Input and output feedback linearization method, device and equipment of rope parallel robot

Abstract

The invention provides an input and output feedback linearization method, device and equipment of a rope parallel robot, the method belongs to a full-closed-loop position control algorithm in the rope parallel robot, the working principle of the method is a technology for performing motion control on the robot by simultaneously using trajectory tracking control, and through improved control, the control precision is improved. By means of the method, accurate motion control over the robot can be achieved, the adaptive capacity of the robot facing external link influence factors can be effectively improved, robustness is improved, and quick response to trajectory planning control over the tail end load of the rope traction robot is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of parallel robots, and in particular to an input-output feedback linearization method, a device and equipment of a rope parallel robot. Background Art

[0002] The fields of aerospace, shipbuilding, infrastructure construction, energy and power are experiencing a booming development. Various major projects have been launched at an accelerated pace, showing a booming development trend. Driven by the pursuit of high-quality development, various industries have given rise to market demands and strict industry standards. In areas such as satellite assembly and launch vehicle assembly in the aerospace field, large-area ship spraying in the shipbuilding field, and on-site assembly and disassembly of shield machines in tunnel excavation projects, there is a demand for large-scale, high-load, and high-precision manufacturing and assembly. In major projects, the cost can be as high as hundreds of billions or even trillions, so precision requirements are crucial. However, traditional lifting machinery often finds it difficult to meet the requirements in these scenarios, especially in terms of precision.

[0003] At present, the assembly and assembling of large ships in my country mainly rely on manually guided traditional lifting machinery to achieve lifting and positioning. However, this operation method has many difficulties, high work risks, low efficiency, limited assembly quality, and it is difficult to meet the efficient needs of manufacturing and assembly.

[0004] Compared with traditional serial robots, rope-pulled parallel robots have the advantages of large carrying capacity, small inertia, high energy utilization, and large working space. In addition, they are easy to reconfigure, have low construction costs, and are easy to transport and assemble. And by combining excellent control algorithms, they can achieve high-precision work and meet the needs of related fields of major projects. Relying on simple structure, high mobility, and strong reconfigurability, they can improve work efficiency, reduce application costs, and simplify the controller design process, which provides potential advantages for the performance of robots in traction tasks.

[0005] In view of this, this application is filed. Summary of the invention

[0006] The present invention provides an input-output feedback linearization method, device and equipment for a rope parallel robot, which can at least partially improve the above-mentioned problems.

[0007] To achieve the above object, the present invention adopts the following technical solutions:

[0008] A method for linearizing input and output feedback of a rope parallel robot, comprising:

[0009] The actual situation of the current rope parallel robot is obtained, a mechanism diagram is constructed, and a calculation process is performed according to the mechanism diagram and the expected trajectory of the end effector of the preset rope parallel robot to obtain the expected rope length, and the Lagrangian method is used to model the rope velocity and rope acceleration of the dynamic platform to obtain a dynamic model;

[0010] Based on the dynamic model, the rope tension is calculated using the static equilibrium equation;

[0011] The system dynamics of the rope tension is transformed into a linear and decoupled form using a multi-input multi-output nonlinear system;

[0012] The dynamic model is rewritten into the state equation form through feedback processing by a multi-input multi-output nonlinear system, and the rope tension in linear and decoupled forms is transformed through a multi-input multi-output nonlinear system to obtain the selection of new inputs to achieve stable trajectory tracking of the end effector;

[0013] The preset infrared head sensor is used to feed back the position of the end effector tracked by the stable trajectory into the control loop, forming a position error with the coordinate position of the current desired trajectory of the end effector. The parameters are adjusted according to the position error, and the adjustment value is fed back to the actual position information to perform closed-loop control of the position.

[0014] The present invention also provides an input-output feedback linearization device for a rope parallel robot, which comprises:

[0015] The dynamic model unit is used to obtain the actual situation of the current rope parallel robot, build a mechanism diagram, perform calculation processing according to the mechanism diagram and the expected trajectory of the end effector of the preset rope parallel robot, obtain the expected rope length, and use the Lagrangian method to model the rope velocity and rope acceleration of the dynamic platform to obtain the dynamic model;

[0016] Rope tension calculation unit, used to calculate rope tension using static equilibrium equation based on dynamic model;

[0017] A conversion unit for converting the system dynamics form of the rope tension into a linear and decoupled form using a multi-input multi-output nonlinear system;

[0018] A stable trajectory tracking unit is used for feedback processing through a multi-input multi-output nonlinear system, rewriting the dynamic model into a state equation form, transforming the rope tension in a linear and decoupled form through the multi-input multi-output nonlinear system, and obtaining a new input selection to achieve stable trajectory tracking of the end effector;

[0019] The closed-loop control unit is used to feed back the position of the end effector tracked by the stable trajectory into the control loop using a preset infrared head sensor, forming a position error with the coordinate position of the current desired trajectory of the end effector, adjusting parameters according to the position error, and feeding back the adjustment value to the actual position information to perform closed-loop control of the position.

[0020] The present invention also provides an input-output feedback linearization device for a rope parallel robot, which includes: a memory and a processor, wherein a computer program is stored in the memory, and the computer program can be executed by the processor to implement the input-output feedback linearization method for the rope parallel robot as described in any one of the above items.

[0021] In summary, the input-output feedback linearization method of the rope parallel robot establishes the dynamic model of the rope-pulled parallel robot, and a nonlinear control scheme is proposed by using the input-output linearization technology, and the linear feedback controller is designed by adopting the pole configuration method. This scheme can effectively cope with the complexity of nonlinear systems, make up for the shortcomings of traditional linear control methods in dealing with such systems, and adjust the dynamic behavior of the system to make the system meet the performance requirements of fast response, good stability, etc. Among them, the application of the input-output feedback linearization control method on the rope-pulled parallel robot is expected to improve the control accuracy and robustness of the system, improve the trajectory tracking accuracy and dynamic response, and realize the high-speed and high-precision operation of the rope-pulled parallel robot, meet the needs of related fields, and replace the use of traditional lifting machinery in occasions requiring large space, large load, and high precision.

[0022] Simply put, the input-output feedback linearization method of the rope parallel robot belongs to the full closed-loop position control algorithm in the rope parallel robot. Its working principle is to use trajectory tracking control technology to control the robot's motion at the same time. Through improved control, more precise motion control of the robot can be achieved, which can effectively improve the robot's adaptability in the face of external factors, improve robustness, and meet the rapid response of trajectory planning control of the end load of the rope traction robot. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a flow chart of an input-output feedback linearization method of a rope parallel robot provided by the first aspect of the present invention;

[0024] Figure 2 It is a schematic diagram of the mechanism of a three-rope three-degree-of-freedom elastic rope robot provided by an embodiment of the present invention;

[0025] Figure 3 It is a schematic diagram of the flow chart of input and output linearization of a rope robot provided by an embodiment of the present invention;

[0026] Figure 4 It is a flow chart of an input-output feedback linearization method of a rope parallel robot provided in the second aspect of an embodiment of the present invention;

[0027] Figure 5 is a control block diagram of a trajectory tracking error system of a rope robot provided by an embodiment of the present invention;

[0028] Figure 6 is a trajectory tracking control flow chart of a rope robot provided by an embodiment of the present invention;

[0029] Figure 7 is a schematic structural diagram of a rope robot provided by an embodiment of the present invention;

[0030] Figure 8 It is a module schematic diagram of an input-output feedback linearization device of a rope parallel robot provided in the second embodiment of the present invention. DETAILED DESCRIPTION

[0031] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0032] refer to Figure 1 , Figure 4 , Figure 7 As shown, the first embodiment of the present invention discloses an input-output feedback linearization method of a rope-connected parallel robot, which can be executed by an input-output feedback linearization device of the rope-connected parallel robot (hereinafter referred to as feedback linearization device), and in particular, executed by one or more processors in the feedback linearization device to implement the following method:

[0033] S1, obtaining the actual situation of the current rope parallel robot, building a mechanism diagram, performing calculation processing according to the mechanism diagram and the expected trajectory of the end effector of the preset rope parallel robot, obtaining the expected rope length, and using the Lagrangian method to model the rope velocity and rope acceleration on the dynamic platform to obtain a dynamic model;

[0034] Specifically, step S1 includes: obtaining the actual situation of the current rope parallel robot, building a mechanism diagram, determining the static coordinate system of the rope parallel robot, and obtaining the coordinates of the center point of the pulley, wherein the origin O of the static coordinate system is the centroid of the equilateral triangle formed by connecting the center points of the three pulleys, the x-axis direction points from point O to the pulley coordinate point O1, the z-axis direction is perpendicular to the x-axis and vertically upward, and the y-axis direction is perpendicular to the xoz plane according to the right-hand rule;

[0035] Acquiring position and posture information of an end effector of the rope parallel robot, wherein the position and posture information includes changes in position, velocity, and acceleration of the end effector;

[0036] The geometric relationship between the position information and the coordinates of the center point of the pulley is calculated to obtain the real-time expected rope length of the rope parallel robot. The calculation formula is: Among them, L i is the column vector composed of the lengths of the three cables, B i is the center coordinate of the i-th pulley, X is the position coordinate of the desired trajectory of the end effector at the current time, and T is the transposition sign;

[0037] Determine the linear relationship between the rope velocity and the end effector velocity, the formula is: in, is the rope speed, J is the length of the three ropes of the rope parallel robot, and the Jacobian matrix is ​​derived by respectively derivation of the x, y, and z trajectories planned by the end effector. is the speed of the end effector, L=[l1,l2,l3] T is the rope length;

[0038] The Lagrangian method is used to model the dynamic platform based on the rope velocity and rope acceleration. Based on the relationship between the pulley and the rope length, the rope length, rope velocity and acceleration information are expressed using differential flatness to express the torque of the motor driver and obtain the dynamic model.

[0039] S2, based on the dynamic model, the rope tension is calculated using the static equilibrium equation;

[0040] Specifically, step S2 includes: calculating the rope tension according to the static equilibrium equation, wherein the static equilibrium equation is: m is the mass, is the end effector acceleration, F j is the tension of the three ropes, j = 1, 2, 3, G = [00mg] T is the gravity vector;

[0041] Consider each rope as a massless length vector ρ j The rigid lightweight link with length vector s j The linear spring is connected to obtain the length vector l of the rope j =s j +ρ j , spring force τ j =ks j , where k is the stiffness and 5 is the spring length;

[0042] According to the length vector l of the rope j =s j +ρj , find the spring length Get rope tension in, Expressed as the change in end effector acceleration and the equations for the end effector position x, y, z, where k is the spring stiffness.

[0043] In this embodiment, the kinematic modeling is accurately established, and a linear feedback controller is designed through the input-output feedback linearization method for controlling the rope-pulled parallel robot. This solution is conducive to improving the rope tension and improving the accuracy and stability of the terminal trajectory tracking control.

[0044] Build a schematic diagram of a parallel mechanical mechanism with elastic ropes, such as Figure 2 shown.

[0045] Determine the static coordinate system of the mechanism. The origin O of the coordinate system is set at the centroid of the equilateral triangle formed by connecting the center points of the three pulleys (R is the radius of the circumscribed circle of the equilateral triangle). The x-axis points from point O to the pulley coordinate point O1. The z-axis is perpendicular to the x-axis and points vertically upward. The y-axis is perpendicular to the xoz plane according to the right-hand rule. The schematic diagram of the rope-pulled parallel robot is shown in the figure below. Figure 2 As shown, the position coordinates of the three points of the pulley are: j =[O jx , O jy , O jz ] T j=1,2,3 represents the jth pulley. Under the action of the pulling force, the end effector realizes the translational motion in the x, y, and z directions on the xyz plane, including the changes in the position, velocity, and acceleration of the end effector, expressed as x, y, z,

[0046] The real-time expected rope length of the rope robot is solved through the geometric relationship between the position and posture of the end effector and the coordinates of the pulley center point.

[0047] The calculation is as follows:

[0048]

[0049] Where: L i is the column vector composed of the lengths of the three cables, B i is the center coordinate of the i-th pulley, and X is the coordinate of the end effector, that is, the position coordinate of the desired trajectory of the end effector at the current time.

[0050] According to the relationship between the rope speed and the end effector speed:

[0051]

[0052] Where L = [l1, l2, l3] T is the rope length, is the rope speed, J is the Jacobian matrix obtained by respectively differentiating the x, y, and z trajectories planned by the end effector with respect to the three rope lengths, describing the linear relationship between the rope speed and the end effector speed. is the speed of the end effector.

[0053] The Lagrangian method is used to model the dynamic platform according to the rope speed and rope acceleration. Then, through the relationship between the pulley and the rope length, the differential flatness is used to express the torque of the motor driver through the rope length, rope speed and acceleration information, that is, the dynamic model. The ropes are connected to their respective winches and drivers by passing through the pulleys. After the control system of the rope traction robot sends the control signal to the driver, it causes the servo motor output shaft to rotate, driving the winch to change the rope tension.

[0054] The tension on the rope is expressed as follows according to the static balance, which is the obtained rope tension:

[0055]

[0056] In the formula, m represents mass, is the end effector acceleration, F j is the tension of the three ropes (j=1,2,3), G(X)=[0 0 mg] T is the gravity vector.

[0057] Treat each rope as a massless length vector ρ j The rigid lightweight connecting rod with length vector s j The length vector of the rope is therefore l j =s j +ρ j The elastic force of the spring, i.e. the tension of the rope, is expressed as:

[0058] τ j =ks j

[0059] That is: In the formula, τ j is the rope tension (j=1,2,3), k represents the stiffness, s j is the spring length (j=1,2,3).

[0060] According to the force balance equation Right now:

[0061]

[0062] τ j =ks j Substituting in:

[0063]

[0064] Arranged:

[0065]

[0066] In the above formula:

[0067]

[0068] Write the formula as:

[0069] (ρ is a lightweight connecting rod, a0 is a constant) Taking ρ1, ρ2, ρ3 as unknowns to solve the three-variable linear equation system, we get:

[0070] This formula shows that ρ1, ρ2, ρ3 are about x, y, z, The function is written as:

[0071] According to l j =s j +ρ j , the length of the spring can be obtained:

[0072]

[0073] Available

[0074]

[0075] Where u1, u2, u3 are the rope tensions, k is the spring stiffness, s1, s2, s3 are the spring lengths, and l1(x, y, z) is an equation about (x, y, z). Expressed as The equation of .

[0076] The above steps establish an accurate dynamic model of the elastic rope traction robot. Matlab can be used to solve the expressions of u1, u2 and u3, and the above elastic rope dynamic model can be used to implement the input-output feedback linearization method.

[0077] S3, using a multi-input multi-output nonlinear system to transform the system dynamics of the rope tension into a linear and decoupled form;

[0078] Specifically, step S3 includes: the mathematical expression of the multi-input multi-output nonlinear system is:

[0079]

[0080] Among them, ui (t) is the control input, y i (t) is the system output, f(x,t), g i (x,t),h i (x, t) are all smooth nonlinear functions, i = 1, 2, ..., n, n is;

[0081] The input rope tension is transformed into the form of the input transformation formula of the multi-input multi-output nonlinear system: Among them, E -1 (x) is the inverse matrix of the matrix E(x), E(x) is an n×n matrix, For the rth n Second Lie derivative, r n is the number of derivatives required for the nth input to appear, h n (x) is a function describing the nonlinear characteristics, and n is the new input of the nth system.

[0082] In this embodiment, a method for input-output feedback linearization is described, the steps are as follows Figure 3 As shown. The multi-input multi-output nonlinear system can be expressed as:

[0083]

[0084] In the formula, u i (t) is the control input, y i (t) is the system output, f(x,t), g i (x,t),h i (x, t) is a smooth nonlinear function (i=1,2...n).

[0085] The basic principle of the input-output feedback linearization method is to find an input transformation of the shape: i =α i (x)+β i (x)ν i , where ν i is the new input of the system, α i (x), β i (x) is a nonlinear function

[0086] Through the above formula, the output y i (t) and the new input v i (t), decouple the interaction between the original input and output, and obtain the relationship between the new input and the new output. The principle is as follows Figure 3 shown.

[0087] To achieve this, the derivative is repeated for each output until the input signal appears in the derivative expression. The output function is the lie derivative of the state quantity, and the partial differential expression of the output function and the lie derivative with respect to the input quantity is: in,

[0088] Through the above formula, the derivation process can be expressed as:

[0089]

[0090] In the formula, r i Represents the number of derivatives required for at least one of the inputs to appear.

[0091] Expand the above derivation to get:

[0092]

[0093] Where E(x) is an n×n matrix.

[0094] The above formula can be used to define the input transformation:

[0095]

[0096] As shown in the equation, a linear control law can be used to propose feedback control for a linear system to ensure the desired behavior of a nonlinear system using conventional control techniques.

[0097] S4, feedback processing is performed through a multi-input multi-output nonlinear system, the dynamic model is rewritten into a state equation form, the rope tension in a linear and decoupled form is transformed through a multi-input multi-output nonlinear system, and a new input selection is obtained to achieve stable trajectory tracking of the end effector;

[0098] Specifically, step S4 includes: expressing the kinetic model as: Among them, M -1 is the inverse matrix of the inertia matrix of the end effector, G(X) is the gravity vector, J T is the transpose of the Jacobian matrix;

[0099] Get the input and output of the state variables in the state space equation: Among them, the state variable x1=X=[xyz] T represents the displacement of the end effector, the state variable Represents the speed of the end effector, input variable u=τ=[u1 u2 u3] Trepresents the tension on the rope, and the output variable y=x1 represents the output as the displacement of the end effector;

[0100] Substitute the input and output of the state variables in the state space equation into the dynamic model and obtain the following formula through the input transformation of the multi-input and multi-output nonlinear system:

[0101] Derivation of the transformed input variables to obtain the new input v i Choices: Among them, x d is the desired position of the end effector in the x direction, is the desired velocity of the end effector in the x direction, is the desired acceleration of the end effector in the x direction, y d is the desired position of the end effector in the y direction, is the desired velocity of the end effector in the y direction, is the desired acceleration of the end effector in the y direction, z d is the desired position of the end effector in the z direction, is the desired velocity of the end effector in the z direction, is the desired acceleration of the end effector in the z direction, k dI and k pI All are control gains, I=1,2,3.

[0102] In this embodiment, the stable trajectory tracking of the end effector is achieved by input-output feedback linearization feedback, the steps are as follows Figure 5 As shown. The power of the rope-driven parallel robot is obtained from step S1.

[0103]

[0104] The model can be expressed as a state equation: y = x1, where M -1 is the inverse matrix of the inertia matrix of the end effector, G(X) is the gravity vector, J T is the transpose of the Jacobian matrix. The state variables, inputs and outputs in the state space equation are: In the formula, the state variable x1=X=[xyz] T represents the displacement of the end effector, the state variable Represents the speed of the end effector, input variable u=τ=[u1 u2 u3] T represents the tension on the rope, and the output variable y=x1 represents the output as the displacement of the end effector.

[0105] Substituting into the dynamic model of the rope-pulling robot, the transformed formula input in step S3 can be obtained: By deducing the above formula, we can get the new input v i Choices: Among them, x d is the desired position of the end effector, is the desired velocity of the end effector, is the expected acceleration of the end effector, is the actual position of the end effector, k dI ,k pI (I = 1, 2, 3) is the control gain. To achieve the optimal control effect, it can be further obtained by pole configuration method. If the optimized control effect still cannot meet the target requirements, the feedback control law can be further modified to meet the end effector motion and accuracy requirements and meet the practical application.

[0106] S5, uses a preset infrared head sensor to feed back the position of the end effector tracked by the stable trajectory to the control loop, forming a position error with the coordinate position of the current desired trajectory of the end effector, adjusts the parameters according to the position error, and feeds back the adjustment value to the actual position information to perform closed-loop control of the position.

[0107] Specifically, step S5 includes: the expression of the trajectory tracking error system of the end effector is: Where, e = xx d is the position error vector of the end effector, is the velocity error vector of the end effector, is the acceleration error vector of the end effector.

[0108] In this embodiment, in order to meet the high requirements for response speed, an infrared head sensor is used to feed back the position of the end effector to the control loop. In this way, a position error is formed with the coordinate position of the current desired trajectory of the end effector. After adjusting the error parameters, the adjustment value is fed back to the actual position information to perform closed-loop control of the position. The trajectory tracking control process is as follows: Figure 6 shown.

[0109] Then the trajectory tracking error system of the end effector can be expressed as: Where, e = xx d The position error vector of the end effector, The velocity error vector of the end effector, Acceleration error vector of the end effector.

[0110] In this embodiment, the input-output feedback linearization method of the rope parallel robot belongs to the full closed-loop position control algorithm in the rope parallel robot. Its working principle is to use trajectory tracking control to control the robot's motion at the same time. Through improved control, more accurate motion control of the robot can be achieved, which can effectively improve the robot's adaptability to external factors, improve robustness, and meet the rapid response of trajectory planning control for the end load of the rope traction robot. Because the rope is different from an ordinary rigid structure and has a certain elasticity when subjected to tension, if you want to accurately control the torque of the flexible mechanism, then the elasticity of the rope must be considered. Simplifying the rope into a linear spring and a lightweight connecting rod model is conducive to the tracking control of the end trajectory.

[0111] Feedback linearization is one of the most important nonlinear control technologies. It allows people to obtain a completely equivalent linear system by using coordinate transformation and state feedback, effectively dealing with the complexity of nonlinear systems. The premise of using the input-output feedback linearization control method is to first accurately establish the kinematic and dynamic models of the rope-pulled robot. On this basis, the input-output feedback linearization method is used to transform the nonlinear system into a linear system, realize the decoupling of output and input, and then accurately control the system.

[0112] In summary, the design focus of the input-output feedback linearization method of the rope parallel robot is to convert the nonlinear dynamics of the system into a linear and decoupled form by using the input-output feedback linearization technology, thereby simplifying the complexity of the control system design. This method helps to improve the controllability and observability of the system. Then, the linear feedback controller designed by the pole configuration method may provide better control performance and robustness, which helps to meet the system's requirements for fast response and anti-interference performance. In short, the key point of the input-output feedback linearization method of the rope parallel robot is to establish a dynamic model of the elastic rope-pulled parallel robot system, simplify the rope into a linear spring and lightweight connecting rod model, and consider the elasticity of the rope. Based on this model, an input-output feedback linearization control method is used for the control of the parallel robot, and an effective feedback controller is designed by using the pole configuration method to stabilize the system output. This scheme can effectively cope with the complexity of nonlinear systems, convert nonlinear systems into linear systems for control, and is conducive to improving trajectory tracking accuracy.

[0113] See also Figure 8 The second embodiment of the present invention provides an input-output feedback linearization device for a cable parallel robot, which includes:

[0114] The dynamic model unit 201 is used to obtain the actual situation of the current rope parallel robot, build a mechanism diagram, perform calculation processing according to the mechanism diagram and the expected trajectory of the end effector of the preset rope parallel robot, obtain the expected rope length, and use the Lagrangian method to model the rope velocity and rope acceleration of the dynamic platform to obtain the dynamic model;

[0115] A rope tension calculation unit 202 is used to calculate the rope tension using a static equilibrium equation based on a dynamic model;

[0116] A conversion unit 203, for converting the system dynamics form of the rope tension into a linear and decoupled form using a multi-input multi-output nonlinear system;

[0117] A stable trajectory tracking unit 204 is used to perform feedback processing through a multi-input multi-output nonlinear system, rewrite the dynamic model into a state equation form, transform the rope tension in a linear and decoupled form through the multi-input multi-output nonlinear system, and obtain a new input selection to achieve stable trajectory tracking of the end effector;

[0118] The closed-loop control unit 205 is used to use a preset infrared head sensor to feed back the position of the end effector tracked by the stable trajectory to the control loop, form a position error with the coordinate position of the current desired trajectory of the end effector, adjust the parameters according to the position error, and feed back the adjustment value to the actual position information to perform closed-loop control of the position.

[0119] The third embodiment of the present invention provides an input-output feedback linearization device of a rope parallel robot, which includes: a memory and a processor, wherein the memory stores a computer program, and the computer program can be executed by the processor to implement the input-output feedback linearization method of the rope parallel robot as described in any one of the above items.

[0120] Exemplarily, the above-mentioned devices and process steps can be implemented by a computer program. The computer program can be divided into one or more units. The one or more units are stored in the memory and executed by the processor to complete the present invention.

[0121] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0122] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the present invention by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (FlashCard), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0123] Wherein, if the electronic device or printer integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0124] It should be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0125] The above is a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the principle of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for linearizing input and output feedback of a rope-connected parallel robot, characterized in that: include: The actual situation of the current rope parallel robot is obtained, a mechanism diagram is constructed, and a calculation process is performed according to the mechanism diagram and the expected trajectory of the end effector of the preset rope parallel robot to obtain the expected rope length, and the Lagrangian method is used to model the rope velocity and rope acceleration of the dynamic platform to obtain a dynamic model; Based on the dynamic model, the rope tension is calculated using the static equilibrium equation; The system dynamics of the rope tension is transformed into a linear and decoupled form using a multi-input multi-output nonlinear system; The dynamic model is rewritten into the state equation form through feedback processing by a multi-input multi-output nonlinear system, and the rope tension in linear and decoupled forms is transformed through a multi-input multi-output nonlinear system to obtain the selection of new inputs to achieve stable trajectory tracking of the end effector; The preset infrared head sensor is used to feed back the position of the end effector tracked by the stable trajectory into the control loop, forming a position error with the coordinate position of the current desired trajectory of the end effector. The parameters are adjusted according to the position error, and the adjustment value is fed back to the actual position information to perform closed-loop control of the position.

2. The input-output feedback linearization method of the cable parallel robot according to claim 1, characterized in that: The actual situation of the current rope parallel robot is obtained, and a mechanism diagram is constructed. According to the mechanism diagram and the expected trajectory of the end effector of the preset rope parallel robot, calculation processing is performed to obtain the expected rope length, and the Lagrangian method is used to model the rope velocity and rope acceleration on the dynamic platform to obtain the dynamic model: Get the actual situation of the current rope parallel robot, build a mechanism diagram, determine the static coordinate system of the rope parallel robot, and obtain the coordinates of the pulley center point, where the origin O of the static coordinate system is the centroid of the equilateral triangle formed by connecting the center points of the three pulleys, the x-axis points from point O to the pulley coordinate point O1, the z-axis is perpendicular to the x-axis and points vertically upward, and the y-axis is perpendicular to the xoz plane according to the right-hand rule; Acquiring position and posture information of an end effector of the rope parallel robot, wherein the position and posture information includes changes in position, velocity, and acceleration of the end effector; The geometric relationship between the position information and the coordinates of the center point of the pulley is calculated to obtain the real-time expected rope length of the rope parallel robot. The calculation formula is: Among them, L i is a column vector composed of three rope lengths, Bi is the coordinate of the center of the i-th pulley, X is the position coordinate of the desired trajectory of the end effector at the current time, and T is the transposition sign; Determine the linear relationship between the rope velocity and the end effector velocity, the formula is: in, is the rope speed, J is the length of the three ropes of the rope parallel robot, and the Jacobian matrix is ​​derived by respectively derivation of the x, y, and z trajectories planned by the end effector. is the speed of the end effector, L = [l1, l2, l3] T is the rope length; The Lagrangian method is used to model the dynamic platform based on the rope velocity and rope acceleration. Based on the relationship between the pulley and the rope length, the rope length, rope velocity and acceleration information are expressed using differential flatness to express the torque of the motor driver and obtain the dynamic model.

3. The input-output feedback linearization method of the cable-connected parallel robot according to claim 2, characterized in that: Based on the dynamic model, the rope tension is calculated using the static equilibrium equation, which is: The rope tension is calculated according to the static equilibrium equation, where the static equilibrium equation formula is: m is the mass, is the end effector acceleration, F j is the tension of the three ropes, j = 1, 2, 3, G = [00mg] T is the gravity vector; Consider each rope as a massless length vector ρ j The rigid lightweight link with the length vector s j The linear spring is connected to obtain the length vector l of the rope j =s j +ρ j , spring force τ j =ks j , where k is the stiffness, s j is the spring length; According to the length vector l of the rope j =s j +ρ j , find the spring length Get rope tension in, Expressed as the change in end effector acceleration and the equations for the end effector position x, y, z, where k is the spring stiffness.

4. The input-output feedback linearization method of the cable parallel robot according to claim 3, characterized in that: The system dynamics of the rope tension is transformed into a linear and decoupled form using a multi-input multi-output nonlinear system, specifically: The mathematical expression of the multi-input multi-output nonlinear system is: Among them, u i (t) is the control input, y i (t) is the system output, f(x, t), g i (x, t), h i (x, t) are all smooth nonlinear functions, i = 1, 2, ..., n, n is a variable; The input rope tension is transformed into the form of the input transformation formula of the multi-input multi-output nonlinear system: Among them, E -1 (x) is the inverse matrix of the matrix E(x), E(x) is an n×n matrix, For the rth n Second Lie derivative, r n is the number of derivatives required for the nth input to appear, h n (x) is a function describing nonlinear characteristics, v n is the new input for the nth system.

5. The input-output feedback linearization method of the cable-connected parallel robot according to claim 4, characterized in that: Feedback processing is performed through a multi-input multi-output nonlinear system, and the dynamic model is rewritten into a state equation form. The rope tension in the linear and decoupled form is transformed through a multi-input multi-output nonlinear system to obtain the selection of new inputs to achieve stable trajectory tracking of the end effector, specifically: The expression of the kinetic model is: Among them, M -1 is the inverse matrix of the inertia matrix of the end effector, G(X) is the gravity vector, J T is the transpose of the Jacobian matrix; Get the input and output of the state variables in the state space equation: Among them, the state variable x1=X=[xyz] T represents the displacement of the end effector, the state variable Represents the speed of the end effector, input variable u=τ=[u1 u2 u3] T represents the tension on the rope, and the output variable y=x1 represents the output as the displacement of the end effector; Substitute the input and output of the state variables in the state space equation into the dynamic model and obtain the following formula through the input transformation of the multi-input and multi-output nonlinear system: Derivation of the transformed input variables to obtain the new input v i Choices: Among them, x d is the desired position of the end effector in the x direction, is the desired velocity of the end effector in the x direction, is the desired acceleration of the end effector in the x direction, yd is the desired position of the end effector in the y direction, is the desired velocity of the end effector in the y direction, is the desired acceleration of the end effector in the y direction, z d is the desired position of the end effector in the z direction, is the desired velocity of the end effector in the z direction, is the desired acceleration of the end effector in the z direction, k dI and k pI All are control gains, I=1, 2, 3.

6. The input-output feedback linearization method of the cable-connected parallel robot according to claim 5, characterized in that: The preset infrared head sensor is used to feed back the position of the end effector tracked by the stable trajectory to the control loop, forming a position error with the coordinate position of the current desired trajectory of the end effector. The parameters are adjusted according to the position error, and the adjustment value is fed back to the actual position information to perform closed-loop control of the position. Specifically: The expression of the trajectory tracking error system of the end effector is: Where, e = xx d is the position error vector of the end effector, is the velocity error vector of the end effector, is the acceleration error vector of the end effector.

7. An input-output feedback linearization device for a rope parallel robot, characterized in that: include: The dynamic model unit is used to obtain the actual situation of the current rope parallel robot, build a mechanism diagram, perform calculation processing according to the mechanism diagram and the expected trajectory of the end effector of the preset rope parallel robot, obtain the expected rope length, and use the Lagrangian method to model the rope velocity and rope acceleration of the dynamic platform to obtain the dynamic model; Rope tension calculation unit, used to calculate rope tension using static equilibrium equation based on dynamic model; a conversion unit for converting the system dynamics form of the rope tension into a linear and decoupled form using a multi-input multi-output nonlinear system; A stable trajectory tracking unit is used for feedback processing through a multi-input multi-output nonlinear system, rewriting the dynamic model into a state equation form, transforming the rope tension in a linear and decoupled form through the multi-input multi-output nonlinear system, and obtaining a new input selection to achieve stable trajectory tracking of the end effector; The closed-loop control unit is used to feed back the position of the end effector tracked by the stable trajectory into the control loop using a preset infrared head sensor, forming a position error with the coordinate position of the current desired trajectory of the end effector, adjusting parameters according to the position error, and feeding back the adjustment value to the actual position information to perform closed-loop control of the position.

8. An input-output feedback linearization device for a rope parallel robot, characterized in that: It comprises a memory and a processor, wherein the memory stores a computer program, and the computer program can be executed by the processor to implement the input-output feedback linearization method of the rope parallel robot as claimed in any one of claims 1 to 6.

Images