CN118192304B - Modularized control dynamics simulation method for heavy-duty industrial robot for aviation - Google Patents
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Abstract
The invention belongs to the technical field of aviation, and particularly relates to a modularized control dynamics simulation method of a heavy-duty industrial robot for aviation, which is characterized in that a dynamics control model of a whole system of the robot is built quickly in an integrated way by combining a dynamic model topological diagram of a robot system by establishing a dynamics simulation method of the heavy-duty industrial robot based on a dynamics modularized control unit, so that control design and performance analysis of the heavy-duty industrial robot are realized, meanwhile, the control precision of the heavy-duty industrial robot is improved by simulating the nonlinear interference characteristic of joints through a neural network model, the efficient and accurate modeling and quick iterative optimization of the dynamics control system of the heavy-duty industrial robot are realized, the problems that the calculation speed of the robot is slow, the optimization efficiency is low and the control design is difficult by adopting a common multi-body dynamics method are solved, and technical support is provided for improving the processing quality and precision of high-end equipment of the heavy-duty industrial robot for aviation.
Description
Technical Field
The invention relates to the technical field of intelligent manufacturing of high-end equipment of aviation, in particular to a modularized control dynamics simulation method of a heavy-duty industrial robot for aviation.
Background
With the gradual promotion of a large number of national special projects such as domestic large aircrafts, new generation fighters, manned space stations and the like, the improvement of the aerospace manufacturing level of China is promoted. But the new generation of aerospace craft has the new characteristics of large size, complex structure, weak rigidity, thin wall structure and the like, and has higher requirements on the processing capacity of manufacturing equipment. The traditional machine tool processing technology has small working space and low flexibility degree, and can not meet the comprehensive requirements of changeable model and short development period of aerospace parts. In recent years, with the advent of highly flexible and efficient in-situ manufacturing modes, intelligent technologies and devices represented by industrial robots are increasingly applied in high-end fields. Compared with the traditional computer numerical control machine tool, the industrial robot has the advantages of low cost, large working space and high flexibility, and the heavy-load industrial robot is successfully applied to machining and assembling operations such as drilling, milling, polishing and the like of aerospace products at present. However, the special open-chain type multi-rod serial structure of the robot, the characteristics of joint clearance, low rigidity and the like, lead to the problems that the robot is difficult to control, the absolute positioning precision of the tail end cannot completely meet the operation requirement and the like. Therefore, a dynamics control model for rapidly and accurately simulating the dynamic characteristics of the robot under the action of nonlinear forces such as control force, joint clearance, friction and the like is an important foundation for control design and performance analysis of a heavy-duty industrial robot, and has important significance for improving and enhancing the control precision of the robot.
Most of the existing commonly used industrial robot dynamics control modeling methods are based on a Newton-Euler method, a Lagrange method, a Kane method and other multi-body dynamics methods, and control performance analysis of the robot is achieved by combining a robot control algorithm. However, the above-mentioned dynamics method must establish the overall dynamics equation of the system, the dynamics modeling and deduction process is complicated, and the dynamics equation of the system must be deduced again once the system structure changes. Meanwhile, the existing robot dynamics control simulation is difficult to accurately simulate nonlinear dynamics characteristics such as joint gaps, friction and the like, so that the accuracy of a robot dynamics control model is low.
Disclosure of Invention
The invention aims to overcome the defects in the prior art, provides a modularized control dynamics simulation method for an aviation heavy-duty industrial robot, and solves the problems of difficult control design and performance analysis of the heavy-duty industrial robot under the action of nonlinear interference force.
In order to achieve the object of the present invention, the present invention will be implemented using the following technical solutions.
A modular control dynamics simulation method of an aviation heavy-duty industrial robot comprises the following steps:
S1, establishing a controlled dynamics model of a connecting rod element of the heavy-duty industrial robot according to an acceleration multi-body system transmission matrix method, and expanding the model into a controlled dynamics equation of the connecting rod element of the heavy-duty industrial robot; wherein the controlled dynamics model comprises a linkage element dynamics model and a controller model;
S2, acquiring motion states of joints under the action of different control driving forces through an independent joint dynamic characteristic test of the heavy-duty industrial robot, training a neural network nonlinear model to obtain a neural network model of the joints, and taking the neural network model as a disturbance external force model of a controller to fit joint gaps and friction nonlinear mechanical characteristics;
S3, forming a dynamic control model of the heavy-load industrial robot system by using the controlled dynamic model and the neural network model, namely forming a modularized control unit of the heavy-load industrial robot by using the disturbance external force model, the connecting rod element dynamic model and the controller model;
S4, constructing a topological graph through a dynamic control model of a system of the heavy-duty industrial robot according to natural attributes of the heavy-duty industrial robot, wherein the natural attributes comprise the configuration and key components of the heavy-duty industrial robot;
s5, based on a topological graph and combined with a self-adaptive PID control algorithm, establishing a total transfer equation and a total transfer matrix of a dynamic control system of the heavy-duty industrial robot and selecting generalized coordinates of the system as integral variables in a mode of integrating a modularized control unit of the heavy-duty industrial robot;
S6, according to a total transfer equation and a total transfer matrix, obtaining a boundary point state vector through calculation, and then calculating the state vector of each connecting point according to the boundary point state vector;
s7, calculating a second derivative of the generalized coordinates according to the state vector and the integral variable of each connecting point;
and S8, solving and obtaining the motion state of the heavy-load industrial robot at each moment through a numerical simulation method according to the second derivative of the generalized coordinates.
As a preferable scheme of the invention, the dynamic model of the connecting rod element is constructed by taking a connecting rod of a heavy-duty industrial robot as a space motion rigid body element with one end input and the other end output; the controller model is a model which is formed by regarding a controller of a heavy-duty industrial robot as a parallel connection with a connecting rod, wherein the input end is a joint rotation angle deviation, and the output end is a joint control moment.
As a preferred embodiment of the present invention, the transmission equation of the link element is:
； (1)
In the method, in the process of the invention, Is the state vector of the output end of the element; is a state vector at the input of the element; is the transfer matrix of the element, is a function of the control moment and the time t; wherein,
The control moment is determined by a self-adaptive PID control algorithm and the control quantity deviation of the current state, and the control equation is as follows:
； (2)
In the method, in the process of the invention, In order for the joint rotation angle deviation to be the same,For the control of the moment of the joint,Is a function of the control algorithm and time t.
As a preferred scheme of the invention, the neural network model is obtained by training a neural network nonlinear model through input and output test data, wherein the expression of the neural network nonlinear model is as follows:
； （3）
In the method, in the process of the invention, Is a data set containing joint structure parameters, corner speed and corner acceleration parameters; As the disturbance moment of the joint, as the actual measurement moment Moment of control with jointDeviation of (2)；Training by inputting and outputting test data is a function related to the input quantity of the motion state of the robot and time t.
As a preferable scheme of the invention, the neural network model is used as a disturbance external force model of the controller, and then a control equation of the controller becomes:
。 （4）
As a preferable scheme of the invention, the dynamic control equation of the modular control unit of the heavy-duty industrial robot is as follows
； （5）
In the method, in the process of the invention,Is the state vector at the input of element a; Is the state vector at the output of element b; The transmission matrix coefficient from the input end of the element a to the output end of the element b comprises structural parameters of the system, joint rotation angle deviation at the current moment, disturbance external moment calculated based on a neural network nonlinear model and the like.
As a preferable mode of the invention, each part in the heavy-duty industrial robot is regarded as a space motion rigid body with one end input and one end output.
As a preferable mode of the invention, each component comprises a base, a turntable, a lower arm, an upper arm, a wrist rotating component, a wrist swinging component, a wrist rotating component, a tail end and a cutter; the adjacent components are connected through a column hinge, and the base is directly fixed on the AGV or the ground.
As a preferred scheme of the invention, the total transfer equation and the total transfer matrix of the heavy-duty industrial robot dynamics control system are as follows:
； （6）
In the method, in the process of the invention, 、、、、、The dynamic control coefficient matrix of each modularized unit is related to structural parameters, control parameters and the like of the system; A state vector for the end output end of the robot system; Is a state vector of the turntable input end of the robot system.
As a preferred scheme of the invention, the total transfer equation and the total transfer matrix of the heavy-duty industrial robot dynamics control system are substituted into the boundary condition of the system to obtain an expression:
； （7）
In the method, in the process of the invention, Is a state vector that deletes zero elements based on system boundary conditions,Matrix relative to rows and columns is pruned based on the state vector; solving the above method to obtain the state vector of the boundary end、Then the state vector of each component connection point can be obtained according to the transmission matrix of the element、、、…、。
As a preferable mode of the invention, the generalized coordinates areThe ordinary differential equation has the form:
； (8）
In the method, in the process of the invention, 、、、…、Input state vectors for elements 1 through 12 respectively,Is a generalized coordinate set describing the system, including generalized linear displacement and generalized angular displacement,Is the first derivative of the generalized coordinate set with respect to time, including the generalized linear velocity and the generalized angular velocity,Is the second derivative of the generalized coordinate set with respect to time, including generalized linear acceleration and generalized angular acceleration.
According to the dynamic characteristic simulation method for the heavy-duty industrial robot based on the dynamic modularized control unit, a dynamic control model of the whole system of the robot can be quickly integrated and built by combining with a dynamic model topological graph of the robot system, control design and performance analysis of the heavy-duty industrial robot are achieved, meanwhile, the control precision of the heavy-duty industrial robot is improved by simulating the nonlinear interference characteristic of joints through a neural network model, efficient and accurate modeling and quick iterative optimization of the dynamic control system of the heavy-duty industrial robot are achieved, the problems that the robot is low in calculation speed, low in optimization efficiency and difficult in control design by adopting a common multi-body dynamic method are solved, and technical support is provided for improving the processing quality and precision of high-end equipment of the heavy-duty industrial robot for aviation.
Drawings
FIG. 1 is a link element of a heavy duty industrial robot of the present invention;
FIG. 2 is a modular control unit of the heavy duty industrial robot of the present invention;
FIG. 3 is a dynamic control model of a heavy duty industrial robot in the present invention;
Fig. 4 is a simulation flow chart of the dynamics control system of the heavy-duty industrial robot in the present invention.
Detailed Description
The invention will be further described with reference to the drawings and examples.
As shown in fig. 1 to 4, as a technical scheme of the present invention, a modular control dynamics simulation method for an aviation heavy-duty industrial robot includes the following steps:
S1, establishing a controlled dynamics model of a connecting rod element of the heavy-duty industrial robot according to an acceleration multi-body system transmission matrix method, and expanding the model into a controlled dynamics equation of the connecting rod element of the heavy-duty industrial robot; wherein the controlled dynamics model comprises a linkage element dynamics model and a controller model;
s2, acquiring motion states of joints under the action of different control driving forces through an independent joint dynamic characteristic test of the heavy-duty industrial robot, and training to obtain a neural network model of the joints, wherein the neural network model is used as a disturbance external force model of a controller so as to fit joint gaps and friction nonlinear mechanical characteristics;
S3, forming a dynamic control model of the heavy-load industrial robot system by using the controlled dynamic model and the neural network model, namely forming a modularized control unit of the heavy-load industrial robot by using the disturbance external force model, the connecting rod element dynamic model and the controller model;
S4, constructing a topological graph through a dynamic control model of a system of the heavy-duty industrial robot according to natural attributes of the heavy-duty industrial robot, wherein the natural attributes comprise the configuration and key components of the heavy-duty industrial robot;
s5, based on a topological graph and combined with a self-adaptive PID control algorithm, establishing a total transfer equation and a total transfer matrix of a dynamic control system of the heavy-duty industrial robot and selecting generalized coordinates of the system as integral variables in a mode of integrating a modularized control unit of the heavy-duty industrial robot;
S6, according to a total transfer equation and a total transfer matrix, obtaining a boundary point state vector through calculation, and then calculating the state vector of each connecting point according to the boundary point state vector;
s7, calculating a second derivative of the generalized coordinates according to the state vector and the integral variable of each connecting point;
and S8, solving and obtaining the motion state of the heavy-load industrial robot at each moment through a numerical simulation method according to the second derivative of the generalized coordinates.
As an embodiment of the invention, as shown in fig. 1 to 4, the modular control dynamics simulation method for the heavy-duty industrial robot for aviation comprises the following specific schemes:
According to an acceleration multi-body system transfer matrix method, a controlled dynamics model of a robot connecting rod element is established, as shown in figure 1, wherein the robot connecting rod is regarded as a space motion rigid body element with one input end and the other output end, and the transfer equation of the connecting rod element is that
； (1)
In the method, in the process of the invention,Is the state vector of the output end of the element; is a state vector at the input of the element; Is the transfer matrix of the element, is a function of the control moment and time t.
The controller is regarded as being connected with the connecting rod in parallel, the input end is joint rotation angle deviation, the output end is joint control moment, the control moment is determined by a control algorithm and the control quantity deviation of the current state, and a control equation is as follows:
； (2)
In the method, in the process of the invention, In order for the joint rotation angle deviation to be the same,For the control of the moment of the joint,Is a function of the control algorithm and time t.
According to the dynamic characteristic test of the independent joint of the robot, acquiring the motion state of the joint under the action of different control driving forces, training a neural network nonlinear model to obtain a neural network model SF of the joint, and taking the neural network model SF as disturbance external force of a controller so as to simulate nonlinear mechanical characteristics such as joint gaps, friction and the like, wherein the neural network nonlinear model is as follows:
； （3）
In the method, in the process of the invention, Is a data set containing joint structure parameters, corner speed and corner acceleration parameters; As the disturbance moment of the joint, as the actual measurement moment Moment of control with jointDeviation of (2)；Training by inputting and outputting test data is a function related to the input quantity of the motion state of the robot and time t. On this basis, the control equation becomes:
。 （4）
As shown in fig. 2, the disturbance external force model, the dynamic model of the connecting rod element and the controller model together form a robot modularized control unit, and the dynamic control equation is as follows:
； （5）
In the method, in the process of the invention, Is the state vector at the input of element a; Is the state vector at the output of element b; The transmission matrix coefficient from the input end of the element a to the output end of the element b comprises structural parameters of the system, joint rotation angle deviation at the current moment, disturbance external moment calculated based on a neural network nonlinear model and the like.
The main components of the heavy-duty industrial robot comprise a base, a turntable, a lower arm, an upper arm, a wrist rotating component, a wrist swinging component, a wrist rotating component, a tail end and a cutter, wherein the wrist rotating component, the tail end and the cutter are usually fixedly connected, the components are all regarded as space motion rigid bodies with one input end and one output end according to an acceleration multi-body system transmission matrix method, adjacent components are connected through a column hinge, and the base is directly fixed on an AGV or the ground. As shown in fig. 3, it can be seen that elements 1 and 2, elements 3 and 4, elements 5 and 6, elements 7 and 8, elements 9 and 10, and elements 11 and 12 form six modularized control units, and after integration, two 0's respectively represent the end boundary and the ground boundary, a dynamics control model of the whole system of the heavy-duty industrial robot can be built, and the total transfer equation and the transfer matrix of the system are as follows
； （6）
In the method, in the process of the invention,、、、、、The dynamic control coefficient matrix of each modularized unit is related to structural parameters, control parameters and the like of the system; A state vector for the end output end of the robot system; Is a state vector of the turntable input end of the robot system. Substituting the boundary conditions of the system to obtain:
； （7）
In the method, in the process of the invention, Is a state vector that deletes zero elements based on system boundary conditions,Matrix relative to rows and columns is pruned based on the state vector; solving the above method to obtain the state vector of the boundary end、Then the state vector of each component connection point can be obtained according to the transmission matrix of the element、、、…、。
As a preferable mode of the invention, the generalized coordinates areThe ordinary differential equation has the form:
； (8）
In the method, in the process of the invention, 、、、…、Input state vectors for elements 1 through 12 respectively,Is a generalized coordinate set describing the system, including generalized linear displacement and generalized angular displacement,Is the first derivative of the generalized coordinate set with respect to time, including the generalized linear velocity and the generalized angular velocity,Is the second derivative of the generalized coordinate set with respect to time, including generalized linear acceleration and generalized angular acceleration. Solving by a numerical integration method to obtain the change rule of the dynamic characteristic of the robot along with time in the control processThe simulation flow chart is shown in fig. 4.
The preferred embodiments of the present application have been described above with reference to the accompanying drawings, and are not thereby limiting the scope of the claims of the embodiments of the present application. Any modifications, equivalent substitutions and improvements made by those skilled in the art without departing from the scope and spirit of the embodiments of the present application shall fall within the scope of the claims of the embodiments of the present application.
Claims (9)
1. The modularized control dynamics simulation method for the heavy-duty industrial robot for aviation is characterized by comprising the following steps of:
s1, establishing a controlled dynamics model of a connecting rod element of the heavy-duty industrial robot according to an acceleration multi-body system transmission matrix method, and expanding the model into a controlled dynamics equation of the connecting rod element of the heavy-duty industrial robot; the controlled dynamics model comprises a connecting rod element dynamics model and a controller model, wherein the connecting rod element dynamics model is constructed by taking a connecting rod of a heavy-duty industrial robot as a spatial motion rigid body element with one end input and the other end output; the controller model is a model which is formed by regarding a controller of a heavy-duty industrial robot as a parallel connection with a connecting rod, wherein the input end is a joint rotation angle deviation, and the output end is a joint control moment;
S2, acquiring motion states of joints under the action of different control driving forces through an independent joint dynamic characteristic test of the heavy-duty industrial robot, training a neural network nonlinear model to obtain a neural network model of the joints, and taking the neural network model as a disturbance external force model of a controller to fit joint gaps and friction nonlinear mechanical characteristics;
S3, forming a dynamic control model of the heavy-load industrial robot system by using the controlled dynamic model and the neural network model, namely forming a modularized control unit of the heavy-load industrial robot by using the disturbance external force model, the connecting rod element dynamic model and the controller model;
S4, constructing a topological graph through a dynamic control model of a system of the heavy-duty industrial robot according to natural attributes of the heavy-duty industrial robot, wherein the natural attributes comprise the configuration and key components of the heavy-duty industrial robot;
s5, based on a topological graph and combined with a self-adaptive PID control algorithm, establishing a total transfer equation and a total transfer matrix of a dynamic control system of the heavy-duty industrial robot and selecting generalized coordinates of the system as integral variables in a mode of integrating a modularized control unit of the heavy-duty industrial robot;
S6, according to a total transfer equation and a total transfer matrix, obtaining a boundary point state vector through calculation, and then calculating the state vector of each connecting point according to the boundary point state vector;
s7, calculating a second derivative of the generalized coordinates according to the state vector and the integral variable of each connecting point;
and S8, solving and obtaining the motion state of the heavy-load industrial robot at each moment through a numerical simulation method according to the second derivative of the generalized coordinates.
2. The modular control dynamics simulation method of an aviation heavy-duty industrial robot according to claim 1, wherein the transfer equation of the connecting rod element is:
zj,O(t)＝Uj(t,τc)zj,I(t)；(1)
Where z j,O (t) is the state vector at the output of the element; z j,I (t) is the state vector at the input of the element; u j(t,τc) is the transfer matrix of the element, a function related to the control moment and time t; wherein,
The control moment is determined by a self-adaptive PID control algorithm and the control quantity deviation of the current state, and the control equation is as follows:
τc＝h(e,t)；(2)
Where e is the joint rotation angle deviation, τ c is the joint control moment, and h is a function related to the control algorithm and time t.
3. The modular control dynamics simulation method of an aviation heavy-duty industrial robot according to claim 1, wherein the neural network model is obtained by training a neural network nonlinear model through input and output test data, wherein the expression of the neural network nonlinear model is:
τ′＝g(x,t)；(3)
Wherein x is a data set containing joint structure parameters, corner speed and corner acceleration parameters; τ' is the joint disturbance moment, which is the measured moment Deviation from the joint control moment τ c G is a neural network nonlinear model, and is trained by inputting and outputting test data and is a function related to the input quantity of the motion state of the robot and time t.
4. A modular control dynamics simulation method of an aviation heavy-duty industrial robot according to claim 3, wherein the neural network model is used as a disturbance external force model of the controller, and then the control equation of the controller becomes:
τc＝h(e,τ′,t)。(4)
5. The simulation method of modular control dynamics of an aviation heavy-duty industrial robot according to claim 1, wherein the dynamic control equation of the modular control unit of the heavy-duty industrial robot is
Where z 1,I (t) is the state vector at the input of element 1; z 2,O (t) is the state vector at the output of element 2; the transmission matrix coefficient from the input end of the element 1 to the output end of the element 2 comprises structural parameters of the system, joint rotation angle deviation at the current moment, disturbance external moment calculated based on a neural network nonlinear model and the like.
6. The modular control dynamics simulation method of an aviation heavy-duty industrial robot according to claim 1, wherein each part in the heavy-duty industrial robot is regarded as a spatial motion rigid body with one input end and one output end.
7. The modular control dynamics simulation method of an airborne heavy-duty industrial robot according to claim 6, wherein each component comprises a base, a turntable, a lower arm, an upper arm, a wrist rotating component, a wrist swinging component, a wrist turning component, a tip and a cutter; the adjacent components are connected through a column hinge, and the base is directly fixed on the AGV or the ground.
8. The modular control dynamics simulation method of an aviation heavy-duty industrial robot according to claim 1, wherein the total transfer equation and the total transfer matrix of the dynamic control system of the heavy-duty industrial robot are as follows:
In the method, in the process of the invention, The dynamic control coefficient matrix of each modularized unit is related to structural parameters, control parameters and the like of the system; z 12,O is a state vector of the end output end of the robot system; z 1,I is a state vector of the input end of the turntable of the robot system;
substituting the boundary conditions of the system to obtain an expression:
In the method, in the process of the invention, Is a state vector that deletes zero elements based on system boundary conditions,Matrix relative to rows and columns is pruned based on the state vector; and solving the above formula to obtain a state vector z 1,I、z12,O of the boundary end, and then obtaining a state vector z 1,I、z2,I、z3,I、…、z12,I of each component connection point according to the transfer matrix of the element.
9. The modular control dynamics simulation method for an aviation heavy-duty industrial robot according to claim 1, wherein the generalized coordinates areThe ordinary differential equation has the form:
Where z 1,I、z2,I、z3,I、…、z12,I is the input state vector for elements 1-12, respectively, y is the generalized coordinate set describing the system, including the generalized linear displacement and the generalized angular displacement, Is the first derivative of the generalized coordinate set with respect to time, including the generalized linear velocity and the generalized angular velocity,Is the second derivative of the generalized coordinate set with respect to time, including generalized linear acceleration and generalized angular acceleration.
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