WO2023010811A1 - Robot control method, apparatus and device based on frictional force - Google Patents

Abstract

A robot control method, apparatus and device based on a frictional force, and a computer-readable storage medium. The method comprises: by means of taking an electrical parameter of a joint electric motor of a target robot and a structural parameter of a target robot joint as a basis in advance, indirectly establishing a frictional force identification model of a motion parameter and a frictional force of the joint electric motor; during actual control, acquiring a real-time motion parameter of the joint electric motor, and substituting same into the frictional force identification model; and after real-time frictional force data is obtained, obtaining actual force control data for the joint electric motor by means of performing calculation on the basis of the real-time frictional force data and target force control data for the joint electric motor, thereby controlling the joint electric motor according to the actual force control data. Therefore, by means of the robot control method based on a frictional force provided in the present application, robot joint control based on a frictional force can be realized without additionally mounting a force/torque sensor, and the method has the beneficial effects of a high control precision and low costs.

Description

A friction-based robot control method, device and equipment

This application claims the priority of the Chinese patent application submitted to the China Patent Office on August 3, 2021 with the application number 202110885508.4 and the title of the invention as "a friction-based robot control method, device and equipment", the entire content of which is passed References are incorporated in this application.

technical field

The present application relates to the technical field of robot control, in particular to a friction-based robot control method, device, equipment, 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 perspective structural 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 provided by the embodiment of the present application. As shown in Figures 1-4, the driving part of the rope-driven joint robot is mainly composed of joint motors 101, drivers 102, reducers 103, rope winches 104, encoders 107 and other mechanical components. 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, and the motor 101 drives the reducer 103 and then drives the rope to control the joint movement 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 inevitably friction. The existence of friction is an important reason for the fluctuation of the system's low-speed operation speed. Harmful characteristics such as stagnation, crawling and limit loops are generated when the closed-loop tracking low-speed moving target and precise positioning are performed.

At present, there is no friction-based control scheme proposed for the rope-driven joint robot. 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 theory of identifying dynamic model parameters.

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

Contents 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 control the robot based on friction without installing a force/torque sensor. Joint control, with the beneficial effects of high control precision and low cost.

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

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

Obtaining real-time motion parameters of the joint motor;

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

calculating and obtaining actual force control data for the joint motor according to the real-time friction data and the force control target 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 joints of the target robot, a friction force identification model for motion parameters and friction of the joint motors is established, specifically including:

Obtain the motor torque constant of the joint motor;

performing speed control on the joint motors to obtain a set of motor speeds and a corresponding set of motor drive currents;

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

Substituting the set of motor speeds into an identification equation based on a steady-state friction model to obtain a set of theoretical friction torque values;

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

Substituting the first friction force identification parameters into the identification equation based on the steady-state friction force model to obtain the friction force identification model.

Optionally, the steady-state friction model is specifically:

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, θ 1} are stiffness coefficients , σ _{2, θ2} are damping coefficients.

Optionally, based on the electrical parameters of the joint motors of the target robot and the structural parameters of the joints of the target robot, a friction force identification model for motion parameters and friction of the joint motors is established, 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;

performing inverse M-sequence position control on the joint motor, collecting motor speed information and current information, and obtaining a motor speed sequence and a corresponding motor current sequence;

Obtaining the second friction force identification parameter of the shutdown motor based on the motor speed sequence and the motor current sequence fitting;

determining 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;

Substituting the moment of inertia and the equivalent damping coefficient into the joint rotation dynamics model to obtain the friction identification model.

Optionally, the friction 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 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 calculation of the actual force control data for the joint motor based on the real-time friction data and the force control target data for the joint motor specifically includes:

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

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

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

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 is the actual force control value of the joint motor.

Optionally, also include:

Obtain 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 working condition model.

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

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

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

a first computing unit, configured to substitute the real-time motion parameters into the friction identification model to obtain real-time friction data;

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

A control unit, configured to control the joint motors according to the actual force control data.

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

A memory for storing instructions, the instructions including 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-mentioned 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 as described in any one of the above-mentioned methods is realized. step.

The friction-based robot control method provided by the present application indirectly establishes a friction identification model of joint motor motion parameters and 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 identification model. After the real-time friction data is obtained, the actual force control data of the joint motor is calculated according to the real-time friction data and the force control target data of 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 in the present application can realize the friction-based robot joint control without adding force/torque sensors, 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-mentioned beneficial effects, and will not be repeated here.

Description of drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application or the prior art, the accompanying drawings that need to be used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the accompanying drawings in the following description are only For some embodiments of the present application, those of ordinary skill in the art can also obtain other drawings based on these drawings without creative effort.

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

Fig. 5 is a schematic diagram of the connection of the driving part of a rope-driven joint robot provided in the embodiment of the present application;

FIG. 6 is a flow chart of a friction-based robot control method provided in an embodiment of the present application;

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

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

FIG. 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 of step S601 in FIG. 6 provided by the embodiment of the present application;

FIG. 11 is a control block diagram of a current loop controller provided in an embodiment of the present application;

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

Fig. 13 is a schematic structural diagram of a friction-based robot control device provided in an embodiment of the present application;

FIG. 14 is a schematic structural diagram of a friction-based robot control device provided in an embodiment of the present 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, 803 is the dynamic Torque speed sensor, 804 is a shaft coupling, 805 is a data acquisition card, 806 is a current controller, and 807 is a motor driver.

Detailed ways

The core 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 control the robot based on friction without installing a force/torque sensor. Force control, with the beneficial effects of high control precision and low cost.

The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, not all of them. Based on the embodiments in this application, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the scope of protection of this application.

FIG. 6 is a flow chart of a friction-based robot control method provided by an embodiment of the present application.

As shown in Figure 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 joints of the target robot in advance, establish a friction force identification model of motion parameters and friction forces of the joint motors.

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

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

S604: According to the real-time friction data and the force control target data on the joint motor, calculate and obtain the actual force control data on the joint motor.

S605: Control the joint motors 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 use measurement method or theoretical identification method. Theoretical identification method obtains the dynamic parameters by analyzing the robot model, but the analysis process is relatively complicated. The measurement rule can accurately measure the steady-state and dynamic characteristics of the motor under different working conditions through the test instrument to extract parameters, combine the identified motor and transmission parameters, further identify the friction model of the joint and the corresponding dynamic parameters, and cooperate with the friction model compensation The controller thus realizes the three-loop control of the motor. Because of its low cost, convenient realization, and high precision, its realization and promotion significance is strong. Therefore, in the embodiment of the present application, it is preferable to use a measurement method for parameter identification.

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

Based on the electrical parameters of the joint motors obtained through identification and the structural parameters of the target robot joints, a friction identification model for the motion parameters and friction of the joint motors is established to realize the real-time friction calculation of the joints without the need for force/torque sensors to obtain external forces force.

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

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

Embodiment two

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

On the basis of the above-mentioned embodiments, the embodiment of the present application provides a specific implementation manner of measuring and identifying the electrical parameters of the joint motor 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 identify the phase resistance R _S. Let the three phases of the brushless motor be U, V, and W phases (the corresponding phase resistances are R _U , R _V , R _W ), and measure the three-phase line resistances R _VW , R _UW , and R _UV . During the measurement, keep the rotor of the motor stationary, measure each of the three sets of terminal resistances several 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 connections:

in,

The specific way to measure the phase inductance L _S can be to use an oscilloscope to excite the motor by applying a step voltage to the three wires of the motor, and to identify the phase inductance L _S by observing the response of the current. Suppose the rated input voltage of the brushless motor is U, and the three phases of the brushless motor are U, V, and W. The experiments are divided into three times, and the steps of U _UV = U, U _VW = U, U _UW = U are applied to the three lines respectively. Voltage, and by observing the current response curve of the three phases, the line inductance value is calculated. During the measurement, keep the rotor of the motor stationary.

Then for star connection:

in,

Respectively, U _UV , U _VW , U _UW three sets of current stable values after being stabilized for a certain period of time (such as 2 minutes) under voltage excitation. τ _UV , τ _VW , τ _UW correspond to

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

The reaction electromotive force K _e of the motor is identified to complete the identification of the electrical dynamic response model of the joint. Specifically, fix the motor on the stand, connect it coaxially to the towed rotating motor through a coupling, and drive the towed rotating motor for one revolution through the position controller, and measure the two-wire voltage of the motor to be tested with an oscilloscope , the corresponding waveform appears continuously on the oscilloscope, and the number of waveform peaks obtained from the test is N, then the number of pole pairs

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

Measure the peak line voltage of the motor according to the speed change method to obtain the corresponding speed

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

Record it as the reaction electromotive force K _e of the joint motor.

The identification of the structural parameters of the target robot joints mainly includes the identification of the motor torque constant, the moment of inertia of the joint, the equivalent damping coefficient and the friction parameter of the joint (including the motor + reducer combination). In order to solve the problem of difficult identification of the moment of inertia and friction coefficient due to the complex transmission form, and to use the high dynamic controller as an important parameter reference for later design, the embodiment of the present application provides the following motor mechanical parameter identification method.

Calibrating the motor torque constant K _T can be realized by using the motor torque constant calibration system shown in Fig. 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 gather different currents

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

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

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 as I _rated , and divide the rated current I _rated evenly (for example, it can be divided into 20 parts) to obtain

Measure the motor 801 according to the way of current gradient to obtain the corresponding torque

Taking (I _i , τ _i ) as the coordinate point for fitting, we get

Denote as the motor torque constant of the joint motor 801 .

For the moment of inertia J _J of the joint, the inverse M-sequence position control of the joint can be adopted. By collecting the current and speed information, and using multiple sets of parameter batch processing, 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 rotational 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.

The parameters to be identified in formulas (1) and (2) include the simplified friction parameters of the positive and negative directions of joint motor movement

and joint moment of inertia J _J .

In order to solve formulas (1) and (2), first fix the joints of the robot on the detection device, use the position controller to drive the joint motors, and reverse the position command of the M sequence, 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. set up:

Among them, W and _W1 are the rotation speed sequence, and _I1 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. set up:

Then the parameter vector to be identified is:

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

Through the identification method provided by 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 three

FIG. 10 is a flow chart of a specific implementation of step S601 in FIG. 6 provided by an embodiment of the present application.

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

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

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

S1002: Perform speed control on the joint motors to obtain a set of motor speeds and a corresponding set of motor drive currents.

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

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

S1005: Aiming at 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: Substituting the first friction force identification parameter into an identification equation based on a steady-state friction force model to obtain a friction force identification model.

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

For step S1002 and step S1003, the speed control of the joint motor is carried out through different motor speed sets

Collect the corresponding current set used to drive the joint motor

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

For step S1004, the steady-state friction model may be the Stribeck friction model. However, the friction fit of the Stribeck friction model is poor at high speeds. Therefore, the embodiment of the present application provides an optimized steady-state friction 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, σ2 _{, θ1} is the stiffness coefficient, σ2 _{, θ2} is the damping coefficient.

Since the slope of the friction force in the high speed range decreases gradually with the increase of the speed, the steady-state friction model shown in formula (14) can have a good fitting effect on the phenomenon of decreasing slope.

Note that the friction parameter in formula (14) is the first friction parameter, including the friction parameter in the positive direction

and friction parameters in the opposite direction

It is necessary to identify the friction parameters in the positive direction and the reverse direction respectively, then the identification equation based on the steady-state friction model is:

Among them, M _{f, theo, i} are the theoretical friction torque values based on the friction model.

After substituting the motor speed set collected in step S1002 into equation (15), the set is obtained

For step S1005, set the following function:

With the goal 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 specific friction model in the positive direction is:

The friction model in the opposite direction is specifically:

The optimized friction identification model proposed in the embodiment of the present application can cover the accurate identification of the high and low speed state of the rotary joint friction, and the identification model can be designed to make up for the low slope and accuracy of the traditional Stribeck friction model at high speeds. Reduce the shortcomings, thereby improving the identification accuracy of friction force and improving the force control accuracy. And it has this beneficial effect on the identification of friction force of all kinds of rotary joints, not limited to the identification of friction force of rope-driven rotary joints.

Embodiment four

Embodiment 3 of the present application provides an optimized steady-state friction model, which can cover the friction identification accuracy of high-speed and low-speed operation. For the scene where the force control performance is not high, in order to simplify the calculation, the joint rotation dynamic model given in the second embodiment of the present application can be used to determine the friction force. Then in step S601, based on the electrical parameters of the joint motors of the target robot and the structural parameters of the joints of the target robot, a friction force identification model for motion parameters and friction of the joint motors is established, which may specifically include:

Obtain the motor torque constant of the joint motor;

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

Carry out inverse M-sequence position control on the joint motor, collect motor speed information and current information, and obtain the motor speed sequence and the corresponding motor current sequence;

Based on the motor speed sequence and the motor current sequence fitting, the second friction force identification parameter of the shutdown motor is obtained;

determining 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 moment of inertia and equivalent damping coefficient are substituted into the dynamic model of joint rotation to obtain the friction identification model.

Then the friction identification model adopts the formula (2):

by

It is the second friction force identification parameter, and the solution process can refer to Embodiment 2 of the present application.

By applying the friction force identification method provided in the embodiment of the present application, the friction force identification model can be determined while identifying the joint moment of inertia and the equivalent damping coefficient, so as to achieve the effects of reducing computational complexity and delay of solution.

Embodiment five

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

The 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-mentioned embodiments, the embodiment of the present application provides a design scheme of a current controller, as shown in Figure 11, a proportional-integral (PI) controller is designed, and the phase resistance R _S The parameters of the proportional controller and the integral controller are obtained by summing the phase inductance L _S and the bandwidth of the current loop to complete the design of the current loop controller.

Wherein, i _{q_ref} is the reference value of the q-axis current control; K _e is the reaction electromotive force of the joint motor identified in Embodiment 2 of the present application; G _{C_ctl} (s) is the PI controller of the current, and the transfer function is

G _{inv_d} (s) is an 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 a current filter whose transfer function is

Where ω _cf is the cut-off frequency of the filter.

The bandwidth of the designed current loop is:

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

Based on the current controller, an impedance controller based on friction feedforward is further designed. As shown in FIG. 12 , according to the friction identification model as feedforward, control input i _{q_ref} is made to the current controller of the joint motor.

Embodiment 1 of the present application provides a scheme in which after the friction identification model is established, the real-time friction data is obtained by collecting the real-time motion parameters of the joint motors and substituting them into the friction identification model, and then the joint motors are controlled based on the friction data. In practical applications, the collected real-time motion parameters of the joint motors may only include the real-time rotational speed of the joint motors, and the real-time rotational speeds of the joint motors are substituted into the friction force identification model describing the relationship between the joint motor speed and the frictional force provided in the above-mentioned embodiments. 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 to the force control target of the joint motor, and the compensation for the friction force can be realized.

In order to adapt to different working conditions, in the embodiment of the present application, the real-time motion parameters may 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.

Then step S604: According to the real-time friction force data and the force control target data on the joint motor, calculate the actual force control data on the joint motor, specifically including:

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

Multiply 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;

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

Multiply the difference between the position control target of the joint motor minus 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 taken as 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.

In a specific implementation, as shown in Figure 12, after determining the real-time rotational speed ω of the joint motor, the real-time position θ of the joint motor and the real-time rotational speed acceleration of the joint motor can be respectively obtained through integral calculation and differential calculation

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

The real-time rotational speed ω of the joint motor is substituted into the friction identification model M _f (ω) to calculate the identified friction force M _f .

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 , virtual friction coefficient M _d and virtual stiffness coefficient K _d are the parameters of the simulated impedance model, which can be adapted to different working conditions by adjusting the virtual damping coefficient B _d , virtual friction coefficient M _d and virtual stiffness coefficient K _d next request.

As shown in FIG. 12 , the embodiment of the present application also adds a working condition model M _L (θ) and a position planner θ _r (t) to the impedance controller (not shown in FIG. 12 ). Among them, the working condition model M _L (θ) is used to determine the force control target of the joint motor according to the current working condition, and the position planner is used to determine the speed control target of the joint motor according to the current working condition

Acceleration control target of joint motor

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

In addition, when the working condition is unknown, the working condition model M _L (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 actual force control value F _d of the joint motor is obtained as follows:

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

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

Based on the impedance controller based on friction feedforward provided by the embodiment of the present application, the rope-driven joint robot can dynamically adjust the joint pull rope according to the friction feedforward provided by the precise friction force and the working condition curve and the designed impedance parameters. The force-position characteristic can not only realize precise force control, but also realize complex force-position control effect similar to impedance characteristic. 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 application also discloses a friction-based robot control device, equipment, and computer-readable storage medium corresponding to the above method.

Embodiment six

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

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

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

The first acquisition unit 1302 is configured to acquire real-time motion parameters of the joint motor;

The first calculation unit 1303 is configured to substitute real-time motion parameters into the friction identification model to obtain real-time friction data;

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

The control unit 1305 is used to control the joint motors 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 calculation unit is used to generate real-time rotational speed, real-time rotational speed acceleration, real-time position and force control target according to the working condition model.

Since the embodiment of the device part corresponds to the embodiment of the method part, please refer to the description of the embodiment of the method part for the embodiment of the device part, and details will not be repeated here.

Embodiment seven

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 Figure 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-mentioned embodiments;

The processor 1420 is configured to execute the instruction.

Wherein, 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 can be realized by at least one hardware form of digital signal processing DSP (Digital Signal Processing), field programmable gate array FPGA (Field-Programmable Gate Array), and programmable logic array PLA (Programmable Logic Array). The processor 1420 may also include a main processor and a coprocessor, the main processor is a processor for processing data in the wake-up state, and is also called a central processing unit CPU (Central Processing Unit); Low-power processor for processing data in standby state. In some embodiments, the processor 1420 may be integrated with an image processor GPU (Graphics Processing Unit), 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 also include an artificial intelligence AI (Artificial Intelligence) processor, which is used to process computing operations related to machine learning.

Memory 1410 may include one or more computer-readable storage media, which may be non-transitory. The memory 1410 may also include high-speed random access memory, and non-volatile memory, such as one or more magnetic disk storage devices, flash memory storage devices. In this embodiment, the memory 1410 is at least used to store the following computer program 1411, wherein, after the computer program 1411 is loaded and executed by the processor 1420, it can realize the correlation in the friction-based robot control method 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 method may be temporary storage or permanent storage. Wherein, the operating system 1412 may be Windows. The data 1413 may include but not limited to the data involved in the above method.

In some embodiments, the friction-based robot control device may further include a display screen 1430 , a power supply 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 in the illustration.

The friction-based robot control device provided by the embodiment of the present application includes a memory and a processor. When the processor executes the program stored in the memory, it can implement the friction-based robot control method described above, with the same effect as above.

Embodiment eight

It should be noted that the above-described device 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. In another point, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces, and the indirect coupling or communication connection of devices or modules may be in electrical, mechanical or other forms. A module described as a separate component may or may not be physically separated, and a component shown as a module may or may not be a physical module, that is, it may be located in one place, or may also be distributed to multiple network modules. Part or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

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

If the integrated modules are realized in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or part of the contribution to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium , executing 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, on which a computer program is stored, 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, mobile 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 contained in the computer-readable storage medium provided in this embodiment can realize the steps of the above-mentioned friction-based robot control method when executed by a processor, and the effect is the same as above.

The friction-based robot control method, device, equipment and computer-readable storage medium provided by the present application have been described in detail above. Each embodiment in the description is described in a progressive manner, each embodiment focuses on the difference from other embodiments, and the same and similar parts of each embodiment can be referred to each other. As for the device, equipment and computer-readable storage medium disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and for relevant details, please refer to the description of the method part. It should be pointed out that those skilled in the art can make some improvements and modifications to the application without departing from the principles of the application, and these improvements and modifications also fall within the protection scope of the claims of the application.

It should also be noted that in this specification, relative terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that these entities or operations There is no such actual relationship or order between the operations. Furthermore, the term "comprises", "comprises" or any other variation thereof is intended to cover a non-exclusive inclusion such that a process, method, article, or apparatus comprising a set of elements includes not only those elements, but also includes elements not expressly listed. other elements of or also include elements inherent in such a process, method, article, or device. Without further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article or apparatus comprising said element.

Claims (10)

1. A method for controlling a robot based on friction, comprising:

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

   Obtaining real-time motion parameters of the joint motor;

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

   calculating and obtaining actual force control data for the joint motor according to the real-time friction data and the force control target 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, characterized in that, based on the electrical parameters of the joint motors of the target robot and the structural parameters of the joints of the target robot, the frictional force between the motion parameters of the joint motors and the frictional force is established Identify models, including:

   Obtain the motor torque constant of the joint motor;

   performing speed control on the joint motors to obtain a set of motor speeds and a corresponding set of motor drive currents;

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

   Substituting the set of motor speeds into an identification equation based on a steady-state friction model to obtain a set of theoretical friction torque values;

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

   Substituting the first friction force identification parameters 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 model is specifically:

   

   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, θ 1} are stiffness coefficients , σ _{2, θ2} are damping coefficients.
4. The robot control method according to claim 1, characterized in that, based on the electrical parameters of the joint motors of the target robot and the structural parameters of the joints of the target robot, the frictional force between the motion parameters of the joint motors and the frictional force is established Identify models, 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;

   performing inverse M-sequence position control on the joint motor, collecting motor speed information and current information, and obtaining a motor speed sequence and a corresponding motor current sequence;

   Obtaining the second friction force identification parameter of the shutdown motor based on the motor speed sequence and the motor current sequence fitting;

   determining 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;

   Substituting the moment of inertia and the equivalent damping coefficient into the joint rotation dynamics model to obtain the friction identification model.
5. The robot control method according to claim 4, wherein the friction 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 calculation of the actual force control data for the joint motor based on the real-time friction data and the force control target data for the joint motor specifically includes:

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

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

   The difference between the acceleration control target of the joint motor minus the real-time rotational speed acceleration is multiplied by 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 by a preset virtual stiffness coefficient to obtain a third compensation force;

   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 is the actual force control value of the joint motor.
7. The robot control method according to claim 6, further comprising:

   Obtain 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 working condition model.
8. A robot control device based on friction, characterized in that it comprises:

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

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

   a first computing unit, configured to substitute the real-time motion parameters into the friction identification model to obtain real-time friction data;

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

   A control unit, configured to control the joint motors according to the actual force control data.
9. A friction-based robot control device, characterized in that it includes:

   A memory for storing instructions, the instructions including 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, wherein, when the computer program is executed by a processor, the steps of the friction-based robot control method according to any one of claims 1 to 7 are realized.

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