Control method of artificial muscle

Abstract

An artificial muscle control method comprises the following steps that S1, an artificial muscle module is connected in series to generate an artificial muscle bundle; s2, establishing an artificial muscle beam voltage displacement model, and obtaining an ideal voltage V (t) required by the target displacement Y * (t) of the artificial muscle beam through the artificial muscle beam voltage displacement model; s3, the feedforward inverse compensation controller compensates the ideal voltage V (t) in advance according to the disturbance signal; the disturbance signals comprise humidity, temperature and load change; 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 value between the ideal voltage V (t) and the actual voltage; s5, according to the ideal voltage V (t), the target displacement Y * (t) of the artificial muscle bundle is achieved. According to the method, the voltage required by a specific displacement target can be accurately calculated, the nonlinearity and external disturbance of an artificial muscle system are effectively counteracted, the control precision is improved, and the displacement of the artificial muscle bundle can accurately reach the target displacement.

Description

Technical Field

[0001] The present invention relates to the field of material science and technology, and in particular to a control method for artificial muscles. Background Art

[0002] Artificial muscle is a high-tech material or device that can change shape under external physical or chemical stimulation. It is widely used in the development of robotics and biological structure research. At present, artificial muscles are 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 muscles through various control strategies and technical means, so that it can simulate the functions and characteristics of real muscles.

[0003] The existing patent with application number CN118025790A discloses a control method for a flexible clamp of a curved block, including: a control mechanism controls the airbag to evacuate air, and the clamping space between the first clamp and the second clamp is aligned with the curved block, so that the first clamp and the second clamp are located on both sides of the curved block; the first clamp moves toward the second clamp to reduce the clamping space, and the curved block is initially clamped, and the initial clamping state of the curved block by the first clamp and the second clamp is maintained; the control mechanism controls the inflation of the airbag and controls the air intake of the airbag, and the control mechanism is provided with an air intake threshold, and inflation is stopped after the air intake threshold is reached; after the airbag is inflated, the curved block is clamped for a second time in the initial clamping state, wherein the clamping force of the secondary clamping is greater than the clamping force of the initial clamping.

[0004] The above-mentioned curved block flexible fixture is a flexible actuator. The defects of the existing flexible actuator control method are: the current control method can often only achieve rough qualitative control, and it is difficult to introduce PID regulation, so it is impossible to achieve precise quantitative control, and it is difficult to expand to more complex application scenarios. This is mainly because the flexible actuator has significant nonlinear characteristics, and its modeling process is complex and difficult, which makes it 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, precise control of the flexible actuator is also difficult to achieve. Summary of the invention

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

[0006] In order to achieve the above object, the technical solution adopted by the present invention is:

[0007] A method for controlling an artificial muscle, characterized in that it comprises the following steps:

[0008] S1: connecting artificial muscle modules in series to generate artificial muscle bundles;

[0009] S2: Establishing an artificial muscle bundle voltage displacement model, and obtaining the target displacement Y of the artificial muscle bundle through the artificial muscle bundle voltage displacement model * (t) The desired ideal voltage V(t);

[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 changes;

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

[0013] Artificial muscle modules are the basic units that make up artificial muscles, and each module can be electrically connected and controlled independently. When multiple artificial muscle modules are connected in series in sequence, 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 artificial muscles can complete more complex tasks.

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

[0015] 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 disturbance 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 certain moment in the future 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 nonlinear effect 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 faster, reduce the time to reach the desired state, reduce the steady-state error of the artificial muscle system, and improve the control accuracy. The feedforward inverse compensation controller can better cope with the nonlinearity and external disturbances of the artificial muscle system and improve the robustness of the system.

[0016] The PID controller is an automatic control system that adjusts the ideal voltage V(t) by calculating and using a combination of the proportional, integral, and differential parts. The PID controller obtains an adjustment value based on the error signal e(t) through its proportional, integral, and differential calculations. This adjustment value is applied to the ideal voltage V(t) to obtain a new ideal 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 ideal voltage V(t).

[0017] In summary, the present invention can achieve precise control of the displacement of artificial muscle bundles by establishing a voltage displacement model of artificial muscle bundles, combined with 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 advance compensation effect of the feedforward inverse compensation controller and the real-time adjustment function of the PID controller enable the artificial muscle system to respond quickly 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 applicable to a single artificial muscle bundle, but can also be extended to a complex artificial muscle system composed of multiple parallel artificial muscle bundles to achieve coordinated control of the entire system.

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

[0019] 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 (t) The Maxwell stress on the artificial muscle module under the condition of Y(t); The driving displacement Y(t) of the artificial muscle module is calculated by the generalized Calvin model.

[0020] The equivalent circuit is used to calculate the equivalent voltage V across the equivalent capacitor of the artificial muscle module c (t). If the equivalent circuit includes resistors 1 and 2 connected in series, and capacitor 1 connected in parallel with resistor 2, the equivalent circuit model is as follows:

[0021]

[0022] Among them, R1, R 2、 C1 is the value of resistor 1, resistor 2, and capacitor 1, respectively, and is determined by the electrochemical properties of PVC gel.

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

[0024]

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

[0026] The generalized Calvin model is as follows:

[0027]

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

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

[0030]

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

[0032] The input of the fuzzy controller includes 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 desired target. And the error change rate It is the rate of change of the error signal e(t) over time, reflecting the speed of error change. It 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 value K of the PID controller parameters. p , K i and K d These adjustment values ​​are used to dynamically adjust the proportional, integral and differential gains of the PID controller, and adjust the control parameters according to the real-time status and performance indicators of the artificial muscle bundle to achieve the best control effect. The fuzzy controller monitors the error signal e(t) and the error change rate in real time. Intelligently adjusting the parameters of the PID controller enables the control system to respond to various uncertainties and dynamic changes more flexibly and effectively.

[0033] Preferably, the step S1 comprises: adding ionic liquid to the gel driving part of the artificial muscle module; controlling the artificial muscle bundle with alternating high and low levels, driving at a high level and sensing at a low level.

[0034] By adding ionic liquid, the integration of drive and transmission for efficient control of artificial muscle is realized. Drive and transmission integration refers to the integration of the motion control of the drive and the sensing detection state function, which can improve the efficiency and response speed of the system. A control strategy of alternating high and low levels is adopted, such as a control method of 1 second high level and 0.5 second low level. The drive is carried out at a high level, the artificial muscle bundle is activated and moves, and the sensing detection is carried out at a low level to evaluate the state of the artificial muscle bundle. Since the gel used in 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 perform state detection during the low level period without affecting the action of the artificial muscle bundle. This drive and transmission integration method improves the efficiency and response speed of the system by integrating the drive 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 achieving more precise control.

[0035] Preferably, the control method comprises: detecting the current magnitude 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 required to prevent damage. If the current is too small, pressure driving is required. If the current is normal, continue to check whether the artificial muscle bundle can work normally. If the artificial muscle bundle cannot work normally, it is judged to be damaged. If the artificial muscle bundle can work normally, the test ends.

[0037] Preferably, the control method comprises: according to the target displacement Y of the artificial muscle bundle in normal operation * (t) and the total ideal voltage required for calculating the artificial muscle bundle voltage-displacement model.

[0038] The artificial muscle system needs to identify 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 it produces. The target displacement Y of each artificial muscle * (t) may be different. According to the target displacement Y of each normally working artificial muscle bundle * (t) Calculate the ideal voltage required for this artificial muscle bundle to achieve this displacement, accumulate the ideal voltages of each artificial muscle bundle, and finally obtain the total ideal voltage required.

[0039] Preferably, the control method is applicable to artificial muscles made of electroactive polymer materials.

[0040] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0041] 1. The control methods of the prior art for flexible actuators can often only perform rough qualitative control, mainly because flexible actuators usually have strong nonlinearity and are difficult and complex to model. Therefore, in most cases, it is difficult to find a transfer function to describe the control object. The present invention establishes an artificial muscle bundle voltage displacement model, combines feedforward inverse compensation and PID control, and can achieve precise control of the displacement of the artificial muscle bundle, so that it can accurately simulate the functions and characteristics of real muscles and complete various complex motion tasks. The advance compensation effect of the feedforward inverse compensation controller and the real-time adjustment function of the PID controller enable the artificial muscle system to respond quickly 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 applicable to a single artificial muscle bundle, but can also be extended to a complex artificial muscle system composed of multiple parallel artificial muscle bundles to achieve coordinated 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 if closed-loop control is desired. The present invention adds ionic liquid to the gel drive part of the artificial muscle module, uses alternating high and low levels to control the artificial muscle bundle, drives at a high level, and senses at a low level, thereby achieving the integration of drive and transmission for efficient control of artificial muscles, and can improve the efficiency and response speed of the artificial muscle system.

[0043] 3. The present invention connects multiple artificial muscle modules in series, so that the artificial muscle bundle can generate greater force and a larger displacement range, thereby being able to perform more complex tasks or withstand greater loads. Each artificial muscle bundle can be controlled independently, which increases the flexibility of the system. Different artificial muscle bundles can be controlled separately as needed to achieve more complex movement 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, durable and robust. When an artificial muscle bundle is slightly damaged (such as when the current is slightly smaller or larger), this artificial muscle bundle can self-adjust the voltage to ensure driving efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

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

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

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

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

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

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

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

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

[0053] In order to make the technical means, creative features, objectives and effects of the invention easier to understand, the invention is further described with reference to specific diagrams. However, the invention is not limited to the following implementation cases.

[0054] It should be noted that the structures, proportions, sizes, etc. illustrated in the drawings in this specification are only used to match the contents disclosed in the specification so as to facilitate understanding and reading by persons familiar with this technology. They are not used to limit the conditions under which the present invention can be implemented, and therefore have no substantive technical significance. Any structural modification, change in proportion or adjustment of size, without affecting the effects and purposes that can be achieved by the present invention, should still fall within the scope of the technical contents disclosed by the present invention.

[0055] Embodiment 1:

[0056] like Figure 1 A control method for a heterogeneous artificial muscle is shown, characterized in that it comprises the following steps:

[0057] S1: connecting artificial muscle modules in series to generate artificial muscle bundles;

[0058] S2: Establish an artificial muscle bundle voltage displacement model, and obtain the target displacement Y of the artificial muscle bundle through the artificial muscle bundle voltage displacement model * (t) The desired ideal voltage V(t);

[0059] S3: The feedforward inverse compensation controller compensates the ideal voltage V(t) in advance 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;

[0060] S4: The PID controller automatically adjusts the ideal voltage V(t) according to 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] Artificial muscle modules are the basic units that make up artificial muscles, and each module can be electrically connected and controlled independently. When multiple artificial muscle modules are connected in series in sequence, 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 force of each artificial muscle bundle, thereby controlling the artificial muscle group units composed of artificial muscle bundles, allowing artificial muscles to complete more complex tasks. Figure 1 shows the specific control method of the artificial muscle group unit, and describes how to use computers and controllers to accurately control artificial muscle bundles to achieve specific displacement targets. Figure 2 As shown, the artificial muscle group unit is composed of multiple artificial muscle bundles, and each artificial muscle bundle can be independently controlled to achieve complex movement 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 according to the overall movement requirements. The actual displacement of each artificial muscle bundle is fed back to the controller. This 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 at 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 disturbance 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 certain moment in the future 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 nonlinear effect 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 faster, reduce the time to reach the desired state, reduce the steady-state error of the artificial muscle system, and improve the control accuracy. The feedforward inverse compensation controller can better cope with the nonlinearity and external disturbances of the artificial muscle system and improve the robustness of the system.

[0065] There are many ways to obtain the empirical formula of the feedforward compensation controller: 1. Data acquisition. Select the key static characteristics of the artificial muscle (such as output force and displacement) as the output, and the voltage and current as the input, and pay attention to the ambient temperature and humidity. Design multiple sets of 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 data accuracy. 2. Data analysis and fitting. Draw a scatter plot to observe the relationship, select a fitting method based on linear or nonlinearity, 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, look at indicators such as mean square error, and adjust the model if it does not meet the standards. 3. Construct a feedforward compensation function. Construct a function based on the fitting model, measure the relationship between displacement and current, and derive the current calculation formula as a feedforward function. Monitor the error in real time, fine-tune the function parameters according to the adaptive strategy, and 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 the proportional, integral, and differential parts. The PID controller obtains an adjustment value based on the error signal e(t) through its proportional, integral, and differential calculations. This adjustment value is applied to the ideal voltage V(t) to obtain a new ideal 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 to continuously adjust until the actual voltage is consistent with the ideal voltage V(t).

[0067] In summary, the present invention can achieve precise control of the displacement of artificial muscle bundles by establishing a voltage displacement model of artificial muscle bundles, combined with 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 advance compensation effect of the feedforward inverse compensation controller and the real-time adjustment function of the PID controller enable the artificial muscle system to respond quickly 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 applicable to a single artificial muscle bundle, but can also be extended to a complex artificial muscle system composed of multiple parallel artificial muscle bundles to achieve coordinated control of the entire system.

[0068] The control method of this embodiment is suitable for artificial muscles made of electroactive polymer materials, such as PVC gel and CPVC gel. In addition to PVC (polyvinyl chloride) inductive materials, the electroactive polymer main body material also adds other inductive and dielectric elastomer materials, such as CPVC (chlorinated polyvinyl chloride), and Polymethyl methacrylate, polyurethane, polystyrene, polyvinyl acetate, PA6, polyvinyl alcohol (PVA), polycarbonate, polyethylene terephthalate, polyacrylonitrile, silicone and other inductive materials that can produce electrodeformation. The thickness of the polymer film is 10 to 1000 microns. Types of plasticizers used in the main body material: In addition to the plasticizer DBA, the main body part also adds other plasticizers with similar functions such as DMA, DESuC, DEA, DOS, DOA, DMP, DBP, DOP, DEHP, DESeb, DBSeb, DOSeb and the like. The ratio of polymer material to plasticizer is 1:1 to 1:10. Mesh or concave-convex anode electrode materials: In addition to metal mesh electrodes, metal electrodes with concave-convex structures (such as metal flat electrodes with evenly distributed columnar bosses on flat electrodes) and resin-woven mesh electrodes are plated with 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 to 500 microns. The wire diameter of the mesh electrode is 25 to 250 microns. Flexible cathode and anode electrode materials: Conductive film electrodes (thickness less than tens of microns) are used, including metals and conductive polymer materials.

[0069] like Figure 3 As shown, the artificial muscle bundle voltage displacement model established in step S2 includes: calculating 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 (t) The Maxwell stress on the artificial muscle module; the driving displacement Y(t) of the artificial muscle module is calculated by the generalized Calvin model. The equivalent circuit is used to calculate the equivalent voltage V across the equivalent capacitor of the artificial muscle module c (t). If the equivalent circuit includes resistors 1 and 2 connected in series, and capacitor 1 connected in parallel with resistor 2, the equivalent circuit model is as follows:

[0070]

[0071] Among them, R1, R 2、C1 is the value of resistor 1, resistor 2, and capacitor 1, which is determined by the electrochemical properties of the PVC gel artificial muscle. The voltage across resistor 1 is the body layer; the parallel voltage across capacitor 1 and resistor 2 is equivalent to the voltage of the PVC drive layer.

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

[0073]

[0074] The SR layer is the driving layer of the artificial muscle module, 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 SR layer of PVC gel under the action of the electric field.

[0075] The generalized Calvin model is as follows:

[0076]

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

[0078] Combining the above three model formulas, the voltage-displacement model of the artificial muscle bundle can be obtained as shown below. This formula directly reflects the dynamic relationship between input voltage and output strain.

[0079]

[0080] Step S4 includes: the fuzzy controller adjusts the PID controller parameters. The input of the fuzzy controller includes 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 It is the rate of change of the error signal e(t) over time, reflecting the speed of error change. It 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 value K of the PID controller parameters. p , K i and K d These adjustment values ​​are used to dynamically adjust the proportional, integral and differential gains of the PID controller, and adjust the control parameters according to the real-time status and performance indicators of the artificial muscle bundle to achieve the best control effect. The fuzzy controller monitors the error signal e(t) and the error change rate in real time. Intelligently adjust the parameters of the PID controller to enable the control system to respond to various uncertainties and dynamic changes more flexibly and effectively. Figure 4 As shown in the table below, Δk p , Δk i , Δk d The fuzzy rule table.

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

[0082] Since the gel used in the artificial muscle module is fibrous, its specific surface area is significantly increased. Adding 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 perform state detection during low-level periods without affecting the action of the artificial muscle bundle. This integrated drive and transmission method improves the efficiency and response speed of the system by integrating drive and sensing functions. Through specific circuit design and control strategies, it can monitor the state of the driver in real time while ensuring the drive effect, thereby achieving more precise control.

[0083] like Figure 5-8As shown, the control method also includes: 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 required to prevent damage. If the current is too small, pressure driving is required. If the current is normal, continue 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. This embodiment implements the detection procedure in the control method through the detection function and the self-sensing module in the system. The self-sensing module determines the real-time displacement of the artificial muscle bundle through the change of the electrical signal, and then predicts the actual voltage corresponding to the displacement.

[0084] The control method also includes: calculating the required total ideal voltage based on the target displacement of the normally 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 workable state, which may involve checking the electrical connection, signal transmission and response capabilities 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 it produces. The target displacement of each artificial muscle may be different. According to the target displacement of each normally working artificial muscle bundle, the ideal voltage required for this artificial muscle bundle to achieve this displacement is calculated, and the ideal voltage of each artificial muscle bundle is accumulated to finally obtain the required total ideal voltage.

[0085] In the artificial muscle system program, each artificial muscle bundle is looped through and the control function is called for each artificial muscle bundle. The control function is based on the artificial muscle bundle voltage displacement model, which is determined by the target displacement Y * (t) Calculate the ideal voltage V(t), and combine the PID controller to control the voltage change function according to the ideal and actual voltage difference e(t). Calculate the target displacement Y after completing the traversal of all artificial muscle bundles * (t) The desired ideal voltage. The control function includes a detection function, which is used to obtain the current state of the artificial muscle bundle. The self-sensing module determines the real-time displacement of the driver through the change of the electrical signal, and then predicts the actual voltage function corresponding to the displacement. The control function also includes determining whether to perform a driving or sensing process. If it is a driving process, voltage is applied. If it is a sensing process, capacitance and resistance are detected. The voltage is adjusted using a PID controller based on the detected capacitance and resistance. Check whether the target displacement Y is reached. * (t), if the target displacement Y is reached * (t), end the control function; otherwise, continue adjusting.

Claims

1. A method for controlling an artificial muscle, characterized in that: The following steps are involved: S1: connecting artificial muscle modules in series to generate artificial muscle bundles; S2: Establishing an artificial muscle bundle voltage displacement model, and obtaining the target displacement Y of the artificial muscle bundle through the artificial muscle bundle voltage displacement model * (t) required ideal voltage V(t); 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 changes; 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; S5: Realizing the target displacement Y of the artificial muscle bundle according to the ideal voltage V(t) * (t).

2. The control method of artificial muscle according to claim 1, characterized in that: The artificial muscle bundle voltage displacement model in 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 (t) The Maxwell stress on the artificial muscle module under the condition of Y(t); The driving displacement Y(t) of the artificial muscle module is calculated by the generalized Calvin model.

3. The control method of artificial muscle according to claim 1, characterized in that: The step S4 includes: the fuzzy controller adjusts the parameters of the PID controller.

4. The control method of artificial muscle according to claim 1, characterized in that: The step S1 comprises: adding ionic liquid to the gel driving part of the artificial muscle module; controlling the artificial muscle bundle with alternating high and low levels, driving at a high level and sensing at a low level.

5. The method for controlling an artificial muscle according to claim 1, characterized in that: The control method comprises: detecting the current magnitude and normal working state of the artificial muscle bundle.

6. The method for controlling an artificial muscle according to claim 5, characterized in that: The control method comprises: calculating the required total ideal voltage according to the target displacement of the artificial muscle bundle in normal operation and the voltage displacement model of the artificial muscle bundle.

7. The method for controlling an artificial muscle according to any one of claims 1 to 6, characterized in that: The control method is applicable to artificial muscles made of electroactive polymer materials.

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