CN113510693B - Robot control method, device and equipment based on friction force - Google Patents
Robot control method, device and equipment based on friction force Download PDFInfo
- Publication number
- CN113510693B CN113510693B CN202110885508.4A CN202110885508A CN113510693B CN 113510693 B CN113510693 B CN 113510693B CN 202110885508 A CN202110885508 A CN 202110885508A CN 113510693 B CN113510693 B CN 113510693B
- Authority
- CN
- China
- Prior art keywords
- motor
- joint motor
- friction
- joint
- force
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 68
- 238000004364 calculation method Methods 0.000 claims abstract description 15
- 238000013016 damping Methods 0.000 claims description 16
- 230000001133 acceleration Effects 0.000 claims description 14
- 238000004590 computer program Methods 0.000 claims description 8
- 230000003068 static effect Effects 0.000 claims description 7
- 230000008569 process Effects 0.000 abstract description 10
- 230000009286 beneficial effect Effects 0.000 abstract description 5
- 238000010586 diagram Methods 0.000 description 17
- 238000012545 processing Methods 0.000 description 10
- 239000003638 chemical reducing agent Substances 0.000 description 6
- 230000000694 effects Effects 0.000 description 6
- 230000006870 function Effects 0.000 description 5
- 238000012360 testing method Methods 0.000 description 5
- 238000006243 chemical reaction Methods 0.000 description 4
- 238000004891 communication Methods 0.000 description 4
- 238000013461 design Methods 0.000 description 4
- 238000005259 measurement Methods 0.000 description 4
- 230000009471 action Effects 0.000 description 3
- 230000005540 biological transmission Effects 0.000 description 3
- 230000008878 coupling Effects 0.000 description 3
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 230000004044 response Effects 0.000 description 3
- 238000012546 transfer Methods 0.000 description 3
- 230000008901 benefit Effects 0.000 description 2
- 238000004422 calculation algorithm Methods 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 238000000691 measurement method Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000005070 sampling Methods 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000011217 control strategy Methods 0.000 description 1
- 230000009193 crawling Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000005284 excitation Effects 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 230000002068 genetic effect Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 230000005055 memory storage Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 230000002085 persistent effect Effects 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000009877 rendering Methods 0.000 description 1
- 230000000630 rising effect Effects 0.000 description 1
- 230000000087 stabilizing effect Effects 0.000 description 1
- 230000001052 transient effect Effects 0.000 description 1
Images
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/10—Programme-controlled manipulators characterised by positioning means for manipulator elements
- B25J9/104—Programme-controlled manipulators characterised by positioning means for manipulator elements with cables, chains or ribbons
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1602—Programme controls characterised by the control system, structure, architecture
Landscapes
- Engineering & Computer Science (AREA)
- Robotics (AREA)
- Mechanical Engineering (AREA)
- Automation & Control Theory (AREA)
- Control Of Electric Motors In General (AREA)
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
Technical Field
The present disclosure relates to the field of robot control technologies, and in particular, to a friction-based robot control method, apparatus, device, and computer-readable storage medium.
Background
FIG. 1 is a schematic structural diagram of a rope-driven articulated robot driving part; FIG. 2 is a front view of the cord driven articulated robot drive section of FIG. 1; FIG. 3 is a side view of the cord driven articulated robot drive section of FIG. 1; FIG. 4 is another schematic view of the rope-driven articulated robot drive shown in FIG. 1; fig. 5 is a connection schematic diagram of a rope-driven joint robot driving portion according to an embodiment of the present disclosure. As shown in fig. 1 to 4, the driving part of the rope-driven articulated robot is mainly composed of joint motors 101, a driver 102, a reducer 103, a rope winch 104, an encoder 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 further drives the rope to control the robot joint to move.
In a force interaction scene, a robot needs to acquire accurate force, speed and position control, friction inevitably exists in a rotary joint transmission system driven by a servo motor, the existence of the friction is an important reason for fluctuation of the low-speed running speed of the system, and harmful characteristics such as sliding, crawling and limit ring are generated when a low-speed moving target is tracked in a closed loop and accurately positioned.
There is currently no proposed friction-based control scheme for rope-driven articulated robots. In other motor control schemes, torque is usually obtained by additionally installing a force sensor and a torque sensor, and then friction is determined and compensated based on an identification dynamic model parameter theory.
However, this way of adding a force/torque sensor adds structural complexity and leads to higher costs, and does not have good economy and versatility.
Disclosure of Invention
The application aims to provide a friction-based robot control method, a friction-based robot control device, friction-based robot control equipment and a computer-readable storage medium, which are used for realizing friction identification and controlling joints of a robot based on friction on the premise of not additionally installing a force/torque sensor and have the advantages of high control precision and low cost.
In order to solve the above technical problem, the present application provides a robot control method based on friction, including:
establishing a friction force identification model of motion parameters and friction force of a joint motor of a target robot in advance based on electric parameters of the joint motor and structural parameters of a joint of the target robot;
acquiring real-time motion parameters of the joint motor;
substituting the real-time motion parameters into the friction force identification model to obtain real-time friction force data;
calculating actual force control data of the joint motor according to the real-time friction force data and force control target data of the joint motor;
and controlling the joint motor according to the actual force control data.
Optionally, the electric parameter of the joint motor based on the target robot and the structural parameter of the joint of the target robot establish a friction force identification model of the motion parameter of the joint motor and the friction force, specifically include:
acquiring a motor torque constant of the joint motor;
carrying out speed control on the joint motor to obtain a motor rotating speed set and a corresponding motor driving current set;
multiplying the motor driving current set by the motor torque constant to obtain a motor driving torque value set;
substituting the motor rotating speed set into an identification equation based on a steady-state friction force model to obtain a theoretical friction torque value set;
with the absolute value of the difference value between the theoretical friction torque value set and the motor driving torque value set minimized as a target, obtaining a first friction force identification parameter of the joint motor;
and substituting 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 model specifically includes:
wherein M is f For identifying friction, M C Is coulomb friction force, M S Is static friction force, omega is the real-time rotating speed of the joint motor, omega s Is the critical speed, sigma, of the joint motor 2,θ1 Is a rigidity coefficient, σ 2,θ2 Is the damping coefficient.
Optionally, the electric parameter of the joint motor based on the target robot and the structural parameter of the joint of the target robot establish a friction force identification model of the motion parameter of the joint motor and the friction force, specifically include:
acquiring a motor torque constant of the joint motor;
establishing a joint rotation dynamic model of the joint motor according to the motor torque constant;
performing position control of an inverse M sequence on the joint motor, and acquiring motor rotating speed information and current information to obtain a motor rotating speed sequence and a corresponding motor current sequence;
fitting based on the motor rotating speed sequence and the motor current sequence to obtain a second friction force identification parameter of the joint motor;
determining the rotational inertia of the joint motor and the equivalent damping coefficient of the joint motor according to the second friction force identification parameter;
and substituting the rotational inertia and the equivalent damping coefficient into the joint rotational dynamics model to obtain the friction force identification model.
Optionally, the friction force identification model specifically includes:
wherein, tau f To identify frictional forces, tau c And the maximum static friction force of the joint motor is omega, the real-time rotating speed of the joint motor is omega, and the equivalent damping coefficient is B.
Optionally, the real-time motion parameters specifically include: the real-time rotating speed of the joint motor, the real-time rotating speed acceleration of the joint motor and the real-time position of the joint motor are calculated;
the calculating actual force control data of the joint motor according to the real-time friction force data and the force control target data of the joint motor specifically comprises:
generating a friction force compensation force according to the real-time friction force data;
multiplying the difference value obtained by subtracting the real-time rotating speed from the rotating speed control target of the joint motor by a preset virtual damping coefficient to obtain a first compensation force;
multiplying the difference value obtained by subtracting the real-time rotating speed and the acceleration from the acceleration control target of the joint motor by a preset virtual friction coefficient to obtain a second compensation force;
multiplying the difference value obtained by subtracting the real-time position from the position control target of the joint motor by a preset virtual stiffness coefficient to obtain a third compensation force;
and taking the sum of the force control target of the joint motor, the frictional force compensation force, the first compensation force, the second compensation force and the third compensation force as an actual force control value of the joint motor.
Optionally, the method further includes:
acquiring a working condition model of the joint motor;
and generating the real-time rotating speed, the real-time rotating speed acceleration, the real-time position and the force control target according to the working condition model.
In order to solve the above technical problem, the present application further provides a robot control device based on a frictional force, including:
the friction force identification unit is used for establishing a friction force identification model of the motion parameters and the friction force of the joint motor based on the electric 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 is used for acquiring real-time motion parameters of the joint motor;
the first calculation unit is used for substituting the real-time motion parameters into the friction force identification model to obtain real-time friction force data;
the second calculation unit is used for calculating actual force control data of the joint motor according to the real-time friction force data and force control target data of the joint motor;
and the control unit is used for controlling the joint motor according to the actual force control data.
In order to solve the above technical problem, the present application further provides a robot control apparatus based on a frictional force, including:
a memory for storing instructions comprising the steps of any of the friction-based robot control methods described above;
a processor to execute the instructions.
To solve the above technical problem, the present application further provides a computer-readable storage medium having a computer program stored thereon, where the computer program is executed by a processor to implement the steps of the friction-based robot control method according to any one of the above aspects.
The application provides a robot control method based on frictional force, through the electrical parameter of the joint motor based on the target robot in advance and the structural parameter of the joint of the target robot, the motion parameter of the joint motor and the frictional force identification model of the frictional force are indirectly established, in the actual control process, the real-time motion parameter of the joint motor is obtained and substituted into the frictional force identification model, after the real-time frictional force data is obtained, the actual force control data of the joint motor is obtained through calculation according to the real-time frictional force data and the 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.
The application also provides a robot control device, equipment and a computer readable storage medium based on friction force, which have the beneficial effects and are not repeated herein.
Drawings
For a clearer explanation of the embodiments or technical solutions of the prior art of the present application, the drawings needed for the description of the embodiments or prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a schematic structural diagram of a rope-driven articulated robot driving part;
FIG. 2 is a front view of the cord actuated articulated robot drive section of FIG. 1;
FIG. 3 is a side view of the cord driven articulated robot drive section of FIG. 1;
FIG. 4 is another perspective view of the drive portion of the rope-driven articulated robot shown in FIG. 1;
FIG. 5 is a schematic connection diagram of a rope-driven articulated robot driving portion according to an embodiment of the present disclosure;
fig. 6 is a flowchart of a friction-based robot control method according to an embodiment of the present disclosure;
FIG. 7 is a schematic view 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 according to an embodiment of the present disclosure;
FIG. 9 is a schematic connection diagram of a motor torque constant calibration system according to an embodiment of the present disclosure;
fig. 10 is a flowchart of an embodiment of the present application, where the flowchart of step S601 in fig. 6 is provided;
fig. 11 is a control block diagram of a current loop controller according to an embodiment of the present application;
FIG. 12 is a control block diagram of a friction feedforward-based impedance controller according to an embodiment of the present disclosure;
fig. 13 is a schematic structural diagram of a friction-based robot control device according to an embodiment of the present disclosure;
fig. 14 is a schematic structural diagram of a friction-based robot control device according to an embodiment of the present disclosure;
wherein 101 is a joint motor, 102 is a driver, 103 is a reducer, 104 is a rope winch, 105 is a controller, 106 is a sensor, 107 is an encoder, 801 is a test bench, 802 is a hysteresis brake, 803 is a dynamic torque and rotation speed sensor, 804 is a coupler, 805 is a data acquisition card, 806 is a current controller, and 807 is a motor driver.
Detailed Description
The core of the application is to provide a friction-based robot control method, a friction-based robot control device, friction-based robot control equipment and a computer-readable storage medium, which are used for realizing friction identification and performing force control on a robot based on friction on the premise of not additionally installing a force/torque sensor, and have the advantages of high control precision and low cost.
The technical solutions in the embodiments of the present application will be described clearly and completely with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only some embodiments of the present application, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Example one
Fig. 6 is a flowchart of a friction-based robot control method according to an embodiment of the present disclosure. As shown in fig. 6, a friction-based robot control method provided in an embodiment of the present application includes:
s601: and establishing a friction force identification model of the motion parameters and the friction force of the joint motor in advance based on the electrical parameters of the joint motor of the target robot and the structural parameters of the joint of the target robot.
S602: and acquiring real-time motion parameters of the joint motor.
S603: and substituting the real-time motion parameters into the friction force identification model to obtain real-time friction force data.
S604: and calculating actual force control data of the joint motor according to the real-time friction force data and the force control target data of the joint motor.
S605: and controlling the joint motor according to the actual force control data.
In a specific implementation, in step S601, in order to control the joint motor, it is necessary to identify an electrical parameter of the joint motor and a structural parameter of the joint. The identification method can adopt a measurement method or a theoretical identification method. The theoretical identification method obtains kinetic parameters by analyzing a robot model, but the analysis process is complicated. The measurement rule can accurately measure the steady state and dynamic characteristics of the motor under different working conditions through a test instrument to extract parameters, further identify a friction model of a joint and corresponding dynamic parameters by combining the identified motor and transmission parameters, and realize three-loop control of the motor by matching with a controller compensated by the friction model. Therefore, in the embodiment of the present application, the parameter identification is preferably performed by using a measurement method.
The problem that motor parameters are few and cannot be known when the motor is selected for use in the prior art is solved by identifying the electrical parameters of the joint motor in advance, so that the problem can be solved, and reference basis can be provided for a follow-up motor control link. For a three-phase brushless motor, the electrical parameter identification mainly relates to identification of phase resistance, identification of phase inductance and identification of reaction electromotive force of the motor. The structural parameter identification aiming at the target robot joint mainly comprises the identification of a motor torque constant, a joint rotational inertia, an equivalent damping coefficient and a friction force parameter of the joint (comprising a motor and a reducer combination).
And establishing a friction force identification model of the motion parameters and the friction force of the joint motor based on the electric parameters of the joint motor and the structural parameters of the target robot joint obtained by identification so as to realize calculation of the real-time friction force of the joint under the condition that an external force is acquired without a force/torque sensor.
In the actual control process, the real-time friction data can be obtained through the friction force identification model by acquiring the real-time motion parameters of the joint motor, then the friction loss compensation is carried out on the force control target data of the joint motor according to the real-time friction data to obtain the actual force control data of the joint motor, and the joint motor is controlled according to the actual force control data to ensure that the joint motor reaches the expected force control target.
The embodiment of the application provides a friction-based robot control method, through the electrical parameter of the joint motor based on the target robot and the structural parameter of the joint of the target robot in advance, the motion parameter of the joint motor and the friction force identification model are indirectly established, in the actual control process, the real-time motion parameter of the joint motor is acquired and substituted into the friction force identification model, after the real-time friction force data is obtained, the actual force control data of the joint motor is obtained through calculation according to the real-time friction force data and the 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 provided by the embodiment of the application 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.
Example two
FIG. 7 is a schematic view 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 according to an embodiment of the present disclosure; fig. 9 is a connection diagram of a motor torque constant calibration system according to an embodiment of the present disclosure.
On the basis of the above embodiments, the embodiments of the present application provide a specific implementation manner for 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, the identification method provided in the embodiments of the present application is not limited to the identification method provided in the embodiments of the present application for different types of motors and robot applications.
Measuring phase resistance R S The specific method of the method can be that after the line resistance is measured by adopting a multimeter, the phase resistance R is carried out S And (4) identifying. Let three phases of the brushless motor be U, V, W phases (corresponding phase resistance is R) U 、R V 、R W ) Separately measuring the three-phase line resistance R VW 、R UW 、R UV . During measurement, the rotor of the motor is kept still, and after each of the three groups of end resistors is measured for multiple times (such as three times), the arithmetic mean value is taken as the value of the end resistor, and R is obtained respectively VW 、R UW 、R UV 。
Then for the star connection:
R U ＝R med -R VW ；
R V ＝R med -R UW ；
R W ＝R med -R UV 。
for delta-connection:
wherein,
measuring phase inductance L S The specific mode of the method can be that an oscilloscope is utilized, the motor is excited by applying step voltage to three lines of the motor, and the phase inductance L is obtained by observing the response of current S And (5) performing identification. Setting the rated input voltage of the brushless motor as U, dividing three phases of the brushless motor into U, V and W into three experiments, and applying the three phases of the brushless motor as U UV ＝U、U VW ＝U、U UW = U step voltage and the line inductance value is calculated by observing the current response curve for the three phases. And during measurement, keeping the rotor of the motor still.
Then for a star connection:
wherein,
are respectively U UV 、U VW 、U UW And stabilizing the current stable value after a certain time (such as 2 minutes) under the excitation of the three groups of voltages. Tau is UV 、τ VW 、τ UW Correspond to
Time (time from line voltage/line current change to 0). The observed current graph is shown in fig. 7.
Reaction electromotive force K to motor e And identifying to complete the identification of the joint electric dynamic response model. Specifically, the motor is fixed on a rack, the motor is coaxially connected to a counter-dragging rotating motor through a coupler, the counter-dragging rotating motor is driven by a position controller in a circle, two-line voltage of the motor to be tested is measured through an oscilloscope, corresponding waveforms continuously appear on the oscilloscope, the number of wave peaks of the waveforms obtained through testing is N, and the number of pole pairs is N
Controlling the counter-dragging motor by using a speed controller, and setting the rated rotating speed of the motor to be tested to be N rated The rated rotation speed is divided equally (for example, 20 parts equally) to obtain
Measuring the peak line voltage of the motor in a mode of rotating speed gradient to obtain the corresponding rotating speed
Line voltage wave downArithmetic mean of peak values and corresponding frequencies
Then the
Taking out multiple groups (such as 20 groups)
Arithmetic mean value of
Reaction electromotive force K marked as joint motor e 。
The structural parameter identification for the target robot joint mainly comprises the identification of a motor torque constant, a joint rotational inertia, an equivalent damping coefficient and a friction force parameter of the joint (comprising a motor and a reducer combination). In order to solve the problems that the rotational inertia and the friction coefficient are complex and difficult to identify due to the complex transmission form and a high-dynamic controller is designed for the later stage to serve as an important parameter reference, the embodiment of the application provides the following motor mechanical parameter identification method.
Calibrating motor torque constant K T This can be accomplished using a motor torque constant calibration system as shown in fig. 8-9. The joint motor 101 is fixed by using a test bench 801, the motor shaft is locked by using a hysteresis brake 802, and different current sets are collected in a current control mode
The motor 101 is driven, information of the dynamic rotation speed torque sensor 803 connected in series is recorded, and the motor torque constant K is identified T . The specific measurement steps include:
fixing the joint motor 101 on a test bench 801, connecting the joint motor to a dynamic torque rotating speed sensor 803 through a coupler 804, and acquiring data of a hysteresis brake 802 through a data acquisition card 805 and inputting the data into a current controller 806; and locked by the hysteresis brake 802.
For joint moment of inertia J J The position control of the joint in inverse M sequence can be adopted, and the integral moment of inertia J of shutdown is identified by collecting current and rotating speed information and utilizing a plurality of groups of parameters for batch processing J And an equivalent damping coefficient B. Specifically, the joint rotation dynamics model is as follows:
J J ·ω＝K T ·I-τ f ， (1)
wherein omega is the real-time rotating speed of the joint motor, K T Is motor torque constant, I is motor current, τ f Is static friction force, tau c The maximum static friction force of the joint motor is B, and the equivalent damping coefficient is B.
The parameters to be identified in the formulas (1) and (2) comprise simplified friction parameters in the positive and negative directions of the motion of the joint motor
And joint moment of inertia J J 。
To solve the equations (1) and (2), the robot joint is first fixed on a detection device and usedThe position controller drives the joint motor to perform position and speed acquisition on the joint motor according to a position instruction of an inverse M sequence, the amplitude of the position instruction is A and is more than or equal to 10 degrees, the amplitude is 2mm, the rotation speed and the current of the joint motor are sampled by using a zero-order retainer, and the sampling time is
Obtaining N more than or equal to 10000 sampling points. Setting:
wherein W and W 1 As a sequence of rotational speeds, I 1 Is a current sequence.
Based on the least square method, let:
thus, parameter vector identification is performed with the goal of minimizing E.
Setting:
the parameter vector to be identified is:
wherein, K θd 、T θs 、p θd And lambda (k) and phi are process parameters, and particularly, an identification parameter vector can be obtained by taking the minimum E as a target through a genetic algorithm. The following results can thus be obtained:
through the identification method provided by the embodiment of the application, the relevant electrical parameters and structural parameters of the joint motor can be accurately obtained, and a foundation is laid for realizing accurate force control.
EXAMPLE III
Fig. 10 is a flowchart of an embodiment of the present application, where the specific implementation manner of step S601 in fig. 6 is provided.
In addition to the identification of the electrical parameters and the structural parameters, friction force parameter identification of the joint (combination of the joint motor and the reducer) is also required, and the friction force parameter identification is used as an important friction force model reference for further identifying the rotational inertia of the joint.
Specifically, as shown in fig. 10, the establishing a friction force identification model of the motion parameter and the friction force of the joint motor based on the electrical parameter of the joint motor of the target robot and the structural parameter of the joint of the target robot in step S601 may specifically include:
s1001: and acquiring a motor torque constant of the joint motor.
S1002: and controlling the speed of the joint motor to obtain a motor rotating speed set and a corresponding motor driving current set.
S1003: and multiplying the motor driving current set by the motor torque constant to obtain a motor driving torque value set.
S1004: and substituting the motor rotating speed set into an identification equation based on a steady-state friction force model to obtain a theoretical friction torque value set.
S1005: and solving a first friction force identification parameter of the joint motor by taking the absolute value of the difference value of the minimum theoretical friction torque value set and the motor driving torque value set as a target.
S1006: and substituting the first friction force identification parameter into an 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 by the above steps.
For step S1002 and step S1003, the speed of the joint motor is controlled by different motor speed sets
Collecting a corresponding set of currents for driving a joint motor
Multiplying to obtain a corresponding motor drive torque value set:
for step S1004, a Stribeck friction model may be selected as the steady-state friction model. However, the Stribeck friction model has a poor friction fit at high rotational speeds. Therefore, the embodiment of the present application provides an optimized steady-state friction model as follows:
wherein M is f For identifying friction, M C Is coulomb friction force, M S Is static friction force, omega is the real-time rotating speed of the joint motor, omega s Is the critical speed, sigma, of the joint motor 2,θ1 Is a rigidity coefficient, σ 2,θ2 Is the damping coefficient.
Because the slope of the friction force in the high rotating speed interval rising along with the speed is gradually reduced, the steady-state friction force model shown in the formula (14) is adopted, and the steady-state friction force model has good fitting effect on the phenomenon of slope reduction.
The friction force parameter in the formula (14) is a first friction force parameter, including the friction force parameter in the positive direction
And friction parameter in the opposite direction
If friction parameters in the positive direction and the negative direction need to be identified respectively, the identification equation based on the steady-state friction model is as follows:
wherein M is f,theo,i Is a theoretical friction torque value based on a friction force model.
Substituting the motor rotating speed set acquired in the step S1002 into an equation (15) to obtain a set
For step S1005, the following function is set:
to minimize L (D) i ) To target, the best fit parameter combination is obtained:
for step S1006, the parameters obtained from equations (17) and (18) are substituted into the identification equation of equation (15), and the friction force identification model is obtained as follows:
the positive direction friction force model specifically comprises:
the counter direction friction force model is specifically as follows:
the friction force identification model of optimization that this application embodiment provided can cover the accurate of rotary joint frictional force when high low-speed state and distinguish, can compensate traditional Stribeck friction force model slope step-down and the shortcoming that the precision reduces when high-speed section through the design identification model to promote the identification precision of frictional force, improve the power control precision. And the friction force identification of various rotary joints has the beneficial effect, and the friction force identification is not only limited to the identification of the friction force of the rope-driven rotary joint.
Example four
The third embodiment of the application provides an optimized steady-state friction force model, and the identification precision of friction force running at high speed and low speed can be covered. For a scene with low force control performance requirement, in order to simplify calculation, the joint rotation dynamic model provided in the second embodiment of the present application may be selected to determine the friction force. Then, in step S601, based on the electrical parameter of the joint motor of the target robot and the structural parameter of the joint of the target robot, a friction force identification model of the motion parameter of the joint motor and the friction force is established, which may specifically include:
acquiring a motor torque constant of a joint motor;
establishing a joint rotation dynamic model of the joint motor according to the motor torque constant;
carrying out position control of an inverse M sequence on a joint motor, and acquiring motor rotating speed information and current information to obtain a motor rotating speed sequence and a corresponding motor current sequence;
fitting based on the motor rotating speed sequence and the motor current sequence to obtain a second friction force identification parameter of the joint motor;
determining the rotational inertia of the joint motor and the equivalent damping coefficient of the joint motor according to the second friction force identification parameter;
and substituting the rotational inertia and the equivalent damping coefficient into the joint rotational dynamics model to obtain a friction force identification model.
The friction force identification model adopts the formula (2):
to be provided with
For a second friction identification parameter, the second embodiment of the present application may be referred to for the solving process.
By applying the friction force identification method provided by the embodiment of the application, the friction force identification model can be determined while the joint moment of inertia and the equivalent damping coefficient are identified, and the effects of reducing the calculation complexity and reducing the calculation delay are achieved.
EXAMPLE five
Fig. 11 is a control block diagram of a current loop controller according to an embodiment of the present application; fig. 12 is a control block diagram of an impedance controller based on friction feedforward according to an embodiment of the present application.
The important link of the force control of the joint motor is the design and the regulation of a current controller. On the basis of the above embodiments, the present application provides a design scheme of a current controller, as shown in fig. 11, a Proportional Integral (PI) controller is designed, and a phase resistance R identified by the second application embodiment is obtained S And phase inductance L S And obtaining parameters of a proportional controller and an integral controller according to the bandwidth of the current loop, and completing the design of the current loop controller.
Wherein i q_ref Controlling a reference value for the q-axis current; k e The reaction electromotive force of the joint motor identified in the second embodiment of the present application; is G C_ctl (s) a PI controller for the current with a transfer function of
G inv_d (s) is an inverter based on SVPWM algorithm, and the transfer function is equivalent to
G s_pmsm (s) is based on i d Motor model of the vector control strategy of =0,
G cf (s) is a current filter having a transfer function of
Wherein ω is cf Is the cut-off frequency of the filter.
The bandwidth of the current loop is designed as follows:
then the PI controller parameters for the current are designed to be:
on the basis of the current controller, an impedance controller based on friction force feedforward is further designed. As shown in FIG. 12, the control input i is performed to the current controller of the joint motor according to the friction force identification model as feedforward q_ref 。
The embodiment of the application provides a scheme that after a friction force identification model is established, real-time motion parameters of a joint motor are collected and substituted into the friction force identification model to obtain real-time friction force data, and then the control of the joint motor is carried out based on the friction force data. In practical application, the acquired real-time motion parameters of the joint motor may only include the real-time rotation speed of the joint motor, and the friction force may be obtained by substituting the real-time rotation speed of the joint motor into the friction force identification model describing the relationship between the rotation speed and the friction force provided in the above embodiment, so that the actual force control value of the joint motor may be obtained by adding the friction force to the force control target of the joint motor, and the compensation of the friction force may 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 rotating speed of the joint motor, the real-time rotating speed acceleration of the joint motor and the real-time position of the joint motor.
Step S604: calculating actual force control data of the joint motor according to the real-time friction force data and the force control target data of the joint motor, and specifically comprising the following steps:
generating a friction force compensation force according to the real-time friction force data;
multiplying the difference value obtained by subtracting the real-time rotating speed from the rotating speed control target of the joint motor by a preset virtual damping coefficient to obtain a first compensation force;
multiplying the difference value of the acceleration control target of the joint motor minus the real-time rotating speed and the acceleration by a preset virtual friction coefficient to obtain a second compensation force;
multiplying the difference value obtained by subtracting the real-time position from the position control target of the joint motor by a preset virtual stiffness coefficient to obtain a third compensation force;
and taking 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 as an actual force control value of the joint motor.
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 may be obtained through integral calculation and differential calculation, respectively
The real-time rotation speed ω may be determined by acquiring the rotation speed of the joint motor in real time through a measuring instrument, or controlling the target with the rotation speed of the joint motor as shown in fig. 12
Substituting real-time rotating speed omega of joint motor into friction force identification model M f (omega) calculating to obtain identification friction force M f 。
In the embodiment of the application, a virtual damping coefficient B is added d Virtual coefficient of friction M d And a virtual stiffness coefficient K d To meet the requirements under complex working conditions. Virtual damping coefficient B d Virtual coefficient of friction M d And a virtual stiffness coefficient K d For the simulated impedance model parameters, by adjusting the virtual damping coefficient B d Virtual coefficient of friction M d And a virtual stiffness coefficient K d The requirements under different working conditions are met.
As shown in fig. 12, in the embodiment of the present application, a working condition model M is further added to the impedance controller L (theta) and position planner theta r (t) (not shown in FIG. 12). Wherein, the working condition model M L (theta) force control target M for determining joint motor according to current working condition L Position planner theta r (t) determining a rotational speed control target of the joint motor according to the current working condition
Acceleration control target of joint motor
Position control target theta of joint motor r . The friction-based robot control method provided by the embodiment of the application further includes: acquiring a working condition model of a joint motor; and generating a real-time rotating speed, a real-time rotating speed acceleration, a real-time position and a force control target according to the working condition model.
In addition, when the working condition is unknown, the working condition model M can be omitted L (theta) while adjusting the impedance controller parameter B d 、M d 、K d To meet the requirements of complex working conditions.
The impedance controller F based on the control block diagram shown in fig. 12 virtual As shown in the following formula:
the obtained actual force control value F of the joint motor d As shown in the following formula:
input to the current controller i q_ref Comprises the following steps:
wherein, T L (θ) is the external demand load curve.
Based on the friction feedforward-based impedance controller provided by the embodiment of the application, the force-position characteristic of the joint pull rope can be dynamically adjusted according to the friction feedforward provided by the accurate friction and the working condition curve and the designed impedance parameter to the rope-driven joint robot, so that the accurate force control can be realized, and the complex force-position control effect similar to the impedance characteristic can be realized. The joint adaptability can be changed by adapting to the requirements of different environments through dynamically adjusting the parameters of the impedance controller, the joint motor control device has a good control effect, and the applicability is strong.
On the basis of the above detailed description of various embodiments corresponding to the friction-based robot control method, the application also discloses a friction-based robot control device, equipment and a computer readable storage medium corresponding to the method.
EXAMPLE six
Fig. 13 is a schematic structural diagram of a robot control device based on friction according to an embodiment of the present application.
As shown in fig. 13, a friction-based robot control device according to an embodiment of the present application includes:
a friction force identification unit 1301 for establishing a friction force identification model of a motion parameter and a friction force of a joint motor based on an electrical parameter of the joint motor of the target robot and a structural parameter of a joint of the target robot in advance;
a first obtaining unit 1302, configured to obtain real-time motion parameters of a joint motor;
the first calculation unit 1303 is used for substituting the real-time motion parameters into the friction force identification model to obtain real-time friction force data;
a second calculating unit 1304, configured to calculate actual force control data of the joint motor according to the real-time friction data and the force control target data of the joint motor;
a control unit 1305 for controlling the joint motor according to the actual force control data.
Optionally, the robot control device based on friction provided in this application embodiment further includes:
the second acquisition unit is used for acquiring a working condition model of the joint motor;
and the third calculation unit is used for generating a real-time rotating speed, a real-time rotating speed acceleration, a real-time position and a force control target according to the working condition model.
Since the embodiments of the apparatus portion and the method portion correspond to each other, please refer to the description of the embodiments of the method portion for the embodiments of the apparatus portion, which is not repeated here.
EXAMPLE seven
Fig. 14 is a schematic structural diagram of a robot control device based on friction according to an embodiment of the present application.
As shown in fig. 14, a friction-based robot control apparatus provided in an embodiment of the present application includes:
a memory 1410 for storing instructions comprising the steps of the friction-based robot control method of any of the above embodiments;
a processor 1420 to execute the instructions.
Among other things, processor 1420 may include one or more processing cores, such as 3-core processors, 8-core processors, and so on. The processor 1420 may be implemented in at least one hardware form of a Digital Signal Processing DSP (Digital Signal Processing), a Field-Programmable Gate Array (FPGA), and a Programmable Logic Array (PLA). Processor 1420 may also include a main processor, which is a processor for Processing data in the wake state and is also called a Central Processing Unit (CPU); a coprocessor is a low power processor for processing data in a standby state. In some embodiments, the processor 1420 may be integrated with an image processor GPU (Graphics Processing Unit) that is responsible for rendering and drawing content that the display screen needs to display. In some embodiments, processor 1420 may also include an Artificial Intelligence (AI) processor for processing computational 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, as well as 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 for storing the following computer program 1411, wherein after the computer program 1411 is loaded and executed by the processor 1420, the relevant steps in the friction-based robot control method disclosed in any of the foregoing embodiments can be implemented. In addition, the resources stored in memory 1410 may also include an operating system 1412, data 1413, and the like, which may be stored in a transient or persistent manner. Operating system 1412 may be Windows, among others. The data 1413 may include, but is not limited to, data involved with the above-described methods.
In some embodiments, the friction-based robotic control device may also include a display 1430, a power source 1440, a communication interface 1450, an input output interface 1460, sensors 1470, and a communication bus 1480.
Those skilled in the art will appreciate that the configuration shown in fig. 14 does not constitute a limitation of friction-based robotic control devices and may include more or fewer components than those shown.
The friction-based robot control device provided by the embodiment of the application comprises the memory and the processor, and when the processor executes the program stored in the memory, the friction-based robot control method can be realized, and the effect is the same as that of the friction-based robot control method.
Example eight
It should be noted that the above-described embodiments of the apparatus and device are merely illustrative, for example, the division of modules is only one division of logical functions, and there may be other divisions when actually implementing, for example, a plurality of modules or components may be combined or integrated into another system, or some features may be omitted or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or modules, and may be in an electrical, mechanical or other form. Modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical modules, may be located in one place, or may be distributed on a plurality of network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, functional modules in the embodiments of the present application may be integrated into one processing module, or each of the modules may exist alone physically, or two or more modules are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode.
The integrated module, if implemented in the form of a software functional module and sold or used as a separate product, may be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which is stored in a storage medium and executes all or part of the steps of the methods described in the embodiments of the present application, or all or part of the technical solutions.
To this end, the present application further provides a computer readable storage medium, on which a computer program is stored, and the computer program, when executed by a processor, implements the steps of the robot control method based on friction.
The computer-readable storage medium may include: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory ROM (Read-Only Memory), a Random Access Memory RAM (Random Access Memory), a magnetic disk, or an optical disk.
The computer program contained in the computer-readable storage medium provided in this embodiment can implement the steps of the friction-based robot control method described above when executed by the processor, and the same effects are obtained.
The friction-based robot control method, apparatus, device and computer-readable storage medium provided in the present application are described in detail above. The embodiments are described in a progressive manner in the specification, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device, the apparatus and the computer-readable storage medium disclosed in the embodiments correspond to the method disclosed in the embodiments, so that the description is simple, and the relevant points can be referred to the description of the method. It should be noted that, for those skilled in the art, without departing from the principle of the present application, the present application can also make several improvements and modifications, and those improvements and modifications also fall into 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, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrases "comprising a," "8230," "8230," or "comprising" does not exclude the presence of additional like elements in a process, method, article, or apparatus that comprises the element.
Claims (10)
1. A friction-based robot control method, comprising:
establishing a friction force identification model of motion parameters and friction force of a joint motor of a target robot in advance based on electric parameters of the joint motor and structural parameters of a joint of the target robot;
acquiring real-time motion parameters of the joint motor;
substituting the real-time motion parameters into the friction force identification model to obtain real-time friction force data;
calculating actual force control data of the joint motor according to the real-time friction force data and force control target data of the joint motor;
and controlling the joint motor according to the actual force control data.
2. The robot control method according to claim 1, wherein the establishing of the friction force recognition model of the motion parameter and the friction force of the joint motor based on the electrical parameter of the joint motor of the target robot and the structural parameter of the joint of the target robot specifically comprises:
acquiring a motor torque constant of the joint motor;
carrying out speed control on the joint motor to obtain a motor rotating speed set and a corresponding motor driving current set;
multiplying the motor driving current set by the motor torque constant to obtain a motor driving torque value set;
substituting the motor rotating speed set into an identification equation based on a steady-state friction force model to obtain a theoretical friction torque value set;
obtaining a first friction force identification parameter of the joint motor by taking a difference absolute value of the theoretical friction torque value set and the motor driving torque value set as a target;
and substituting 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 model is specifically:
wherein M is f To identify frictional forces, M C Is coulomb friction force, M S Is static friction force, omega is the real-time rotating speed of the joint motor, omega s Is the critical speed, sigma, of the joint motor 2,θ1 Is a rigidity coefficient, σ 2,θ2 Is the damping coefficient.
4. The robot control method according to claim 1, wherein the establishing of the friction force recognition model of the motion parameter and the friction force of the joint motor based on the electrical parameter of the joint motor of the target robot and the structural parameter of the joint of the target robot specifically comprises:
acquiring a motor torque constant of the joint motor;
establishing a joint rotation dynamic model of the joint motor according to the motor torque constant;
performing position control of an inverse M sequence on the joint motor, and acquiring motor rotating speed information and current information to obtain a motor rotating speed sequence and a corresponding motor current sequence;
fitting based on the motor rotating speed sequence and the motor current sequence to obtain a second friction force identification parameter of the joint motor;
determining the rotational inertia of the joint motor and the equivalent damping coefficient of the joint motor according to the second friction force identification parameter;
and substituting the rotational inertia and the equivalent damping coefficient into the joint rotational dynamics model to obtain the friction force identification model.
5. A robot control method according to claim 4, wherein the friction force identification model is specifically:
wherein, tau f To identify friction, τ c And the maximum static friction force of the joint motor is omega, the real-time rotating speed of the joint motor is omega, and the equivalent damping coefficient is B.
6. The robot control method according to claim 1, wherein the real-time motion parameters specifically include: the real-time rotating speed of the joint motor, the real-time rotating speed acceleration of the joint motor and the real-time position of the joint motor are calculated;
the calculating actual force control data of the joint motor according to the real-time friction force data and the force control target data of the joint motor specifically comprises:
generating friction compensation force according to the real-time friction data;
multiplying the difference value obtained by subtracting the real-time rotating speed from the rotating speed control target of the joint motor by a preset virtual damping coefficient to obtain a first compensation force;
multiplying the difference value obtained by subtracting the real-time rotating speed and the acceleration from the acceleration control target of the joint motor by a preset virtual friction coefficient to obtain a second compensation force;
multiplying the difference value obtained by subtracting the real-time position from the position control target of the joint motor by a preset virtual stiffness coefficient to obtain a third compensation force;
and taking 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 as the actual force control value of the joint motor.
7. The robot control method according to claim 6, further comprising:
acquiring a working condition model of the joint motor;
and generating the real-time rotating speed, the real-time rotating speed acceleration, the real-time position and the force control target according to the working condition model.
8. A friction-based robot control apparatus, comprising:
the friction force identification unit is used for establishing a friction force identification model of the motion parameters and the friction force of the joint motor based on the electric 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 is used for acquiring real-time motion parameters of the joint motor;
the first calculation unit is used for substituting the real-time motion parameters into the friction force identification model to obtain real-time friction force data;
the second calculation unit is used for calculating actual force control data of the joint motor according to the real-time friction force data and force control target data of the joint motor;
and the control unit is used for controlling the joint motor according to the actual force control data.
9. A friction-based robotic control device, comprising:
a memory for storing instructions comprising the steps of the friction-based robot control method of any of claims 1 to 7;
a processor to execute the instructions.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the friction-based robot control method according to any one of claims 1 to 7.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110885508.4A CN113510693B (en) | 2021-08-03 | 2021-08-03 | Robot control method, device and equipment based on friction force |
| PCT/CN2022/070908 WO2023010811A1 (en) | 2021-08-03 | 2022-01-10 | Robot control method, apparatus and device based on frictional force |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110885508.4A CN113510693B (en) | 2021-08-03 | 2021-08-03 | Robot control method, device and equipment based on friction force |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113510693A CN113510693A (en) | 2021-10-19 |
| CN113510693B true CN113510693B (en) | 2022-10-25 |
Family
ID=78068730
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110885508.4A Active CN113510693B (en) | 2021-08-03 | 2021-08-03 | Robot control method, device and equipment based on friction force |
Country Status (2)
| Country | Link |
|---|---|
| CN (1) | CN113510693B (en) |
| WO (1) | WO2023010811A1 (en) |
Families Citing this family (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113510693B (en) * | 2021-08-03 | 2022-10-25 | 香港中文大学（深圳） | Robot control method, device and equipment based on friction force |
| CN114454166B (en) * | 2022-02-11 | 2023-04-28 | 苏州艾利特机器人有限公司 | Impedance control method and device for mechanical arm |
| CN114734437B (en) * | 2022-03-25 | 2024-09-20 | 中国科学院自动化研究所 | Robot joint control method and device |
| CN116038692A (en) * | 2022-12-12 | 2023-05-02 | 东南大学 | Rope force control method for time-varying configuration |
| CN116442220A (en) * | 2023-03-30 | 2023-07-18 | 之江实验室 | Parameter identification method and device for robot joint friction model and moment estimation method and device |
| CN117706931A (en) * | 2023-12-15 | 2024-03-15 | 苏州康多机器人有限公司 | Lifting teaching control method, device, equipment and medium for surgical robot |
| CN118067175B (en) * | 2024-04-22 | 2024-08-27 | 科弛医疗科技(北京)有限公司 | Combined verification method, control method of surgical robot, and storage medium |
| CN118331168B (en) * | 2024-04-30 | 2024-10-11 | 上海卫尼医疗器械有限公司 | Motion control method and system |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH10249763A (en) * | 1997-03-18 | 1998-09-22 | Kobe Steel Ltd | Friction parameter control method for robot manipulator |
| JP2015018496A (en) * | 2013-07-12 | 2015-01-29 | 三菱重工業株式会社 | Friction compensation device, friction compensation method and servo control device |
| CN107263467A (en) * | 2017-05-11 | 2017-10-20 | 广州视源电子科技股份有限公司 | Method and device for controlling movement of rotary joint of robot and robot |
| CN109483591A (en) * | 2018-10-23 | 2019-03-19 | 华南理工大学 | Joint of robot frictional force discrimination method based on LuGre friction model |
| CN111216120A (en) * | 2019-11-13 | 2020-06-02 | 遨博（北京）智能科技有限公司 | Joint friction force moment compensation method and device and robot |
| KR102170591B1 (en) * | 2019-11-01 | 2020-10-27 | 주식회사 뉴로메카 | Friction Compensation Method for Multi-DOF Cooperative Robots |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112528434B (en) * | 2020-12-04 | 2023-01-06 | 上海新时达机器人有限公司 | Information identification method and device, electronic equipment and storage medium |
| CN113510693B (en) * | 2021-08-03 | 2022-10-25 | 香港中文大学（深圳） | Robot control method, device and equipment based on friction force |
-
2021
- 2021-08-03 CN CN202110885508.4A patent/CN113510693B/en active Active
-
2022
- 2022-01-10 WO PCT/CN2022/070908 patent/WO2023010811A1/en active Application Filing
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH10249763A (en) * | 1997-03-18 | 1998-09-22 | Kobe Steel Ltd | Friction parameter control method for robot manipulator |
| JP2015018496A (en) * | 2013-07-12 | 2015-01-29 | 三菱重工業株式会社 | Friction compensation device, friction compensation method and servo control device |
| CN107263467A (en) * | 2017-05-11 | 2017-10-20 | 广州视源电子科技股份有限公司 | Method and device for controlling movement of rotary joint of robot and robot |
| CN109483591A (en) * | 2018-10-23 | 2019-03-19 | 华南理工大学 | Joint of robot frictional force discrimination method based on LuGre friction model |
| KR102170591B1 (en) * | 2019-11-01 | 2020-10-27 | 주식회사 뉴로메카 | Friction Compensation Method for Multi-DOF Cooperative Robots |
| CN111216120A (en) * | 2019-11-13 | 2020-06-02 | 遨博（北京）智能科技有限公司 | Joint friction force moment compensation method and device and robot |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113510693A (en) | 2021-10-19 |
| WO2023010811A1 (en) | 2023-02-09 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113510693B (en) | Robot control method, device and equipment based on friction force | |
| CN107359837A (en) | Torsion control system of synchronization generator with everlasting magnetic and method based on sliding mode observer and Active Disturbance Rejection Control | |
| CN106533303A (en) | Permanent magnet brushless DC motor driver control method | |
| JP5839111B2 (en) | Control device for three-phase AC induction motor and control method for three-phase AC induction motor | |
| CN111656674B (en) | Control device, control method, and motor drive system for power conversion device | |
| CN111656676B (en) | Control device for power conversion device and motor drive system | |
| WO2023029903A1 (en) | Load inertia identification method and apparatus, electronic device and system | |
| KR20200033478A (en) | Automated torque ripple reduction apparatus of motor | |
| US11757390B2 (en) | Motor inductance measurement device, motor drive system, and motor inductance measurement method | |
| CN106602950B (en) | Electric current loop decoupling control method and system based on complex vector | |
| Jannati et al. | Speed sensorless fault-tolerant drive system of 3-phase induction motor using switching extended kalman filter | |
| Horen et al. | Simple mechanical parameters identification of induction machine using voltage sensor only | |
| CN114499334A (en) | Permanent magnet three-phase alternating current motor and load simulation device and control method thereof | |
| CN111781839B (en) | Adaptive robust control method of electric loading system and electric loading system | |
| Naumov et al. | Modeling of three-phase electric motor operation by the MATLAB system with deteriorated power quality in the 0.38 kV distribution networks | |
| JP2024524676A (en) | Adaptive AC servo motor current control method and system | |
| Versèle et al. | Implementation of advanced control schemes using dSPACE material for teaching induction motor drives | |
| CN112269127A (en) | DQ0 and inverse DQ0 conversion for three-phase inverter, motor, and drive designs | |
| Gupta et al. | Dynamic modeling of induction motor using rotor rotating reference frame | |
| CN106877767B (en) | The method and device of on-line measurement time constant of rotor of asynchronous machine | |
| CN110504883A (en) | A kind of magnetic linkage discrimination method of permanent magnet synchronous motor | |
| Lopac et al. | Application of model-based design tool X2C in induction machine vector control | |
| JP2014204489A (en) | Rotary machine control device | |
| CN113708672B (en) | Control method for high-voltage high-speed switch driving motor | |
| CN111181447B (en) | Motor group current sensor cooperation system based on self-generated detection signal and correction method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |