CN114815602A - Dynamic electromagnetic loading force multi-parameter optimization control system and control method for water lubricated bearing - Google Patents
Dynamic electromagnetic loading force multi-parameter optimization control system and control method for water lubricated bearing Download PDFInfo
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Abstract
Description
技术领域technical field
本发明属于机械设备状态监测技术领域,具体涉及水润滑轴承动态电磁加载力多参数优化控制系统,还涉及水润滑轴承动态电磁加载力多参数优化控制方法。The invention belongs to the technical field of mechanical equipment state monitoring, in particular to a multi-parameter optimal control system for dynamic electromagnetic loading force of a water-lubricated bearing, and a multi-parameter optimal control method for the dynamic electromagnetic loading force of a water-lubricated bearing.
背景技术Background technique
电磁加载装置在机械设备状态监测中逐渐得到了广泛应用。电磁加载装置可提供非接触式载荷,相比接触式非接触式电磁加载装置,其避免了摩擦、振动等问题,应用效果更好。在电磁加载装置动态施加电磁加载力的过程中,加载力会因为轴系转速、轴心位移、磁场变化等因素而不稳定,给载荷模拟,甚至轴承测试试验带来较大影响,是目前面临的棘手问题。因此,对动态电磁加载力实施精准控制具有重要意义与工程应用价值。Electromagnetic loading devices have gradually been widely used in the condition monitoring of mechanical equipment. The electromagnetic loading device can provide non-contact load. Compared with the contact-type non-contact electromagnetic loading device, it avoids problems such as friction and vibration, and has a better application effect. In the process of dynamically applying the electromagnetic loading force by the electromagnetic loading device, the loading force will be unstable due to factors such as shafting speed, axis displacement, magnetic field changes, etc., which will have a great impact on the load simulation and even the bearing test. thorny problem. Therefore, it is of great significance and engineering application value to implement precise control of dynamic electromagnetic loading force.
申请号CN201911147320.9的发明专利提供了一种磁悬浮周希跌落轨迹识别与重新悬浮的控制方法及装置,提出通过检测轴系的径向位移以及通过希尔伯特变换所得瞬时频率的期望进行轨迹响应的识别。The invention patent with the application number CN201911147320.9 provides a control method and device for identifying and re-suspending the magnetic levitation Zhouxi drop trajectory, and proposes to detect the radial displacement of the shafting and obtain the desired instantaneous frequency through Hilbert transform. Identification of the response.
申请号CN201710471181.X的发明专利提出了一种控制主动磁悬浮轴承系统的控制器及其控制方法,提出了通过三段级联控制结构并引入了单层神经网络调节器分别控制一堆主动磁悬浮轴承中的两个线圈中的电流。The invention patent with the application number CN201710471181.X proposes a controller and a control method for controlling an active magnetic suspension bearing system, and proposes to control a stack of active magnetic suspension bearings respectively through a three-stage cascade control structure and the introduction of a single-layer neural network regulator. current in the two coils.
上述专利提出了在轴系发生径向位移时让轴系重新进入稳定的方法,但没有解决在某一方向上轴系发生径向位移时如何稳定加载力的问题。The above-mentioned patent proposes a method of re-entering stability of the shafting when the shafting is radially displaced, but does not solve the problem of how to stabilize the loading force when the shafting is radially displaced in a certain direction.
发明内容SUMMARY OF THE INVENTION
本发明的目的是提供水润滑轴承动态电磁加载力控制系统,具有在长时间运行情形下精度更高的控制效果,且在不同突变情形下均具有比单控制算法更短的调节时间。The purpose of the present invention is to provide a dynamic electromagnetic loading force control system for a water lubricated bearing, which has a control effect with higher precision under long-term operation, and has a shorter adjustment time than a single control algorithm under different sudden changes.
本发明的另一目的是提供水润滑轴承动态电磁加载力多参数优化控制方法,保证了控制精度的同时,缩小了超调量,稳定了负载系统的动态性能。Another object of the present invention is to provide a multi-parameter optimization control method for the dynamic electromagnetic loading force of the water-lubricated bearing, which ensures the control precision, reduces the overshoot, and stabilizes the dynamic performance of the load system.
本发明所采用的技术方案是,水润滑轴承动态电磁加载力控制系统,包括轴承主轴两端各设置有的一对非接触式电磁加载装置,非接触式电磁加载装置连接有负载装置驱动器,轴承主轴两端均还设置有电涡流传感器,且轴承主轴近电动机端设置有扭矩转速传感器,非接触式电磁加载装置底部设置有压阻式测力传感器,电涡流传感器、扭矩转速传感器和压阻式测力传感器连接有负载控制器,负载控制器连接有储存在服务器硬盘中的数据库,负载控制器还连接有电流调节器,电流调节器又与负载装置驱动器连接。The technical scheme adopted by the present invention is that the dynamic electromagnetic loading force control system of the water lubricated bearing includes a pair of non-contact electromagnetic loading devices arranged at both ends of the bearing main shaft, the non-contact electromagnetic loading device is connected with a load device driver, and the bearing Both ends of the spindle are also provided with eddy current sensors, and the bearing spindle is provided with a torque speed sensor near the motor end, and a piezoresistive load cell, eddy current sensor, torque speed sensor and piezoresistive type are provided at the bottom of the non-contact electromagnetic loading device. The load cell is connected with the load controller, the load controller is connected with the database stored in the hard disk of the server, the load controller is also connected with a current regulator, and the current regulator is connected with the driver of the load device.
本发明的特点还在于,The present invention is also characterized in that,
负载控制器为三维路径跟踪控制器;The load controller is a three-dimensional path tracking controller;
电流调节器为滑模控制器;The current regulator is a sliding mode controller;
数据库为Oracle数据库。The database is an Oracle database.
本发明所采用的另一技术方案是,水润滑轴承动态电磁加载力多参数优化控制方法,应用本发明的水润滑轴承动态电磁加载力控制系统进行电磁加载力控制,设定目标加载力,采集扭矩转速传感器、压阻式测力传感器、两对电涡流传感器和负载系统控制器读数并将信号传递给负载控制器,负载控制器读取传感器读数并分别作均值处理,得到压阻式测力传感器信号的加载力测量平均值电流传感器信号的励磁电流测量平均值扭矩转速传感器信号的转速测量平均值判断非接触式电磁加载力是否与设定值匹配,若匹配则各传感器继续重复上述过程,若不匹配则将均值处理后的传感信号作为查询条件对数据库进行预处理,预处理后得到数据块,负载控制器采用遗传算法优化的三维数据路径跟踪算法,读取数据块并计算得到参考电流Ir,将参考电流Ir输入至电流调节器中,电流调节器采用滑模算法计算得到控制输出电流Ismc,输入负载装置驱动器中以控制水润滑轴承非接触式电磁加载装置的电磁加载力F,从而达到提高电磁加载力精度和不同工况下鲁棒性的目的。Another technical solution adopted by the present invention is that the multi-parameter optimization control method for the dynamic electromagnetic loading force of the water-lubricated bearing is to use the dynamic electromagnetic loading force control system of the water-lubricated bearing of the present invention to control the electromagnetic loading force, set the target loading force, and collect the Torque speed sensor, piezoresistive load cell, two pairs of eddy current sensors and load system controller read the readings and transmit the signal to the load controller. The load controller reads the sensor readings and averages them respectively to obtain the piezoresistive force measurement Load force measurement mean value of sensor signal Excitation current measurement average value of the current sensor signal Average value of the speed measurement of the torque speed sensor signal Determine whether the non-contact electromagnetic loading force matches the set value. If it matches, each sensor continues to repeat the above process; The load controller adopts the three-dimensional data path tracking algorithm optimized by genetic algorithm, reads the data block and calculates the reference current I r , and inputs the reference current I r into the current regulator, and the current regulator uses the sliding mode algorithm to calculate and control The output current I smc is input into the driver of the load device to control the electromagnetic loading force F of the non-contact electromagnetic loading device of the water-lubricated bearing, so as to improve the accuracy of the electromagnetic loading force and the robustness under different working conditions.
本发明的特点还在于,The present invention is also characterized in that,
计算采样周期内电涡流传感器信号的轴心距测量平均值的计算公式如下:Calculates the mean value of the shaft center distance measurement of the eddy current sensor signal during the sampling period The calculation formula is as follows:
式(1)中,k为一个采样周期内采集到的点数,xr(i)为一个采样周期内水平安装的电涡流传感器采集到的所有水平方向上轴心距的值,yr(i)为一个采样周期内竖直安装的电涡流传感器采集到的所有竖直方向上轴心距的值,δ(i)为一个采样周期内采集到的所有无方向轴心距的值;In formula (1), k is the number of points collected in a sampling period, x r (i) is the value of the axial center distance in all horizontal directions collected by the eddy current sensor installed horizontally in a sampling period, y r (i ) is the value of the axial center distance in all vertical directions collected by the eddy current sensor installed vertically in one sampling period, and δ(i) is the value of all non-directional axial center distance collected in one sampling period;
计算采样周期内压阻式测力传感器信号的加载力测量平均值的计算公式如下:Calculate the mean value of the load force measurement of the piezoresistive load cell signal during the sampling period The calculation formula is as follows:
式(2)中,k为一个采样周期内采集到的点数,F(i)为一个采样周期内采集到的所有加载力的值;In formula (2), k is the number of points collected in one sampling period, and F(i) is the value of all loading forces collected in one sampling period;
计算采样周期内电流传感器信号的励磁电流测量平均值的计算公式如下:Calculates the average value of the excitation current measurement of the current sensor signal during the sampling period The calculation formula is as follows:
式(3)中k为一个采样周期内采集到的点数,Ie(i)为一个采样周期内采集到的所有励磁电流的值;In formula (3), k is the number of points collected in one sampling period, and I e (i) is the value of all excitation currents collected in one sampling period;
计算采样周期内扭矩&转速传感器信号的转速测量平均值的计算公式如下:Calculates the average value of the speed measurements of the torque & speed sensor signals over the sampling period The calculation formula is as follows:
式(4)中k为一个采样周期内采集到的点数,n(i)为一个采样周期内采集到的所有转速的值;In formula (4), k is the number of points collected in one sampling period, and n(i) is the value of all rotational speeds collected in one sampling period;
通过将非接触式电磁加载装置在采样周期内电磁加载力的测量平均值与阈值F0*ε1进行比较,判断非接触式电磁加载力是否与设定值匹配,判别公式如下:By measuring the average value of the electromagnetic loading force of the non-contact electromagnetic loading device in the sampling period Compare with the threshold value F 0 *ε 1 to judge whether the non-contact electromagnetic loading force matches the set value. The judgment formula is as follows:
为电磁加载力的测量平均值,F0为目标电磁加载力,ε1为电磁力波动阈值。 is the measured average value of the electromagnetic loading force, F 0 is the target electromagnetic loading force, and ε 1 is the electromagnetic force fluctuation threshold.
数据库由数据储存端、预调用数据区、多个处理进程、用户进程、服务器进程和备份日志文件构成,数据储存端包括:实际试验数据组成的数据表空间、优化参数组成的参数表空间和由调用语句、表头、表说明等构成的共享池;The database consists of a data storage end, a pre-invoked data area, multiple processing processes, user processes, server processes and backup log files. The data storage end includes: the data table space composed of actual test data, the parameter table space composed of optimization parameters, and the A shared pool composed of call statements, headers, table descriptions, etc.;
实际试验数据组成的数据表空间由试验数据表和数据索引段构成,试验数据表储存着实际测得的在不同轴心距δ、不同转速n、不同励磁电流I下电磁加载力F的试验数据,以轴心距δ为一级查询条件将试验数据表分为不同轴心距下三维数据表,这些三维数据表均以转速n、励磁电流I和电磁加载力F构成的三维数据组成;The data table space composed of the actual test data is composed of the test data table and the data index segment. The test data table stores the actual measured test data of the electromagnetic loading force F under different shaft center distances δ, different rotational speeds n, and different excitation currents I. , taking the axle center distance δ as the first-level query condition, the test data table is divided into three-dimensional data tables under different axle center distances, and these three-dimensional data tables are composed of three-dimensional data consisting of rotational speed n, excitation current I and electromagnetic loading force F;
三维数据表以转速范围、励磁电流范围和加载力范围细分为多个数据块,其中,轴心距δ为一级查询条件,加载力区间作为二级查询条件,转速区间和励磁电流区间作为三级查询条件,数据索引段以索引关键字构成,索引关键字由一级查询条件、二级查询条件和三级查询条件构成;The three-dimensional data table is subdivided into multiple data blocks based on the range of rotational speed, excitation current and loading force. Among them, the axial distance δ is the first-level query condition, the loading force interval is the second-level query condition, and the rotational speed interval and the excitation current interval are Three-level query conditions, the data index segment is composed of index keywords, and the index keywords are composed of first-level query conditions, second-level query conditions, and third-level query conditions;
参数表空间由优化参数表和参数索引段构成,优化参数表储存着各个优化参数,如最大步长λmax、最大视距smax、步长增益系数K、曲线路径权重ζ1、ζ2、ζ3、η1、η2、η3,以类别作为一级查询条件,分别储存在堆栈形式的数据表中。The parameter table space is composed of an optimization parameter table and a parameter index segment. The optimization parameter table stores various optimization parameters, such as the maximum step size λ max , the maximum sight distance s max , the step size gain coefficient K, the curve path weight ζ 1 , ζ 2 , ζ 3 , η 1 , η 2 , η 3 , take the category as the first-level query condition, and are stored in the data table in stack form respectively.
对数据库进行预处理得到数据块具体为,The data blocks obtained by preprocessing the database are as follows:
发出调用指令,先以轴心距平均值作为一级查询条件查询,指定数据表空间;以当前加载力平均值作为二级查询条件,指定数据区后,查询目标坐标点加载力F的值是否位于该数据区中,若位于该数据区则该数据区为预调用数据区,若不位于该数据区,则调用目标加载力F至当前加载力平均值的所有数据区,构成预调用数据区;通过当前工况转速和励磁电流作为三级查询条件,在预调用数据区中指定数据段,读取该数据段并同索引段一起放入位于预调用数据区的局部数据中,得到预处理后的数据块,等待负载控制器读取。Issue a call command, first take the average value of the axis distance As a first-level query condition query, specify the data table space; use the current average loading force As a secondary query condition, after specifying the data area, query whether the value of the loading force F of the target coordinate point is located in the data area. Call the target loading force F to the current average loading force All data areas of the and excitation current As the third-level query condition, specify the data segment in the pre-call data area, read the data segment and put it into the local data in the pre-call data area together with the index segment, obtain the pre-processed data block, and wait for the load controller read.
负载控制器采用遗传算法优化的三维数据路径跟踪算法由全局路径规划层、局部路径规划层、路径重构层和行为执行层构成,具体为,The 3D data path tracking algorithm optimized by the load controller using the genetic algorithm is composed of a global path planning layer, a local path planning layer, a path reconstruction layer and a behavior execution layer. Specifically,
负载控制器初始化,唤醒数据库,读取轴心距轴转速励磁电流加载力输入值,调用存储在预调用数据区中局部数据的数据段,进入全局路径规划层,建立三维图,确定当前输入值坐标点和目标值坐标点,规划全局路径;进入局部路径规划层,添加约束条件,在全局路径中寻找最优路径;进入行为执行层,读取最优路径,判断选用点对点式跟踪方式或点对线式跟踪方式,并用模拟退火算法和粒子群算法分别进行优化,根据励磁电流坐标变化值计算出参考电流Ir,并将参考电流Ir输出给电流控制器;Load controller initialization, wake up database, read axle center distance shaft speed Excitation current loading force Enter the value, call the data segment of the local data stored in the pre-call data area, enter the global path planning layer, build a 3D map, determine the coordinate points of the current input value and the coordinate point of the target value, and plan the global path; enter the local path planning layer, add Constraints, find the optimal path in the global path; enter the behavior execution layer, read the optimal path, determine whether to use the point-to-point tracking method or the point-to-line tracking method, and use the simulated annealing algorithm and the particle swarm algorithm to optimize respectively. The reference current I r is calculated from the coordinate change value of the excitation current, and the reference current I r is output to the current controller;
同时,实时判断轴心距平均值是否改变,若改变,进入路径重构层,更换数据库中的表空间,不再进行索引,直接调用表空间中指定数据段,再次进入局部路径规划层,进行路径重新规划,添加约束条件,在全局路径中寻找最优路径;进入行为执行层,读取最优路径,选择最优路径并确定路径跟踪方式,根据励磁电流坐标变化值计算出参考电流Ir。At the same time, the average value of the axis distance is judged in real time If it changes, enter the path reconstruction layer, replace the table space in the database, no longer index, directly call the specified data segment in the table space, enter the local path planning layer again, re-plan the path, add constraints, and Find the optimal path in the global path; enter the behavior execution layer, read the optimal path, select the optimal path and determine the path tracking method, and calculate the reference current I r according to the coordinate change value of the excitation current.
局部路径规划层作为三维数据路径跟踪算法的局部规划部分,接收全局地图生成的以当前工况下电磁加载力三维坐标到目标电磁加载力三维坐标局部地图信息,添加约束条件以选取局部最优路径,得到的最优路径储存在数据库的指定数据块中,方便实时更迭,具体为,The local path planning layer, as the local planning part of the 3D data path tracking algorithm, receives the local map information generated from the global map from the 3D coordinates of the electromagnetic loading force under the current working conditions to the 3D coordinates of the target electromagnetic loading force, and adds constraints to select the local optimal path. , the obtained optimal path is stored in the specified data block of the database, which is convenient for real-time change. Specifically,
调用储存在数据库中的三个低阶曲线路径权重ζ1、ζ2、ζ3和三个高阶曲线路径权重η1、η2、η3,读取三维数据图,确认当前坐标和目标坐标,计算电磁加载力误差e,通过该误差进行如下判断:Call the three low-order curve path weights ζ 1 , ζ 2 , ζ 3 and the three high-order curve path weights η 1 , η 2 , η 3 stored in the database, read the three-dimensional data map, and confirm the current coordinates and target coordinates , calculate the electromagnetic loading force error e, and make the following judgments through this error:
误差e是否在目标电磁力的30%以内,若误差小于目标电磁力的30%,确定低阶曲线路径权重ζ1为参数1;反之,则确定高阶曲线路径权重η1为参数1;三维数据图上是否存在不连续点,以导数是否连续判定,若存在不连续点,确定低阶曲线路径权重ζ2为参数2;反之,则确定高阶曲线路径权重η2为参数2;是否允许工况的微小改变,控制系统精度为目标值±2%~±5%内,若允许最优路径为低阶曲线时转速波动在控制系统精度内,则确定低阶曲线路径权重η3为参数3;反之,则确定高阶曲线路径权重η3为参数3;Whether the error e is within 30% of the target electromagnetic force, if the error is less than 30% of the target electromagnetic force, determine the low-order curve path weight ζ 1 as
整合路径权重ζ1、ζ2、ζ3、η1、η2、η3,被选择的路径权重通过遗传算法迭代优化,调整参数,低阶曲线路径权重ζ1、ζ2、ζ3求和得到低阶曲线路径总权重ζ,高阶曲线路径权重η1、η2、η3求和得到高阶曲线路径总权重η;Integrate the path weights ζ 1 , ζ 2 , ζ 3 , η 1 , η 2 , η 3 , the selected path weights are iteratively optimized by the genetic algorithm, the parameters are adjusted, and the low-order curve path weights ζ 1 , ζ 2 , ζ 3 are summed The total weight ζ of the low-order curve path is obtained, and the total weight η of the high-order curve path is obtained by summing the weights η 1 , η 2 , and η 3 of the high-order curve path;
ζ若大于η,则判定为寻找最优低阶曲线路径组,反之,则判定为寻找最优高阶曲线路径组,并计算权重差值Δ=|η-ζ|,通过遗传算法调整权重差值阈值Δ*,以判断是选择最优曲线组中较为高阶的还是选择较为低阶的,判断条件如下:If ζ is greater than η, it is determined to find the optimal low-order curve path group; otherwise, it is determined to find the optimal high-order curve path group, and the weight difference Δ=|η-ζ| is calculated, and the weight difference is adjusted by genetic algorithm. The value threshold Δ * is used to judge whether to choose a higher-order or a lower-order one in the optimal curve group. The judgment conditions are as follows:
最优高阶曲线路径组采用D*Lite路径搜索算法来选取,而最优低阶曲线路径组选取采用Dijkstra算法。The optimal high-order curve path group is selected by the D*Lite path search algorithm, and the optimal low-order curve path group is selected by the Dijkstra algorithm.
进入行为执行层,读取最优路径,判断选用点对点式跟踪方式或点对线式跟踪方式,并用模拟退火算法和粒子群算法分别进行优化,根据励磁电流坐标变化值计算出参考电流Ir具体为,Enter the behavior execution layer, read the optimal path, determine whether to use the point-to-point tracking method or the point-to-line tracking method, and use the simulated annealing algorithm and the particle swarm algorithm to optimize respectively, and calculate the reference current I r according to the coordinate change value of the excitation current. for,
从数据库中读取视距s、最大视距smax、步长λ、最大步长λmax和步长增益系数K,以及由局部路径规划层得到的最优路径L,判断最优路径L的阶数 m是否大于2;Read the sight distance s, the maximum sight distance s max , the step size λ, the maximum step size λ max and the step size gain coefficient K from the database, as well as the optimal path L obtained by the local path planning layer, and judge the optimal path L Whether the order m is greater than 2;
若m>2,则选用点线式跟踪方式,该方式流程如下:判断电流信号误差 e和最大视距smax的关系;若e<smax,则以最大步长λmax在最优路径上进行选择前进点;若e>smax,即此时可以“看到”目标点,此时通过粒子群优化算法按照误差e的大小对步长增益系数K进行修正,并计算修正后的步长增益系数与最大步长λmax乘积得到步长λ,从而在最优路径上选择前进点,在达到目标点的前一个点时,步长λ应为0;若步长λ内存在不连续点,则以不连续点作为前进点,分段进行路径跟踪;得到每一步的励磁电流坐标,每一步计算得到各自的参考电流Irn,逐步达到最终的参考电流Ir;If m>2, the point-line tracking method is selected. The process of this method is as follows: determine the relationship between the current signal error e and the maximum line of sight s max ; if e<s max , use the maximum step size λ max on the optimal path Select the forward point; if e>s max , that is, the target point can be "seen" at this time. At this time, the particle swarm optimization algorithm is used to correct the step gain coefficient K according to the size of the error e, and calculate the corrected step size The step size λ is obtained by multiplying the gain coefficient and the maximum step size λ max , so as to select the forward point on the optimal path. When reaching the previous point of the target point, the step size λ should be 0; if there are discontinuous points in the step size λ , then take the discontinuous point as the advance point, and carry out the path tracking in sections; obtain the excitation current coordinates of each step, calculate the respective reference current I rn in each step, and gradually reach the final reference current I r ;
若m<2,则选用点点式跟踪方式,该方式流程如下:根据电流信号误差 e大小,通过粒子群优化算法调整分割点数q;根据分割点数q,将最优低阶曲线分割为当前点A、中间点A1、A2、…、Aq、目标点B;将中间点A1设为下一个目标点,确认励磁电流坐标修正值,计算得到参考电流Ir1;逐点进行,分别得到参考电流Ir2、Ir3、…、Irq,最终得到目标点参考电流Ir。If m<2, the point-to-point tracking method is selected. The process of this method is as follows: according to the size of the current signal error e, adjust the number of split points q through the particle swarm optimization algorithm; according to the number of split points q, split the optimal low-order curve into the current point A , intermediate points A 1 , A 2 , ..., A q , target point B; set the intermediate point A 1 as the next target point, confirm the correction value of the excitation current coordinates, and calculate the reference current I r1 ; With reference to the currents I r2 , I r3 , . . . , I rq , the target point reference current I r is finally obtained.
电流控制器为滑模控制器,基于以下数学模型设计:The current controller is a sliding mode controller and is designed based on the following mathematical model:
式(5)中, R为加载盘外圆半径,为加载盘厚度,μ0为真空磁导率,N为线圈匝数,I为励磁电流,l为气隙长度,n为谐波次数, vx为加载盘线速度;In formula (5), R is the outer circle radius of the loading disc, is the thickness of the loading disc, μ 0 is the vacuum permeability, N is the number of coil turns, I is the excitation current, l is the length of the air gap, n is the harmonic order, and vx is the loading disc line speed;
滑模控制器的滑模变量s选取为:The sliding mode variable s of the sliding mode controller is selected as:
式(6)中,c为调整误差的速度,电流信号误差e=Ir-Ie, In formula (6), c is the speed of the adjustment error, the current signal error e=I r -I e ,
滑模控制器采用趋近率方式设计,趋近率为:The sliding mode controller is designed using the approach rate method, and the approach rate is:
式(7)中,ks和γ均为正常数,sgn(s)为符号函数;In formula (7), k s and γ are both constants, and sgn(s) is a sign function;
计算得到滑模控制器的控制输出电流Ismc为:The control output current I smc of the sliding mode controller is calculated as:
Ismc=ce+ks+γsgn(s) (8)。 Ismc =ce+ks+γsgn( s ) (8).
本发明的有益效果是:The beneficial effects of the present invention are:
本发明水润滑轴承动态电磁加载力控制系统,相比于传统单一控制算法的控制系统,具有在长时间运行情形下精度更高的控制效果,且在不同突变情形下均具有比单控制算法更短的调节时间;采用数据库储存海量试验数据和优化参数,不仅可以查看历史数据,也可以针对不同结构的非接触式电磁加载装置进行修正,扩大应用范围;表空间的查询方式设计为在切换表空间时直接调用指定数据段,简化了查询步骤,节约时间和内存;轴系在运动过程中气隙是不间断变化的,数据库的设计保证了在轴系动态运作过程中,控制系统的灵敏性和响应速度;且数据库采用Oracle形式,其特性之一在于锁机制策略,即对于参数的写操作不会阻塞读操作,这意味着在实时运行过程中,可以通过优化算法对数据库内参量进行合理修正,进而不断提高控制精度、复杂工况下的鲁棒性以及应对突变工况的适应性。Compared with the control system of the traditional single control algorithm, the dynamic electromagnetic loading force control system of the water lubricated bearing of the present invention has a control effect with higher precision in the case of long-term operation, and has a higher control effect than the single control algorithm under different sudden changes. Short adjustment time; using database to store massive test data and optimization parameters, not only can view historical data, but also correct for non-contact electromagnetic loading devices of different structures to expand the scope of application; the query method of table space is designed to switch the table The specified data segment is directly called in the space, which simplifies the query steps, saves time and memory; the air gap changes continuously during the movement of the shaft system, and the design of the database ensures the sensitivity of the control system during the dynamic operation of the shaft system. and the response speed; and the database adopts the form of Oracle, one of its characteristics is the locking mechanism strategy, that is, the write operation of the parameter will not block the read operation, which means that in the real-time operation process, the parameters in the database can be reasonably adjusted through the optimization algorithm. Correction, and then continuously improve the control accuracy, robustness under complex working conditions and adaptability to deal with sudden changes.
本发明水润滑轴承动态电磁加载力多参数优化控制方法,采用分散控制,针对不同的非接触式电磁加载装置均有不同的励磁电流进行驱动,在复杂工况和大负载情形引起的轴心偏移情形下具有比集中控制更精确的控制效果,提高了轴系的刚度;回避了解耦困难等问题,读取实际运行下采集到的数据,运用路径规划算法和滑模控制算法,通过计算坐标的修正值和设计滑膜面的形式,得到输出电流有效值,不仅无需对转速、电流和电磁加载力之间进行数学建模上的解耦,而且在数据中已包含有当前工况下的静态损耗和动态损耗,不需要进一步的补偿。本方法在有效保证了控制效果的同时,简化了控制难度;设计了“视距”、步长和步长增益三个参数,保证了在变化过程的初始阶段有着较高的“速度”,从而保证了整体较低的调节时间;同时在视距的缩小过程中,步长增益参数也在缩小,保证了控制精度的同时,缩小了超调量,稳定了负载系统的动态性能。The multi-parameter optimization control method for the dynamic electromagnetic loading force of the water lubricated bearing of the present invention adopts decentralized control, and different non-contact electromagnetic loading devices are driven by different excitation currents. It has a more accurate control effect than centralized control in the moving situation, and improves the stiffness of the shaft system; avoids problems such as decoupling difficulties, reads the data collected under actual operation, and uses the path planning algorithm and sliding mode control algorithm. The corrected value of the coordinates and the form of the designed synovial surface are used to obtain the effective value of the output current, which not only does not need to decouple the mathematical modeling between the rotational speed, the current and the electromagnetic loading force, but also includes the current working conditions in the data. The static loss and dynamic loss do not require further compensation. This method simplifies the control difficulty while effectively ensuring the control effect; three parameters of "line of sight", step length and step gain are designed to ensure a high "speed" in the initial stage of the change process, thereby The overall low adjustment time is ensured; at the same time, the step gain parameter is also reduced in the process of reducing the line of sight, which ensures the control accuracy, reduces the overshoot, and stabilizes the dynamic performance of the load system.
附图说明Description of drawings
图1是本发明水润滑轴承动态电磁加载力控制系统结构图;1 is a structural diagram of a dynamic electromagnetic loading force control system for a water-lubricated bearing of the present invention;
图2是本发明水润滑轴承动态电磁加载力多参数优化控制方法的控制流程图;Fig. 2 is the control flow chart of the multi-parameter optimization control method of the dynamic electromagnetic loading force of the water-lubricated bearing of the present invention;
图3是Oracle数据库结构图;Figure 3 is the Oracle database structure diagram;
图4是数据表空间结构图;Figure 4 is a data table space structure diagram;
图5是数据库调用流程图;Fig. 5 is the database calling flow chart;
图6是预调用数据区结构;Fig. 6 is the structure of pre-calling data area;
图7是三维数据路径跟踪算法流程图;7 is a flowchart of a three-dimensional data path tracking algorithm;
图8是局部路径规划算法流程图;Fig. 8 is the flow chart of local path planning algorithm;
图9是行为执行层算法流程图;Fig. 9 is the algorithm flow chart of the behavior execution layer;
图10是实施例的全局三维图;Figure 10 is a global three-dimensional view of an embodiment;
图11是实施例的局部三维图。Figure 11 is a partial three-dimensional view of the embodiment.
具体实施方式Detailed ways
下面结合附图和具体实施方式对本发明进行详细说明。The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.
本发明水润滑轴承动态电磁加载力控制系统,如图1所示,包括轴承主轴两端各设置有的一对非接触式电磁加载装置,非接触式电磁加载装置连接有负载装置驱动器,负载控制器为三维路径跟踪控制器,轴承主轴两端均还设置有电涡流传感器,且轴承主轴近电动机端设置有扭矩转速传感器,非接触式电磁加载装置底部设置有压阻式测力传感器,电涡流传感器、扭矩转速传感器和压阻式测力传感器通过采集卡、模数转换器连接有负载控制器,负载控制器连接有储存在服务器硬盘中的Oracle数据库,负载控制器还连接有电流调节器,电流调节器为滑模控制器,电流调节器又与负载装置驱动器连接。The dynamic electromagnetic loading force control system of the water lubricated bearing of the present invention, as shown in Figure 1, includes a pair of non-contact electromagnetic loading devices provided at both ends of the bearing main shaft. The non-contact electromagnetic loading device is connected with a load device driver, and the load controls The device is a three-dimensional path tracking controller. Eddy current sensors are also installed at both ends of the bearing spindle, and a torque speed sensor is installed at the end of the bearing spindle near the motor. A piezoresistive load cell is installed at the bottom of the non-contact electromagnetic loading device. The sensor, torque speed sensor and piezoresistive load cell are connected to the load controller through the acquisition card and the analog-to-digital converter. The load controller is connected to the Oracle database stored in the hard disk of the server, and the load controller is also connected to the current regulator. The current regulator is a sliding mode controller, and the current regulator is connected with the load device driver.
本发明水润滑轴承动态电磁加载力多参数优化控制方法,应用本发明的水润滑轴承动态电磁加载力控制系统进行电磁加载力控制,如图2所示,系统初始化后,设定目标加载力,采集扭矩转速传感器、压阻式测力传感器、两对电涡流传感器和负载系统控制器读数并将信号传递给负载控制器,负载控制器读取传感器读数并分别作均值处理,得到压阻式测力传感器信号的加载力测量平均值电流传感器信号的励磁电流测量平均值扭矩转速传感器信号的转速测量平均值判断非接触式电磁加载力是否与设定值匹配,若匹配则各传感器继续重复上述过程,若不匹配则将均值处理后的传感信号作为查询条件对数据库进行预处理,预处理后得到数据块,负载控制器采用遗传算法优化的三维数据路径跟踪算法,读取数据块并计算得到参考电流Ir,将参考电流Ir输入至电流调节器中,电流调节器采用滑模算法计算得到控制输出电流Ismc,输入负载装置驱动器中以控制水润滑轴承非接触式电磁加载装置的电磁加载力F,从而达到提高电磁加载力精度和不同工况下鲁棒性的目的。The multi-parameter optimization control method of the dynamic electromagnetic loading force of the water lubricated bearing of the present invention uses the dynamic electromagnetic loading force control system of the water lubricated bearing of the present invention to control the electromagnetic loading force. As shown in Figure 2, after the system is initialized, the target loading force is set, Collect the readings of torque speed sensor, piezoresistive load cell, two pairs of eddy current sensors and load system controller and transmit the signal to the load controller. Loading force measurement mean value of the force sensor signal Excitation current measurement average value of the current sensor signal Average value of the speed measurement of the torque speed sensor signal Determine whether the non-contact electromagnetic loading force matches the set value. If it matches, each sensor continues to repeat the above process; The load controller adopts the three-dimensional data path tracking algorithm optimized by genetic algorithm, reads the data block and calculates the reference current I r , and inputs the reference current I r into the current regulator, and the current regulator uses the sliding mode algorithm to calculate and control The output current I smc is input into the driver of the load device to control the electromagnetic loading force F of the non-contact electromagnetic loading device of the water-lubricated bearing, so as to improve the accuracy of the electromagnetic loading force and the robustness under different working conditions.
计算采样周期内电涡流传感器信号的轴心距测量平均值的计算公式如下:Calculates the mean value of the shaft center distance measurement of the eddy current sensor signal during the sampling period The calculation formula is as follows:
式(1)中,k为一个采样周期内采集到的点数,xr(i)为一个采样周期内水平安装的电涡流传感器采集到的所有水平方向上轴心距的值,yr(i)为一个采样周期内竖直安装的电涡流传感器采集到的所有竖直方向上轴心距的值,δ(i)为一个采样周期内采集到的所有无方向轴心距的值;In formula (1), k is the number of points collected in a sampling period, x r (i) is the value of the axial center distance in all horizontal directions collected by the eddy current sensor installed horizontally in a sampling period, y r (i ) is the value of the axial center distance in all vertical directions collected by the eddy current sensor installed vertically in one sampling period, and δ(i) is the value of all non-directional axial center distance collected in one sampling period;
计算采样周期内压阻式测力传感器信号的加载力测量平均值的计算公式如下:Calculate the mean value of the load force measurement of the piezoresistive load cell signal during the sampling period The calculation formula is as follows:
式(2)中,k为一个采样周期内采集到的点数,F(i)为一个采样周期内采集到的所有加载力的值;In formula (2), k is the number of points collected in one sampling period, and F(i) is the value of all loading forces collected in one sampling period;
计算采样周期内电流传感器信号的励磁电流测量平均值的计算公式如下:Calculates the average value of the excitation current measurement of the current sensor signal during the sampling period The calculation formula is as follows:
式(3)中k为一个采样周期内采集到的点数,Ie(i)为一个采样周期内采集到的所有励磁电流的值;In formula (3), k is the number of points collected in one sampling period, and I e (i) is the value of all excitation currents collected in one sampling period;
计算采样周期内扭矩&转速传感器信号的转速测量平均值的计算公式如下:Calculates the average value of the speed measurements of the torque & speed sensor signals over the sampling period The calculation formula is as follows:
式(4)中k为一个采样周期内采集到的点数,n(i)为一个采样周期内采集到的所有转速的值;In formula (4), k is the number of points collected in one sampling period, and n(i) is the value of all rotational speeds collected in one sampling period;
通过将非接触式电磁加载装置在采样周期内电磁加载力的测量平均值与阈值F0*ε1进行比较,判断非接触式电磁加载力是否与设定值匹配,判别公式如下:By measuring the average value of the electromagnetic loading force of the non-contact electromagnetic loading device in the sampling period Compare with the threshold value F 0 *ε 1 to judge whether the non-contact electromagnetic loading force matches the set value. The judgment formula is as follows:
为电磁加载力的测量平均值,F0为目标电磁加载力,ε1为电磁力波动阈值。 is the measured average value of the electromagnetic loading force, F 0 is the target electromagnetic loading force, and ε 1 is the electromagnetic force fluctuation threshold.
Oracle数据库储存在服务器硬盘中,随时等待上位机发出用户指令进行调用,数据库结构如图3所示,Oracle数据库由数据储存端、预调用数据区、多个处理进程、用户进程、服务器进程和备份日志文件构成。其中,数据储存端保存着完整的数据表空间、完整的参数表空间以及相对应的索引文件;调用的参数、预处理后得到的数据块以及库名文件、镜像文件等储存在预调用数据区中等待调用指令;处理进程包括CKPT进程、SMON进程、LGWR进程、DBWN进程、ARCN进程和PMON进程,分别负责检验、调用、记录、删除等功能;用户进程为上位机发送的调用指令或修改指令,进而在服务器进程处理后对数据库进行修改。The Oracle database is stored in the hard disk of the server, and waits for the host computer to issue user instructions to call at any time. The database structure is shown in Figure 3. The Oracle database consists of a data storage end, a pre-invoked data area, multiple processing processes, user processes, server processes and backups. Log file composition. Among them, the data storage side saves the complete data table space, the complete parameter table space and the corresponding index file; the called parameters, the data blocks obtained after preprocessing, the library name file, the image file, etc. are stored in the pre-call data area Waiting for the call instruction in the middle; the processing process includes the CKPT process, the SMON process, the LGWR process, the DBWN process, the ARCN process and the PMON process, which are respectively responsible for functions such as checking, calling, recording, and deleting; the user process is the calling instruction or modification instruction sent by the host computer , and then modify the database after processing by the server process.
数据储存端包括:实际试验数据组成的数据表空间、优化参数组成的参数表空间和由调用语句、表头、表说明等构成的共享池;The data storage end includes: the data table space composed of actual test data, the parameter table space composed of optimization parameters, and the shared pool composed of calling statements, table headers, table descriptions, etc.;
实际试验数据组成的数据表空间由试验数据表和数据索引段构成,试验数据表储存着实际测得的在不同轴心距δ、不同转速n、不同励磁电流I下加载力F的试验数据,以轴心距δ为一级查询条件将试验数据表分为不同轴心距下三维数据表,这些三维数据表均以转速n、励磁电流I和电磁加载力F构成的三维数据组成;The data table space composed of actual test data is composed of test data table and data index segment. The test data table stores the actual measured test data of loading force F under different axis distance δ, different rotational speed n, and different excitation current I. Taking the axle center distance δ as the first-level query condition, the test data table is divided into three-dimensional data tables under different axle center distances. These three-dimensional data tables are composed of three-dimensional data composed of rotational speed n, excitation current I and electromagnetic loading force F;
数据表空间结构如图4所示,三维数据表以转速范围、励磁电流范围和加载力范围细分为多个数据块,其中,轴心距δ为一级查询条件,加载力区间作为二级查询条件,转速区间和励磁电流区间作为三级查询条件,数据索引段以索引关键字构成,索引关键字由一级查询条件、二级查询条件和三级查询条件构成;The spatial structure of the data table is shown in Figure 4. The three-dimensional data table is subdivided into multiple data blocks according to the speed range, excitation current range and loading force range. Among them, the axial distance δ is the first-level query condition, and the loading force interval is the second-level query condition. The query conditions, the speed interval and the excitation current interval are used as the third-level query conditions, the data index segment is composed of index keywords, and the index keywords are composed of the first-level query conditions, the second-level query conditions and the third-level query conditions;
参数表空间由优化参数表和参数索引段构成,优化参数表储存着各个优化参数,如最大步长λmax、最大视距smax、步长增益系数K、曲线路径权重ζ1、ζ2、ζ3、η1、η2、η3,以类别作为一级查询条件,分别储存在堆栈形式的数据表中。The parameter table space is composed of an optimization parameter table and a parameter index segment. The optimization parameter table stores various optimization parameters, such as the maximum step size λ max , the maximum sight distance s max , the step size gain coefficient K, the curve path weight ζ 1 , ζ 2 , ζ 3 , η 1 , η 2 , η 3 , take the category as the first-level query condition, and are stored in the data table in stack form respectively.
各个进程在三维数据库读取过程中发挥不同的作用,在数据储存端, CKPT进程检验数据库完整性后,SMON进程清理不再使用的临时段,保证数据库具有足够数据空间记录修改记录。在运行过程中,DBWR进程将优化过程中产生的旧参数保存在修改记录中,若有数据表空间内数据修改,同样会保存在修改记录中。数据库是有限的,修改记录和修改后的数据会分别通过LGWR进程和DBWN进程送回数据储存端中进行分别保存。而修改记录是有时效性的,为了保证能够生成参数的长时间变化曲线,ARCN进程会调用数据库中的修改记录进行另外保存。Each process plays a different role in the 3D database reading process. On the data storage side, after the CKPT process checks the integrity of the database, the SMON process cleans up the temporary segments that are no longer used to ensure that the database has enough data space to record modification records. During the running process, the DBWR process saves the old parameters generated during the optimization process in the modification record. If there is data modification in the data tablespace, it will also be saved in the modification record. The database is limited, and the modified records and modified data will be sent back to the data storage end through the LGWR process and the DBWN process respectively for preservation. The modification record is time-sensitive. In order to ensure that the long-term change curve of the parameters can be generated, the ARCN process will call the modification record in the database to save it separately.
调用流程图如图5所示,结合图4,用户进程发出调用指令,先查询一级查询条件——轴心距平均值指定数据表空间;以当前加载力平均值确认二级查询条件,指定数据区后,查询目标坐标点加载力F的值是否位于该数据区中,若不位于该数据区,则调用目标加载力F至当前加载力平均值的所有数据区,构成预调用数据区,其结构如图6所示;通过当前工况——转速和励磁电流确认三级查询条件,在预调用数据区中指定数据段,读取该数据段并同索引段一起放入位于预调用数据区的局部数据中,等待读取。The call flow chart is shown in Figure 5. Combined with Figure 4, the user process issues a call command, first query the first-level query condition - the average value of the axis distance Specify the data tablespace; take the current load force average After confirming the secondary query conditions, after specifying the data area, check whether the value of the loading force F of the target coordinate point is located in the data area. If it is not located in the data area, call the target loading force F to the current average value of the loading force All data areas of the and excitation current Confirm the three-level query condition, specify the data segment in the pre-call data area, read the data segment and put it into the local data in the pre-call data area together with the index segment, and wait for reading.
对数据库进行预处理得到数据块具体为,The data blocks obtained by preprocessing the database are as follows:
发出调用指令,先以轴心距平均值作为一级查询条件查询,指定数据表空间;以当前加载力平均值作为二级查询条件,指定数据区后,查询目标坐标点加载力F的值是否位于该数据区中,若位于该数据区则该数据区为预调用数据区,若不位于该数据区,则调用目标加载力F至当前加载力平均值的所有数据区,构成预调用数据区;通过当前工况转速和励磁电流作为三级查询条件,在预调用数据区中指定数据段,读取该数据段并同索引段一起放入位于预调用数据区的局部数据中,得到预处理后的数据块,等待负载控制器读取。Issue a call command, first take the average value of the axis distance As a first-level query condition query, specify the data table space; use the current average loading force As a secondary query condition, after specifying the data area, query whether the value of the loading force F of the target coordinate point is located in the data area. Call the target loading force F to the current average loading force All data areas of the and excitation current As the third-level query condition, specify the data segment in the pre-call data area, read the data segment and put it into the local data in the pre-call data area together with the index segment, obtain the pre-processed data block, and wait for the load controller read.
负载控制器采用遗传算法优化的三维数据路径跟踪算法由全局路径规划层、局部路径规划层、路径重构层和行为执行层构成,如图7所示。具体为,The 3D data path tracking algorithm optimized by the load controller using genetic algorithm consists of a global path planning layer, a local path planning layer, a path reconstruction layer and a behavior execution layer, as shown in Figure 7. Specifically,
负载控制器初始化,唤醒数据库,读取轴心距轴转速励磁电流加载力输入值,调用存储在预调用数据区中局部数据的数据段,进入全局路径规划层,建立三维图,确定当前输入值坐标点和目标值坐标点,规划全局路径;进入局部路径规划层,添加约束条件,在全局路径中寻找最优路径;进入行为执行层,读取最优路径,判断选用点对点式跟踪方式或点对线式跟踪方式,并用模拟退火算法和粒子群算法分别进行优化,根据励磁电流坐标变化值计算出参考电流Ir,并将参考电流Ir输出给电流控制器;Load controller initialization, wake up database, read axle center distance shaft speed Excitation current loading force Enter the value, call the data segment of the local data stored in the pre-call data area, enter the global path planning layer, build a 3D map, determine the coordinate points of the current input value and the coordinate point of the target value, and plan the global path; enter the local path planning layer, add Constraints, find the optimal path in the global path; enter the behavior execution layer, read the optimal path, determine whether to use the point-to-point tracking method or the point-to-line tracking method, and use the simulated annealing algorithm and the particle swarm algorithm to optimize respectively. The reference current I r is calculated from the coordinate change value of the excitation current, and the reference current I r is output to the current controller;
同时,实时判断轴心距平均值是否改变,若改变,进入路径重构层,更换数据库中的表空间,不再进行索引,直接调用表空间中指定数据段,再次进入局部路径规划层,进行路径重新规划,添加约束条件,在全局路径中寻找最优路径;进入行为执行层,读取最优路径,选择最优路径并确定路径跟踪方式,根据励磁电流坐标变化值计算出参考电流Ir。At the same time, the average value of the axis distance is judged in real time If it changes, enter the path reconstruction layer, replace the table space in the database, no longer index, directly call the specified data segment in the table space, enter the local path planning layer again, re-plan the path, add constraints, and Find the optimal path in the global path; enter the behavior execution layer, read the optimal path, select the optimal path and determine the path tracking method, and calculate the reference current I r according to the coordinate change value of the excitation current.
局部路径规划层作为三维数据路径跟踪算法的局部规划部分,接收全局地图生成的以当前工况下电磁加载力三维坐标到目标电磁力三维坐标局部地图信息,添加约束条件以选取局部最优路径,得到的最优路径储存在数据库的指定数据块中,方便实时更迭,如图8所示,具体为,The local path planning layer, as the local planning part of the 3D data path tracking algorithm, receives the local map information generated from the global map from the 3D coordinates of the electromagnetic loading force under the current working conditions to the 3D coordinates of the target electromagnetic force, and adds constraints to select the local optimal path. The obtained optimal path is stored in the specified data block of the database, which is convenient for real-time change, as shown in Figure 8. Specifically,
调用储存在数据库中的三个低阶曲线路径权重ζ1、ζ2、ζ3和三个高阶曲线路径权重η1、η2、η3,读取三维数据图,确认当前坐标和目标坐标,计算电磁加载力误差e,通过该误差进行如下判断:Call the three low-order curve path weights ζ 1 , ζ 2 , ζ 3 and the three high-order curve path weights η 1 , η 2 , η 3 stored in the database, read the three-dimensional data map, and confirm the current coordinates and target coordinates , calculate the electromagnetic loading force error e, and make the following judgments through this error:
误差e是否在目标电磁力的30%以内,若误差小于目标电磁力的30%,确定低阶曲线路径权重ζ1为参数1;反之,则确定高阶曲线路径权重η1为参数1;三维数据图上是否存在不连续点,以导数是否连续判定,若存在不连续点,确定低阶曲线路径权重ζ2为参数2;反之,则确定高阶曲线路径权重η2为参数2;是否允许工况的微小改变,控制系统精度为目标值±2%~±5%内,若允许最优路径为低阶曲线时转速波动在控制系统精度内,则确定低阶曲线路径权重η3为参数3;反之,则确定高阶曲线路径权重η3为参数3;Whether the error e is within 30% of the target electromagnetic force, if the error is less than 30% of the target electromagnetic force, determine the low-order curve path weight ζ 1 as
整合路径权重ζ1、ζ2、ζ3、η1、η2、η3,被选择的路径权重通过遗传算法迭代优化,调整参数,低阶曲线路径权重ζ1、ζ2、ζ3求和得到低阶曲线路径总权重ζ,高阶曲线路径权重η1、η2、η3求和得到高阶曲线路径总权重η;Integrate the path weights ζ 1 , ζ 2 , ζ 3 , η 1 , η 2 , η 3 , the selected path weights are iteratively optimized by the genetic algorithm, the parameters are adjusted, and the low-order curve path weights ζ 1 , ζ 2 , ζ 3 are summed The total weight ζ of the low-order curve path is obtained, and the total weight η of the high-order curve path is obtained by summing the weights η 1 , η 2 , and η 3 of the high-order curve path;
ζ若大于η,则判定为寻找最优低阶曲线路径组,反之,则判定为寻找最优高阶曲线路径组,并计算权重差值Δ=|η-ζ|,通过遗传算法调整权重差值阈值Δ*,以判断是选择最优曲线组中较为高阶的还是选择较为低阶的,判断条件如下:If ζ is greater than η, it is determined to find the optimal low-order curve path group; otherwise, it is determined to find the optimal high-order curve path group, and the weight difference Δ=|η-ζ| is calculated, and the weight difference is adjusted by genetic algorithm. The value threshold Δ * is used to judge whether to choose a higher-order or a lower-order one in the optimal curve group. The judgment conditions are as follows:
最优高阶曲线路径组采用D*Lite路径搜索算法来选取,而最优低阶曲线路径组选取采用Dijkstra算法。The optimal high-order curve path group is selected by the D*Lite path search algorithm, and the optimal low-order curve path group is selected by the Dijkstra algorithm.
进入行为执行层,读取最优路径,判断选用点对点式跟踪方式或点对线式跟踪方式,并用模拟退火算法和粒子群算法分别进行优化,根据励磁电流坐标变化值计算出参考电流Ir,如图9所示,具体为,Enter the behavior execution layer, read the optimal path, determine whether to use the point-to-point tracking method or the point-to-line tracking method, and use the simulated annealing algorithm and the particle swarm algorithm to optimize respectively, and calculate the reference current I r according to the coordinate change value of the excitation current, As shown in Figure 9, specifically,
从数据库中读取视距s、最大视距smax、步长λ、最大步长λmax和步长增益系数K,以及由局部路径规划层得到的最优路径L,判断最优路径L的阶数 m是否大于2;Read the sight distance s, the maximum sight distance s max , the step size λ, the maximum step size λ max and the step size gain coefficient K from the database, as well as the optimal path L obtained by the local path planning layer, and judge the optimal path L Whether the order m is greater than 2;
若m>2,则选用点线式跟踪方式,该方式流程如下:判断电流信号误差 e和最大视距smax的关系;若e<smax,则以最大步长λmax在最优路径上进行选择前进点;若e>smax,即此时可以“看到”目标点,此时通过粒子群优化算法按照误差e的大小对步长增益系数K进行修正,并计算修正后的步长增益系数与最大步长λmax乘积得到步长λ,从而在最优路径上选择前进点,在达到目标点的前一个点时,步长λ应为0;若步长λ内存在不连续点,则以不连续点作为前进点,分段进行路径跟踪;得到每一步的励磁电流坐标,每一步计算得到各自的参考电流Irn,逐步达到最终的参考电流Ir;If m>2, the point-line tracking method is selected. The process of this method is as follows: determine the relationship between the current signal error e and the maximum line of sight s max ; if e<s max , use the maximum step size λ max on the optimal path Select the forward point; if e>s max , that is, the target point can be "seen" at this time. At this time, the particle swarm optimization algorithm is used to correct the step gain coefficient K according to the size of the error e, and calculate the corrected step size The step size λ is obtained by multiplying the gain coefficient and the maximum step size λ max , so as to select the forward point on the optimal path. When reaching the previous point of the target point, the step size λ should be 0; if there are discontinuous points in the step size λ , then take the discontinuous point as the advance point, and carry out the path tracking in sections; obtain the excitation current coordinates of each step, calculate the respective reference current I rn in each step, and gradually reach the final reference current I r ;
若m<2,则选用点点式跟踪方式,该方式流程如下:根据电流信号误差 e大小,通过粒子群优化算法调整分割点数q;根据分割点数q,将最优低阶曲线分割为当前点A、中间点A1、A2、…、Aq、目标点B;将中间点A1设为下一个目标点,确认励磁电流坐标修正值,计算得到参考电流Ir1;逐点进行,分别得到参考电流Ir2、Ir3、…、Irq,最终得到目标点参考电流Ir。If m<2, the point-to-point tracking method is selected. The process of this method is as follows: according to the size of the current signal error e, adjust the number of split points q through the particle swarm optimization algorithm; according to the number of split points q, split the optimal low-order curve into the current point A , intermediate points A 1 , A 2 , ..., A q , target point B; set the intermediate point A 1 as the next target point, confirm the correction value of the excitation current coordinates, and calculate the reference current I r1 ; With reference to the currents I r2 , I r3 , . . . , I rq , the target point reference current I r is finally obtained.
电流控制器为滑模控制器,基于以下数学模型设计:The current controller is a sliding mode controller and is designed based on the following mathematical model:
式(5)中, R为加载盘外圆半径,为加载盘厚度,μ0为真空磁导率,N为线圈匝数,I为励磁电流,l为气隙长度,n为谐波次数, vx为加载盘线速度;In formula (5), R is the outer circle radius of the loading disc, is the thickness of the loading disc, μ 0 is the vacuum permeability, N is the number of coil turns, I is the excitation current, l is the length of the air gap, n is the harmonic order, and vx is the loading disc line speed;
滑模控制器的滑模变量s选取为:The sliding mode variable s of the sliding mode controller is selected as:
式(6)中,滑模变量s是通过滑模面设计得到,其中x为状态向量,C是矩阵[c1…cn-1 1]T,在滑模控制中,参数c1c2…cn-1应满足多项式pn-1+cn-1pn-2+…+c2p+c1为hurwitz,其中p为laplace算子,本式中n取2,x2为调节c的大小可以调节状态趋近于零的速度,c 越大表示调整误差的速度也就越快,电流信号误差e=Ir-Ie, In formula (6), the sliding mode variable s is designed by the sliding mode surface get, where x is the state vector, C is the matrix [c 1 ... c n-1 1] T , in sliding mode control, the parameters c 1 c 2 ... c n-1 should satisfy the polynomial p n-1 +c n- 1 p n-2 +…+c 2 p+c 1 is hurwitz, where p is the laplace operator, n is 2 in this formula, and x 2 is Adjusting the size of c can adjust the speed of the state approaching zero. The larger the c is, the faster the adjustment error will be. The current signal error e=I r -I e ,
滑模控制器采用趋近率方式设计,趋近率为:The sliding mode controller is designed using the approach rate method, and the approach rate is:
式(7)中,ks和γ均为正常数,sgn(s)为符号函数;In formula (7), k s and γ are both constants, and sgn(s) is a sign function;
计算得到滑模控制器的控制输出电流Ismc为:The control output current I smc of the sliding mode controller is calculated as:
Ismc=ce+ks+γsgn(s) (8)。 Ismc =ce+ks+γsgn( s ) (8).
实施例Example
本实施例按照水润滑轴承动态电磁加载力多参数优化控制方法,设定目标加载力,采集扭矩转速传感器、压阻式测力传感器、两对电涡流传感器和负载系统控制器读数并将信号传递给负载控制器,负载控制器读取传感器读数并分别作均值处理,得到压阻式测力传感器信号的加载力测量平均值电流传感器信号的励磁电流测量平均值扭矩转速传感器信号的转速测量平均值判断非接触式电磁加载力是否与设定值匹配,若匹配则各传感器继续重复上述过程;若不匹配则将均值处理后的传感信号作为查询条件对数据库进行预处理,预处理后得到数据块,负载控制器采用遗传算法优化的三维数据路径跟踪算法,读取数据块并计算得到参考电流Ir,将参考电流Ir输入至电流调节器中,采用滑模算法计算得到电流调节器的控制输出电流Ismc,输入负载装置驱动器中以控制水润滑轴承非接触式电磁加载装置的电磁加载力F,从而达到提高电磁加载力精度和不同工况下鲁棒性的目的。In this embodiment, according to the multi-parameter optimization control method of the dynamic electromagnetic loading force of the water-lubricated bearing, the target loading force is set, the readings of the torque and rotational speed sensor, the piezoresistive load cell, the two pairs of eddy current sensors and the load system controller are collected, and the signals are transmitted. To the load controller, the load controller reads the sensor readings and performs average processing respectively to obtain the average value of the loading force measurement of the piezoresistive load cell signal. Excitation current measurement average value of the current sensor signal Average value of the speed measurement of the torque speed sensor signal Determine whether the non-contact electromagnetic loading force matches the set value. If it matches, each sensor continues to repeat the above process; block, the load controller adopts the three-dimensional data path tracking algorithm optimized by genetic algorithm, reads the data block and calculates the reference current I r , inputs the reference current I r into the current regulator, and uses the sliding mode algorithm to calculate the value of the current regulator. The output current I smc is controlled and input into the driver of the load device to control the electromagnetic loading force F of the non-contact electromagnetic loading device of the water-lubricated bearing, so as to achieve the purpose of improving the accuracy of the electromagnetic loading force and the robustness under different working conditions.
如图10所示,为某一气隙下,由励磁电流、转速和电磁加载力构成的表空间数据所转换而来的三维图,三维图上所有点坐标形式为:(Ie,n,F),该三维图的形成包括调用数据库和全局路径规划层两步骤。该假设当前工况坐标为点A,目标工况坐标为点B,需要通过在该三维图上构建最优路径,以变化水平坐标的形式将当前工况坐标点移动至点B。首先,进入局部路径规划层,取出点A至点B的所属数据段,如图11所示。As shown in Figure 10, it is a three-dimensional map converted from table space data composed of excitation current, rotational speed and electromagnetic loading force under a certain air gap. The coordinates of all points on the three-dimensional map are in the form: (I e , n, F ), the formation of the three-dimensional map includes two steps of calling the database and the global path planning layer. It is assumed that the coordinate of the current working condition is point A, and the coordinate of the target working condition is point B. It is necessary to construct the optimal path on the three-dimensional map, and move the coordinate point of the current working condition to point B in the form of changing horizontal coordinates. First, enter the local route planning layer, and extract the data segment from point A to point B, as shown in Figure 11 .
图11同时给出了3条路径规划层可能选择的最优路径L1、Le、L3,其中,L1、L2为从三维图表面经过的高阶曲线路径;L3为从三维图内部经过的低阶曲线路径,且L3与L1都有转折点。路径规划层开始按照如下条件进行判断:Figure 11 also shows the optimal paths L 1 , Le , and L 3 that may be selected by the three path planning layers. Among them, L 1 and L 2 are high-order curve paths passing through the surface of the three-dimensional graph; L 3 is the path from the three-dimensional graph. The low - order curve path that the graph passes through, and both L3 and L1 have turning points. The path planning layer begins to judge according to the following conditions:
1)误差e是否在目标电磁力的30%以内;1) Whether the error e is within 30% of the target electromagnetic force;
2)三维数据图上是否存在不连续点;2) Whether there are discontinuous points on the 3D data map;
3)是否允许工况的微小改变;3) Whether minor changes in working conditions are allowed;
假设当前选择分别为:Suppose the current selections are:
1)误差e在目标电磁力的30%以内;1) The error e is within 30% of the target electromagnetic force;
2)三维数据图上不存在不连续点;2) There are no discontinuous points on the 3D data map;
3)允许工况的微小改变;3) Minor changes in working conditions are allowed;
此时,路径权重的选择为:At this time, the choice of path weight is:
1)低阶曲线路径权重1——ζ1 1) Low-order curve path weight 1 - ζ 1
2)高阶曲线路径权重2——η2;2) High-order
3)低阶曲线路径权重3——ζ3;3) Low-order
通过遗传算法迭代并分别求和得到低阶曲线路径总权重ζ和高阶曲线路径总权重η后,判断ζ和η的大小,此时η>ζ,且Δ>Δ*,选择L2作为最优曲线路径,存储于数据库中的指定数据块内,等待行为执行层的调用。After iterating through the genetic algorithm and summing up the total weight ζ of the low-order curve path and the total weight of the high-order curve path η, judge the size of ζ and η, at this time η>ζ, and Δ>Δ * , choose L 2 as the most The optimal curve path is stored in the specified data block in the database, waiting for the call of the behavior execution layer.
行为执行层调用最优曲线L2后,首先进行判断L2的阶数。显然,L2阶数大于2,因此选用点线式跟踪方式。从数据库中读取视距s、最大视距smax、步长λ、最大步长λmax和步长增益系数K,从起点A开始,以点A为圆心,最大视距smax为半径构成视距圆Os,寻找视距圆Os与最优曲线的L2交点,以该交点为中间目标点,通过视距s所对应的步长λ进行多步前进。After the behavior execution layer calls the optimal curve L 2 , it first determines the order of L 2 . Obviously, the L 2 order is greater than 2, so the point-line tracking method is selected. Read the sight distance s, the maximum sight distance s max , the step size λ, the maximum step size λ max and the step gain coefficient K from the database, starting from the starting point A, with the point A as the center, and the maximum sight distance s max as the radius. Look for the line of sight circle O s , find the intersection of the line of sight circle O s and the optimal curve L 2 , take the intersection as the intermediate target point, and proceed in multiple steps through the step size λ corresponding to the line of sight s.
由于视距圆内存在拐点,因此以拐点A1作为目标点进行前行,保证坐标点(IA,nA,FA)变换到过程中,同时只有励磁电流I或者转速n在变化,避免因为同时变化造成轴系的不稳定。如图9所示,从第k个前进点Ak到第k+1个前进点Ak+1时,路径上没有拐点或不连续点,因此|Ak,Ak+1|= smax,步长为最大步长λmax。而在前进点An至目标点B这段,由于|An,B|<smax,因此步长λ=K*λmax,在最优路径上选择前进点,得到每一步的励磁电流坐标,每一步计算得到各自的参考电流Irn,逐步达到最终的参考电流Ir。Since there is an inflection point in the line of sight circle, take the inflection point A 1 as the target point to move forward to ensure that the coordinate points (I A , n A , F A ) are transformed to During the process, only the excitation current I or the rotational speed n is changing at the same time, so as to avoid the instability of the shaft system caused by the simultaneous change. As shown in Figure 9, when going from the k-th advance point A k to the k+1-th advance point A k+1 , there are no inflection points or discontinuities on the path, so |A k ,A k+1 |= s max , the step size is the maximum step size λ max . From the advance point A n to the target point B, since |A n , B|<s max , the step size λ=K*λ max , select the advance point on the optimal path, and obtain the excitation current coordinates of each step , each step calculates to obtain the respective reference current I rn , and gradually reaches the final reference current I r .
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Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
DE202011051969U1 (en) * | 2011-06-15 | 2012-09-17 | Kendrion Linnig Gmbh Commercial Vehicle Systems | Eddy current clutch |
CN108051299A (en) * | 2017-12-06 | 2018-05-18 | 湖南湘建检测有限公司 | A kind of brittleness construction material test device and its test method |
CN112649192A (en) * | 2020-11-27 | 2021-04-13 | 陕西理工大学 | Dynamic electromagnetic force control system and control method of electromagnetic loading device |
-
2022
- 2022-04-02 CN CN202210345655.7A patent/CN114815602B/en active Active
Patent Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
DE202011051969U1 (en) * | 2011-06-15 | 2012-09-17 | Kendrion Linnig Gmbh Commercial Vehicle Systems | Eddy current clutch |
CN108051299A (en) * | 2017-12-06 | 2018-05-18 | 湖南湘建检测有限公司 | A kind of brittleness construction material test device and its test method |
CN112649192A (en) * | 2020-11-27 | 2021-04-13 | 陕西理工大学 | Dynamic electromagnetic force control system and control method of electromagnetic loading device |
Non-Patent Citations (4)
Title |
---|
I.V.GULYAEV: "A contorl system of an electromagnetic bearing", RUSSIAN ELECTRICAL ENGINEERING, 23 August 2017 (2017-08-23), pages 342 - 346 * |
NAN WANG: "Dynamic eletromagnetic force variation mechanism and energy loss of a non-contact loading device for a water-lubricated bearing", JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY, 22 May 2021 (2021-05-22), pages 2645 - 2656, XP037472965, DOI: 10.1007/s12206-021-0535-y * |
易可可: "直线电磁模拟加载系统的性能分析及控制研究", 中国优秀硕士学位论文全文数据库工程科技Ⅱ辑, 15 February 2018 (2018-02-15), pages 029 - 212 * |
王楠: "非接触式电磁加载水润滑轴承监测系统", 中国机械工程, 24 December 2019 (2019-12-24), pages 3004 - 3009 * |
Cited By (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN116126056A (en) * | 2023-04-04 | 2023-05-16 | 国网山东省电力公司潍坊供电公司 | Method, system, terminal and medium for generating dynamic control strategy of material processing temperature |
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