Long arm spread heavy load robot and force-position hybrid control and high precision dynamic compensation method

Abstract

The application discloses a long-arm-span heavy-load robot and a force-position hybrid control and high-precision dynamic compensation method, and belongs to the technical field of forging robots.The long-arm-span heavy-load robot comprises a 4R type serial main chain and five parallel closed-loop structures, wherein the end effector, the static platform and the connecting rod part can realize seven-axis movement under the driving of motors and hydraulic drives.The force-position hybrid control and high-precision dynamic compensation method of the application realizes high-precision forging control by collecting the position and force feedback signals of the robot in real time and combining an adaptive control algorithm; meanwhile, the set coordinate values of the end effector and the force conditions of the connecting rod are obtained, the deformation amount of the connecting rod is calculated, the deformation amount is compensated by controlling the driving oil cylinder, the on-line compensation of mechanical deformation and the compensation of multi-connecting rod deformation are realized by using the closed-loop feedback mechanism, and the high-precision control of the robot is achieved.The application has the advantages of large load, high rigidity, long arm span and high precision, and can realize the rapid and accurate transfer of medium and large forgings in a forging workshop.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of forging robots, and particularly relates to a long-arm-span heavy-load robot and a force-position hybrid control and high-precision dynamic compensation method. BACKGROUND

[0002] In modern manufacturing, the forging process is a key link for producing high-strength and high-performance metal parts, and is of great significance to improving the production efficiency and product quality of the entire industry. With the continuous development of forging robot technology, its application in the forging field has gradually attracted attention, providing the possibility for the automation and intelligentization of the forging process, and helping to reduce labor intensity, improve production efficiency and ensure the consistency of product quality. Therefore, higher requirements are put forward for forging robots, and thus it is an inevitable trend for the development of key industries to design and develop a high-performance forging robot.

[0003] At present, existing forging robots mainly have deficiencies in stroke, load, heat resistance and precision. For example, the stroke is generally short, which cannot meet the forging range requirements of large and complex parts; the load capacity is also limited, and problems such as insufficient torque and decreased precision are prone to occur when forging large and heavy parts; in terms of heat resistance, due to the high-temperature environment generated during the forging process, ordinary robot materials and structures are difficult to withstand, which can easily lead to equipment damage or performance degradation; in terms of precision, affected by factors such as structural design and transmission system error, it is difficult to meet the requirements of high-precision forging, affecting the dimensional accuracy and surface quality of the parts. These limitations make the existing forging robots unable to meet the high-quality and high-efficiency production requirements of modern manufacturing for forging large and complex metal parts, limiting their wide application in the forging industry.

[0004] In order to solve the above-mentioned deficiencies of forging robots and better meet the high-quality and high-efficiency production requirements of modern manufacturing for forging large and complex metal parts, it is urgent to invent a seven-degree-of-freedom long-arm-span heavy-load robot and a high-precision dynamic compensation control method, which is of great significance to improving the automation level, production efficiency and product quality of the forging industry. SUMMARY

[0005] The problem to be solved by the present application is to provide a seven-degree-of-freedom long-arm-span heavy-load robot and a force-position hybrid control and high-precision dynamic compensation method.

[0006] To solve the above technical problems, the technical scheme adopted by the present application is: a long-arm-span heavy-load robot, comprising a 4R type serial main chain and five parallel closed loop structures, the five parallel closed loop structures comprising a parallel closed loop one, a parallel closed loop two, a parallel closed loop three, a parallel closed loop four and a parallel closed loop five, the parallel closed loop one comprising a static platform, a connecting rod six, a connecting rod nine and a connecting rod ten, the parallel closed loop two comprising a connecting rod four, a connecting rod ten, a connecting rod twelve and a connecting rod thirteen, the parallel closed loop three comprising a connecting rod two, a connecting rod three, a connecting rod four and a connecting rod six, the parallel closed loop four comprising a connecting rod one, a connecting rod four, a connecting rod five and a connecting rod eight, the parallel closed loop five comprising an end effector, a connecting rod one, a connecting rod fourteen and a connecting rod fifteen, the parallel closed loop one being connected with the parallel closed loop four through a connecting rod seven, the parallel closed loop four and the parallel closed loop five having a common connecting rod one, and the parallel closed loop two and the parallel closed loop three having a common connecting rod four.

[0007] The 4R type serial main chain realizes the functions of coupling degrees of freedom, changing joint motion states and amplifying joint strokes by adding the five parallel closed loop structures of the parallel closed loop one, the parallel closed loop two, the parallel closed loop three, the parallel closed loop four and the parallel closed loop five.

[0008] The present application further comprises a lifting hydraulic cylinder, an extension hydraulic cylinder, a posture hydraulic cylinder and an auxiliary oil cylinder, the lifting hydraulic cylinder lifting through pushing the connecting rod twelve, amplifying through the parallel closed loop two and the parallel closed loop three, and then controlling the lifting motion of the end effector to realize the lifting motion of the end effector in a plane, the extension hydraulic cylinder driving the parallel closed loop one, the parallel closed loop four and the parallel closed loop five to extend and retract through pushing the connecting rod eleven, and then controlling the extension and retraction motion of the end effector to realize the extension and retraction motion of the end effector in a plane, the posture hydraulic cylinder driving the parallel closed loop five to control the posture of the end effector, decoupling the position and the posture, and realizing the pitching motion of the end effector in a plane, and the auxiliary oil cylinder assisting the extension and retraction of the extension hydraulic cylinder.

[0009] The connecting rod one, the connecting rod two, the connecting rod three, the connecting rod four, the connecting rod five, the connecting rod six, the connecting rod seven, the connecting rod eight, the connecting rod nine, the connecting rod ten, the connecting rod eleven, the connecting rod twelve, the connecting rod thirteen, the connecting rod fourteen and the connecting rod fifteen constitute a connecting rod mechanism, the static platform, the connecting rod mechanism and the end effector are installed on a mobile vehicle body as a robot mechanism main body, the mobile vehicle body is located on a guide rail, and a rotary motor is installed at the center of the mobile vehicle body to control the translation and rotation of the whole robot and realize two-axis motion of overall translation and overall rotation.

[0010] The connecting rods of the connecting rod mechanism are connected through parallel rotary pairs, and are driven by the lifting hydraulic cylinder, the extension hydraulic cylinder and the posture hydraulic cylinder to control the planar motion of the robot mechanism main body.

[0011] Further, the end effector is an end gripper, the end gripper is controlled by a motor to realize two-axis motion of gripper head clamping and gripper head rotation.

[0012] The application also provides a force-position hybrid control method of a long-arm heavy-load robot, which realizes high-precision control by collecting position signals and force feedback signals of the robot in real time and combining an adaptive control algorithm, and comprises the following steps:

[0013] S1, position signal collection: collecting position signals of each joint of the robot through an encoder or a photoelectric sensor, and calculating an actual position of an end effector through forward kinematics;

[0014] Further, each joint of the robot is equipped with an encoder for collecting position signals, and the end effector is equipped with a force sensor for collecting force feedback signals.

[0015] Supposing that the robot has n joints, the position of the i-th joint is , and the joint position vector is:

[0016]

[0017] The position of the end effector is x The position of the end effector is calculated through a forward kinematics model:

[0018]

[0019] wherein, is a forward kinematics function of the robot, which is established through a Denavit-Hartenberg (D-H) parameter method or a geometric method.

[0020] S2, force feedback signal collection: collecting force feedback signals of the end effector of the robot through a force sensor or a torque sensor, and calculating a deviation between an actual force and a target force;

[0021] Supposing that the actual force of the end effector is , and the target force is , the force deviation is The calculation formula is:

[0022]

[0023] S3, control instruction calculation: calculating a control instruction according to a preset forging trajectory and a force target, in combination with the position signal and the force feedback signal;

[0024] Further, supposing that the target position of the end effector is , the actual position is , and the position deviation is ,

[0025]

[0026] the position control instruction The force control command can be calculated by a proportional-integral (PI) controller:

[0027]

[0028] wherein, Kp is a position proportional gain, Kd is a differential gain, t is a time;

[0029] The force control command The force control command can be calculated by a proportional-integral (PI) controller:

[0030]

[0031] wherein, Kfp is a force proportional gain, Kfi is an integral gain;

[0032] Finally, the hybrid control command u is a weighted sum of the position control command and the force control command

[0033]

[0034] wherein, α is a weight coefficient, used to adjust the relative importance of position control and force control.

[0035] S4, adaptive control algorithm: an adaptive control algorithm is used to adjust the motion state of the robot, to realize accurate control of position and force;

[0036] Further, the adaptive control algorithm adopts a model reference adaptive control (MRAC) method, by adjusting the control parameters in real time, to make the motion state of the robot approach the reference model. The specific steps are as follows:

[0037] A dynamic model of the robot is established, and the dynamic model of the robot is set as:

[0038]

[0039] wherein, is a mass matrix, is a Coriolis force and centrifugal force matrix, G(q) is a gravity vector, is a first-order derivative of the joint position vector, is a second-order derivative of the joint position vector.

[0040] A reference model is designed, and the desired motion state is determined, and the reference model is set as:

[0041] ​

[0042] where r is a reference input, K 1 and K 2 are two gain matrices of the reference model, is a joint position vector of the reference model, is a first derivative of the joint position vector of the reference model, is a second derivative of the joint position vector of the reference model;

[0043] The control parameters are adjusted by the adaptive law to make the actual motion state approach the reference model, and the adaptive control law adjusts the control parameters θ to minimize the tracking error e :

[0044]

[0045]

[0046] where Γ is an adaptive gain matrix, is a regression matrix, is a first derivative of the control parameter.

[0047] The application also provides a high-precision dynamic compensation method for a long-arm heavy-load robot, comprising the following steps: analyzing the deformation amount of the mechanical arm of the robot by a finite element method, fitting a deformation function by a response surface method, and proposing an iterative compensation strategy for the deformation to realize online compensation of mechanical deformation.

[0048] Further, fitting the deformation function needs to perform finite element-based experimental design and select a high-precision response surface model. Based on the end effector coordinates, an experimental method of central composite design (CCD) is used to generate multiple sets of experimental design points required for fitting the response surface model. A joint simulation platform of Solidworks and ANSYS Workbench software is built to perform simulation experiments on each set of experimental points. Each set of experimental point parameters is updated to each experimental model after each parameter is updated by the joint software, and each updated model is sequentially subjected to finite element simulation until all experimental points are simulated to obtain the final experimental design data.

[0049] The response surface function for fitting the model includes the following types:

[0050]

[0051] wherein, is a first-order response surface model, is a second-order response surface model, is a third-order response surface model, is a fourth-order response surface model, is a function constant term, a first coefficient of a quadratic term of the model function, a second coefficient of a quadratic term of the model function, a second coefficient of a quadratic term of the model function, a third coefficient of a cubic term of the model function, a fourth coefficient of a quartic term of the model function, a coordinate of the end effector, a coordinate of the end effector a specific value at i a specific value at a coordinate of the end effector at j a coordinate of the end effector at

[0052] After completing the response surface model fitting, the fitting accuracy of the response surface model needs to be evaluated. By selecting a small number of experimental points, the calculated value of the response surface model is compared with the true value of the experimental points, and four precision evaluation indexes, relative average absolute error (RAAE), relative maximum absolute error (RMAE), root mean square error (RMSE), and variance (R 2 ), are used to analyze the fitting accuracy of the response surface model. The formulas of the four evaluation indexes are as follows:

[0053]

[0054] Among them, RAAE is the relative average absolute error, RMAE is the relative maximum absolute error, RMSE is the root mean square error, and R 2 is the variance, is the number of selected experimental points, is the true value of the i th experimental point, is the predicted value of the i th experimental point calculated by the response surface model, is the average value of

[0055] As can be seen from the above formula, the larger R 2 , the smaller the relative average absolute error, the relative maximum absolute error, and the root mean square error, indicating that the response surface fitting accuracy is higher. Using the above theoretical method, the data obtained by experimental design is used to establish a response surface model with the help of Isight software.

[0056] An iterative compensation strategy is proposed for deformation to realize online compensation of mechanical deformation. By obtaining the set coordinate values of the end effector and the force conditions of the link mechanism, the link deformation is calculated, and then the driving auxiliary oil cylinder is controlled to compensate the deformation amount. The combination of fuzzy controller and PID controller and the closed-loop feedback mechanism are used to realize online compensation of mechanical deformation and multi-link deformation compensation, achieving high-precision control of the robot. ​

[0057] The end gripper, static platform and connecting rod mechanism of the present application are used as the main body of the long-arm heavy load robot mechanism, which comprises a 4R type serial main chain L1 and five closed loop coupling structures; the connecting rod mechanism comprises fifteen connecting rods, which are connected through parallel rotary pairs, realizing three-axis movement of the gripper head lifting, the gripper head stretching and retracting, and the gripper head pitching; the end gripper realizes two-axis movement of the gripper head clamping and the gripper head rotating; the moving vehicle body controls the overall mechanism translation and rotation of the long-arm heavy load robot, realizing two-axis movement of the overall translation and overall rotation. Under the driving of the motor and the hydraulic drive, the forging robot can realize seven-axis movement. At the same time, by collecting the position and force feedback signals of the robot in real time, combined with the adaptive control algorithm, high-precision forging control is realized. The seven-degree-of-freedom long-arm heavy load forging robot has the advantages of large load, high stiffness, long arm, high precision, and can realize the rapid and accurate transfer of medium and large forgings in the forging workshop.

[0058] The specific effects of the present application are as follows:

[0059] The robot mechanism body of the present application is composed of a 4R type serial main chain by adding five parallel closed loop structures, which realize the coupling degree of freedom, change the joint movement state, and at the same time amplify the joint stroke, improve the rigidity and load capacity of the mechanism, have the advantages of large load, high stiffness, long arm, high precision, and can realize the rapid and accurate transfer of medium and large forgings in the forging workshop.

[0060] The moving vehicle body, static platform, connecting rod mechanism and end gripper of the present application can realize seven-axis movement of the gripper head lifting, the gripper head stretching and retracting, the gripper head pitching, the gripper head clamping, the gripper head rotating, the overall translation and the overall rotation under the driving of the motor and the hydraulic drive, improve the robot operation flexibility and adaptability, so that the robot can complete complex forging tasks in multiple postures and movement modes, flexibly adjust the position and posture of the gripper head through multi-axis cooperative movement, ensure accurate grabbing and operation of the forgings, expand the working range and efficiency, and improve the process precision and quality.

[0061] The present application adopts a force and position hybrid control method, collects position signals and force feedback signals in real time, and combines with an adaptive control algorithm to realize high-precision control. The hybrid control method can accurately adjust the movement state of the robot according to the preset forging trajectory and force target, ensure that the force applied during forging always meets the process requirements, improve the forging precision and quality, and the adaptive control algorithm can adjust the control parameters in real time to adapt to the dynamic changes of the load and the uncertainty of the working environment, enhance the adaptability and stability of the equipment, and improve the production efficiency and safety.

[0062] The application realizes online compensation of mechanical deformation through high-precision dynamic compensation technology, can monitor and compensate the deformation amount of the mechanical arm in real time, ensures the precision and stability of the robot under long-time and high-intensity working conditions, improves the operation precision of the robot, reduces stress concentration and fatigue damage caused by mechanical deformation through real-time compensation of the deformation amount, thereby prolongs the service life of the equipment, reduces the maintenance cost and replacement frequency of the equipment, and improves the production efficiency and economy. BRIEF DESCRIPTION OF DRAWINGS

[0063] The advantages and implementation modes of the application will be more obvious by referring to the drawings and combining the examples, wherein the contents shown in the drawings are only used for explaining and describing the application, and do not constitute any limitation on the application in any sense, and in the drawings:

[0064] Figure 1 is a structural schematic diagram of a long-arm heavy-load robot of the application.

[0065] Figure 2 is a front view of the long-arm heavy-load robot of the application.

[0066] Figure 3 is a schematic diagram of the motion state of the long-arm heavy-load robot of the application at a moment.

[0067] Figure 4 is a schematic diagram of the high-precision dynamic compensation control strategy of the long-arm heavy-load robot of the application.

[0068] In the drawings:

[0069] 1, mobile vehicle body; 2, static platform; 3, connecting rod mechanism; 4, end gripper; 31, parallel closed loop one; 32, parallel closed loop two; 33, parallel closed loop three; 34, parallel closed loop four; 35, parallel closed loop five; 301, connecting rod one; 302, connecting rod two; 303, connecting rod three; 304, connecting rod four; 305, connecting rod five; 306, connecting rod six; 307, connecting rod seven; 308, connecting rod eight; 309, connecting rod nine; 3010, connecting rod ten; 3011, connecting rod eleven; 3012, connecting rod twelve; 3013, connecting rod thirteen; 3014, connecting rod fourteen; 3015, connecting rod fifteen; L1, 4R type series main chain; P1, lifting hydraulic cylinder; P2, telescopic hydraulic cylinder; P3, attitude hydraulic cylinder; P4, auxiliary oil cylinder. DETAILED DESCRIPTION

[0070] As Figures 1 to 3As shown, a long-arm-span heavy-load robot includes a 4R type serial main chain L1 and five parallel closed loop structures, the five parallel closed loop structures include parallel closed loop one 31, parallel closed loop two 32, parallel closed loop three 33, parallel closed loop four 34 and parallel closed loop five 35, the parallel closed loop one 31 includes a static platform 2, a connecting rod six 306, a connecting rod nine 309, a connecting rod ten 3010; the parallel closed loop two 32 includes a connecting rod four 304, a connecting rod ten 3010, a connecting rod twelve 3012, a connecting rod thirteen 3013; the parallel closed loop three 33 includes a connecting rod two 302, a connecting rod three 303, a connecting rod four 304, a connecting rod six 306; the parallel closed loop four 34 includes a connecting rod one 301, a connecting rod four 304, a connecting rod five 305, a connecting rod eight 308; the parallel closed loop five 35 includes an end effector (in this embodiment, an end gripper 4), a connecting rod one 301, a connecting rod fourteen 3014, a connecting rod fifteen 3015, the parallel closed loop one 31 is connected with the parallel closed loop four 34 through a connecting rod seven 307, and the parallel closed loop four 34 and the parallel closed loop five 35 have a common connecting rod one 301; the parallel closed loop two 32 and the parallel closed loop three 33 have a common connecting rod four 304.

[0071] The 4R type serial main chain L1 realizes the functions of coupling degrees of freedom, changing joint motion states and amplifying joint strokes by adding the five parallel closed loop structures of the parallel closed loop one 31, the parallel closed loop two 32, the parallel closed loop three 33, the parallel closed loop four 34 and the parallel closed loop five 35.

[0072] The application also includes a lifting hydraulic cylinder P1, a telescopic hydraulic cylinder P2, a posture hydraulic cylinder P3 and an auxiliary oil cylinder P4, the lifting hydraulic cylinder P1 lifts by pushing the connecting rod twelve 3012, is amplified through the parallel closed loop two 32 and the parallel closed loop three 33, and then controls the lifting motion of the end gripper 4 to realize the in-plane gripper head lifting motion; the telescopic hydraulic cylinder P2 drives the parallel closed loop one 31, the parallel closed loop four 34 and the parallel closed loop five 35 to jointly telescope by pushing the connecting rod eleven 3011, and then controls the telescopic motion of the end gripper 4 to realize the in-plane gripper head telescopic motion (that is, the telescopic hydraulic cylinder P2 drives the connecting rod thirteen 3013 by pushing the connecting rod eleven 3011, is transmitted to the parallel closed loop three 33 through the parallel closed loop two 32, and then drives the parallel closed loop one 31 through the connecting rod six 306, and finally drives the parallel closed loop four 34 and the parallel closed loop five 35 to jointly telescope through the connecting rod seven 307 and the connecting rod one 301); the posture hydraulic cylinder P3 drives the parallel closed loop five 35 to control the posture of the end gripper 4, decouples the position and the posture, and realizes the in-plane end gripper 4 pitching motion (that is, the posture hydraulic cylinder P3 realizes the accurate adjustment of the end effector posture by simultaneously driving the connecting rod two 302 (belonging to the parallel closed loop three 33) and the connecting rod fifteen 3015 (belonging to the parallel closed loop five 35) in the manner of the former assisting positioning and the latter leading control); the auxiliary oil cylinder P4 assists the driving of the telescopic hydraulic cylinder P2.

[0073] The connecting rod one 301, the connecting rod two 302, the connecting rod three 303, the connecting rod four 304, the connecting rod five 305, the connecting rod six 306, the connecting rod seven 307, the connecting rod eight 308, the connecting rod nine 309, the connecting rod ten 3010, the connecting rod eleven 3011, the connecting rod twelve 3012, the connecting rod thirteen 3013, the connecting rod fourteen 3014 and the connecting rod fifteen 3015 constitute a connecting rod mechanism 3, the static platform 2, the connecting rod mechanism 3 and the end gripper 4 are installed on the moving vehicle body 1 as a robot mechanism main body, the moving vehicle body 1 is located on a guide rail, and a rotary motor is installed at the center of the moving vehicle body 1, so that the translation and rotation of the whole robot can be controlled, and two-axis motion of overall translation and overall rotation can be realized.

[0074] The connecting rods of the connecting rod mechanism 3 are connected through parallel rotating pairs, and are jointly driven by the lifting hydraulic cylinder P1, the telescopic hydraulic cylinder P2 and the attitude hydraulic cylinder P3 to control the planar motion of the robot mechanism main body.

[0075] The end gripper 4 is controlled by a motor to realize two-axis motion of gripper head clamping and gripper head rotation.

[0076] The application also provides a force-position control method of the long-arm heavy load robot, which realizes high-precision control by collecting position signals and force feedback signals of the robot in real time and combining an adaptive control algorithm, and comprises the following steps:

[0077] S1, position signal collection: collecting position signals of each joint of the robot through an encoder or a photoelectric sensor, and calculating an actual position of the end gripper 4 through forward kinematics;

[0078] Specifically, the robot is provided with an encoder at each joint for collecting position signals, and the end gripper 4 is provided with a force sensor for collecting force feedback signals.

[0079] Supposing that the robot has n joints, the position of the i-th joint is Then, the joint position vector is:

[0080]

[0081] The position of the end gripper 4 is x The position is calculated through a forward kinematics model:

[0082]

[0083] Wherein, is a forward kinematics function of the robot, which is established through a Denavit-Hartenberg (D-H) parameter method or a geometric method.

[0084] S2, force feedback signal collection: collecting force feedback signals of the end gripper 4 of the robot through a force sensor or a torque sensor, and calculating the deviation between the actual force and the target force.

[0085] The actual force of the end gripper 4 is , and the target force is , then the force deviation is The calculation formula is:

[0086]

[0087] S3, control instruction calculation: according to the preset forging trajectory and force target, combined with the position signal and force feedback signal, the control instruction is calculated;

[0088] Let the target position of the end gripper 4 be , and the actual position be , then the position deviation is ,

[0089]

[0090] The position control instruction can be calculated by a proportional-differential (PD) controller:

[0091]

[0092] Where, Kp is the position proportional gain, Kd is the differential gain, t is the time;

[0093] The force control instruction can be calculated by a proportional-integral (PI) controller:

[0094]

[0095] Where, Kfp is the force proportional gain, Kfi is the integral gain;

[0096] Finally, the hybrid control instruction u is the weighted sum of the position control instruction and the force control instruction :

[0097]

[0098] Where, α is the weight coefficient, used to adjust the relative importance of position control and force control.

[0099] S4, adaptive control algorithm: adopt an adaptive control algorithm to adjust the motion state of the robot, to realize accurate control of position and force;

[0100] The adaptive control algorithm adopts a model reference adaptive control (MRAC) method, and through real-time adjustment of control parameters, the motion state of the robot is approximated to a reference model.

[0101] A dynamic model of the robot is established, and the dynamic model of the robot is:

[0102]

[0103] wherein, is a mass matrix, is a Coriolis force and centrifugal force matrix, and G(q) is a gravity vector, is a first-order derivative of the joint position vector, is a second-order derivative of the joint position vector.

[0104] A reference model is designed, and a desired motion state is determined, and the reference model is:

[0105]

[0106] wherein, r is a reference input, K 1 and K 2 are two gain matrices of the reference model, is a joint position vector of the reference model, is a first-order derivative of the joint position vector of the reference model, is a second-order derivative of the joint position vector of the reference model.

[0107] An adaptive law is used to adjust the control parameters, so that the actual motion state approximates to the reference model, and the adaptive control law adjusts the control parameters θ to minimize the tracking error e .

[0108]

[0109]

[0110] wherein, Γ is an adaptive gain matrix, is a regression matrix, is a first-order derivative of the control parameter.

[0111] The application also provides a high-precision dynamic compensation method for a long-arm heavy-load robot, comprising the following steps:

[0112] The deformation amount of the mechanical arm is analyzed by a finite element method, a deformation function is fitted by a response surface method, and an iterative compensation strategy is proposed for deformation, so as to realize online compensation of mechanical deformation. In particular, the fitting of the response surface deformation function needs to be based on finite element experimental design and selection of a high-precision response surface model.

[0113] Fitting a response surface model requires a large amount of sample data generated through experimental design. This experimental design employs the Central Composite Design (CCD) method to generate the experimental points needed to fit the response surface model. This method expands the design space and obtains higher-order information, providing sample data for the approximate model. The experimental design is simple, and a high-accuracy fitting model can be obtained with fewer experiments.

[0114] Based on the 4-axis coordinate system of the end clamp, the experimental method of center composite design (CCD) was used to generate multiple sets of experimental design points. A joint simulation platform of Solidworks and ANSYS Workbench software was built to conduct simulation experiments on each set of experimental points. The parameters of each set of experimental points were updated by the joint software to correspond to each experimental model with updated parameters. Each updated model was then subjected to finite element simulation in sequence until the simulation of all experimental points was completed, and the final experimental design data was obtained.

[0115] The response surface functions used to fit the model include the following:

[0116]

[0117] in, It is a first-order response surface model. It is a second-order response surface model. It is a third-order response surface model. It is a fourth-order response surface model. For the function constant term, The coefficients of the first-order term of the model function, The first coefficient of the quadratic term of the model function, The second coefficient of the quadratic term in the model function. The coefficients of the cubic term in the model function, The coefficients of the fourth term in the model function, For the end clamp 4 coordinates, 4-axis end clamp exist i The specific value at that location, For the end clamp 4 coordinates in j The specific value at the location, where s is the total number of experimental points.

[0118] After fitting the response surface model, it is necessary to evaluate the fitting accuracy. This is done by substituting a small number of experimental points and comparing the calculated values ​​of the response surface model with the actual values ​​at the experimental points. The relative mean absolute error (RAAE), relative maximum absolute error (RMAE), root mean square error (RMSE), and variance (R²) are used as the evaluation metrics. 2 These four accuracy evaluation indicators analyze the fitting accuracy of the response surface model. The formulas for these four evaluation indicators are as follows:

[0119]

[0120] Where RAAE is the relative mean absolute error, RMAE is the relative maximum absolute error, RMSE is the root mean square error, and R... 2 For variance, The number of experimental points selected. For the first i The true value of the experimental point For the first i The predicted values ​​obtained from the response surface model for each experimental point. for The average value.

[0121] From the above formula, we can see that R 2 The larger the value, the smaller the relative mean absolute error, relative maximum absolute error, and root mean square error, indicating a higher accuracy in the response surface fitting. Using the above theoretical methods and data obtained from experimental design, a response surface model was established using Isight software.

[0122] An iterative compensation strategy is proposed to address deformation, enabling online compensation of mechanical deformation. For example... Figure 4 As shown, by acquiring the set coordinate values ​​of the end gripper 4 and the force conditions of the linkage mechanism, the deformation of the linkage is calculated, and then the drive auxiliary cylinder P4 is controlled to compensate for the deformation. By using a combination of fuzzy controller and PID controller and a closed-loop feedback mechanism, online compensation for mechanical deformation and multi-link deformation compensation are achieved, thus realizing high-precision control of the robot.

[0123] The actual execution end control accuracy is ≤ ±2mm.

[0124] Specifically, the fitting deformation function and response surface model accuracy in high-precision dynamic compensation control are shown in Table 1:

[0125] Table 1 Accuracy of Response Surface Model

[0126]

[0127] Based on the model fitting results, the required evaluation metrics for the model fitting accuracy are set as follows: RAAE < 0.2, RMAE < 0.3, RMSE < 0.2, R² < 0.9. The data in the table shows that the first-order fitting function between the deformation and coordinate values ​​has the highest accuracy. Therefore, the function expression for the deformation is as follows:

[0128]

[0129] Assuming the coordinates at this time for , get the fitting value 18.9mm, namely the end clamp 4 is located in this coordinate, the robot end deformation is 18.9mm. Deformation K as the input value of fuzzy PID closed-loop control system, after the system first compares the input value with the signal feedback by the measuring transmitter, get the deviation E, and calculate the change rate dE / dt. Fuzzy controller according to E and dE / dt adjust the parameters of PID controller, PID controller according to the adjusted parameters and deviation E calculate the control signal, act on the controlled object, make the output of the controlled object change in the direction of reducing the deviation, measuring transmitter constantly feedback the actual output of the controlled object to the system, form a closed-loop control, to realize the control of the drive auxiliary oil cylinder P4 to compensate the deformation, the compensation error can be as low as 2mm, namely the end deformation is 18.9mm, through the high-precision dynamic compensation control technology, can realize the compensation of at least 16.9mm, at most 20.9mm deformation.

[0130] The above detailed description of the embodiments of the present application, but the content is only the preferred embodiments of the present application, and cannot be considered as used to limit the scope of the present application. Any equivalent changes and improvements made within the scope of the present application should still belong to the scope of the present application.

Claims

1. A long reach heavy duty robot, characterized by: It includes 4R type series main chain and five parallel closed loop structures, five parallel closed loop structures include parallel closed loop one, parallel closed loop two, parallel closed loop three, parallel closed loop four and parallel closed loop five, it also includes lifting hydraulic cylinder, telescopic hydraulic cylinder, attitude hydraulic cylinder and auxiliary oil cylinder, the lifting hydraulic cylinder is lifted through the push link twelve, through the amplification of parallel closed loop two and parallel closed loop three, control end effector lifting movement;The telescopic hydraulic cylinder drives parallel closed loop one, parallel closed loop four and parallel closed loop five to stretch out and shrink through the push link eleven, control end effector telescopic movement;The attitude hydraulic cylinder drives parallel closed loop five to control the attitude of end effector, decouples position and attitude, realizes end effector pitching movement in plane;The auxiliary oil cylinder assists the drive of telescopic hydraulic cylinder;The parallel closed loop one includes static platform, link six, link nine and link ten;The parallel closed loop two includes link four, link ten, link twelve and link thirteen;The parallel closed loop three includes link two, link three, link four and link six;The parallel closed loop four includes link one, link four, link five and link eight;The parallel closed loop five includes end effector, link one, link fourteen and link fifteen, the parallel closed loop one is connected with the parallel closed loop four through link seven, and the parallel closed loop four and the parallel closed loop five have common link one;The parallel closed loop two and the parallel closed loop three have common link four.

2. The long-arm span heavy duty robot according to claim 1, characterized in that: The link one, link two, link three, link four, link five, link six, link seven, link eight, link nine, link ten, link eleven, link twelve, link thirteen, link fourteen and link fifteen constitute a link mechanism, the static platform, the link mechanism and the end effector are installed on the mobile vehicle body as the main body of the robot mechanism, the mobile vehicle body is located on the guide rail, and a rotary motor is installed at the center of the mobile vehicle body to control translation and rotation, realize two-axis movement of translation and rotation.

3. A force-position hybrid control method of a long-reach heavy-duty robot, implemented based on the long-reach heavy-duty robot of claim 1 or 2, characterized in that: It includes the following steps: S1, position signal acquisition: collect the position signal of each joint of the robot, and calculate the actual position of the end effector through forward kinematics; S2, force feedback signal acquisition: collect the force feedback signal of the end effector of the robot, and calculate the deviation between the actual force and the target force; S3, control instruction calculation: according to the preset forging trajectory and force target, combining the end effector position signal and the force feedback signal, calculate the control instruction; S4, adaptive control algorithm: adopt adaptive control algorithm to adjust the motion state of the robot, realize accurate control of position and force.

4. The force-position hybrid control method of the long-arm heavy-lifting robot according to claim 3, characterized in that: In step S1, each joint of the robot is equipped with an encoder for collecting position signals;The end effector is equipped with a force sensor for collecting force feedback signals; Let the robot have n joints, and let the position of the ith joint be The joint position vector is then Position of the end effector x By forward kinematic model calculation: wherein, is the forward kinematics function of the robot, established by Denavit-Hartenberg (D-H) parameter method or geometric method.

5. The force-position hybrid control method of the long-arm heavy-lifting robot according to claim 4, characterized in that: In step S2, assuming that the actual force of the end effector is , the target force is , and the force deviation is The calculation formula is: 。 6. The force-position hybrid control method of the long-arm heavy-lifting robot according to claim 5, characterized in that: In step S3, let the target position of the end effector be , the actual position be , and the position deviation be , Position control command may be calculated by a proportional-derivative controller: wherein, Kp is a position proportional gain, Kd is a derivative gain, t is time; Force control command may be calculated by a proportional-integral controller: wherein Kfp is a force proportional gain, Kfi is an integral gain; Finally, the hybrid control command u is a weighted sum of the position control command and the force control command ​ wherein α are weight factors for adjusting the relative importance of position control and force control.

7. The force-position hybrid control method of the long-arm heavy-lifting robot according to claim 6, characterized in that: In step S4, the adaptive control algorithm adopts model reference adaptive control method, adjusts the control parameters in real time, so that the motion state of the robot approaches the reference model, and the specific steps are as follows: Establish the dynamic model of the robot, and set the dynamic model of the robot as: wherein is a mass matrix, is a Coriolis and centrifugal force matrix, G(q) is a gravity vector, is a first order derivative of the joint position vector, is a second order derivative of the joint position vector; Design the reference model, determine the expected motion state, and set the reference model as: where r is a reference input, K 1 and K 2 are two gain matrices of a reference model, is a joint position vector of the reference model, is a first derivative of the joint position vector of the reference model, is a second derivative of the joint position vector of the reference model; By adjusting the control parameters using an adaptive law, the actual motion state is made to approximate the reference model. The adaptive control law adjusts the control parameters... θ To minimize tracking error e : where Γ is an adaptive gain matrix, is a regression matrix, is a first order derivative of the control parameter.

8. A high-precision dynamic compensation method for a long-reach heavy-load robot, implemented based on the long-reach heavy-load robot as claimed in claim 1 or 2, characterized in that: It includes the following steps: The deformation of the robot's mechanical arm is analyzed by the finite element method, the deformation function is fitted by the response surface method, and the deformation is compensated iteratively to realize online compensation of mechanical deformation.

9. The method of claim 8, wherein: Fitting the deformation function requires experimental design based on finite elements and selecting a high-precision response surface model: Based on the end effector coordinates, use the central composite design experimental method to generate multiple sets of experimental design points required for fitting the response surface model, build a simulation platform, and perform simulation experiments on each set of experimental points. Each set of experimental point parameters corresponds to the updated experimental model of each parameter. Each updated model will be subjected to finite element simulation in turn until all experimental points are simulated. The final experimental design data is obtained. The response surface function used to fit the model is: in, It is a first-order response surface model. It is a second-order response surface model. It is a third-order response surface model. It is a fourth-order response surface model. For the function constant term, The coefficients of the first-order term of the model function, The first coefficient of the quadratic term in the model function. The second coefficient of the quadratic term in the model function. The coefficients of the cubic term in the model function, The coefficients of the fourth term in the model function, For the coordinates of the end effector, coordinates of the end effector exist i The specific value at that location, For the coordinates of the end effector j The specific value at the location, where s is the total number of experimental points; After the response surface model fitting is completed, the fitting accuracy of the response surface model is evaluated, a small number of experimental points are selected and substituted, the calculated value of the response surface model is compared with the true value of the experimental points, and the relative average absolute error, the relative maximum absolute error, the root mean square error, R 2 The four accuracy evaluation indexes analyze the fitting accuracy of the response surface model, and the four evaluation index formulas are as follows: wherein RAAE is the relative average absolute error, RMAE is the relative maximum absolute error, RMSE is the root mean square error, R 2 is the variance, is the number of selected experimental points, is the true value of the i th experimental point, is the predicted value of the i th experimental point calculated by the response surface model, is the average value, Using the data obtained from the experimental design, a response surface model is established, and iterative compensation is proposed for deformation to realize online compensation of mechanical deformation. By obtaining the set coordinate values of the end effector and the force conditions of the link mechanism, the link deformation is calculated, the auxiliary oil cylinder is controlled to compensate for the deformation, and the fuzzy controller and PID controller are combined to realize online compensation of mechanical deformation and multi-link deformation compensation, achieving high-precision control of the robot.

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