A control method for an artificial muscle

Abstract

A control method of artificial muscle, comprising the following steps: S1: generating an artificial muscle bundle by connecting artificial muscle modules in series; S2: establishing an artificial muscle bundle voltage displacement model, and obtaining a target displacement Y of the artificial muscle bundle through the artificial muscle bundle voltage displacement model * (t) required ideal voltage V(t); S3: a feedforward inverse compensation controller compensates the ideal voltage V(t) in advance according to a disturbance signal; the disturbance signal includes humidity, temperature and load change; S4: a PID controller automatically adjusts the ideal voltage V(t) according to an error signal e(t); the error signal e(t) is the difference between the ideal voltage V(t) and an actual voltage; S5: the target displacement Y of the artificial muscle bundle is realized according to the ideal voltage V(t) * (t). The application can accurately calculate the voltage required for a specific displacement target, effectively offset the nonlinearity and external disturbance of the artificial muscle system, improve the control accuracy, and make the displacement of the artificial muscle bundle accurately reach the target displacement.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of material science, in particular to a control method of artificial muscle. BACKGROUND

[0002] Artificial muscle is a kind of high-tech material or device that can change shape under external physical or chemical stimulation, and is widely used in the development of robot technology and the study of biological structure. At present, artificial muscle is mainly composed of multiple electrically driven flexible material units, which are called flexible actuators or artificial muscle modules. The control method of artificial muscle refers to the precise control of the movement and behavior of artificial muscle through various control strategies and technical means, so that it can simulate the function and characteristics of real muscle.

[0003] The existing patent with application number CN118025790A discloses a control method of a curved block flexible clamp, which comprises: a control mechanism controls the air bag to exhaust, the clamping space between the first clamping plate and the second clamping plate is aligned with the curved block, and the first clamping plate and the second clamping plate are located on both sides of the curved block; the first clamping plate moves towards the second clamping plate to reduce the clamping space and preliminarily clamps the curved block, and the first clamping plate and the second clamping plate maintain the preliminary clamping state of the curved block; the control mechanism controls the air bag to inflate, and controls the air intake amount of the air bag, the control mechanism is provided with an air intake amount threshold, and the inflation is stopped after the air intake amount threshold is reached; the curved block is clamped again under the preliminary clamping state after the air bag is inflated, and the clamping force of the secondary clamping is greater than that of the preliminary clamping.

[0004] The above-mentioned curved block flexible clamp is a flexible actuator, and the defects of the existing control method of the flexible actuator are: the current control method can only realize rough qualitative control, it is difficult to introduce PID adjustment, so it is difficult to realize accurate quantitative control, and it is also difficult to expand to more complex application scenarios. This is mainly because the flexible actuator has significant nonlinear characteristics, its modeling process is complex and difficult, and it is difficult to find a suitable transfer function to accurately describe the control characteristics of the flexible actuator in many cases. Due to the lack of in-depth understanding of its physical model, accurate control of the flexible actuator is also difficult to achieve. SUMMARY

[0005] In view of the above defects of the prior art, the present application provides a control method of artificial muscle, which can accurately calculate the voltage required for a specific displacement target, effectively offset the nonlinearity and external disturbance of the artificial muscle system, improve the control accuracy, and make the displacement of the artificial muscle bundle accurately reach the target displacement.

[0006] To achieve the above-mentioned purpose, the technical scheme adopted by the present application is:

[0007] A control method of artificial muscle, characterized in that it comprises the following steps:

[0008] S1: Artificial muscle modules are connected in series to form an artificial muscle bundle;

[0009] S2: An artificial muscle bundle voltage displacement model is established to obtain the target displacement Y of the artificial muscle bundle through the artificial muscle bundle voltage displacement model * (t) the ideal voltage V(t) required;

[0010] S3: The feedforward inverse compensation controller compensates the ideal voltage V(t) in advance according to the disturbance signal; the disturbance signal includes humidity, temperature and load change;

[0011] S4: The PID controller automatically adjusts the ideal voltage V(t) according to the error signal e(t); the error signal e(t) is the difference between the ideal voltage V(t) and the actual voltage;

[0012] S5: The target displacement Y of the artificial muscle bundle is realized according to the ideal voltage V(t) * (t).

[0013] An artificial muscle module is the basic unit of an artificial muscle, and each module can be independently electrically connected and controlled. When multiple artificial muscle modules are connected in series, an artificial muscle bundle is formed. Each artificial muscle bundle contains several artificial muscle modules connected in series, and each artificial muscle bundle can be controlled as an independent minimum control loop. Applying the control method from a single artificial muscle bundle to each artificial muscle bundle can achieve more precise and complex control, and can accurately control the displacement and output force of each artificial muscle bundle, so that the artificial muscle can complete more complex tasks.

[0014] The artificial muscle bundle voltage displacement model describes the displacement behavior of the artificial muscle bundle under different voltages, allowing the calculation of the voltage required to achieve a specific displacement target.

[0015] Feedforward inverse compensation is a control strategy whose core idea is to use the inverse model of the controlled object as a feedforward compensation controller to compensate for the input signal or known disturbances in advance, thereby improving the performance of the control system. The feedforward inverse compensation controller predicts the possible error of the artificial muscle bundle at a future time based on the disturbance signal. Based on the predicted error, the controller calculates the compensation amount that needs to be applied in advance to offset the delay or nonlinearity of the artificial muscle bundle. The calculated compensation amount is applied to the ideal voltage V(t) to obtain the adjusted ideal voltage V(t). Through advance compensation, the artificial muscle system can respond to the control signal more quickly, reducing the time to reach the desired state, and can reduce the steady-state error of the artificial muscle system, improving control accuracy. The feedforward inverse compensation controller can better cope with the nonlinearity and external disturbances of the artificial muscle system, improving the robustness of the system.

[0016] A PID controller is an automatic control system that adjusts the desired voltage V(t) by calculating and using a combination of proportional, integral, and derivative components. The PID controller calculates an adjustment value based on the error signal e(t) through its proportional, integral, and derivative components. This adjustment value is applied to the desired voltage V(t) to obtain a new desired voltage V(t). The artificial muscle system will continuously monitor the actual voltage and repeat the above steps based on the new error signal e(t) to continuously adjust until the actual voltage is consistent with the desired voltage V(t).

[0017] In summary, the present application can achieve precise control of artificial muscle bundle displacement by establishing an artificial muscle bundle voltage displacement model, combining feedforward inverse compensation and PID control, so that it can accurately simulate the functions and characteristics of real muscles and complete various complex motion tasks. The pre-compensation effect of the feedforward inverse compensation controller and the real-time adjustment function of the PID controller enable the artificial muscle system to quickly respond to control signals, reduce the time to reach the desired state, and improve the dynamic performance of the system. This control method is not only suitable for a single artificial muscle bundle, but can also be applied to complex artificial muscle systems composed of multiple parallel artificial muscle bundles to achieve coordinated control of the entire system.

[0018] As a preferred, the artificial muscle bundle voltage displacement model in step S2 comprises:

[0019] calculating the equivalent voltage V c (t) across the equivalent capacitor of the artificial muscle module in the equivalent circuit; calculating the Maxwell stress suffered by the artificial muscle module under the equivalent voltage V c (t); and calculating the driving displacement Y(t) of the artificial muscle module through the generalized Kalvin model.

[0020] The equivalent circuit is used to calculate the equivalent voltage V c (t) across the equivalent capacitor of the artificial muscle module. For example, if the equivalent circuit includes a resistor one and a resistor two in series, and a capacitor one in parallel with the resistor two, the equivalent circuit model is as follows:

[0021]

[0022] where R1, R 2、 C1 are the values of the resistor one, the resistor two, and the capacitor one, respectively, which are determined by the electrochemical properties of the PVC gel.

[0023] The Maxwell stress model of the artificial muscle module is as follows:

[0024]

[0025] The S-R layer is the driving layer of the artificial muscle module, and ε0, εv The relative dielectric constant of the m-PVC gel, S is the electrode area, and v0 is the volume of the S-R layer under the action of the electric field.

[0026] The generalized Kelvin model is as follows:

[0027]

[0028] wherein, is the delay time constant. E0, E1, E2 are spring parameters in the generalized Kelvin model, and η0, η1, η2 are damping parameters in the generalized Kelvin model.

[0029] The artificial muscle bundle voltage displacement model can be obtained by simultaneously solving the above three model formulas, as shown below. This formula directly reflects the dynamic relationship between the input voltage and the output strain.

[0030]

[0031] As a preferred, the step S4 comprises: the fuzzy controller adjusts the PID controller parameters.

[0032] The inputs of the fuzzy controller include the following two main variables: the error signal e(t), which is the difference between the ideal voltage V(t) and the actual voltage, directly reflects the deviation between the system performance and the expected target; and the error change rate , which is the rate of change of the error signal e(t) over time, reflects the speed of error change, which can help the control system to predict the future trend of the error and make adjustments in advance, thereby improving the dynamic response performance of the system. The fuzzy controller processes these two inputs according to the preset fuzzy rules, and outputs the adjustment values K p , K i and K d of the PID controller parameters. These adjustment values are used to dynamically adjust the proportional, integral and derivative gains of the PID controller, and adjust the control parameters according to the real-time state and performance indicators of the artificial muscle bundle, so as to achieve the optimal control effect. The fuzzy controller intelligently adjusts the parameters of the PID controller by real-time monitoring of the error signal e(t) and the error change rate , so that the control system can more flexibly and effectively cope with various uncertainties and dynamic changes.

[0033] As a preferred, the step S1 comprises: adding an ionic liquid to the gel driving part of the artificial muscle module; and using alternating high and low level control for the artificial muscle bundle, driving at high level and sensing detection at low level.

[0034] The method of adding ionic liquid realizes the integration of high-efficiency control and driving of artificial muscle. The integration of driving action control and sensing state function can improve the efficiency and response speed of the system. The high-low level alternating control strategy is adopted, such as 1 second high level and 0.5 second low level control mode. The driving is carried out in high level, the artificial muscle bundle is activated, and the action is carried out. The sensing detection is carried out in low level to evaluate the state of the artificial muscle bundle. Because the gel used by the artificial muscle module is fibrous, its specific surface area is significantly increased, and the self-sensing ability of the gel can be significantly improved by adding ionic liquid. The improvement of self-sensing ability makes it possible to obtain the state information of the driver by detecting voltage, current and other signals without adding additional sensors. The dynamic response time of the artificial muscle bundle is generally not less than 1 second, which means that the system has enough time to detect the state during the low level, and will not affect the action of the artificial muscle bundle. This method of driving and sensing integration improves the efficiency and response speed of the system. Through specific circuit design and control strategy, the state of the driver can be monitored in real time while ensuring the driving effect, so as to realize more accurate control.

[0035] As preferred, the control method comprises: detecting the current size and normal working state of the artificial muscle bundle.

[0036] When the artificial muscle bundle is driven, the self-sensing function is used to check whether the current of the artificial muscle bundle is normal. If the current is too large, power-off protection is needed to prevent damage. If the current is too small, pressure driving is needed. If the current is normal, it is continued to detect whether the artificial muscle bundle can work normally. If the artificial muscle bundle cannot work normally, it is determined as damaged. If the artificial muscle bundle can work normally, the detection is ended.

[0037] As preferred, the control method comprises: calculating the total ideal voltage required according to the target displacement Y * (t) of the normal working artificial muscle bundle and the artificial muscle bundle voltage displacement model.

[0038] The artificial muscle system needs to identify and determine whether each artificial muscle bundle is in a working state, which may involve checking the electrical connection, signal transmission and response ability of each muscle bundle. The artificial muscle bundle voltage displacement model describes the relationship between the voltage applied to the artificial muscle module and the displacement generated thereby. The target displacement Y * (t) of each artificial muscle may be different. According to the target displacement Y * (t) of each normal working artificial muscle bundle, the ideal voltage required for this artificial muscle bundle to reach this displacement is calculated, and the ideal voltages of each artificial muscle bundle are accumulated to finally obtain the total ideal voltage required.

[0039] As a preference, said control method is applicable to artificial muscles made of electroactive polymer materials.

[0040] Compared with the prior art, the beneficial effects of the present application are embodied in:

[0041] 1. The control method for flexible actuators in the prior art can only perform rough qualitative control, mainly because flexible actuators usually have strong nonlinearity, and modeling is difficult and complex, so it is difficult to find a transfer function to describe the control object. The present application can realize accurate control of artificial muscle bundle displacement by establishing an artificial muscle bundle voltage displacement model, combining feedforward inverse compensation and PID control, so that it can accurately simulate the functions and characteristics of real muscles and complete various complex motion tasks. The pre-compensation effect of the feedforward inverse compensation controller and the real-time adjustment function of the PID controller enable the artificial muscle system to quickly respond to control signals, reduce the time to reach the desired state, and improve the dynamic performance of the system. This control method is not only suitable for a single artificial muscle bundle, but can also be applied to complex artificial muscle systems composed of multiple parallel artificial muscle bundles to realize collaborative control of the entire system.

[0042] 2. The control method for flexible actuators in the prior art cannot achieve good self-sensing, and sensors need to be added for closed-loop control. The present application adds ionic liquid to the gel driving part of the artificial muscle module, controls the artificial muscle bundle with alternating high and low levels, drives at high level, and senses at low level, realizes the integration of driving and sensing of artificial muscle efficient control, and can improve the efficiency and response speed of the artificial muscle system.

[0043] 3. The present application can generate greater force and greater displacement range by connecting multiple artificial muscle modules in series, so as to be able to perform more complex tasks or withstand greater loads. Each artificial muscle bundle can be controlled independently, increasing the flexibility of the system, and different artificial muscle bundles can be controlled as needed to achieve more complex motion patterns.

[0044] 4. Since each artificial muscle bundle is independently controlled, if one of the muscle bundles is damaged, it will not affect the work of other artificial muscle bundles, making the entire artificial muscle system more reliable and durable, and having good robustness. When a slight damage occurs to an artificial muscle bundle (such as a slight current or a slight current), the artificial muscle bundle can adjust the voltage to ensure driving efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0045] Figure 1 is a schematic diagram of the method of Example 1;

[0046] Figure 2is a schematic diagram of a control method of the artificial muscle group of Example 1;

[0047] Figure 3 is a schematic diagram of an artificial muscle bundle voltage displacement model of Example 1;

[0048] Figure 4 is a fuzzy rule table of the fuzzy controller of Example 1;

[0049] Figure 5 is a schematic diagram of a self-sensing function of Example 1;

[0050] Figure 6 is a main function flow chart of the control method of Example 1;

[0051] Figure 7 is a control function flow chart of the control method of Example 1;

[0052] Figure 8 is a detection function flow chart of the control method of Example 1. DETAILED DESCRIPTION

[0053] In order to make the technical means, creative features, purposes and effects of the application easy to understand, the application will be further described in conjunction with specific drawings. However, the application is not limited to the following examples.

[0054] It should be noted that the structures, proportions, sizes, etc. shown in the drawings attached to the present specification are only used to cooperate with the content disclosed in the specification, to be understood and read by those skilled in the art, and do not have technical substantive significance, and any modification of the structure, change of the proportion relationship or adjustment of the size, without affecting the effects and purposes that can be achieved by the application, should still fall within the scope of the technical content disclosed by the application.

[0055] Example 1:

[0056] As shown in Figure 1 a control method of a heterogeneous artificial muscle, characterized in that it comprises the following steps:

[0057] S1: generating an artificial muscle bundle by connecting artificial muscle modules in series;

[0058] S2: establishing an artificial muscle bundle voltage displacement model, and obtaining a target displacement Y * (t) of the artificial muscle bundle through the artificial muscle bundle voltage displacement model;

[0059] S3: a feedforward inverse compensation controller compensates in advance for the ideal voltage V(t) according to an error signal e(t); the error signal e(t) is the difference between the ideal voltage V(t) and the actual voltage;

[0060] S4: The PID controller automatically adjusts the ideal voltage V(t) based on the error signal e(t);

[0061] S5: Achieve the target displacement Y of the artificial muscle bundle according to the ideal voltage V(t) * (t).

[0062] An artificial muscle module is the basic unit that makes up an artificial muscle, and each module can be independently electrically connected and controlled. When multiple artificial muscle modules are connected in series, an artificial muscle bundle is formed. Each artificial muscle bundle contains several series-connected artificial muscle modules, and each artificial muscle bundle can be controlled as an independent minimum control loop. Applying the control method from a single artificial muscle bundle to each artificial muscle bundle can achieve more precise and complex control, accurately control the displacement and force of each artificial muscle bundle, and thus control the artificial muscle group unit composed of artificial muscle bundles, enabling the artificial muscle to complete more complex tasks. The diagram shows the specific control method of the artificial muscle group unit, describing how to accurately control the artificial muscle bundle through a computer and a controller to achieve a specific displacement target. As shown in Figure 2 , the artificial muscle group unit is composed of multiple artificial muscle bundles, and each artificial muscle bundle can be independently controlled to achieve complex motion and force control. According to the application scenario, the computer first calculates the target displacement of each artificial muscle bundle based on the target displacement of the artificial muscle group unit. This means that the computer needs to distribute the target displacement to each individual muscle bundle based on the overall motion requirements. The actual displacement of each artificial muscle bundle is fed back to the controller. These feedback information is used to adjust the control signal to ensure that the actual displacement of the artificial muscle bundle matches the target displacement.

[0063] The artificial muscle bundle voltage-displacement model describes the displacement behavior of the artificial muscle bundle under different voltages, allowing the calculation of the voltage required to achieve a specific displacement target.

[0064] The feedforward inverse compensation channel is a control strategy whose core idea is to use the inverse model of the controlled object as a feedforward compensation controller to compensate for the input signal or known disturbances in advance, thereby improving the performance of the control system. The feedforward inverse compensation controller predicts the possible error of the artificial muscle bundle at a future time based on the error signal e(t). Based on the predicted error, the controller calculates the compensation amount that needs to be applied in advance to offset the delay or nonlinearity of the artificial muscle bundle. The calculated compensation amount is applied to the ideal voltage V(t) to obtain the adjusted ideal voltage V(t). By compensating in advance, the artificial muscle system can respond to the control signal more quickly, reducing the time to reach the desired state, and can reduce the steady-state error of the artificial muscle system, improving control accuracy. The feedforward inverse compensation controller can better cope with the nonlinearity and external disturbances of the artificial muscle system, improving the robustness of the system.

[0065] There are several ways to obtain the empirical formula of the feedforward compensation controller: 1. Data collection. Select the key static characteristics of the artificial muscle (such as output force, displacement) as the output, and voltage, current as the input, pay attention to the environmental temperature, humidity. Design multiple experiments, drive the artificial muscle according to different voltage or current combinations, accurately measure the corresponding static characteristics, and take the average of multiple measurements to ensure the accuracy of the data. 2. Data analysis and fitting. Draw a scatter plot to observe the relationship, select a fitting method like linear or nonlinear, such as linear regression, polynomial regression, etc., and use the least squares method to determine the model coefficients. Divide the training and validation sets, use the training set to fit and the validation set to verify the model, and check the mean square error and other indicators. If it does not meet the standard, adjust the model. 3. Construct the feedforward compensation function. According to the fitting model, build a function, measure the displacement and current relationship, and derive the current calculation formula as the feedforward function. Real-time monitor the error, and adjust the function parameters according to the adaptive strategy to optimize the feedforward compensation effect.

[0066] The PID controller is an automatic control system that adjusts the ideal voltage V(t) by calculating and using a combination of proportional, integral, and derivative components. The PID controller calculates an adjustment value based on the error signal e(t) through its proportional, integral, and derivative components. Apply this adjustment value to the ideal voltage V(t) to get a new ideal voltage V(t). The artificial muscle system will continuously monitor the actual voltage and repeat the above steps according to the new error signal e to continuously adjust until the actual voltage and ideal voltage V(t) are consistent.

[0067] In summary, the present application can achieve precise control of artificial muscle bundle displacement by establishing an artificial muscle bundle voltage displacement model, combining feedforward inverse compensation and PID control, so that it can accurately simulate the functions and characteristics of real muscles and complete various complex motion tasks. The feedforward inverse compensation controller has an advance compensation effect, and the PID controller has a real-time adjustment function, so that the artificial muscle system can quickly respond to the control signal, reduce the time to reach the desired state, and improve the dynamic performance of the system. This control method is not only suitable for a single artificial muscle bundle, but also can be applied to complex artificial muscle systems composed of multiple parallel artificial muscle bundles to achieve coordinated control of the entire system.

[0068] The control method of the embodiment is suitable for artificial muscles made of electroactive polymer materials, such as PVC gel and CPVC gel. The electroactive polymer host materials include PVC (polyvinyl chloride) electroactive materials and other electroactive and dielectric elastomer materials, such as CPVC (chlorinated polyvinyl chloride), Polymethyl methacrylate, polyurethane, polystyrene, polyvinyl acetate, PA6, polyvinyl alcohol (PVA), polycarbonate, polyethylene terephthalate, polyacrylonitrile, silicone, etc. The thickness of the polymer film is 10-1000 microns. The types of plasticizers used in the host materials include, in addition to DBA, other plasticizers with similar functions, such as DMA, DESuC, DEA, DOS, DOA, DMP, DBP, DOP, DEHP, DESeb, DBSeb, DOSeb, etc. The ratio of polymer materials to plasticizers is 1:1-1:10. The mesh or concave-convex anode electrode material: in addition to the metal mesh electrode, a metal electrode with a concave-convex structure (such as a metal flat plate electrode with uniformly distributed columnar bosses on the flat plate electrode) is added, and a resin-woven mesh electrode is added by plating a conductive metal layer on the surface, which can greatly reduce the electrode mass and the overall mass of the driver without affecting the performance. The thickness of the electrode is 50-500 microns. The wire diameter of the mesh electrode is 25-250 microns. The flexible anode and cathode electrode materials: conductive thin film electrodes (thickness less than several tens of microns) are used, including metal and conductive polymer materials.

[0069] As shown in Figure 3 , the step S2 of establishing the artificial muscle bundle voltage displacement model includes: calculating the equivalent voltage V c (t) across the equivalent capacitor of the artificial muscle module in the equivalent circuit; calculating the Maxwell stress suffered by the artificial muscle module under the equivalent voltage V c (t); and calculating the driving displacement Y(t) of the artificial muscle module through the generalized Kalvin model. The equivalent circuit is used to calculate the equivalent voltage V c (t) across the equivalent capacitor of the artificial muscle module. As shown in the equivalent circuit, it includes resistor one and resistor two in series, and capacitor one in parallel with resistor two, and the equivalent circuit model is as follows:

[0070]

[0071] where R1, R 2、C1 is the value of resistance one, resistance two, and capacitance one, which is determined by the electrochemical characteristics of the PVC gel artificial muscle. The voltage across resistance one is the body layer; the parallel voltage across capacitance one and resistance two can be equivalent to the voltage of the PVC driving layer.

[0072] The Maxwell stress model of the artificial muscle module is as follows:

[0073]

[0074] The S-R layer is the driving layer of the artificial muscle module, and ε0, ε v are the dielectric constant of vacuum and the relative dielectric constant of PVC gel, respectively, S is the electrode area, and v0 is the volume of the S-R layer under the action of the electric field.

[0075] The generalized Kelvin model is as follows:

[0076]

[0077] wherein, is the delay time constant. E0, E1, and E2 are spring parameters in the generalized Kelvin model, η0, η1, and η2 are damping parameters in the generalized Kelvin model, D(t) is the creep compliance, and τ n is the relaxation time

[0078] The artificial muscle bundle voltage displacement model can be obtained by combining the above three model formulas as shown below, which directly reflects the dynamic relationship between the input voltage and the output strain.

[0079]

[0080] Step S4 includes adjusting the PID controller parameters by the fuzzy controller. The inputs of the fuzzy controller include the following two main variables: the error signal e(t), which is the difference between the ideal voltage V(t) and the actual voltage, directly reflecting the deviation between the system performance and the expected target; and the error change rate , which is the change rate of the error signal e(t) over time, reflecting the speed of error change, which can help the control system predict the future trend of the error, so as to make adjustments in advance and improve the dynamic response performance of the system. The fuzzy controller processes these two inputs according to the preset fuzzy rules, and outputs the adjustment values K p , K i , and K d of the PID controller parameters. These adjustment values are used to dynamically adjust the proportional, integral, and derivative gains of the PID controller, and adjust the control parameters according to the real-time state and performance indicators of the artificial muscle bundle, so as to achieve the optimal control effect. The fuzzy controller monitors the error signal e(t) and the error change rate The parameters of the PID controller are intelligently adjusted to make the control system more flexible and effective in dealing with various uncertainties and dynamic changes. As shown in Figure 4 The following table is the fuzzy rule table of Δk p , Δk i , and Δk d .

[0081] Step S1 includes adding an ionic liquid, such as 1-allyl-3-methylimidazolium bis(trifluoromethanesulfonyl)imide, to the gel driving part of the artificial muscle module to make it have better conductivity, thus having self-sensing function, which can be used for negative feedback channel. The artificial muscle bundle is controlled by alternating high and low levels, and is driven at high level and sensed at low level. By adding ionic liquid, the drive-sensing integration of artificial muscle efficient control is realized. Drive-sensing integration refers to integrating the action control of driving and the state function of sensing detection together, which can improve the efficiency and response speed of the system. The alternating control strategy of high and low levels is adopted, such as the control mode of 1 second high level and 0.5 second low level. The artificial muscle bundle is activated and moves at high level, and the state of the artificial muscle bundle is sensed at low level to evaluate the state of the artificial muscle bundle. The realization of drive-sensing integration requires a voltage boosting module for increasing voltage, a voltage controller for accurately controlling the voltage applied to the driver, a switch for controlling the circuit, a resistor and a capacitor for monitoring the driving state.

[0082] Since the gel used by the artificial muscle module is fibrous, its specific surface area is significantly increased, and the addition of ionic liquid can significantly improve the self-sensing ability of the gel. The improvement of self-sensing ability makes it possible to obtain the state information of the driver by detecting signals such as voltage and current without adding additional sensors. The dynamic response time of the artificial muscle bundle is generally not less than 1 second, which means that the system has enough time to detect the state during the low level period without affecting the movement of the artificial muscle bundle. This drive-sensing integration method improves the efficiency and response speed of the system by integrating the driving and sensing functions. Through specific circuit design and control strategy, the state of the driver can be monitored in real time while ensuring the driving effect, thereby realizing more accurate control.

[0083] As shown in Figures 5-8As shown, the control method further comprises detecting the current size and normal working state of the artificial muscle bundle. When the artificial muscle bundle is driven, the self-sensing function is used to check whether the current of the artificial muscle bundle is normal. If the current is too large, power-off protection is needed to prevent damage. If the current is too small, pressure driving is needed. If the current is normal, it is continued to detect whether the artificial muscle bundle can work normally. If the artificial muscle bundle cannot work normally, it is determined to be damaged. If the artificial muscle bundle can work normally, the detection is ended. In this embodiment, the detection procedure in the control method is realized by the system detection function and the self-sensing module. The self-sensing module judges the real-time displacement of the artificial muscle bundle through the change of the electrical signal, and further predicts the actual voltage corresponding to the displacement.

[0084] The control method further comprises calculating the total ideal voltage required according to the target displacement of the normal working artificial muscle bundle and the artificial muscle bundle voltage displacement model. The artificial muscle system needs to identify and determine whether each artificial muscle bundle is in a working state, which may involve checking the electrical connection, signal transmission and response capability of each muscle bundle. The artificial muscle bundle voltage displacement model describes the relationship between the voltage applied to the artificial muscle module and the displacement generated thereby. The target displacement of each artificial muscle may be different. The ideal voltage required for each normal working artificial muscle bundle to reach the target displacement is calculated according to the target displacement of each normal working artificial muscle bundle, and the ideal voltage of each artificial muscle bundle is accumulated, and finally the total ideal voltage required is obtained.

[0085] In the artificial muscle system program, each artificial muscle bundle is traversed in a loop and a control function is called for each artificial muscle bundle. The control function is based on the artificial muscle bundle voltage displacement model, and the target displacement Y * (t) is calculated, and a PID controller is combined to control the voltage change function according to the difference e(t) between the ideal and actual voltages. After the traversal of all artificial muscle bundles is completed, the ideal voltage required for the target displacement Y * (t) is calculated. The control function includes a detection function for obtaining the current state of the artificial muscle bundle. The self-sensing module judges the real-time displacement of the driver through the change of the electrical signal, and further predicts the actual voltage corresponding to the displacement. The control function also includes a function for determining whether to drive or sense. If it is a driving process, the voltage is applied, and if it is a sensing process, the capacitance and resistance are detected. According to the detected capacitance and resistance, the voltage is adjusted using a PID controller. It is checked whether the target displacement Y * (t) is reached. If the target displacement Y * (t) is reached, the control function is ended; otherwise, the adjustment is continued.

Claims

1. A control method of an artificial muscle, characterized by, The control method comprises the following steps: S1: generating an artificial muscle bundle by connecting artificial muscle modules in series; S2: establishing an artificial muscle bundle voltage displacement model, and obtaining a target displacement Y of the artificial muscle bundle through the artificial muscle bundle voltage displacement model * (t) the ideal voltage V(t) required S3: a feedforward inverse compensation controller compensates the ideal voltage V(t) in advance according to a disturbance signal; the disturbance signal includes humidity, temperature and load change; S4: a PID controller automatically adjusts the ideal voltage V(t) according to an error signal e(t); the error signal e(t) is the difference between the ideal voltage V(t) and the actual voltage; S5: achieving the target displacement Y of the artificial muscle bundle according to the ideal voltage V(t) * (t); The artificial muscle bundle voltage displacement model in the step S2 comprises: Calculate the equivalent voltage V across the equivalent capacitor of the artificial muscle module in the equivalent circuit. c (t); Calculate the equivalent voltage V. c The Maxwell stress on the artificial muscle module is calculated using the generalized Calvin model; the driving displacement Y(t) of the artificial muscle module is calculated using the generalized Calvin model. The equivalent circuit comprises a resistor one and a resistor two connected in series, and a capacitor one connected in parallel with the resistor two, and the equivalent circuit model is as follows: Wherein R1, R2 and C1 are the values of the resistor one, the resistor two and the capacitor one respectively, which are determined by the electrochemical characteristics of the PVC gel; The Maxwell stress model of the artificial muscle module is as follows: S-R layer is the driving layer of artificial muscle module, ε0, ε v respectively the dielectric constant of vacuum and the relative dielectric constant of m-PVC gel, S is the electrode area, v0 is the volume of S-R layer under the action of electric field The generalized Kalvin model is as follows: wherein, is the retardation time constant, E0, E1, E2are spring parameters in the generalized Kelvin model, η0, η1, η2are damping parameters in the generalized Kelvin model, and D(t) is the creep compliance. The artificial muscle bundle voltage displacement model can be obtained by combining the above three model formulas, and the formula directly reflects the dynamic relationship between the input voltage and the output strain as follows:

2. The control method of the artificial muscle according to claim 1, characterized by, The step S4 comprises: a fuzzy controller adjusts the parameters of the PID controller.

3. The control method of the artificial muscle according to claim 1, characterized by, The step S1 comprises: adding an ionic liquid to the gel driving part of the artificial muscle module; and controlling the artificial muscle bundle by using alternating high and low levels, driving at the high level and sensing and detecting at the low level.

4. The control method of the artificial muscle according to claim 1, characterized by, The control method comprises: detecting the current size and normal working state of the artificial muscle bundle.

5. The control method of the artificial muscle according to claim 4, characterized by, The control method comprises: calculating the total ideal voltage required according to the target displacement of the artificial muscle bundle in normal working state and the artificial muscle bundle voltage displacement model.

6. The control method of the artificial muscle according to any one of claims 1 to 5, characterized by, The control method is suitable for artificial muscles prepared from electroactive polymer materials.

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