CN114986499B - Mechanical arm motion control method, system and equipment and readable storage medium - Google Patents

Mechanical arm motion control method, system and equipment and readable storage medium Download PDF

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CN114986499B
CN114986499B CN202210563749.1A CN202210563749A CN114986499B CN 114986499 B CN114986499 B CN 114986499B CN 202210563749 A CN202210563749 A CN 202210563749A CN 114986499 B CN114986499 B CN 114986499B
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mechanical arm
linear predictor
disturbance
motion
tube
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CN114986499A (en
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赵东东
阎石
杨晓迪
周兴文
李弘历
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Lanzhou University
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Programme-controlled manipulators
    • B25J9/16—Programme controls
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Programme-controlled manipulators
    • B25J9/16—Programme controls
    • B25J9/1656—Programme controls characterised by programming, planning systems for manipulators
    • B25J9/1664—Programme controls characterised by programming, planning systems for manipulators characterised by motion, path, trajectory planning

Abstract

The invention discloses a mechanical arm motion control method, a system, equipment and a readable storage medium, which fully consider the interference possibly suffered by a mechanical arm in the motion process, apply random disturbance to the mechanical arm, collect pose data of the mechanical arm in the motion process based on the random disturbance, then construct a linear predictor required in the control process by using the pose data and the applied disturbance quantity, and further complete the motion control task of the mechanical arm by using the constructed linear predictor. Compared with the prior art, the method has the advantages that a data set with random interference is collected to construct the linear predictor required in the control process, so that the motion control task of the mechanical arm is completed, and the accuracy of the linear predictor in controlling the motion process of the mechanical arm is improved; and the method for constructing the linear predictor by applying random disturbance does not need to carry out in-depth research on the nonlinear system of the mechanical arm in advance, and has better universality.

Description

Mechanical arm motion control method, system and equipment and readable storage medium
Technical Field
The invention relates to the crossing field of automatic control and computer technology, in particular to a method, a system and equipment for controlling the motion of a mechanical arm and a readable storage medium.
Background
After the rapid development of computer technology and computing power, MPC (Model Predictive Control) methods, which are highly correlated with computing power, have been well developed in recent decades. The basic idea of the MPC strategy is to convert global optimization into local optimization, which is embodied as solving open-loop optimal control on a set prediction time domain in each sampling period. However, the current MPC method has certain limitations and does not take into account the effect of noise in the system. In reality, noise is inevitably mixed when data of a dynamic system are acquired, and the accuracy of model prediction is reduced to a certain extent.
At present, most of methods for controlling the motion of a mechanical arm do not sufficiently utilize data of the system, so that the prediction accuracy of a constructed linear predictor is low, an optimal control strategy is often difficult to obtain, and even when the disturbance on the mechanical arm is increased, the prediction result of the linear predictor completely deviates from the actual result, so that the control task on the mechanical arm system fails.
Disclosure of Invention
The present invention is directed to solving at least one of the problems of the prior art. Therefore, the invention provides a mechanical arm motion control method, which improves the accuracy of a linear predictor in controlling the mechanical arm motion process.
The invention also provides a system, equipment and a readable storage medium for executing the mechanical arm motion control method.
According to a robot arm motion control method of an embodiment of a first aspect of the present invention, the robot arm motion control method includes:
applying a random disturbance and an input voltage to a robotic arm to cause the robotic arm to generate a motion based on the random disturbance and the input voltage;
acquiring pose data generated by the mechanical arm in the motion;
constructing a linear predictor according to the pose data, the disturbance quantity of the random disturbance and the voltage value of the input voltage;
and performing MPC control on the mechanical arm according to the linear predictor.
The control method provided by the embodiment of the invention at least has the following beneficial effects:
in order to solve the problem of interference in the motion control process of the mechanical arm, the mechanical arm motion control method of the embodiment sufficiently considers the interference possibly suffered by the mechanical arm in the motion process, applies random disturbance to the mechanical arm, collects pose data of the mechanical arm in the motion process based on the random disturbance, and then constructs a linear predictor required in the control process by using the pose data and the applied disturbance quantity, so that the constructed linear predictor is used for completing the motion control task of the mechanical arm. Compared with the prior scheme, the method fully considers the problem of possible interference to the mechanical arm in the motion process, acquires a data set with random interference to construct a linear predictor required in the control process, further completes the motion control task of the mechanical arm, and improves the accuracy of the linear predictor in controlling the motion process of the mechanical arm; in addition, the method for constructing the linear predictor by applying random disturbance does not need to carry out in-depth study on the nonlinear system of the mechanical arm in advance, has better universality and provides a brand-new solution for the motion control problem of the mechanical arm.
According to some embodiments of the invention, the MPC controlling the robotic arm according to the linear predictor comprises:
designing Tube according to the linear predictor;
calculating a control sequence of the robotic arm using the Tube-based MPC method;
and taking the control sequence as the input of the mechanical arm to control the mechanical arm to complete the action.
According to some embodiments of the invention, before the applying the random disturbance to the robot arm, the robot arm motion control method further comprises:
the random perturbations are generated programmatically.
According to some embodiments of the invention, the robotic arm motion control method collects the pose data through a depth camera and a six-axis sensor.
According to some embodiments of the invention, constructing the representation of the linear predictor according to the pose data and the disturbance amount of the random disturbance comprises:
Z k+1 ＝AZ k +Bu k +Gω k
Z 0 ＝Φ(x 0 )
Figure BDA0003657459480000021
wherein A, B, G, C represent coefficients of the linear predictor,
Figure BDA0003657459480000022
representing the predicted value, x, of the linear predictor at time k 0 Represents the state of the robot arm at time 0, u k Represents the input voltage, ω, of the mechanical arm at time k k Represents the disturbance of the arm at time k, phi represents a set of Koopman eigenfunctions, and>
Figure BDA0003657459480000023
according to some embodiments of the invention, said designing the Tube according to the linear predictor comprises:
deforming the linear predictor, and applying state constraint, control constraint and terminal constraint; wherein the linear predictor is deformed to: y is k+1 ＝A 1 y k +B 1 u+G 1 ω，
Figure BDA0003657459480000024
B 1 ＝CB,G 1 ＝CG，/>
Figure BDA0003657459480000025
Represents a pseudo-inverse symbol, said y k+1 ＝A 1 y k +B 1 u+G 1 The ω deformation is: y is + ＝A 1 y+B 1 u+G 1 ω, said y + ＝A 1 y+B 1 u+G 1 The nominal system for ω is:
Figure BDA0003657459480000026
the state constraint, the control constraint and the terminal constraint are as follows: />
Figure BDA0003657459480000027
Figure BDA0003657459480000028
Z is a minimum robust positive invariant set and-K is a coefficient of an optimal feedback law;
constructing a cost equation, wherein the cost equation is as follows:
Figure BDA0003657459480000029
solving the cost equation to obtain an optimal feedback law u n ＝-Ky n Minimizing the cost equation;
solving the minimum robust positive invariant set; wherein said y + ＝A 1 y+B 1 u+G 1 Rewriting of omega to
Figure BDA0003657459480000031
Figure BDA0003657459480000032
If the perturbation is bounded, and ω ∈ W, then the minimum robust positive invariant set is:
Figure BDA0003657459480000033
generating a Tube and a strategy corresponding to the Tube according to the minimum robust positive invariant set and the optimal feedback law; the Tube and the corresponding policy of the Tube comprise:
Figure BDA0003657459480000034
and
Figure BDA0003657459480000035
according to a robot arm motion control system according to an embodiment of the second aspect of the present invention, the robot arm motion control system includes:
a disturbance applying unit for applying a random disturbance and an input voltage to a robot arm to cause the robot arm to generate a motion based on the random disturbance and the input voltage;
the data acquisition unit is used for acquiring pose data generated by the mechanical arm in the motion;
the predictor construction unit is used for constructing a linear predictor according to the pose data, the disturbance quantity of the random disturbance and the voltage value of the input voltage;
and the MPC control unit is used for performing MPC control on the mechanical arm according to the linear predictor.
The mechanical arm motion control system adopts all the technical schemes of the mechanical arm motion control method of the embodiment, so that the mechanical arm motion control system at least has all the beneficial effects brought by the technical schemes of the embodiment.
According to some embodiments of the invention, the robotic arm motion control system further comprises: a disturbance generation unit for generating random disturbances by programming.
An electronic device according to an embodiment of the third aspect of the present invention includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor when executing the computer program implementing: the method for controlling the movement of the mechanical arm. Since the electronic device adopts all the technical solutions of the robot arm motion control method of the above embodiment, at least all the advantages brought by the technical solutions of the above embodiments are achieved.
A computer-readable storage medium according to a fourth aspect embodiment of the present invention comprises a computer-readable storage medium storing computer-executable instructions for implementing, when executed by a processor: the method for controlling the movement of the mechanical arm. Since the computer-readable storage medium adopts all the technical solutions of the robot arm motion control method of the above embodiment, at least all the advantages brought by the technical solutions of the above embodiments are achieved.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
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The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a schematic flow chart diagram of a robot motion control method according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart diagram illustrating a method for controlling the movement of a robotic arm according to another embodiment of the present invention;
FIG. 3 is a flow chart of constructing a linear predictor with perturbation terms according to another embodiment of the present invention;
FIG. 4 is a schematic flow chart of designing a Tube and performing robust MPC control in accordance with another embodiment of the present invention;
fig. 5 is a schematic structural diagram of a robot arm motion control system according to another embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
In the description of the present invention, if there are first, second, etc. described, it is only for the purpose of distinguishing technical features, and it is not understood that relative importance is indicated or implied or that the number of indicated technical features is implicitly indicated or that the precedence of the indicated technical features is implicitly indicated.
In the description of the present invention, it should be understood that the orientation or positional relationship referred to, for example, the upper, lower, etc., is indicated based on the orientation or positional relationship shown in the drawings, and is only for convenience of description and simplification of description, but does not indicate or imply that the device or element referred to must have a specific orientation, be constructed in a specific orientation, and be operated, and thus should not be construed as limiting the present invention.
In the description of the present invention, it should be noted that unless otherwise explicitly defined, terms such as setup, installation, connection, etc. should be understood in a broad sense, and those skilled in the art can reasonably determine the specific meanings of the above terms in the present invention by combining the detailed contents of the technical solutions.
After the explosion of computer technology and computing power, MPC methods that have a large correlation to computing power have evolved well in the last few decades. The basic idea of the MPC strategy is to convert global optimization into local optimization, which is embodied as solving open-loop optimal control on a set prediction time domain in each sampling period.
However, the current MPC method has certain limitations and does not take into account the effect of noise in the system. In reality, noise is inevitably mixed when data of a dynamic system are acquired, and the accuracy of model prediction is reduced to a certain extent.
The advent of Tube-based robust MPC has solved the noise problem, but most Tube MPC studies are conducted on linear systems, and for complex nonlinear systems, the current studies lack a systematic method for constructing a linear predictor with perturbation terms, which limits further applications of Tube MPC in complex nonlinear systems.
Most of current methods for controlling the motion of the mechanical arm do not sufficiently utilize data of the system, so that the prediction accuracy of a constructed linear predictor is low, an optimal control strategy is often difficult to obtain, and even when the disturbance on the mechanical arm is increased, the prediction result of the linear predictor completely deviates from the actual result, so that the control task of the mechanical arm system fails.
Therefore, in order to effectively solve the noise problem of the mechanical arm control system, the application provides a mechanical arm motion control method, a mechanical arm motion control system, a mechanical arm motion control device and a readable storage medium.
Referring to fig. 1, an embodiment of the present application provides a robot motion control method, including the following steps:
and step S101, generating random disturbance. Random perturbations are generated and applied to the robotic arm by programming or other means.
And S102, applying random disturbance and input voltage to the mechanical arm so that the mechanical arm generates movement based on the random disturbance and the input voltage.
And S103, acquiring pose data generated by the mechanical arm in the motion. The method comprises the steps of capturing the motion trail of a mechanical arm subjected to bounded disturbance by using devices such as a depth camera and a six-axis sensor, collecting input voltage data (driving the mechanical arm to move through the input voltage), disturbance quantity of random disturbance and output pose data of the mechanical arm, and establishing a data set.
And step S104, constructing a linear predictor according to the pose data, the disturbance amount of random disturbance and the voltage value of the input voltage. And (4) constructing a linear predictor by using the data set acquired in the step S103 based on the Koopman operator theory. In some embodiments, the expression of the constructed linear predictor includes:
Z k+1 ＝AZ k +Bu k +Gω k
Z 0 ＝Φ(x 0 )
Figure BDA0003657459480000051
wherein A, B, G, C represent coefficients of a linear predictor,
Figure BDA0003657459480000052
representing the predicted value, x, of the linear predictor at time k 0 Indicates the state of the robot arm at time 0, u k Representing the input voltage, omega, of the arm at time k k Represents the disturbance of the arm at time k, phi represents a set of Koopman eigenfunctions, <' > or>
Figure BDA0003657459480000053
And S105, performing MPC control on the mechanical arm according to the linear predictor. And finally, performing robust MPC control on the mechanical arm based on the linear predictor constructed in the step S104.
In order to solve the problem of interference in the motion control process of the mechanical arm, the mechanical arm motion control method of the embodiment fully considers the interference possibly suffered by the mechanical arm in the motion process, applies random disturbance to the mechanical arm, collects pose data of the mechanical arm in the motion process based on the random disturbance, and then constructs a linear predictor required in the control process by using the pose data and the applied disturbance quantity, so that the constructed linear predictor is used for completing the motion control task of the mechanical arm. Compared with the prior scheme, the method fully considers the problem of possible interference to the mechanical arm in the motion process, acquires a data set with random interference to construct a linear predictor required in the control process, further completes the motion control task of the mechanical arm, and improves the accuracy of the linear predictor in controlling the motion process of the mechanical arm; and the method for constructing the linear predictor by applying random disturbance does not need to carry out in-depth research on the nonlinear system of the mechanical arm in advance, has better universality and provides a brand-new solution for the motion control problem of the mechanical arm.
In some embodiments, the MPC control of the robot arm based on the linear predictor in step S105 comprises the steps of:
and step S1051, designing Tube according to the linear predictor.
Designing Tube from a linear predictor includes:
deforming the linear predictor, and applying state constraint, control constraint and terminal constraint; wherein the linear predictor is deformed as: y is k+1 ＝A 1 y k +B 1 u+G 1 ω，
Figure BDA0003657459480000061
B 1 ＝CB,G 1 ＝CG，/>
Figure BDA0003657459480000062
Representing a pseudo-inverse symbol, y k+1 ＝A 1 y k +B 1 u+G 1 The ω deformation is: y is + ＝A 1 y+B 1 u+G 1 ω，y + ＝A 1 y+B 1 u+G 1 The nominal system for ω is: />
Figure BDA0003657459480000063
The state constraints, control constraints and terminal constraints are: />
Figure BDA0003657459480000064
Figure BDA0003657459480000065
Z is the minimum robust positive invariant set and K is the coefficient of the optimal feedback law.
Constructing a cost equation, wherein the cost equation is as follows:
Figure BDA0003657459480000066
solving a cost equation to obtain an optimal feedback law u n ＝-Ky n Minimizing the cost equation.
Solving a minimum robust positive invariant set; wherein y is + ＝A 1 y+B 1 u+G 1 Rewriting omega to y + ＝(A 1 +KB 1 )y+G 1 ω＝A k y+G 1 ω, if the perturbation is bounded, and ω ∈ W, then the minimum robust positive invariant set is:
Figure BDA0003657459480000067
generating Tube and a strategy corresponding to the Tube according to the minimum robust positive invariant set and the optimal feedback law; the policy corresponding to the Tube comprises
Figure BDA0003657459480000068
And &>
Figure BDA0003657459480000069
Step S1052 calculates a control sequence of the robot arm using the Tube-based MPC method.
And step S1053, taking the control sequence as the input of the mechanical arm to control the mechanical arm to finish the action.
The method constructs the linear predictor with the disturbance term of the mechanical arm system through the step S104, so that the robust MPC method based on Tube, which originally can only be applied to the linear system, can also be applied to the nonlinear mechanical arm system. It should be noted that the robust MPC method based on Tube is used to solve the control problem of the disturbed linear system, i.e. the robust MPC method based on Tube can only be used in the disturbed linear system, and the construction of the linear predictor with disturbance term expands the application range of the method.
With reference to fig. 2 to 4, another embodiment of the present application provides a robot arm movement control method, including the following steps:
step S201, giving a random disturbance and an input voltage to the mechanical arm in a programming mode; then capturing the pose data of the mechanical arm subjected to bounded disturbance by using devices such as a depth camera, a six-axis sensor and the like, and establishing the pose data, the input voltage data and the disturbance quantity of given random disturbance as a data set:
Figure BDA00036574594800000610
/>
wherein x is k Representing pose data u of the mechanical arm at the kth sampling moment k Representing the input voltage, ω, to the arm at time k k And the random disturbance of the mechanical arm at the time k is shown, and the data at the time M are collected in total.
In step S201, an input voltage is applied to the robot arm in a programming manner to allow the robot arm to complete an operation; the random disturbance magnitude is randomly given in the program, and the value is determined by the user according to the actual situation, so as to simulate the situation that the mechanical arm is disturbed. The data acquisition process comprises the following steps: at a certain moment, a random disturbance and a certain input voltage are given to the mechanical arm through a programming method, the random disturbance and the input voltage are recorded, the mechanical arm executes corresponding actions, pose data after the mechanical arm executes the actions are recorded, and therefore the three data of the input voltage, the pose and the random disturbance at the moment are collected.
Step S202, constructing a linear predictor form with disturbance terms of the mechanical arm system based on the Koopman operator theory:
Z k+1 ＝AZ k +Bu k +Gω k
Z 0 ＝Φ(x 0 )
Figure BDA0003657459480000071
in the formula, x 0 Indicates the state of the robot arm at time 0, i.e., the initial state of the robot arm, u k Indicating the input voltage of the arm at time k, i.e. the control quantity, omega, of the arm system at time k k Represents the amount of disturbance experienced by the arm at time k, and phi represents a set of Koopman eigenfunctions of the system, namely:
Figure BDA0003657459480000072
Figure BDA0003657459480000073
is the output of the predictor, i.e. the prediction made of the quantity of interest in the robot arm system, here assumed to be h (x);
the coefficient matrix A, B, C, G of the linear predictor is obtained by the following method:
solving A:
Figure BDA0003657459480000074
wherein λ is 1 ,…,λ n Is and intrinsicFunction phi 1 ,…,φ n Corresponding characteristic values;
solving C:
Figure BDA0003657459480000075
the resulting analytical solution is:
Figure BDA0003657459480000076
wherein, the upper label
Figure BDA0003657459480000077
Represents a pseudo-inverse symbol;
solving coefficients B, G:
Figure BDA0003657459480000078
expression pair Z from linear predictor k Successive iterations are performed to obtain the following
Figure BDA0003657459480000079
Expression (c): />
Figure BDA00036574594800000710
Since the vector operator of the matrix has an important formula:
Figure BDA0003657459480000081
the above equation can be rewritten as:
Figure BDA0003657459480000082
wherein Q, R, theta are respectively:
Figure BDA0003657459480000083
Figure BDA0003657459480000084
Figure BDA0003657459480000085
therefore, formula (1) can be rewritten as:
Figure BDA0003657459480000086
the resulting analytical solution is:
Figure BDA0003657459480000087
and constructing and finishing the linear predictor with the disturbance term through the operation.
And S203, performing Tube MPC control on the mechanical arm system according to the constructed linear predictor.
Performing equivalent deformation on the constructed linear predictor to obtain:
y k+1 ＝A 1 y k +B 1 u+G 1 ω
wherein the content of the first and second substances,
Figure BDA0003657459480000088
B 1 ＝CB,G 1 = CG, the above formula can also be rewritten as:
Figure BDA0003657459480000089
/>
this system satisfies state and control constraints
Figure BDA00036574594800000810
Wherein->
Figure BDA00036574594800000811
Is compact and is either>
Figure BDA00036574594800000812
Is closed.
The corresponding nominal systems are:
Figure BDA00036574594800000813
the nominal system satisfies the following state constraints, control constraints, and terminal constraints:
Figure BDA0003657459480000091
Figure BDA0003657459480000092
Figure BDA0003657459480000093
wherein, Z is the minimum robust positive invariant set, and-K is the coefficient of the optimal feedback law.
Constructing a cost equation:
Figure BDA0003657459480000094
by solving the cost equation, the optimal feedback law u can be obtained n ＝-Ky n Minimizing the cost equation described above. Formula (2) can be rewritten as:
y + ＝(A 1 +KB 1 )y+G 1 ω＝A k y+G 1 ω
assuming that the disturbance is bounded and ω ∈ W, the minimum robust positive invariant set of the system
Figure BDA0003657459480000095
Thus, the Tube form and associated strategy pi selected when implementing robust MPC control is:
Figure BDA0003657459480000096
through the steps, model prediction control is carried out on the mechanical arm through a robust MPC method, random disturbance of the mechanical arm is given, a data set of any motion track is obtained, a linear predictor with disturbance items is constructed by means of a Koopman operator, a designed Tube is added into the predictor, an optimal control sequence can be obtained through the robust MPC theory, the first item of the control sequence is used as the input of the mechanical arm, and the mechanical arm can complete the action expected by a user. A set of continuous optimal control sequences can be obtained by using MPC theory, but only the first item is taken as the input of the mechanical arm due to the adoption of a rolling time domain optimization mode.
Compared with the prior art, the method solves the problem that the mechanical arm is interfered in the motion process, the method for constructing the linear predictor does not need to deeply research a nonlinear system of the mechanical arm in advance, the linear predictor required in the control process can be constructed only by acquiring a data set with random interference, and the motion control task of the mechanical arm is completed, so that the method has good universality, and a brand-new solution is provided for the motion control problem of the mechanical arm.
Referring to fig. 5, in an embodiment of the present application, there is provided a robot motion control system, including: the system comprises a disturbance generating unit 1000, a disturbance applying unit 2000, a data acquisition unit 3000, a predictor constructing unit 4000 and an MPC control unit 5000, wherein:
the disturbance generating unit 1000 is used to generate random disturbances.
The disturbance applying unit 2000 is configured to apply random disturbance and input voltage to the robot arm, so that the robot arm generates motion based on the random disturbance and the input voltage.
The data acquisition unit 3000 is used to acquire pose data generated by the mechanical arm in motion.
The predictor construction unit 4000 is used for constructing a linear predictor according to the pose data, the disturbance amount of random disturbance and the voltage value of the input voltage.
The MPC control unit 5000 is used to perform MPC control of the robot arm according to a linear predictor.
Since the robot arm motion control system adopts all the technical solutions of the robot arm motion control method of the above embodiment, at least all the advantages brought by the technical solutions of the above embodiment are achieved, and the embodiment of the system and the embodiment of the method are based on the same inventive concept, so that the contents of the embodiment of the method are also applicable to the embodiment of the system, and are not described in detail herein.
The present application further provides an electronic device, comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor when executing the computer program implementing: the robot arm motion control method is described above.
The processor and memory may be connected by a bus or other means.
The memory, which is a non-transitory computer readable storage medium, may be used to store non-transitory software programs as well as non-transitory computer executable programs. Further, the memory may include high speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory optionally includes memory located remotely from the processor, and these remote memories may be connected to the processor through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
Non-transitory software programs and instructions required to implement the robot arm motion control method of the above-described embodiment are stored in the memory, and when executed by the processor, perform the robot arm motion control method of the above-described embodiment, for example, perform the above-described method steps S101 to S105 in fig. 1.
The present application further provides a computer-readable storage medium having stored thereon computer-executable instructions for performing: the method for controlling the movement of the mechanical arm.
The computer-readable storage medium stores computer-executable instructions, which are executed by a processor or controller, for example, by a processor in the above-mentioned electronic device embodiment, and can make the above-mentioned processor execute the robot arm motion control method in the above-mentioned embodiment, for example, the above-mentioned method steps S101 to S105 in fig. 1 are executed.
It will be understood by those of ordinary skill in the art that all or some of the steps, systems, and methods disclosed above may be implemented as software, firmware, hardware, or suitable combinations thereof. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). The term computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of data such as computer readable instructions, data structures, program modules or other data, as is well known to those of ordinary skill in the art. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired data and which can accessed by the computer. In addition, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any data delivery media as known to one of ordinary skill in the art.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an illustrative embodiment," "an example," "a specific example," or "some examples" or the like mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The embodiments of the present invention have been described in detail with reference to the drawings, but the present invention is not limited to the embodiments, and various changes can be made within the knowledge of those skilled in the art without departing from the gist of the present invention.

Claims (7)

1. A method for controlling the motion of a robot arm, comprising:
applying a random disturbance and an input voltage to a robotic arm to cause the robotic arm to generate motion based on the random disturbance and the input voltage;
acquiring pose data generated by the mechanical arm in the motion;
constructing a linear predictor according to the pose data, the disturbance quantity of the random disturbance and the voltage value of the input voltage; wherein the construction of the expression form of the linear predictor according to the pose data and the disturbance quantity of the random disturbance comprises the following steps:
Figure QLYQS_1
Figure QLYQS_2
Figure QLYQS_3
wherein the content of the first and second substances,
Figure QLYQS_5
coefficient representing the linear predictor, is selected>
Figure QLYQS_7
Indicating that the linear predictor is->
Figure QLYQS_10
The predicted value of the time of day,
Figure QLYQS_6
indicates the status of the mechanical arm at time 0>
Figure QLYQS_8
Indicating that the mechanical arm is->
Figure QLYQS_11
The input voltage at that moment>
Figure QLYQS_13
Indicating that the mechanical arm is->
Figure QLYQS_4
The disturbance value at that moment is greater or less>
Figure QLYQS_9
Represents a set of Koopman eigenfunctions, said->
Figure QLYQS_12
；
Performing MPC control of the robotic arm according to the linear predictor, comprising: designing Tube according to the linear predictor; calculating a control sequence of the robotic arm using the Tube-based MPC method; taking the control sequence as the input of the mechanical arm to control the mechanical arm to complete the action; wherein the designing of the Tube according to the linear predictor comprises:
deforming the linear predictor, and applying state constraint, control constraint and terminal constraint; wherein the linear predictor is deformed to:
Figure QLYQS_16
，/>
Figure QLYQS_19
，/>
Figure QLYQS_22
represents a pseudo-inverted symbol, said->
Figure QLYQS_17
The deformation is as follows: />
Figure QLYQS_20
The above-mentioned
Figure QLYQS_23
The nominal system of (a) is: />
Figure QLYQS_25
(ii) a The state constraint, the control constraint and the terminal constraint are as follows: />
Figure QLYQS_15
，/>
Figure QLYQS_18
，/>
Figure QLYQS_21
，/>
Figure QLYQS_24
For a least robust positive invariant set, <' >>
Figure QLYQS_14
Coefficients that are the optimal feedback law;
constructing a cost equation, wherein the cost equation is as follows:
Figure QLYQS_26
；
solving the cost equation to obtain the optimal feedback law
Figure QLYQS_27
Minimizing the cost equation;
solving the minimum robust positive invariant set; wherein said
Figure QLYQS_28
Is rewritten as
Figure QLYQS_29
If the perturbation is bounded, and->
Figure QLYQS_30
Then the minimum robust positive invariant set is:
Figure QLYQS_31
；
generating a Tube and a strategy corresponding to the Tube according to the minimum robust positive invariant set and the optimal feedback law; the Tube and the corresponding policy of the Tube comprise:
Figure QLYQS_32
and &>
Figure QLYQS_33
。
2. The robot arm motion control method according to claim 1, wherein before the applying of the random disturbance to the robot arm, the robot arm motion control method further comprises:
the random perturbations are generated programmatically.
3. The robot arm motion control method according to claim 2, wherein the robot arm motion control method collects the pose data by a depth camera and a six-axis sensor.
4. A robot arm motion control system, comprising:
a disturbance applying unit for applying a random disturbance and an input voltage to a robot arm to cause the robot arm to generate a motion based on the random disturbance and the input voltage;
the data acquisition unit is used for acquiring pose data generated by the mechanical arm in the motion;
the predictor construction unit is used for constructing a linear predictor according to the pose data, the disturbance quantity of the random disturbance and the voltage value of the input voltage; wherein the construction of the expression form of the linear predictor according to the pose data and the disturbance quantity of the random disturbance comprises the following steps:
Figure QLYQS_34
Figure QLYQS_35
Figure QLYQS_36
wherein the content of the first and second substances,
Figure QLYQS_38
coefficient representing the linear predictor, is selected>
Figure QLYQS_41
Indicating that the linear predictor is->
Figure QLYQS_43
The predicted value of the time of day,
Figure QLYQS_39
indicates the status of the mechanical arm at time 0>
Figure QLYQS_42
Indicating that the mechanical arm is->
Figure QLYQS_44
The input voltage at that moment is greater or less>
Figure QLYQS_46
Indicating that the mechanical arm is->
Figure QLYQS_37
Disturbance at a moment in time>
Figure QLYQS_40
Represents a set of Koopman eigenfunctions, said +>
Figure QLYQS_45
；
An MPC control unit for MPC controlling the robot arm according to the linear predictor, comprising: designing Tube according to the linear predictor; calculating a control sequence of the robotic arm using the Tube-based MPC method; taking the control sequence as the input of the mechanical arm to control the mechanical arm to complete the action; wherein the designing of the Tube according to the linear predictor comprises:
deforming the linear predictor, and applying state constraint, control constraint and terminal constraint; wherein the linear predictor is deformed to:
Figure QLYQS_47
，/>
Figure QLYQS_52
，/>
Figure QLYQS_55
represents a pseudo-inverted symbol, said->
Figure QLYQS_48
The deformation is as follows: />
Figure QLYQS_51
Said
Figure QLYQS_54
The nominal system of (a) is: />
Figure QLYQS_57
(ii) a The state constraint, the control constraint and the terminal constraint are as follows: />
Figure QLYQS_50
，/>
Figure QLYQS_53
，/>
Figure QLYQS_56
，/>
Figure QLYQS_58
For a least robust positive invariant set, <' >>
Figure QLYQS_49
Coefficients that are the optimal feedback law;
constructing a cost equation, wherein the cost equation is as follows:
Figure QLYQS_59
；
solving the cost equation to obtain the optimal feedback law
Figure QLYQS_60
Minimizing the cost equation;
solving the minimum robust positive invariant set; wherein said
Figure QLYQS_61
Is rewritten as
Figure QLYQS_62
If the perturbation is bounded, and->
Figure QLYQS_63
Then the minimum robust positive invariant set is:
Figure QLYQS_64
；
generating a Tube and a strategy corresponding to the Tube according to the minimum robust positive invariant set and the optimal feedback law; the Tube and the corresponding policy of the Tube comprise:
Figure QLYQS_65
and &>
Figure QLYQS_66
。
5. The robot arm motion control system of claim 4, further comprising: a disturbance generation unit for generating random disturbances by programming.
6. An electronic device, comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor when executing the computer program implements:
a robot arm movement control method as claimed in any one of claims 1 to 3.
7. A computer-readable storage medium storing computer-executable instructions for performing, when executed by a processor,:
the robot arm motion control method according to any one of claims 1 to 3.
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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110647042A (en) * 2019-11-11 2020-01-03 中国人民解放军国防科技大学 Robot robust learning prediction control method based on data driving
CN111409068A (en) * 2020-03-13 2020-07-14 兰州大学 Bionic manipulator control system and bionic manipulator
CN112016194A (en) * 2020-08-10 2020-12-01 天津大学 All-directional mobile mechanical arm data driving model prediction control method based on Koopman operator
CN112135717A (en) * 2018-05-18 2020-12-25 谷歌有限责任公司 System and method for pixel-based model predictive control

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CA3081708C (en) * 2009-10-19 2023-10-03 Theranos Ip Company, Llc Integrated health data capture and analysis system
US11833681B2 (en) * 2018-08-24 2023-12-05 Nvidia Corporation Robotic control system

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112135717A (en) * 2018-05-18 2020-12-25 谷歌有限责任公司 System and method for pixel-based model predictive control
CN110647042A (en) * 2019-11-11 2020-01-03 中国人民解放军国防科技大学 Robot robust learning prediction control method based on data driving
CN111409068A (en) * 2020-03-13 2020-07-14 兰州大学 Bionic manipulator control system and bionic manipulator
CN112016194A (en) * 2020-08-10 2020-12-01 天津大学 All-directional mobile mechanical arm data driving model prediction control method based on Koopman operator

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
Milan Korda.Linear predictors for nonlinear dynamical systems: Koopman operator meets model predictive control.《Linear predictors for nonlinear dynamical systems: Koopman operator meets model predictive control》.2018,第1-27页. *
Xinglong Zhang.Robust Learning-Based Predictive Control for Discrete-Time Nonlinear Systems With Unknown Dynamics and State Constraints.《Robust Learning-Based Predictive Control for Discrete-Time Nonlinear Systems With Unknown Dynamics and State Constraints》.2022,第7314-7327页. *
何德峰.有界扰动下约束非线性系统鲁棒经济模型预测控制.《有界扰动下约束非线性系统鲁棒经济模型预测控制》.2020,第48卷(第2期),第572-581页. *
周兴文.基于BBO算法的加速器电源数字控制器的设计与实现.《基于BBO算法的加速器电源数字控制器的设计与实现》.2021,(第1（2021）期),C040-97. *

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