Robot machining vibration control method and system

Abstract

The invention provides a robot machining vibration control method and system, and belongs to the technical field of robot vibration control. The method comprises the steps that tail end six-axis acceleration decoupling is carried out to sense robot tail end vibration; building a robot joint and tail end acceleration model, and obtaining a load side and tail end acceleration relation through a Jacobian matrix; estimating a load side joint speed by using a Kalman filter; designing an active damping controller, analyzing disturbance force influence, establishing a feedback item, and introducing a feedforward item to eliminate steady-state errors; and compensating the correction speed in the active damping controller into the speed ring controller to realize active vibration control. Vibration of the flexible joint robot can be accurately sensed and controlled, a body does not need to be transformed, tail end six-axis acceleration signals are introduced to achieve damping self-adaptive adjustment, and stability is improved.

Description

Technical Field

[0001] The present invention relates to the field of vibration control technology, and in particular to a robot machining vibration control method and system. Background Art

[0002] With the continuous improvement of the level of intelligence in the manufacturing industry, industrial robots are increasingly used in various fields of the manufacturing industry, especially in high-precision processing such as assembly, welding, spraying, handling, testing and grinding. The application of industrial robots not only improves production efficiency and reduces labor costs, but also improves the stability of product quality through automated control. However, due to the flexibility of the joints, there is a difference in the motion state between the motor side and the load side of the robot, that is, the input motion of the joint cannot represent the output motion of the joint. At present, most robot joints on the market only have one encoder installed on the motor side. Therefore, it is difficult to ensure system stability by controlling the robot through the existing motor side motion state information, which restricts the robot's operating performance.

[0003] Due to the flexibility of the joints, the robot system has sensor mismatch characteristics, which leads to serious vibration problems during the robot movement. In response to the sensor mismatch problem, relevant scholars install secondary encoders or torque sensors on the robot joint side to sense the motion state of the robot load side, and then feed back to the robot joint controller to achieve full closed-loop control to improve the stability of the robot system. However, the addition of sensors to the robot joints requires the modification of the robot body structure, resulting in a more complex robot system structure and higher costs. In addition, the torque sensor is obtained by deforming the internal elastic structure, which further reduces the stiffness of the robot joint. In addition, there is a certain risk when performing torque loop control on the robot, and the underlying torque loop control of most industrial robots is not open, making it difficult to achieve torque control.

[0004] In order not to change the robot body structure, the second-order differential of the joint position can be used to obtain the joint vibration state. Due to the presence of noise, the joint vibration information will be submerged. Acceleration sensors can also be installed on the robot body to sense the state of the robot during movement and feedback to the robot controller, thereby improving the stability of the robot. Some scholars install multiple acceleration sensors on the robot connecting rod and combine control algorithms to realize the load-side vibration information perception and vibration control of flexible joint robots. However, it is necessary to consider the layout and number of acceleration sensors. The implementation method is relatively complicated and it is difficult to improve the robot control efficiency. Another group of scholars install a three-axis acceleration sensor at the end of the robot and realize the motion state estimation of the joint load side through an intelligent algorithm. However, industrial robots have six degrees of freedom in Cartesian space, namely translation along three axes and rotation around three axes in Cartesian space, and it is difficult for three-axis acceleration sensors to decouple the end angular acceleration information, which will lead to low decoupling accuracy of the robot load-side motion state, resulting in poor robot vibration control effect. Installing a gyro sensor at the end of the robot to obtain angular acceleration differentially will cause problems such as large bias drift, low impact resistance, poor durability, and high noise, resulting in low information decoupling accuracy on the joint load side and poor vibration suppression effect.

[0005] In summary, the existing robot acceleration feedback vibration control technology has shortcomings. At present, three-axis acceleration, gyroscopes and other methods are mostly used to sense and decouple the load side information, but the decoupling accuracy is low, resulting in poor robot vibration control effect. When using intelligent control algorithms such as sliding mode control and adaptive integral sliding mode controller to achieve vibration suppression of robot motion, these methods mainly rely on interference observers or motor side information to achieve control. In addition, the control algorithm has high requirements on the accuracy of robot modeling and the control structure is complex. Therefore, it is difficult for existing technologies to achieve accurate, efficient and stable vibration control of flexible joint robots. Summary of the invention

[0006] In order to solve the above problems, the present invention proposes a robot machining vibration control method and system, which establishes a terminal six-axis acceleration decoupling strategy, adopts the load-side speed estimation method to obtain the robot's speed information, establishes an active damping controller and feeds back to the robot controller in real time, thereby realizing flexible vibration suppression of the robot joints. While improving the vibration control effect, the implementation difficulty and complexity are reduced, making the implementation process of vibration control smoother and more feasible.

[0007] In order to achieve the above object, the present invention adopts the following technical solution:

[0008] In a first aspect, the present invention provides a robot machining vibration control method, comprising:

[0009] A six-axis acceleration measurement unit is built at the end of the robot to obtain the six-axis acceleration and vibration state of the end of the robot;

[0010] Based on the motor side and the load side of the robot joint, a dual-inertia model is constructed; the six-axis acceleration measurement unit and the dual-inertia model are solved using the Jacobian matrix to obtain a mapping relationship between the six-axis acceleration in Cartesian space and the joint space acceleration; the terminal vibration state is substituted into the mapping relationship to obtain the load-side acceleration of the robot joint;

[0011] According to the obtained joint load-side acceleration, the load-side joint velocity is estimated using a self-updating Kalman filter to obtain the load-side joint estimated velocity;

[0012] The feedback control rate is used to eliminate the steady-state error of the estimated velocity of the load-side joint and obtain the corrected velocity;

[0013] The correction speed is compensated to the robot speed loop controller, and the control gain in the damping controller is adjusted according to the vibration state of the robot end, so as to realize the active control of the robot vibration.

[0014] Preferably, the six-axis acceleration of the robot terminal includes three-axis acceleration and three-axis angular acceleration; the acquisition process is specifically as follows:

[0015] The specific process of obtaining the six-axis acceleration of the robot terminal is as follows:

[0016] A six-axis acceleration measurement unit is constructed for the robot end based on a spatial rigid body coordinate system; four three-axis accelerometers are respectively set on the X, Y, Z axes and the origin of the six-axis acceleration measurement unit;

[0017] According to the relationship between the spatial rigid body coordinate system and the inertial coordinate system, the acceleration of the three-axis accelerometer in the six-axis acceleration measurement unit is obtained, and the accelerometer output value is corrected based on the specific force;

[0018] Convert 4 triaxial accelerometers into 12 uniaxial accelerometers and construct system measurement equations;

[0019] The system measurement equation is simplified, and the corrected accelerometer output value is substituted to obtain the six-axis acceleration.

[0020] Preferably, the dual-inertia model is constructed based on the motor side and the load side of the robot joint, specifically: the n-degree-of-freedom dual-inertia model is established for the robot joint using the Lagrangian method; the dual-inertia model includes the motor side dynamic equation and the load side dynamic equation.

[0021] Preferably, the six-axis acceleration measurement unit and the dual inertia model are solved by using the Jacobian matrix to obtain a mapping relationship between the six-axis acceleration in Cartesian space and the joint space acceleration; the terminal vibration state is substituted into the mapping relationship to obtain the load-side acceleration of the robot joint; specifically including:

[0022] Obtain the terminal six-axis acceleration in the six-axis acceleration measurement unit and calculate the terminal Cartesian velocity;

[0023] Obtain the load-side dynamic equation in the dual-inertia model and obtain the load-side velocity vector;

[0024] The Jacobian matrix is ​​used to construct a speed mapping model based on the load-side speed vector and the terminal Cartesian speed;

[0025] The vibration state of the robot end is input into the mapping relationship to obtain the robot joint acceleration.

[0026] Preferably, the method of estimating the load-side joint velocity using a self-updating Kalman filter based on the obtained joint load-side acceleration to obtain the load-side joint estimated velocity specifically includes:

[0027] Based on the kinematic relationship between the robot's joint position, velocity, and acceleration, the kinematic differential equation is established;

[0028] The motion state of each joint of the robot is input into the motion differential equation, and the load-side joint position and velocity are solved using a self-updating Kalman filter to obtain the load-side joint estimated velocity.

[0029] Preferably, the method of eliminating the steady-state error of the estimated speed of the load-side joint by using the feedback control rate to obtain the corrected speed specifically includes:

[0030] The dual-inertia model with external disturbance is transformed into the frequency domain by Laplace transform method; the external disturbance includes Coriolis force, centrifugal force and gravity;

[0031] The load-side dynamic equation after frequency domain conversion is derived to obtain the relationship between the connecting rod side speed and the motor side speed; the relationship between the connecting rod side speed and the motor side speed is solved by using the feedback control law to obtain the trajectory reference joint speed;

[0032] The trajectory reference joint velocity is introduced into the load-side joint estimated velocity to eliminate the steady-state error.

[0033] Preferably, the correction speed is compensated to the robot speed loop controller, and the control gain in the damping controller is adjusted according to the vibration state of the robot terminal to achieve active control of the robot vibration. Specifically, the correction speed is fed back to the robot speed loop controller, and the damping gain and stiffness gain of the active damping controller are adjusted according to the terminal six-axis vibration state signal during the robot movement.

[0034] In a second aspect, the present invention provides a robot processing vibration control system, comprising:

[0035] The terminal modeling module is used to build a six-axis acceleration measurement unit for the robot terminal and obtain the six-axis acceleration and vibration state of the robot terminal;

[0036] A joint-end relationship mapping module is used to construct a dual-inertia model based on the motor side and the load side of the robot joint; the six-axis acceleration measurement unit and the dual-inertia model are solved by using the Jacobian matrix to obtain a mapping relationship between the six-axis acceleration in Cartesian space and the joint space acceleration; the end vibration state is substituted into the mapping relationship to obtain the load-side acceleration of the robot joint;

[0037] The estimated speed acquisition module is used to estimate the load-side joint speed by using a self-updating Kalman filter according to the obtained joint load-side acceleration to obtain the load-side joint estimated speed;

[0038] A correction speed acquisition module is used to eliminate the steady-state error of the estimated speed of the load-side joint by using the feedback control rate to obtain the correction speed;

[0039] The active control module is used to compensate the correction speed into the robot speed loop controller, adjust the control gain in the damping controller according to the vibration state of the robot end, and realize active control of the robot vibration.

[0040] In a third aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of a robot machining vibration control method described in the first aspect.

[0041] In a fourth aspect, the present invention provides a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps in the robot machining vibration control method described in the first aspect are implemented.

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] (1) The present invention can obtain the terminal vibration state more accurately and improve the information perception accuracy by constructing a six-axis acceleration measurement unit to replace the three-axis acceleration; construct a dual inertia model based on the motor side and the load side, and obtain the Cartesian and joint space acceleration mapping through the Jacobian matrix solution, which overcomes the problem of low accuracy when decoupling the motor side and the load side motion information; use a self-updating Kalman filter to estimate the load side joint speed, which reduces the high requirements for modeling accuracy and the complexity of the control structure compared to the method that relies on the disturbance observer or the motor side information; finally, the correction speed is compensated to the speed loop controller and the damping gain is adjusted to achieve effective active control of the robot vibration. The method proposed by the present invention is simple, reliable, and easy to implement, and accurately and efficiently solves the vibration problem generated by the flexible joint during the robot movement process, and improves the stability of the robot movement process.

[0044] (2) The present invention adopts four three-axis accelerometers to fuse and design a six-axis acceleration measurement unit that does not require gyroscope differential approximate angular acceleration, thereby obtaining the three-axis acceleration and three-axis angular acceleration of the robot end, avoiding the problem of modifying the robot body system to realize the load-side motion state perception of the flexible joint robot.

[0045] (3) The present invention proposes a method for sensing the motion state of the robot load side, adopts the terminal six-axis acceleration and combines the Jacobian matrix to establish the robot load side joint acceleration and terminal acceleration model, avoids the use of motor side position signal differential or three-axis acceleration to approximate decoupling of joint vibration state, and uses a self-updating Kalman filter to estimate the joint velocity on the load side, which can avoid the shortcomings of the existing approximate decoupling method and more accurately sense the motion state of the load side.

[0046] (4) The active damping control method proposed in the present invention establishes an active damping controller through the joint side velocity, trajectory reference velocity and the output signal of the robot position controller, and then outputs the correction speed and feeds it back to the robot speed control loop to achieve adaptive adjustment of the robot system damping.

[0047] Advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The accompanying drawings constituting a part of the present invention are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their description are used to explain the present invention but do not constitute a limitation of the present invention.

[0049] Figure 1 A main flow chart of a robot machining vibration control method provided by an embodiment of the present invention;

[0050] Figure 2 A schematic diagram of modeling a robot flexible joint provided in an embodiment of the present invention;

[0051] Figure 3 A schematic diagram of a six-axis acceleration measurement unit provided in an embodiment of the present invention;

[0052] Figure 4 A schematic diagram of the geometric configuration of a six-axis acceleration measurement unit provided in an embodiment of the present invention;

[0053] Figure 5 A control principle diagram of the overall method of the present invention provided for an embodiment of the present invention;

[0054] Figure 6 A frequency response diagram of the first joint of the robot provided by an embodiment of the present invention with and without an active damping control algorithm;

[0055] Figure 7 A schematic diagram of a reference trajectory of a robot end provided by an embodiment of the present invention;

[0056] Figure 8 A transient error diagram of robot joint trajectory tracking with and without active damping control algorithms provided by an embodiment of the present invention;

[0057] Fig. 9 A six-axis acceleration curve of the robot terminal with and without the active damping control algorithm provided by the embodiment of the present invention;

[0058] Fig.10 A schematic diagram of a machining process of a robot grinding a curved surface workpiece provided in an embodiment of the present invention;

[0059] Fig.11 A six-axis acceleration curve of the robot terminal with and without the active damping control algorithm provided by the embodiment of the present invention;

[0060] Fig.12 A fast Fourier transform diagram of the robot terminal acceleration with and without the active damping control algorithm provided in an embodiment of the present invention.

[0061] Wherein: 101-flexible joint motor end; 102-joint flexible part; 103-flexible joint load end; 201-six-axis acceleration measurement unit housing 1; 202-six-axis acceleration measurement unit bracket; 203-three-axis acceleration sensor TB; 204-three-axis acceleration sensor TD; 205-three-axis acceleration sensor TA; 206-three-axis acceleration sensor TC; 207-six-axis acceleration measurement unit housing 2; 301-robot; 302 robot end flange; 303-grinding tool; 304-six-axis acceleration measurement unit; 305-curved workpiece. DETAILED DESCRIPTION

[0062] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0063] Embodiment 1

[0064] like Figure 1 As shown, this embodiment discloses a robot machining vibration control method, which establishes a terminal six-axis acceleration decoupling strategy, adopts a load-side velocity estimation method to obtain the velocity information of the robot, establishes an active damping controller and feeds back to the robot controller in real time, and realizes the robot joint flexible vibration suppression. Specifically, it includes the following steps:

[0065] S1: Build a six-axis acceleration measurement unit at the end of the robot to obtain the six-axis acceleration and vibration state of the end of the robot;

[0066] S2: Based on the motor side and the load side of the robot joint, a dual-inertia model is constructed; the six-axis acceleration measurement unit and the dual-inertia model are solved using the Jacobian matrix to obtain a mapping relationship between the six-axis acceleration in Cartesian space and the joint space acceleration; the terminal vibration state is substituted into the mapping relationship to obtain the load-side acceleration of the robot joint;

[0067] S3: according to the obtained joint load-side acceleration, a self-updating Kalman filter is used to estimate the load-side joint velocity to obtain the load-side joint estimated velocity;

[0068] S4: using the feedback control rate to eliminate the steady-state error of the estimated speed of the load-side joint and obtain the corrected speed;

[0069] S5: Compensating the correction speed into the robot speed loop controller, adjusting the control gain in the damping controller according to the vibration state of the robot terminal, and realizing active control of the robot vibration.

[0070] Next, combine Figure 1 , a robot processing vibration control method disclosed in this embodiment is described in detail.

[0071] In S1, the terminal six-axis acceleration is decoupled: four three-axis accelerometers are combined into a six-axis acceleration measurement unit, and the six-axis acceleration measurement unit is installed on the robot terminal flange. According to the three-axis acceleration of each measurement point in the six-axis acceleration measurement unit and the rigid body kinematic model, the three-axis acceleration and three-axis angular acceleration of the robot terminal are obtained.

[0072] Further, if Figure 2 Schematic diagram of modeling a robot flexible joint is shown, including a flexible joint motor end 101, a joint flexible part 102 and a flexible joint load end 103. Figure 3 As shown, the terminal six-axis acceleration decoupling includes the following steps:

[0073] S101: The six-axis acceleration measurement unit is configured as a tetrahedron, with four three-axis accelerometers installed at the four vertices of the tetrahedron;

[0074] Install the three-axis acceleration sensor TB203, three-axis acceleration sensor TD204, three-axis acceleration sensor TA205, and three-axis acceleration sensor TC206 on Figure 4 On the four vertices of the tetrahedron B, D, A, and C;

[0075] S102: The four three-axis accelerometers output the acceleration values ​​along the XYZ three axes at the four vertices respectively. According to the kinematic model of the spatial rigid body, the acceleration of the measurement point on the spatial rigid body is solved.

[0076]

[0077] In the formula, is the measuring point P j The linear acceleration value of acceleration relative to the inertial coordinate system {i}; is the linear acceleration of the rigid body coordinate system {b} relative to the inertial coordinate system {i}; is the angular velocity of the rigid body coordinate system {b} relative to the inertial coordinate system {i}; is the angular acceleration of the rigid body coordinate system {b} relative to the inertial coordinate system {i}; i r j P j The position vector of the point in the inertial coordinate system {i}; P j The velocity of the point relative to the rigid body coordinate system {b}; P j The acceleration of the point relative to the rigid body coordinate system {b}.

[0078] S103: Due to the installation in P j The accelerometer output value at point is not the motion acceleration relative to the inertial coordinate system {i}, but the specific force at the installation position. Using the relationship between specific force and motion acceleration, the value of the acceleration sensor output is obtained.

[0079]

[0080] In the formula, b s j Indicates P j The sensitive axis direction of the uniaxial acceleration sensor at point in the rigid body coordinate system {b}, b f j Indicates P j The specific force detected by the accelerometer at the point bf j The projection in the {b} system, b f ib represents the projection of the specific force at the origin of the {b} system in the {i} system, express The antisymmetric matrix of b r j Indicates P j The position vector of the point in the rigid body coordinate system {b}, b Ω ib express b ω ib The antisymmetric matrix, b ω ib is the angular velocity of the rigid body coordinate system {b} relative to the inertial coordinate system {i} i ω ib The projection on {b}, for b ω ib The angular acceleration value.

[0081] S104: Convert the four triaxial acceleration sensors into 12 uniaxial acceleration sensors, and then obtain the system measurement equation of the 12 uniaxial acceleration sensors:

[0082]

[0083] Where P represents the configuration state matrix of the uniaxial acceleration sensor, Q represents the coupling matrix of the uniaxial acceleration sensor configuration, F( b ω ib ) is expressed as the coupling term of the angular velocity of the rigid body, A W×1 =[a1...a j ...a 12 ] T Collect values ​​for 12 single-axis acceleration sensors;

[0084]

[0085] S105: Rewrite the system equation in step S104 as in, Configure the matrix for the acceleration sensor, c j =[P j Q j ] 1×12 , is the rigid body motion parameter, and then the six-axis acceleration of a certain measurement point on the rigid body is obtained:

[0086]

[0087] In S2, the robot joint acceleration and terminal acceleration models are established: the robot joint flexibility model is assumed to be a linear torsion spring, and the motor side and the load side are modeled as dual-inertia models, thereby obtaining the n-degree-of-freedom robot dynamics model with joint flexibility (including the load side dynamics equations and the motor side dynamics equations), and the Jacobian matrix is ​​used to obtain the mapping relationship between the Cartesian space velocity of the robot terminal (including the three-axis velocity and the three-axis angular velocity) and the joint velocity. By differentiating the time on both sides of the mapping relationship, the robot load side joint acceleration model and the terminal acceleration model are obtained.

[0088] S201, such as Figure 2 As shown, the Lagrangian method is used to establish the dynamic model of the n-DOF robot:

[0089] Load side dynamic equation:

[0090]

[0091] Motor side dynamic equation:

[0092]

[0093] In the formula, are the load side and motor side position vectors respectively, represents the load side velocity vector, represents the load side acceleration vector, represents the motor side speed vector, Represents the acceleration vector on the motor side. is the load side torque, is the torque on the motor side. is the load side inertia matrix, is the Coriolis force and centrifugal force matrix, is the gravity vector. m , K J , D j and It is a diagonal matrix, and the diagonal elements are motor inertia, reducer stiffness, reducer damping, and gear reduction ratio.

[0094] S202: Using Jacobian Matrix Establish the relationship between the robot joint velocity and the end Cartesian velocity:

[0095]

[0096] In the formula, is the translation speed of the robot end and angular velocity The Cartesian velocity vector.

[0097] S203: The velocity mapping model in S202 is differentiated in time to obtain the mapping relationship between the six-axis acceleration in Cartesian space and the acceleration in joint space:

[0098]

[0099] In the formula, is the Cartesian space acceleration of the robot end; is the load-side acceleration of the robot joint.

[0100] S204: Using the six-axis acceleration measurement unit to obtain the vibration state a of the robot end e , according to the mapping relationship between the six-axis acceleration and the joint space acceleration in S203, the load-side acceleration of the robot joint is obtained.

[0101] q l :

[0102]

[0103] In the formula, is the Jacobian matrix J(q l ) is the Moore-Penrose pseudoinverse of . is J(q l ) with respect to time.

[0104] In S3, the robot load-side velocity is acquired: based on the obtained joint load-side acceleration, a self-updating Kalman filter is used to estimate the joint velocity on the load side. By selecting the parameters in the Kalman filter, the accuracy of the joint load-side velocity estimation is improved, thereby avoiding the direct use of integration to obtain the load-side joint velocity, and solving the cumulative error and signal distortion problems caused by integration.

[0105] S301: Establish the motion differential equation through the motion relationship between the robot joint position, velocity and acceleration:

[0106]

[0107] In the formula, k is the time step, A is the system state matrix, B is the system control matrix, and C is the measurement matrix. It is the system process noise and conforms to Gaussian distribution; is the observation noise of the system at time k and conforms to Gaussian distribution, x k is the real state of the system at time k, y k is the observed value of the system at time k, u k is the input of the system at time k.

[0108] S302: The motion state of each joint of the robot is estimated using a self-updating Kalman filter to solve the joint position and velocity on the load side.

[0109] Among them, the self-updating Kalman filter estimation method is adopted, and the specific steps are:

[0110] S3021: The self-updating Kalman filter is represented as a prediction process and an update process:

[0111] Prediction process:

[0112]

[0113] In the formula, is to predict the state at time k+1 based on the system at time k; P k+1|k is the state covariance predicted at time k+1 based on the system at time k; P k|k is the state covariance at time k+1 after filtering.

[0114] Update process:

[0115]

[0116] In the formula, is the state of the system at time k+1 after filtering; P k+1|k+1 The state covariance at time k+1 after filtering; K k+1 is the Kalman gain at time k; y k+1 is the actual measured signal; is the filtered signal after Kalman filter estimation.

[0117] S3022: In order to improve the estimation effect of the Kalman filter, a self-updating Kalman filter method considering the filter residual is adopted to improve the dynamic filtering accuracy of the Kalman filter. The observation noise covariance matrix R is dynamically adjusted according to the error estimated by the Kalman filter, and the process noise covariance matrix Q is dynamically adjusted according to the new information matrix. k and residual ε k They are:

[0118]

[0119] S3023: Introduce the forgetting factor α for dynamic update of the observation noise covariance matrix R and the process noise covariance matrix Q:

[0120]

[0121] In S4, the active damping controller is designed: by establishing a robot dynamics model considering joint flexibility, based on the Laplace transform method, the time domain equation is converted into the s domain to facilitate the design of the damping controller; the load-side dynamics equation is differentiated to obtain the relationship between the connecting rod side speed and the motor side speed; the disturbance function is used to analyze the influence of the external disturbance force on the motion performance of the robot system, and then the active damping controller feedback term is obtained; the feedback term is introduced into the robot speed loop control loop to obtain the motor speed loop reference signal; by analyzing the steady-state error between the load-side joint speed and the trajectory reference joint speed, the trajectory reference speed feedforward control is introduced to eliminate the influence of the steady-state error.

[0122] S401: Based on the robot dynamics model (dual inertia model) established in S201, the Coriolis force, centrifugal force term, and gravity term are unified into the external disturbance torque τ ext . And the robot motion space is regarded as each single point motion, so the coupling term in the load side inertia matrix is ​​temporarily ignored. Using the Laplace transform method, the expression model of the robot dynamics model in the s domain is obtained:

[0123] Load side:

[0124]

[0125] Motor side:

[0126]

[0127] Among them, q li represents the joint position on the load side of the i-th joint, s is the Laplace operator, is the load side inertia matrix M l The inverse of the corresponding element; k i and d i are parameters related to stiffness and damping respectively; θ mi is the motor side position; is the motor side inertia matrix M m The inverse of the corresponding element; τ m,i is the motor side torque, τ f,i is the friction torque, N -1 Represents the inverse of the gear ratio diagonal matrix.

[0128] S402: Derivative the load-side dynamic equation to obtain the relationship between the connecting rod side speed and the motor side speed: The disturbance transfer function is used to analyze the disturbance during the operation of the robot:

[0129]

[0130] S403: Design feedback control law G(s) to suppress system disturbances and introduce a correction speed into the system As a feedback signal, In order to realize active compensation of damping and stiffness, the control gain is introduced: damping gain K d,i and stiffness gain K k,i , the feedback control law G(s) is set as:

[0131]

[0132] In the formula, represents the joint velocity on the load side of the i-th joint, and the numerator It is to actively compensate for damping to enhance the damping effect of the system and reduce vibration; It is an active compensation for stiffness, increasing the stiffness of the system, which helps the system to return to a stable state faster; the denominator d i s+k i The purpose of suppressing disturbance is achieved by estimating the system damping and stiffness and introducing the dynamic characteristics of the controlled system.

[0133] S404: Add the feedback control law H(s) to the speed loop of the robot controller. The reference input signal of the motor speed control loop is:

[0134]

[0135] in, represents the speed of the robot position loop controller output, Represents the joint velocity of the reference trajectory.

[0136] S405: Substitute the motor side reference signal into the robot dynamics model established in S401 to obtain the disturbance function between the input and output signals on the load side:

[0137]

[0138] S406: Using Laplace transform, calculate the system steady-state error:

[0139]

[0140] The expected trajectory feedforward method is used to eliminate the steady-state error of the system. When controlling the robot motor, the performance of the motor-side speed controller is sufficient to meet the control requirements. The complete control law of the active damping controller is:

[0141]

[0142] In S5, the robot speed loop feedback control: the correction speed in the active damping controller is compensated to the robot speed loop controller, and the control gain in the damping controller is adjusted according to the vibration suppression effect during the robot movement, thereby realizing active control of the robot vibration.

[0143] S501: The correction speed obtained by the active damping controller in S4 Feedback to the robot speed loop controller;

[0144] S502: Adjust the damping gain K of the active damping controller according to the six-axis vibration signal of the end of the robot during movement d,i and stiffness gain K k,i .

[0145] Example 1

[0146] In this embodiment, the robot flexible joint dual inertia model parameters are set to the parameters in Table 1. Figure 5 The control schematic diagram in Figure 6 Frequency response diagram of the robot's first joint with and without active damping control algorithm. Figure 6 It can be found that the system amplitude response peak is reduced when the active damping control algorithm is adopted. This is because the introduction of damping makes the robot system response smoother and improves the stability of the system.

[0147] Table 1 Parameter settings of dual inertia model

[0148]

[0149] use Figure 7 The robot end reference trajectory in Figure 5 The control principle of the overall method is used to control the robot motion and select the damping gain K in the active damping controller. d,i and stiffness gain K k,i According to the six-axis acceleration measurement unit at the end of the robot, the correction speed in the active damping controller is fed back to the robot speed loop controller in real time to achieve vibration suppression of the robot's flexible joints. Using transient response ||e||=||q l,r -q l || indicates that the vibration control effect and tracking performance of the controller are evaluated and the obtained Figure 8The transient error diagram of robot joint trajectory tracking with and without active damping control algorithm is shown in the figure. The transient response fluctuation is reduced from 0.004rad to 0.015rad under the non-active damping control algorithm to 0rad to 0.004rad under the active damping control algorithm. When the active damping control algorithm is adopted, due to the feedback of the six-axis acceleration at the end of the robot, the vibration of the robot joint trajectory is significantly controlled, especially in the initial response stage. In terms of the later trajectory tracking, the robot trajectory tracking error is smaller than the error under the non-active damping control algorithm.

[0150] The six-axis acceleration measurement unit installed on the end flange of the robot decouples the linear acceleration and angular acceleration as shown in Fig. 9 As shown in the figure, when the active damping control algorithm is used, the vibration amplitude of the six axes of the robot end is the smallest compared with the non-active damping control algorithm. Therefore, this method not only effectively suppresses the trajectory vibration, but also improves the robot trajectory tracking accuracy.

[0151] Example 2

[0152] This embodiment provides a robot processing vibration control method. Since robots need to meet different task requirements, the method proposed in this invention is used to control the robot to Fig.10 The curved surface workpiece is ground to suppress the flexible vibration of the robot during the grinding process.

[0153] like Fig.10 As shown, a six-axis acceleration measurement unit 304 is installed on the robot end flange 302 at the end of the robot 301, and a grinding tool 303 is installed at the bottom of the robot end flange 302. By setting the robot grinding processing trajectory, the curved workpiece 305 is ground.

[0154] use Figure 5 The six-axis acceleration feedback robot vibration active control method in the present invention suppresses the vibration of the robot during processing, and obtains the three-axis acceleration and three-axis angular acceleration decoupled by the six-axis acceleration measurement unit 304, such as Fig.11 As shown in the figure, when the active damping control algorithm is used, the vibration amplitude of the six axes of the robot end is the smallest compared with the non-active damping control algorithm, and by adjusting the adjacent Fig.11 The Y-axis and Z-axis accelerations and the angular acceleration around the x-axis are transformed by fast Fourier transform, as shown in the attached figure. Fig.12 The fast Fourier transform diagram of the robot end acceleration with and without the active damping control algorithm. The active damping control algorithm effectively suppresses the vibration of the robot end.

[0155] This specific embodiment utilizes four three-axis accelerometers to form a six-axis acceleration measurement unit, and installs the six-axis acceleration measurement unit on the robot end flange, obtains the three-axis acceleration and three-axis angular acceleration of the robot end through the system measurement equation of the acceleration sensor; obtains the mapping relationship between the Cartesian space acceleration of the robot end and the joint space acceleration using the Jacobian matrix; uses a self-updating Kalman filter to estimate the joint velocity on the load side, thereby improving the estimation accuracy of the joint load side velocity, thereby avoiding the direct use of integration to obtain the load side joint velocity, and solving the cumulative error and signal distortion problems caused by integration; by establishing a robot dynamics model considering joint flexibility, using a disturbance function to analyze the influence of external disturbance force on the motion performance of the robot system, obtaining the feedback term of the active damping controller, and introducing a trajectory reference velocity feedforward control to eliminate the influence of steady-state errors, thereby obtaining a motor speed loop reference signal, which is fed back to the robot control loop to achieve active control of the robot vibration.

[0156] In view of the existing problems of robot acceleration feedback vibration control technology, the present invention proposes a robot processing vibration control method, which realizes flexible vibration suppression of robot joints and improves the stability of the robot system by establishing a terminal six-axis acceleration decoupling strategy, feeding back the terminal vibration state in real time, establishing an active damping controller and feeding back to the robot controller in real time.

[0157] Embodiment 2

[0158] This embodiment provides a robot machining vibration control system, including:

[0159] The terminal modeling module is used to build a six-axis acceleration measurement unit for the robot terminal and obtain the six-axis acceleration and vibration state of the robot terminal;

[0160] A joint-end relationship mapping module is used to construct a dual-inertia model based on the motor side and the load side of the robot joint; the six-axis acceleration measurement unit and the dual-inertia model are solved by using the Jacobian matrix to obtain a mapping relationship between the six-axis acceleration in Cartesian space and the joint space acceleration; the end vibration state is substituted into the mapping relationship to obtain the load-side acceleration of the robot joint;

[0161] The estimated speed acquisition module is used to estimate the load-side joint speed by using a self-updating Kalman filter according to the obtained joint load-side acceleration to obtain the load-side joint estimated speed;

[0162] A correction speed acquisition module is used to eliminate the steady-state error of the estimated speed of the load-side joint by using the feedback control rate to obtain the correction speed;

[0163] The active control module is used to compensate the correction speed into the robot speed loop controller, adjust the control gain in the damping controller according to the vibration state of the robot end, and realize active control of the robot vibration.

[0164] Embodiment 3

[0165] This embodiment provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps in the robot machining vibration control method described in the above-mentioned embodiment 1 are implemented.

[0166] Embodiment 4

[0167] This embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps in a robot machining vibration control method as described in the first embodiment above are implemented.

[0168] The steps or modules involved in the above embodiments 2 to 4 correspond to those in embodiment 1. For the specific implementation, please refer to the relevant description of embodiment 1. The term "computer-readable storage medium" should be understood as a single medium or multiple media including one or more instruction sets; it should also be understood to include any medium that can store, encode or carry an instruction set for execution by a processor and enable the processor to execute any method in the present invention.

[0169] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A robot machining vibration control method, characterized in that: include: A six-axis acceleration measurement unit is built at the end of the robot to obtain the six-axis acceleration and vibration state of the end of the robot; Based on the motor side and load side of the robot joint, a dual-inertia model is constructed; The six-axis acceleration measurement unit and the dual inertia model are solved by using the Jacobian matrix to obtain a mapping relationship between the six-axis acceleration in Cartesian space and the joint space acceleration; the terminal vibration state is substituted into the mapping relationship to obtain the load-side acceleration of the robot joint; According to the obtained joint load-side acceleration, the load-side joint velocity is estimated using a self-updating Kalman filter to obtain the load-side joint estimated velocity; The feedback control rate is used to eliminate the steady-state error of the estimated velocity of the load-side joint and obtain the corrected velocity; The correction speed is compensated to the robot speed loop controller, and the control gain in the damping controller is adjusted according to the vibration state of the robot end, so as to realize the active control of the robot vibration.

2. A robot processing vibration control method as claimed in claim 1, characterized in that: The six-axis acceleration of the robot terminal includes three-axis acceleration and three-axis angular acceleration; the acquisition process is specifically as follows: The specific process of obtaining the six-axis acceleration of the robot terminal is as follows: A six-axis acceleration measurement unit is constructed for the robot end based on a spatial rigid body coordinate system; four three-axis accelerometers are respectively set on the X, Y, Z axes and the origin of the six-axis acceleration measurement unit; According to the relationship between the spatial rigid body coordinate system and the inertial coordinate system, the acceleration of the three-axis accelerometer in the six-axis acceleration measurement unit is obtained, and the accelerometer output value is corrected based on the specific force; Convert 4 triaxial accelerometers into 12 uniaxial accelerometers and construct system measurement equations; The system measurement equation is simplified, and the corrected accelerometer output value is substituted to obtain the six-axis acceleration.

3. A robot processing vibration control method as claimed in claim 1, characterized in that: The dual-inertia model is constructed based on the motor side and the load side of the robot joint, specifically: the n-degree-of-freedom dual-inertia model is established for the robot joint using the Lagrangian method; the dual-inertia model includes the motor side dynamic equation and the load side dynamic equation.

4. A robot processing vibration control method as claimed in claim 3, characterized in that: The Jacobian matrix is ​​used to solve the six-axis acceleration measurement unit and the dual inertia model to obtain a mapping relationship between the six-axis acceleration in Cartesian space and the joint space acceleration; Substituting the terminal vibration state into the mapping relationship to obtain the load-side acceleration of the robot joint; specifically including: Obtain the terminal six-axis acceleration in the six-axis acceleration measurement unit and calculate the terminal Cartesian velocity; Obtain the load-side dynamic equation in the dual-inertia model and obtain the load-side velocity vector; The Jacobian matrix is ​​used to construct a speed mapping model based on the load-side speed vector and the terminal Cartesian speed; The vibration state of the robot end is input into the mapping relationship to obtain the robot joint acceleration.

5. A robot processing vibration control method as claimed in claim 1, characterized in that: The method estimates the load-side joint velocity using a self-updating Kalman filter according to the obtained joint load-side acceleration to obtain the load-side joint estimated velocity; specifically includes: Based on the kinematic relationship between the robot's joint position, velocity, and acceleration, the kinematic differential equation is established; The motion state of each joint of the robot is input into the motion differential equation, and the load-side joint position and velocity are solved using a self-updating Kalman filter to obtain the load-side joint estimated velocity.

6. A robot machining vibration control method as claimed in claim 1, characterized in that: The feedback control rate is used to eliminate the steady-state error of the estimated speed of the load-side joint to obtain the corrected speed; Specifically include: The dual-inertia model with external disturbance is transformed into the frequency domain by Laplace transform method; the external disturbance includes Coriolis force, centrifugal force and gravity; The load-side dynamic equation after frequency domain conversion is derived to obtain the relationship between the connecting rod side speed and the motor side speed; the relationship between the connecting rod side speed and the motor side speed is solved by using the feedback control law to obtain the trajectory reference joint speed; The trajectory reference joint velocity is introduced into the load-side joint estimated velocity to eliminate the steady-state error.

7. A robot processing vibration control method as claimed in claim 1, characterized in that: The correction speed is compensated to the robot speed loop controller, and the control gain in the damping controller is adjusted according to the vibration state of the robot terminal to realize active control of the robot vibration. Specifically, the correction speed is fed back to the robot speed loop controller, and the damping gain and stiffness gain of the active damping controller are adjusted according to the terminal six-axis vibration state signal during the robot movement.

8. A robot processing vibration control system, characterized in that: include: The terminal modeling module is used to build a six-axis acceleration measurement unit for the robot terminal and obtain the six-axis acceleration and vibration state of the robot terminal; The joint-end relationship mapping module is used to build a dual-inertia model based on the motor side and load side of the robot joint; The six-axis acceleration measurement unit and the dual-inertia model are solved by using the Jacobian matrix to obtain a mapping relationship between the six-axis acceleration in Cartesian space and the acceleration in joint space; Substituting the terminal vibration state into the mapping relationship to obtain the load-side acceleration of the robot joint; The estimated speed acquisition module is used to estimate the load-side joint speed by using a self-updating Kalman filter according to the obtained joint load-side acceleration to obtain the load-side joint estimated speed; A correction speed acquisition module is used to eliminate the steady-state error of the estimated speed of the load-side joint by using the feedback control rate to obtain the correction speed; The active control module is used to compensate the correction speed into the robot speed loop controller, adjust the control gain in the damping controller according to the vibration state of the robot end, and realize active control of the robot vibration.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of a robot machining vibration control method as described in any one of claims 1 to 7 are implemented.

10. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps of a robot machining vibration control method as described in any one of claims 1-7 are implemented.

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