WO2024036651A1 - Friction force compensation method and compensation apparatus for linear guide rail displacement system - Google Patents

Friction force compensation method and compensation apparatus for linear guide rail displacement system Download PDF

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WO2024036651A1
WO2024036651A1 PCT/CN2022/114148 CN2022114148W WO2024036651A1 WO 2024036651 A1 WO2024036651 A1 WO 2024036651A1 CN 2022114148 W CN2022114148 W CN 2022114148W WO 2024036651 A1 WO2024036651 A1 WO 2024036651A1
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friction
friction force
model
speed
feedforward
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PCT/CN2022/114148
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韩昱
范玉娇
张翔宇
朱庆娜
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北京华卓精科科技股份有限公司
北京优微精密测控技术研究有限公司
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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance

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  • the invention belongs to the technical field of precision displacement motion. Specifically, it relates to a friction compensation method and a compensation device for a linear guide displacement system.
  • Linear guide rails have the characteristics of small motion damping, large allowable load, high positioning accuracy, high stiffness in all directions, and good high-speed performance. They are widely used in linear guide rail displacement systems. In order to compensate for the guide rail damping, when identifying the friction parameters, the system response data under the frequency sweep or PRBS signal is usually used. The linear damping of the guide rail can be calculated from this data. In high-speed and light-load linear motion systems, guide rail damping usually does not affect the control accuracy of the system.
  • the Stribeck model is usually used to describe the friction.
  • the Stribeck model curve is shown in Figure 1.
  • the friction force is relatively large; when the running speed gradually increases to the critical friction speed, the friction force decreases rapidly as the speed increases; as the speed exceeds the critical friction speed, the friction force increases again in direct proportion to the speed. .
  • high modeling accuracy is required.
  • linear damping compensation algorithms are usually used to improve control accuracy.
  • the model parameters are more complex, making it difficult to design a suitable nonlinear compensation model. There is currently no better method. to compensate for friction.
  • This application uses the particle swarm algorithm to identify the nonlinear friction model (friction-velocity model) parameters of the heavy-load linear guide displacement system, balances the global retrieval and local optimization performance of the particle swarm algorithm through the compression factor, and uses the nonlinear model to design compensation Algorithms improve the control capabilities of servo systems.
  • the technical solutions adopted in this application are as follows:
  • a friction compensation method for a linear guide displacement system including the following steps:
  • F(v) is the kinetic friction between the moving platform and the guide rail
  • v is the running speed of the moving platform
  • F c is the Coulomb friction force
  • F s is the maximum static friction force
  • b is the viscous damping coefficient
  • v s is the Stribeck critical speed
  • ⁇ is the model empirical coefficient
  • Sgn() returns an integer variable, giving the sign of the parameter v;
  • the input of the servo controller is the design displacement of the moving platform, and the output is the actual displacement of the moving platform.
  • the servo controller includes a position controller, a current controller, a motor thrust constant, and a linear motor dynamics model connected in sequence. , the output of the linear motor dynamics model is fed back to the input end of the position controller, the friction feedforward compensator is connected in parallel with the position controller, and the output of the current controller is also connected to the input end of the current controller, which is connected with the position controller, The output of the friction feedforward compensator is commonly input to the current controller.
  • the value of ⁇ is in the range of 0.5 to 2.
  • solving the model parameters of the friction force-velocity model through particle swarm optimization includes:
  • v i+1,j ⁇ (v i,j +c 1 ⁇ rand(0,1)(p i,j -x i,j )+c 2 ⁇ rand(0,1)(p i,g -x i,j ))
  • c 1 and c 2 are learning factors
  • ⁇ is the compression factor
  • rand(0,1) means taking a random number between 0 and 1;
  • v i,j and x i,j are the speed and position of the j-th particle in the i-th iteration
  • p i,j is the optimal solution for particle j during the iteration to i times;
  • p i,g is the optimal solution among all particles in the particle swarm during the iteration to i times;
  • the fitness e norm(F( vi )-F f,vi ), where F( vi ) represents the dynamic friction solved by the friction-speed model when the moving platform is at different speeds vi.
  • Force, F f,vi represents the driving force measured at different speeds v i of the moving platform.
  • the particle swarm algorithm is one of a compression factor particle swarm algorithm, an adaptive compression factor particle swarm algorithm, a time-varying compression factor particle swarm algorithm, and a variable weight particle swarm algorithm.
  • the invention also provides a friction compensation device for a linear guide rail displacement system, which includes:
  • the friction model building module is used to establish the friction-velocity model. Its expression is as follows:
  • F(v) is the kinetic friction between the moving platform and the guide rail
  • v is the running speed of the moving platform
  • F c is the Coulomb friction force
  • F s is the maximum static friction force
  • b is the viscous damping coefficient
  • v s is the Stribeck critical speed
  • ⁇ is the model empirical coefficient
  • Sgn() returns an integer variable, giving the sign of the parameter v;
  • the dynamic friction force acquisition module is used to collect the speed and driving force of the uniform motion section of the moving platform, and use the driving force as the dynamic friction force of the uniform motion section;
  • a model parameter solving module used to solve the model parameters of the friction-velocity model through particle swarm algorithm to obtain a complete friction-velocity model
  • the compensation module is used to write the complete friction force-speed model into the feedforward compensator C f so that the dynamic friction force and the corresponding friction force feedforward current signal are calculated based on the command speed according to the complete friction force-speed model, and the friction force feedforward current signal is calculated according to the command speed.
  • the force feedforward current signal is provided to the servo controller current loop as feedforward compensation data for friction compensation.
  • the present invention utilizes the global search capability and local optimization capability of the compression factor balanced particle swarm algorithm to better identify the nonlinear friction model of the linear guide rail displacement system.
  • the present invention can provide a current feedforward compensation signal for the servo system through the nonlinear friction model. , improve the control performance of the linear guide displacement system, and reduce the impact of nonlinear friction in the linear guide displacement system on tracking errors and system stability.
  • Figure 1 is a schematic diagram of the Stribeck friction model
  • Figure 2 is a friction force-speed model curve diagram of multiple identifications according to the embodiment of the present invention.
  • Figure 3 is a control signal flow chart of the servo controller according to the embodiment of the present invention.
  • the friction compensation method of the linear guide displacement system in this embodiment is used to establish a friction-velocity model for the linear guide displacement system, identify the model parameters through the compression factor particle swarm algorithm, and write the model into the feedforward compensator.
  • the feedforward compensator is used to compensate the linear guide rail displacement system.
  • the linear guide rail displacement system includes linear grating, linear motor, linear guide rail, servo controller and moving platform.
  • the linear motor is used to drive the moving platform to move on the linear guide rail.
  • the servo controller is used to control the linear motor.
  • the linear grating is used to measure the movement of the moving platform. Displacement.
  • the friction compensation method of linear guide displacement system includes the following steps:
  • Step S1 establish the friction force-velocity model, its expression is as follows:
  • the friction force has a nonlinear relationship with the running speed, where F(v) is the kinetic friction force, v is the running speed, F c is the Coulomb friction force, F s is the maximum static friction force, b is the viscous damping coefficient, v s is the Stribeck critical speed, and ⁇ is the model empirical coefficient, which can range from 0.5 to 2.
  • Sgn() returns an integer variable indicating the sign of parameter v. If v is greater than 0, Sgn returns 1, if v is equal to 0, returns 0, and if v is less than 0, returns -1.
  • Step S2 use the servo controller to move the moving platform to the stroke limit, configure the data recording function in the servo controller, record the linear motor drive current and the moving platform movement speed, set the moving platform to reciprocate at different speeds, and collect the running speed. and driving force (or driving current), and filter out the speed and driving force (or driving current) of the uniform motion segment.
  • the dynamic friction force on the moving platform can be obtained by experimentally collecting the thrust data (or thrust current) of the moving platform under different constant speed motion states. Then use the optimization algorithm to identify the parameters F c , F s , b and v s in the formula, and finally obtain the complete friction-speed model.
  • Step S3 Solve the model parameters through the compression factor particle swarm algorithm to obtain a complete model.
  • the model curve is shown in Figure 2, where the abscissa is the speed of the moving platform and the ordinate is the friction force.
  • the viscous damping coefficient is often very small (parameter b in Figure 2). From Figure 2, the damping is approximately constant in the high-speed section.
  • Particle swarm optimization does not require derivation of the objective function and has global search and local optimization capabilities. It is a commonly used heuristic optimization algorithm.
  • the individual learning factor c 1 and the global learning factor c 2 in the particle swarm algorithm affect the balance between local optimization and global retrieval of the particle swarm. When the settings are unreasonable, it will cause the particle flight speed to be too low and fall into the local optimum or fall into the local optimum. The flight speed is too high and exceeds the optimal solution.
  • the compression factor ⁇ related to the learning factor the particle flight speed is effectively controlled, the global search and local search capabilities of the particle swarm algorithm are balanced, and the convergence of the algorithm is ensured.
  • Particle swarm optimization includes the following steps:
  • Step S31 initialize the position and speed of the particles
  • Each individual in the particle swarm contains all the variables to be determined.
  • the number of particle swarm individuals can be set from 50 to 100, so that the optimal solution with ideal accuracy can be obtained in a short time; the particle swarm algorithm can define the solution range of each variable.
  • the corresponding reasonable value range can be set by querying the theoretical value table of each variable; based on the solution range, the initial position and initial velocity of each individual in the particle swarm can be randomly generated.
  • Step S32 evaluate the fitness of each particle.
  • F( vi ) represents the dynamic friction force solved based on the friction force-speed model when the moving platform is at different speeds v i
  • F f, vi represents the driving force measured at different speeds v i of the moving platform (or according to the driving force
  • the equivalent driving force calculated by the current) outputs the norm.
  • the current position and fitness of each particle are stored in the pbest of each particle, and the positions of the optimal particles in all pbest are summed. Fitness is stored in gbetst;
  • Step S33 update the speed and position of the particles according to the following formula:
  • v i+1,j ⁇ (v i,j +c 1 ⁇ rand(0,1)(p i,j -x i,j )+c 2 ⁇ rand(0,1)(p i,g -x i,j ))
  • c 1 and c 2 are learning factors
  • ⁇ is the compression factor
  • rand(0,1) means taking a random number between 0 and 1;
  • v i,j and x i,j are the speed and position of the j-th particle in the i-th iteration
  • p i,j is the optimal solution for particle j during the iteration to i times;
  • p i,g is the optimal solution among all particles in the particle swarm during the iteration to i times, and g is the abbreviation of global;
  • Step S34 Determine whether the number of iterations has been reached. If the number of iterations has been reached, go to step S35; if the number of iterations has not been reached, add 1 to the number of iterations and go to step S33;
  • Step S35 Output gbest, that is, obtain the parameters F c , F s , b and v s .
  • Step S4 write the complete friction force-speed model into the feedforward compensator C f , calculate the friction force and the corresponding friction force feedforward current signal i f based on the command speed according to the complete friction force-speed model, and convert the friction force into the feedforward compensator C f
  • the feedforward current signal i f is provided to the servo controller current loop as feedforward compensation data for friction compensation.
  • the input of the servo controller is the design displacement of the moving platform, and the output is the actual displacement of the moving platform.
  • the servo controller includes a position controller, a current controller, a motor thrust constant, and a linear motor dynamics model that are connected in sequence.
  • the output of the linear motor dynamics model is fed back to the input end of the position controller, and the friction feedforward compensator
  • the output of the current controller is also connected to the input of the current controller, and is input to the current controller together with the outputs of the position controller and the friction feedforward compensator.
  • Input the design displacement r of the moving platform determine the design speed v based on the design displacement, and calculate the required friction feedforward compensation current i f through the friction feedforward compensator C f .
  • the position controller C P calculates the current required to control the position deviation based on the design displacement r and the actual position r*, and combines it with the feedforward compensation current i f to generate the required design drive current i.
  • the current controller outputs the actual drive current i based on i. *, and is converted into the actual position r* of the linear motor load through the motor thrust constant K f and the linear motor transmission mechanism (ie, the linear motor dynamics model P).
  • control signal flow chart of the servo controller is shown in Figure 3, and the meaning of the symbols is shown in Table 1.

Abstract

A friction force compensation method and compensation apparatus for a linear guide rail displacement system. The method comprises: establishing a friction force-speed model, collecting moving speeds and driving force of a movable platform, and screening the speed and the driving force of a uniform-motion section therefrom, the driving force being used as the friction force of the uniform-motion section; by means of a compression factor particle swarm algorithm, solving model parameters of the friction force-speed model to obtain a complete friction force-speed model; writing into a feedforward compensator the complete friction force-speed model, and according to the friction force-speed model, calculating a dynamic friction force and a corresponding friction force feedforward current signal on the basis of an instruction speed; and providing to a servo controller the friction force feedforward current signal as feedforward compensation data, and performing friction force compensation. The method can improve the control performance of heavy-load precision displacement devices, and reduce influences caused by nonlinear friction forces on tracking errors and system stability in linear guide rail servo systems.

Description

一种直线导轨位移系统的摩擦力补偿方法及补偿装置Friction compensation method and compensation device for linear guide rail displacement system 技术领域Technical field
本发明属于精密位移运动技术领域，具体的，涉及一种直线导轨位移系统的摩擦力补偿方法及补偿装置。The invention belongs to the technical field of precision displacement motion. Specifically, it relates to a friction compensation method and a compensation device for a linear guide displacement system.
背景技术Background technique
直线导轨具有运动阻尼小，允许载荷大，定位精度高，各方向刚度高，高速性能好等特点，在直线导轨位移系统中应用极其广泛。为补偿导轨阻尼，在进行摩擦力参数辨识时，通常利用在扫频或PRBS信号下的系统响应数据，通过该数据可以计算出导轨的线性阻尼。在高速轻载的直线运动系统中，导轨阻尼通常不会对系统的控制精度产生影响。Linear guide rails have the characteristics of small motion damping, large allowable load, high positioning accuracy, high stiffness in all directions, and good high-speed performance. They are widely used in linear guide rail displacement systems. In order to compensate for the guide rail damping, when identifying the friction parameters, the system response data under the frequency sweep or PRBS signal is usually used. The linear damping of the guide rail can be calculated from this data. In high-speed and light-load linear motion systems, guide rail damping usually does not affect the control accuracy of the system.
而在低速重载情况下，直线导轨的摩擦力表现为非线性，通常采用Stribeck模型对摩擦力进行描述，Stribeck模型曲线如图1所示。导轨滑台启动时，所受摩擦力较大；当运行速度逐渐增大到临界摩擦速度时，摩擦力随速度增加快速降低；随着速度超过临界摩擦速度后，摩擦力再度与速度成正比增长。为了对这种非线性阻尼进行补偿，需要较高的建模精度。对于当前主流的PID控制系统来说，通常会采用线性阻尼补偿算法提高控制精度，而对于非线性阻尼来说，其模型参数较为复杂，难以设计合适的非线性补偿模型，目前没有较好的办法来补偿摩擦力。Under low speed and heavy load conditions, the friction of linear guides is nonlinear. The Stribeck model is usually used to describe the friction. The Stribeck model curve is shown in Figure 1. When the guide rail slide is started, the friction force is relatively large; when the running speed gradually increases to the critical friction speed, the friction force decreases rapidly as the speed increases; as the speed exceeds the critical friction speed, the friction force increases again in direct proportion to the speed. . To compensate for this nonlinear damping, high modeling accuracy is required. For current mainstream PID control systems, linear damping compensation algorithms are usually used to improve control accuracy. However, for nonlinear damping, the model parameters are more complex, making it difficult to design a suitable nonlinear compensation model. There is currently no better method. to compensate for friction.
发明内容Contents of the invention
本申请利用粒子群算法辨识重载直线导轨位移系统的非线性摩擦模型(摩擦力-速度模型)参数，通过压缩因子平衡粒子群算法的全局检索及局部寻优性能，并利用非线性模型设计补偿算法改善伺服系统的控制能力。本申请所采用的技术方案如下：This application uses the particle swarm algorithm to identify the nonlinear friction model (friction-velocity model) parameters of the heavy-load linear guide displacement system, balances the global retrieval and local optimization performance of the particle swarm algorithm through the compression factor, and uses the nonlinear model to design compensation Algorithms improve the control capabilities of servo systems. The technical solutions adopted in this application are as follows:
一种直线导轨位移系统的摩擦力补偿方法，包括以下步骤：A friction compensation method for a linear guide displacement system, including the following steps:
建立摩擦力-速度模型，其表达式如下：Establish a friction-velocity model with the following expression:
Figure PCTCN2022114148-appb-000001
Figure PCTCN2022114148-appb-000001
式中F(v)是动平台与导轨的动摩擦力，v是动平台运行速度，F c为库伦摩擦力，F s为最大静摩擦力，b是粘滞阻尼系数，v s是Stribeck临界速度，δ是模型经验系数，Sgn()是返回一个整型变量，给出参数v的正负号； In the formula, F(v) is the kinetic friction between the moving platform and the guide rail, v is the running speed of the moving platform, F c is the Coulomb friction force, F s is the maximum static friction force, b is the viscous damping coefficient, v s is the Stribeck critical speed, δ is the model empirical coefficient, and Sgn() returns an integer variable, giving the sign of the parameter v;
采集动平台匀速运动段的速度及驱动力，并将所述驱动力作为匀速段的动摩擦力；Collect the speed and driving force of the uniform motion section of the moving platform, and use the driving force as the dynamic friction force of the uniform section;
通过粒子群算法对所述摩擦力-速度模型求解模型参数，获得完整摩擦力-速度模型；Use the particle swarm algorithm to solve the model parameters of the friction-velocity model to obtain a complete friction-velocity model;
将完整摩擦力-速度模型写入前馈补偿器C f，基于指令速度根据所述完整摩擦力-速度模型计算动摩擦力及对应的摩擦力前馈电流信号，将该摩擦力前馈电流信号作为前馈补偿数据提供给伺服控制器电流环，进行摩擦力补偿。 Write the complete friction force-speed model into the feedforward compensator C f , calculate the dynamic friction force and the corresponding friction force feedforward current signal based on the command speed according to the complete friction force-speed model, and use the friction force feedforward current signal as The feedforward compensation data is provided to the servo controller current loop for friction compensation.
可选地，所述伺服控制器的输入是动平台设计位移，输出是动平台实际位移，所述伺服控制器包括依次连接的位置控制器、电流控制器、电机推力常数、直线电机动力学模型，直线电机动力学模型的输出再反馈至位置控制器的输入端，摩擦力前馈补偿器与位置控制器并联，电流控制器的输出还连接至电流控制器的输入端，与位置控制器、摩擦力前馈补偿器的输出共同输 入到电流控制器。Optionally, the input of the servo controller is the design displacement of the moving platform, and the output is the actual displacement of the moving platform. The servo controller includes a position controller, a current controller, a motor thrust constant, and a linear motor dynamics model connected in sequence. , the output of the linear motor dynamics model is fed back to the input end of the position controller, the friction feedforward compensator is connected in parallel with the position controller, and the output of the current controller is also connected to the input end of the current controller, which is connected with the position controller, The output of the friction feedforward compensator is commonly input to the current controller.
可选地，δ的取值在0.5～2范围内。Optionally, the value of δ is in the range of 0.5 to 2.
可选地，所述通过粒子群算法对所述摩擦力-速度模型求解模型参数，包括：Optionally, solving the model parameters of the friction force-velocity model through particle swarm optimization includes:
初始化粒子的位置和速度；Initialize the position and velocity of particles;
评价每个粒子的适应度，将当前各粒子的位置和适应度存储在各粒子的pbest中，将所有的pbest中最优粒子的位置和适应度存储在gbetst中；Evaluate the fitness of each particle, store the current position and fitness of each particle in the pbest of each particle, and store the position and fitness of the optimal particle in all pbest in gbetst;
根据下式更新粒子的速度和位置：Update the particle's velocity and position according to:
C＝c 1+c 2 C＝c 1 +c 2
Figure PCTCN2022114148-appb-000002
Figure PCTCN2022114148-appb-000002
v i+1,j＝λ·(v i,j+c 1·rand(0,1)(p i,j-x i,j)+c 2·rand(0,1)(p i,g-x i,j)) v i+1,j =λ·(v i,j +c 1 ·rand(0,1)(p i,j -x i,j )+c 2 ·rand(0,1)(p i,g -x i,j ))
x i+1,j＝x i,j+v i+1,j x i+1,j =x i,j +v i+1,j
其中，c 1、c 2为学习因子； Among them, c 1 and c 2 are learning factors;
λ为压缩因子；λ is the compression factor;
rand(0,1)表示取0～1之间随机数；rand(0,1) means taking a random number between 0 and 1;
v i,j、x i,j为第i次迭代中第j个粒子的速度和位置； v i,j and x i,j are the speed and position of the j-th particle in the i-th iteration;
p i,j是迭代至i次期间粒子j的最优解； p i,j is the optimal solution for particle j during the iteration to i times;
p i,g是迭代至i次期间粒子群中所有粒子中的最优解； p i,g is the optimal solution among all particles in the particle swarm during the iteration to i times;
判断是否达到了迭代次数，若达到迭代次数则输出gbest；若没有达到迭代次数，则迭代次数加1，重新更新粒子的速度和位置。Determine whether the number of iterations has been reached. If the number of iterations has been reached, gbest will be output; if the number of iterations has not been reached, the number of iterations will be increased by 1 and the speed and position of the particles will be updated again.
可选地，所述适应度e＝norm(F(v i)-F f,vi)，式中F(v i)表示动平台不同速度v i时采用所述摩擦力-速度模型求解的动摩擦力，F f,vi表示动平台不同速 度v i下测得的驱动力。 Optionally, the fitness e=norm(F( vi )-F f,vi ), where F( vi ) represents the dynamic friction solved by the friction-speed model when the moving platform is at different speeds vi. Force, F f,vi represents the driving force measured at different speeds v i of the moving platform.
可选地，所述粒子群算法是压缩因子粒子群算法、自适应压缩因子粒子群算法、时变压缩因子粒子群算法以及变权重粒子群算法中的一种。Optionally, the particle swarm algorithm is one of a compression factor particle swarm algorithm, an adaptive compression factor particle swarm algorithm, a time-varying compression factor particle swarm algorithm, and a variable weight particle swarm algorithm.
本发明还提供一种直线导轨位移系统的摩擦力补偿装置，包括：The invention also provides a friction compensation device for a linear guide rail displacement system, which includes:
摩擦力模型构建模块，用于建立摩擦力-速度模型，其表达式如下：The friction model building module is used to establish the friction-velocity model. Its expression is as follows:
Figure PCTCN2022114148-appb-000003
Figure PCTCN2022114148-appb-000003
式中F(v)是动平台与导轨的动摩擦力，v是动平台运行速度，F c为库伦摩擦力，F s为最大静摩擦力，b是粘滞阻尼系数，v s是Stribeck临界速度，δ是模型经验系数，Sgn()是返回一个整型变量，给出参数v的正负号； In the formula, F(v) is the kinetic friction between the moving platform and the guide rail, v is the running speed of the moving platform, F c is the Coulomb friction force, F s is the maximum static friction force, b is the viscous damping coefficient, v s is the Stribeck critical speed, δ is the model empirical coefficient, and Sgn() returns an integer variable, giving the sign of the parameter v;
动摩擦力采集模块，用于采集动平台匀速运动段的速度及驱动力，并将所述驱动力作为匀速段的动摩擦力；The dynamic friction force acquisition module is used to collect the speed and driving force of the uniform motion section of the moving platform, and use the driving force as the dynamic friction force of the uniform motion section;
模型参数求解模块，用于通过粒子群算法对所述摩擦力-速度模型求解模型参数，获得完整摩擦力-速度模型；A model parameter solving module, used to solve the model parameters of the friction-velocity model through particle swarm algorithm to obtain a complete friction-velocity model;
补偿模块，用于将完整摩擦力-速度模型写入前馈补偿器C f，使得基于指令速度根据所述完整摩擦力-速度模型计算动摩擦力及对应的摩擦力前馈电流信号，将该摩擦力前馈电流信号作为前馈补偿数据提供给伺服控制器电流环，进行摩擦力补偿。 The compensation module is used to write the complete friction force-speed model into the feedforward compensator C f so that the dynamic friction force and the corresponding friction force feedforward current signal are calculated based on the command speed according to the complete friction force-speed model, and the friction force feedforward current signal is calculated according to the command speed. The force feedforward current signal is provided to the servo controller current loop as feedforward compensation data for friction compensation.
本发明利用压缩因子平衡粒子群算法的全局搜索能力及局部寻优能力可以更好的辨识直线导轨位移系统的非线性摩擦模型，本发明可以通过非线性摩擦模型为伺服系统提供电流前馈补偿信号，提高直线导轨位移系统的控制性能，减少直线导轨位移系统中非线性摩擦力对跟踪误差及系统稳定性带来的影响。The present invention utilizes the global search capability and local optimization capability of the compression factor balanced particle swarm algorithm to better identify the nonlinear friction model of the linear guide rail displacement system. The present invention can provide a current feedforward compensation signal for the servo system through the nonlinear friction model. , improve the control performance of the linear guide displacement system, and reduce the impact of nonlinear friction in the linear guide displacement system on tracking errors and system stability.
附图说明Description of drawings
图1为Stribeck摩擦模型示意图；Figure 1 is a schematic diagram of the Stribeck friction model;
图2为本发明实施例的多次辨识的摩擦力-速度模型曲线图；Figure 2 is a friction force-speed model curve diagram of multiple identifications according to the embodiment of the present invention;
图3为本发明实施例的伺服控制器的控制信号流程图。Figure 3 is a control signal flow chart of the servo controller according to the embodiment of the present invention.
具体实施方式Detailed ways
下面将结合附图对本发明的技术方案进行清楚、完整地描述，显然，所描述的实施例是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.
本实施例的直线导轨位移系统的摩擦力补偿方法，用于对直线导轨位移系统建立摩擦力-速度模型，并通过压缩因子粒子群算法辨识出模型参数，并将模型写入前馈补偿器，利用前馈补偿器对直线导轨位移系统进行补偿控制。The friction compensation method of the linear guide displacement system in this embodiment is used to establish a friction-velocity model for the linear guide displacement system, identify the model parameters through the compression factor particle swarm algorithm, and write the model into the feedforward compensator. The feedforward compensator is used to compensate the linear guide rail displacement system.
直线导轨位移系统包括直线光栅、直线电机、直线导轨、伺服控制器以及动平台，直线电机用于驱动动平台在直线导轨上移动，伺服控制器用于控制直线电机，直线光栅用于测量动平台的位移。The linear guide rail displacement system includes linear grating, linear motor, linear guide rail, servo controller and moving platform. The linear motor is used to drive the moving platform to move on the linear guide rail. The servo controller is used to control the linear motor. The linear grating is used to measure the movement of the moving platform. Displacement.
直线导轨位移系统的摩擦力补偿方法，包括以下步骤：The friction compensation method of linear guide displacement system includes the following steps:
步骤S1，建立摩擦力-速度模型，其表达式如下：Step S1, establish the friction force-velocity model, its expression is as follows:
Figure PCTCN2022114148-appb-000004
Figure PCTCN2022114148-appb-000004
根据模型可知摩擦力与运行速度呈非线性关系，式中F(v)为动摩擦力，v是运行速度，F c为库伦摩擦力，F s为最大静摩擦力，b是粘滞阻尼系数，v s是 Stribeck临界速度，δ是模型经验系数，可以取值0.5～2。Sgn()返回一个整型变量，指出参数v的正负号，如果v大于0，则Sgn返回1，如果v等于0，返回0，如果v小于0，则返回-1。 According to the model, it can be seen that the friction force has a nonlinear relationship with the running speed, where F(v) is the kinetic friction force, v is the running speed, F c is the Coulomb friction force, F s is the maximum static friction force, b is the viscous damping coefficient, v s is the Stribeck critical speed, and δ is the model empirical coefficient, which can range from 0.5 to 2. Sgn() returns an integer variable indicating the sign of parameter v. If v is greater than 0, Sgn returns 1, if v is equal to 0, returns 0, and if v is less than 0, returns -1.
步骤S2，通过伺服控制器将动平台移动至行程限位处，配置伺服控制器中的数据记录功能，记录直线电机驱动电流及动平台运动速度，设置动平台以不同速度往复运行，采集运行速度及驱动力(或驱动电流)，并从中筛选出匀速运动段的速度及驱动力(或驱动电流)。Step S2, use the servo controller to move the moving platform to the stroke limit, configure the data recording function in the servo controller, record the linear motor drive current and the moving platform movement speed, set the moving platform to reciprocate at different speeds, and collect the running speed. and driving force (or driving current), and filter out the speed and driving force (or driving current) of the uniform motion segment.
由于动平台匀速运动时推力与摩擦力大小相同方向相反，通过实验采集动平台不同匀速运动状态下的推力数据(或推力电流)，即可得到动平台所受的动摩擦力。再利用优化算法对公式中参数F c，F s,b以及v s进行辨识，最终即可获得完整摩擦力-速度模型。 Since the thrust and friction force have the same magnitude and opposite directions when the moving platform is moving at a constant speed, the dynamic friction force on the moving platform can be obtained by experimentally collecting the thrust data (or thrust current) of the moving platform under different constant speed motion states. Then use the optimization algorithm to identify the parameters F c , F s , b and v s in the formula, and finally obtain the complete friction-speed model.
步骤S3，通过压缩因子粒子群算法求解模型参数，获得完整模型，其模型曲线见图2，其中横坐标是动平台的速度，纵坐标是摩擦力。对于大多数工业环境使用的直线导轨来说，粘滞阻尼系数往往很小(图2中参数b)，从图2来看在高速段阻尼近似为常数。Step S3: Solve the model parameters through the compression factor particle swarm algorithm to obtain a complete model. The model curve is shown in Figure 2, where the abscissa is the speed of the moving platform and the ordinate is the friction force. For most linear guides used in industrial environments, the viscous damping coefficient is often very small (parameter b in Figure 2). From Figure 2, the damping is approximately constant in the high-speed section.
粒子群算法无需对目标函数求导，且具备全局检索及局部寻优能力，是一种较常用的启发式优化算法。粒子群算法中的个体学习因子c 1以及全局学习因子c 2影响了粒子群在局部寻优及全局检索之间的平衡，当设置不合理时，会导致粒子飞行速度过低陷入局部最优或者飞行速度过大而越过最优解。通过加入与学习因子相关的压缩因子λ，有效控制了粒子飞行速度，平衡了粒子群算法的全局检索及局部寻能力，保证了算法收敛性。 Particle swarm optimization does not require derivation of the objective function and has global search and local optimization capabilities. It is a commonly used heuristic optimization algorithm. The individual learning factor c 1 and the global learning factor c 2 in the particle swarm algorithm affect the balance between local optimization and global retrieval of the particle swarm. When the settings are unreasonable, it will cause the particle flight speed to be too low and fall into the local optimum or fall into the local optimum. The flight speed is too high and exceeds the optimal solution. By adding the compression factor λ related to the learning factor, the particle flight speed is effectively controlled, the global search and local search capabilities of the particle swarm algorithm are balanced, and the convergence of the algorithm is ensured.
粒子群算法包括以下步骤：Particle swarm optimization includes the following steps:
步骤S31，初始化粒子的位置和速度；Step S31, initialize the position and speed of the particles;
粒子群中每个个体均包含了所有待求变量，粒子群个体数量可以设置为50至100，即可在较短时间内得到精度理想的最优解；粒子群算法可以定义各个变量的求解范围，可通过查询各个变量的理论数值表格设置对应的合理取值范围；基于求解范围可随机生成粒子群中各个个体的初始位置及初始速度。Each individual in the particle swarm contains all the variables to be determined. The number of particle swarm individuals can be set from 50 to 100, so that the optimal solution with ideal accuracy can be obtained in a short time; the particle swarm algorithm can define the solution range of each variable. , the corresponding reasonable value range can be set by querying the theoretical value table of each variable; based on the solution range, the initial position and initial velocity of each individual in the particle swarm can be randomly generated.
步骤S32，评价每个粒子的适应度，本文所涉及的适应度e＝norm(F(v i)-F f,vi)， Step S32, evaluate the fitness of each particle. The fitness involved in this article is e=norm(F( vi )-F f,vi ),
式中F(v i)表示动平台不同速度v i时基于所述摩擦力-速度模型求解的动摩擦力，F f,vi表示动平台不同速度v i下测得的驱动力(或根据驱动力电流计算出的等效驱动力)，norm函数输出的是范数。适应度e越小则说明该粒子所含的各待求量更靠近最优解，将当前各粒子的位置和适应度存储在各粒子的pbest中，将所有的pbest中最优粒子的位置和适应度存储在gbetst中； In the formula, F( vi ) represents the dynamic friction force solved based on the friction force-speed model when the moving platform is at different speeds v i , and F f, vi represents the driving force measured at different speeds v i of the moving platform (or according to the driving force The equivalent driving force calculated by the current), the norm function outputs the norm. The smaller the fitness e is, it means that the quantities to be sought in the particle are closer to the optimal solution. The current position and fitness of each particle are stored in the pbest of each particle, and the positions of the optimal particles in all pbest are summed. Fitness is stored in gbetst;
步骤S33，根据下式更新粒子的速度和位置：Step S33, update the speed and position of the particles according to the following formula:
C＝c 1+c 2 C＝c 1 +c 2
Figure PCTCN2022114148-appb-000005
Figure PCTCN2022114148-appb-000005
v i+1,j＝λ·(v i,j+c 1·rand(0,1)(p i,j-x i,j)+c 2·rand(0,1)(p i,g-x i,j)) v i+1,j =λ·(v i,j +c 1 ·rand(0,1)(p i,j -x i,j )+c 2 ·rand(0,1)(p i,g -x i,j ))
x i+1,j＝x i,j+v i+1,j x i+1,j =x i,j +v i+1,j
其中，c 1、c 2为学习因子； Among them, c 1 and c 2 are learning factors;
λ为压缩因子；λ is the compression factor;
rand(0,1)表示取0～1之间随机数；rand(0,1) means taking a random number between 0 and 1;
v i,j、x i,j为第i次迭代中第j个粒子的速度和位置； v i,j and x i,j are the speed and position of the j-th particle in the i-th iteration;
p i,j是迭代至i次期间粒子j的最优解； p i,j is the optimal solution for particle j during the iteration to i times;
p i,g是迭代至i次期间粒子群中所有粒子中的最优解，g为全局global的缩写； p i,g is the optimal solution among all particles in the particle swarm during the iteration to i times, and g is the abbreviation of global;
步骤S34：判断是否达到了迭代次数，若达到迭代次数转到步骤S35；若没有达到迭代次数，迭代次数加1，转到步骤S33；Step S34: Determine whether the number of iterations has been reached. If the number of iterations has been reached, go to step S35; if the number of iterations has not been reached, add 1 to the number of iterations and go to step S33;
步骤S35：输出gbest，即获得了参数F c，F s,b以及v s。 Step S35: Output gbest, that is, obtain the parameters F c , F s , b and v s .
步骤S4，将完整摩擦力-速度模型写入前馈补偿器C f，基于指令速度根据所述完整摩擦力-速度模型计算摩擦力及对应的摩擦力前馈电流信号i f，将该摩擦力前馈电流信号i f作为前馈补偿数据提供给伺服控制器电流环，进行摩擦力补偿。 Step S4, write the complete friction force-speed model into the feedforward compensator C f , calculate the friction force and the corresponding friction force feedforward current signal i f based on the command speed according to the complete friction force-speed model, and convert the friction force into the feedforward compensator C f The feedforward current signal i f is provided to the servo controller current loop as feedforward compensation data for friction compensation.
伺服控制器的输入是动平台的设计位移，输出是动平台的实际位移。所述伺服控制器包括依次连接的位置控制器、电流控制器、电机推力常数、直线电机动力学模型，直线电机动力学模型的输出再反馈至位置控制器的输入端，摩擦力前馈补偿器与位置控制器并联，电流控制器的输出还连接至电流控制器的输入，与位置控制器、摩擦力前馈补偿器的输出共同输入到电流控制器。The input of the servo controller is the design displacement of the moving platform, and the output is the actual displacement of the moving platform. The servo controller includes a position controller, a current controller, a motor thrust constant, and a linear motor dynamics model that are connected in sequence. The output of the linear motor dynamics model is fed back to the input end of the position controller, and the friction feedforward compensator In parallel with the position controller, the output of the current controller is also connected to the input of the current controller, and is input to the current controller together with the outputs of the position controller and the friction feedforward compensator.
输入动平台的设计位移r，根据设计位移确定设计速度v，并通过摩擦力前馈补偿器C f计算所需的摩擦力前馈补偿电流i f。位置控制器C P根据设计位移r和实际位置r*计算控制位置偏差所需的电流，并结合前馈补偿电流i f生成所需的设计驱动电流i，电流控制器根据i输出实际驱动电流i*，并通过电机推力常数K f及直线电机传动机构(即直线电机动力学模型P)转变为直线电机负载的实际位置r*。 Input the design displacement r of the moving platform, determine the design speed v based on the design displacement, and calculate the required friction feedforward compensation current i f through the friction feedforward compensator C f . The position controller C P calculates the current required to control the position deviation based on the design displacement r and the actual position r*, and combines it with the feedforward compensation current i f to generate the required design drive current i. The current controller outputs the actual drive current i based on i. *, and is converted into the actual position r* of the linear motor load through the motor thrust constant K f and the linear motor transmission mechanism (ie, the linear motor dynamics model P).
伺服控制器的控制信号流程图如图3所示，其中符号含义如表1所示。The control signal flow chart of the servo controller is shown in Figure 3, and the meaning of the symbols is shown in Table 1.
表1.控制信号流程图参数Table 1. Control signal flow chart parameters
Figure PCTCN2022114148-appb-000006
Figure PCTCN2022114148-appb-000006
当然，本发明还可有其它多种实施例，在不背离本发明精神及其实质的情况下，本领域技术人员可根据本发明做出各种相应的改变和变形，但这些相应的改变和变形都属于本发明的权利要求的保护范围。Of course, the present invention can also have various other embodiments. Without departing from the spirit and essence of the present invention, those skilled in the art can make various corresponding changes and modifications according to the present invention. However, these corresponding changes and All modifications fall within the protection scope of the claims of the present invention.

Claims (7)

  1. 一种直线导轨位移系统的摩擦力补偿方法，其特征在于，包括以下步骤：A friction compensation method for a linear guide displacement system, which is characterized by including the following steps:
    建立摩擦力-速度模型，其表达式如下：Establish a friction-velocity model with the following expression:
    Figure PCTCN2022114148-appb-100001
    Figure PCTCN2022114148-appb-100001
    式中F(v)是动平台与导轨的动摩擦力，v是动平台运行速度，F c为库伦摩擦力，F s为最大静摩擦力，b是粘滞阻尼系数，v s是Stribeck临界速度，δ是模型经验系数，Sgn()是返回一个整型变量，给出参数v的正负号； In the formula, F(v) is the kinetic friction between the moving platform and the guide rail, v is the running speed of the moving platform, F c is the Coulomb friction force, F s is the maximum static friction force, b is the viscous damping coefficient, v s is the Stribeck critical speed, δ is the model empirical coefficient, and Sgn() returns an integer variable, giving the sign of the parameter v;
    采集动平台匀速运动段的速度及驱动力，并将所述驱动力作为匀速段的动摩擦力；Collect the speed and driving force of the uniform motion section of the moving platform, and use the driving force as the dynamic friction force of the uniform section;
    通过粒子群算法对所述摩擦力-速度模型求解模型参数，获得完整摩擦力-速度模型；Use the particle swarm algorithm to solve the model parameters of the friction-velocity model to obtain a complete friction-velocity model;
    将完整摩擦力-速度模型写入前馈补偿器C f，基于指令速度根据所述完整摩擦力-速度模型计算动摩擦力及对应的摩擦力前馈电流信号，将该摩擦力前馈电流信号作为前馈补偿数据提供给伺服控制器电流环，进行摩擦力补偿。 Write the complete friction force-speed model into the feedforward compensator C f , calculate the dynamic friction force and the corresponding friction force feedforward current signal based on the command speed according to the complete friction force-speed model, and use the friction force feedforward current signal as The feedforward compensation data is provided to the servo controller current loop for friction compensation.
  2. 根据权利要求1所述的直线导轨位移系统的摩擦力补偿方法，其特征在于，The friction compensation method of linear guide rail displacement system according to claim 1, characterized in that:
    所述伺服控制器的输入是动平台设计位移，输出是动平台实际位移，所述伺服控制器包括依次连接的位置控制器、电流控制器、电机推力常数、直线电机动力学模型，直线电机动力学模型的输出再反馈至位置控制器的输入端，摩擦力前馈补偿器与位置控制器并联，电流控制器的输出还连接至电流 控制器的输入端，与位置控制器、摩擦力前馈补偿器的输出共同输入到电流控制器。The input of the servo controller is the design displacement of the moving platform, and the output is the actual displacement of the moving platform. The servo controller includes a position controller, a current controller, a motor thrust constant, a linear motor dynamics model, and a linear motor power connected in sequence. The output of the learning model is fed back to the input end of the position controller. The friction feedforward compensator is connected in parallel with the position controller. The output of the current controller is also connected to the input end of the current controller, which is connected with the position controller and friction feedforward. The output of the compensator is a common input to the current controller.
  3. 根据权利要求1所述的直线导轨位移系统的摩擦力补偿方法，其特征在于，The friction compensation method of linear guide rail displacement system according to claim 1, characterized in that:
    δ的取值在0.5～2范围内。The value of δ is in the range of 0.5 to 2.
  4. 根据权利要求1所述的直线导轨位移系统的摩擦力补偿方法，其特征在于，所述通过粒子群算法对所述摩擦力-速度模型求解模型参数，包括：The friction compensation method of a linear guide displacement system according to claim 1, characterized in that the method of solving the model parameters of the friction-velocity model through particle swarm optimization includes:
    初始化粒子的位置和速度；Initialize the position and velocity of particles;
    评价每个粒子的适应度，将当前各粒子的位置和适应度存储在各粒子的pbest中，将所有的pbest中最优粒子的位置和适应度存储在gbetst中；Evaluate the fitness of each particle, store the current position and fitness of each particle in the pbest of each particle, and store the position and fitness of the optimal particle in all pbest in gbetst;
    根据下式更新粒子的速度和位置：Update the particle's velocity and position according to:
    C＝c 1+c 2 C＝c 1 +c 2
    Figure PCTCN2022114148-appb-100002
    Figure PCTCN2022114148-appb-100002
    v i+1,j＝λ·(v i,j+c 1·rand(0,1)(p i,j-x i,j)+c 1·rand(0,1)(p i,g-x i,j)) v i+1,j =λ·(v i,j +c 1 ·rand(0,1)(p i,j -x i,j )+c 1 ·rand(0,1)(p i,g -x i,j ))
    x i+1,j＝x i,j+v i+1,j x i+1,j =x i,j +v i+1,j
    其中，c 1、c 2为学习因子； Among them, c 1 and c 2 are learning factors;
    λ为压缩因子；λ is the compression factor;
    rand(0,1)表示取0～1之间随机数；rand(0,1) means taking a random number between 0 and 1;
    v i,j、x i,j为第i次迭代中第j个粒子的速度和位置； v i,j and x i,j are the speed and position of the j-th particle in the i-th iteration;
    p i,j是迭代至i次期间粒子j的最优解； p i,j is the optimal solution for particle j during the iteration to i times;
    p i,g是迭代至i次期间粒子群中所有粒子中的最优解； p i,g is the optimal solution among all particles in the particle swarm during the iteration to i times;
    判断是否达到了迭代次数，若达到迭代次数则输出gbest；若没有达到迭 代次数，则迭代次数加1，重新更新粒子的速度和位置。Determine whether the number of iterations has been reached. If the number of iterations has been reached, gbest will be output; if the number of iterations has not been reached, the number of iterations will be increased by 1 and the velocity and position of the particles will be updated again.
  5. 根据权利要求4所述的直线导轨位移系统的摩擦力补偿方法，其特征在于，所述适应度e＝norm(F(v i)-F f,vi)，式中F(v i)表示动平台不同速度v i时采用所述摩擦力-速度模型求解的动摩擦力，F f,vi表示动平台不同速度v i下测得的驱动力。 The friction compensation method of linear guide rail displacement system according to claim 4, characterized in that the fitness e=norm(F( vi )-F f,vi ), where F( vi ) represents dynamic The dynamic friction force is solved using the friction force-velocity model when the platform is at different speeds v i . F f,vi represents the driving force measured at different speeds v i of the moving platform.
  6. 根据权利要求1所述的直线导轨位移系统的摩擦力补偿方法，其特征在于，所述粒子群算法是压缩因子粒子群算法、自适应压缩因子粒子群算法、时变压缩因子粒子群算法以及变权重粒子群算法中的一种。The friction compensation method of a linear guide rail displacement system according to claim 1, wherein the particle swarm algorithm is a compression factor particle swarm algorithm, an adaptive compression factor particle swarm algorithm, a time-varying compression factor particle swarm algorithm, and a variable compression factor particle swarm algorithm. One of the weighted particle swarm algorithms.
  7. 一种直线导轨位移系统的摩擦力补偿装置，其特征在于，包括：A friction compensation device for a linear guide rail displacement system, which is characterized by including:
    摩擦力模型构建模块，用于建立摩擦力-速度模型，其表达式如下：The friction model building module is used to establish the friction-velocity model. Its expression is as follows:
    Figure PCTCN2022114148-appb-100003
    Figure PCTCN2022114148-appb-100003
    式中F(v)是动平台与导轨的动摩擦力，v是动平台运行速度，F c为库伦摩擦力，F s为最大静摩擦力，b是粘滞阻尼系数，v s是Stribeck临界速度，δ是模型经验系数，Sgn()是返回一个整型变量，给出参数v的正负号； In the formula, F(v) is the kinetic friction between the moving platform and the guide rail, v is the running speed of the moving platform, F c is the Coulomb friction force, F s is the maximum static friction force, b is the viscous damping coefficient, v s is the Stribeck critical speed, δ is the model empirical coefficient, and Sgn() returns an integer variable, giving the sign of the parameter v;
    动摩擦力采集模块，用于采集动平台匀速运动段的速度及驱动力，并将所述驱动力作为匀速段的动摩擦力；The dynamic friction force acquisition module is used to collect the speed and driving force of the uniform motion section of the moving platform, and use the driving force as the dynamic friction force of the uniform motion section;
    模型参数求解模块，用于通过粒子群算法对所述摩擦力-速度模型求解模型参数，获得完整摩擦力-速度模型；A model parameter solving module, used to solve the model parameters of the friction-velocity model through particle swarm algorithm to obtain a complete friction-velocity model;
    补偿模块，用于将完整摩擦力-速度模型写入前馈补偿器C f，使得基于指令速度根据所述完整摩擦力-速度模型计算动摩擦力及对应的摩擦力前馈电流信号，将该摩擦力前馈电流信号作为前馈补偿数据提供给伺服控制器电流环，进行摩擦力补偿。 The compensation module is used to write the complete friction force-speed model into the feedforward compensator C f so that the dynamic friction force and the corresponding friction force feedforward current signal are calculated based on the command speed according to the complete friction force-speed model, and the friction force feedforward current signal is calculated according to the command speed. The force feedforward current signal is provided to the servo controller current loop as feedforward compensation data for friction compensation.
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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105700470A (en) * 2016-02-01 2016-06-22 华中科技大学 Method for reducing tracking error of machine tool servo feeding system
CN107036963A (en) * 2017-04-20 2017-08-11 中南大学 The frictional behavior test device and method of testing of engineering machinery hydraulic cylinder and guide rail
CN110460277A (en) * 2019-07-22 2019-11-15 南京理工大学 Single motor servo system friction non-linear compensation method based on particle swarm algorithm
JP2021002248A (en) * 2019-06-24 2021-01-07 富士電機株式会社 Friction compensation device
CN114527710A (en) * 2022-02-21 2022-05-24 天津大学 Feed direct torque control method and device based on friction compensation and storage medium

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105700470A (en) * 2016-02-01 2016-06-22 华中科技大学 Method for reducing tracking error of machine tool servo feeding system
CN107036963A (en) * 2017-04-20 2017-08-11 中南大学 The frictional behavior test device and method of testing of engineering machinery hydraulic cylinder and guide rail
JP2021002248A (en) * 2019-06-24 2021-01-07 富士電機株式会社 Friction compensation device
CN110460277A (en) * 2019-07-22 2019-11-15 南京理工大学 Single motor servo system friction non-linear compensation method based on particle swarm algorithm
CN114527710A (en) * 2022-02-21 2022-05-24 天津大学 Feed direct torque control method and device based on friction compensation and storage medium

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
CONGPENG ZHANG, LIU QIANG: "Friction modeling and compensation of positioning stage driven by linear motors", JOURNAL OF BEIJING UNIVERSITY OF AERONAUTICS AND ASTRONAUTICS, vol. 34, no. 1, 15 January 2008 (2008-01-15), pages 47 - 50, XP093140819 *

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