Real-time measurement method and device for integrated shape memory alloy driver

Abstract

The application discloses a kind of integrated shape memory alloy driver real-time measurement method and device, it is related to material driving technical field.SMA wire linear driver temperature is changed by using power-up heating and power-off cooling strategy to drive SMA wire to generate deformation, current control circuit, data acquisition center, sensor module and host computer are integrated.Sensor module obtains deformation multi-parameter dynamic data, data acquisition center processes and transmits data, and adjusts SMA driving current.Resistance-strain compensation model is constructed, time-varying linear relationship between resistance and strain is established, target strain is converted into target resistance, real resistance is corrected and feedback current is generated.Adaptive fuzzy PID controller is designed, and double-mode control strategy is constructed to realize self-feedback control, and host computer synchronously acquires and displays data.The problem that the prior art cannot accurately control the intermediate state of driving process, and limits the control precision and intelligent level in complex working conditions is solved.

Description

Technical Field

[0001] This application relates to the field of materials-driven technology, and in particular to a real-time measurement method and device for an integrated shape memory alloy actuator. Background Technology

[0002] Shape memory alloys (SMAs), as a new type of smart material actuators, have advantages such as high power-to-weight ratio, strong integrability, large actuation stroke and good durability, and are regarded as an ideal choice to replace traditional actuators.

[0003] However, existing technologies mostly focus on controlling the start and stop states of the SMA phase transition, making it difficult to achieve precise control of the intermediate states during the drive process. Although introducing external sensors for closed-loop control improves accuracy, it significantly increases system complexity, size, and cost. Furthermore, existing technologies are mostly limited to single-parameter monitoring and feedback, restricting their control accuracy and intelligence level in complex operating conditions. Summary of the Invention

[0004] In this application embodiment, by providing an integrated shape memory alloy actuator real-time measurement method, the problem that existing technologies mostly focus on controlling the start and stop states before and after the SMA phase transition, making it difficult to achieve precise control of the intermediate state during the driving process, and are mostly limited to single parameter monitoring and feedback, thus limiting their control accuracy and intelligence level in complex working conditions.

[0005] In a first aspect, embodiments of this application provide a real-time measurement method for an integrated shape memory alloy actuator. The method includes: employing a control strategy of energizing and de-energizing to change the temperature of an SMA filament linear actuator with a two-way shape memory effect, thereby driving the SMA filament in the actuator to deform; integrating a current control circuit, a data acquisition center, a sensor module, and a host computer based on the control strategy; the sensor module is configured to acquire multi-parameter dynamic change data during the SMA filament deformation process; the data acquisition center acquires and stores the multi-parameter dynamic change data and transmits the data to the host computer; simultaneously, the current control circuit is used to adjust the SMA driving current; wherein the SMA driving current is the driving current input to the SMA filament linear actuator; and based on the multi-parameter dynamic change data, constructing a resistance-strain compensation model and adjusting it through online identification. The parameters in the resistance-strain compensation model are used to establish a time-varying linear relationship between resistance and strain; strain is a quantitative indicator of the deformation degree of SMA filament. Based on the time-varying linear relationship between resistance and strain, the target strain is converted into a target resistance in real time. The actual resistance value measured by the sensor module is corrected, and the corrected actual resistance value is used as the feedback quantity to calculate the resistance error between it and the target resistance. A feedback current driven by the resistance error is generated based on the resistance error. An adaptive fuzzy PID controller is designed. Based on the feedback current driven by the resistance error, and according to the asymmetric response characteristics of the SMA filament linear actuator in the power-on heating and power-off cooling stages, a dual-modal control strategy is constructed to complete the construction of an SMA actuator with self-feedback control capability. The host computer communicates with the data acquisition center in real time to synchronously collect and display the driving force, driving strain, surface temperature, and resistance value of the SMA actuator.

[0006] In one possible implementation, the current control circuit includes a power supply, a DC circuit breaker, and a programmable constant current output module. The power supply uses a rechargeable lithium battery pack as the main power source and is configured to power the SMA filament linear actuator. The DC circuit breaker is used to provide safety protection for the system and power supply in case of emergencies. The programmable constant current output module is used to regulate the current of the SMA filament in the SMA filament linear actuator. Its input terminal is connected to the positive and negative terminals of the rechargeable lithium battery pack, its output terminal is connected to both ends of the measured section of the SMA filament linear actuator, and its communication terminal is connected to the data acquisition center.

[0007] In one possible implementation, a resistance-strain compensation model is constructed based on multi-parameter dynamic change data. The parameters in the resistance-strain compensation model are adjusted online to establish a time-varying linear relationship between resistance and strain. Here, strain is a quantitative indicator of the degree of deformation of the SMA filament, and the multi-parameter dynamic change data includes ambient temperature, load stress, current, and voltage. The expression for the resistance-strain compensation model is: ;in, In response, This is a compensation coefficient used to characterize the strength of the linear relationship between resistance change and strain. This is the actual resistance value. To compensate for zero-point drift in the resistance-strain relationship, the parameters in the resistance-strain compensation model are adjusted online, and the expression is as follows: , ;in, The initial compensation coefficient, This serves as the initial compensation bias. This is the sensitivity coefficient of temperature to the compensation coefficient. This is the sensitivity coefficient of temperature to the compensation bias. This is the sensitivity coefficient of stress to the compensation coefficient. Let be the sensitivity coefficient of stress to compensation bias. The current ambient temperature. For reference temperature, Given the current load stress, The reference stress is used; the expression for the time-varying linear relationship between resistance and strain is: ;in, For the target resistance, Adapt to the target.

[0008] In one possible implementation, based on the time-varying linear relationship between resistance and strain, the target strain is converted into a target resistance in real time. The actual resistance value measured by the sensor module is corrected, and the corrected actual resistance value is used as a feedback quantity to calculate the resistance error between it and the target resistance. A feedback current driven by the resistance error is generated based on the resistance error. The calculation method of the actual resistance value is as follows: ;in, This is the actual resistance value. This is the voltage value. Let be the current value; the expression for the feedback current driven by the resistor error is: ;in, For feedback current, For proportional gain, This is due to resistance error; ;in, For the target resistance, This is the corrected actual resistance value.

[0009] In one possible implementation, the dual-modal control strategy includes: during the power-on heating phase, enhancing the derivative action of the PID controller to suppress overshoot; and during the power-off cooling phase, enhancing the integral action of the PID controller to eliminate steady-state residuals.

[0010] In one possible implementation, the adaptive fuzzy PID controller is designed based on the feedback current driven by the resistance error. According to the asymmetric response characteristics of the SMA wire linear actuator during the power-on heating and power-off cooling stages, a dual-modal control strategy is constructed to complete the construction of an SMA actuator with self-feedback control capability. This includes: defining the current error and its rate of change as inputs, and the output as the PID parameter increment; wherein the current error is the difference between the feedback current and the set current; during the fuzzy inference process, the Mamdani maximum-minimum synthesis method is used to fuzzify the input current error and its rate of change according to a pre-defined fuzzy subset, and the corresponding dual-modal control strategy is adjusted according to the constructed dual-modal fuzzy rule base; the fuzzy output calculated by each activation rule is defuzzified using the weighted centrifugal method to obtain the PID parameter increment for each output; the expression for the PID parameter increment is: ;in, To calculate the increment of the PID parameters, The number of rules to activate. For the first The increment of the PID parameter corresponding to each activation rule. For the first The membership degree of an activation rule. For index; the increment of the PID parameter to be calculated. Including the increment of the proportional coefficient Increment of integral coefficient and the increment of the differential coefficient The PWM duty cycle instruction output by the PID controller is determined based on the incremental PID parameters. ;in, This refers to the PWM duty cycle command output by the PID controller at the current moment. This refers to the PWM duty cycle command output by the PID controller at the previous moment. This is the initial value of the proportionality coefficient. The increment of the proportionality coefficient, This represents the change in error at the current moment. The initial value of the integral coefficient is . For the increment of the integral coefficient, The current error at the current moment. These are the initial values ​​of the differential coefficients. For the increment of the differential coefficient, The error change is the amount of the previous moment; the PWM duty cycle command is amplified by the programmable constant current output module and regulates the current flowing through the SMA wire to form a closed-loop control, so as to complete the construction of an SMA driver with self-feedback control capability.

[0011] Secondly, embodiments of this application provide an integrated shape memory alloy actuator real-time measurement device, which includes: a temperature changing module, used to change the temperature of an SMA filament linear actuator with a two-way shape memory effect using a control strategy of energizing and de-energizing to drive the SMA filament in the SMA filament linear actuator to deform; an integration module, used to integrate a current control circuit, a data acquisition center, a sensor module, and a host computer based on the control strategy; the sensor module is configured to acquire multi-parameter dynamic change data during the deformation process of the SMA filament, the data acquisition center acquires and stores the multi-parameter dynamic change data, and transmits the multi-parameter dynamic change data to the host computer, while the current control circuit is used to adjust the SMA driving current; wherein, the SMA driving current is the driving current input to the SMA filament linear actuator; and a construction module, used to construct a resistance-strain compensation model based on the multi-parameter dynamic change data, and adjust the model through online identification. The parameters in the resistance-strain compensation model are used to establish a time-varying linear relationship between resistance and strain; where strain is a quantitative indicator of the degree of deformation of SMA filament. A conversion module is used to convert the target strain into a target resistance in real time based on the time-varying linear relationship between resistance and strain, correct the actual resistance value measured by the sensor module, and use the corrected actual resistance value as feedback to calculate the resistance error between it and the target resistance, generating a feedback current driven by the resistance error. A design module is used to design an adaptive fuzzy PID controller, based on the feedback current driven by the resistance error, and construct a dual-modal control strategy according to the asymmetric response characteristics of the SMA filament linear actuator during the power-on heating and power-off cooling stages, to complete the construction of an SMA actuator with self-feedback control capability. A data acquisition module is used for the host computer to synchronously acquire and display the driving force, driving strain, surface temperature, and resistance value of the SMA actuator through real-time communication with the data acquisition center.

[0012] Thirdly, embodiments of this application provide an integrated shape memory alloy driver real-time measurement server, including a memory and a processor; the memory is used to store computer-executable instructions; the processor is used to execute the computer-executable instructions to implement the method described in the first aspect or any possible implementation of the first aspect.

[0013] Fourthly, embodiments of this application provide a computer-readable storage medium storing executable instructions, which, when executed by a computer, enable the method described in the first aspect or any possible implementation thereof.

[0014] One or more technical solutions provided in this application embodiment have at least the following technical effects: This application embodiment provides an integrated shape memory alloy actuator real-time measurement method, which uses a power-on heating and power-off cooling strategy to change the temperature of the SMA filament linear actuator to drive the SMA filament to deform. It integrates a current control circuit, a data acquisition center, a sensor module, and a host computer. The sensor module acquires dynamic data of multiple deformation parameters, the data acquisition center processes and transmits the data, and adjusts the SMA driving current. A resistance-strain compensation model is constructed to establish a time-varying linear relationship between resistance and strain, converting the target strain into a target resistance, correcting the actual resistance, and generating a feedback current. An adaptive fuzzy PID controller is designed, and a dual-modal control strategy is constructed to achieve self-feedback control. The host computer synchronously acquires and displays the data. This application constructs a self-feedback control system with resistance as the feedback signal. It abandons the dependence on external displacement, force, or temperature sensors, and utilizes the inherent electrical parameter of the SMA's own resistance to achieve precise closed-loop control of the driven displacement (strain), providing a new technical path for the miniaturization, integration, and low cost of SMA actuators. This solves the problem that existing technologies mostly focus on controlling the start and stop states of SMA before and after the phase transition, making it difficult to achieve precise control of the intermediate state during the drive process. Furthermore, they are mostly limited to single parameter monitoring and feedback, which restricts their control accuracy and intelligence level in complex working conditions. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments of this application or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 A flowchart illustrating a real-time measurement method for an integrated shape memory alloy actuator provided in this application embodiment.

[0017] Figure 2 This is a schematic diagram of a multi-parameter synchronous measurement and self-feedback control system provided in an embodiment of this application.

[0018] Figure 3 This is a schematic diagram of an integrated shape memory alloy actuator real-time measurement device provided in an embodiment of this application.

[0019] Figure 4 This is a schematic diagram of an integrated shape memory alloy driver real-time measurement server provided in an embodiment of this application. Detailed Implementation

[0020] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0021] The following description of some technologies involved in the embodiments of this application is provided to aid understanding and should be considered merely exemplary. Therefore, those skilled in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this application. Similarly, for clarity and brevity, some descriptions of well-known functions and structures are omitted in the following description.

[0022] This application provides a real-time measurement method for an integrated shape memory alloy actuator, such as... Figure 1 As shown, the method includes steps S101 to S107. Wherein, Figure 1 This is merely one execution order shown in the embodiments of this application and does not represent the only execution order of an integrated shape memory alloy actuator real-time measurement method. Where the final result can be achieved, Figure 1 The steps shown can be performed in parallel or in reverse order.

[0023] S101: The temperature of the SMA filament linear actuator with two-way shape memory effect is changed by the control strategy of heating up when powered on and cooling down when powered off, thereby driving the SMA filament in the SMA filament linear actuator to deform.

[0024] Specifically, the SMA wire (shape memory alloy wire) in this application is a double-pass SMA wire.

[0025] By applying electricity to heat the SMA filament, an internal phase change is triggered, resulting in deformation. When it is necessary to restore or change the deformed state, the power is cut off (or the current is reduced) to cool it down, triggering the phase change process again. This temperature change directly drives the SMA filament in the SMA filament linear actuator to produce the corresponding deformation, laying the foundation for subsequent measurement and control operations.

[0026] S102: Based on control strategy, it integrates current control circuit, data acquisition center, sensor module and host computer.

[0027] Figure 2This is a schematic diagram of a multi-parameter synchronous measurement and self-feedback control system provided in an embodiment of this application. The system is used to implement a real-time measurement method for an integrated shape memory alloy actuator. The current control circuit includes a power supply, a DC circuit breaker, and a programmable constant current output module. The power supply uses a rechargeable lithium battery pack as the main power source and is configured to power the SMA filament linear actuator. The DC circuit breaker is used to provide safety protection for the system and power supply in case of emergencies. To overcome the self-bending phenomenon of the double-pass SMA filament and apply preload, this application includes a fixed pulley and a fixed load counterweight. The fixed load counterweight (such as a standard weight) is connected by a rope, which passes over the fixed pulley and its other end is connected to the moving part of the displacement measurement module, thereby converting gravity into a constant tension along the axial direction of the double-pass SMA filament.

[0028] Specifically, the power supply circuit is equipped with an instantaneous trip DC circuit breaker to provide safety protection for the system and power supply in case of emergencies such as overload, short circuit or deflagration.

[0029] The programmable constant current output module is used to regulate the current of the SMA wire in the SMA wire linear actuator. Its input terminal is connected to the positive and negative terminals of the rechargeable lithium battery pack, its output terminal is connected to the two ends of the measured section of the SMA wire linear actuator, and its communication terminal is connected to the data acquisition center.

[0030] Specifically, to simplify debugging and achieve system miniaturization, the same sensors and system architecture were used in both the multi-parameter measurement and self-feedback control stages. The data acquisition center was miniaturized and included components such as an Arduino development board, a development board regulator, a main regulator, and an RS485-TTL converter module. Its core is the Arduino GIGA R1 development board, equipped with a dual-core microcontroller capable of high-frequency computation and high-frequency acquisition and processing of multiple signals. The system connection logic is as follows: The positive terminal of the main power supply is split into three paths along with the negative terminal after passing through a DC circuit breaker. The first path connects to the development board regulator, stepping down the voltage to 5.0V to independently power the Arduino development board. The second path connects to the power supply terminal of the high-power programmable constant current output module, powering it and the double-ended SMA cable; the output of this module is connected to both ends of the double-ended SMA cable. The third path connects to the input of the main regulator, stepping down the 24V voltage to a constant 5.0V, and its output is split into multiple paths to power various sensors and conversion modules. Since the development board has built-in TTL and I2C protocols but lacks an RS485 protocol, an RS485-TTL converter module is used to achieve communication conversion. The RS485 terminals (ports A and B) of this converter module are connected to the communication ports (ports A and B) of the high-power programmable constant current output module; the power supply for its TTL terminals is taken from the main voltage regulator, and the communication ports (RX and TX) are connected to the corresponding serial ports on the development board, thereby enabling the development board to control the constant current module at high speed and with precision.

[0031] Specifically, the programmable constant current output module adopts the RS485 Modbus-RTU communication protocol, has an output current readback function, a minimum starting current of 8.0mA, a maximum output current of 8.0A, and a maximum output power of 182W under a 24V input voltage. Based on PWM modulation and inductor filtering technology, the module can receive control signals and output current with a specified waveform and period. The current control resolution is 5.0mA, the maximum adjustment frequency is 50Hz, and the maximum current ramp rate is 0.36A / ms.

[0032] S103: The sensor module is configured to acquire multi-parameter dynamic change data during the SMA filament deformation process. The data acquisition center collects and stores the multi-parameter dynamic change data and transmits it to the host computer. Simultaneously, a current control circuit is used to adjust the SMA drive current. The SMA drive current is the drive current input to the SMA filament linear actuator.

[0033] The dynamic data of multiple parameters include ambient temperature, load stress, current and voltage.

[0034] Specifically, the displacement measurement in this application employs a micron-level high-precision grating ruler with a measuring range of 200 mm and an accuracy of 1 μm, suitable for measuring the reciprocating linear deformation of SMA wires. A mechanical connection structure is provided between the moving end of the double-pass SMA wire and the reading head of the grating ruler. One side of this structure secures the double-pass SMA wire with an M5 set screw, while the other side connects to the reading head of the grating ruler with an M5 screw, and is electrically insulated. The grating ruler is mounted on a support plate. The fixed end of the double-pass SMA wire is fixed and insulated using connectors made of PLA material. During assembly, it is ensured that the double-pass SMA wire is horizontal and the connectors at both ends are coaxial, resulting in only uniaxial linear displacement, suitable for measuring the reciprocating linear deformation of SMA wires, with a maximum operating speed of 0.33 m / s. Temperature measurement in this application is performed by a K-type thermocouple wire and a TI-INA826 temperature sensor module. The temperature sensor head of the K-type thermocouple wire is insulated and then tightly and parallelly attached to the surface of the double-pass SMA wire with high-temperature resistant tape. Its pin end is connected to the input terminal of the thermocouple temperature sensor module (such as the TI-INA826). This module has cold junction compensation, which calibrates the thermocouple signal to an accurate temperature value and sends the data to the Arduino development board via serial port (RX / TX). The measurement accuracy is 1℃, the thermocouple outer diameter is less than 0.2mm, and the temperature range is -20℃ to 320℃. Current measurement in this application uses a digital power meter, which is connected in series in the power supply circuit of the double-pass SMA wire 4 as a high-side ammeter. The measurement accuracy is 1mA, and it communicates with the Arduino development board via the I2C bus (SCL, SDA). Voltage measurement employs an analog-to-digital converter module (such as the ADS1117) with an accuracy of 0.09mV and a range of 6.134V. It is connected in parallel across the two ends of a double-ended SMA cable as a voltmeter, providing 15-bit resolution. The development board synchronously reads current and voltage data and calculates the resistance value in real time. The resistance value is calculated using Ohm's law. A power meter is connected in series with the SMA power supply circuit as an ammeter, while the ADS1117 module is connected in parallel across the two ends of the SMA cable as a voltmeter. Both communicate with the development board via I2C protocol and analog input channel, respectively.

[0035] S104: Based on multi-parameter dynamic variation data, a resistance-strain compensation model is constructed. The parameters in the resistance-strain compensation model are adjusted through online identification to establish a time-varying linear relationship between resistance and strain. Strain is a quantitative indicator of the degree of deformation of the SMA filament.

[0036] Based on multi-parameter dynamic variation data, a resistance-strain compensation model is constructed. The parameters in the resistance-strain compensation model are adjusted through online identification to establish a time-varying linear relationship between resistance and strain. Strain is a quantitative indicator of the degree of deformation of the SMA filament, including the following:

[0037] The dynamic data of multiple parameters include ambient temperature, load stress, current and voltage.

[0038] Specifically, this application can extract the values ​​of different working conditions for different combinations of temperature T∈[14, 35]°C and stress σ∈[0, 200]N (Newtons). and The linear relationship between the slope and intercept was calculated, and the temperature / stress sensitivity coefficient was fitted. , , , Therefore, a resistance-strain compensation model was established.

[0039] The expression for the resistance-strain compensation model is: .in, In response, This is a compensation coefficient used to characterize the strength of the linear relationship between resistance change and strain. This is the actual resistance value. This is a compensation bias used to correct zero-point drift in the resistance-strain relationship.

[0040] The parameters in the resistance-strain compensation model are adjusted through online identification, as expressed in the following expression: , .in, The initial compensation coefficient, This serves as the initial compensation bias. This is the sensitivity coefficient of temperature to the compensation coefficient. This is the sensitivity coefficient of temperature to the compensation bias. This is the sensitivity coefficient of stress to the compensation coefficient. Let be the sensitivity coefficient of stress to compensation bias. The current ambient temperature. For reference temperature, Given the current load stress, This is the reference stress.

[0041] The expression for the time-varying linear relationship between resistance and strain is: .in, For the target resistance, Adapt to the target.

[0042] S105: Based on the time-varying linear relationship between resistance and strain, the target strain is converted into the target resistance in real time. The real resistance value measured by the sensor module is corrected, and the corrected real resistance value is used as the feedback quantity to calculate the resistance error between it and the target resistance. The feedback current driven by the resistance error is generated based on the resistance error.

[0043] Based on the time-varying linear relationship between resistance and strain, the target strain is converted into the target resistance in real time. The actual resistance value measured by the sensor module is corrected, and the corrected actual resistance value is used as the feedback quantity to calculate the resistance error between it and the target resistance. Based on the resistance error, a feedback current driven by the resistance error is generated, including the following:

[0044] Specifically, after the system starts up, it enters the online control phase. First, the host computer (based on the Python platform) sends the target strain to the Arduino development board via USB serial port. The controller first queries the piecewise cubic spline model to locate... Current range The inverse function was solved using the Newton-Raphson iterative method. .in, for The minimum current it belongs to, for The maximum current it belongs to, This is the current value. This represents the strain as a function of current. Since the relationship between current and strain is often nonlinear, the piecewise cubic spline model divides the entire current range into multiple intervals, and uses a cubic polynomial in each interval to approximate the functional relationship between current and strain.

[0045] Initial value of iteration The iterative formula is: , where the derivative Iterate to The convergence occurs, and the obtained That is, feedforward current .in, This is a counter for the number of iterations. For the first Current during the next iteration For the first Current during the next iteration When the current is The corresponding strain value at time, Let the value of the derivative of strain with respect to current at a certain point be . For the derivative of strain with respect to current, , and These are the coefficients of the cubic polynomial for the corresponding interval in the piecewise cubic spline model. This represents the current being calculated.

[0046] Meanwhile, the system acquires five-dimensional physical quantities in real time. The Arduino development board generates a global synchronization trigger signal with a 1ms period through its internal high-precision timer. This signal simultaneously triggers: interrupt decoding of the edges of the AB phase signal (the encoder output signal type used to measure position and velocity) by the grating ruler, serial data reading from the K-type thermocouple (a type of base metal thermocouple) temperature sensing wire and thermocouple temperature sensor module, access to the I²C (synchronous serial bus) register of the digital power meter, and continuous sampling via the SPI (communication bus) of the analog-to-digital converter module. All acquisition tasks are completed in the hardware interrupt service routine and appended with a unified timestamp. Due to differences in the response delays of various peripherals, the raw data has a microsecond-level deviation. Therefore, a timestamp alignment algorithm is adopted: using the grating ruler displacement data as the reference time axis, the data of other channels are aligned to the same moment through linear interpolation, ultimately compressing the multi-channel data deviation to ≤10μs to ensure... , , , It strictly corresponds to the same transient state of the SMA filament.

[0047] Calculate the current load stress: ;in, The actual load force acting on the SMA wire is calculated from the preload provided by the constant load counterweight and the displacement feedback. This represents the cross-sectional area of ​​the SMA wire. (Combined) and The current resistance-strain compensation model is used to calculate the resistance-strain compensation model. and , and thus adapt to the target Convert to target resistance: .

[0048] The actual resistance value is calculated as follows: .in, This is the actual resistance value. This is the voltage value. This represents the current value.

[0049] The expression for the feedback current driven by the resistor error is: .in, For feedback current, For proportional gain, This is the resistance error.

[0050] Specifically, proportional gain Preset according to SMA yarn specifications.

[0051] .in, For the target resistance, This is the corrected actual resistance value.

[0052] S106: Design an adaptive fuzzy PID controller based on the feedback current driven by the resistance error. According to the asymmetric response characteristics of the SMA wire linear actuator in the power-on heating and power-off cooling stages, a dual-mode control strategy is constructed to complete the construction of the SMA actuator with self-feedback control capability.

[0053] The dual-mode control strategy includes: during the power-on heating phase, enhancing the derivative action of the PID controller to suppress overshoot; and during the power-off cooling phase, enhancing the integral action of the PID controller to eliminate steady-state residuals.

[0054] Design an adaptive fuzzy PID controller based on feedback current driven by resistance error. Based on the asymmetric response characteristics of the SMA wire linear actuator during the power-on heating and power-off cooling stages, construct a dual-modal control strategy to complete the construction of an SMA actuator with self-feedback control capability. This includes the following:

[0055] The current error and its rate of change are defined as inputs, and the output is the increment of the PID parameters. The current error is the difference between the feedback current and the set current.

[0056] In the fuzzy inference process, the Mamdani max-min synthesis method is employed to fuzzify the input current error and its rate of change according to a pre-defined fuzzy subset. Then, the corresponding bimodal control strategy is adjusted based on the constructed bimodal fuzzy rule base. The constructed bimodal fuzzy rule base includes multiple rules. When the system's current error and its rate of change satisfy the antecedent condition of a rule, that rule is activated, and subsequent adjustments to the bimodal control strategy are made based on the activated rule.

[0057] Specifically, feedforward current With feedback current Superimposed to form the total reference current : For precise tracking The system enables an adaptive fuzzy PID controller. This controller uses the current error... Its rate of change is the input, and the output is the increment of the PID parameter. , and .in, For actual current measurement. The controller has a built-in dual-modal fuzzy rule library, and the input quantity (current error) is... Error change Both the output quantity (PID parameter increment) and the output quantity are divided into 7 fuzzy subsets: negative large (NB), negative medium (NM), negative small (NS), zero (ZO), positive small (PS), positive medium (PM) and positive large (PB).

[0058] Specifically, for example, one rule is: "If the current error is negatively large (NB) and the rate of change of the current error is positively small (PS), then the increment of the differential coefficient..." "It is negative medium (NM)". In this example, "the current error is negative large (NB) and the rate of change of the current error is positive small (PS)" is the antecedent condition of this rule.

[0059] when (Heating phase) Activate the heating rule set and focus on the increment of the differential coefficient. The adjustment is to suppress current overshoot caused by the rapid phase transition of SMA, and the corresponding dual-modal fuzzy rule base is shown in Table 1.

[0060] Table 1 Heating Stage Rule table

[0061] when (Cooling-down phase) Activate the cooling rule set and focus on the increment of the integral coefficient. The enhancement is used to eliminate the steady-state residuals caused by slow natural cooling, and the corresponding dual-modal fuzzy rule base is shown in Table 2.

[0062] Table 2 Cooling Stage Rule table

[0063] The fuzzy output calculated for each activation rule is defuzzified using a weighted method to obtain the PID parameter increment for each output.

[0064] The expression for the PID parameter increment is: .in, To calculate the increment of the PID parameters, The number of rules to activate. For the first The increment of the PID parameter corresponding to each activation rule. For the first The membership degree of an activation rule. For indexing.

[0065] PID parameter increments to be calculated Including the increment of the proportional coefficient Increment of integral coefficient and the increment of the differential coefficient .

[0066] The PWM duty cycle instruction output by the PID controller is determined based on the increment of the PID parameters. .in, This refers to the PWM duty cycle command output by the PID controller at the current moment. This refers to the PWM duty cycle command output by the PID controller at the previous moment. This is the initial value of the proportionality coefficient. The increment of the proportionality coefficient, This represents the change in error at the current moment. The initial value of the integral coefficient is . For the increment of the integral coefficient, The current error at the current moment. These are the initial values ​​of the differential coefficients. For the increment of the differential coefficient, This represents the change in error at the previous moment.

[0067] The PWM duty cycle command is amplified by the programmable constant current output module and regulates the current flowing through the SMA wire to form a closed-loop control, thereby completing the construction of an SMA driver with self-feedback control capability.

[0068] S107: The host computer communicates with the data acquisition center in real time to synchronously collect and display the driving force, driving strain, surface temperature and resistance value of the SMA driver.

[0069] This application also provides an integrated shape memory alloy actuator real-time measurement device 300, such as... Figure 3 As shown, the device includes: a temperature changing module 301, an integration module 302, construction modules 303 and 304, a design module 305, and a data acquisition module 306.

[0070] The temperature change module 301 is used to change the temperature of the SMA filament linear actuator with two-way shape memory effect by adopting a control strategy of heating up when powered on and cooling down when powered off, thereby driving the SMA filament in the SMA filament linear actuator to deform.

[0071] The integrated module 302 integrates a current control circuit, a data acquisition center, a sensor module, and a host computer based on a control strategy. The sensor module is configured to acquire multi-parameter dynamic change data during the SMA filament deformation process. The data acquisition center collects and stores this multi-parameter dynamic change data and transmits it to the host computer. Simultaneously, the current control circuit regulates the SMA drive current. The SMA drive current is the drive current input to the SMA filament linear actuator.

[0072] Module 303 is used to construct a resistance-strain compensation model based on multi-parameter dynamic change data. The parameters in the resistance-strain compensation model are adjusted through online identification to establish a time-varying linear relationship between resistance and strain. Here, strain is a quantitative indicator of the degree of deformation of the SMA filament.

[0073] The conversion module 304 is used to convert the target strain into the target resistance in real time based on the time-varying linear relationship between resistance and strain, correct the real resistance value measured by the sensor module, and use the corrected real resistance value as a feedback quantity to calculate the resistance error between it and the target resistance, and generate a feedback current driven by the resistance error based on the resistance error.

[0074] Design module 305 is used to design an adaptive fuzzy PID controller. Based on the feedback current driven by the resistance error, and according to the asymmetric response characteristics of the SMA wire linear actuator in the power-on heating and power-off cooling stages, a dual-mode control strategy is constructed to complete the construction of an SMA actuator with self-feedback control capability.

[0075] The acquisition module 306 is used by the host computer to synchronously acquire and display the driving force, driving strain, surface temperature and resistance value of the SMA driver through real-time communication with the data acquisition center.

[0076] Some modules in the apparatus described in this application can be described in the general context of computer-executable instructions that are executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, classes, etc., that perform a specific task or implement a specific abstract data type. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0077] The apparatus or module described in the above embodiments can be implemented by a computer chip or physical entity, or by a product with a certain function. For ease of description, the above apparatus is described by dividing it into various modules according to their functions. When implementing the embodiments of this application, the functions of each module can be implemented in one or more software and / or hardware. Of course, a module that implements a certain function can also be implemented by combining multiple sub-modules or sub-units.

[0078] The methods, apparatus, or modules described in this application can be implemented in a computer-readable program code manner. The controller can be implemented in any suitable manner, such as a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers, and embedded microcontrollers. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicon Labs C8051F320. A memory controller can also be implemented as part of the control logic of a memory. Those skilled in the art will also recognize that, in addition to implementing the controller in purely computer-readable program code manner, the same functionality can be achieved by logically programming the method steps to make the controller take the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers. Therefore, such a controller can be considered a hardware component, and the means included within it for implementing various functions can also be considered as structures within the hardware component. Alternatively, the device used to implement various functions can be viewed as either a software module implementing the method or a structure within a hardware component.

[0079] like Figure 4 As shown in the figure, this application embodiment also provides an integrated shape memory alloy actuator real-time measurement server, including a memory 401 and a processor 402; the memory 401 is used to store computer-executable instructions; the processor 402 is used to execute the computer-executable instructions to implement the integrated shape memory alloy actuator real-time measurement method described above in this application embodiment.

[0080] This application also provides a computer-readable storage medium storing executable instructions, which, when executed by a computer, enable the real-time measurement method of an integrated shape memory alloy actuator described above in this application.

[0081] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary hardware. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product, or it can be embodied in the process of data migration. The computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, mobile terminal, server, or network device, etc.) to execute the methods described in the embodiments of this application.

[0082] The various embodiments described in this specification are presented in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on its differences from other embodiments. All or part of this application can be used in numerous general-purpose or special-purpose computer system environments or configurations.

[0083] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit this application. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of this application.

Claims

1. A real-time measurement method for an integrated shape memory alloy actuator, characterized in that, include: A control strategy of heating up by powering on and cooling down by powering off is adopted to change the temperature of the SMA filament linear actuator with two-way shape memory effect, thereby driving the SMA filament in the SMA filament linear actuator to deform. Based on the control strategy, the system integrates current control circuit, data acquisition center, sensor module and host computer; The sensor module is configured to acquire multi-parameter dynamic change data during the deformation process of SMA filament. The data acquisition center collects and stores the multi-parameter dynamic change data and transmits the multi-parameter dynamic change data to the host computer. At the same time, the current control circuit is used to adjust the SMA drive current. The SMA drive current is the drive current input to the SMA filament linear actuator. Based on multi-parameter dynamic change data, a resistance-strain compensation model is constructed. The parameters in the resistance-strain compensation model are adjusted through online identification to establish a time-varying linear relationship between resistance and strain. Among them, strain is a quantitative index of the degree of deformation of SMA filament. Based on the time-varying linear relationship between resistance and strain, the target strain is converted into the target resistance in real time. The real resistance value measured by the sensor module is corrected, and the corrected real resistance value is used as the feedback quantity to calculate the resistance error between it and the target resistance. The feedback current driven by the resistance error is generated based on the resistance error. An adaptive fuzzy PID controller is designed based on the feedback current driven by the resistance error. According to the asymmetric response characteristics of the SMA wire linear actuator in the power-on heating and power-off cooling stages, a dual-mode control strategy is constructed to complete the construction of the SMA actuator with self-feedback control capability. The host computer communicates with the data acquisition center in real time to synchronously collect and display the driving force, driving strain, surface temperature and resistance value of the SMA driver.

2. The real-time measurement method for the integrated shape memory alloy actuator according to claim 1, characterized in that, The current control circuit includes a power supply, a DC circuit breaker, and a programmable constant current output module. The power supply uses a rechargeable lithium battery pack as the main power source and is configured to power the SMA filament linear driver. DC circuit breakers are used to provide safety protection for systems and power supplies in case of emergencies; The programmable constant current output module is used to regulate the current of the SMA wire in the SMA wire linear actuator. Its input terminal is connected to the positive and negative terminals of the rechargeable lithium battery pack, its output terminal is connected to the two ends of the measured section of the SMA wire linear actuator, and its communication terminal is connected to the data acquisition center.

3. The real-time measurement method for the integrated shape memory alloy actuator according to claim 1, characterized in that, Based on multi-parameter dynamic change data, a resistance-strain compensation model is constructed. The parameters in the resistance-strain compensation model are adjusted online to establish a time-varying linear relationship between resistance and strain. Strain is a quantitative indicator of the degree of deformation of the SMA filament, including: Multi-parameter dynamic change data includes ambient temperature, load stress, current, and voltage; The expression for the resistance-strain compensation model is: ;in, In response, This is a compensation coefficient used to characterize the strength of the linear relationship between resistance change and strain. This is the actual resistance value. This is a compensation bias used to correct zero-point drift in the resistance-strain relationship; The parameters in the resistance-strain compensation model are adjusted through online identification, as expressed in the following expression: , ;in, The initial compensation coefficient, This serves as the initial compensation bias. This is the sensitivity coefficient of temperature to the compensation coefficient. This is the sensitivity coefficient of temperature to the compensation bias. This is the sensitivity coefficient of stress to the compensation coefficient. Let be the sensitivity coefficient of stress to compensation bias. The current ambient temperature. For reference temperature, Given the current load stress, For reference stress; The expression for the time-varying linear relationship between resistance and strain is: ;in, For the target resistance, Adapt to the target.

4. The real-time measurement method for the integrated shape memory alloy actuator according to claim 1, characterized in that, The method, based on the time-varying linear relationship between resistance and strain, converts the target strain into a target resistance in real time, corrects the actual resistance value measured by the sensor module, and uses the corrected actual resistance value as a feedback quantity to calculate the resistance error between it and the target resistance. A feedback current driven by the resistance error is then generated based on this error, including: The actual resistance value is calculated as follows: ;in, This is the actual resistance value. This is the voltage value. This is the current value; The expression for the feedback current driven by the resistor error is: ;in, For feedback current, For proportional gain, This is due to resistance error; ;in, For the target resistance, This is the corrected actual resistance value.

5. The real-time measurement method for the integrated shape memory alloy actuator according to claim 1, characterized in that, The dual-mode control strategy includes: during the power-on heating phase, enhancing the derivative action of the PID controller to suppress overshoot; and during the power-off cooling phase, enhancing the integral action of the PID controller to eliminate steady-state residuals.

6. The real-time measurement method for the integrated shape memory alloy actuator according to claim 5, characterized in that, The design of the adaptive fuzzy PID controller, based on the feedback current driven by the resistance error, and according to the asymmetric response characteristics of the SMA filament linear actuator during the power-on heating and power-off cooling stages, constructs a dual-modal control strategy to complete the construction of an SMA actuator with self-feedback control capability, including: Define the current error and its rate of change as inputs, and the output as the increment of the PID parameters; where the current error is the difference between the feedback current and the set current. In the fuzzy inference process, the Mamdani maximum-minimum synthesis method is adopted to fuzzify the input current error and its rate of change according to a pre-set fuzzy subset, and to adjust the corresponding dual-modal control strategy according to the constructed dual-modal fuzzy rule base. The fuzzy output calculated for each activation rule is defuzzified using a weighted mindset to obtain the PID parameter increment for each output. The expression for the PID parameter increment is: ;in, To calculate the increment of the PID parameters, The number of rules to activate. For the first The increment of the PID parameter corresponding to each activation rule. For the first The membership degree of an activation rule. For indexing; PID parameter increments to be calculated Including the increment of the proportional coefficient Increment of integral coefficient and the increment of the differential coefficient ; The PWM duty cycle instruction output by the PID controller is determined based on the increment of the PID parameters. ;in, This refers to the PWM duty cycle command output by the PID controller at the current moment. This refers to the PWM duty cycle command output by the PID controller at the previous moment. This is the initial value of the proportionality coefficient. The increment of the proportionality coefficient, This represents the change in error at the current moment. The initial value of the integral coefficient is . For the increment of the integral coefficient, The current error at the current moment. These are the initial values ​​of the differential coefficients. For the increment of the differential coefficient, This represents the change in error at the previous moment; The PWM duty cycle command is amplified by the programmable constant current output module and regulates the current flowing through the SMA wire to form a closed-loop control, thereby completing the construction of an SMA driver with self-feedback control capability.

7. A real-time measurement device for an integrated shape memory alloy actuator, characterized in that, include: The temperature module is used to change the temperature of the SMA filament linear actuator with two-way shape memory effect by adopting a control strategy of power-on heating and power-off cooling, thereby driving the SMA filament in the SMA filament linear actuator to deform. An integrated module is used to integrate a current control circuit, a data acquisition center, a sensor module, and a host computer based on a control strategy. The sensor module is configured to acquire multi-parameter dynamic change data during the deformation process of SMA filament. The data acquisition center acquires and stores the multi-parameter dynamic change data and transmits it to the host computer. At the same time, the current control circuit is used to adjust the SMA drive current. The SMA drive current is the drive current input to the SMA filament linear actuator. The module is used to build a resistance-strain compensation model based on multi-parameter dynamic change data. The parameters in the resistance-strain compensation model are adjusted through online identification to establish a time-varying linear relationship between resistance and strain. Among them, strain is a quantitative index of the degree of deformation of SMA filament. The conversion module is used to convert the target strain into the target resistance in real time based on the time-varying linear relationship between resistance and strain. It corrects the real resistance value measured by the sensor module, and uses the corrected real resistance value as a feedback quantity to calculate the resistance error between it and the target resistance. Based on the resistance error, it generates a feedback current driven by the resistance error. The design module is used to design an adaptive fuzzy PID controller. Based on the feedback current driven by the resistance error, and according to the asymmetric response characteristics of the SMA wire linear actuator in the power-on heating and power-off cooling stages, a dual-mode control strategy is constructed to complete the construction of an SMA actuator with self-feedback control capability. The acquisition module is used by the host computer to synchronously acquire and display the driving force, driving strain, surface temperature and resistance value of the SMA driver through real-time communication with the data acquisition center.

8. A real-time measurement server with an integrated shape memory alloy actuator, characterized in that, Including memory and processor; The memory is used to store computer-executable instructions; The processor is configured to execute the computer-executable instructions to implement the method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores executable instructions, which, when executed by a computer, enable the implementation of the method as described in any one of claims 1-6.

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