CN113510693B - A friction-based robot control method, device and equipment - Google Patents

Abstract

The application discloses a friction-based robot control method, a device, equipment and a computer-readable storage medium, wherein a friction force identification model of motion parameters and friction force of a joint motor is indirectly established through electrical parameters of the joint motor based on a target robot and structural parameters of a joint of the target robot in advance, in the actual control process, real-time motion parameters of the joint motor are obtained and substituted into the friction force identification model, after real-time friction force data are obtained, actual force control data of the joint motor are obtained through calculation according to the real-time friction force data and force control target data of the joint motor, and therefore the joint motor is controlled according to the actual force control data. Therefore, the robot control method based on the friction force can realize the robot joint control based on the friction force without additionally arranging a force/torque sensor, and has the beneficial effects of high control precision and low cost.

Description

A friction-based robot control method, device and equipment

technical field

The present application relates to the technical field of robot control, and in particular, to a friction-based robot control method, apparatus, device, and computer-readable storage medium.

Background technique

Fig. 1 is a structural schematic diagram of the driving part of a rope-driven joint robot; Fig. 2 is a front view of the driving part of the rope-driven joint robot shown in Fig. 1; Fig. 3 is a side view of the driving part of the rope-driven joint robot shown in Fig. 1; Fig. 4 is another angular structural schematic diagram of the driving part of the rope-driven joint robot shown in FIG. 1 ; FIG. 5 is a schematic connection diagram of the driving part of a rope-driven joint robot according to an embodiment of the present application. As shown in Figures 1-4, the driving part of the rope-driven joint robot is mainly composed of mechanical components such as a joint motor 101, a driver 102, a reducer 103, a rope winch 104, and an encoder 107. As shown in FIG. 5 , the controller 105 receives the state quantity of the robot obtained by the sensor 106 , and controls the driver 102 to drive the motor 101 according to the control model. The motor 101 drives the reducer 103 and then drives the rope to control the joint motion of the robot.

In the scene of force interaction, the robot needs to obtain precise force, speed and position control. In the rotary joint transmission system driven by the servo motor, there is inevitable friction. The existence of friction is an important reason for the fluctuation of the system's low-speed running speed. In closed-loop tracking of low-speed moving targets and precise positioning, harmful characteristics such as stick-slip, creep, and limit cycles are generated.

At present, there is no proposed friction-based control scheme for rope-driven articulated robots. In other motor control schemes, the torque is usually obtained by adding a force sensor and a torque sensor, and then the friction force is determined and compensated based on the identification of the dynamic model parameter theory.

However, this way of adding a force/torque sensor increases the structural complexity and leads to higher cost, and does not have good economy and generality.

SUMMARY OF THE INVENTION

The purpose of this application is to provide a friction-based robot control method, device, device, and computer-readable storage medium, which are used to realize friction identification and perform friction-based robot control without adding a force/torque sensor. Joint control has the beneficial effects of high control accuracy and low cost.

In order to solve the above-mentioned technical problems, the present application provides a friction-based robot control method, comprising:

Based on the electrical parameters of the joint motor of the target robot and the structural parameters of the joint of the target robot, a friction force identification model of the motion parameters of the joint motor and the friction force is established;

obtaining real-time motion parameters of the joint motor;

Substitute the real-time motion parameters into the friction identification model to obtain real-time friction data;

According to the real-time friction force data and the force control target data for the joint motor, calculate the actual force control data for the joint motor;

The joint motor is controlled according to the actual force control data.

Optionally, based on the electrical parameters of the joint motors of the target robot and the structural parameters of the target robot joints, establish a friction force identification model between the motion parameters of the joint motors and the friction force, specifically including:

Obtain the motor torque constant of the joint motor;

performing speed control on the joint motor to obtain a motor speed set and a corresponding motor drive current set;

multiplying the motor drive current set by the motor torque constant to obtain a motor drive torque value set;

Substituting the motor speed set into the identification equation based on the steady-state friction force model to obtain a theoretical friction torque value set;

With the goal of minimizing the absolute value of the difference between the theoretical friction torque value set and the motor driving torque value set, the first friction force identification parameter of the joint motor is obtained;

Substitute the first friction force identification parameter into the identification equation based on the steady state friction force model to obtain the friction force identification model.

Optionally, the steady state friction force model is specifically:

Wherein, M _f is the identification friction force, M _C is the Coulomb friction force, M _S is the static friction force, ω is the real-time rotational speed of the joint motor, ω _s is the critical rotational speed of the joint motor, σ _{2, θ 1} are the stiffness coefficients , σ _{2, θ 2} are damping coefficients.

Optionally, based on the electrical parameters of the joint motors of the target robot and the structural parameters of the target robot joints, establish a friction force identification model between the motion parameters of the joint motors and the friction force, specifically including:

Obtain the motor torque constant of the joint motor;

establishing a joint rotation dynamics model of the joint motor according to the motor torque constant;

Perform position control of inverse M sequence on the joint motor, collect motor speed information and current information, and obtain a motor speed sequence and a corresponding motor current sequence;

The second friction identification parameter of the joint motor is obtained by fitting based on the motor speed sequence and the motor current sequence;

Determine the moment of inertia of the joint motor and the equivalent damping coefficient of the joint motor according to the second friction force identification parameter;

The friction force identification model is obtained by substituting the moment of inertia and the equivalent damping coefficient into the joint rotational dynamics model.

Optionally, the friction force identification model is specifically:

Wherein, τ _f is the identification friction force, τ _c is the maximum static friction force of the joint motor, ω is the real-time rotational speed of the joint motor, and B is the equivalent damping coefficient.

Optionally, the real-time motion parameters specifically include: the real-time rotation speed of the joint motor, the real-time rotation speed acceleration of the joint motor, and the real-time position of the joint motor;

The actual force control data for the joint motor is calculated and obtained according to the real-time friction force data and the force control target data for the joint motor, which specifically includes:

generating friction compensation force according to the real-time friction force data;

Multiplying the difference between the speed control target of the joint motor minus the real-time speed and a preset virtual damping coefficient to obtain a first compensation force;

Multiplying the difference between the acceleration control target of the joint motor minus the real-time rotational speed acceleration and a preset virtual friction coefficient to obtain a second compensation force;

Multiplying the difference between the position control target of the joint motor minus the real-time position and a preset virtual stiffness coefficient to obtain a third compensation force;

The actual force control value of the joint motor is the sum of the force control target of the joint motor, the friction force compensation force, the first compensation force, the second compensation force and the third compensation force.

Optionally, also include:

obtaining the working condition model of the joint motor;

The real-time rotational speed, the real-time rotational speed acceleration, the real-time position and the force control target are generated according to the operating condition model.

In order to solve the above-mentioned technical problems, the present application also provides a friction-based robot control device, comprising:

a friction force identification unit for establishing a friction force identification model between the motion parameters of the joint motor and the friction force based on the electrical parameters of the joint motor of the target robot and the structural parameters of the target robot joint in advance;

a first acquisition unit, configured to acquire real-time motion parameters of the joint motor;

a first computing unit, used for substituting the real-time motion parameters into the friction force identification model to obtain real-time friction force data;

a second computing unit, configured to calculate the actual force control data for the joint motor according to the real-time friction force data and the force control target data for the joint motor;

The control unit is configured to control the joint motor according to the actual force control data.

In order to solve the above-mentioned technical problems, the application also provides a friction-based robot control device, including:

a memory for storing instructions, the instructions comprising the steps of any one of the friction-based robot control methods described above;

a processor for executing the instructions.

In order to solve the above technical problems, the present application also provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the friction-based robot control method described in any of the above is realized. step.

The friction-based robot control method provided by this application indirectly establishes the friction force identification model of the motion parameters of the joint motor and the friction force based on the electrical parameters of the joint motor of the target robot and the structural parameters of the target robot joint in advance. In the actual control process, the real-time motion parameters of the joint motor are obtained and substituted into the friction force identification model. After obtaining the real-time friction force data, the actual force control data for the joint motor is calculated according to the real-time friction force data and the force control target data for the joint motor. , so as to control the joint motor according to the actual force control data. Therefore, the friction-based robot control method provided by the present application can realize the friction-based robot joint control without adding a force/torque sensor, and has the beneficial effects of high control accuracy and low cost.

The present application also provides a friction-based robot control device, equipment, and computer-readable storage medium, which have the above beneficial effects, and are not repeated here.

Description of drawings

In order to illustrate the technical solutions of the embodiments of the present application or the prior art more clearly, the following briefly introduces the accompanying drawings that need to be used in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only For some embodiments of the present application, for those of ordinary skill in the art, other drawings can also be obtained according to these drawings without any creative effort.

Fig. 1 is a kind of structural schematic diagram of the driving part of a rope-driven joint robot;

Fig. 2 is the front view of the driving part of the rope-driven joint robot shown in Fig. 1;

Fig. 3 is a side view of the driving part of the rope-driven joint robot shown in Fig. 1;

Fig. 4 is another angular structural schematic diagram of the driving part of the rope-driven joint robot shown in Fig. 1;

FIG. 5 is a schematic connection diagram of a driving part of a rope-driven joint robot according to an embodiment of the present application;

6 is a flowchart of a friction-based robot control method provided by an embodiment of the present application;

FIG. 7 is a schematic diagram of an observation current provided by an embodiment of the present application;

8 is a schematic structural diagram of a motor torque constant calibration system provided by an embodiment of the present application;

9 is a schematic connection diagram of a motor torque constant calibration system provided by an embodiment of the present application;

FIG. 10 is a flowchart of a specific implementation manner of step S601 in FIG. 6 provided by an embodiment of the present application;

11 is a control block diagram of a current loop controller provided by an embodiment of the application;

12 is a control block diagram of an impedance controller based on friction feedforward provided by an embodiment of the application;

13 is a schematic structural diagram of a friction-based robot control device provided by an embodiment of the application;

14 is a schematic structural diagram of a friction-based robot control device provided by an embodiment of the application;

Among them, 101 is the joint motor, 102 is the driver, 103 is the reducer, 104 is the rope winch, 105 is the controller, 106 is the sensor, 107 is the encoder, 801 is the test bench, 802 is the hysteresis brake, and 803 is the dynamic Torque speed sensor, 804 is a coupling, 805 is a data acquisition card, 806 is a current controller, and 807 is a motor driver.

Detailed ways

The core of the present application is to provide a friction-based robot control method, device, device and computer-readable storage medium, which are used to realize friction force identification and perform friction-based robot control without adding a force/torque sensor. Force control has the beneficial effects of high control accuracy and low cost.

The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, but not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

Example 1

FIG. 6 is a flowchart of a friction-based robot control method provided by an embodiment of the present application. As shown in FIG. 6 , the friction-based robot control method provided by the embodiment of the present application includes:

S601: Based on the electrical parameters of the joint motors of the target robot and the structural parameters of the target robot joints in advance, establish a friction force identification model of the motion parameters of the joint motors and the friction force.

S602: Acquire real-time motion parameters of the joint motor.

S603: Substitute the real-time motion parameters into the friction force identification model to obtain real-time friction force data.

S604: Calculate the actual force control data for the joint motor according to the real-time friction force data and the force control target data for the joint motor.

S605: Control the joint motor according to the actual force control data.

In a specific implementation, for step S601, in order to realize the control of the joint motor, it is necessary to identify the electrical parameters of the joint motor and the structural parameters of the joint. The identification method can be a measurement method or a theoretical identification method. The theoretical identification method obtains the dynamic parameters by analyzing the robot model, but the analysis process is more complicated. The measurement method can accurately measure the steady-state and dynamic characteristics of the motor under different working conditions through the test instrument to extract parameters, and combine the identified motor and transmission parameters to further identify the friction model of the joint and the corresponding dynamic parameters. The controller thus realizes the three-loop control of the motor. Because of its low cost, convenient implementation and high precision, its realization and promotion are of great significance. Therefore, in the embodiment of the present application, the measurement method is preferably used for parameter identification.

In view of the problem that the motor parameters are few and cannot be known in the prior art, the embodiment of the present application identifies the electrical parameters of the joint motor in advance, which can not only solve the aforementioned problems, but also provide a reference for subsequent motor control links. For the three-phase brushless motor, the electrical parameter identification mainly involves the identification of the phase resistance of the motor, the identification of the phase inductance and the identification of the reaction electromotive force. The structural parameter identification of the target robot joint mainly includes the identification of the motor torque constant, the joint rotational inertia, the equivalent damping coefficient and the friction parameter of the joint (including the motor + reducer combination).

Based on the identified electrical parameters of the joint motor and the structural parameters of the target robot joint, a friction force identification model of the motion parameters of the joint motor and the friction force is established to calculate the real-time friction of the joint without the need for a force/torque sensor to obtain the external force. force.

In the actual control process, by collecting the real-time motion parameters of the joint motor, the real-time friction force data can be obtained through the friction force identification model, and then the friction loss compensation is performed on the force control target data of the joint motor according to the real-time friction force data. The actual force control data for the joint motor is obtained, and the joint motor is controlled according to the actual force control data to ensure that the joint motor achieves the desired force control target.

The friction-based robot control method provided by the embodiment of the present application indirectly establishes the friction force identification model of the motion parameters of the joint motor and the friction force based on the electrical parameters of the joint motor of the target robot and the structural parameters of the target robot joint in advance, In the actual control process, the real-time motion parameters of the joint motor are obtained and substituted into the friction force identification model. After obtaining the real-time friction force data, the actual force control of the joint motor is calculated according to the real-time friction force data and the force control target data of the joint motor. data, so as to control the joint motor according to the actual force control data. Therefore, the friction-based robot control method provided by the embodiments of the present application can realize the friction-based robot joint control without adding a force/torque sensor, and has the beneficial effects of high control accuracy and low cost.

Embodiment 2

FIG. 7 is a schematic diagram of an observation current provided by an embodiment of the application; FIG. 8 is a schematic structural diagram of a motor torque constant calibration system provided by an embodiment of the application; FIG. 9 is a motor torque constant calibration provided by an embodiment of the application System connection diagram.

On the basis of the above embodiments, the embodiments of the present application provide a specific implementation manner of measuring and identifying the electrical parameters of the joint motors and the structural parameters of the target robot joint. It should be noted that, in practical applications, different types of motors and robot applications are not limited to the identification methods provided in the embodiments of the present application.

The specific method of measuring the phase resistance R _S may be to use a multimeter to measure the line resistance, and then perform the identification of the phase resistance R _S . Assume that the three phases of the brushless motor are U, V, and W phases (the corresponding phase resistances are R _U , R _V , R _W ), and the three-phase line resistances R _VW , R _UW , and R _UV are measured respectively. During the measurement, keep the rotor of the motor still, and measure each group of the three groups of terminal resistance multiple times (for example, three times), and then take the arithmetic mean value as the value of the terminal resistance, and obtain R _VW , R _UW , and R _UV respectively.

Then for star connection:

R _U =R _med -R _VW ;

R _V =R _med -R _UW ;

R _W =R _med - R _UV .

For delta connection:

in,

The specific method of measuring the phase inductance L _S can be to use an oscilloscope to excite the motor by applying a step voltage to the third wire of the motor, and to identify the phase inductance L _S by observing the response of the current. Let the rated input voltage of the brushless motor be U, and the three-phase of the brushless motor are U, V, and W. Divide three experiments, and apply the steps of U _UV = U, U _VW = U, U _UW = U to the three lines respectively. voltage, and calculate the line inductance value by observing the current response curve of the three-phase. When measuring, keep the rotor of the motor stationary.

Then for star connection:

L_Bcos＝L_B·cos2θ＝L_B2；

L _Bcos =L _B ·cos2θ=L _B2 ;

分别为U_UV、U_VW、U_UW三组电压激励下稳定一定时间(如2分钟)后的电流稳定值。τ_UV、τ_VW、τ_UW对应为

的时刻(从线电压/线电流变化为0的时刻)。观测的电流图如图7所示。in,

They are the current stable values after a certain period of time (such as 2 minutes) under the excitation of three groups of voltages U _UV , U _VW , and U _UW respectively. τ _UV , τ _VW , τ _UW correspond to

the moment (the moment when the line voltage/line current changes to 0). The observed current diagram is shown in Figure 7.

使用速度控制器控制对拖电机，设待测电机的额定转速为N_rated，将额定转速均分(例如可以均分为20份)后得到

按照转速递变的方式对电机峰值线电压进行测定，得到对应转速

下的线电压波峰值的算数平均值及对应的频率

则

取多组(如20组)

算数平均值得到

记为关节电机的反应电动势K_e。The reaction electromotive force _Ke of the motor is identified, and the identification of the joint electrical dynamic response model is completed. Specifically, fix the motor on the bench, connect it coaxially to the towed rotary motor through the coupling, drive the towed rotary motor for one circle through the position controller, and measure the two-line voltage of the motor to be tested by the oscilloscope , the corresponding waveform appears continuously on the oscilloscope, and the number of peaks of the waveform obtained from the test is N, then the number of pole pairs

Use the speed controller to control the tow motor, set the rated speed of the motor to be tested as N _rated , divide the rated speed equally (for example, it can be divided into 20 parts) to get

The peak line voltage of the motor is measured according to the speed gradient, and the corresponding speed is obtained.

The arithmetic mean value of the peak value of the line voltage wave and the corresponding frequency

but

Take multiple groups (such as 20 groups)

The arithmetic mean is obtained

It is recorded as the reaction electromotive force _Ke of the joint motor.

The structural parameter identification of the target robot joint mainly includes the identification of the motor torque constant, the joint rotational inertia, the equivalent damping coefficient and the friction parameter of the joint (including the motor + reducer combination). In order to solve the problem that the moment of inertia and friction coefficient are complex and difficult to identify due to the complex transmission form, and to serve as an important parameter reference for the later design of a highly dynamic controller, the embodiments of the present application provide the following method for identifying the mechanical parameters of the motor.

驱动电机101，记录串联的动态转速扭矩传感器803的信息，辨识出电机扭矩常数K_T。具体测量步骤包括：The calibration of the motor torque constant K _T can be achieved by using the motor torque constant calibration system shown in Figure 8-9. Use the test bench 801 to fix the joint motor 101, use the hysteresis brake 802 to lock the motor shaft, and use the current control method to set different currents

Drive the motor 101, record the information of the series-connected dynamic rotational speed torque sensor 803, and identify the motor torque constant K _T . The specific measurement steps include:

Fix the joint motor 101 on the test bench 801, connect it to the dynamic torque and speed sensor 803 through the coupling 804, collect the data of the hysteresis brake 802 through the data acquisition card 805 and input it to the current controller 806; The brake 802 is locked.

按照电流递变的方式对电机801进行测定得到对应的扭矩

以(I_i,τ_i)为坐标点进行拟合，得到

记为关节电机801的电机扭矩常数K_T。Use the current controller 806 of the joint motor 801 to control the joint motor 801 through the motor driver 807, set the rated current of the joint motor 801 to I _rated , and divide the rated current I _rated equally (for example, it can be divided into 20 equal parts) to obtain

The motor 801 is measured according to the current gradient to obtain the corresponding torque

Fitting with (I _i ,τ _i ) as the coordinate point, we get

It is denoted as the motor torque constant K _T of the joint motor 801 .

For the moment of inertia J _J of the joint, the position control of the inverse M sequence of the joint can be used. By collecting the current and speed information, and using multiple sets of parameters to batch process, the overall moment of inertia J _J and the equivalent damping coefficient B of the shutdown can be identified. Specifically, the joint rotation dynamics model is as follows:

J _J ·ω=K _T ·I-τ _f , (1)

Among them, ω is the real-time speed of the joint motor, K _T is the motor torque constant, I is the motor current, τ _f is the static friction force, τ _c is the maximum static friction force of the joint motor, and B is the equivalent damping coefficient.

以及关节转动惯量J_J。The parameters to be identified in formulas (1) and (2) include the simplified friction parameters of the forward and reverse directions of the joint motor motion

and joint moment of inertia J _J .

得到N≥10000个采样点。设：In order to solve formulas (1) and (2), first fix the robot joint on the detection device, use the position controller to drive the joint motor, and use the position command of the inverse M sequence, and the amplitude of the position command is A≥10° Drive the joint motor with an amplitude of 2mm, collect the position and speed of the joint motor, use the zero-order holder to sample the speed and current of the joint motor, and the sampling time is

Get N ≥ 10000 sampling points. Assume:

Among them, W and W ₁ are the rotation speed sequence, and I ₁ is the current sequence.

Based on the least squares method, let:

Therefore, with the goal of minimizing E, the parameter vector identification is carried out.

Assume:

Then the parameter vector to be identified is:

Among them, K _θd , T _θs , p _θd , λ(k), and Φ are process parameters. Specifically, the identification parameter vector can be obtained by genetic algorithm with the goal of minimizing E. This results in the following results:

Through the identification method provided in the embodiment of the present application, the electrical parameters and structural parameters related to the joint motor can be accurately known, which lays a foundation for realizing precise force control.

Embodiment 3

FIG. 10 is a flowchart of a specific implementation manner of step S601 in FIG. 6 according to an embodiment of the present application.

In addition to the identification of the above electrical parameters and structural parameters, it is also necessary to identify the friction parameters of the joint (joint motor and reducer combination), in order to further identify the moment of inertia of the joint as an important friction model reference.

Specifically, as shown in FIG. 10 , in step S601, based on the electrical parameters of the joint motor of the target robot and the structural parameters of the target robot joint, a friction force identification model of the motion parameters of the joint motor and the friction force is established, which may specifically include:

S1001: Obtain the motor torque constant of the joint motor.

S1002: Control the speed of the joint motor to obtain a motor speed set and a corresponding motor drive current set.

S1003: Multiply the motor drive current set by the motor torque constant to obtain the motor drive torque value set.

S1004: Substitute the motor speed set into the identification equation based on the steady-state friction force model to obtain the theoretical friction torque value set.

S1005: With the goal of minimizing the absolute value of the difference between the theoretical friction torque value set and the motor driving torque value set, obtain the first friction force identification parameter of the joint motor.

S1006: Substitute the first friction force identification parameter into the identification equation based on the steady state friction force model to obtain the friction force identification model.

Wherein, the motor torque constant K _T can be obtained through the above steps.

采集到用于驱动关节电机的对应的电流集合

相乘得到对应的电机驱动扭矩值集合：For steps S1002 and S1003, speed control is performed on the joint motor, through different sets of motor speeds

The corresponding current set used to drive the joint motor is collected

Multiply to get the corresponding set of motor drive torque values:

For step S1004, the Stribeck friction model can be selected as the steady-state friction model. However, the friction fit of the Stribeck friction model is poor at high rotational speeds. Therefore, the embodiment of the present application provides an optimized steady-state friction force model as follows:

Among them, M _f is the identification friction force, M _C is the Coulomb friction force, M _S is the static friction force, ω is the real-time speed of the joint motor, ω _s is the critical speed of the joint motor, σ _{2, θ1} are the stiffness coefficients, σ _{2, θ2} is the damping coefficient.

Since the slope of the friction force decreases gradually with the increase of the speed in the high-speed range, the steady-state friction force model shown in formula (14) can be used to fit the phenomenon of decreasing slope.

和反方向的摩擦力参数

需要分别对正方向和反方向的摩擦力参数进行辨识，则基于稳态摩擦力模型的辨识方程为：Denote the frictional force parameter in formula (14) as the first frictional force parameter, including the frictional force parameter in the positive direction

and the friction parameters in the opposite direction

The friction parameters in the forward and reverse directions need to be identified separately, then the identification equation based on the steady-state friction model is:

Among them, M _f,theo,i is the theoretical friction torque value based on the friction force model.

Substitute the motor speed set collected in step S1002 into equation (15) to obtain the set

For step S1005, the following function is set:

With the objective of minimizing L(D _i ), the best fitting parameter combination is obtained:

For step S1006, the parameters obtained by formulas (17) and (18) are substituted into the identification equation of formula (15), and the friction force identification model is obtained as follows:

The positive direction friction model is specifically:

The friction force model in the opposite direction is as follows:

The optimized friction force identification model proposed in the embodiment of the present application can cover the accurate identification of the friction force of the rotating joint at high and low speeds. By designing the identification model, the slope of the traditional Stribeck friction force model can be reduced and the accuracy of the friction force becomes lower at high speed. Therefore, the identification accuracy of friction force is improved, and the force control accuracy is improved. Moreover, it has this beneficial effect on the friction force identification of various types of rotary joints, and is not limited to the identification of the friction force of rope-driven rotary joints.

Embodiment 4

The third embodiment of the present application provides an optimized steady-state friction force model, which can cover the friction force identification accuracy of high-speed and low-speed operation. For scenarios with low requirements on force control performance, in order to simplify the calculation, the joint rotation dynamics model given in the second embodiment of the present application can be selected to determine the friction force. Then in step S601, based on the electrical parameters of the joint motor of the target robot and the structural parameters of the target robot joint, a friction force identification model of the motion parameters of the joint motor and the friction force is established, which may specifically include:

Get the motor torque constant of the joint motor;

The joint rotation dynamics model of the joint motor is established according to the motor torque constant;

Perform the position control of the inverse M sequence on the joint motor, collect the motor speed information and current information, and obtain the motor speed sequence and the corresponding motor current sequence;

The second friction identification parameter of the joint motor is obtained by fitting based on the motor speed sequence and the motor current sequence;

Determine the moment of inertia of the joint motor and the equivalent damping coefficient of the joint motor according to the second friction force identification parameter;

The friction force identification model is obtained by substituting the moment of inertia and equivalent damping coefficient into the joint rotational dynamics model.

以

为第二摩擦力辨识参数，求解过程可以参考本申请实施例二。Then the friction force identification model adopts formula (2):

by

For the second friction force identification parameter, reference may be made to the second embodiment of the present application for the solution process.

By applying the friction force identification method provided by the embodiments of the present application, the friction force identification model can be determined while the joint rotational inertia and equivalent damping coefficient are identified, thereby achieving the effects of reducing computational complexity and solving delay.

Embodiment 5

FIG. 11 is a control block diagram of a current loop controller provided by an embodiment of the application; FIG. 12 is a control block diagram of a friction feedforward based impedance controller provided by an embodiment of the application.

An important part of the force control of the joint motor is the design and regulation of the current controller. On the basis of the above embodiment, the embodiment of the present application provides a design scheme of a current controller. As shown in FIG. 11 , a proportional integral (PI) controller is designed, and the phase resistance R _S obtained through the identification of the second embodiment of the present application And the phase inductance L _S , and the bandwidth of the current loop get the parameters of the proportional controller and the integral controller to complete the design of the current loop controller.

G_{inv_d}(s)为基于SVPWM算法的逆变器，传递函数等效为

G_{s_pmsm}(s)为基于i_d＝0矢量控制策略的电机模型，

G_cf(s)为电流滤波器，其传递函数为

其中ω_cf为滤波器的截止频率。Wherein, i _{q_ref} is the q-axis current control reference value; _{Ke is the reaction electromotive force of the joint motor identified in the second embodiment of the application; G C_ctl (} s) is the PI controller of the current, and the transfer function is

G _{inv_d} (s) is the inverter based on the SVPWM algorithm, and the transfer function is equivalent to

G _{s_pmsm} (s) is the motor model based on _id = 0 vector control strategy,

G _cf (s) is the current filter, and its transfer function is

where ω _cf is the cutoff frequency of the filter.

则电流的PI控制器参数设计为：The bandwidth of the designed current loop is:

Then the PI controller parameters of the current are designed as:

On the basis of the current controller, an impedance controller based on friction feedforward is further designed. As shown in FIG. 12 , according to the friction force identification model being feedforward, the current controller of the joint motor is controlled and input i _{q_ref} .

The first embodiment of the present application provides a solution for obtaining real-time friction force data by collecting real-time motion parameters of the joint motor and substituting them into the friction force identification model after the friction force identification model is established, and then controlling the joint motor based on the friction force data. In practical applications, the collected real-time motion parameters of the joint motor may only include the real-time rotational speed of the joint motor, and the real-time rotational speed of the joint motor is substituted into the friction force identification model provided in the foregoing embodiment to describe the relationship between the rotational speed of the joint motor and the friction force, namely The friction force can be obtained, so that the actual force control value of the joint motor can be obtained by adding the friction force on the basis of the force control target of the joint motor, so as to realize the compensation of the friction force.

In order to adapt to different working conditions, in this embodiment of the present application, the real-time motion parameters may specifically include: the real-time rotation speed of the joint motor, the real-time rotation speed acceleration of the joint motor, and the real-time position of the joint motor.

Then step S604: According to the real-time friction force data and the force control target data for the joint motor, calculate and obtain the actual force control data for the joint motor, which specifically includes:

Generate friction compensation force based on real-time friction force data;

Multiplying the difference between the speed control target of the joint motor minus the real-time speed and the preset virtual damping coefficient to obtain the first compensation force;

Multiplying the difference between the acceleration control target of the joint motor and the real-time speed acceleration and the preset virtual friction coefficient to obtain the second compensation force;

Multiplying the difference between the position control target of the joint motor and the real-time position and the preset virtual stiffness coefficient to obtain the third compensation force;

The actual force control value of the joint motor is the sum of the force control target of the joint motor, the friction compensation force, the first compensation force, the second compensation force and the third compensation force.

实时转速ω的确定方法可以为通过测量仪器实时采集关节电机的转速，也可以如图12所示的以关节电机的转速控制目标

为关节电机的实时转速ω代入摩擦力辨识模型M_f(ω)计算得到辨识摩擦力M_f。In a specific implementation, as shown in FIG. 12 , after determining the real-time rotation speed ω of the joint motor, the real-time position θ of the joint motor and the real-time rotation speed acceleration of the joint motor can be obtained through integral calculation and differential calculation, respectively.

The method for determining the real-time rotational speed ω can be to collect the rotational speed of the joint motor in real time through a measuring instrument, or to control the target with the rotational speed of the joint motor as shown in Figure 12.

The identified friction force M _f is obtained by substituting the friction force identification model M _f (ω) for the real-time speed ω of the joint motor.

In the embodiment of the present application, a virtual damping coefficient B _d , a virtual friction coefficient M _d and a virtual stiffness coefficient K _d are added to meet the requirements under complex working conditions. The virtual damping coefficient B _d , the virtual friction coefficient M _d and the virtual stiffness coefficient K _d are the simulated impedance model parameters. By adjusting the virtual damping coefficient B _d , the virtual friction coefficient M _d and the virtual stiffness coefficient K _d to adapt to different working conditions the requirements below.

关节电机的加速度控制目标

关节电机的位置控制目标θ_r。则本申请实施例提供的基于摩擦力的机器人控制方法还包括：获取关节电机的工况模型；根据工况模型生成实时转速、实时转速加速度、实时位置和力控制目标。As shown in FIG. 12 , in this embodiment of the present application, a working condition model _ML (θ) and a position planner θ _r (t) (not shown in FIG. 12 ) are also added to the impedance controller. Among them, the working condition model _ML (θ) is used to determine the force control target _ML of the joint motor according to the current working condition, and the position planner θ _r (t) is used to determine the rotational speed control target of the joint motor according to the current working condition.

The acceleration control target of the joint motor

The position control target θ _r of the joint motor. The friction-based robot control method provided by the embodiment of the present application further includes: acquiring a working condition model of the joint motor; and generating real-time rotational speed, real-time rotational speed acceleration, real-time position and force control targets according to the working condition model.

In addition, when the working condition is unknown, the working condition model _ML (θ) can be omitted, and the impedance controller parameters B _d , M _d , and K _d can be adjusted to meet the requirements of complex working conditions.

Then the impedance controller F _virtual based on the control block diagram shown in Figure 12 is shown in the following formula:

Then the obtained actual force control value F _d of the joint motor is shown in the following formula:

For the input i _{q_ref} of the current controller is:

Among them, _TL (θ) is the external demand load curve.

Based on the friction feedforward-based impedance controller provided in the embodiment of the present application, the rope-driven joint robot can dynamically adjust the joint pull rope according to the precise friction force, the friction force feedforward provided by the working condition curve, and the designed impedance parameters. The force-position characteristic can not only achieve precise force control, but also achieve complex force-position control effects similar to impedance characteristics. And by dynamically adjusting the parameters of the impedance controller, the adaptability of the joint can be changed to meet the requirements of different environments, and it has a good control effect on the joint motor and has strong applicability.

Various embodiments corresponding to the friction-based robot control method are described in detail above. On this basis, the present application also discloses a friction-based robot control device, equipment and computer-readable storage medium corresponding to the above method.

Embodiment 6

FIG. 13 is a schematic structural diagram of a friction-based robot control device according to an embodiment of the present application.

As shown in FIG. 13 , the friction-based robot control device provided by the embodiment of the present application includes:

The friction force identification unit 1301 is used to establish a friction force identification model of the motion parameters of the joint motor and the friction force based on the electrical parameters of the joint motor of the target robot and the structural parameters of the target robot joint in advance;

a first obtaining unit 1302, configured to obtain real-time motion parameters of the joint motor;

The first calculation unit 1303 is used for substituting real-time motion parameters into the friction force identification model to obtain real-time friction force data;

The second calculation unit 1304 is configured to calculate the actual force control data for the joint motor according to the real-time friction force data and the force control target data for the joint motor;

The control unit 1305 is used to control the joint motor according to the actual force control data.

Optionally, the friction-based robot control device provided in the embodiment of the present application further includes:

The second acquisition unit is used to acquire the working condition model of the joint motor;

The third computing unit is used for generating real-time rotational speed, real-time rotational speed acceleration, real-time position and force control targets according to the working condition model.

Since the embodiment of the apparatus part corresponds to the embodiment of the method part, for the embodiment of the apparatus part, please refer to the description of the embodiment of the method part, which will not be repeated here.

Embodiment 7

FIG. 14 is a schematic structural diagram of a friction-based robot control device provided by an embodiment of the present application.

As shown in FIG. 14 , the friction-based robot control device provided by the embodiment of the present application includes:

The memory 1410 is used to store instructions, and the instructions include the steps of the friction-based robot control method described in any one of the above embodiments;

A processor 1420 for executing the instructions.

The processor 1420 may include one or more processing cores, such as a 3-core processor, an 8-core processor, and the like. The processor 1420 may be implemented in at least one hardware form among digital signal processing (DSP), field-programmable gate array (FPGA), and programmable logic array (PLA). The processor 1420 may also include a main processor and a coprocessor. The main processor is a processor used to process data in the wake-up state, also called a central processing unit (CPU); the coprocessor is used for processing data in the wake-up state. A low-power processor that processes data in a standby state. In some embodiments, the processor 1420 may be integrated with a graphics processing unit (GPU), and the GPU is used for rendering and drawing the content that needs to be displayed on the display screen. In some embodiments, the processor 1420 may further include an artificial intelligence (Artificial Intelligence) processor for processing computing operations related to machine learning.

Memory 1410 may include one or more computer-readable storage media, which may be non-transitory. Memory 1410 may also include high-speed random access memory, as well as non-volatile memory, such as one or more disk storage devices, flash storage devices. In this embodiment, the memory 1410 is at least used to store the following computer program 1411 , where, after the computer program 1411 is loaded and executed by the processor 1420 , it can implement the related friction-based robot control methods disclosed in any of the foregoing embodiments. step. In addition, the resources stored in the memory 1410 may also include an operating system 1412 and data 1413, etc., and the storage mode may be short-term storage or permanent storage. The operating system 1412 may be Windows. The data 1413 may include, but is not limited to, the data involved in the above methods.

In some embodiments, the friction-based robot control device may further include a display screen 1430 , a power source 1440 , a communication interface 1450 , an input and output interface 1460 , a sensor 1470 , and a communication bus 1480 .

Those skilled in the art can understand that the structure shown in FIG. 14 does not constitute a limitation to the friction-based robot control device, and may include more or less components than those shown.

The friction-based robot control device provided by the embodiments of the present application includes a memory and a processor. When the processor executes a program stored in the memory, the above-mentioned friction-based robot control method can be implemented, and the effect is the same as above.

Embodiment 8

It should be noted that the above-described apparatus and device embodiments are only illustrative. For example, the division of modules is only a logical function division. In actual implementation, there may be other division methods, such as multiple modules or components. May be combined or may be integrated into another system, or some features may be omitted, or not implemented. On the other hand, the shown or discussed mutual coupling or direct coupling or communication connection may be through some interfaces, indirect coupling or communication connection of devices or modules, and may be in electrical, mechanical or other forms. Modules described as separate components may or may not be physically separated, and components shown as modules may or may not be physical modules, that is, they may be located in one place, or may be distributed to multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution in this embodiment.

In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically alone, or two or more modules may be integrated into one module. The above-mentioned integrated modules can be implemented in the form of hardware, and can also be implemented in the form of software function modules.

The integrated modules, if implemented in the form of software functional modules and sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the present application can be embodied in the form of software products in essence, or the parts that contribute to the prior art, or all or part of the technical solutions, and the computer software products are stored in a storage medium , execute all or part of the steps of the methods described in the various embodiments of the present application.

To this end, an embodiment of the present application further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, steps such as a friction-based robot control method are implemented.

The computer-readable storage medium may include: U disk, removable hard disk, read-only memory ROM (Read-Only Memory), random access memory RAM (Random Access Memory), magnetic disk or optical disk and other media that can store program codes.

The computer program included in the computer-readable storage medium provided in this embodiment can, when executed by the processor, implement the steps of the friction-based robot control method described above, and the effects are the same as above.

The friction-based robot control method, device, device, and computer-readable storage medium provided by the present application have been described above in detail. The various embodiments in the specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments, and the same and similar parts between the various embodiments can be referred to each other. For the apparatuses, devices, and computer-readable storage media disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple, and reference may be made to the descriptions of the methods for related parts. It should be pointed out that for those of ordinary skill in the art, without departing from the principles of the present application, several improvements and modifications can also be made to the present application, and these improvements and modifications also fall within the protection scope of the claims of the present application.

It should also be noted that, in this specification, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply these entities or operations. There is no such actual relationship or sequence between operations. Moreover, the terms "comprising", "comprising" or any other variation thereof are intended to encompass a non-exclusive inclusion such that a process, method, article or device that includes a list of elements includes not only those elements, but also includes not explicitly listed or other elements inherent to such a process, method, article or apparatus. Without further limitation, an element qualified by the phrase "comprising a..." does not preclude the presence of additional identical elements in a process, method, article or apparatus that includes the element.

Claims (10)

1. a friction-based robot control method, is characterized in that, comprises: Based on the electrical parameters of the joint motor of the target robot and the structural parameters of the joint of the target robot, a friction force identification model of the motion parameters of the joint motor and the friction force is established; obtaining real-time motion parameters of the joint motor; Substitute the real-time motion parameters into the friction identification model to obtain real-time friction data; According to the real-time friction force data and the force control target data for the joint motor, calculate the actual force control data for the joint motor; The joint motor is controlled according to the actual force control data. 2. The robot control method according to claim 1, wherein the electrical parameters of the joint motor of the target robot and the structural parameters of the joint motor of the target robot are used to establish the relationship between the motion parameters of the joint motor and the frictional force. Friction identification model, including: Obtain the motor torque constant of the joint motor; performing speed control on the joint motor to obtain a motor speed set and a corresponding motor drive current set; multiplying the motor drive current set by the motor torque constant to obtain a motor drive torque value set; Substituting the motor speed set into the identification equation based on the steady-state friction force model to obtain a theoretical friction torque value set; With the goal of minimizing the absolute value of the difference between the theoretical friction torque value set and the motor driving torque value set, the first friction force identification parameter of the joint motor is obtained; Substitute the first friction force identification parameter into the identification equation based on the steady state friction force model to obtain the friction force identification model. 3. The robot control method according to claim 2, wherein the steady state friction force model is specifically:

Wherein, M _f is the identification friction force, M _C is the Coulomb friction force, M _S is the static friction force, ω is the real-time rotational speed of the joint motor, ω _s is the critical rotational speed of the joint motor, σ _{2, θ 1} are the stiffness coefficients , σ _{2, θ 2} are damping coefficients. 4. The robot control method according to claim 1, wherein the electrical parameters of the joint motor of the target robot and the structural parameters of the joint motor of the target robot are used to establish the relationship between the motion parameters of the joint motor and the friction force. Friction identification model, including: Obtain the motor torque constant of the joint motor; establishing a joint rotation dynamics model of the joint motor according to the motor torque constant; Perform position control of inverse M sequence on the joint motor, collect motor speed information and current information, and obtain a motor speed sequence and a corresponding motor current sequence; The second friction identification parameter of the joint motor is obtained by fitting based on the motor speed sequence and the motor current sequence; Determine the moment of inertia of the joint motor and the equivalent damping coefficient of the joint motor according to the second friction force identification parameter; The friction force identification model is obtained by substituting the moment of inertia and the equivalent damping coefficient into the joint rotational dynamics model. 5. The robot control method according to claim 4, wherein the friction force identification model is specifically:

Wherein, τ _f is the identification friction force, τ _c is the maximum static friction force of the joint motor, ω is the real-time rotational speed of the joint motor, and B is the equivalent damping coefficient. 6. The robot control method according to claim 1, wherein the real-time motion parameters specifically include: the real-time rotational speed of the joint motor, the real-time rotational speed acceleration of the joint motor and the real-time position of the joint motor; The actual force control data for the joint motor is calculated and obtained according to the real-time friction force data and the force control target data for the joint motor, which specifically includes: generating friction compensation force according to the real-time friction force data; Multiplying the difference between the speed control target of the joint motor minus the real-time speed and a preset virtual damping coefficient to obtain a first compensation force; Multiplying the difference between the acceleration control target of the joint motor minus the real-time rotational speed acceleration and a preset virtual friction coefficient to obtain a second compensation force; Multiplying the difference between the position control target of the joint motor minus the real-time position and a preset virtual stiffness coefficient to obtain a third compensation force; The actual force control value of the joint motor is the sum of the force control target of the joint motor, the friction force compensation force, the first compensation force, the second compensation force and the third compensation force. 7. The robot control method according to claim 6, further comprising: obtaining the working condition model of the joint motor; The real-time rotational speed, the real-time rotational speed acceleration, the real-time position and the force control target are generated according to the operating condition model. 8. A friction-based robot control device, comprising: a friction force identification unit for establishing a friction force identification model between the motion parameters of the joint motor and the friction force based on the electrical parameters of the joint motor of the target robot and the structural parameters of the target robot joint in advance; a first acquisition unit, configured to acquire real-time motion parameters of the joint motor; a first computing unit, used for substituting the real-time motion parameters into the friction force identification model to obtain real-time friction force data; a second computing unit, configured to calculate the actual force control data for the joint motor according to the real-time friction force data and the force control target data for the joint motor; The control unit is configured to control the joint motor according to the actual force control data. 9. A friction-based robot control device, comprising: a memory for storing instructions, the instructions comprising the steps of the friction-based robot control method according to any one of claims 1 to 7; a processor for executing the instructions. 10. A computer-readable storage medium on which a computer program is stored, characterized in that, when the computer program is executed by a processor, the control method of the friction-based robot control method according to any one of claims 1 to 7 is realized. step.

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