Model-free adaptive control method and system based on 4D printing crawling robot

Abstract

The application discloses a model-free adaptive control method based on a 4D printing crawling robot, and comprises the following steps: a step of establishing a dynamic linearization data model; a step of calculating the value of the pseudo partial derivative at the current time by using a pseudo partial derivative estimation algorithm and a model-free adaptive controller; and a step of calculating the input signal value at each time by using the model-free adaptive control algorithm, and realizing step control and behavior control of an executing device of the 4D printing crawling robot by using input and output data of the model-free adaptive control algorithm. According to the application, the data-driven control of the 4D printing soft crawling robot can be realized, and the accurate behavior control and step control of the bidirectional crawling robot can be realized.

Description

TECHNICAL FIELD

[0001] The present application relates to 4D printing technology, in particular to a model-free adaptive control method and system based on a 4D printing crawling robot. BACKGROUND

[0002] In recent years, soft robots have become a research field that people pay more and more attention to because they have almost infinite degrees of freedom in theory, light materials, and good compliance.

[0003] Generally speaking, compared with traditional rigid robots, soft robots have softer materials, better compliance, and stronger adaptability to the environment. Therefore, in complex environments, tasks that traditional rigid robots cannot perform can be completed using soft robots.

[0004] 4D printing technology is based on 3D printing technology and adds a fourth dimension, time. Based on the programmable characteristics of smart materials, a three-dimensional object is printed by 3D printing, which will deform under external stimuli. Based on the bidirectional crawling robot of 4D printing, the crawling robot can realize precise step control and behavior control through reasonable structural design.

[0005] Nowadays, there is a relatively complete theoretical system for model-free adaptive control, which is widely used in various machines and rigid systems, and can accurately control the controlled system under the condition of only having part of the model or even no model. For complex nonlinear systems and soft crawling robots, modeling requires material constitutive model and dynamics model, so it is very difficult to design. Model-free adaptive control can accurately control soft crawling robots using input and output data, thereby overcoming the unmodeled problem of the system.

[0006] In the prior art, the Chinese invention application with publication number CN110523979A and the name of "spider crawling robot based on 4D printing technology" discloses a spider crawling robot structure, the main material of which is memory alloy, and the bionic mechanical leg is made of 3D printing technology. It has no complicated mechanical structure, and can realize the functions of propulsion, balance and crawling by using the shape memory function of memory alloy. However, it cannot accurately control the step and behavior through external stimulation.

[0007] A soft climbing robot and a control method thereof are disclosed in Chinese patent application CN113927616A entitled "A soft climbing robot and a control method thereof". The driving principle of the climbing soft robot is through a front-end pneumatic actuator, a rear-end actuator and a center actuator. The climbing and control of the soft robot are realized by charging and discharging of the above three actuators. The climbing mode depends on the structure design and the air bag structure design, and is not realized based on the deformation mechanism of 4D printing intelligent materials, so that only the simple control principle can be used to realize the control of the soft robot body moving forward and backward in the pipeline, and accurate action control cannot be realized. SUMMARY

[0008] Therefore, the main purpose of the present application is to provide a model-free adaptive control method and system for a 4D printing climbing robot, which can realize precise behavior control and step control of a bidirectional climbing robot based on data-driven control of a 4D printing soft climbing robot.

[0009] To achieve the above purpose, the technical scheme of the present application is as follows:

[0010] A model-free adaptive control method for a 4D printing climbing robot, comprising:

[0011] a step of establishing a dynamic linearization data model;

[0012] a step of calculating the value of the pseudo partial derivative at the current time using a model-free adaptive controller and a pseudo partial derivative estimation algorithm; wherein in the step of calculating the value of the pseudo partial derivative at the current time using a model-free adaptive controller and a pseudo partial derivative estimation algorithm, the pseudo partial derivative at each time is calculated by the following formula:

[0013] (1)

[0014] wherein, represents the pseudo partial derivative at the current time evaluated by input and output data; represents the pseudo partial derivative at the previous sampling time evaluated by input and output data; is a step factor; is to prevent the pseudo partial derivative estimation value from changing too much; is the input difference of the previous two times; is the input difference of the current time and the previous time; further comprising a reset algorithm to prevent the pseudo partial derivative from falling into a dead zone:

[0015] (2)

[0016] wherein, represents the pseudo partial derivative at the current time evaluated by input and output data; The pseudo-partial derivatives represent the values ​​at the initial time step. It is a sufficiently small positive number;

[0017] The input signal value at each moment is calculated by the model-free adaptive controller. Using the input and output data of the model-free adaptive control algorithm, the steps of stride control and behavior control of the actuator of the 4D-printed crawling robot are implemented. The step of calculating the input signal value at each moment by the model-free adaptive controller is as follows:

[0018] (3)

[0019] in, This is the current voltage input value; The voltage input value at the previous sampling time; Step size factor; The introduction of this is to prevent excessive changes in input; The pseudo-partial derivative at the current moment; This represents the expected output value at the next moment. The output value at the current moment.

[0020] A model-free adaptive control system based on a 4D-printed crawling robot includes: a system construction module, a behavior control module, and a stride control module; wherein:

[0021] The system building module transmits system input information to the built-in microcontroller through the built-in acquisition circuit. The microcontroller then implements a model-free adaptive control algorithm and calculates the effective system output value at the current moment.

[0022] The behavior control module is used to receive the valid system output value at the current moment in order to control the 4D printed crawling robot to perform forward and backward movements.

[0023] The stride control module changes stride length by altering the relative angle between the behavior control module and / or other behavior control modules.

[0024] The behavior control module is specifically used to control the first execution device and / or the second execution device to enable the first execution device to perform a forward movement or the second execution device to perform a backward movement, and also to enable the first execution device and the second execution device to cooperate with each other to achieve the forward movement and / or backward movement of the 4D printed crawling robot.

[0025] A 4D printing crawling robot, comprising a first execution device 1, a support device 2 and a second execution device 3;Wherein, the first execution device 1 is connected with the second execution device 3 through the support device 2, and the first execution device 1 or / and the second execution device 3 realizes the control of the forward movement, the backward movement and the step control of the 4D printing crawling robot by executing the model-free adaptive control method based on the 4D printing crawling robot.

[0026] Wherein, the first execution device 1 further comprises: a first main body module 13, a first driving layer module 12, a first sensor module 14 and a first friction module 11;Wherein, the first driving layer module 12 is arranged at the lower part of the first main body module 13, so that the first driving layer module 12 contacts a plane;When the first driving layer module 12 is connected with voltage or current, the first driving layer module 12 generates heat, so that the first main body module 13 is heated to generate upward bending deformation;The first sensor module 14 is arranged on the other side of the first main body module 13;The first friction module 11 is arranged at the front end of the 4D printing crawling robot body.

[0027] The second execution device 3 further comprises: a second main body module 32, a second driving layer module 31, a second sensor module 33 and a second friction module 34;Wherein:

[0028] The second driving layer module 31 is arranged at the upper part of the second main body module 32;When the current is connected, the second driving layer module 31 generates heat, so that the second main body module 32 is heated to generate downward bending deformation, and the second sensor module 33 is placed at the lower part of the second main body module 32 and contacts a plane;The second friction module 34 is arranged at the end of the crawling robot, and the second main body module 32 at the part where the second friction module 34 is arranged is slightly bent upward until the second friction module 34 is separated from the ground;For making the ground friction of the second friction module 34 of the second execution device 3 greater than the ground friction of the first driving layer module 12 of the first execution device 1, or making the ground friction of the second friction module 34 less than the ground friction of the first friction module 11.

[0029] An electronic device readable storage medium, the electronic device readable storage medium stores one or more program modules, the program modules are one or more of system building modules, behavior control modules and step control modules;When the program modules are read or called by the processor of the electronic device, the electronic device executes the steps of the model-free adaptive control method based on the 4D printing crawling robot.

[0030] The model-free adaptive control method based on the 4D printing crawling robot and the system thereof have the following beneficial effects:

[0031] The present application adopts a model-free adaptive control method, executes a model-free adaptive control algorithm, uses input and output data of a crawling robot to accurately control the bending angle of an execution device, realizes controllability of a single execution device, and realizes step control and behavior control through cooperative control of the first execution device 1 and the second execution device 3 of the bidirectional crawling robot, so as to realize data-driven control of a soft crawling robot based on 4D printing, and achieve the effects of accurate behavior control and step control of the bidirectional crawling robot. BRIEF DESCRIPTION OF DRAWINGS

[0032] Figure 1 It is a schematic diagram of the three-dimensional structure of the 4D printing crawling robot of the present application.

[0033] Figure 2 It is a schematic diagram of the dynamic linearization data model of the present application.

[0034] Figure 3 It is a control principle block diagram of the model-free adaptive control method / algorithms adopted by the bidirectional crawling robot of the present application.

[0035] Figure 4 It is a schematic diagram of the control system of the 4D printing bidirectional crawling robot of the present application.

[0036] Figure 5 It is a schematic diagram of the simulation implementation of the input of the single actuator based on model-free adaptive control in the present application.

[0037] Figure 6 It is a schematic diagram of the simulation implementation of the pseudo partial derivative of the single actuator based on model-free adaptive control in the present application.

[0038] Figure 7 It is a schematic diagram of the simulation implementation of the tracking of the expected value of the single actuator based on model-free adaptive control in the present application.

[0039]

MAIN COMPONENT / ASSEMBLY SYMBOL DESCRIPTION

[0040] 1: first execution device

[0041] 11: first friction module 12: first drive layer module 13: first main body module

[0042] 14: first sensor module

[0043] 2: support device

[0044] 3: second execution device

[0045] 31: second drive layer module 32: second main body module 33: second sensor module

[0046] 34: second friction module. DETAILED DESCRIPTION

[0047] The application will be described in further detail below with reference to the drawings and embodiments of the application.

[0048] For more clearly illustrating and describing the embodiments of the application, reference can be made to one or more accompanying drawings, but the additional details or examples described in the drawings are not intended to limit the scope of any one of the application, the presently described embodiments or the preferred modes. Unless otherwise defined, all technical or scientific terms used in the specification of the application have the same meaning as commonly understood by one skilled in the art to which the application belongs. The terms used in the specification of the application are only for the purpose of describing specific embodiments and are not intended to limit the application.

[0049] The application aims at the defects or deficiencies of existing rigid robots in complex environments. Based on the good compliance and environmental adaptability of soft materials, a self-adaptive control technology suitable for soft robots is developed to enable the 4D printed bidirectional crawling robot to realize continuous reversible deformation, sensing and driving integrated precise control. With the further research of intelligent materials and the improvement of the structure of the crawling robot and the control method, the bidirectional crawling robot technology based on 4D printing will develop rapidly and will gradually replace some rigid robots.

[0050] Due to the lack of effective and precise control methods for soft robots at present, most of the existing technologies are model-dependent control algorithms, while the model of soft robots has strong nonlinearity and it is difficult to establish an accurate model. The bidirectional crawling robot of the application, the main part of the first execution device 1 and the second execution device 2 are made of 4D printed flexible liquid crystal elastomer (LCE) material, which has the characteristics of softness, good compliance and strong adaptability. Through the model-free adaptive control method, the crawling robot can be accurately controlled in behavior and step under the condition of only input and output data.

[0051] Here, the bidirectional crawling robot based on 4D printing is a design that can realize continuous reversible deformation, sensing, driving and control integration, and can also be referred to as a 4D printing crawling robot. The liquid crystal elastomer (LCE) refers to a liquid crystal polymer that displays elasticity in isotropic state or liquid crystal state after moderate crosslinking, has dual characteristics of liquid crystal and elastomer, retains the original performance of non-crosslinked liquid crystal polymer, and becomes a very popular field in material research in countries around the world due to its excellent orientation, piezoelectricity, ferroelectricity, soft elasticity and other characteristics under the action of external stimuli such as heat, light, electricity, magnetism, PH value and humidity. As a most representative intelligent material, the liquid crystal elastomer (LCE) can change the phase state or molecular structure under the action of external stimuli such as heat, light, electricity, magnetism, PH value and humidity, thereby changing the arrangement order of liquid crystal units, and causing the material itself to deform macroscopically. When the external stimulus is removed, the liquid crystal elastomer (LCE) can restore to the original shape.

[0052] Figure 1 It is a schematic diagram of the three-dimensional structure of the 4D printing crawling robot.

[0053] As shown in the drawing, Figure 1 The bidirectional crawling robot based on 4D printing mainly comprises a first execution device 1, a support device 2 and a second execution device 3. Wherein:

[0054] The first execution device 1 comprises a first main body module 13, a first driving layer module 12, a first sensor module 14 and a first friction module 11.

[0055] Here, the first main body module 13 is made of liquid crystal elastomer (LCE) material. The first driving layer module 12 is composed of a polyimide film with a grid structure. The first sensor module 14 detects the bending angle of the first main body module 13 through a bending curvature detection sensor. The first friction module 11 is made of silicone glue.

[0056] The first driving layer module 12 of the first execution device 1 is arranged at the lower part of the first main body module 13, so that the first driving layer module 12 contacts the plane of an object, such as the ground or the desktop. When the first driving layer module 12 is supplied with voltage / current, the first driving layer module 12 generates heat, so that the first main body module 13 is heated to generate upward bending deformation. The first sensor module 14 is arranged on the other side (i.e. the upper part) of the first main body module 13. The first friction module 11 is arranged at the front end of the crawling robot body.

[0057] The support device 2 is composed of double-layer polyimide film and is used to connect the first execution device 1 and the second execution device 3.

[0058] The second actuator 3 mainly includes: a second main body module 32, a second drive layer module 31, a second sensor module 33, and a second friction module 34. Wherein:

[0059] The second main body module 32 is also made of liquid crystal elastomer (LCE) material. The second driving layer module 31 is composed of a polyimide film with a mesh structure. The second sensor module 32 detects the bending angle of the second main body module 32 through a bending curvature detection sensor. The second friction module 34 is made of polydimethylsiloxane.

[0060] The second driving layer module 31 is disposed on the upper part of the second main body module 32, and the second driving layer module 31 does not contact the plane. When current is applied, the second driving layer module 31 generates heat, causing the second main body module 32 to bend downward due to the heat. The second sensor module 33 is placed on the lower part of the second main body module 32, in contact with a plane (such as the ground).

[0061] The second friction module 34 is located at the very end of the crawling robot. The second main body module 32 of the part in which it is located is slightly bent upward until the second friction module 34 does not contact the ground, so that the friction force of the second friction module 34 on the ground is greater than the friction force of the first drive layer module 12 on the ground; or, so that the friction force of the second friction module 34 on the ground is less than the friction force of the first friction module 11 on the ground.

[0062] Figure 3 This is a block diagram illustrating the control principle of the model-free adaptive control method / algorithm used in the 4D-printed crawling robot, i.e., the bidirectional crawling robot, described in this invention.

[0063] like Figure 3 As shown in the control principle diagram of this model-free adaptive control method / algorithm:

[0064] 1 / z represents a delayed sampler, which delays the transmission of the sampled information obtained in the previous step.

[0065] Here, the previous stage refers to the previous one or several timing / clock control cycles.

[0066] The pseudo-partial derivative estimation algorithm module is used to receive the sampled information after multiple delays and evaluate the value of the pseudo-partial derivative based on the output value of the bidirectional crawling robot.

[0067] A model-free adaptive controller is used to solve for the input value u(k) of the control signal at the current moment (of the bidirectional crawling robot) by using the difference between the output value and the expected value at the previous moment and the pseudo-partial derivative value at the current moment.

[0068] Here, the input value u(k) can be a voltage value, a current value, or the like.

[0069] Then, the control signal u(k) is input into the bidirectional crawling robot to achieve precise motion control of the 4D printing crawling robot.

[0070] Here, the model-free adaptive control method mainly executes the model-free adaptive control algorithm, that is, the following steps are executed:

[0071] First, the step of establishing a dynamic linearization data model: for a nonlinear system, the pseudo-derivative represents the value of a certain point in the adjacent time interval, and the curve fitting is used to represent the data model, which is called a dynamic linearization model. As shown in Figure 2 The dynamic linearization data model is fitted to the real model by calculating the pseudo-derivative value existing between two points through the values of the adjacent two sampling data.

[0072] Second, the step of determining the model-free adaptive controller to be used: the pseudo-derivative at each time is evaluated by the following evaluation algorithm:

[0073] (1)

[0074] to calculate the pseudo-derivative (value) at the current time.

[0075] In formula (1), represents the pseudo-derivative at the current time evaluated by input and output data. is the pseudo-derivative at the previous sampling time evaluated by input and output data. is a step factor. is to prevent the pseudo-derivative estimate from changing too much. is the input difference of the previous two times. is the input difference of the current time and the previous time.

[0076] Preferably, in order to prevent the pseudo-derivative from falling into a dead zone, a reset algorithm can also be used:

[0077] (2)

[0078] In formula (2), is the pseudo-derivative at the current time evaluated by input and output data. represents the pseudo-derivative at the initial time. is a sufficiently small positive number.

[0079] Third, the input signal of each time system can be calculated by the model-free adaptive controller:

[0080] (3)

[0081] In formula (3), is the voltage input value at the current time. is the voltage input value at the previous sampling time. is a step factor. The introduction of is to prevent the input from changing too much. is the pseudo partial derivative at the current time. is the expected output value at the next time. is the output value at the current time.

[0082] Here, only the input data and output data of the previous time are needed to track the expected value well. Next, the appropriate parameters are set according to the adjustment of the parameters.

[0083] The present application uses the input and output data of the crawling robot to accurately control the bending angle of the execution device by using the model-free adaptive control algorithm, and the single execution device is controllable. Through the cooperative control of the first execution device 1 and the second execution device 3 of the bidirectional crawling robot, the step control and behavior control are realized.

[0084] Figure 4 It is a control system schematic diagram of the bidirectional crawling robot of the present application 4D printing.

[0085] As Figure 4 shown, the control system of the bidirectional crawling robot mainly includes a system building module, a behavior control module and a step control module. Among them:

[0086] The system building module transmits the system input information to the built-in single-chip microcomputer through the built-in acquisition circuit, transplants the model-free adaptive control algorithm in the single-chip microcomputer, and obtains the effective system output value at the current time through calculation.

[0087] The behavior control module receives the effective system output value at the current time to control the bidirectional crawling robot to realize the forward action and the backward action. Among them:

[0088] The principle of realizing the forward action is that the state is that the first friction module 11 contacts the ground, and the second main body module 32 starts to bend the angle to 90 degrees on the ground, showing an arch shape. Because the friction force of the first friction module 11 of the first execution device 1 is greater than that of the second friction module 34 of the second execution device 3, the second execution device 3 is pulled forward as a whole. At this time, the angle of the second execution device 3 is maintained, the first execution device 1 is bent upward, showing a reverse arch shape. The friction force of the first execution device 1 on the ground is small, and the static friction coefficient of the second friction module 34 of the second execution device 3 is greater than that of the first execution device 1. The whole shows forward movement.

[0089] The principle of realizing the backward movement is that the state is that the first friction module 11 contacts the ground, the first main body module 13 is bent to 30 degrees, and a micro-arch shape is presented. At this time, the first execution device 1 contacts the ground, and the friction is very small. The second main body module 32 is bent to 90 degrees. The static friction coefficient of the second friction module 34 is greater than that of the first execution device 1, so that the first execution device 1 is pulled backward by the supporting device 2. The bending angle of the second main body module 32 is maintained, the first main body module 13 is bent to 0 degrees, and the first friction module 11 contacts the ground. Because the friction of the second friction module 34 is smaller than that of the first friction module 11, the whole shows a backward movement.

[0090] The principle of realizing the step size adjustment through the step control module is that the change of the step size is realized by changing the relative angle of the behavior control module or / and other behavior control modules. For example, in the embodiment of the application, the change of the step size can be realized by changing the relative angle of the first execution device 1 and the second execution device 3.

[0091] The application applies the concept and method of model-free adaptive control to the crawling robot structure based on 4D printing, and verifies the control effect of the control algorithm on a single actuator through simulation experiments.

[0092] Because the tree model of the crawling robot is difficult to establish, a nonlinear model is used to replace the model of the single actuator.

[0093] (4)

[0094]

[0095] In formula (4), is the input value at the current moment. is the output value at the current moment. is the output value at the next moment.

[0096] The input and output of the assumed model are used to verify that the desired value can be well tracked only by the input and output. The expected trajectory is set to a square wave, and it can be seen from the simulation implementation diagram that the tracking effect is good.

[0097] The embodiment of the application also provides an electronic device readable storage medium, the electronic device readable storage medium stores one or more program modules, the program modules are one or more of a system building module, a behavior control module and a step control module; when the program modules are read or called by a processor of an electronic device, the electronic device is caused to execute the steps of the model-free adaptive control method based on the 4D printing crawling robot. The electronic device can be a computer or a computer system, and can also be other intelligent electronic devices configured similarly to the computer system. ​

[0098] Figure 5 , Figure 6 , Figure 7 This is a simulation diagram illustrating the model-free adaptive control algorithm for the bidirectional crawling robot of this invention. Wherein:

[0099] Figure 5 This is a schematic diagram of the input simulation implementation of a single actuator based on model-free adaptive control in this invention.

[0100] like Figure 5 As shown, the input curves clearly illustrate that model-free adaptive control can effectively control the input at the current moment.

[0101] Figure 6 This is a simulation diagram illustrating the pseudo-partial derivatives of a single actuator based on model-free adaptive control in this invention.

[0102] like Figure 6 As shown, its pseudo-partial derivative is the estimate of the partial derivative between two adjacent points during the sampling process.

[0103] Figure 7 This is a simulation diagram illustrating the tracking expectation value of a single actuator based on model-free adaptive control in this invention.

[0104] like Figure 7 As shown, the dashed line is the actual output curve of a single actuator, while the solid line is the expected trajectory of a single actuator.

[0105] refer to Figure 7 The control effect diagram of the control system in this invention, as shown, demonstrates that the desired trajectory can be tracked well within several sampling periods. The crawling robot, through cooperative control, controls the first actuator 1 and the second actuator 3, achieving excellent control performance.

[0106] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention.

Claims

1. A model-free adaptive control method based on a 4D-printed crawling robot, characterized in that, include: Steps for establishing a dynamic linearized data model; The step of calculating the value of the pseudo-partial derivative at the current time using a model-free adaptive controller and a pseudo-partial derivative estimation algorithm; in the step of calculating the value of the pseudo-partial derivative at the current time using a model-free adaptive controller and a pseudo-partial derivative estimation algorithm, the pseudo-partial derivative at each time step is calculated by the following formula: （1） in, This represents the pseudo-partial derivative at the current moment, evaluated using the input and output data. This is the pseudo-partial derivative of the previous sampling time evaluated using the input and output data; Step size factor; To prevent excessive changes in the spurious partial derivative estimates; This represents the input difference between the first two time points; It represents the input difference between the current time step and the previous time step; it also includes a reset algorithm to prevent pseudo-partial derivatives from getting stuck in the dead zone. （2） in, This is the pseudo-partial derivative of the current moment, evaluated using the input and output data; The pseudo-partial derivatives represent the values ​​at the initial time step. It is a sufficiently small positive number; The input signal value at each moment is calculated by the model-free adaptive controller. Using the input and output data of the model-free adaptive control algorithm, the steps of stride control and behavior control of the actuator of the 4D-printed crawling robot are implemented. The step of calculating the input signal value at each moment by the model-free adaptive controller is as follows: （3） in, This is the current voltage input value; The voltage input value at the previous sampling time; Step size factor; The introduction of this is to prevent excessive changes in input; The pseudo-partial derivative at the current moment; This represents the expected output value at the next moment. The output value at the current moment.

2. A model-free adaptive control system based on the model-free adaptive control method for a 4D-printed crawling robot as described in claim 1, characterized in that, include: The system consists of three modules: a behavior control module and a stride control module; among which: The system building module transmits system input information to the built-in microcontroller through the built-in acquisition circuit. The microcontroller then implements a model-free adaptive control algorithm and calculates the effective system output value at the current moment. The behavior control module is used to receive the valid system output value at the current moment in order to control the 4D printed crawling robot to perform forward and backward movements. The stride control module changes stride length by altering the relative angle between the behavior control module and / or other behavior control modules.

3. The model-free adaptive control system according to claim 2, characterized in that, The behavior control module is specifically used to control the first execution device and / or the second execution device to enable the first execution device to perform a forward movement or the second execution device to perform a backward movement, and also to enable the first execution device and the second execution device to cooperate with each other to achieve the forward movement and / or backward movement of the 4D printed crawling robot.

4. A 4D-printed crawling robot, characterized in that, It includes a first actuator (1), a support device (2), and a second actuator (3); wherein the first actuator (1) is connected to the second actuator (3) through the support device (2), and the first actuator (1) or / and the second actuator (3) control the forward movement, backward movement, and stride of the 4D printed crawling robot by executing the model-free adaptive control method based on the 4D printed crawling robot according to claim 1.

5. The 4D-printed crawling robot according to claim 4, characterized in that, The first actuator (1) further includes: a first main body module (13), a first driving layer module (12), a first sensor module (14), and a first friction module (11); wherein, the first driving layer module (12) is located at the lower part of the first main body module (13), so that the first driving layer module (12) contacts a plane; when voltage or current is applied to the first driving layer module (12), the first driving layer module (12) generates heat, causing the first main body module (13) to bend upward due to heat; the first sensor module (14) is located on the other side of the first main body module (13); and the first friction module (11) is located at the front end of the 4D printed crawling robot body.

6. The 4D-printed crawling robot according to claim 4, characterized in that, The second actuator (3) further includes: a second main body module (32), a second drive layer module (31), a second sensor module (33), and a second friction module (34); wherein: The second drive layer module (31) is located on the upper part of the second main body module (32). When current is applied, the second drive layer module (31) generates heat, causing the second main body module (32) to bend downwards due to the heat, placing the second sensor module (33) on the lower part of the second main body module (32) and contacting a plane. The second friction module (34) is located at the very end of the crawling robot, and the second main body module (32) in which it is located bends slightly upwards until the second friction module (34) is removed from contact with the ground. It is used to make the friction force of the second friction module (34) of the second actuator (3) on the ground greater than the friction force of the first drive layer module (12) of the first actuator (1) on the ground; or, to make the friction force of the second friction module (34) on the ground less than the friction force of the first friction module (11).

7. An electronic device readable storage medium, characterized in that, The electronic device's readable storage medium stores one or more program modules, which are one or more of a system building module, a behavior control module, and a stride control module; when the program module is read or invoked by the electronic device's processor, the electronic device executes the steps of the model-free adaptive control method based on a 4D-printed crawling robot as described in claim 1.

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