CN116604546A - Industrial mechanical arm control method, device, equipment and storage medium - Google Patents

Abstract

The invention discloses a control method, a device, equipment and a storage medium for an industrial mechanical arm, which relate to the technical field of intelligent control, wherein the method comprises the following steps: acquiring physical characteristic data and preset performance indexes of an industrial mechanical arm; constructing a constrained system model of the industrial mechanical arm according to the physical characteristic data and the preset performance index; performing equivalent system conversion on the constrained system model to obtain an unconstrained system model; constructing a Hamiltonian-Jacobian-Belman equation according to the unconstrained system model and an optimal control strategy of the industrial mechanical arm; and solving a Hamiltonian-Jacobian-Bellman equation by a self-adaptive dynamic programming method to obtain an optimal control law so as to control the industrial mechanical arm according to the optimal control law. The invention solves the problem that the existing industrial mechanical arm control method can not realize optimal control while meeting the constraint of the preset performance index, and realizes high-precision control of the industrial mechanical arm.

Description

Industrial mechanical arm control method, device, equipment and storage medium

technical field

The present invention relates to the technical field of intelligent control, in particular to an industrial mechanical arm control method, device, equipment and storage medium.

Background technique

With the continuous development of technology and the continuous improvement of production efficiency and operation accuracy, industrial robotic arms have been widely used in machining, assembly, welding and other industries that have high requirements for trajectory tracking accuracy. In order to improve the control accuracy of industrial manipulators, it is usually necessary to put forward requirements for performance indicators. Therefore, it is of great significance to combine preset performance indicators in the process of controller design for control. Optimal control is a control strategy that considers system control performance and energy-saving effects. The motion process of industrial manipulators is a highly coupled nonlinear system, which brings great challenges to traditional optimal control methods.

Therefore, it is an urgent problem to design an industrial manipulator controller that satisfies the preset performance index constraints and can achieve optimal control.

Contents of the invention

The main purpose of the present invention is to provide an industrial manipulator control method, device, equipment and storage medium, aiming to solve the technical problem that the existing industrial manipulator control method cannot achieve optimal control while satisfying the preset performance index constraints .

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

In a first aspect, the present invention provides a method for controlling an industrial robot arm, the method comprising:

Obtain physical characteristic data and preset performance indicators of industrial robotic arms;

Constructing a constrained system model of the industrial manipulator according to the physical characteristic data and the preset performance index;

performing an equivalent system conversion on the constrained system model to obtain an unconstrained system model;

Constructing the Hamilton-Jacobi-Bellman equation according to the unconstrained system model and the optimal control strategy of the industrial manipulator;

The Hamilton-Jacobi-Bellman equation is solved by an adaptive dynamic programming method to obtain an optimal control law, so as to control the industrial mechanical arm according to the optimal control law.

Optionally, in the above method for controlling the industrial manipulator, the step of constructing the constrained system model of the industrial manipulator according to the physical characteristic data and the preset performance index includes:

Constructing a state space equation of the industrial manipulator according to the physical characteristic data;

defining a preset performance function according to the preset performance index;

The constrained system model is obtained according to the state space equation and the preset performance function.

Optionally, in the above method for controlling the industrial manipulator, the step of constructing the state space equation of the industrial manipulator according to the physical characteristic data includes:

Modeling the physical characteristic data to obtain a dynamic model of the industrial mechanical arm;

The dynamic model is converted to obtain the state space equation of the industrial mechanical arm.

Optionally, in the above method for controlling the industrial manipulator, the step of constructing the Hamilton-Jacobi-Bellman equation according to the unconstrained system model and the optimal control strategy of the industrial manipulator includes:

Define a cost function according to the unconstrained system model and the optimal control strategy of the industrial manipulator;

According to the cost function, the Hamilton-Jacobi-Bellman equation is constructed.

Optionally, in the above method for controlling an industrial manipulator, the step of defining a cost function according to the unconstrained system model and the optimal control strategy of the industrial manipulator includes:

According to the unconstrained system model, define position tracking error and mixing error;

Obtain an error vector according to the position tracking error and the mixed error;

A cost function is defined according to the error vector and the optimal control strategy of the industrial manipulator.

Optionally, in the above method for controlling an industrial manipulator, the step of constructing the Hamilton-Jacobi-Bellman equation according to the cost function includes:

According to the cost function, define a Hamiltonian function and an optimal cost function;

Solving the optimal cost function by using Bellman's optimal principle to obtain the optimal solution of the optimal cost function;

The optimal solution is substituted into the Hamiltonian function to obtain the Hamilton-Jacobi-Bellman equation.

Optionally, in the above method for controlling an industrial manipulator, the step of solving the Hamilton-Jacobi-Bellman equation by an adaptive dynamic programming method to obtain an optimal control law includes:

An adaptive dynamic programming method based on a neural network architecture is used to solve the Hamilton-Jacobi-Bellman equation to obtain an optimal control law.

In a second aspect, the present invention provides an industrial robotic arm control device, the device comprising:

The data acquisition module is used to acquire the physical characteristic data and preset performance indicators of the industrial mechanical arm;

A model construction module, configured to construct a constrained system model of the industrial manipulator according to the physical characteristic data and the preset performance index;

A system conversion module, configured to perform equivalent system conversion on the constrained system model to obtain an unconstrained system model;

An equation construction module, configured to construct the Hamilton-Jacobi-Bellman equation according to the unconstrained system model and the optimal control strategy of the industrial manipulator;

The optimal control module is used to solve the Hamilton-Jacobi-Bellman equation through an adaptive dynamic programming method to obtain an optimal control law, so as to control the industrial mechanical arm according to the optimal control law.

In a third aspect, the present invention provides an industrial manipulator control device, the device includes a processor and a memory, an industrial manipulator control program is stored in the memory, and the industrial manipulator control program is executed by the processor , realize the above-mentioned industrial manipulator control method.

In a fourth aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by one or more processors, the above-mentioned method for controlling an industrial robot arm is realized.

The above one or more technical solutions provided by the present invention may have the following advantages or at least achieve the following technical effects:

An industrial manipulator control method, device, equipment, and storage medium proposed by the present invention construct a constrained system model of an industrial manipulator based on the physical characteristic data and preset performance indicators of the industrial manipulator, and perform an equalization process on the constrained system model. Then, the unconstrained system model is obtained, and the Hamilton-Jacobi-Bellman equation is constructed according to the unconstrained system model and the optimal control strategy of the industrial manipulator, and the Hamilton-Jacobi-Bellman equation is solved by the adaptive dynamic programming method. Mann equation to obtain the optimal control law, thereby controlling the industrial mechanical arm according to the optimal control law, and performing optimal control on the basis of ensuring the preset performance index, and realizing high-precision control of the industrial mechanical arm; The method can enable the system of the industrial manipulator to output an effective tracking reference signal, and make the tracking error and the like meet preset requirements, improve the control accuracy, and also have an energy-saving effect.

Description of drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings that need to be used in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only These are some embodiments of the present invention. Those skilled in the art can also obtain other drawings according to the provided drawings without creative work.

Fig. 1 is the schematic flow sheet of industrial manipulator control method of the present invention;

Fig. 2 is a schematic diagram of the hardware structure of the industrial manipulator control device involved in the present invention;

3 is a schematic diagram of the physical characteristics of the single-link robotic arm in Embodiment 1 of the present invention;

FIG. 4 is a graph of convergence curves for evaluating network weights in Embodiment 1 of the present invention;

5 is a graph of reference signal _yd and system state _x1 in Embodiment 1 of the present invention;

Figure 6 is a reference signal in Embodiment 1 of the present invention plot against system state _x2 ;

FIG. 7 is a graph of tracking error and preset performance boundary in Embodiment 1 of the present invention;

Fig. 8 is a schematic diagram of the functional modules of the industrial robot control device of the present invention.

The realization of the purpose of the present invention, functional characteristics and advantages will be further described in conjunction with the embodiments and with reference to the accompanying drawings.

Detailed ways

In order to make the purpose, technical solutions and advantages of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only the present invention. Some, but not all, embodiments of the invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

It should be noted that in the present invention, the term "comprising", "comprising" or any other variation thereof is intended to cover a non-exclusive inclusion, so that a process, method, article or system comprising a series of elements includes not only those elements, but also Other elements not expressly listed, or inherent to the process, method, article, or system are also included. Without further limitations, an element defined by the phrase "comprising..." does not preclude the presence of additional identical elements in the process, method, article or system comprising the element. In addition, in the present invention, use of suffixes such as 'module', 'part' or 'unit' for denoting elements is only for facilitating description of the present invention and has no specific meaning by itself. Therefore, 'module', 'part' or 'unit' may be used in combination. Those of ordinary skill in the art can understand the specific meanings of the above terms in the present invention according to specific situations. In addition, the technical solutions of various embodiments can be combined with each other, but it is based on the realization of those skilled in the art. When the combination of technical solutions is contradictory or cannot be realized, it should be considered that the combination of technical solutions does not exist. Also not within the scope of protection required by the present invention.

The analysis of the existing technology found that fast response and high-precision position tracking control has always been a research hotspot of industrial manipulators. At present, most of the control methods for industrial manipulators are still in the stage of ensuring the gradual convergence of tracking errors. In order to further improve the control accuracy of industrial manipulators due to problems such as slow response speed and high overshoot, it is usually necessary to put forward requirements on performance indicators such as convergence rate, maximum overshoot, and steady-state error. Therefore, it is of great significance to control with preset performance indicators in the process of controller design.

In addition, while paying attention to the high-precision control of industrial manipulators, the industry has also put forward higher requirements for its energy consumption. It is particularly important for the current industrial development to reduce the control cost of industrial robotic arms and reduce energy consumption. Optimal control is a kind of control strategy that considers system control performance and energy-saving effect. Studies have shown that the motion process of industrial manipulators is a highly coupled nonlinear system, which brings great challenges to traditional optimal control methods.

There are adaptive dynamic programming methods for solutions to optimal control problems of strongly nonlinear systems. Based on the adaptive dynamic programming design technology, the obtained optimal controller not only ensures the stability of the controlled system, but also optimizes the performance of the system. However, at present, for the high-precision tracking control problem of industrial manipulators, it is still an urgent problem to design an optimal controller that satisfies the constraints of preset performance indicators.

In view of the technical problem that the industrial manipulator control method in the prior art cannot achieve optimal control while satisfying the preset performance index constraints, the present invention provides an industrial manipulator control method, the general idea is as follows:

Obtain the physical characteristic data and preset performance indicators of the industrial manipulator; construct the constrained system model of the industrial manipulator according to the physical characteristic data and preset performance indicators; perform equivalent system conversion on the constrained system model to obtain the unconstrained system model ; According to the unconstrained system model and the optimal control strategy of the industrial manipulator, the Hamilton-Jacobi-Bellman equation is constructed; the optimal control law is obtained by solving the Hamilton-Jacobi-Bellman equation through the adaptive dynamic programming method, In order to control the industrial manipulator according to the optimal control law.

Through the above technical scheme, optimal control is carried out on the basis of ensuring the preset performance indicators, and high-precision control of the industrial manipulator is realized; the method of the present invention can make the system of the industrial manipulator output an effective tracking reference signal, and make the tracking The error and the like meet the preset requirements, which improves the control accuracy and also has energy-saving effects.

The industrial manipulator control method, device, equipment and storage medium provided by the present invention will be described in detail below through specific embodiments and implementations with reference to the accompanying drawings.

Embodiment one

Referring to the schematic flowchart of FIG. 1 , a first embodiment of the method for controlling an industrial robot arm of the present invention is proposed, and the method for controlling an industrial robot arm is applied to an industrial robot arm control device.

Industrial robotic arm control equipment refers to terminal equipment or control equipment that can realize data transmission, which can be terminal equipment such as mobile phones, computers, embedded industrial computers, etc., or control equipment such as controllers and processors located in the system.

As shown in Fig. 2, it is a schematic diagram of the hardware structure of the control equipment of the industrial manipulator. The industrial robot arm control device may include: a processor 1001 , such as a CPU (Central Processing Unit, central processing unit), a communication bus 1002 , a user interface 1003 , a network interface 1004 , and a memory 1005 .

Specifically, the communication bus 1002 is used to realize connection and communication between these components; the user interface 1003 is used to connect the client and perform data communication with the client, and the user interface 1003 may include an output unit, such as a display screen, and an input unit, such as a keyboard The network interface 1004 is used to connect the background server, and carries out data communication with the background server, and the network interface 1004 can include input/output interfaces, such as standard wired interfaces, wireless interfaces, such as Wi-Fi interfaces; memory 1005 is used to store various types of These data, for example, can include instructions of any application program or method in the industrial manipulator control device, as well as application-related data, and the memory 1005 can be a high-speed RAM memory, or a stable memory, such as a disk memory; Optionally, the memory 1005 can also be a storage device independent of the processor 1001. With continued reference to FIG. 2, the memory 1005 can include an operating system, a network communication module, a user interface module, and an industrial manipulator control program;

The processor 1001 is used to call the industrial manipulator control program stored in the memory 1005, and perform the following operations:

Obtain physical characteristic data and preset performance indicators of industrial robotic arms;

Construct a constrained system model of an industrial manipulator based on physical characteristic data and preset performance indicators;

Perform equivalent system transformation on the constrained system model to obtain the unconstrained system model;

According to the unconstrained system model and the optimal control strategy of the industrial manipulator, the Hamilton-Jacobi-Bellman equation is constructed;

The Hamilton-Jacobi-Bellman equation is solved by the adaptive dynamic programming method, and the optimal control law is obtained, so as to control the industrial manipulator according to the optimal control law.

Based on the above-mentioned industrial manipulator control device, the method for controlling an industrial manipulator in this embodiment will be described in detail below in conjunction with the schematic flowchart shown in FIG. 1 . The method may include the steps of:

Step S100: Obtain physical characteristic data and preset performance indicators of the industrial robotic arm.

Specifically, the industrial robot arm may be a single-link robot arm, such as a type of nonlinear single-link robot arm. The physical characteristic data refers to the mechanical characteristics corresponding to the hardware structure of the industrial robotic arm, such as the mass of the payload of the industrial robotic arm, the length of the robotic arm, the moment of inertia, the coefficient of friction, the angle of rotation, and control input. The preset performance index refers to the parameter index for the industrial manipulator to realize the preset performance control, such as the tracking error requirement of the industrial manipulator. The tracking error is the difference between the output signal of the industrial manipulator and the input signal that needs to be tracked. The tracking error index can be is a range value.

Step S200: Construct a constrained system model of the industrial manipulator according to the physical characteristic data and the preset performance index.

Specifically, after the control device obtains the physical characteristic data and preset performance indicators of the industrial manipulator, it can construct a constrained system model of the industrial manipulator. The model can be a kinetic model or an equation of state model.

Further, step S200 may include:

Step S210: Construct a state space equation of the industrial robot arm according to the physical characteristic data.

Specifically, when constructing the state space equation according to the physical characteristic data of the industrial manipulator, a dynamic model can be established first and then converted into the form of the state space equation. The modeling software directly inputs the physical characteristic data to obtain the corresponding state space equation.

Further, step S210 may include:

Step S211: Modeling the physical characteristic data to obtain a dynamic model of the industrial mechanical arm.

In this embodiment, a class of single-link manipulator is taken as an example. Figure 1 is a schematic diagram of the physical characteristics of the single-link manipulator. The mass M of the load, the acceleration of gravity g, the length H of the single-link manipulator, the rotation angle θ(t), the moment of inertia G, and the control input u(t) of the control device to the single-link manipulator. The physical characteristics of the single-link manipulator are modeled, and the dynamic model obtained is:

Among them, M is the mass of the payload of the single-link manipulator, g is the acceleration of gravity, H is the length of the single-link manipulator, G is the moment of inertia, D is the coefficient of friction, θ(t) is the rotation angle, u (t) represents a control input, and t represents time.

Step S212: Convert the dynamic model to obtain the state space equation of the industrial manipulator.

In this embodiment, according to the physical characteristics of the single-link manipulator, the equation of the dynamic model obtained by modeling can be transformed into a state space equation. Let x ₁ =θ(t), The state space equation of the single link manipulator is obtained as:

y=x ₁

Among them, y represents the output signal of the industrial manipulator.

Assuming that the input signal to be tracked of the single-link manipulator is y _d , its first derivative second derivative /> Both exist, then the compact structure of the state space equation of the single-link manipulator can be obtained:

y=x ₁

in,

Step S220: Define a preset performance function according to the preset performance index;

Preset performance control is a practical technology that can predetermine dynamic performance indicators such as convergence speed and control accuracy. It can keep the tracking error of the industrial manipulator within a limited range composed of two specified performance functions, thus ensuring the stability of the industrial machinery. High dynamic performance of the arm.

In this embodiment, the tracking error of the single-link manipulator is the difference between the output signal y and the input signal y _d that needs to be tracked. Assuming that the tracking error of the single-link manipulator is required to be between b and B, the predicted Assuming that the performance index is b<yy _d <B, the preset performance function determined according to the preset performance index of the single-link manipulator is:

Among them, δ ₁ , l ₁ , γ ₁ , δ ₂ , l ₂ and γ ₂ are all positive design parameters, which can be set according to actual needs.

Step S230: Obtain the constrained system model according to the state space equation and the preset performance function.

In this embodiment, according to the preset performance function, the output constraint of the single-link manipulator can be obtained as b+y _d <y<B+y _d , combined with the aforementioned state space equation, the constrained system model can be obtained.

Step S300: performing an equivalent system transformation on the constrained system model to obtain an unconstrained system model.

In this embodiment, in order to achieve the goal of high-precision control, a system conversion technology based on a preset performance function is introduced. For the output signal of a single-link manipulator, a nonlinear mapping function is defined as:

ρ ₁ =T(x ₁ ,B,b,y _d )

in, a tanh( ) means the arc tangent function;

Deriving the nonlinear mapping function, we can get:

make continue to get:

in,

The system transformation technology is used to deal with the problem of preset performance constraints, and the constrained system can be transformed into an unconstrained equivalent system by defining a nonlinear mapping function based on the preset performance index. The control strategy designed for the converted unconstrained system can keep the tracking error of the industrial manipulator within a limited range composed of two preset performance functions, such as the above b and B ranges, thereby effectively improving the control accuracy.

Step S400: Construct the Hamilton-Jacobi-Bellman equation according to the unconstrained system model and the optimal control strategy of the industrial manipulator.

Further, step S400 may include:

Step S410: Define a cost function according to the unconstrained system model and the optimal control strategy of the industrial manipulator.

Specifically, in order to balance the control accuracy and the energy consumed by the control input, so as to achieve the goal of optimal control, a cost function is defined.

Further, step S410 may include:

Step S411: According to the unconstrained system model, define position tracking error and mixing error.

In this embodiment, for the converted unconstrained system, the position tracking error z is defined as:

z=ρ ₁ -s _d

in,

And define the mixing error θ as:

in, is a normal number and can be set according to actual needs, /> Indicates the rotation rate tracking error, /> is the first derivative of s _d .

Step S412: Obtain an error vector according to the position tracking error and the mixed error.

In this embodiment, based on the previously defined position tracking error z and mixed error θ, a new error vector X is defined as:

Thus, the dynamic equation of the error vector X can be obtained as:

in,

Step S413: Define a cost function according to the error vector and the optimal control strategy of the industrial manipulator.

In this embodiment, according to the error vector and the optimal control strategy of the industrial manipulator, the defined cost function is:

in, is a positive definite matrix, R is a positive constant, can be set according to actual needs, R>0.

Step S420: Construct the Hamilton-Jacobi-Bellman equation according to the cost function.

Specifically, by deriving the Hamilton-Jacobi-Bellman equation based on the cost function defined by the optimal control strategy, the optimal control problem can be transformed into solving the Hamilton-Jacobi-Bellman equation.

Further, step S420 may include:

Step S421: According to the cost function, define a Hamiltonian function and an optimal cost function.

In this embodiment, according to the cost function defined in step S413, the Hamiltonian function is defined as:

in,

In optimal control, the cost function should be minimized to obtain the desired control accuracy with the minimum control input. In this embodiment, the optimal cost function is defined as:

Wherein, Ω represents a set of allowable control strategies for a single-link manipulator, and V ^* (X) satisfies V ^* (0)=0.

Step S422: Solving the optimal cost function by using the Bellman optimality principle to obtain an optimal solution of the optimal cost function.

According to the Bellman optimality principle, we can get

in,

Depend on The optimal solution of the optimal cost function can be obtained as:

Step S423: Substituting the optimal solution into the Hamiltonian function to obtain the Hamilton-Jacobi-Bellman equation.

In the present embodiment, substituting the optimal solution u ^* (t) into the Hamiltonian function defined in step S421, the Hamilton-Jacobi-Bellman equation can be obtained as:

Step S500: solving the Hamilton-Jacobi-Bellman equation by an adaptive dynamic programming method to obtain an optimal control law, so as to control the industrial manipulator according to the optimal control law.

Specifically, due to the strongly nonlinear nature of the Hamilton-Jacobi-Bellman equation, it is difficult to directly solve the optimal cost function and optimal control law. Therefore, the Hamilton-Jacobi-Bellman equation can be approximated by using the adaptive dynamic programming method of a single network.

Further, step S500 may include:

Step S510: Solving the Hamilton-Jacobi-Bellman equation by using an adaptive dynamic programming method based on a neural network architecture to obtain an optimal control law.

In this embodiment, a single-network evaluation network is established based on the neural network architecture, and the optimal cost function V ^* (X) can be approximated by the evaluation network as:

in, is the ideal weight vector, /> is the basis function vector, /> is the approximate error, m represents the number of neural network nodes, and />

make Represents the estimated value of the ideal weight, the optimal cost function can be estimated as:

Then the approximate optimal control law can be obtained as:

Therefore, the control device can control the single-link manipulator correspondingly according to the optimal control law, perform optimal control on the basis of ensuring preset performance indicators, and realize high-precision control of the single-link manipulator.

Approximately obtain the optimal control law through a single network adaptive dynamic programming method, realize the control of industrial manipulators, and use a single evaluation network to approximate the optimal cost function, compared with the traditional execution-evaluation dual network structure, it is helpful Reduce computation and memory requirements.

Optionally, the weight update law of the designed evaluation network can be:

Among them, β is a positive design parameter, which can be set according to actual needs,

In order to verify the effectiveness of the industrial manipulator control method provided in this embodiment, the following simulation experiments are carried out:

In the simulation experiment, the control target is set to make the output signal y of the single-link manipulator track the upper reference signal y _d =0.2 sin(t) optimally. According to the actual system of the single-link manipulator, the physical characteristic data are M=1kg, g=9.8m/s ² , l=1m, D=2N·m·s/rad. The preset performance functions are b=-0.1-e ^-t and B=0.1+e ^-t , and the initial state values of the single-link manipulator are x ₁ (0)=0.6, x ₂ (0)=-1.5. The function of the evaluation network is σ _c (X)=[z ² ,θ ² ,zθ] ^T , and its weight initial value is w _c (0)=[100,250,50] ^T . In addition, set other parameters as Q=[800,0;0,800], R=1, β=2.

Choose the Lyapunov function The time derivative method is used to analyze the results. Based on the parameters designed in the actual situation in the simulation experiment, no examples are given here. After the analysis, you can get /> According to the Lyapunov stability theorem, it can be known that the tracking error z and mixing error θ and the weight estimation error of the evaluation network are consistent and ultimately bounded, which means that the output signal y of the single-link manipulator can track the upper reference signal y _d , and the weight of the evaluation network can converge close to the ideal value, as shown in Figure 4. The convergence of the evaluation network weight In the graph, in the graph, the horizontal axis represents time in seconds (s), and the vertical axis represents the value of the evaluation network weight. It can be seen from the figure that the evaluation network can accurately approximate the cost function, so the obtained control input u(t) can be regarded as optimal.

As shown in Fig. 5, it is the curve diagram of reference signal y _d and system state x ₁ in the present embodiment, among the figure, horizontal axis represents time, and unit is second (s), and vertical axis represents the value corresponding to each curve; Fig. 6 Shown is the reference signal in this example and the graph of the system state x ₂ , in the figure, the horizontal axis represents time, and the unit is second (s), and the vertical axis represents the value corresponding to each curve; As shown in Figure 7, it is the tracking error and the preset performance boundary in the present embodiment In the graph, the horizontal axis represents time in seconds (s), and the vertical axis represents the value corresponding to each curve, wherein the preset performance boundary is the curve corresponding to the value of the preset performance index. From Figures 5 to 7, it can be seen that the system state of the single-link manipulator corresponding to the output signal y is consistent with the reference signal y _d , the tracking effect of this control method is good, and the tracking error meets the preset The requirements of the performance index can realize the high-precision control of the single-link manipulator.

The control method of the industrial manipulator provided in this embodiment constructs the constrained system model of the industrial manipulator according to the physical characteristic data and preset performance indicators of the industrial manipulator, performs equivalent system conversion on the constrained system model, and obtains an unconstrained system Then construct the Hamilton-Jacobi-Bellman equation according to the unconstrained system model and the optimal control strategy of the industrial manipulator, and solve the Hamilton-Jacobi-Bellman equation through the adaptive dynamic programming method to obtain the optimal control law , so that the industrial manipulator is controlled according to the optimal control law, the optimal control is carried out on the basis of ensuring the preset performance index, and the high-precision control of the industrial manipulator is realized; the method of the present invention can make the system of the industrial manipulator The effective tracking reference signal is output, and the tracking error and the like meet the preset requirements, which improves the control precision and also has an energy-saving effect.

Embodiment two

Based on the same inventive concept, referring to FIG. 8 , a first embodiment of the control device for industrial manipulators of the present invention is proposed. The device can be a virtual device, which is applied to control equipment for industrial manipulators.

The industrial manipulator control device provided in this embodiment will be described in detail below in conjunction with the functional module schematic diagram shown in FIG. 8 . The device may include:

The data acquisition module is used to acquire the physical characteristic data and preset performance indicators of the industrial mechanical arm;

A model construction module, configured to construct a constrained system model of the industrial manipulator according to the physical characteristic data and the preset performance index;

A system conversion module, configured to perform equivalent system conversion on the constrained system model to obtain an unconstrained system model;

An equation construction module, configured to construct the Hamilton-Jacobi-Bellman equation according to the unconstrained system model and the optimal control strategy of the industrial manipulator;

The optimal control module is used to solve the Hamilton-Jacobi-Bellman equation through an adaptive dynamic programming method to obtain an optimal control law, so as to control the industrial mechanical arm according to the optimal control law.

It should be noted that the functions and corresponding technical effects achieved by each module in the industrial manipulator control device provided in this embodiment can refer to the description of the specific implementation in each embodiment of the industrial manipulator control method of the present invention. For the sake of brevity of the description, I won't repeat them here.

Embodiment three

Based on the same inventive concept, referring to the schematic diagram of the hardware structure in Fig. 2, this embodiment provides an industrial manipulator control device, which may include a processor and a memory, and an industrial manipulator control program is stored in the memory, and the industrial manipulator control When the program is executed by the processor, all or part of the steps of the various embodiments of the industrial robot arm control method of the present invention are realized.

Specifically, industrial robotic arm control equipment refers to terminal equipment or control equipment capable of data transmission, which can be terminal equipment such as mobile phones, computers, embedded industrial computers, etc., or control equipment such as controllers and processors located in the system .

Those skilled in the art can understand that the hardware structure shown in FIG. 2 does not constitute a limitation to the industrial robot arm control device of the present invention, and may include more or less components than those shown in the illustration, or combine certain components, or have different Part placement.

It can be understood that the industrial manipulator control device may also include a communication bus, a user interface and a network interface. Among them, the communication bus is used to realize the connection and communication between these components; the user interface is used to connect the client and perform data communication with the client. The user interface can include an output unit, such as a display screen, and an input unit, such as a keyboard; For connecting to the background server and performing data communication with the background server, the network interface may include an input/output interface, such as a standard wired interface and a wireless interface.

The memory is used to store various types of data, which may include, for example, instructions of any application program or method in the industrial robot arm control device, as well as data related to the application program. The memory may be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM for short), Random Access Memory (RAM for short), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (EPROM) -Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic storage, flash memory, magnetic disk or optical disk, optionally, the memory can also be a storage device independent of the processor.

The processor is used to call the industrial manipulator control program stored in the memory, and execute the above-mentioned industrial manipulator control method. The processor can be an application specific integrated circuit (ASIC for short), a digital signal processor (Digital Signal Processor) , DSP for short), digital signal processing device (Digital Signal Processing Device, DSPD for short), programmable logic device (Programmable Logic Device, PLD for short), field programmable gate array (Field Programmable Gate Array, FPGA for short), controller, microcontroller A controller, a microprocessor or other electronic components are used to execute all or part of the steps in the various embodiments of the above-mentioned industrial robot control method.

Embodiment four

Based on the same inventive concept, this embodiment provides a computer-readable storage medium, such as flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), Read Only Memory (ROM), Programmable Read Only Memory (PROM), Erasable Programmable Read Only Memory (EPROM), Electrically Erasable Programmable Read Only Memory (EEPROM), Magnetic Memory, Disk, CD, server, etc., the storage medium stores a computer program, the computer program can be executed by one or more processors, when the computer program is executed by the processor, all of the various embodiments of the industrial manipulator control method of the present invention can be realized or partial steps.

It should be noted that the serial numbers of the above embodiments of the present invention are for description only, and do not represent the advantages and disadvantages of the embodiments. The above embodiments are only optional embodiments of the present invention, and are not therefore limiting the patent scope of the present invention. Under the inventive concept of the present invention, the equivalent structure or equivalent process transformation made by utilizing the description of the present invention and the contents of the accompanying drawings, Or directly or indirectly used in other related technical fields, all are included in the patent protection scope of the present invention.

Claims (10)

1. An industrial robot control method, comprising:

acquiring physical characteristic data and preset performance indexes of an industrial mechanical arm;

constructing a constrained system model of the industrial mechanical arm according to the physical characteristic data and the preset performance index;

performing equivalent system conversion on the constrained system model to obtain an unconstrained system model;

constructing a Hamiltonian-Jacobian-Belman equation according to the unconstrained system model and an optimal control strategy of the industrial mechanical arm;

and solving the Hamiltonian-Jacobian-Bellman equation by a self-adaptive dynamic programming method to obtain an optimal control law, so as to control the industrial mechanical arm according to the optimal control law.

2. The industrial robot control method of claim 1, wherein the step of constructing a constrained system model of the industrial robot based on the physical characteristic data and the preset performance index comprises:

constructing a state space equation of the industrial mechanical arm according to the physical characteristic data;

defining a preset performance function according to the preset performance index;

and obtaining the constrained system model according to the state space equation and the preset performance function.

3. The industrial robot control method according to claim 2, wherein the step of constructing a state space equation of the industrial robot based on the physical characteristic data comprises:

modeling the physical characteristic data to obtain a dynamic model of the industrial mechanical arm;

and converting the dynamic model to obtain a state space equation of the industrial mechanical arm.

4. The industrial robot control method of claim 1, wherein the constructing a hamilton-jacobian-bellman equation according to the unconstrained system model and the optimal control strategy of the industrial robot comprises:

defining a cost function according to the unconstrained system model and an optimal control strategy of the industrial mechanical arm;

and constructing a Hamiltonian-Jacobian-Belman equation according to the cost function.

5. The method of claim 4, wherein defining a cost function based on the unconstrained system model and an optimal control strategy for the industrial robot comprises:

defining a position tracking error and a mixing error according to the unconstrained system model;

obtaining an error vector according to the position tracking error and the mixed error;

and defining a cost function according to the error vector and an optimal control strategy of the industrial mechanical arm.

6. The industrial robot control method of claim 4, wherein constructing a hamilton-jacobian-bellman equation from the cost function comprises:

according to the cost function, a Hamiltonian function and an optimal cost function are defined;

solving the optimal cost function by utilizing a Belman optimal principle to obtain an optimal solution of the optimal cost function;

substituting the optimal solution into the Hamiltonian to obtain the Hamiltonian-Jacobian-Belman equation.

7. The industrial robot control method according to claim 1, wherein the step of solving the hamilton-jacobian-bellman equation by an adaptive dynamic programming method to obtain an optimal control law comprises:

and solving the Hamiltonian-Jacobian-Bellman equation by adopting a self-adaptive dynamic programming method based on a neural network architecture to obtain an optimal control law.

8. An industrial robot control device, the device comprising:

the data acquisition module is used for acquiring physical characteristic data and preset performance indexes of the industrial mechanical arm;

the model construction module is used for constructing a constrained system model of the industrial mechanical arm according to the physical characteristic data and the preset performance index;

the system conversion module is used for carrying out equivalent system conversion on the constrained system model to obtain an unconstrained system model;

the equation construction module is used for constructing a Hamiltonian-Jacobian-Bellman equation according to the unconstrained system model and an optimal control strategy of the industrial mechanical arm;

and the optimal control module is used for solving the Hamiltonian-Jacobian-Bellman equation through a self-adaptive dynamic programming method to obtain an optimal control law so as to control the industrial mechanical arm according to the optimal control law.

9. An industrial robot control device, characterized in that the device comprises a processor and a memory, on which an industrial robot control program is stored, which when executed by the processor, implements the industrial robot control method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that the storage medium has stored thereon a computer program which, when executed by one or more processors, implements the industrial robot control method according to any one of claims 1 to 7.