CN113359458B - 一种高速并联机器人的模糊前馈控制方法 - Google Patents

一种高速并联机器人的模糊前馈控制方法 Download PDF

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CN113359458B
CN113359458B CN202110691349.4A CN202110691349A CN113359458B CN 113359458 B CN113359458 B CN 113359458B CN 202110691349 A CN202110691349 A CN 202110691349A CN 113359458 B CN113359458 B CN 113359458B
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CN113359458A (zh
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刘祺
蔺景龙
马跃
李彬
张冕
刘振忠
宋晨阳
张智涛
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Tianjin University of Technology
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Abstract

本发明公开了一种高速并联机器人的模糊前馈控制方法，属于机器人运动控制领域，包括以下步骤，S1、确定输入及输出变量；S2、将各输入、各输出划分为3个区间，构建模糊规则；S3、采集S1中的三个输入变量并按照步骤S2定义的三个语言变量对其进行分类；然后，根据步骤S2定义的模糊规则进行推理，进而确定两个输出的语言变量；最后，将输出变量清晰化，得到速度与加速度前馈控制器参数的模糊调整量ΔKvi与ΔKai，则可根据如下算法计算速度与加速度前馈控制器参数。本发明可利用简单的模糊规则实现前馈控制器参数的自动快速精确调节，进一步调高各驱动关节的跟随精度。

Description

一种高速并联机器人的模糊前馈控制方法
技术领域
本发明属于机器人运动控制领域，涉及机器人技术和自动化领域，可有效提高这种机器人在高速运行时的运动控制精度，尤其涉及一种高速并联机器人的模糊前馈控制方法。
背景技术
对于现代工业普遍采用的PID控制器，其优点是结构简单，方便计算，但动态控制性能较差，自适应能力有限。考虑到高速并联机器人通常执行高速、高加速度运动轨迹，对于精度要求极高。然而对于时变性的被控对象，固定增益的控制器很难满足高速并联机器人高精度的需求。模糊PID作为一种新型的智能控制器，具有较好的鲁棒性，但无法调节前馈控制器参数。在机器人系统高速、高加速度运行时，因前馈控制器参数整定不准确导致的跟随误差，严重降低关节跟随精度。因此，亟需在原有速度、加速度前馈控制器的基础上实施一种模糊前馈控制算法，实现前馈控制器参数的自动快速精确调节，进一步提高驱动关节的跟随精度。
发明内容
本部分的目的是在于概述本发明的实施例的一些方面以及简要介绍一些较佳实施例。在本部分以及本申请的说明书摘要和发明名称中可能会做些简化或省略以避免使本部分、说明书摘要和发明名称的目的模糊，而这种简化或省略不能用于限制本发明的范围。
鉴于上述背景技术描述中存在的问题，提出了本发明，因此，本发明其中一个目的是提供一种高速并联机器人的模糊前馈控制方法，可根据设定的接触刚度与阻尼自适应调节位置环与速度环的补偿信号，进而改善抛光质量，提高抛光效率。
2、为解决上述技术问题，本发明采用的技术方案是：一种高速并联机器人的模糊前馈控制方法，包括以下步骤，
S1、确定输入及输出变量；
采用三输入双输出的模糊控制结构：输入变量为驱动关节跟随误差ei(i＝1,2,3,4)、关节速度
Figure BDA0003126281470000021
关节加速度
Figure BDA0003126281470000022
输出变量为速度前馈控制器参数调整量ΔKvi(i＝1,2,3,4)与加速度前馈控制器参数调整量ΔKai(i＝1,2,3,4)；
S2、构建模糊规则；
将各输入、各输出划分为3个区间，定义对应语言变量为NB-负大，ZO-零，PB-正大，则相应的模糊子集表示为{NB，ZO，PB}，定义模糊规则如下：
If e is PB and
Figure BDA0003126281470000023
is PB and
Figure BDA0003126281470000024
is PB，then ΔKv is PB and ΔKa is PB；
If e is PB and
Figure BDA0003126281470000025
is PB and
Figure BDA0003126281470000026
is NB，then ΔKv is PB and ΔKa is NB；
If e is PB and
Figure BDA0003126281470000027
is PB and
Figure BDA0003126281470000028
is ZO，then ΔKv is PB and ΔKa is ZO；
If e is PB and
Figure BDA0003126281470000029
is NB and
Figure BDA00031262814700000210
is PB，then ΔKv is NB and ΔKa is PB；
If e is PB and
Figure BDA00031262814700000211
is NB and
Figure BDA00031262814700000212
is NB，then ΔKv is NB and ΔKa is NB；
If e is PB and
Figure BDA00031262814700000213
is NB and
Figure BDA00031262814700000214
is ZO，then ΔKv is NB and ΔKa is ZO；
If e is PB and
Figure BDA00031262814700000215
is ZO and
Figure BDA00031262814700000216
is PB，then ΔKv is ZO and ΔKa is PB；
If e is PB and
Figure BDA00031262814700000217
is ZO and
Figure BDA00031262814700000218
is NB，then ΔKv is ZO and ΔKa is NB；
If e is PB and
Figure BDA00031262814700000219
is ZO and
Figure BDA00031262814700000220
is ZO，then ΔKv is ZO and ΔKa is ZO；
If e is NB and
Figure BDA00031262814700000221
is PB and
Figure BDA00031262814700000222
is PB，then ΔKv is NB and ΔKa is NB；
If e is NB and
Figure BDA00031262814700000223
is PB and
Figure BDA00031262814700000224
is NB，then ΔKv is NB and ΔKa is PB；
If e is NB and
Figure BDA00031262814700000225
is PB and
Figure BDA00031262814700000226
is ZO，then ΔKv is NB and ΔKa is ZO；
If e is NB and
Figure BDA00031262814700000227
is NB and
Figure BDA00031262814700000228
is PB，then ΔKv is PB and ΔKa is NB；
If e is NB and
Figure BDA00031262814700000229
is NB and
Figure BDA00031262814700000230
is NB，then ΔKv is PB and ΔKa is PB；
If e is NB and
Figure BDA00031262814700000231
is NB and
Figure BDA00031262814700000232
is ZO，then ΔKv is PB and ΔKa is ZO；
If e is NB and
Figure BDA00031262814700000233
is ZO and
Figure BDA00031262814700000234
is PB，then ΔKv is ZO and ΔKa is NB；
If e is NB and
Figure BDA00031262814700000235
is ZO and
Figure BDA00031262814700000236
is NB，then ΔKv is ZO and ΔKa is PB；
If e is NB and
Figure BDA0003126281470000031
is ZO and
Figure BDA0003126281470000032
is ZO，then ΔKv is ZO and ΔKa is ZO；
If e is ZO and
Figure BDA0003126281470000033
is PB and
Figure BDA0003126281470000034
is PB，then ΔKv is ZO and ΔKa is ZO；
If e is ZO and
Figure BDA0003126281470000035
is PB and
Figure BDA0003126281470000036
is NB，then ΔKv is ZO and ΔKa is ZO；
If e is ZO and
Figure BDA0003126281470000037
is PB and
Figure BDA0003126281470000038
is ZO，then ΔKv is ZO and ΔKa is ZO；
If e is ZO and
Figure BDA0003126281470000039
is NB and
Figure BDA00031262814700000310
is PB，then ΔKv is ZO and ΔKa is ZO；
If e is ZO and
Figure BDA00031262814700000311
is NB and
Figure BDA00031262814700000312
is NB，then ΔKv is ZO and ΔKa is ZO；
If e is ZO and
Figure BDA00031262814700000313
is NB and
Figure BDA00031262814700000314
is ZO，then ΔKv is ZO and ΔKa is ZO；
If e is ZO and
Figure BDA00031262814700000315
is ZO and
Figure BDA00031262814700000316
is PB，then ΔKv is ZO and ΔKa is ZO；
If e is ZO and
Figure BDA00031262814700000317
is ZO and
Figure BDA00031262814700000318
is NB，then ΔKv is ZO and ΔKa is ZO；
If e is ZO and
Figure BDA00031262814700000319
is ZO and
Figure BDA00031262814700000320
is ZO，then ΔKv is ZO and ΔKa is ZO；
S3、前馈控制器参数模糊调节；
采集S1中的三个输入变量并按照步骤S2定义的三个语言变量对其进行分类；然后，根据步骤S2定义的模糊规则进行推理，进而确定两个输出的语言变量；最后，将输出变量清晰化，得到速度与加速度前馈控制器参数的模糊调整量ΔKvi与ΔKai，则可根据如下算法计算速度与加速度前馈控制器参数，
Figure BDA00031262814700000321
Kai＝Ka0i+ΔKai
式中，
Figure BDA00031262814700000322
与
Figure BDA00031262814700000323
分别表示模糊调节前的速度与加速度前馈控制器参数初值。
进一步的，在步骤S3中，设定步骤S1中的三个输入变量的阈值分别为[e]、
Figure BDA00031262814700000324
Figure BDA00031262814700000325
通过采集驱动关节跟随误差ei、关节速度
Figure BDA00031262814700000326
关节加速度
Figure BDA00031262814700000327
与阈值进行分步比对，确定模糊调节算法执行流程；
首先，将驱动关节跟随误差e与对应阈值[e]比对；
进一步将关节速度
Figure BDA00031262814700000328
关节加速度
Figure BDA00031262814700000329
与对应阈值
Figure BDA00031262814700000330
比对。
进一步的，在步骤S3中，若满足ei≤[e]，则认为速度与加速度前馈控制器参数准确，无需调节；
若满足ei＞[e]，则认为速度与加速度前馈控制器参数不准确，需调用前馈控制器参数模糊调节算法进行调节。
进一步的，在步骤S3中，若满足
Figure BDA0003126281470000041
且
Figure BDA0003126281470000042
则认为机器人系统处于低速运行状态，跟随误差增大与前馈控制器参数无关，需调用模糊PID控制方法调节反馈控制器参数；
若满足
Figure BDA0003126281470000043
且
Figure BDA0003126281470000044
则认为机器人系统处于高速运行状态，跟随误差增大与前馈控制器参数有关。
进一步的，包括模糊控制器、前馈控制器和反馈控制器，模糊控制器用于确定输入及输出变量，作为反馈控制器的输入，θdi关节转过的角度作为前馈控制器和反馈控制器的输入，前馈控制器和反馈控制器的输出与被控伺服系统连接，被控伺服系统的输出θai作为反馈控制器的输入。
与现有技术相比，本发明具有的优点和积极效果如下。
1、本发明采用前馈校正控制策略，构造了一种输入为关节误差、关节速度与关节加速度，输出为速度前馈控制器参数调整量与加速度前馈控制器参数调整量的三输入双输出的前馈模糊算法，自动模糊调节速度与加速度前馈控制器参数，该算法被存储在一个独立的运算寄存器中，可利用简单的模糊规则实现前馈控制器参数的自动快速精确调节，进一步调高各驱动关节的跟随精度；
2、本发明可有效应对机器人高速运行时速度、加速度前馈控制器参数整定不准确对控制品质的影响，从而提高控制精度；核心在于通过根据关节运动状态自动模糊调节速度与加速度前馈控制器参数，本发明的优点在于可自动化调节前馈控制器参数，避免因前馈控制器参数整定不准确导致跟随误差过大的情况，算法简单，占用硬件资源少，易于实现；
3、本发明构造了一种三输入双输出的模糊前馈控制策略，可根据驱动关节的运动状态自动调节前馈控制器参数，提升控制精度。
附图说明
为了更清楚地说明本发明实施例的技术方案，下面将对实施例描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本发明的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动性的前提下，还可以根据这些附图获得其它的附图。
图1是本发明一种高速并联机器人的模糊前馈控制方法的控制框图；
图2是本发明一种高速并联机器人的模糊前馈控制方法的策略实施流程框图。
具体实施方式
为使本发明的上述目的、特征和优点能够更加明显易懂，下面结合附图对本发明的具体实施方式做详细的说明。
在下面的描述中阐述了很多具体细节以便于充分理解本发明，但是本发明还可以采用其他不同于在此描述的其它方式来实施，本领域技术人员可以在不违背本发明内涵的情况下做类似推广，因此本发明不受下面公开的具体实施例的限制。
其次，本发明结合示意图进行详细描述，在详述本发明实施例时，为便于说明，表示器件结构的剖面图会不依一般比例作局部放大，而且所述示意图只是示例，其在此不应限制本发明保护的范围。此外，在实际制作中应包含长度、宽度及深度的三维空间尺寸
再次，需要说明的是，在不冲突的情况下，本发明中的实施例及实施例中的特征可以相互组合。
在本发明的描述中，需要理解的是，术语“中心”、“纵向”、“横向”、“上”、“下”、“前”、“后”、“左”、“右”、“竖直”、“水平”、“顶”、“底”、“内”、“外”等指示的方位或位置关系为基于附图所示的方位或位置关系，仅是为了便于描述本发明和简化描述，而不是指示或暗示所指的装置或元件必须具有特定的方位、以特定的方位构造和操作，因此不能理解为对本发明的限制。此外，术语“第一”、“第二”等仅用于描述目的，而不能理解为指示或暗示相对重要性或者隐含指明所指示的技术特征的数量。由此，限定有“第一”、“第二”等的特征可以明示或者隐含地包括一个或者更多个该特征。在本发明的描述中，除非另有说明，“多个”的含义是两个或两个以上。
在本发明的描述中，需要说明的是，除非另有明确的规定和限定，术语“安装”、“相连”、“连接”应做广义理解，例如，可以是固定连接，也可以是可拆卸连接，或一体地连接；可以是机械连接，也可以是电连接；可以是直接相连，也可以通过中间媒介间接相连，可以是两个元件内部的连通。对于本领域的普通技术人员而言，可以通过具体情况理解上述术语在本发明中的具体含义。
为使本发明的目的、技术方案和优点更加清楚，下面结合附图对本发明的具体实施例做详细说明。
如图1和图2所示，一种高速并联机器人的模糊前馈控制方法，包括以下步骤：
1、确定输入及输出变量
本发明采用三输入双输出的模糊控制结构：输入变量为驱动关节跟随误差ei(i＝1,2,3,4)、关节速度
Figure BDA0003126281470000061
关节加速度
Figure BDA0003126281470000062
输出变量为速度前馈控制器参数调整量ΔKvi(i＝1,2,3,4)与加速度前馈控制器参数调整量ΔKai(i＝1,2,3,4)。
2、构建模糊规则
本发明采用3个语言变量，将各输入、各输出划分为3个区间，定义对应语言变量为NB(负大)，ZO(零)，PB(正大)，则相应的模糊子集表示为{NB，ZO，PB}，定义模糊规则如下：
If e is PB and
Figure BDA0003126281470000063
is PB and
Figure BDA0003126281470000064
is PB，then ΔKv is PB and ΔKa is PB；
If e is PB and
Figure BDA0003126281470000065
is PB and
Figure BDA0003126281470000066
is NB，then ΔKv is PB and ΔKa is NB；
If e is PB and
Figure BDA0003126281470000067
is PB and
Figure BDA0003126281470000068
is ZO，then ΔKv is PB and ΔKa is ZO；
If e is PB and
Figure BDA0003126281470000069
is NB and
Figure BDA00031262814700000610
is PB，then ΔKv is NB and ΔKa is PB；
If e is PB and
Figure BDA00031262814700000611
is NB and
Figure BDA00031262814700000612
is NB，then ΔKv is NB and ΔKa is NB；
If e is PB and
Figure BDA00031262814700000613
is NB and
Figure BDA00031262814700000614
is ZO，then ΔKv is NB and ΔKa is ZO；
If e is PB and
Figure BDA00031262814700000615
is ZO and
Figure BDA00031262814700000616
is PB，then ΔKv is ZO and ΔKa is PB；
If e is PB and
Figure BDA0003126281470000071
is ZO and
Figure BDA0003126281470000072
is NB，then ΔKv is ZO and ΔKa is NB；
If e is PB and
Figure BDA0003126281470000073
is ZO and
Figure BDA0003126281470000074
is ZO，then ΔKv is ZO and ΔKa is ZO；
If e is NB and
Figure BDA0003126281470000075
is PB and
Figure BDA0003126281470000076
is PB，then ΔKv is NB and ΔKa is NB；
If e is NB and
Figure BDA0003126281470000077
is PB and
Figure BDA0003126281470000078
is NB，then ΔKv is NB and ΔKa is PB；
If e is NB and
Figure BDA0003126281470000079
is PB and
Figure BDA00031262814700000710
is ZO，then ΔKv is NB and ΔKa is ZO；
If e is NB and
Figure BDA00031262814700000711
is NB and
Figure BDA00031262814700000712
is PB，then ΔKv is PB and ΔKa is NB；
If e is NB and
Figure BDA00031262814700000713
is NB and
Figure BDA00031262814700000714
is NB，then ΔKv is PB and ΔKa is PB；
If e is NB and
Figure BDA00031262814700000715
is NB and
Figure BDA00031262814700000716
is ZO，then ΔKv is PB and ΔKa is ZO；
If e is NB and
Figure BDA00031262814700000717
is ZO and
Figure BDA00031262814700000718
is PB，then ΔKv is ZO and ΔKa is NB；
If e is NB and
Figure BDA00031262814700000719
is ZO and
Figure BDA00031262814700000720
is NB，then ΔKv is ZO and ΔKa is PB；
If e is NB and
Figure BDA00031262814700000721
is ZO and
Figure BDA00031262814700000722
is ZO，then ΔKv is ZO and ΔKa is ZO；
If e is ZO and
Figure BDA00031262814700000723
is PB and
Figure BDA00031262814700000724
is PB，then ΔKv is ZO and ΔKa is ZO；
If e is ZO and
Figure BDA00031262814700000725
is PB and
Figure BDA00031262814700000726
is NB，then ΔKv is ZO and ΔKa is ZO；
If e is ZO and
Figure BDA00031262814700000727
is PB and
Figure BDA00031262814700000728
is ZO，then ΔKv is ZO and ΔKa is ZO；
If e is ZO and
Figure BDA00031262814700000729
is NB and
Figure BDA00031262814700000730
is PB，then ΔKv is ZO and ΔKa is ZO；
If e is ZO and
Figure BDA00031262814700000731
is NB and
Figure BDA00031262814700000732
is NB，then ΔKv is ZO and ΔKa is ZO；
If e is ZO and
Figure BDA00031262814700000733
is NB and
Figure BDA00031262814700000734
is ZO，then ΔKv is ZO and ΔKa is ZO；
If e is ZO and
Figure BDA00031262814700000735
is ZO and
Figure BDA00031262814700000736
is PB，then ΔKv is ZO and ΔKa is ZO；
If e is ZO and
Figure BDA00031262814700000737
is ZO and
Figure BDA00031262814700000738
is NB，then ΔKv is ZO and ΔKa is ZO；
If e is ZO and
Figure BDA00031262814700000739
is ZO and
Figure BDA00031262814700000740
is ZO，then ΔKv is ZO and ΔKa is ZO；
3、前馈控制器参数模糊调节
设定步骤1中的三个输入变量的阈值分别为[e]、
Figure BDA00031262814700000741
通过采集驱动关节跟随误差ei、关节速度
Figure BDA0003126281470000081
关节加速度
Figure BDA0003126281470000082
与阈值进行分步比对，可确定模糊调节算法执行流程如下；
首先，将驱动关节跟随误差e与对应阈值[e]比对：
若满足ei≤[e]，则认为速度与加速度前馈控制器参数准确，无需调节；
若满足ei＞[e]，则认为速度与加速度前馈控制器参数不准确，需调用前馈控制器参数模糊调节算法进行调节。此时，需进一步将关节速度
Figure BDA0003126281470000083
关节加速度
Figure BDA0003126281470000084
与对应阈值
Figure BDA0003126281470000085
比对：
若满足
Figure BDA0003126281470000086
且
Figure BDA0003126281470000087
则认为机器人系统处于低速运行状态，跟随误差增大与前馈控制器参数无关，需调用模糊PID控制方法调节反馈控制器参数。
若满足
Figure BDA0003126281470000088
且
Figure BDA0003126281470000089
则认为机器人系统处于高速运行状态，跟随误差增大与前馈控制器参数有关，需调用前馈控制器参数模糊调节方法：首先，采集步骤1中的三个输入变量并按照步骤2定义的三个语言变量对其进行分类；然后，根据步骤2定义的模糊规则进行推理，进而确定两个输出的语言变量；最后，将输出变量清晰化，得到速度与加速度前馈控制器参数的模糊调整量ΔKvi与ΔKai，则可根据如下算法计算速度与加速度前馈控制器参数
Figure BDA00031262814700000810
Kai＝Ka0i+ΔKai
式中，
Figure BDA00031262814700000811
与
Figure BDA00031262814700000812
分别表示模糊调节前的速度与加速度前馈控制器参数初值。
在图1中，θdi为关节转过的角度，是电机编码器反馈的关节位置；
Figure BDA00031262814700000813
为关节速度，是电机编码器反馈的关节位置的一次微分；
Figure BDA00031262814700000814
为关节加速度，是电机编码器反馈的关节位置的二次微分，θai为被控伺服系统输出的关节转过的角度。
应说明的是，以上实施例仅用以说明本发明的技术方案而非限制，尽管参照较佳实施例对本发明进行了详细说明，本领域的普通技术人员应当理解，可以对本发明的技术方案进行修改或者等同替换，而不脱离本发明技术方案的精神和范围，其均应涵盖在本发明的权利要求范围当中。

Claims (1)

1.一种高速并联机器人的模糊前馈控制方法，其特征在于：包括以下步骤，
S1、确定输入及输出变量；
采用三输入双输出的模糊控制结构：输入变量为驱动关节跟随误差ei(i＝1,2,3,4)、关节速度
Figure FDA0003914139280000011
关节加速度
Figure FDA0003914139280000012
输出变量为速度前馈控制器参数调整量ΔKvi(i＝1,2,3,4)与加速度前馈控制器参数调整量ΔKai(i＝1,2,3,4)；
S2、构建模糊规则；
将各输入、各输出划分为3个区间，定义对应语言变量为NB-负大，ZO-零，PB-正大，则相应的模糊子集表示为{NB，ZO，PB}，定义模糊规则如下：
If e is PB and
Figure FDA0003914139280000013
is PB and
Figure FDA0003914139280000014
is PB，then ΔKv is PB and ΔKa is PB；
If e is PB and
Figure FDA0003914139280000015
is PB and
Figure FDA0003914139280000016
is NB，then ΔKv is PB and ΔKa is NB；
If e is PB and
Figure FDA0003914139280000017
is PB and
Figure FDA0003914139280000018
is ZO，then ΔKv is PB and ΔKa is ZO；
If e is PB and
Figure FDA0003914139280000019
is NB and
Figure FDA00039141392800000110
is PB，then ΔKv is NB and ΔKa is PB；
If e is PB and
Figure FDA00039141392800000111
is NB and
Figure FDA00039141392800000112
is NB，then ΔKv is NB and ΔKa is NB；
If e is PB and
Figure FDA00039141392800000113
is NB and
Figure FDA00039141392800000114
is ZO，then ΔKv is NB and ΔKa is ZO；
If e is PB and
Figure FDA00039141392800000115
is ZO and
Figure FDA00039141392800000116
is PB，then ΔKv is ZO and ΔKa is PB；
If e is PB and
Figure FDA00039141392800000117
is ZO and
Figure FDA00039141392800000118
is NB，then ΔKv is ZO and ΔKa is NB；
If e is PB and
Figure FDA00039141392800000119
is ZO and
Figure FDA00039141392800000120
is ZO，then ΔKv is ZO and ΔKa is ZO；
If e is NB and
Figure FDA00039141392800000121
is PB and
Figure FDA00039141392800000122
is PB，then ΔKv is NB and ΔKa is NB；
If e is NB and
Figure FDA00039141392800000123
is PB and
Figure FDA00039141392800000124
is NB，then ΔKv is NB and ΔKa is PB；
If e is NB and
Figure FDA00039141392800000125
is PB and
Figure FDA00039141392800000126
is ZO，then ΔKv is NB and ΔKa is ZO；
If e is NB and
Figure FDA00039141392800000127
is NB and
Figure FDA00039141392800000128
is PB，then ΔKv is PB and ΔKa is NB；
If e is NB and
Figure FDA00039141392800000129
is NB and
Figure FDA00039141392800000130
is NB，then ΔKv is PB and ΔKa is PB；
If e is NB and
Figure FDA0003914139280000021
is NB and
Figure FDA0003914139280000022
is ZO，then ΔKv is PB and ΔKa is ZO；
If e is NB and
Figure FDA0003914139280000023
is ZO and
Figure FDA0003914139280000024
is PB，then ΔKv is ZO and ΔKa is NB；
If e is NB and
Figure FDA0003914139280000025
is ZO and
Figure FDA0003914139280000026
is NB，then ΔKv is ZO and ΔKa is PB；
If e is NB and
Figure FDA0003914139280000027
is ZO and
Figure FDA0003914139280000028
is ZO，then ΔKv is ZO and ΔKa is ZO；
If e is ZO and
Figure FDA0003914139280000029
is PB and
Figure FDA00039141392800000210
is PB，then ΔKv is ZO and ΔKa is ZO；
If e is ZO and
Figure FDA00039141392800000211
is PB and
Figure FDA00039141392800000212
is NB，then ΔKv is ZO and ΔKa is ZO；
If e is ZO and
Figure FDA00039141392800000213
is PB and
Figure FDA00039141392800000214
is ZO，then ΔKv is ZO and ΔKa is ZO；
If e is ZO and
Figure FDA00039141392800000215
is NB and
Figure FDA00039141392800000216
is PB，then ΔKv is ZO and ΔKa is ZO；
If e is ZO and
Figure FDA00039141392800000217
is NB and
Figure FDA00039141392800000218
is NB，then ΔKv is ZO and ΔKa is ZO；
If e is ZO and
Figure FDA00039141392800000219
is NB and
Figure FDA00039141392800000220
is ZO，then ΔKv is ZO and ΔKa is ZO；
If e is ZO and
Figure FDA00039141392800000221
is ZO and
Figure FDA00039141392800000222
is PB，then ΔKv is ZO and ΔKa is ZO；
If e is ZO and
Figure FDA00039141392800000223
is ZO and
Figure FDA00039141392800000224
is NB，then ΔKv is ZO and ΔKa is ZO；
If e is ZO and
Figure FDA00039141392800000225
is ZO and
Figure FDA00039141392800000226
is ZO，then ΔKv is ZO and ΔKa is ZO；
S3、前馈控制器参数模糊调节；
采集S1中的三个输入变量并按照步骤S2定义的三个语言变量对其进行分类；然后，根据步骤S2定义的模糊规则进行推理，进而确定两个输出的语言变量；最后，将输出变量清晰化，得到速度与加速度前馈控制器参数的模糊调整量ΔKvi与ΔKai，则可根据如下算法计算速度与加速度前馈控制器参数，
Figure FDA00039141392800000227
Kai＝Ka0i+ΔKai
式中，
Figure FDA00039141392800000228
与
Figure FDA00039141392800000229
分别表示模糊调节前的速度与加速度前馈控制器参数初值；
在步骤S3中，设定步骤S1中的三个输入变量的阈值分别为[e]、
Figure FDA00039141392800000230
通过采集驱动关节跟随误差ei、关节速度
Figure FDA00039141392800000231
关节加速度
Figure FDA00039141392800000232
与阈值进行分步比对，确定模糊调节算法执行流程；
首先，将驱动关节跟随误差e与对应阈值[e]比对；
进一步将关节速度
Figure FDA0003914139280000031
关节加速度
Figure FDA0003914139280000032
与对应阈值
Figure FDA0003914139280000033
比对；
包括模糊控制器、前馈控制器和反馈控制器，模糊控制器用于确定输入及输出变量，作为反馈控制器的输入，θdi关节转过的角度作为前馈控制器和反馈控制器的输入，前馈控制器和反馈控制器的输出与被控伺服系统连接，被控伺服系统的输出θai作为反馈控制器的输入；在步骤S3中，若满足ei≤[e]，则认为速度与加速度前馈控制器参数准确，无需调节；
若满足ei＞[e]，则认为速度与加速度前馈控制器参数不准确，需调用前馈控制器参数模糊调节算法进行调节；在步骤S3中，若满足
Figure FDA0003914139280000034
且
Figure FDA0003914139280000035
则认为机器人系统处于低速运行状态，跟随误差增大与前馈控制器参数无关，需调用模糊PID控制方法调节反馈控制器参数；
若满足
Figure FDA0003914139280000036
且
Figure FDA0003914139280000037
则认为机器人系统处于高速运行状态，跟随误差增大与前馈控制器参数有关。
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