic bearing system's collision count per unit time to exceed an upper threshold, the system immediately switches to the next set of parameters to prevent magnetic bearing system failure. Because the optimization targets are all based on a per-unit-time basis, the protection process does not require interrupting the auto-tuning process.
  • the evaluation indicators of the suspension performance of the PID-controlled magnetic bearing system include the normalized area per unit time of displacement and the number of collisions per unit time.
  • the normalized area per unit time S * refers to:
  • Ns is the number of sampling points for a single set of parameters during the auto-tuning process
  • x(n) is the distance from the rotor's center reference point for the nth sampling point at each moment.
  • the total number of collisions per unit time is:
  • the single-set parameter sampling time refers to the time it takes to record displacements when the PID controller uses the current parameters.
  • the total collision count is calculated by assuming that the displacement range for a degree of freedom is -Xp to + Xp . If the rotor offset from the center reference point for that degree of freedom is greater than aXp or less than -aXp , where a is a number greater than 0 and less than 1, the total collision count increases by one.
  • the optimization objectives of this self-tuning method are to minimize the normalized area per unit time and to minimize the number of collisions per unit time.
  • the optimization targets are the P and D parameters for each degree of freedom of the magnetic bearing system using PID displacement control.
  • the I parameter is not optimized because it does not directly affect the performance of the magnetic bearing.
  • the present invention proposes a PD parameter self-tuning method for a magnetic levitation bearing system, which has a specific structure of a PD parameter self-tuning method that combines an empirical compensator with a heuristic algorithm in parallel, wherein the rules of the empirical compensator are as follows: when the number of collisions per unit time is 0, the P and D parameters are not affected; when the number of collisions per unit time is 1, the D parameter is reduced; when the number of collisions per unit time is small but not 0 or 1, the P parameter is increased; when the number of collisions per unit time is large, the P parameter is reduced; and in other cases, the P and D parameters are increased simultaneously.
  • the steps of the heuristic algorithm are as follows: in the same round of iteration, the P and D parameters of all parameter groups are applied to the magnetic levitation bearing system, and the normalized area per unit time of each group during its operation and sampling time is counted.
  • Each degree of freedom should have multiple P and D parameter combinations; according to the normalized area per unit time, all parameter groups move toward the group of parameters with the smallest normalized area per unit time, and at the same time, all parameter groups move away from the group of parameters with the largest normalized area per unit time; except for the group of parameters with the smallest normalized area per unit time, all other parameter groups are randomly changed within a smaller range; and the correction values of the P and D parameters are output according to the above rules.
  • the parameters output by the experience compensator and the heuristic algorithm are combined before starting a new round of iterations to achieve the purpose of parallel execution of the two.
  • the heuristic algorithm requires a set of initial value parameters for each degree of freedom.
  • the method for determining the initial value parameters is as follows.
  • Two different differential control currents are injected into a single-degree-of-freedom winding, allowing it to run from one end to the other. The displacement during the process is recorded.
  • the two sets of data can be used to solve the following two-variable linear equation:
  • parameters are randomly selected within a smaller range to obtain the parameter set for the start of the self-tuning of the degree of freedom.
  • the number of rotor collisions is monitored in real time. If a parameter set is unstable and high-frequency rotor collisions are detected, the system immediately switches to the next parameter set. Since the evaluation metrics used in auto-tuning are all per unit time, data acquisition during the auto-tuning process is not affected. Furthermore, since the tuning of each degree of freedom is independent of each other, the proposed method can be applied to magnetic bearing systems with any degree of freedom.
  • the present invention also provides a PD parameter self-tuning system for a magnetic bearing system, comprising: a computer-readable storage medium and a processor;
  • the computer-readable storage medium is used to store executable instructions
  • the processor is used to read the executable instructions stored in the computer-readable storage medium and execute the above-mentioned PD parameter self-tuning method for the magnetic bearing system.
  • the method proposed in the present invention can realize the self-tuning of the P and D parameters of the magnetic bearing system using PID displacement control, and provides a method for determining the self-tuning starting point parameters. It can also be used for magnetic bearing systems with unknown parameters, thereby lowering the threshold for the use of magnetic bearings.
  • the method proposed in the present invention also includes a method for dealing with instability during the self-tuning process, and will not affect the normal progress of the self-tuning process, and is suitable for application in actual engineering.
  • FIG1 is a control block diagram of a magnetic bearing system applicable to an embodiment of the present invention.
  • FIG2 shows the insertion position of an example of the present invention in a control block diagram of a magnetic bearing system to which the present invention is applicable.
  • FIG3 is a flowchart of an algorithm in actual application of an example of the present invention.
  • the present invention provides a PD parameter self-tuning method for a magnetic bearing system, wherein the magnetic bearing system uses PID displacement control, comprising:
  • a parameter self-tuning algorithm is established in which the empirical compensator and the heuristic algorithm are executed in parallel to self-tune the P and D parameters of the magnetic bearing system; wherein, both the empirical compensator and the heuristic algorithm take the optimization objective as input and the correction amount of the P parameter and the D parameter as output.
  • Figure 1 shows a system block diagram of a magnetic bearing. Its displacement control section uses a PID controller.
  • the PID controller's input is the reference position signal minus the displacement feedback signal from the displacement sensor, and its output is a command current signal.
  • This command current signal enters a current controller, which drives a power amplifier, which converts the command current signal into actual winding current for control.
  • This type of magnetic bearing control system can utilize the algorithm of the present invention.
  • Figure 2 shows the position where the self-tuning algorithm proposed in the present invention is inserted into the magnetic bearing control system.
  • the self-tuning algorithm of the present invention requires the system to input a displacement signal, and it outputs P and D control parameters and is applied to the PID displacement controller of the magnetic bearing system.
  • Figure 3 shows a flow chart of the self-tuning algorithm proposed in the present invention.
  • the self-tuning algorithm proposed in the present invention can follow the process shown in Figure 3, or the calculation process can be adjusted to suit the controller.
  • the process steps shown in Figure 3 are as follows.
  • the first step is to inject two different differential control currents into the single-degree-of-freedom winding, allowing it to run from one end to the other.
  • the displacement during the process is recorded.
  • the two sets of data are used to solve the two-variable linear equation to estimate the force/displacement parameter k i and the force/current parameter k x :
  • parameters are randomly selected within a smaller range to obtain the parameter set for the start of the self-tuning of the degree of freedom.
  • the second step starting from the first set of parameters in the starting parameter group, they are applied to the magnetic bearing system in sequence, and the displacement during the process is recorded to obtain the number of collisions per unit time and the normalized area per unit time until all the parameters in the starting parameter group are applied.
  • the third step is to determine whether the system has converged and entered a stable suspension state. If it has entered a stable suspension state, the self-tuning ends; otherwise, proceed to the next step.
  • the parameter group is adjusted using the experience compensator and heuristic algorithm to obtain a new set of parameters.
  • the rules of the experience compensator are as follows: when the number of collisions per unit time is 0, the P and D parameters are not affected; when the number of collisions per unit time is 1, the D parameter is reduced; when the number of collisions per unit time is small (single digit) but not 0 or 1, the P parameter is increased; when the number of collisions per unit time is large (three digits or more), the P parameter is reduced; in all other cases, the P and D parameters are increased at the same time.
  • the steps of the heuristic algorithm are as follows: in the same round of iteration, the P and D parameters of all parameter groups are applied to the magnetic bearing system, and the normalized area per unit time of each group during its operation and sampling time is counted.
  • Each degree of freedom should have multiple P and D parameter combinations; according to the normalized area per unit time, all parameter groups move toward the group of parameters with the smallest normalized area per unit time, and at the same time, all parameter groups move away from the group of parameters with the largest normalized area per unit time; except for the group of parameters with the smallest normalized area per unit time, all other parameter groups are randomly changed within a smaller range; and a new round of iteration is started until the magnetic bearing can operate stably.
  • a new round of iteration is started based on the new parameter set obtained in the fourth step, and then the process returns to the first step.
  • the collision count statistics are always running, and the displacement collision count is counted in real time. If the displacement collision count is large, it will immediately switch to the next set of parameters to obtain the current collision count per unit time and the normalized area per unit time, and continue to run the self-tuning algorithm.
  • the magnetic bearing system After obtaining the parameters that can support the stable suspension of the magnetic bearing system, the magnetic bearing system enters a closed-loop stable state, and the system can be swept or other model fitting can be performed for further analysis and optimization design.

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  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Automation & Control Theory (AREA)
  • Magnetic Bearings And Hydrostatic Bearings (AREA)

Abstract

一种用于磁悬浮轴承系统的PD参数自整定方法及系统，该磁悬浮轴承系统各个自由度的P参数与D参数的初始值，以位移单位时间归一化面积最小，单位时间碰撞次数为0为优化目标；根据优化目标建立经验补偿器与启发式算法并行执行的参数自整定算法，对磁悬浮轴承系统的P、D参数进行自整定；其中，经验补偿器与启发式算法均以优化目标为输入，P参数与D参数的修正量为输出。该磁悬浮轴承系统的PD参数自整定方法，能够适用于参数未知的磁悬浮轴承系统，不受磁悬浮轴承自由度限制，评价指标获取简单，具有无需先验知识，适用性广、可以在线使用等特点。

Description

一种用于磁悬浮轴承系统的PD参数自整定方法及系统 【技术领域】
本发明属于磁悬浮轴承控制领域，更具体的，涉及一种用于磁悬浮轴承系统的比例-微分(PD)参数自整定方法及系统。
【背景技术】
磁悬浮轴承是指利用电磁力使转子悬浮，从而实现转子与定子的无接触运行。磁悬浮轴承具有无摩擦、无污染、寿命长等特点，适合应用于高速、超高速以及需要无接触、无润滑、无污染的高性能传动场合。
通常磁悬浮轴承都使用比例-积分-微分(PID)控制器进行其位移控制，但由于磁悬浮轴承物理特性，其自身开环不稳定；加工难度高，非线性强，实际参数与设计参数有较大差异；自由度多，通常有4个以上；使得磁悬浮轴承无法使用经典控制理论中的频域方法进行参数设计，PID控制器的参数选定难度大，需要有丰富经验的工程师花费大量时间进行调整，限制了磁悬浮轴承的使用场景和使用门槛。
【发明内容】
针对现有技术的缺陷，本发明的目的在于提出了一种用于磁悬浮轴承系统的比例-微分(PD)参数自整定方法，旨在加快实际使用比例-积分-微分(PID)位移控制的磁悬浮轴承系统的现场参数调整过程，降低磁悬浮轴承系统的使用门槛，解决磁悬浮轴承开环不稳定条件下的位置闭环控制参数设计问题。
为实现上述目的，本发明提供了一种用于磁悬浮轴承系统的PD参数自整定方法，确定了PID位移控制的磁悬浮轴承系统悬浮性能评价指标、优化目标与优化对象；根据所述的优化目标和优化对象，建立了经验补偿器与启发式算法并行执行的参数自整定算法；给出了自整定开始时参数的起点确定方法；同时，给出了自整定过程中系统的保护方法，且该保护方法不影响自整定进程。
本发明提供的用于磁悬浮轴承系统的PD参数自整定方法，所述磁悬浮轴承系统使用PID位移控制，包括：
(1)确定磁悬浮轴承系统各个自由度的P参数与D参数的初始值，以位移单位时间归一化面积最小，单位时间碰撞次数为0为优化目标；
(2)根据所述优化目标，建立了经验补偿器与启发式算法并行执行的参数自整定算法，对磁悬浮轴承系统的P、D参数进行自整定；其中，经验补偿器与启发式算法均以优化目标为输入，P参数与D参数的修正量为输出。
(3)PD自整定算法持续运行，直到磁悬浮轴承系统能够使转子稳定悬浮。
在整定过程中，如果某一组参数使得磁悬浮轴承系统单位时间碰撞次数超过上限阈值，则立即切换到下一组参数，避免磁悬浮轴承系统故障。因为优化对象均基于单位时间，保护流程无需中断自整定过程。
其中，PID控制的磁悬浮轴承系统悬浮性能评价指标包括位移单位时间归一化面积与单位时间碰撞次数。单位时间归一化面积S*指的是：
其中，-Xp～+Xp为磁悬浮轴承该自由度的位移范围，Ns为自整定过程中单组参数采样点数，x(n)为每一时刻第n个采样点该自由度转子偏移中心参考点的距离。单位时间碰撞总次数则指的是：
单位时间碰撞次数＝总碰撞次数/单组参数采样时间
其中，单组参数采样时间指的是PID控制器使用当前参数时，记录位移的时间。总碰撞次数的统计方法为，假设该自由度的位移范围为-Xp～+Xp，该自由度转子偏移中心参考点的距离大于aXp或小于-aXp，a是一个大于0小于1的数，则总碰撞次数增加一次。
基于上述的评价指标，该自整定方法的优化目标为使单位时间归一化面积应尽可能小，单位时间碰撞次数应当为0。而优化对象是使用PID位移控制的磁悬浮轴承系统各个自由度的P参数与D参数的数值，I参数由于不直接影响磁悬浮轴承性能因此不作为优化对象。
本发明提出的一种用于磁悬浮轴承系统的PD参数自整定方法具体结构为经验补偿器与启发式算法并行的PD参数自整定方法，其中经验补偿器的规则为：单位时间碰撞次数为0时，不影响P、D参数；单位时间碰撞次数为1时，减小D参数；单位时间碰撞次数较小但不为0或1时，增大P参数；单位时间碰撞次数较大时，减小P参数；其余情况下同时增大P、D参数。
其中启发式算法的步骤为：在同一轮迭代中，将全部参数组的P、D参数应用于磁悬浮轴承系统，并统计每一组其运行过程中、采样时间内的单位时间归一化面积，每个自由度应有多个P、D参数组合；根据单位时间归一化面积，全部参数组向单位时间归一化面积最小的一组参数移动，同时，全部参数组背离单位时间归一化面积最大的一组参数移动；除去单位时间归一化面积最小的一组参数，其余全部参数组在其一较小的领域内随机变动；按上述规则输出P、D参数的修正量。
经验补偿器与启发式算法输出的参数在启发式算法输出的参数在开始进行新一轮迭代前组合，达成两者并行执行的目的。
启发式算法对每一自由度都需要有一组初始值参数，初始值参数的确定方法如下。
给单一自由度的绕组中注入两个不同差分控制电流，使其分别能够从一段运行至另一端，记录过程中的位移，两组数据可以用来解如下的二元一次方程：
其中，x1与x2为两次得到的位移，Ts为采样时间，N为采样点数，m为转子的重量；ki为未知数1，被称为力/电流系数；kx为未知数2，被称为力/位移系数。
从而，可以得到该自由度的起点参数为KP0与KD0，计算方法为：
基于参数初始值KP0与KD0，在其一较小的领域内随机选取参数，即可得到该自由度自整定开始时的参数组。
在自整定过程中实时监测转子的碰撞次数，当检测到某组参数不稳定转子发生高频碰撞时，立即切换至下一组参数；由于自整定所使用的评价指标均为单位时间内的指标，因此不影响自整定过程中数据的获取。同时，由于各个自由度的整定相互独立，因此本发明提出的方法可以对任意自由度的磁悬浮轴承系统使用。
本发明还提供了一种用于磁悬浮轴承系统的PD参数自整定系统，包括：计算机可读存储介质和处理器；
所述计算机可读存储介质用于存储可执行指令；
所述处理器用于读取所述计算机可读存储介质中存储的可执行指令，执行上述的用于磁悬浮轴承系统的PD参数自整定方法。
总体而言，通过本发明所构思的以上技术方案，与现有技术相比，能够取得以下有益效果：
(1)本发明所提出的方法能够实现使用PID位移控制的磁悬浮轴承系统的P、D参数自整定，并给出了自整定起点参数的确定方法，并且可以用于参数未知的磁悬浮轴承系统，降低了磁悬浮轴承的使用门槛。
(2)本发明所提出的方法中还包含了自整定过程中失稳的应对方法，并且不会影响自整定过程的正常进行，适合实际工程中应用。
【附图说明】
图1为本发明实例所适用的一种磁悬浮轴承系统控制框图。
图2为本发明实例在所适用的一种磁悬浮轴承系统控制框图中的插入位置。
图3为本发明实例在实际运用中的一种算法流程框图。
【具体实施方式】
为了使本发明的目的、技术方案及优点更加清楚明白，以下结合附图及实施例，对本发明进行进一步详细说明。应当理解，此处所描述的具体实施例仅仅用以解释本发明，并不用于限定本发明。此外，下面所描述的本发明各个实施方式中所涉及到的技术特征只要彼此之间未构成冲突就可以相互组合，重新排序。
本发明提供的用于磁悬浮轴承系统的PD参数自整定方法，所述磁悬浮轴承系统使用PID位移控制，包括：
(1)确定磁悬浮轴承系统各个自由度的P参数与D参数的初始值，以位移单位时间归一化面积最小，单位时间碰撞次数为0为优化目标；
(2)根据所述优化目标，建立了经验补偿器与启发式算法并行执行的参数自整定算法，对磁悬浮轴承系统的P、D参数进行自整定；其中，经验补偿器与启发式算法均以优化目标为输入，P参数与D参数的修正量为输出。
(3)PD自整定算法持续运行，直到磁悬浮轴承系统能够使转子稳定悬浮。
图1展示了一种磁悬浮轴承的系统框图，其位移控制部分采用的是PID控制器，PID控制器的输入为参考位置信号减去位移传感器提供的位移反馈信号，输出为指令电流信号。指令电流信号进入电流控制器，电流控制器驱动功率放大器，由功率放大器将指令电流信号转化为实际绕组电流实现控制。此种结构的磁悬浮轴承控制系统即可使用本发明的算法。
图2展示了本发明所提出的自整定算法插入这种磁悬浮轴承控制系统的位置，本发明的自整定算法需要系统输入位移信号，其输出P、D控制参数并应用于磁悬浮轴承系统的PID位移控制器。
图3展示了本发明所提出的自整定算法的一种流程图。本发明所提出的自整定算法在实际运用中可以遵循如图3所示的流程，也可以对运算流程做适合控制器执行的调整，图3所示的流程步骤如下。
第一步，给单一自由度的绕组中注入两个不同差分控制电流，使其分别能够从一段运行至另一端，记录过程中的位移，两组数据用来解估算力/位移参数ki和力/电流参数kx的二元一次方程：
其中，x1与x2为两次得到的位移，Ts为采样时间，N为采样点数，m为转子的重量。进一步得到该自由度的起点参数为KP0与KD0，计算方法为：
基于起点参数KP0与KD0，在其一较小的领域内随机选取参数，即可得到该自由度自整定开始时的参数组。
第二步，从起始参数组中的第一组参数开始，依次将其应用于磁悬浮轴承系统，并记录过程中的位移，得到单位时间碰撞次数与单位时间归一化面积，直至起始参数组中的全部参数都被应用了一遍。
第三步，判断系统是否已经收敛，进入稳定悬浮状态，如果已经进入稳定悬浮状态则自整定结束，否则进入下一步。
第四步，基于先前得到的单位时间碰撞次数与单位时间归一化面积，运用经验补偿器与启发式算法调整参数组，得到一组新参数。
其中经验补偿器的规则为：单位时间碰撞次数为0时，不影响P、D参数；单位时间碰撞次数为1时，减小D参数；单位时间碰撞次数较小(为个位数)但不为0或1时，增大P参数；单位时间碰撞次数较大(为三位数或更大)时，减小P参数；其余情况下同时增大P、D参数。
其中启发式算法的步骤为：在同一轮迭代中，将全部参数组的P、D参数应用于磁悬浮轴承系统，并统计每一组其运行过程中、采样时间内的单位时间归一化面积，每个自由度应有多个P、D参数组合；根据单位时间归一化面积，全部参数组向单位时间归一化面积最小的一组参数移动，同时，全部参数组背离单位时间归一化面积最大的一组参数移动；除去单位时间归一化面积最小的一组参数，其余全部参数组在其一较小的领域内随机变动；开始新一轮迭代，直到磁悬浮轴承能够稳定运行。
第五步，基于第四步得到的新参数组重新开始一轮迭代，回到第一步。
碰撞次数统计则始终运行，实时统计位移碰撞次数，如果位移碰撞次数较大则立即切换至下一组参数，得到当前单位时间碰撞次数和单位时间归一化面积，继续自整定算法的运行。
得到能够支持磁悬浮轴承系统稳定悬浮的参数后，磁悬浮轴承系统进入闭环稳定状态，便可以对系统进行扫频或其它模型拟合，从而进行进一步分析和优化设计。
本领域的技术人员容易理解，以上所述仅为本发明的较佳实施例而已，并不用以限制本发明，凡在本发明的精神和原则之内所作的任何修改、等同替换和改进等，均应包含在本发明的保护范围之内。

Claims (9)

  1. 一种用于磁悬浮轴承系统的PD参数自整定方法，所述磁悬浮轴承系统使用PID位移控制，其特征在于，包括：
    (1)确定磁悬浮轴承系统各个自由度的P参数与D参数的初始值，以位移单位时间归一化面积最小，单位时间碰撞次数为0为优化目标；
    (2)根据所述优化目标，建立经验补偿器与启发式算法并行执行的参数自整定算法，对磁悬浮轴承系统的P、D参数进行自整定；其中，经验补偿器与启发式算法均以优化目标为输入，P参数与D参数的修正量为输出。
  2. 根据权利要求1所述的一种用于磁悬浮轴承系统的PD参数自整定方法，如果某一组参数使得磁悬浮轴承系统单位时间碰撞次数超过上限阈值，则立即切换到下一组参数，避免磁悬浮轴承系统故障。
  3. 根据权利要求2所述的一种用于磁悬浮轴承系统的PD参数自整定方法，其特征在于，所述位移单位时间归一化面积为：
    其中，磁悬浮轴承该自由度的位移范围为-Xp～+Xp，自整定过程中单组参数采样点数为Ns，每一时刻该自由度转子偏移中心参考点的距离为x(n)。
  4. 根据权利要求2所述的一种用于磁悬浮轴承系统的PD参数自整定方法，其特征在于，所述单位时间碰撞次数指的是：
    单位时间碰撞次数＝总碰撞次数/单组参数采样时间
    其中，单组参数采样时间指的是PID控制器使用当前参数时，记录位移的时间；总碰撞次数的统计方法为，假设该自由度的位移范围为-Xp～+Xp，该自由度转子偏移中心参考点的距离大于上阈值或小于下阈值，则总碰撞次数增加一次。
  5. 根据权利要求1所述的一种用于磁悬浮轴承系统的PD参数自整定方法，所述经验补偿器指的是：
    单位时间碰撞次数为0时，不影响P、D参数；单位时间碰撞次数为1时，减小D参数；单位时间碰撞次数小于预设值但不为0或1时，增大P参数；单位时间碰撞次数不小于预设值时，减小P参数；其余情况下同时增大P、D参数。
  6. 根据权利要求1所述的一种用于磁悬浮轴承系统的PD参数自整定方法，所述启发式算法指的是：
    在同一轮迭代中，将全部参数组的P、D参数应用于磁悬浮轴承系统，并统计每一组其运行过程中、采样时间内的单位时间归一化面积，每个自由度应有多个P、D参数组合；
    根据单位时间归一化面积，全部参数组向单位时间归一化面积最小的一组参数移动，同时，全部参数组背离单位时间归一化面积最大的一组参数移动；
    除去单位时间归一化面积最小的一组参数，其余全部参数组在预设领域内随机变动；
    根据上述规则输出P、D参数修正量。
  7. 根据权利要求6所述的一种用于磁悬浮轴承系统的PD参数自整定方法，在新一轮迭代开始前，相加所述经验补偿器与启发式算法输出的P、D参数修正量，更新参数，并应用于PID控制器，直到磁悬浮轴承能够稳定运行。
  8. 根据权利要求1所述的一种用于磁悬浮轴承系统的PD参数自整定方法，所述确定磁悬浮轴承系统各个自由度的P参数与D参数的初始值指的是：给单一自由度的绕组中注入两个不同差分控制电流，使其分别能够从一段运行至另一端，记录过程中的位移，两组数据用来解如下的二元一次方程：
    其中，x1与x2为两次得到的位移，Ts为采样时间，N为采样点数，m为转子的重量；ki为未知数1，被称为力/电流系数；kx为未知数2，被称为力/位移系数；
    从而，得到该自由度的参数初始值为KP0与KD0，计算方法为：
    从参数初始值KP0与KD0，在预设领域内随机波动，得到该自由度自整定开始时的参数组。
  9. 一种用于磁悬浮轴承系统的PD参数自整定系统，其特征在于，包括：计算机可读存储介质和处理器；
    所述计算机可读存储介质用于存储可执行指令；
    所述处理器用于读取所述计算机可读存储介质中存储的可执行指令，执行权利要求1至8任一项所述的用于磁悬浮轴承系统的PD参数自整定方法。
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