CN110788859B - A Global Adaptive Adjustment System of Controller Parameters - Google Patents

A Global Adaptive Adjustment System of Controller Parameters Download PDF

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CN110788859B
CN110788859B CN201911022463.7A CN201911022463A CN110788859B CN 110788859 B CN110788859 B CN 110788859B CN 201911022463 A CN201911022463 A CN 201911022463A CN 110788859 B CN110788859 B CN 110788859B
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黄田
刘祺
刘海涛
肖聚亮
郭浩
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Tianjin University
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    • BPERFORMING OPERATIONS; TRANSPORTING
    • B25HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
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    • B25J9/00Programme-controlled manipulators
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    • BPERFORMING OPERATIONS; TRANSPORTING
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Abstract

The invention discloses a controller parameter universe self-adaptive adjusting system, which comprises the following steps of firstly, determining a clustering center of three driving joint load inertias of a parallel mechanism in a dynamic platform reference point working space universe and the membership degree of a sample inertia about each center by means of a fuzzy clustering algorithm; then, off-line setting parameters of feedback and feedforward controllers at corresponding characteristic points of each clustering center, and storing the parameters and membership data in a control system database; and finally, calculating and updating the controller parameters of the three driving joints in real time by using the data in the database and the current coordinate information of the reference point of the movable platform of the parallel mechanism by adopting a gravity center method.

Description

一种控制器参数全域自适应调节系统A Global Adaptive Adjustment System of Controller Parameters

技术领域technical field

本发明涉及一种五自由度混联机器人的控制器参数全域自适应调节方法,涉及机器人技术和自动化领域,可有效提高这种五自由度混联机器人末端执行器的运动控制精度。The invention relates to a global self-adaptive adjustment method for controller parameters of a five-degree-of-freedom hybrid robot, which relates to the field of robotics and automation, and can effectively improve the motion control accuracy of the end-effector of the five-degree-of-freedom hybrid robot.

背景技术Background technique

机器人控制系统普遍采用“反馈+前馈”的复合控制策略,反馈控制器确定系统带宽并保障稳定性;前馈控制器进一步提高系统的跟随精度。然而,对于含有并联机构的混联机器人系统,折算到并联机构驱动关节的负载惯量随机器人位形变化。采用固定增益的控制器难于满足此类机器人在工作空间中高速、高精度运动的要求,制约了其在运动精度要求较高(如机械加工)场合的应用。The robot control system generally adopts the composite control strategy of "feedback + feedforward". The feedback controller determines the system bandwidth and ensures the stability; the feedforward controller further improves the following accuracy of the system. However, for a hybrid robot system with a parallel mechanism, the load inertia converted to the drive joint of the parallel mechanism varies with the robot configuration. The controller with fixed gain is difficult to meet the requirements of high-speed and high-precision motion of such robots in the workspace, which restricts its application in occasions with high requirements for motion accuracy (such as machining).

针对此类强非线性时变系统,目前已提出诸如动力学控制算法、神经网络及遗传算法等先进控制算法,但由于算法复杂、计算量大且缺乏硬件支持,难以付诸实际应用。因此,亟需一种适用于此类机器人系统且算法简单易于实施的控制器参数自适应调节方法,使参数能够匹配负载惯量在工作空间全域的变化,实现在机器人不同位形下的在线调节。For such strongly nonlinear time-varying systems, advanced control algorithms such as dynamic control algorithms, neural networks and genetic algorithms have been proposed. However, due to the complexity of the algorithms, the large amount of computation and the lack of hardware support, it is difficult to put them into practical applications. Therefore, there is an urgent need for an adaptive adjustment method of controller parameters suitable for such robot systems with simple and easy-to-implement algorithms, so that the parameters can match the changes of the load inertia in the entire workspace, and realize online adjustment in different robot configurations.

发明内容SUMMARY OF THE INVENTION

针对专利CN104985596A所公开的一种含转动支架的五自由度混联机器人,本发明提出一种控制器参数全域自适应调节系统,可有效应对并联机构驱动关节运动时,负载惯量随位形变化这一因素对控制品质的影响。本发明的核心在于先借助模糊聚类算法确定并联机构三个驱动关节负载惯量在动平台参考点工作空间全域内的聚类中心及样本惯量关于各中心的隶属度,并离线整定各聚类中心对应特征点处的控制器参数,然后利用重心法在线估计动平台参考点位于工作空间中任意点时三个驱动关节的控制器参数。本发明的优点在于仅需离线整定有限若干特征点处的控制器参数,便可使参数匹配负载惯量变化实现全域自适应调节,且算法简单,占用硬件资源少,易于实现。Aiming at a five-degree-of-freedom hybrid robot with a rotating bracket disclosed in patent CN104985596A, the present invention proposes a global self-adaptive adjustment system for controller parameters, which can effectively deal with the load inertia that changes with the configuration when the parallel mechanism drives the joints to move. The influence of a factor on the quality of control. The core of the present invention is to first determine the cluster center of the load inertia of the three drive joints of the parallel mechanism in the whole domain of the reference point work space of the moving platform and the membership degree of the sample inertia about each center by means of a fuzzy clustering algorithm, and set each cluster center offline. Corresponding to the controller parameters at the feature points, and then using the center of gravity method to estimate the controller parameters of the three drive joints online when the reference point of the moving platform is located at any point in the workspace. The invention has the advantages that only the controller parameters at a limited number of characteristic points need to be adjusted offline, so that the parameters can be matched with the load inertia change to realize the global self-adaptive adjustment, and the algorithm is simple, occupies less hardware resources, and is easy to implement.

一种控制器参数全域自适应调节系统,该系统包括粗插补模块、位置逆解模块和控制算法模块,该系统还包括控制器参数估算模块,所述控制器参数估算模块执行如下步骤对参数匹配负载惯量变化调整,包括:A controller parameter global adaptive adjustment system, the system includes a rough interpolation module, a position inverse solution module and a control algorithm module, the system also includes a controller parameter estimation module, the controller parameter estimation module performs the following steps to adjust the parameters Adjustment to match load inertia changes, including:

将聚类分析得到的隶属度与离线整定得到的控制器参数作为全局变量写入相应的寄存器以备调用,并将在工作空间中点整定的控制器参数作为其初始值;The membership degree obtained by cluster analysis and the controller parameters obtained by offline tuning are written into the corresponding registers as global variables for calling, and the controller parameters tuned in the work space are used as their initial values;

粗插补模块根据运动规律对NC代码做粗插补,计算一个粗插补周期完成后的末端位姿;The rough interpolation module performs rough interpolation on the NC code according to the motion law, and calculates the end pose after a rough interpolation cycle is completed;

位置逆解模块计算与之对应的动平台参考点坐标和驱动关节指令,并将动平台参考点坐标作为全局变量写入临时堆栈寄存器;The position inverse solution module calculates the corresponding moving platform reference point coordinates and drive joint commands, and writes the moving platform reference point coordinates as global variables into the temporary stack register;

粗插补模块按照粗插补周期的整倍数,以中断方式调用控制器参数估算模块,从相应的寄存器中读取动平台参考点坐标,隶属度和n个特征点控制器参数Ki,k(k=1,2…,n),利用重心法估算与参考点坐标对应的第i个驱动关节的控制器参数

Figure BDA0002247663130000021
并作为全局变量写入相应的寄存器;The coarse interpolation module calls the controller parameter estimation module in an interrupt mode according to the integer multiple of the coarse interpolation period, and reads the moving platform reference point coordinate, membership degree and n characteristic point controller parameters K i,k from the corresponding register (k=1,2...,n), use the center of gravity method to estimate the controller parameters of the i-th drive joint corresponding to the reference point coordinates
Figure BDA0002247663130000021
And write to the corresponding register as a global variable;

调用控制算法模块,从相应的寄存器中读取

Figure BDA0002247663130000022
利用更新后的
Figure BDA0002247663130000023
计算控制器的输出指令,并在下一次估算前保持
Figure BDA0002247663130000024
不变。Call the control algorithm module and read from the corresponding register
Figure BDA0002247663130000022
Use the updated
Figure BDA0002247663130000023
Calculate the output command of the controller and hold it until the next evaluation
Figure BDA0002247663130000024
constant.

所述控制器参数

Figure BDA0002247663130000025
是动平台的参考点位于工作空间中任意点时的驱动关节的控制器参数;该关节在所有特征点处控制器参数的加权估计,权重由与当前参考点距离最为接近的样本点确定,定义该样本点序号为m(j=m);控制器参数估计算法为the controller parameters
Figure BDA0002247663130000025
is the controller parameter of the drive joint when the reference point of the moving platform is located at any point in the workspace; the weighted estimation of the controller parameters of the joint at all feature points, the weight is determined by the sample point with the closest distance to the current reference point, the definition The sequence number of the sample point is m (j=m); the controller parameter estimation algorithm is

Figure BDA0002247663130000026
Figure BDA0002247663130000026

所述聚类分析步骤包括:The cluster analysis step includes:

(1)网格划分并联机构动平台参考点的工作空间,定义网格节点为样本点

Figure BDA0002247663130000027
Figure BDA0002247663130000028
其中,根据机器人数学模型,由样本点计算各驱动关节负载惯量,定义第i个关节的第j个样本惯量为Ii,j;(1) Grid divide the working space of the reference point of the parallel mechanism moving platform, and define the grid nodes as sample points
Figure BDA0002247663130000027
Figure BDA0002247663130000028
Wherein, according to the robot mathematical model, the load inertia of each drive joint is calculated from the sample points, and the jth sample inertia of the ith joint is defined as I i,j ;

(2)依据模糊聚类分析算法,给定聚类类别数为n(n<<N),根据公式计算在第l次迭代时,第j个样本惯量关于第k(k=1,2,…,n)个聚类中心的隶属度μi,j,k,l以及新的聚类中心Ii,k,l+1(2) According to the fuzzy clustering analysis algorithm, given the number of cluster categories is n (n<<N), according to the formula to calculate in the lth iteration, the jth sample inertia is about the kth (k=1,2, ...,n) the membership degrees of the cluster centers μ i,j,k,l and the new cluster center I i,k,l+1 ;

Figure BDA0002247663130000029
Figure BDA0002247663130000029

式中,α表示大于1的模糊指数,通常取α=2;In the formula, α represents a fuzzy index greater than 1, usually α=2;

(3)根据上式不断更新μi,j,k,l与Ii,k,l+1,直至在第l次迭代时满足||Ii,k,l+1-Ii,k,l||≤ε。此处,ε表示聚类中心迭代精度。(3) Continuously update μ i,j,k,l and I i,k,l+1 according to the above formula until it satisfies ||I i,k,l+1 -I i,k, l ||≤ε. Here, ε represents the cluster center iteration accuracy.

有益效果beneficial effect

为了保证系统运行的稳定性,调用控制器参数估算模块的任务优先级低于调用伺服算法模块的优先级,并采用伺服任务中断方式来实现该模块的调用。控制器参数估算模块的调用周期可根据任务需求与硬件运算能力来确定,一般可设为粗插补周期的整数(nT)倍。据此,在每nT个粗插补周期执行一次控制器参数估算与更新,并在下一次估算前保持不变。In order to ensure the stability of the system operation, the task priority of calling the controller parameter estimation module is lower than that of calling the servo algorithm module, and the servo task interruption method is used to realize the calling of the module. The calling cycle of the controller parameter estimation module can be determined according to the task requirements and hardware computing capability, and can generally be set to an integer (n T ) multiple of the coarse interpolation cycle. Accordingly, the controller parameters are estimated and updated every n T coarse interpolation cycles and remain unchanged until the next estimation.

本发明提出的五自由度混联机器人控制器参数全域自适应调节方法,借助聚类分析算法确定特征点以及工作空间内任意点关于特征点的隶属度,并据此在线估计参考点位于任意点时的控制器参数。其优点在于,仅需离线整定有限若干特征点处的控制器参数,便可使参数匹配负载惯量变化实现全域自适应调节;算法简单,占用硬件资源少;与伺服算法相互独立,可在保证伺服控制稳定的前提下实时调节参数。The global self-adaptive adjustment method for the controller parameters of the five-degree-of-freedom hybrid robot proposed by the present invention uses the cluster analysis algorithm to determine the feature points and the degree of membership of any point in the workspace with respect to the feature points, and based on the online estimation reference point is located at any point controller parameters at the time. The advantage is that it only needs to tune the controller parameters at a limited number of characteristic points offline, so that the parameters can be matched with the load inertia change to achieve global adaptive adjustment; the algorithm is simple and occupies less hardware resources; independent from the servo algorithm, it can ensure the servo Real-time adjustment of parameters under the premise of stable control.

附图说明Description of drawings

图1混联机器人全域自适应控制策略框图Figure 1. Block diagram of the global adaptive control strategy of the hybrid robot

图2工作空间网格划分与特征点示意图Figure 2 Schematic diagram of workspace grid division and feature points

图3控制器参数自适应调节计算流程图Fig. 3 Calculation flow chart of adaptive adjustment of controller parameters

具体实施方式Detailed ways

为了使本发明的技术方案更加清晰,以下结合附图对本发明做进一步详细说明。应当理解,此处所述的具体实例仅用以解释本发明,但并不限定于本例。以专利CN104985596A所公开的一种含转动支架的五自由度混联机器人为例,对本发明的具体实施方式的说明如下。In order to make the technical solutions of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings. It should be understood that the specific examples described herein are only used to explain the present invention, but are not limited to this example. Taking a five-degree-of-freedom hybrid robot with a rotating bracket disclosed in patent CN104985596A as an example, the specific implementation of the present invention is described as follows.

本发明的一种控制器参数全域自适应调节系统(如图1所示),包括以下步骤:A controller parameter global adaptive adjustment system of the present invention (as shown in FIG. 1 ) includes the following steps:

1、模糊聚类分析1. Fuzzy cluster analysis

网格划分并联机构动平台参考点(后称为参考点)的工作空间,定义网格节点为样本点。根据机器人数学模型,由样本点计算各驱动关节负载惯量,定义为样本惯量。借助模糊聚类算法分析样本惯量,计算聚类中心以及各样本惯量关于聚类中心的隶属度。The grid is divided into the working space of the reference point (referred to as the reference point) of the parallel mechanism motion platform, and the grid nodes are defined as sample points. According to the mathematical model of the robot, the load inertia of each drive joint is calculated from the sample points, which is defined as the sample inertia. The sample inertia is analyzed by means of fuzzy clustering algorithm, and the cluster center and the membership degree of each sample inertia to the cluster center are calculated.

2、控制器参数离线整定2. Offline tuning of controller parameters

定义聚类中心惯量所对应的样本点为特征点,离线整定并记录参考点达到这些特征点时并联机构三个驱动关节的控制器参数。The sample points corresponding to the inertia of the cluster center are defined as feature points, and the controller parameters of the three drive joints of the parallel mechanism are set and recorded offline when the reference points reach these feature points.

3、定义全局变量3. Define global variables

为了提高控制器参数的更新速度,需要实时读取特征点的控制器参数、样本惯量关于聚类中心(样本点关于特征点)的隶属度和动平台参考点的当前坐标。为此,将上述参数定义为全局变量,以提高对它们的读写速度。In order to improve the update speed of the controller parameters, it is necessary to read the controller parameters of the feature points, the membership degree of the sample inertia about the cluster center (the sample points about the feature points) and the current coordinates of the reference point of the moving platform in real time. To do this, define the above parameters as global variables to increase the speed of reading and writing to them.

4、控制器参数在线估计与调整4. Online estimation and adjustment of controller parameters

在执行运动程序过程中,利用位置逆解模块计算得到参考点的当前坐标,并作为全局变量写入临时堆栈寄存器,由控制器参数估算模块读取当前参考点坐标、特征点处控制器参数,判断与当前参考点最为接近的样本点并读取其关于各特征点的隶属度,利用重心法估算与当前参考点坐标对应的驱动关节控制器参数,并用其更新控制算法模块中的控制器参数。In the process of executing the motion program, the current coordinate of the reference point is calculated by the position inverse solution module, and is written into the temporary stack register as a global variable, and the current reference point coordinate and the controller parameters at the feature point are read by the controller parameter estimation module. Determine the sample point closest to the current reference point and read its membership degree about each feature point, use the center of gravity method to estimate the drive joint controller parameters corresponding to the coordinates of the current reference point, and use it to update the controller parameters in the control algorithm module .

步骤一:模糊聚类分析Step 1: Fuzzy Cluster Analysis

网格划分并联机构动平台参考点(后称为参考点)的工作空间,定义网格节点为样本点。根据机器人数学模型,由样本点计算各驱动关节负载惯量,定义为样本惯量。借助模糊聚类算法分析样本惯量,计算聚类中心以及各样本惯量关于聚类中心的隶属度。The grid is divided into the working space of the reference point (referred to as the reference point) of the parallel mechanism motion platform, and the grid nodes are defined as sample points. According to the mathematical model of the robot, the load inertia of each drive joint is calculated from the sample points, which is defined as the sample inertia. The sample inertia is analyzed by means of fuzzy clustering algorithm, and the cluster center and the membership degree of each sample inertia to the cluster center are calculated.

(1)如图2所示,网格划分并联机构动平台参考点(后称为参考点)的工作空间,定义网格节点为样本点

Figure BDA0002247663130000041
根据机器人数学模型,由样本点计算各驱动关节负载惯量,定义第i个关节的第j个样本惯量为Ii,j。(1) As shown in Figure 2, the grid is divided into the working space of the reference point (hereinafter referred to as the reference point) of the parallel mechanism motion platform, and the grid node is defined as the sample point
Figure BDA0002247663130000041
According to the robot mathematical model, the load inertia of each drive joint is calculated from the sample points, and the j-th sample inertia of the i-th joint is defined as I i,j .

(2)依据模糊聚类分析算法,给定聚类类别数为n(n<<N),根据公式计算在第l次迭代时,第j个样本惯量关于第k(k=1,2,…,n)个聚类中心的隶属度μi,j,k,l以及新的聚类中心Ii,k,l+1(2) According to the fuzzy clustering analysis algorithm, given the number of cluster categories is n (n<<N), according to the formula to calculate in the lth iteration, the jth sample inertia is about the kth (k=1,2, ...,n) membership degrees of cluster centers μ i,j,k,l and new cluster centers I i,k,l+1 :

Figure BDA0002247663130000042
Figure BDA0002247663130000042

式中,α表示大于1的模糊指数,通常取α=2。In the formula, α represents a blur index greater than 1, and α=2 is usually taken.

(3)根据上式不断更新μi,j,k,l与Ii,k,l+1,直至在第l次迭代时满足||Ii,k,l+1-Ii,k,l||≤ε。此处,ε表示聚类中心迭代精度。(3) Continuously update μ i,j,k,l and I i,k,l+1 according to the above formula until it satisfies ||I i,k,l+1 -I i,k, l ||≤ε. Here, ε represents the cluster center iteration accuracy.

步骤二:控制器参数离线整定Step 2: Offline tuning of controller parameters

定义与聚类中心惯量Ii,k,l所对应的样本点为特征点

Figure BDA0002247663130000051
离线整定并记录参考点达到这些特征点时并联机构三个驱动关节的控制器参数Define the sample points corresponding to the cluster center inertia I i, k, l as feature points
Figure BDA0002247663130000051
Off-line tuning and recording of the controller parameters of the three drive joints of the parallel mechanism when the reference point reaches these characteristic points

Ki,k={Ki,k,1 Ki,k,2 Ki,k,3 Ki,k,4 Ki,k,5}Ki ,k = {K i,k,1 Ki ,k,2 Ki ,k,3 Ki ,k,4 Ki ,k,5 }

={Ki,k,P Ki,k,I Ki,k,D Ki,k,vff Ki,k,aff}={K i,k,P K i,k,I K i,k,D K i,k,vff K i,k,aff }

式中,Ki,k表示在第i(i=1,2,3)个驱动关节在第k(k=1,2,…,n)个特征点处的控制参数序列。Ki,k,P,Ki,k,I,Ki,k,D,Ki,k,vff,Ki,k,aff分别表示控制器参数序列中的比例、积分、微分、速度前馈和加速度前馈控制器的增益。In the formula, K i,k represents the control parameter sequence of the i-th (i=1, 2, 3) drive joint at the k-th (k=1 , 2, ..., n) feature point. K i,k,P ,K i,k,I ,K i,k,D ,K i,k,vff ,K i,k,aff represent the proportional, integral, differential, speed front Gains of feedforward and acceleration feedforward controllers.

根据机构和工作空间的对称性,第2与第3驱动关节的特征点关于中平面对称,整定二者其中一个关节的特征点参数便可得到另一关节的参数。According to the symmetry of the mechanism and the workspace, the feature points of the second and third drive joints are symmetrical about the midplane, and the parameters of the other joint can be obtained by adjusting the feature point parameters of one of the two joints.

步骤三:定义全局变量Step 3: Define global variables

为了提高控制器参数的更新速度,需要实时读取特征点的控制器参数、样本惯量关于聚类中心(样本点关于特征点)的隶属度和动平台参考点的当前坐标。为此,将上述参数定义为全局变量,以提高对它们的读写速度。In order to improve the update speed of the controller parameters, it is necessary to read the controller parameters of the feature points, the membership degree of the sample inertia about the cluster center (the sample points about the feature points) and the current coordinates of the reference point of the moving platform in real time. To do this, define the above parameters as global variables to increase the speed of reading and writing to them.

步骤四:控制器参数在线估计与调整Step 4: Online estimation and adjustment of controller parameters

本发明在执行运动程序过程中,利用位置逆解模块计算得到参考点的当前坐标,并作为全局变量写入临时堆栈寄存器,由控制器参数估算模块读取当前参考点坐标、特征点处控制器参数,判断与当前参考点最为接近的样本点并读取其关于各特征点的隶属度,利用重心法估算与当前参考点坐标对应的驱动关节控制器参数,并用其更新控制算法模块中的控制器参数。In the process of executing the motion program, the present invention uses the position inverse solution module to calculate and obtain the current coordinates of the reference point, and writes it into the temporary stack register as a global variable, and the controller parameter estimation module reads the coordinates of the current reference point and the controller at the feature point. parameters, determine the sample point closest to the current reference point and read its membership degree with respect to each feature point, use the center of gravity method to estimate the parameters of the drive joint controller corresponding to the coordinates of the current reference point, and use it to update the control algorithm in the control algorithm module. device parameters.

所述的重心法假设参考点位于工作空间中任意点时的控制器参数是所有特征点控制器参数的加权估计,权重(即隶属度)由与当前参考点距离最为接近的样本点确定,定义该样本点序号为m(j=m)。据此,控制器参数估计算法可表示为The described centroid method assumes that the controller parameters when the reference point is located at any point in the workspace is a weighted estimate of the controller parameters of all feature points, and the weight (ie, the degree of membership) is determined by the sample point with the closest distance to the current reference point, which is defined as The sample point serial number is m (j=m). Accordingly, the controller parameter estimation algorithm can be expressed as

Figure BDA0002247663130000052
Figure BDA0002247663130000052

控制器参数全域自适应调节计算流程见图3。由图可见,在实施过程中,需要将聚类分析得到的隶属度与离线整定得到的控制器参数作为全局变量写入相应的寄存器以备调用,并将在工作空间中点整定的控制器参数作为其初始值。在数控程序执行过程中,首先根据运动规律对NC代码做粗插补,计算一个粗插补周期完成后的末端位姿,然后利用位置逆解模块计算与之对应的动平台参考点坐标和驱动关节指令,并将动平台参考点坐标作为全局变量写入临时堆栈寄存器。按照粗插补周期的整倍数,以中断方式调用控制器参数估算模块,从相应的寄存器中读取动平台参考点坐标,隶属度和特征点控制器参数,利用重心法估算与参考点坐标对应的第i个驱动关节的控制器参数

Figure BDA0002247663130000061
并作为全局变量写入相应的寄存器。调用控制算法模块,从相应的寄存器中读取
Figure BDA0002247663130000062
利用更新后的
Figure BDA0002247663130000063
计算控制器的输出指令,并在下一次估算前保持
Figure BDA0002247663130000064
不变。The calculation flow of the global adaptive adjustment of the controller parameters is shown in Figure 3. It can be seen from the figure that in the implementation process, the membership degree obtained by cluster analysis and the controller parameters obtained by offline tuning need to be written into the corresponding registers as global variables for calling, and the controller parameters tuned in the workspace will be set. as its initial value. During the execution of the NC program, the NC code is firstly subjected to rough interpolation according to the motion law to calculate the end pose after a rough interpolation cycle is completed. joint instruction, and write the coordinate of the moving platform reference point into the temporary stack register as a global variable. According to the integer multiple of the rough interpolation period, the controller parameter estimation module is called in an interrupt mode, and the reference point coordinates of the moving platform, the degree of membership and the controller parameters of the feature point are read from the corresponding registers, and the center of gravity method is used to estimate the coordinates corresponding to the reference point. The controller parameters of the ith driven joint of
Figure BDA0002247663130000061
And write to the corresponding register as a global variable. Call the control algorithm module and read from the corresponding register
Figure BDA0002247663130000062
Use the updated
Figure BDA0002247663130000063
Calculate the output command of the controller and hold it until the next evaluation
Figure BDA0002247663130000064
constant.

Claims (3)

1.一种控制器参数全域自适应调节系统,该系统包括粗插补模块、位置逆解模块和控制算法模块,其特征在于,该系统还包括控制器参数估算模块,所述控制器参数估算模块执行如下步骤对参数匹配负载惯量变化调整,包括:1. a controller parameter global adaptive adjustment system, the system comprises a rough interpolation module, a position inverse solution module and a control algorithm module, it is characterized in that, this system also comprises a controller parameter estimation module, described controller parameter estimation The module performs the following steps to adjust the parameter matching load inertia change, including: 将聚类分析得到的隶属度与离线整定得到的特征点控制器参数作为全局变量写入相应的寄存器以备调用,并将在工作空间中点整定的控制器参数作为其初始值;Write the membership degree obtained by cluster analysis and the characteristic point controller parameters obtained by offline tuning as global variables into the corresponding registers for calling, and use the controller parameters tuned in the workspace as its initial values; 粗插补模块根据运动规律对NC代码做粗插补,计算一个粗插补周期完成后的末端位姿;The rough interpolation module performs rough interpolation on the NC code according to the motion law, and calculates the end pose after a rough interpolation cycle is completed; 位置逆解模块计算与之对应的动平台参考点坐标和驱动关节指令,并将动平台参考点坐标作为全局变量写入临时堆栈寄存器;The position inverse solution module calculates the corresponding moving platform reference point coordinates and drive joint commands, and writes the moving platform reference point coordinates as global variables into the temporary stack register; 粗插补模块按照粗插补周期的整倍数,以中断方式调用控制器参数估算模块,从相应的寄存器中读取动平台参考点坐标,隶属度和特征点控制器参数,利用重心法估算与参考点坐标对应的第i个驱动关节的控制器参数
Figure FDA0003669746440000011
并作为全局变量写入相应的寄存器;
The coarse interpolation module calls the controller parameter estimation module in an interrupt mode according to the integer multiple of the coarse interpolation period, reads the coordinates of the reference point of the moving platform, the degree of membership and the controller parameters of the feature point from the corresponding registers, and uses the center of gravity method to estimate and The controller parameters of the i-th drive joint corresponding to the coordinates of the reference point
Figure FDA0003669746440000011
And write to the corresponding register as a global variable;
调用控制算法模块,从相应的寄存器中读取
Figure FDA0003669746440000012
利用更新后的
Figure FDA0003669746440000013
计算控制器的输出指令,并在下一次估算前保持
Figure FDA0003669746440000014
不变。
Call the control algorithm module and read from the corresponding register
Figure FDA0003669746440000012
Use the updated
Figure FDA0003669746440000013
Calculate the output command of the controller and hold it until the next evaluation
Figure FDA0003669746440000014
constant.
2.根据权利要求1所述的一种控制器参数全域自适应调节系统,其特征在于,所述控制器参数
Figure FDA0003669746440000015
是动平台的参考点位于工作空间中任意点时的驱动关节的控制器参数;该关节在所有特征点处控制器参数的加权估计,权重由与当前参考点距离最为接近的样本点确定,定义该样本点序号为m(j=m);控制器参数估计算法为
2 . The global adaptive adjustment system for controller parameters according to claim 1 , wherein the controller parameters
Figure FDA0003669746440000015
is the controller parameter of the drive joint when the reference point of the moving platform is located at any point in the workspace; the weighted estimation of the controller parameters of the joint at all feature points, the weight is determined by the sample point with the closest distance to the current reference point, and is defined by The sample point serial number is m (j=m); the controller parameter estimation algorithm is
Figure FDA0003669746440000016
Figure FDA0003669746440000016
3.根据权利要求1所述的一种控制器参数全域自适应调节系统,其特征在于,所述全局变量写入相应的寄存器以备调用步骤包括:3. a kind of controller parameter global self-adaptive adjustment system according to claim 1, is characterized in that, described global variable is written into corresponding register to prepare for invocation step comprises: (1)网格划分并联机构动平台参考点的工作空间,定义网格节点为样本点
Figure FDA0003669746440000017
Figure FDA0003669746440000018
其中,根据机器人数学模型,由样本点计算各驱动关节负载惯量,定义第i个关节的第j个样本惯量为Ii,j
(1) Grid divide the working space of the reference point of the parallel mechanism moving platform, and define the grid nodes as sample points
Figure FDA0003669746440000017
Figure FDA0003669746440000018
Wherein, according to the robot mathematical model, the load inertia of each drive joint is calculated from the sample points, and the jth sample inertia of the ith joint is defined as I i,j ;
(2)依据模糊聚类分析算法,给定聚类类别数为n(n<<N),根据公式计算在第l次迭代时,第j个样本惯量关于第k(k=1,2,…,n)个聚类中心的隶属度μi,j,k,l以及新的聚类中心Ii,k,l+1(2) According to the fuzzy clustering analysis algorithm, given the number of cluster categories is n (n<<N), according to the formula to calculate in the lth iteration, the jth sample inertia is about the kth (k=1,2, ...,n) the membership degrees of the cluster centers μ i,j,k,l and the new cluster center I i,k,l+1 ;
Figure FDA0003669746440000021
Figure FDA0003669746440000021
式中,α表示大于1的模糊指数;In the formula, α represents a fuzzy index greater than 1; (3)根据上式不断更新μi,j,k,l与Ii,k,l+1,直至在第l次迭代时满足||Ii,k,l+1-Ii,k,l||≤ε;此处,ε表示聚类中心迭代精度。(3) Continuously update μ i,j,k,l and I i,k,l+1 according to the above formula until it satisfies ||I i,k,l+1 -I i,k, l ||≤ε; here, ε represents the cluster center iteration accuracy.
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