Milling motion control method for double hybrid robots

Abstract

The invention discloses a milling motion control method of a double-hybrid robot, and relates to the technical field of robot thin-wall workpiece machining, and the method comprises the steps: firstly obtaining a milling motion control system equation, taking the high-speed motorized spindle current of the milling motion control system equation as a state variable and an output variable, and obtaining a high-speed motorized spindle current model equation; a linear expansion state observer is used for observing a milling tool nose displacement signal collected by a laser displacement sensor, the speed and displacement of a high-speed motorized spindle of a milling motion control system serve as state variables, the speed and displacement are identified through expansion Kalman, and a matrix vector form of an augmented state equation model is obtained; and active-disturbance-rejection extended Kalman convergence analysis is improved, a speed signal and a displacement signal of a milling motion control system tracking differentiator are established, and a motion control working condition design tracking differentiator discrete form of the high-speed motorized spindle is obtained. The position in the milling process can be accurately controlled, and the machining precision and stability are improved.

Description

Technical Field

[0001] The invention relates to the technical field of robot thin-walled workpiece processing, in particular to a milling motion control method of a dual hybrid robot. Background Art

[0002] During the processing of thin-walled parts, the robot's support head driven by the high-speed electric spindle is prone to large deformation and violent vibration, which makes the surface processing quality of the workpiece fail to meet the expected effect and makes it difficult to ensure the wall thickness accuracy. The dual-hybrid robot has the characteristics of large workspace and strong flexibility, and is widely used in the milling control of large structural parts such as aircraft skins and aviation transparent parts. At present, the most commonly used methods for robot milling motion control are pure data-driven workpiece deformation prediction methods based on sparse Bayesian learning, PID linear control methods, disturbance observers and improved Smith predictors, and robust model adaptive control methods with state feedback.

[0003] After searching relevant patents and literature at home and abroad, regarding the robot's milling motion control method for thin-walled parts, there are papers such as "Prediction of Modal Characteristics of Robot Processing System Based on Deep Neural Network", which takes the ABB robot processing system as the research object, proposes a modal prediction method based on deep neural network, and uses hyperparameter optimization method to establish a deep neural network prediction model, and finally achieves accurate prediction of multi-order modal parameters related to the posture in the workspace of the industrial robot, but the control effect of this method is reduced under unknown disturbances. The patent application with application number 202211649077.2 and invention name "A method for generating robot grinding parameters based on the stiffness change of large thin-walled parts" discloses a method for generating robot grinding parameters based on the stiffness change of large thin-walled parts. The initial value of the dynamic contact force curve obtained is the basic contact force value in the dynamic process generation constraint condition, and the change trend of the curve is determined by the workpiece stiffness change trend line. The invention patent with application number 202011215803.0 and invention name "A dual-arm robot collaborative vibration reduction method for grinding large thin-walled parts" discloses a dual-arm robot collaborative vibration reduction method for grinding large thin-walled parts. The handling end of the robot is connected to a vibration absorber, and the grinding end of the grinding robot is connected to a force-controlled end effector. The force-controlled end effector grinds one end of the thin-walled part in the left and right direction, thereby reducing the vibration when grinding the thin-walled part.

[0004] Although the above-mentioned patent application has studied many robot milling motion control methods for thin-walled parts, the solution in the above-mentioned patent application is only applicable to the thinning processing of small thin-walled parts. The vibration and deformation of the thin-walled parts during the processing may easily lead to large milling errors. Summary of the invention

[0005] The technical problem to be solved by the present invention is to overcome the shortcomings of the prior art and provide a milling motion control method for a dual-hybrid robot. This method can make the support head driven by the high-speed electric spindle of the milling motion control system of the dual-hybrid robot have higher synchronization position accuracy, reliable structure, and high calculation efficiency; it exhibits higher robustness under external interference, eliminates the vibration of the support head driven by the high-speed electric spindle of the dual-hybrid robot caused by load disturbance during operation, can accurately control the position during the milling process, and improve processing accuracy and stability.

[0006] The present invention adopts the following technical solutions to solve the above technical problems:

[0007] In a first aspect, a milling motion control method of a dual hybrid robot proposed in the present invention comprises:

[0008] According to the support head driven by the high-speed electric spindle of the milling motion control system of the dual hybrid robot, the milling motion control system equation is obtained;

[0009] The high-speed electric spindle current of the milling motion control system equation is used as the state variable and output variable, thereby obtaining the high-speed electric spindle current model equation;

[0010] The laser displacement sensor collects the milling tool tip displacement signal extracted from the high-speed electric spindle current model equation. The linear extended state observer is used to observe the milling tool tip displacement signal collected by the laser displacement sensor. The high-speed electric spindle speed and displacement of the milling motion control system are used as state variables. The extended Kalman method is used to identify the speed and displacement, and the matrix vector form of the augmented state equation model is obtained.

[0011] By performing self-disturbance rejection extended Kalman convergence analysis on the matrix-vector form of the augmented state equation model, the velocity signal and displacement signal of the tracking differentiator of the milling motion control system are established, and the discrete form of the tracking differentiator for the motion control condition design of the high-speed electric spindle is obtained.

[0012] As a further optimization scheme of the milling motion control method of a dual hybrid robot described in the present invention, the milling motion control system equation is obtained according to the support head driven by the high-speed electric spindle of the dual hybrid robot milling motion control system; specifically including:

[0013] According to the dynamic system transfer function of the milling tool tip and ball of the support head driven by the high-speed electric spindle of the dual hybrid robot milling motion control system, the milling motion control system equation is obtained as follows:

[0014]

[0015] Among them, ρ1 and ρ2 are parameters related to the dynamic characteristics of the milling tool tip and the ball, y mIndicates the displacement signal of the milling tool tip sensor, Yes m The second-order derivative of , u is the speed signal of the high-speed electric spindle; i represents the current of the high-speed electric spindle of the milling motion control system, is the first-order derivative of i, and F is the external disturbance of the milling motion control system.

[0016] As a further optimization scheme of the milling motion control method of a dual hybrid robot described in the present invention, the high-speed electric spindle current model equation is:

[0017]

[0018] Among them, A, B, and C represent the first to third rate-of-change matrices of the high-speed electric spindle current model equation, respectively, f(·) represents the rate-of-change matrix of F, ψ(k) represents the displacement parameter value collected at time k, and t is time.

[0019] As a further optimization scheme of the milling motion control method of a dual hybrid robot described in the present invention,

[0020] The laser displacement sensor collects the milling tool tip displacement signal, and the linear expansion state observer in the self-disturbance rejection method is used to observe the milling tool tip displacement signal collected by the laser displacement sensor; the details are as follows:

[0021]

[0022] Among them, z1(k) and z2(k) are the output signal of the laser displacement sensor and the differential form of the output signal of the laser displacement sensor at time k, respectively; z3(k) is the uncertain disturbance of the milling motion control system at time k; η is the displacement compensation factor of the milling motion control system, and η is used to eliminate the vibration compensation of the high-speed electric spindle; e(k) and e(k+1) are the error change rates of the milling motion control system at time k and time k+1, respectively; and h(·) is the sampling period.

[0023] As a further optimization scheme of the milling motion control method of a dual hybrid robot described in the present invention, the matrix vector form of the augmented state equation model is constructed using the extended Kalman method:

[0024] X(k+1)=f(X(k),u(k))+Q(k)W(k) (4)

[0025] y m (k) = e(k+1) + R(k)V(k) (5)

[0026] Among them, y m (k) represents the displacement signal y of the milling tool tip sensor mDiscrete function at time k, X(k+1) is the speed signal at time k+1, X(k) is the speed signal at time k, W(k) and V(k) are the displacement noise and speed noise at time k respectively, Q(k) and R(k) are the displacement noise covariance matrix and speed noise covariance matrix at time k respectively, and u(k) is the speed signal of the high-speed electric spindle at time k.

[0027] As a further optimization scheme of the milling motion control method of a dual hybrid robot described in the present invention, the discrete form of the tracking differentiator of the motion control working condition design of the high-speed electric spindle is:

[0028]

[0029] Wherein, R1(k) and R2(k) are the speed tracking value and displacement tracking value of the input signal at time k, respectively; R1(k+1) and R2(k+1) are the speed tracking value and displacement tracking value of the input signal at time k+1, respectively; fhan(·) is the fastest control comprehensive function; ξ is the speed variable parameter; τ is the displacement variable parameter; and fh is the tracking differentiator.

[0030] In a second aspect, an embodiment of the present invention further provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein when the processor executes the computer program, the steps of the milling motion control method of a dual hybrid robot as described in the first aspect above or any corresponding embodiment thereof are implemented.

[0031] In a third aspect, an embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the milling motion control method of a dual hybrid robot as described in the first aspect above or any corresponding embodiment thereof are implemented.

[0032] Compared with the prior art, the present invention adopts the above technical solution and has the following technical effects:

[0033] (1) Compared with the traditional extended Kalman method, the milling motion control strategy proposed by the self-disturbance rejection extended Kalman method of the present invention can ensure that the support head driven by the high-speed electric spindle of the dual hybrid robot has high synchronization position accuracy, reliable structure, high calculation efficiency, and solves the problem of difficulty in establishing the control model and the influence of the control parameter uncertainty on the system control performance;

[0034] (2) Compared with the traditional fuzzy PID control method, the self-disturbance rejection extended Kalman method adopted in the present invention has better control effect and shows higher robustness under external interference. It eliminates the vibration of the support head driven by the high-speed electric spindle of the dual hybrid robot caused by load disturbance during operation, can accurately control the position during milling, and improve processing accuracy and stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a flow chart of the present invention;

[0036] Figure 2 This is the effect diagram of the milling motion control of the dual hybrid robot. DETAILED DESCRIPTION

[0037] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0038] like Figure 1 As shown, a milling motion control method of a dual hybrid robot includes the following steps:

[0039] First, according to the support head driven by the high-speed electric spindle of the milling motion control system of the dual hybrid robot, the milling motion control system equation is obtained. In order to simplify the algebraic identification controller gain and the various interference problems caused by the change of system parameters, the high-speed electric spindle current of the milling motion control system equation is used as the state variable and output variable, so as to obtain the high-speed electric spindle current model equation. The laser displacement sensor collects the milling tool tip displacement signal extracted from the high-speed electric spindle current model equation. The milling tool tip displacement signal collected by the laser displacement sensor is observed by the linear expansion state observer. The high-speed electric spindle speed and displacement of the milling motion control system are used as state variables. The extended Kalman is used to identify the speed and displacement to obtain the matrix vector form of the augmented state equation model. The matrix vector form of the augmented state equation model is subjected to the self-disturbance rejection extended Kalman convergence analysis, and the speed signal and displacement signal of the tracking differentiator of the milling motion control system are established, and the discrete form of the tracking differentiator of the motion control working condition design of the high-speed electric spindle is obtained. The self-disturbance rejection extended Kalman method adopted in the present invention reaches a steady state faster than the extended Kalman method, and the displacement is always kept at a very small amplitude.

[0040] According to the support head driven by the high-speed electric spindle of the dual hybrid robot milling motion control system, the milling motion control system equation is obtained; specifically including:

[0041] According to the dynamic system transfer function of the milling tool tip and ball of the support head driven by the high-speed electric spindle of the dual hybrid robot milling motion control system, the milling motion control system equation is obtained as follows:

[0042]

[0043] Among them, ρ1 and ρ2 are parameters related to the dynamic characteristics of the milling tool tip and the ball, y m Indicates the displacement signal of the milling tool tip sensor, Yes m The second-order derivative of , u is the speed signal of the high-speed electric spindle; i represents the current of the high-speed electric spindle of the milling motion control system, is the first-order derivative of i, and F is the external disturbance of the milling motion control system.

[0044] Among them, the milling motion control system usually uses algebraic identification of controller gains and various interference problems caused by changes in system parameters, but this method is computationally complex. Here, the high-speed electric spindle current of the robot milling motion control system is used as the state variable and output variable to obtain the high-speed electric spindle current model equation.

[0045]

[0046] Among them, A, B, and C represent the first to third rate-of-change matrices of the high-speed electric spindle current model equation, respectively, f(·) represents the rate-of-change matrix of F, ψ(k) represents the displacement parameter value collected at time k, and t is time.

[0047] The linear extended state observer is the core of the ADRS. It collects the milling tool tip displacement signal from the laser displacement sensor and observes it.

[0048]

[0049] Among them, z1(k) and z2(k) are the output signal of the laser displacement sensor and the differential form of the output signal of the laser displacement sensor at time k, respectively; z3(k) is the uncertain disturbance of the milling motion control system at time k; η is the displacement compensation factor of the milling motion control system, and η is used to eliminate the vibration compensation of the high-speed electric spindle; e(k) and e(k+1) are the error change rates of the milling motion control system at time k and time k+1, respectively; and h(·) is the sampling period.

[0050] The electrical time constant of the milling motion control system is much smaller than the mechanical time constant, the sampling period is small, the speed and displacement differentials are considered to be zero, and it is a high-order nonlinear and strongly coupled system. The high-speed electric spindle speed and displacement of the milling motion control system are regarded as state variables, and the extended Kalman method is used to identify the speed and displacement. The matrix vector form of the augmented state equation model is obtained as follows:

[0051] X(k+1)=f(X(k),u(k))+Q(k)W(k) (4)

[0052] ym (k) = e(k+1) + R(k)V(k) (5)

[0053] Among them, y m (k) represents the displacement signal y of the milling tool tip sensor m Discrete function at time k, X(k+1) is the speed signal at time k+1, X(k) is the speed signal at time k, W(k) and V(k) are the displacement noise and speed noise at time k respectively, Q(k) and R(k) are the displacement noise covariance matrix and speed noise covariance matrix at time k respectively, and u(k) is the speed signal of the high-speed electric spindle at time k.

[0054] Through the self-disturbance rejection extended Kalman convergence analysis, the velocity signal and displacement signal of the milling motion control system tracking differentiator fh are established, and the tracking differentiator is designed according to the motion control working condition of the high-speed electric spindle. The discrete form is expressed as:

[0055]

[0056] Wherein, R1(k) and R2(k) are the speed tracking value and displacement tracking value of the input signal at time k, respectively; R1(k+1) and R2(k+1) are the speed tracking value and displacement tracking value of the input signal at time k+1, respectively; fhan(·) is the fastest control comprehensive function; ξ is the speed variable parameter; τ is the displacement variable parameter; and fh is the tracking differentiator.

[0057] The active disturbance rejection method of the present invention comprises a linear extended state observer and a tracking differentiator.

[0058] Depend on Figure 2 Analysis shows that for milling motion control systems, the time it takes for the self-disturbance rejection extended Kalman method to reach steady state is about twice as fast as that of the extended Kalman method. In addition, compared with the extended Kalman method, the self-disturbance rejection extended Kalman method can quickly recover to stability for working condition disturbances. Although it will fluctuate again after a period of time, the fluctuation amplitude is much smaller than that of the extended Kalman method, and the displacement can always be kept at a very small amplitude. Comparative analysis further shows that the self-disturbance rejection extended Kalman method can achieve stable control of milling cutting depth, and its anti-interference ability is higher than that of the extended Kalman method, and it has strong robustness and anti-interference ability.

[0059] The dual-hybrid robot has high requirements for the milling motion control of the support head driven by the high-speed electric spindle, and can quickly deal with the uncertainty of the control system parameters, so as to accurately control the displacement during the milling process and improve the processing accuracy and stability. The self-disturbance rejection extended Kalman method adopted in this method can make the support head driven by the high-speed electric spindle of the dual-hybrid robot milling motion control system have high synchronization position accuracy, reliable structure, and high calculation efficiency. It solves the problem that the control model of the dual-hybrid robot is difficult to establish, and the influence of the uncertainty of the control parameters of the dual-hybrid robot on the control performance of the system. It shows higher robustness in the case of external interference, eliminates the vibration caused by the load disturbance during the operation of the support head driven by the high-speed electric spindle of the dual-hybrid robot, and can accurately control the position during the milling process, improving the processing accuracy and stability.

[0060] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes. The schemes in the embodiments of the present invention may be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0061] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0062] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0063] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0064] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0065] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A milling motion control method of a dual hybrid robot, characterized in that: include: According to the support head driven by the high-speed electric spindle of the milling motion control system of the dual hybrid robot, the milling motion control system equation is obtained; The high-speed electric spindle current of the milling motion control system equation is used as the state variable and output variable, thereby obtaining the high-speed electric spindle current model equation; The laser displacement sensor collects the milling tool tip displacement signal extracted from the high-speed electric spindle current model equation. The linear extended state observer is used to observe the milling tool tip displacement signal collected by the laser displacement sensor. The high-speed electric spindle speed and displacement of the milling motion control system are used as state variables. The extended Kalman method is used to identify the speed and displacement, and the matrix vector form of the augmented state equation model is obtained. By performing self-disturbance rejection extended Kalman convergence analysis on the matrix-vector form of the augmented state equation model, the velocity signal and displacement signal of the tracking differentiator of the milling motion control system are established, and the discrete form of the tracking differentiator for the motion control condition design of the high-speed electric spindle is obtained.

2. The milling motion control method of a dual hybrid robot according to claim 1, characterized in that: According to the support head driven by the high-speed electric spindle of the dual hybrid robot milling motion control system, the milling motion control system equation is obtained; specifically including: According to the dynamic system transfer function of the milling tool tip and ball of the support head driven by the high-speed electric spindle of the dual hybrid robot milling motion control system, the milling motion control system equation is obtained as follows: Among them, ρ1 and ρ2 are parameters related to the dynamic characteristics of the milling tool tip and the ball, y m Indicates the displacement signal of the milling tool tip sensor, Yes m The second-order derivative of , u is the speed signal of the high-speed electric spindle; i represents the current of the high-speed electric spindle of the milling motion control system, is the first-order derivative of i, and F is the external disturbance of the milling motion control system.

3. The milling motion control method of a dual hybrid robot according to claim 2, characterized in that: The high-speed electric spindle current model equation is: Among them, A, B, and C represent the first to third rate-of-change matrices of the high-speed electric spindle current model equation, respectively, f(·) represents the rate-of-change matrix of F, ψ(k) represents the displacement parameter value collected at time k, and t is time.

4. The milling motion control method of a dual hybrid robot according to claim 3 is characterized in that: The laser displacement sensor collects the milling tool tip displacement signal, and the linear expansion state observer in the self-disturbance rejection method is used to observe the milling tool tip displacement signal collected by the laser displacement sensor; the details are as follows: Among them, z1(k) and z2(k) are the output signal of the laser displacement sensor and the differential form of the output signal of the laser displacement sensor at time k, respectively; z3(k) is the uncertain disturbance of the milling motion control system at time k; η is the displacement compensation factor of the milling motion control system, and η is used to eliminate the vibration compensation of the high-speed electric spindle; e(k) and e(k+1) are the error change rates of the milling motion control system at time k and time k+1, respectively; and h(·) is the sampling period.

5. The milling motion control method of a dual hybrid robot according to claim 4, characterized in that: The matrix-vector form of the augmented state equation model is constructed using the extended Kalman method: X(k+1)=f(X(k),u(k))+Q(k)W(k) (4) y m (k)=e(k+1)+R(k)V(k) (5) Among them, y m (k) represents the displacement signal y of the milling tool tip sensor m Discrete function at time k, X(k+1) is the speed signal at time k+1, X(k) is the speed signal at time k, W(k) and V(k) are the displacement noise and speed noise at time k respectively, Q(k) and R(k) are the displacement noise covariance matrix and speed noise covariance matrix at time k respectively, and u(k) is the speed signal of the high-speed electric spindle at time k.

6. The milling motion control method of a dual hybrid robot according to claim 5, characterized in that: The discrete form of the tracking differentiator for the motion control condition design of the high-speed electric spindle is: Wherein, R1(k) and R2(k) are the speed tracking value and displacement tracking value of the input signal at time k, respectively; R1(k+1) and R2(k+1) are the speed tracking value and displacement tracking value of the input signal at time k+1, respectively; fhan(·) is the fastest control comprehensive function; ξ is the speed variable parameter; τ is the displacement variable parameter; and fh is the tracking differentiator.

7. A computer device comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that: When the processor executes the computer program, the steps of the milling motion control method of the dual hybrid robot as described in any one of claims 1 to 6 are implemented.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the milling motion control method of the dual hybrid robot as described in any one of claims 1 to 6 are implemented.

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