Stiffness-variable actuator force planning and active stiffness matching disturbance control method and system

Abstract

The application belongs to the field of robot control, and discloses a kind of polishing robot end variable stiffness actuator force planning and active stiffness matching anti-disturbance control method and system, the method comprises: stable contact before force planning method, stable contact after force planning method, active compliance anti-disturbance force control method;The stable contact before force planning method is by controlling the total impulse of input system to thereby constrain the maximum collision force size generated collision;The stable contact after force planning method is by considering environmental stiffness constraint, actuator stiffness constraint, robot dynamics and kinematics constraint, actuator drive error modeling, material removal constraint, optimization is carried out with all constraints to optimal efficiency T as target, obtains optimal force, position, stiffness control target value;The active compliance anti-disturbance force control method includes a kind of noise and disturbance suppression environmental stiffness estimator, stiffness controller, sliding film controller, radial basis function (RBF) network, extended state observer (ESO), switching controller.The application shows excellent noise suppression and stiffness estimation accuracy.In robot polishing, it shows excellent force control accuracy.

Description

Technical Field

[0001] The present invention belongs to the field of robot control, and in particular relates to a method and system for force planning and active stiffness matching anti-disturbance control of a variable stiffness actuator at the end of a grinding and polishing robot. Background Art

[0002] With the increasing requirements for surface quality of free-form workpieces, such as large fan blades, aircraft turbine blades and propellers, the polishing process has become more complicated and required an increasingly higher level of precision. At present, most polishing still requires skilled technicians to perform manually, which has the problems of high risk, high labor intensity, high processing cost, low operating efficiency and low precision. In order to solve the limitations and shortcomings of manual polishing, robotic polishing systems and other types of automatic polishing equipment have been proposed and developed. Among them, industrial robots have the advantages of low price, large workspace and strong flexibility, but they lack force control technology for the workpiece polishing process. Therefore, force control technology is the key technology to realize the surface processing of free-form workpieces by robots and needs to be solved urgently. Active compliant motion control method is the main research trend of high polishing quality. However, there are still the following major problems in the research field of force-controlled end effectors:

[0003] (1) The contact distance between the end effector tool of the robot grinding and polishing force control and the workpiece is unknown, which easily leads to overshoot at the moment of contact. The existing method uses force-position switching control, which on the one hand increases the demand for sensors, and on the other hand, it is difficult to ensure that the force caused by the collision at the moment of contact will not overshoot through the detection of the force threshold.

[0004] (2) In order to ensure the material removal accuracy of robot grinding and polishing, it is often necessary to perform force-speed planning of the robot. However, the current planning ignores the changes in the force response performance of the actuator caused by changes in environmental stiffness, which leads to inaccurate force-speed planning results.

[0005] (3) Research on variable stiffness actuators for polishing robot terminals is gradually increasing. However, current variable stiffness actuator stiffness control mainly focuses on the actuator’s own stiffness control (i.e., controlling its own stiffness to achieve the expected value), thus ignoring the estimation of the stiffness of the actuator. The estimation of environmental stiffness is seriously affected by noise, which also seriously affects the practical application of variable stiffness actuators.

[0006] (4) When the robot faces a complex curved surface, it is affected by multiple sources of disturbance and interference, which leads to a decrease in the robot's force control accuracy and a decrease in the surface quality of the workpiece. Summary of the Invention

[0007] In view of the problems existing in the prior art, the present invention provides a method for force planning and active stiffness matching and anti-disturbance control of a variable stiffness actuator at the end of a grinding and polishing robot.

[0008] The present invention is achieved by providing a method for force planning and active stiffness matching anti-disturbance control of a variable stiffness actuator at the end of a grinding and polishing robot, the method comprising: a stable pre-contact force planning method, a stable post-contact force planning method, and an active compliant anti-disturbance force control method;

[0009] The stable pre-contact force planning method is to control the total impulse of the input system to constrain the maximum collision force generated by the collision;

[0010] The stable post-contact force planning method considers environmental stiffness constraints, actuator stiffness constraints, robot dynamics and kinematics constraints, actuator drive error modeling, and material removal constraints, and combines all constraints to optimize with the optimal efficiency T as the goal to obtain the optimal force, position, and stiffness control target values;

[0011] The active compliant anti-disturbance force control method includes an environmental stiffness estimator for noise and disturbance suppression, a stiffness controller, a sliding film controller, a radial basis function (RBF) network, an extended state observer (ESO), and a switching controller.

[0012] Furthermore, the stable pre-contact force planning method specifically includes:

[0013] S1.1: First, establish the system's motion equation. Assuming the total input impulse of the system before contact is p, and the contact process is considered to be a spring model with an environmental stiffness of k, the system's motion equation can be established as Where m is the mass of the actuator, c f is the actuator damping, k is the environment stiffness, and F is the driving force of the actuator during collision;

[0014] S1.2: Obtain the general expression of the maximum collision force-impulse relationship. The initial solution of the motion equation can be obtained by the initial collision conditions:

[0015]

[0016] Where x is the displacement after collision with time t, and A, B, C, α, and β are auxiliary expression parameters. The maximum collision displacement of the system can be obtained as:

[0017]

[0018] The general expression of the maximum collision force can be further obtained as

[0019]

[0020] S1.3: Given an impulse control strategy, obtain a special expression for the maximum collision force-impulse relationship. The control impulse is a step force control, and the driving force is controlled to be F before the total impulse of the system reaches the preset impulse. mAfter the total impulse of the system reaches the preset value, the driving force is maintained To ensure that the total impulse value of the system input remains unchanged;

[0021] When the collision occurs before the total impulse of the system reaches the preset impulse, the maximum collision expression can be further derived as:

[0022]

[0023] When the collision occurs after the total impulse of the system reaches the preset impulse, the maximum collision expression can be further derived as:

[0024]

[0025] S1.4: Design to obtain the maximum allowable total input impulse. When constraining, F max1 , F max2 Take the maximum collision force of the two for impulse constraint and calculate the maximum allowable impulse p max ,By controlling the maximum impulse allowed by the driver input ,system, it can be effectively ensured that the contact collision force does not ,overshoot.

[0026] Furthermore, the environmental stiffness constraint considers the environmental stiffness variation range k at different workpiece positions. range The actuator stiffness constraint considers the variable stiffness actuator stiffness control capability limit constraint k limit The dynamic constraints include the positions of the six joints of the robot range , speed v range and acceleration a range Restrictions and constraints.

[0027] Furthermore, the actuator error modeling is achieved by establishing the transfer function G(s) of the entire system and considering the response capability of the driver servo capability in each control cycle, thereby obtaining the actuator response capability E of each control cycle under the system position and system stiffness (including the environmental stiffness and the stiffness generated by the actuator). limit .

[0028] Furthermore, the material removal constraint is to consider the relationship between the material removal amount and the contact force, and ensure the material removal accuracy constraint d limit .

[0029] Furthermore, based on the construction of the actuator control desired stiffness B-spline signal curve, the contact desired force B-spline signal curve, and the robot motion desired speed B-spline signal curve, the particle swarm optimization algorithm is used to optimize the B-spline nodes, with the optimization target being T and the optimization constraint being k. range ,k limit ,p range ,vrange a range ,E limit ,d limit , so as to plan the contact expected force of the polishing robot end variable stiffness actuator, control the expected stiffness, and realize the force-speed-stiffness collaborative planning control of the robot polishing.

[0030] Further, the environment stiffness estimator obtains the environment stiffness by observing the environment, and inputs the stiffness controller, and the stiffness controller outputs the stiffness control signal to control the output stiffness of the actuator;

[0031] The stiffness controller takes the expected stiffness signal as the reference to control the output stiffness when the expected stiffness signal is input, and takes the system damping ratio as 1 to calculate the expected stiffness signal by the feedback environment stiffness estimation value when there is no expected stiffness signal input, and takes the expected stiffness signal as the reference to output.

[0032] Further, the sliding film controller is responsible for tracking the contact force size to ensure that the contact force is consistent with the expected force;

[0033] The RBF network is responsible for adaptively adjusting the sliding film controller parameters to improve the robustness and anti-interference of the controller.

[0034] Further, the extended state observer serves as a feedforward control to reduce the control burden of the sliding film controller and further improve the anti-interference of the control system;

[0035] The switching controller is responsible for switching the force planning control strategy before and after contact.

[0036] Further, the environment stiffness estimator is designed as follows.

[0037] S2.1: First, construct the environment stiffness estimation rate:

[0038]

[0039] In the formula is the initial estimated stiffness, the initial estimated distance between the actuator and the environment, describes the estimated stiffness, F0 represents the contact force, and x e describes the estimated distance between the actuator and the environment, and r1 and r2 are the identified constants;

[0040] S2.2: Construct the normalized residual matrix

[0041] At time i, the force data F0 and displacement data X0 sequences with a cache length of n

[0042]

[0043] According to the environment stiffness estimation rate, a calculated estimated stiffness sequence can be obtained

[0044]

[0045] According to the force data, displacement data, and estimated stiffness sequence, normalized residuals can be calculated

[0046]

[0047] Herein, are the normalized residuals of force, displacement, and stiffness, respectively. are the mean values of force, displacement, and stiffness, respectively. are the 587 standard deviation values of force, displacement, and stiffness, respectively; the online weighting matrix Ω is obtained by setting a retention threshold based on the normalized residuals;

[0048] Based on the normalized residuals, a retention threshold of 95% can be set to obtain a real-time weighting matrix:

[0049]

[0050] S2.3: Calculate the stiffness estimation result of the normalized residual weighting matrix, multiply the weighting matrix with the stiffness estimation matrix, and calculate the mean value after excluding zero values, thereby calculating the output value of the stiffness estimation at time i:

[0051]

[0052] wherein mean() refers to the mean value after excluding zero values, K e (i) represents the final output stiffness estimation value at time i.

[0053] Another purpose of the present application is to provide a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the computer program is executed by the processor to enable the processor to perform the steps of the force planning and active stiffness matching anti-disturbance control method of the polishing robot end variable stiffness actuator.

[0054] In combination with the above technical solutions and the technical problems solved, the technical solutions to be protected by the present application have the advantages and positive effects that: technical problems are solved, excellent noise suppression and stiffness

[0055] Firstly, the present application effectively solves the existing force control end actuator stiffness estimation accuracy. In robot polishing, excellent force control accuracy is shown.

[0056] The present application effectively solves the problems existing in the current force control end effector technology, and shows excellent noise suppression and stiffness estimation accuracy. In robot polishing and grinding, it shows excellent force control accuracy.

[0057] Secondly, the expected income and commercial value of the technical solution of the present application after transformation are:

[0058] Improve production efficiency: the present application optimizes the force planning and active stiffness matching disturbance rejection control method of the robot polishing and grinding end variable stiffness actuator, realizes efficient automatic polishing operation, and can significantly improve production efficiency, reduce labor cost and labor intensity compared with traditional manual polishing.

[0059] Improve polishing quality: the force control accuracy and stiffness estimation accuracy of the present application are extremely high, which ensures the high quality polishing of complex curved surface workpieces, reduces surface defects and errors, and improves the surface quality and precision of the final product.

[0060] Market competitive advantage: since the present application can solve the key problems in the prior art, its technical solution has high market competitiveness, and is expected to be widely used in high-end manufacturing industries such as aerospace, automobile manufacturing and medical equipment, thereby bringing significant economic benefits and market share.

[0061] The technical solution of the present application fills the gap in the industry at home and abroad:

[0062] Innovative force planning method: the present application proposes a stable pre-contact and post-contact force planning method, which realizes accurate control of contact force by controlling the total impulse of the input system and optimizing various constraint conditions, filling the gap of traditional methods in force control accuracy and stiffness estimation.

[0063] Active compliant disturbance rejection control technology: by designing key components such as noise and disturbance suppression environment stiffness estimator, stiffness controller, sliding film controller, RBF network, extended state observer (ESO) and switching controller, the present application realizes efficient disturbance rejection control and stiffness matching, filling the gap of existing force control end effector technology in disturbance rejection and compliance.

[0064] The technical solution of the present application solves the technical problems that people have been eager to solve but have failed to succeed:

[0065] Force control end effector contact overshoot problem: in the prior art, the robot polishing and grinding force control end effector tool is easy to cause overshoot when contacting with the workpiece, which affects the machining precision. The present application effectively restricts the size of the collision force by the stable pre-contact force planning method, solving the problem of overshoot at the moment of contact.

[0066] Impact of environmental stiffness changes on force-speed planning: Traditional planning methods ignore the impact of environmental stiffness changes, resulting in inaccurate force-speed planning results. This invention optimizes the force, position, and stiffness control targets by considering both environmental and actuator stiffness constraints, ensuring the accuracy of planning results.

[0067] Problems of multi-source disturbances and interferences on complex surfaces: This paper designs an active and compliant anti-disturbance force control method, which adopts technologies such as sliding film controller, RBF network and extended state observer to effectively suppress multi-source disturbances and interferences on complex surfaces, thereby improving the robot force control accuracy and the surface quality of workpiece grinding and polishing.

[0068] The technical solution of the present invention overcomes technical prejudice:

[0069] Fusion innovation of force control technology: This invention combines a variety of advanced force control technologies and control methods, such as synovial control, RBF network, extended state observation, etc., breaking through the limitations of traditional single control methods, overcoming the technical bias of force control accuracy and stiffness estimation in complex environments, and achieving more efficient and precise control effects.

[0070] Innovative design of environmental stiffness estimation: By constructing the environmental stiffness estimation rate and the normalized residual matrix, the present invention achieves accurate estimation of environmental stiffness, overcomes the technical bias of traditional environmental stiffness estimation methods that are greatly affected by noise, and provides a reliable foundation for achieving high-precision force control.

[0071] Force planning for precision material removal: Force overshoot at the moment of contact can damage the workpiece surface and reduce machining accuracy and quality. This invention controls the total impulse of the input system, thereby constraining the maximum collision force generated, avoiding the problem of force overshoot at the moment of contact and improving the stability and reliability of the system.

[0072] Third, this invention achieves significant technological breakthroughs in force planning and active stiffness matching for anti-disturbance control of variable-stiffness end effectors in polishing and grinding robots, primarily through the application of key parameters, innovative algorithms, and mathematical models. The following is a four-paragraph summary of the technical challenges and technological advancements addressed by this invention:

[0073] First, through carefully designed key parameters such as total impulse, estimated environmental stiffness, and actuator responsiveness within the control cycle, this invention precisely controls the impact and contact forces during the grinding and polishing process, effectively avoiding workpiece damage or actuator failure caused by excessive impact forces. The precise setting of these parameters not only improves operational stability but also ensures machining accuracy and efficiency.

[0074] Secondly, the application adopts advanced algorithms such as Bayesian optimization, particle swarm optimization algorithm and adaptive adjustment strategy based on radial basis function (RBF) network, to realize the optimization solution of force, position and stiffness control target values. The application of these algorithms enables the application to quickly and accurately adjust the control strategy in a complex and variable working environment to adapt to different processing requirements and environmental changes.

[0075] Furthermore, the application constructs a series of innovative mathematical models such as system motion equation, maximum collision force-impulse relationship expression and environment stiffness estimation rate, providing a solid theoretical basis for the precise control of the grinding and polishing process. These mathematical models not only reveal the physical laws in the grinding and polishing process, but also provide strong support for the design and optimization of control algorithms.

[0076] In summary, the application successfully solves the technical problems of force planning and stiffness matching of existing grinding and polishing robots through precise key parameter setting, advanced algorithm application and innovative mathematical model construction. The implementation of the application not only significantly improves the precision and efficiency of grinding and polishing operations, but also reduces maintenance costs and failure rates, making an important contribution to the development of industrial automation and intelligent manufacturing.

[0077] Fourthly, the application proposes a new method for force planning and active stiffness matching disturbance control of the variable stiffness actuator at the end of the grinding and polishing robot. This method realizes precise control of force, position and stiffness in the grinding and polishing process through stable pre-contact and post-contact force planning, as well as active compliant disturbance force control. This innovative method not only improves the precision and efficiency of grinding and polishing operations, but also effectively reduces disturbances caused by factors such as collision, improving the stability and reliability of the robot's operation.

[0078] In terms of pre-contact force planning, the application controls the total impulse of the input system to constrain the maximum collision force during collision, thereby avoiding excessive impact force from causing damage to the workpiece and the actuator. In terms of post-contact force planning, the application considers various constraint conditions such as environmental stiffness, actuator stiffness, robot dynamics and kinematics, optimizes with the goal of optimal efficiency, and obtains optimal force, position and stiffness control target values, achieving fine control of the grinding and polishing process.

[0079] In terms of active compliant disturbance force control, the application introduces an environment stiffness estimator, a stiffness controller, a sliding film controller, a radial basis function (RBF) network, an extended state observer (ESO) and a switching controller, which together form a powerful disturbance control system. This system can estimate the environmental stiffness in real time, adjust the output stiffness of the actuator, track the contact force, and adaptively adjust the controller parameters, thereby effectively suppressing the influence of noise and disturbances on the grinding and polishing process.

[0080] The implementation of the present application successfully solves the technical problems of the existing polishing robot end effector in force planning and stiffness matching, and achieves significant technical progress. Through the application of the present application, accurate control of the polishing process can be achieved, improving the machining quality and efficiency of the workpiece, while reducing the maintenance cost and failure rate of the robot, and having wide application prospect and practical value. BRIEF DESCRIPTION OF DRAWINGS

[0081] Figure 1 is a contact pre-force planning method schematic diagram provided by the embodiment of the present application;

[0082] Figure 2 is a stable contact post-force planning method schematic diagram provided by the embodiment of the present application;

[0083] Figure 3 is a schematic diagram of the active compliant disturbance force control method provided by the embodiment of the present application;

[0084] Figure 4 is a schematic diagram of the environment stiffness estimator design provided by the embodiment of the present application;

[0085] Figure 5 is a schematic diagram of the stiffness estimator performance provided by the embodiment of the present application;

[0086] Figure 6 is a schematic diagram of the force control performance of the variable stiffness effector force planning and active stiffness matching disturbance control method provided by the embodiment of the present application;

[0087] Figure 7 is a schematic diagram of the variable stiffness environment polishing force control effect provided by the embodiment of the present application;

[0088] Figure 8 is a schematic diagram of the contact instantaneous force response when the contact distance is 10mm provided by the embodiment of the present application;

[0089] Figure 9 is a schematic diagram of the contact instantaneous force response when the contact distance is 5mm provided by the embodiment of the present application. DETAILED DESCRIPTION

[0090] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application will be further described in detail below with examples. It should be understood that the specific examples described herein are only used to explain the present application, and are not used to limit the present application.

[0091] Example 1: Surface polishing of automobile parts

[0092] During the automotive manufacturing process, many parts (such as engine housings and drive shafts) require surface polishing to improve their surface finish and corrosion resistance. Traditional polishing methods struggle to meet the high precision and efficiency requirements and have limitations when processing complex parts.

[0093] 1) Stable pre-contact force planning:

[0094] The system motion equation and impulse control strategy are used to control the total input impulse of the polishing robot before it contacts the surface of the component, avoiding damage to the component surface due to sudden collision.

[0095] By presetting the impulse strategy, the magnitude and direction of the driving force are controlled before the polishing robot contacts the surface of the component to ensure that the maximum collision force is within the allowable range of the component material.

[0096] 2) Stable contact force planning:

[0097] The control target values ​​of polishing force, displacement and stiffness are optimized by combining the surface environment stiffness of automotive parts, the stiffness control capability of the polishing actuator, the dynamic and kinematic constraints of the robot, and the material removal amount during the polishing process.

[0098] Ensure the force control and position control accuracy during the polishing process, and ensure the polishing effect and consistency of the component surface.

[0099] 3) Active and flexible anti-interference force control:

[0100] The environmental stiffness estimator is used to monitor the stiffness changes of the component surface in real time, and the estimation results are input into the stiffness controller.

[0101] The stiffness controller dynamically adjusts the output stiffness of the polishing actuator based on the real-time stiffness estimation results to ensure the force control stability between the robot and the component surface during the polishing process.

[0102] The synovial controller and RBF network work together to adaptively adjust the parameters of the synovial controller and improve the robustness and anti-interference performance of the system.

[0103] Through the above method, the polishing robot can achieve high-precision and high-efficiency polishing operations when polishing the surface of automotive parts, while effectively avoiding surface damage caused by collisions, thereby improving the surface quality and production efficiency of parts.

[0104] Example 2: Mobile phone screen glass grinding

[0105] Surface grinding of mobile phone screen glass is a critical step in ensuring its smoothness and transparency. Traditional grinding methods can easily cause scratches or cracks on the glass surface, affecting product quality and user experience.

[0106] 1) Stable pre-contact force planning:

[0107] The motion equation of the grinding robot is established. The total impulse of the input of the system before contact is assumed, and the environmental stiffness of the mobile phone screen glass is considered to ensure that the collision force is constrained by controlling the total impulse before the grinding robot contacts the glass surface.

[0108] Through the maximum collision force-impulse relationship expression, a reasonable impulse control strategy is designed to avoid excessive collision force during the contact process, which may cause damage to the glass surface.

[0109] 2) Stable contact force planning:

[0110] By combining the environmental stiffness variation range of mobile phone screen glass, the stiffness control capability limitations of the grinding actuator, and the robot's dynamics and kinematic constraints, the force control and position control target values ​​during the grinding process are optimized.

[0111] Consider the relationship between material removal and contact force during glass grinding to ensure the accuracy and uniformity of material removal and improve the smoothness and transparency of the glass surface.

[0112] 3) Active and flexible anti-interference force control:

[0113] The environmental stiffness estimator monitors the stiffness changes of the mobile phone screen glass surface in real time and inputs the stiffness controller, which then outputs the corresponding stiffness control signal to ensure the force control stability during the grinding process.

[0114] The sliding membrane controller and RBF network adaptively adjust the control parameters to enhance the robustness and anti-interference performance of the system and reduce the force control deviation caused by external disturbances during the grinding process.

[0115] The extended state observer is used as feedforward control to further reduce the control burden of the synovial controller and improve the anti-disturbance performance of the control system.

[0116] Through the above method, the grinding robot can achieve high-precision force control and position control during the grinding process of mobile phone screen glass, avoiding scratches or cracks on the glass surface, improving the surface quality and user experience of the product, and at the same time improving production efficiency.

[0117] In response to the problems existing in the prior art, the present invention provides a method for force planning and active stiffness matching and anti-disturbance control of a variable stiffness actuator at the end of a grinding and polishing robot. The present invention is described in detail below with reference to the accompanying drawings.

[0118] The method includes a stable pre-contact force planning method, a stable post-contact force planning method, and an active compliant anti-disturbance force control method.

[0119] like Figure 1As shown in Figure 1, the pre-contact force planning method constrains the maximum collision force by controlling the total impulse of the input system. The steps are as follows:

[0120] S1.1 First, establish the system's motion equation. Assuming that the total input impulse of the system before contact is p, and the contact process is considered to be a spring model with an environmental stiffness of k, the system's motion equation can be established as Where m is the mass of the actuator, c f is the actuator damping, k is the environment stiffness, and F is the driving force of the actuator during collision.

[0121] S1.2 Obtain the general expression of the maximum collision force-impulse relationship. The initial solution of the motion equation can be obtained by the initial collision conditions:

[0122]

[0123] Where x is the displacement after collision with time t, and A, B, C, α, and β are auxiliary expression parameters. The maximum collision displacement of the system can be obtained as:

[0124]

[0125] The general expression of the maximum collision force can be further obtained as

[0126]

[0127] S1.3 Given the impulse control strategy, obtain the special expression of the maximum collision force-impulse relationship. The control impulse is step force control, and the driving force is controlled to be F before the total impulse of the system reaches the preset impulse. m After the total impulse of the system reaches the preset value, the driving force is maintained To ensure that the total impulse value of the system input remains unchanged.

[0128] When the collision occurs before the total impulse of the system reaches the preset impulse, the maximum collision expression can be further derived as:

[0129]

[0130] Furthermore, when the collision occurs after the total impulse of the system reaches the preset impulse, the expression of the maximum collision can be further derived as:

[0131]

[0132] S1.4 Design to obtain the maximum allowable total input impulse. When constraining, F max1 , F max2 Take the maximum collision force of the two for impulse constraint and calculate the maximum allowable impulse p max,By controlling the maximum impulse allowed by the driver input ,system, it can be effectively ensured that the contact collision force does not ,overshoot, e.g. Figure 7 , as shown in 8.

[0133] like Figure 2 As shown in the figure, the stable post-contact force planning method considers the environmental stiffness constraints, actuator stiffness constraints, robot dynamics and kinematics constraints, actuator drive error modeling, and material removal constraints, and combines all constraints to optimize with the optimal efficiency T as the goal to obtain the optimal force, position, and stiffness control target values.

[0134] The environmental stiffness constraint considers the environmental stiffness variation range k at different workpiece positions. range .

[0135] Actuator stiffness constraint Considering the variable stiffness actuator stiffness control capability limit constraint k limit .

[0136] The dynamic constraints include the positions of the six joints of the robot range , speed v range and acceleration a range Restrictions and constraints.

[0137] Furthermore, the actuator error modeling is achieved by establishing the transfer function G(s) of the entire system and considering the response capability of the driver servo capability in each control cycle, thereby obtaining the actuator response capability E of the system in each control cycle under the position and system stiffness (including the environmental stiffness and the stiffness generated by the actuator). limit .

[0138] The material removal constraint is to consider the relationship between the material removal amount and the contact force, and ensure the material removal accuracy constraint d limit .

[0139] Based on the construction of the actuator control expected stiffness B-spline signal curve, the contact expected force B-spline signal curve, and the robot motion expected velocity B-spline signal curve, the particle swarm optimization algorithm is used to optimize the B-spline nodes, with the optimization target as T and the optimization constraint as k. range ,k limit ,p range ,v range ,a range ,E limit ,d limit Thus, the expected contact force, expected control stiffness, and expected robot motion speed of the variable stiffness actuator at the end of the grinding and polishing robot are planned to realize the coordinated planning and control of force, speed and stiffness of the robot grinding and polishing.

[0140] like Figure 3As shown, the active compliant anti-disturbance force control method includes an environmental stiffness estimator for noise and disturbance suppression, a stiffness controller, a sliding film controller, a radial basis function (RBF) network, an extended state observer (ESO), and a switching controller.

[0141] The environment stiffness estimator obtains the environment stiffness by observing the environment and inputs it into the stiffness controller. The stiffness controller outputs a stiffness control signal to control the output stiffness of the actuator.

[0142] When a desired stiffness signal is input, the stiffness controller controls the output stiffness based on the desired stiffness signal. When no desired stiffness signal is input, the system damping ratio is set to 1 and the desired stiffness signal is calculated based on the feedback of the estimated ambient stiffness value, which is then used as the output.

[0143] The synovial controller is responsible for tracking the contact force to ensure that the contact force is consistent with the desired force.

[0144] The RBF network is responsible for adaptively adjusting the parameters of the synovial controller to improve the robustness and anti-interference performance of the controller.

[0145] The extended state observer is used as feedforward control to reduce the control burden of the sliding membrane controller and further improve the anti-disturbance performance of the control system.

[0146] The switching controller is responsible for switching the force planning control strategy before and after contact

[0147] like Figure 4 As shown, the environment stiffness estimator is designed as follows.

[0148] S2.1 First construct the environmental stiffness estimation rate

[0149]

[0150] In the formula is the initial estimated stiffness, the initial estimated distance between the actuator and the environment, describes the estimated stiffness, F0 represents the contact force, x e describes the estimated distance between the actuator and the environment, and r1 and r2 are identified constants.

[0151] S2.2 Constructing the normalized residual matrix

[0152] At time i, there is a sequence of force data F0 and displacement data X0 with a buffer length of n

[0153]

[0154] According to the environmental stiffness estimation rate, the estimated stiffness sequence can be calculated

[0155]

[0156] The normalized residual can be calculated based on the three sequences of force data, displacement data and estimated stiffness sequence

[0157]

[0158] Here are the normalized residuals of force, displacement, and stiffness, respectively. are the average values ​​of force, displacement and stiffness, respectively. These are the 587 standard deviation values ​​of force, displacement, and stiffness, respectively. The online weighting matrix Ω is obtained by setting a retention threshold based on the normalized residual. Furthermore, a 95% retention threshold can be set based on the normalized residual to obtain a real-time weighting matrix.

[0159]

[0160] S2.3 Calculate the stiffness estimation result of the normalized residual weighting matrix. Multiply the weighting matrix by the stiffness estimation matrix and calculate the mean after removing zero values ​​to obtain the output value of the stiffness estimation at time i.

[0161]

[0162] Among them, mean() refers to the average value after excluding zero values, K e (i) represents the stiffness estimate finally output at time i.

[0163] like Figure 5 The following is the performance demonstration result of the stiffness estimator, which shows excellent noise suppression and stiffness estimation accuracy. Figure 6 The figure shows the performance of a force planning and active compliant anti-disturbance control method for a variable stiffness actuator at the end of a grinding and polishing robot. It shows excellent force control accuracy in robot grinding and polishing.

[0164] An embodiment of the present invention provides a computer device, which includes a memory and a processor. The memory stores a computer program. When the computer program is executed by the processor, the processor executes the steps of the force planning and active stiffness matching anti-disturbance control method of the variable stiffness actuator at the end of the grinding and polishing robot.

[0165] It should be noted that the embodiments of the present invention can be implemented by hardware, software, or a combination of software and hardware. The hardware portion can be implemented using dedicated logic; the software portion can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated design hardware. Those skilled in the art will appreciate that the above-mentioned devices and methods can be implemented using computer-executable instructions and / or contained in processor control code, for example, such as a carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The devices and modules of the present invention can be implemented by hardware circuits such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., can also be implemented by software executed by various types of processors, or can be implemented by a combination of the above-mentioned hardware circuits and software, such as firmware.

[0166] Through simulation and experimental verification, Figure 7 、 Figure 8 As shown in FIG, a comparison of different control methods at different contact distances shows that the force planning provided by the present invention can effectively suppress the overshoot force of the collision at different distances.

[0167] Figure 9 As shown, experiments using standard parts show that the stiffness estimation method provided by the present invention can effectively reduce the stiffness estimation noise peak by more than 50%.

[0168] The above description is only a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by any technician familiar with this technical field within the technical scope disclosed by the present invention and within the spirit and principles of the present invention should be covered by the scope of protection of the present invention.

Claims

1. A method for force planning and active stiffness matching and anti-disturbance control of a variable stiffness actuator at the end of a grinding and polishing robot, characterized in that: The method includes: a stable pre-contact force planning method, a stable post-contact force planning method, and an active compliant anti-disturbance force control method; The stable pre-contact force planning method is to control the total impulse of the input system to constrain the maximum collision force generated by the collision; The stable post-contact force planning method considers environmental stiffness constraints, actuator stiffness constraints, robot dynamics and kinematics constraints, actuator drive error modeling, and material removal constraints, and combines all constraints to optimize with the optimal efficiency T as the goal to obtain the optimal force, position, and stiffness control target values; The active compliant anti-disturbance force control method includes an environmental stiffness estimator for noise and disturbance suppression, a stiffness controller, a sliding film controller, a radial basis function (RBF) network, an extended state observer (ESO), and a switching controller.

2. The method for force planning and active stiffness matching and anti-disturbance control of the variable stiffness actuator at the end of a polishing robot according to claim 1 is characterized in that: The stable pre-contact force planning method specifically includes: S1.1: First, establish the system's motion equation; assume that the total input impulse of the system before contact is p, and that the contact process is a spring model with an environmental stiffness of k. The system's motion equation is established as follows: Where m is the mass of the actuator, c f is the actuator damping, k is the environment stiffness, and F is the driving force of the actuator during collision; S1.2: Obtain the general expression of the maximum collision force-impulse relationship. The initial solution of the motion equation is obtained by the initial collision conditions: Where x is the displacement after collision with time t, A, B, C, α, and β are auxiliary expression parameters; the maximum collision displacement of the system is obtained as: The general expression for obtaining the maximum collision force is: S1.3: Given an impulse control strategy, obtain a special expression for the maximum collision force-impulse relationship; the impulse is controlled as a step force control, and the driving force is controlled to be F before the total impulse of the system reaches the preset impulse. m After the total impulse of the system reaches the preset value, the driving force is maintained To ensure that the total impulse value of the system input remains unchanged; When the collision occurs before the total impulse of the system reaches the preset impulse, the maximum collision force expression is derived as: When the collision occurs after the total impulse of the system reaches the preset impulse, the maximum collision force expression is derived as: S1.4: Design to obtain the maximum allowable total input impulse; when constraining, F max1 , F max2 Take the maximum collision force of the two for impulse constraint and calculate the maximum allowable impulse p max ,By controlling the maximum impulse allowed by the driver input system, it ,can effectively ensure that the contact collision force does not overshoot.

3. The method for force planning and active stiffness matching and anti-disturbance control of the variable stiffness actuator at the end of a polishing robot according to claim 1, characterized in that: The environmental stiffness constraint considers the environmental stiffness variation range k at different workpiece positions. range The actuator stiffness constraint considers the variable stiffness actuator stiffness control capability limit constraint k limit The robot dynamics and kinematics constraints include the position of the robot's six joints p range , speed v range and acceleration a range Restrictions and constraints.

4. The method for force planning and active stiffness matching and anti-disturbance control of the variable stiffness actuator at the end of a polishing robot according to claim 3 is characterized in that: The actuator drive error modeling is achieved by establishing the transfer function G(s) of the entire system and considering the response capability of the driver servo capability in each control cycle, thereby obtaining the actuator response capability E of the system in each control cycle under the conditions of position and system stiffness. limit , the system stiffness includes the environmental stiffness and the stiffness generated by the actuator.

5. The method for force planning and active stiffness matching and anti-disturbance control of the variable stiffness actuator at the end of a polishing robot according to claim 4, characterized in that: The material removal constraint is to consider the relationship between the material removal amount and the contact force, and to ensure the material removal accuracy constraint d limit .

6. The method for force planning and active stiffness matching and anti-disturbance control of the variable stiffness actuator at the end of a polishing robot according to claim 5, characterized in that: Based on the construction of the actuator control expected stiffness B-spline signal curve, the contact expected force B-spline signal curve, and the robot motion expected velocity B-spline signal curve, the particle swarm optimization algorithm is used to optimize the B-spline nodes, with the optimization target as T and the optimization constraint as k. range ,k limit ,p range ,v range ,a range ,E limit ,d limit , thereby planning the expected contact force, control expected stiffness, and robot movement expected speed of the variable stiffness actuator at the end of the grinding and polishing robot to achieve force-speed-stiffness coordinated planning control of the robot grinding and polishing.

7. The method for force planning and active stiffness matching and anti-disturbance control of a variable stiffness actuator at the end of a polishing robot according to claim 1, characterized in that: The environmental stiffness estimator obtains environmental stiffness by observing the environment and inputs the information into the stiffness controller, which outputs a stiffness control signal to control the output stiffness of the actuator. When there is an expected stiffness signal input, the stiffness controller controls the output stiffness based on the expected stiffness signal. When there is no expected stiffness signal input, the system damping ratio is 1, and the expected stiffness signal is calculated through the feedback environmental stiffness estimation value, and outputted based on this.

8. The method for force planning and active stiffness matching and anti-disturbance control of a variable stiffness actuator at the end of a polishing robot according to claim 1, characterized in that: The synovial controller is responsible for tracking the contact force to ensure that the contact force is consistent with the desired force; The RBF network is responsible for adaptively adjusting the parameters of the synovial controller to improve the robustness and anti-interference performance of the controller.

9. The method for force planning and active stiffness matching and anti-disturbance control of a variable stiffness actuator at the end of a polishing robot according to claim 1, characterized in that: The extended state observer acts as a feedforward control to reduce the control burden of the synovial controller and improve the anti-disturbance performance of the control system; The switching controller is responsible for switching the force planning control strategy before and after contact.

10. The method for force planning and active stiffness matching and anti-disturbance control of a variable stiffness actuator at the end of a polishing robot according to claim 1, characterized in that: The environment stiffness estimator is designed as follows: S2.1: First construct the environmental stiffness estimate: In the formula is the initial estimated stiffness, the initial estimated distance between the actuator and the environment, describes the estimated stiffness, F0 represents the contact force, x e describes the estimated distance between the actuator and the environment, r1, r2 are identified constants; S2.2: Construct normalized residual matrix At time i, there is a sequence of force data F0 and displacement data X0 with a buffer length of n According to the environmental stiffness estimation rate, the estimated stiffness sequence is calculated Calculate the normalized residual based on the three sequences of force data, displacement data and estimated stiffness sequence Here are the normalized residuals of force, displacement, and stiffness, respectively; are the average values ​​of force, displacement and stiffness, respectively; These are 587 standard deviation values ​​of force, displacement, and stiffness, respectively; The online weight matrix Ω is obtained by setting the retention threshold based on the normalized residual; A real-time weighting matrix is ​​obtained by setting a 95% retention threshold based on the normalized residual: S2.3: Calculate the stiffness estimation result of the normalized residual weighted matrix, multiply the weighted matrix by the stiffness estimation matrix, and calculate the mean after removing zero values ​​to obtain the output value of the stiffness estimation at time i: Among them, mean() refers to the average value after excluding zero values, K e (i) represents the stiffness estimate finally output at time i.

11. A computer device, characterized in that: The computer device includes a memory and a processor, the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the force planning and active stiffness matching anti-disturbance control method of the variable stiffness actuator at the end of the grinding and polishing robot as described in any one of claims 1-10.

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