CN108058188A - robot health monitoring and fault diagnosis system and its control method - Google Patents
robot health monitoring and fault diagnosis system and its control method Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J19/00—Accessories fitted to manipulators, e.g. for monitoring, for viewing; Safety devices combined with or specially adapted for use in connection with manipulators
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J19/00—Accessories fitted to manipulators, e.g. for monitoring, for viewing; Safety devices combined with or specially adapted for use in connection with manipulators
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Abstract
The invention belongs to robotic technology fields, it is related to a kind of robot health monitoring and fault diagnosis system, including being located at the data acquiring section at movable joint, for judging the monitoring running state portion of robot current operating conditions, for judging the health status monitoring portion of robot current health state, the state processing portion of alignment processing, the state recording portion of recorder people's state parameter are made according to current robot operating status and health status.The present invention is by establishing joint of robot torque prediction model and robotic identification model, the operating status and health status of real-time judge robot, and carry out timely processing for various abnormalities, the mechanical breakdown that may effectively can occur in early detection robot reduces economic loss.
Description
Technical Field
The invention relates to the technical field of robots, in particular to a robot health monitoring and fault diagnosis system and a control method thereof.
Background
Robots have been widely used in various fields of automated production, and currently, there is no effective means for monitoring the health status of robots. Sudden damage to the robot may cause the entire production line to be stalled, resulting in significant economic losses. Therefore, the health monitoring and fault diagnosis of the robot are of great significance to avoid loss.
Therefore, it is necessary to provide a new control system to solve the above problems.
Disclosure of Invention
The invention mainly aims to provide a robot health monitoring and fault diagnosis system and a control method thereof.
The invention realizes the purpose through the following technical scheme: a robotic health monitoring and fault diagnosis system, comprising:
the data acquisition part is positioned at the motion joint and used for acquiring the state parameters of the current robot in real time, such as mechanical parameters, the motion quantity of each joint and the actual measured value of each joint torque;
the running state monitoring part is used for establishing a robot joint torque prediction model, substituting the joint motion amount and the mechanical parameters obtained by the data acquisition part to calculate a current joint torque predicted value, comparing the joint torque predicted value with an actual value to calculate a torque deviation, and judging the current running state of the robot;
the health state monitoring part is used for establishing a robot mechanical parameter identification model, substituting the joint motion amount and the joint torque obtained by the data acquisition part to calculate the current robot mechanical parameter, and comparing the current robot mechanical parameter with the factory mechanical parameter to judge the current health state of the robot;
the state processing part is used for performing corresponding processing according to the current running state and the health state of the robot, and updating the mechanical parameters of the robot if the state is normal; if the state is abnormal, performing exception handling in a mode of deceleration movement and movement stop;
and a state recording part for recording the robot state parameters.
The control method applying the robot health monitoring and fault diagnosis system comprises the following steps:
s1, obtaining a mechanical parameter vector P of the robot when leaving a factory, wherein the P is the mechanical parameter vector, the dimension is m multiplied by 1, and the mechanical parameters comprise the load and the mass, the mass center, the inertia and the friction coefficient of each joint;
s2, acquiring the motion quantity of each joint of the robot: joint angle q, joint velocityJoint acceleration, all three of which are vector of Nx 1;
s3, acquiring actual torque T of each joint of the robot r ；
S4, establishing a prediction model T of robot joint torque p B = B × P, B is a known parameter matrix;
s5, substituting the mechanical parameters P of the S1 and the joint motion quantity obtained by the S2 into the model established in the S4, and solving the predicted value T of the joint torque p ；
S6, comparing the actual joint torque T obtained in the S3 r And the joint predicted torque T obtained in S5 p Judging the motion state of each joint of the robot at the current moment;
s7, establishing an identification model P of mechanical parameters of the robot k ＝(B T B) -1 B T T r ；
S8, the motion data obtained in the S2 and the joint actual torque T obtained in the S3 are used r Solving the mechanical parameter P of the robot at the current moment by substituting the model established in S7 k ；
S9, comparing the mechanical parameters P of the robot at the current moment obtained in the S8 k And the factory parameters P of the robot 0 Judging the health state of the robot body at the current moment;
s10, recording the motion state of each joint of the robot obtained in the S6 and the health state of the robot body obtained in the S9;
s11, judging the working state of the robot according to the judgment result obtained in the S6 and the judgment result obtained in the S9;
s12, according to the result obtained in the S11, if the state of the robot is abnormal, performing abnormal processing;
s13, according to the result obtained in the S11, if the state of the robot is normal, according to a formula P k+1 ＝(1-λ)P k + λ P updating mechanical parameters to obtain new value P of S1 k+1 λ is the update rate, and the value range is [0.01,0.1]]；
Wherein S1 to S3 are completed by the data acquisition part, S4 to S6 are completed by the running state detection part, S7 to S9 are completed by the health state detection part, S10 is completed by the state recording part, and S11 to S13 are completed by the state processing part.
Specifically, the actual torque T of each joint of the robot r Calculated by the following formula
Wherein tau is mi For the i-th shaft motor to output torque, G i Is the reduction ratio of the ith joint,is the angular velocity of the motor of the ith axis,angular acceleration of the i-th axis, J mi Is the moment of inertia of the rotor of the i-th axis motor, c mi Is the i-th axis motor viscous damping coefficient, f mi Is the friction torque of the i-th shaft motor rotor. The output torque of the motor is equal to the current of the motor multiplied by a torque constant, and the actual torque T of the robot r Is a column vector of Nx 1 dimension, N represents the degree of freedom of the robot, and the ith element is the ith axis joint torque tau i I.e. has T r ＝[τ 1 ，τ 2 ，…，τ N ] T 。
Specifically, the step S6 includes: and subtracting the predicted joint torque from the actual joint torque to obtain a torque deviation, and judging the current running state of the robot according to the torque deviation.
Further, the torque deviation signal is high-pass filtered, if the absolute value of the filtered signal is larger than a second threshold value T 2 Determining that the robot body is subjected to strong external high-frequency disturbance; if the absolute value of the filtered signal is greater than a first threshold value T 1 And is less than a second threshold value T 2 If the robot body receives the common external high-frequency disturbance, the robot body is determined to receive the common external high-frequency disturbance; second threshold value T 2 Greater than a first threshold value T 1 。
Further, the torque deviation signal is low-pass filtered, if the absolute value of the filtered signal is larger than a second threshold value T 2 If so, determining that the robot body is subjected to strong external low-frequency disturbance; if the absolute value of the filtered signal is greater than a first threshold value T 1 And is less than a second threshold value T 2 If the robot body receives the common external low-frequency disturbance, the robot body is determined to receive the common external low-frequency disturbance; second threshold value T 2 Greater than a first threshold value T 1 。
Specifically, the abnormality processing manner in step S12 includes deceleration movement and movement stop.
Compared with the prior art, this grinding jig's beneficial effect lies in:
according to the invention, the running state and the health state of the robot are judged in real time by establishing the robot joint torque prediction model and the robot mechanical parameter identification model, and various abnormal states are processed in time, so that mechanical faults possibly occurring in the robot can be effectively found in early stage, and the economic loss is reduced.
Drawings
FIG. 1 is a schematic diagram of a structure and a flowchart corresponding to the system for health monitoring and fault diagnosis of a robot according to an embodiment;
FIG. 2 is a logic block diagram of an embodiment of a robot health monitoring and fault diagnosis system during operation.
Fig. 3 is a comparison graph of the actual value and the predicted value of the torque of the first axis joint of the Scara robot in the normal operation state.
Detailed Description
The present invention will be described in further detail with reference to specific examples.
Example (b):
as shown in fig. 1, a robot health monitoring and fault diagnosis system of the present invention includes:
the data acquisition part is positioned at the motion joint and used for acquiring the state parameters of the current robot in real time, such as mechanical parameters, the motion amount of each joint and the torque measured value of each joint;
the running state monitoring part is used for establishing a robot joint torque prediction model, substituting the joint motion amount and the mechanical parameters obtained by the data acquisition part to calculate a current joint torque predicted value, comparing the joint torque predicted value with an actual value to calculate a torque deviation, and judging the current running state of the robot;
the health state monitoring part is used for establishing a robot mechanical parameter identification model, substituting the joint motion amount and the joint torque obtained by the data acquisition part to calculate the current robot mechanical parameter, and comparing the current robot mechanical parameter with the factory mechanical parameter to judge the current health state of the robot;
the state processing part is used for performing corresponding processing according to the current running state and the health state of the robot, and updating the mechanical parameters of the robot if the state is normal; if the state is abnormal, performing exception handling in a mode of deceleration movement and movement stop;
and a state recording part for recording the robot state parameters.
As shown in fig. 2, the control method using the robot health monitoring and fault diagnosis system includes the following steps:
s1, obtaining a mechanical parameter vector P of the robot when leaving a factory, wherein the P is the mechanical parameter vector, the dimension is m multiplied by 1, and the mechanical parameters comprise the load and the mass, the mass center, the inertia and the friction coefficient of each joint;
s2, acquiring the motion quantity of each joint of the robot: joint angle q, joint velocityAcceleration of jointAll three are Nx 1 vectors;
s3, acquiring actual torque T of each joint of the robot r ；
S4, establishing a prediction model T of robot joint torque p B = B × P, B is a known parameter matrix;
s5, substituting the mechanical parameters P of the S1 and the joint motion quantity obtained by the S2 into the model established in the S4, and solving the predicted value T of the joint torque p ；
S6, comparing the actual joint torque T obtained in the S3 r And the joint predicted torque T obtained in S5 p Judging the motion state of each joint of the robot at the current moment;
s7, establishing an identification model P of mechanical parameters of the robot k ＝(B T B) -1 B T T r ；
S8, the motion data obtained in the S2 and the joint actual torque T obtained in the S3 are used r Solving the mechanical parameter P of the robot at the current moment by substituting the model established in S7 k ；
S9, comparing the mechanical parameters P of the robot at the current moment obtained in the S8 k And the factory parameters P of the robot 0 Judging the health state of the robot body at the current moment;
s10, recording the motion state of each joint of the robot obtained in the S6 and the health state of the robot body obtained in the S9;
s11, judging the working state of the robot according to the judgment result obtained in the S6 and the judgment result obtained in the S9;
s12, according to the result obtained in the S11, if the state of the robot is abnormal, performing abnormal processing;
s13, according to the result obtained in the S11, if the state of the robot is normal, according to a formula P k+1 ＝(1-λ)P k + λ P updating mechanical parameters to obtain new value P of S1 k+1 λ is the update rate, and the value range is [0.01,0.1]]；
Wherein S1 to S3 are completed by the data acquisition part, S4 to S6 are completed by the running state detection part, S7 to S9 are completed by the health state detection part, S10 is completed by the state recording part, and S11 to S13 are completed by the state processing part.
For S1, the mechanical parameters of the robot body comprise load, mass center, inertia, friction coefficient and the like of each joint, and the initial values of the mechanical parameters pointed by S1 are determined before leaving a factory.
S2 the motion amount of the joint includes a joint angle q and a joint speedAcceleration of jointAll three are Nx 1 vectors, q = [ q ] 1 ，q 2 ，...，q N ] T ，q i 、Andthe angle, angular velocity and angular acceleration of the ith joint, respectively.
The actual torque of the joint indicated by S3 is the output torque of the drive mechanism, and is calculated by the following formula
The index i represents the ith axis of the robot, where λ i Is the motor torque constant, I i As a current of the motor，τ mi For the output of torque of the motor, G i In order to reduce the joint speed by a predetermined ratio,in order to determine the angular velocity of the motor,for angular acceleration of the motor, J mi Is the moment of inertia of the rotor of the motor, c mi Is the viscous damping coefficient of the motor, f mi Is the friction torque of the motor rotor. The output torque of the motor is equal to the current of the motor multiplied by a torque constant, and the actual torque T of the robot r Is a column vector with dimension of Nx 1, N represents the degree of freedom of the robot, and the ith element is the ith axis joint torque tau i I.e. has T r ＝[τ 1 ,τ 2 ,…,τ N ] T 。
The actual torque prediction model of the robot indicated by the S4 is obtained by the following method: a joint torque prediction model is a robot inverse dynamics model, a force and torque balance equation of a single joint is established by using a Newton Euler method, the angular velocity, the angular acceleration, the mass center velocity and the mass center acceleration of a rod piece are obtained through forward iteration of kinematics, and the force and the torque applied to the robot joint can be obtained through reverse iteration of dynamics.
The above-mentioned pushing process is described in detail below by taking the first two joints of the Scara robot as an example, wherein the forward kinematics iterative formula and the reverse dynamics iterative formula are as follows:
the parameters are defined as follows:
the forward derivation equation of the kinematics of the front two joints of Scara is
Wherein b is 1 ＝[0，0，1] T ，b 2 ＝[0，0，1] T ，r 0，1 ＝[l 1 ，0，0]，r 0，c1 ＝[l 1c ，0，0]，r 1，2 ＝[l 2 ，0，0]，r 1，c2 ＝[l 2c ，0，0]Geometric parameter l 1 ，l ic Are all known quantities, rotation matrixAndrespectively as follows:
the inverse derivation formula of the Scara anterior two-joint dynamics is as follows:
f 2 =m 2 a c,2 -m 2 g 2
＝-f 2 ×r 1,c2 +ω 2 ×(J 1 ω 2 )+J 2 α 2
wherein g is 1 ＝[0，0，-9.8]，g 2 ＝[0，0，-9.8]，m 1 Is the mass of the first rod member, m 2 Mass of the second rod member, J 1 The moment of inertia of the first rod piece relative to the first rotating shaft is a 3 x 3 matrix; j. the design is a square 2 The moment of inertia of the second rod with respect to the second rotation axis is also a 3 x 3 matrix. Since the Scara axis is parallel to the Scara axis, the calculation result is not affected, and J can be assumed 1 And J 2 Has the following forms:
derivation J according to parallel axis theorem of moment of inertia 1zz And J 2zz The value of (c). The moment of inertia of the first rod relative to the center of mass is known as H 1 The second rod piece has a mass inertia moment H relative to the mass center 2 Then there is J 1zz ＝H 1 +m 1 *l 1c *l 1c ，J 2zz ＝H 2 +m 2 *l 2c *l 2c . Push to the result of
Considering the friction of joints, let f 1 And f 2 The friction torques of the front two joints of the Scara robot are respectively obtained, and the joint torque equation is modified to
The friction torque comprises three parts of Coulomb friction, linear viscous damping and square damping, and the friction torque expressions of the front two joints of the Scara robot are as follows
Wherein f is di Damping the torque coefficient for dry friction, c i1 The viscous damping torque coefficient ci2 is a square damping torque coefficient, and an inverse dynamic model expression of the Scara robot is arranged and can be written into the following matrix form.
F＝B*P
Wherein the expressions of the matrix B, the vector P and the vector F are respectively
F＝[τ 1 ，τ 2 ] T
c 2 ＝cos(q 2 ) s 2 =sin(q 2 )
For a single sample, the dimension of the B matrix is 2 × 9,P and the dimension of the B matrix is 9 × 1,F and the dimension of the B matrix is 2 × 1. For Y sets of sample data, the dimension of the B matrix is 2Y × 9,P and the dimension of the B matrix is 9 × 1,F and the dimension of the B matrix is 2Y × 1.
Let the predicted value of joint torque be T p Replacing F with T p An expression of the joint torque prediction model can be obtained:
T p ＝B*P
the predicted value of the joint torque indicated by S5 can be calculated through a prediction model indicated by S4, at the moment, the robot mechanical parameter vector P is known and is specified by S1, the model input is a joint motion variable q indicated by S2, the joint motion amount is substituted into a B matrix, and a joint torque predicted value T is obtained through matrix point multiplication p 。
Movement indicated by S6The state judgment method comprises the following steps: definition of T di Is the i-th axis joint torque deviation
T di ＝T ri -T pi
Wherein T is ri Is T r Is the actual value of the i-th axis joint torque, T, calculated in S3 pi Is T p The ith element of (1), the predicted value of the i-th axis joint torque calculated in S5.
Defining a first decision threshold T 1 Second determination threshold T 2 And has T 2 >T 1 >0。
Will T di High-pass filtering is carried out to obtain a filtered signal T hd . If T is 1 <|T hd |<T 2 Then the robot is determined to be subjected to general external high-frequency disturbance if T hd |>T 2 The robot is deemed to be subjected to strong external high frequency disturbances, where | is in absolute sign. The disturbance may be caused by the following factors:
1) The transmission mechanisms such as the speed reducer, the motor and the like have faults.
2) The robot body is impacted.
3) Looseness and even part falling-off of connection among the load, the clamp and the robot flange occur.
4) Other factors.
Will T di Low-pass filtering to obtain filtered signal T sd . If T is 1 <|T sd |<T 2 Then the robot is determined to be subjected to general external high-frequency disturbance if T sd |>T 2 The robot is deemed to be subjected to strong external low frequency disturbances, where | is in absolute sign. The disturbance may be caused by the following factors:
1) The load mechanics parameters are incorrectly filled.
2) The transmission lacks lubrication.
3) The transmission mechanism ages.
4) The robot body is subjected to additional forces.
5) The outside environment is either too cold or too hot.
6) Other factors.
The mechanical parameter identification model S7 can be obtained by using a least square method, and the solving formula is as follows
P k ＝(B T B) -1 B T T r
The mechanical parameters of the current moment pointed by S8 can be calculated through a model shown by S7, the model is input into the joint motion quantity obtained by S2 and the actual torque of the joint obtained by S3, wherein the joint motion quantity can initialize a B matrix, and the actual torque of the joint can initialize a vector T r And the model output is the mechanical parameter P of the robot at the current moment k 。
The method for judging the health state of the robot in the S9 mode comprises the following steps: i.e. P 0 For the mechanical parameter vector, P, of the robot leaving the factory k Defining a new variable delta P = | | P for the mechanical parameter vector at the current moment obtained by calculation of S8 k -P 0 | | where the symbol | | | P k -P 0 I represents the vector P k -P 0 The two norms of (a).
If Δ P>P 1 And considering the health state of the robot to be abnormal, otherwise, considering the health state of the robot to be normal. Wherein P is 1 &And 0 is a judgment threshold value, and the abnormal state can be caused by the following factors:
1) Problems arise with the motor.
2) The transmission wears or ages.
Here, the details are also described using Scara robot as an example, and the parameter vector P is written in two parts, P k ＝[P 1 ，P 2 ]Similarly, P is 0 Written in two parts, P 0 ＝[P 10 ，P 20 ]In which P is 10 Is a subset of vectors, P, related to a quality parameter of the robot 20 Is a subset of vectors related to the robot friction torque, whereinP 1 ＝[c 11 ,c 12 ,c 21 ,c 22 ,f d1 ,f d2 ]，ΔP 1 ＝||P 1 -P 10 ||，ΔP 2 ＝||P 2 -P 20 If Δ P |, if | | | 1 If the value of | | is abnormal, two possibilities exist, namely, the motor fails, and the robot body has additional mass. If | | | Δ P 2 If the value of | | is abnormal, the transmission mechanism has problems, such as the lubrication of the speed reducer is not ideal enough or the speed reducer is damaged.
And S10, recording the motion state of each joint of the robot obtained in S6 and the health state of the robot body obtained in S9.
And S11, making a decision according to judgment results of S6 and S9, and if the running state and the health state of the robot are normal, considering that the robot state is normal, or else, considering that the robot state is abnormal.
And S12, responding to the abnormal working state of the robot obtained in S11, switching the robot to a stop mode when the robot is subjected to strong external disturbance, and otherwise switching the robot to a low-speed running mode.
S13, responding to the normal working state of the robot obtained in S11, specifically, updating the mechanical parameters of the robot, and assuming that the mechanical parameters S1 of the robot at the current moment are P, the mechanical parameters of the robot calculated in S8 at the current moment are P k If the robot mechanical parameter S1 at the next moment is P k+1 :
P k+1 ＝(1-λ)P k +λP
In the formula, λ is an update rate, a value interval is [0.01,0.1], and the larger the value of λ is, the faster the mechanical parameter of the robot indicated by S1 is updated.
In order to verify the correctness of the invention, the actual torque of the first joint is calculated by collecting the motion amount data and the current data of the front two joints of the Scara robot, the current mechanical parameters of the robot are calculated by utilizing an S7 mechanical parameter identification model, then the mechanical parameters and the resampled motion amount are brought into an S4 joint torque Prediction model, the predicted value of the first joint torque is calculated, the related result is shown in figure 3, wherein a solid line Prediction represents the predicted value of the joint torque, and a dotted line Real represents the actual value of the joint torque, so that the predicted value and the actual value of the joint torque are highly consistent under the normal working state, and the accuracy of the S4 joint torque Prediction model is verified.
What has been described above are merely some embodiments of the present invention. It will be apparent to those skilled in the art that various changes and modifications can be made without departing from the inventive concept thereof, and these changes and modifications can be made without departing from the spirit and scope of the invention.
Claims (7)
1. A robotic health monitoring and fault diagnosis system, comprising:
the data acquisition part is positioned at the motion joint and used for acquiring the state parameters of the current robot in real time, such as mechanical parameters, the motion quantity of each joint and the actual measured value of each joint torque;
the running state monitoring part is used for establishing a robot joint torque prediction model, substituting the joint motion amount and the mechanical parameters obtained by the data acquisition part to calculate a current joint torque predicted value, comparing the joint torque predicted value with an actual value to calculate a torque deviation, and judging the current running state of the robot;
the health state monitoring part is used for establishing a robot mechanical parameter identification model, substituting the joint motion amount and the joint torque obtained by the data acquisition part to calculate the current robot mechanical parameter, and comparing the current robot mechanical parameter with the factory mechanical parameter to judge the current health state of the robot;
the state processing part is used for performing corresponding processing according to the current running state and the health state of the robot, and updating the mechanical parameters of the robot if the state is normal; if the state is abnormal, performing exception handling in a mode of deceleration movement and movement stop;
and a state recording part for recording the robot state parameters.
2. The control method of the robot health monitoring and fault diagnosis system according to claim 1, characterized by comprising the steps of:
s1, obtaining a mechanical parameter vector P of the robot when leaving a factory, wherein the P is the mechanical parameter vector, the dimension is m multiplied by 1, and the mechanical parameters comprise the load and the mass, the mass center, the inertia and the friction coefficient of each joint;
s2, acquiring the motion quantity of each joint of the robot: joint angle q, joint velocityAcceleration of jointAll three are Nx 1 vectors;
s3, acquiring actual torque T of each joint of the robot r ；
S4, establishing a prediction model T of robot joint torque p B = B × P, B is a known parameter matrix;
s5, substituting the mechanical parameters P of the S1 and the joint motion quantity obtained by the S2 into the model established in the S4, and solving the predicted value T of the joint torque p ；
S6, comparing the actual joint torque T obtained in the S3 r And the joint predicted torque T obtained in S5 p Judging the motion state of each joint of the robot at the current moment;
s7, establishing an identification model P of mechanical parameters of the robot k ＝(B T B) -1 B T T r ；
S8, the motion data obtained in the S2 and the joint actual torque T obtained in the S3 are used r Solving mechanical parameter P of robot at current moment by substituting model established in S7 k ；
S9, comparing the mechanical parameters P of the robot at the current moment obtained in the S8 k And the factory parameters P of the robot 0 Judging the health state of the robot body at the current moment;
s10, recording the motion state of each joint of the robot obtained in the S6 and the health state of the robot body obtained in the S9;
s11, judging the working state of the robot according to the judgment result obtained in the S6 and the judgment result obtained in the S9;
s12, according to the result obtained in the S11, if the state of the robot is abnormal, performing abnormal processing;
s13, according to the result obtained in the S11, if the state of the robot is normal, according to a formula P k+1 ＝(1-λ)P k +λP updating mechanical parameters to obtain new value P of S1 k+1 λ is the update rate, and the value range is [0.01,0.1]]；
Wherein S1 to S3 are completed by the data acquisition part, S4 to S6 are completed by the running state detection part, S7 to S9 are completed by the health state detection part, S10 is completed by the state recording part, and S11 to S13 are completed by the state processing part.
3. The control method according to claim 2, characterized in that: the actual torque T of each joint of the robot is calculated by the following formula
Wherein tau is mi For the i-th shaft motor to output torque, G i Is the reduction ratio of the ith joint,is the angular velocity of the motor of the ith axis,angular acceleration of the i-th axis, J mi Is the moment of inertia of the rotor of the i-th axis motor, c mi Is the i-th axis motor viscous damping coefficient, f mi Is the friction torque of the i-th shaft motor rotor. The output torque of the motor is equal to the current of the motor multiplied by a torque constant, and the actual torque T of the robot r Is a column vector of Nx 1 dimension, N represents the degree of freedom of the robot, and the ith element is the ith axis joint torque tau i I.e. has T r ＝[τ 1 ，τ 2 ，…，τ N ] T 。
4. The control method according to claim 2, characterized in that: the step S6 includes: and subtracting the predicted joint torque from the actual joint torque to obtain a torque deviation, and judging the current running state of the robot according to the torque deviation.
5. According to claim4, the control method characterized by: high-pass filtering the torque deviation signal, if the absolute value of the filtered signal is greater than a second threshold value T 2 If the robot body is subjected to strong external high-frequency disturbance, determining that the robot body is subjected to strong external high-frequency disturbance; if the absolute value of the filtered signal is greater than a first threshold value T 1 And is less than a second threshold value T 2 If the robot body receives the common external high-frequency disturbance, the robot body is determined to receive the common external high-frequency disturbance; second threshold value T 2 Greater than a first threshold value T 1 。
6. The control method according to claim 4, characterized in that: low-pass filtering the torque deviation signal, if the absolute value of the filtered signal is greater than a second threshold value T 2 Determining that the robot body is subjected to strong external low-frequency disturbance; if the absolute value of the filtered signal is greater than a first threshold T 1 And is less than a second threshold value T 2 If the robot body receives the common external low-frequency disturbance, the robot body is determined to receive the common external low-frequency disturbance; second threshold value T 2 Greater than a first threshold value T 1 。
7. The control method according to claim 2, characterized in that: the abnormality processing manner in step S12 includes deceleration movement and movement stop.
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Cited By (15)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN108908345A (en) * | 2018-08-31 | 2018-11-30 | 上海大学 | A kind of under-actuated delicacy hand transmission system state perception system |
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| CN108908345A (en) * | 2018-08-31 | 2018-11-30 | 上海大学 | A kind of under-actuated delicacy hand transmission system state perception system |
| CN108908345B (en) * | 2018-08-31 | 2023-07-14 | 上海大学 | Under-actuated dexterous hand transmission system state sensing system |
| CN109465823A (en) * | 2018-11-06 | 2019-03-15 | 泰康保险集团股份有限公司 | Study of Intelligent Robot Control method and device, electronic equipment, storage medium |
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| CN109571549A (en) * | 2018-12-29 | 2019-04-05 | 上海新时达机器人有限公司 | The friction force monitoring methods and system and equipment of a kind of robot body |
| CN112123371A (en) * | 2019-06-25 | 2020-12-25 | 株式会社日立制作所 | Robot fault prediction device and system, and robot fault prediction method |
| CN110988526A (en) * | 2019-11-21 | 2020-04-10 | 珠海格力电器股份有限公司 | Robot assembly inspection method and device and storage medium |
| CN110988526B (en) * | 2019-11-21 | 2021-01-29 | 珠海格力电器股份有限公司 | Robot assembly inspection method and device and storage medium |
| CN113021411A (en) * | 2019-12-24 | 2021-06-25 | 株式会社日立制作所 | Robot failure prediction device, robot failure prediction system, and robot failure prediction method |
| CN113021411B (en) * | 2019-12-24 | 2023-06-20 | 株式会社日立制作所 | Robot failure prediction device and system, and robot failure prediction method |
| CN111086025A (en) * | 2019-12-25 | 2020-05-01 | 南京熊猫电子股份有限公司 | Multi-fault-cause diagnosis system and method applied to industrial robot |
| CN111283731A (en) * | 2020-03-17 | 2020-06-16 | 安徽智训机器人技术有限公司 | Industrial robot operation fault determination method and system |
| CN111532988B (en) * | 2020-04-26 | 2021-07-30 | 成都见田科技有限公司 | Remote intelligent monitoring method and monitoring computer applied to elevator |
| CN111532988A (en) * | 2020-04-26 | 2020-08-14 | 成都见田科技有限公司 | Remote intelligent monitoring method and monitoring computer applied to elevator |
| CN111761576A (en) * | 2020-06-15 | 2020-10-13 | 上海高仙自动化科技发展有限公司 | Health monitoring method and system, intelligent robot and readable storage medium |
| WO2022041064A1 (en) * | 2020-08-27 | 2022-03-03 | Rethink Robotics Gmbh | Method and apparatus for robot joint status monitoring |
| CN113776791A (en) * | 2021-08-04 | 2021-12-10 | 深圳优地科技有限公司 | Method and device for monitoring health state of robot, robot and storage medium |
| CN114323718A (en) * | 2021-12-14 | 2022-04-12 | 合肥欣奕华智能机器有限公司 | Robot fault prediction method and device |
| CN114323718B (en) * | 2021-12-14 | 2023-12-15 | 合肥欣奕华智能机器股份有限公司 | Robot fault prediction method and device |
| CN114770607A (en) * | 2022-06-20 | 2022-07-22 | 深圳希研工业科技有限公司 | Robot health monitoring method and system based on big data |
| CN114770607B (en) * | 2022-06-20 | 2022-09-02 | 深圳希研工业科技有限公司 | Robot health monitoring method and system based on big data |
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