Intelligent control system of elastic floating clamping self-locking mechanism

Abstract

The invention discloses an intelligent control system of an elastic floating clamping self-locking mechanism, and relates to the technical field of mechanical and electrical integration equipment and automatic control. The intelligent control system comprises a core processing module which is in communication connection with a sensing module and is used for receiving physical state information and controlling the clamping target to be in a self-adaptive state based on pre-stored clamping target parameters and a self-adaptive control strategy; generating a dynamic control instruction; and the driving execution module is in communication connection with the core processing module and is used for receiving the dynamic control instruction and driving an action part of the elastic floating clamping self-locking mechanism to generate corresponding self-adaptive motion. A self-adaptive control strategy fusing feedforward prediction, fuzzy PID closed-loop adjustment and multivariable comprehensive compensation is executed through the core processing module, force control and position control parameters can be dynamically optimized, vibration is effectively restrained, deformation and load disturbance are effectively compensated, and it is ensured that the clamping process is rapid and stable and finally converges to the ideal uniform clamping and stable locking state.

Description

Technical Field

[0001] This invention relates to the field of mechatronics equipment and automatic control technology, and in particular to an intelligent control system for an elastic floating clamping self-locking mechanism. Background Technology

[0002] With the rapid development of modern intelligent manufacturing and precision machining technologies, especially in fields such as aerospace, precision instruments, microelectronics assembly, and flexible automated production lines, increasingly stringent requirements are being placed on the accuracy, reliability, adaptability, and intelligence level of workpiece clamping. Traditional rigid clamping methods often lack flexibility and real-time adjustment capabilities, easily leading to stress concentration, surface damage, or positioning deviations when clamping irregularly shaped, fragile, or high-precision workpieces, making it difficult to meet the demands of modern production characterized by high dynamics, diverse varieties, and small batches. Therefore, clamping mechanisms with elastic floating functions and self-locking characteristics have emerged. These mechanisms can provide necessary constraints while adapting to workpiece geometric errors through their own elastic deformation and achieving reliable retention through mechanical self-locking, becoming an important direction for resolving the aforementioned contradictions.

[0003] Currently, research and practice on elastic floating clamping self-locking mechanisms have made some progress, with technological development mainly reflected in two aspects: mechanism innovation and local control. In terms of mechanism design, elastic floating structures using components such as shape memory alloys, polymer elastomers, precision springs, or flexible hinges have emerged, often combined with self-locking mechanisms such as wedges, threads, or eccentric wheels, achieving smooth application of clamping force and reliable locking. At the control level, existing technologies mostly employ simple open-loop or single-variable closed-loop control, such as controlling the start and stop of clamping actions by setting motor stroke or current thresholds, or using constant force control through feedback from a single force sensor. These methods improve the automation level of clamping to some extent, but generally suffer from problems such as a single system perception dimension, static and rigid control strategies, and a lack of ability to coordinate and regulate multiple physical quantities throughout the entire clamping process.

[0004] Despite advancements in existing technology, several prominent issues remain in practical applications. First, the clamping process lacks real-time, precise sensing of multi-dimensional states such as contact pressure distribution, micro-deformation, and environmental vibration, making it difficult to guarantee clamping uniformity. This is especially problematic for complex curved surfaces or fragile workpieces, easily leading to localized overpressure or clamping instability. Second, traditional control strategies are mostly based on preset programs or simple PID adjustments, unable to learn and adaptively adjust based on workpiece characteristics, environmental disturbances, and mechanism status. Performance degrades significantly when facing workpieces of different specifications or dynamic conditions, even causing clamping failure. Third, existing systems typically lack intelligent confirmation mechanisms for locking status and the ability to autonomously diagnose and compensate for operational anomalies. Workpiece slippage, overload, or external interference often necessitates manual intervention, reducing the system's autonomy and reliability. Furthermore, the system lacks the ability to continuously optimize based on historical data, making it difficult to improve performance or adapt to long-term changes such as equipment wear through accumulated experience.

[0005] Therefore, it is necessary to invent an intelligent control system for an elastic floating clamping self-locking mechanism to solve the above problems. Summary of the Invention

[0006] The purpose of this invention is to provide an intelligent control system for an elastic floating clamping self-locking mechanism to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: an intelligent control system for an elastic floating clamping self-locking mechanism, comprising the following modules:

[0008] The sensing module is used to acquire physical state information of the clamping mechanism related to the clamping and locking states in real time;

[0009] The core processing module is communicatively connected to the sensing module and is used to receive the physical state information and generate dynamic control commands based on the pre-stored clamping target parameters and adaptive control strategy.

[0010] The drive execution module is communicatively connected to the core processing module and is used to receive the dynamic control command and drive the moving parts of the elastic floating clamping self-locking mechanism to generate corresponding adaptive motion.

[0011] The locking confirmation module is communicatively connected to the sensing module and the core processing module. It is used to determine whether the mechanism has reached a preset stable self-locking state based on the physical state information, and to send a locking confirmation signal back to the core processing module.

[0012] The anomaly handling and optimization module is communicatively connected to the core processing module and the sensing module. It is used to monitor abnormal operating conditions during system operation and trigger corresponding adaptive adjustment or safety protection programs according to the anomaly type. At the same time, it optimizes the adaptive control strategy based on historical operating data.

[0013] The technical effects and advantages of this invention are as follows:

[0014] 1. This invention constructs a complete closed-loop control system that includes a sensing module, a core processing module, a drive execution module, a locking confirmation module, and an anomaly handling and optimization module. This system enables full-state perception, intelligent decision-making, and adaptive execution of the elastic floating clamping self-locking mechanism, significantly improving the accuracy, stability, and reliability of the clamping process.

[0015] 2. By employing array-arranged sensing units, simultaneous monitoring of multiple physical quantities, and high-precision sensing technology, this invention achieves refined real-time perception of clamping force distribution, mechanism motion state, and external disturbances, providing an accurate and comprehensive data foundation for adaptive control.

[0016] 3. This invention executes an adaptive control strategy that integrates feedforward prediction, fuzzy PID closed-loop regulation, and multivariable comprehensive compensation through a core processing module. This strategy can dynamically optimize force control and position control parameters, effectively suppress vibration, compensate for deformation and load disturbances, and ensure that the clamping process is fast, stable, and ultimately converges to an ideal uniform clamping and stable locking state.

[0017] 4. This invention uses a composite drive unit consisting of a servo motor and a piezoelectric ceramic driver, combined with instruction parsing and collaborative control, to achieve an organic combination of macroscopic rapid positioning and microscopic high-frequency force control, which not only ensures clamping efficiency, but also achieves high-resolution adjustment of clamping force and dynamic vibration suppression.

[0018] 5. This invention monitors abnormal operating conditions such as overload and slippage in real time through an anomaly handling and optimization module and triggers corresponding protection or compensation programs. At the same time, it continuously optimizes control strategy parameters based on a historical successful case library, enabling the system to have the ability to self-handle faults, self-learn performance, and self-evolve strategies, thereby improving the long-term adaptability and intelligence level of the system. Attached Figure Description

[0019] Figure 1 This is a diagram of the overall system architecture of the present invention.

[0020] Figure 2 This is a flowchart of the adaptive control strategy of the present invention.

[0021] Figure 3 This is a diagram showing the composition of the driver execution module of the present invention.

[0022] Figure 4This is a flowchart of the locking confirmation judgment process of the present invention. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] This invention provides, for example Figure 1 The intelligent control system of the elastic floating clamping self-locking mechanism shown includes the following modules:

[0025] The sensing module is used to acquire physical state information of the clamping mechanism related to the clamping and locking states in real time;

[0026] Furthermore, in the above technical solution, the sensing module specifically includes:

[0027] The first sensing unit is arranged in an array on the elastic floating element of the clamping mechanism to measure the pressure distribution and micro-deformation of the contact area between the clamping surface and the workpiece, and outputs a real-time clamping force distribution matrix.

[0028] The second sensing unit is located on the transmission link of the clamping mechanism and is used to measure the output displacement, output speed and output torque of the drive source.

[0029] The third sensing unit is located on the workpiece bearing base and the clamping mechanism body, and is used to detect the amount of vibration and load disturbance in the external environment.

[0030] It should be noted that the first sensing unit can specifically employ a flexible thin-film pressure sensor array or a micro fiber optic grating sensor array, embedded or attached to the clamping working surface of the elastic floating element in an M-row × N-column matrix. Each sensor node independently measures the local normal pressure, and the pressure values ​​of all nodes are synchronously read at a preset sampling frequency (e.g., 1 kHz) through a multiplexed acquisition circuit, forming the real-time clamping force distribution matrix. Simultaneously, by measuring the strain of the substrate material of the elastic floating element under force, or through a micro laser displacement sensor located at the edge of the array, the overall or local micron-level deformation of the clamping working surface is obtained, and this deformation is used to calculate the deformation compensation amount.

[0031] The second sensing unit specifically includes: a high-precision photoelectric encoder or magnetic encoder mounted on the output shaft of the drive source (such as a servo motor) for measuring the output displacement and output speed; and a strain gauge torque sensor or non-contact magnetoelastic torque sensor connected in series on the transmission link (such as a ball screw or harmonic reducer) for real-time measurement of the output torque. The output displacement, output speed, and output torque signals are converted into digital quantities by a signal conditioning circuit and then sent to the core processing module via a fieldbus (such as an EtherCAT or CAN bus).

[0032] The third sensing unit specifically includes a triaxial accelerometer arranged on the key rigid structure of the workpiece bearing base and the clamping mechanism body, used to detect the external environmental vibration, and its output signal is transformed by Fourier transform to obtain the environmental vibration spectrum V. f ; and a multi-dimensional force sensor installed between the workpiece bearing base and the foundation support, for real-time monitoring and calculation of the static and dynamic load disturbance caused by changes in workpiece quality, sudden changes in processing force, etc., the disturbance being provided in the form of force and torque vectors.

[0033] The core processing module is communicatively connected to the sensing module and is used to receive the physical state information and generate dynamic control commands based on the pre-stored clamping target parameters and adaptive control strategy.

[0034] Furthermore, in the above technical solution, the core processing module is configured to perform the following operations:

[0035] Receive the real-time clamping force distribution matrix, output displacement, output speed, output torque, external environmental vibration, and load disturbance from the sensing module;

[0036] By comparing the real-time clamping force distribution matrix with the pre-stored ideal clamping force reference matrix, the clamping uniformity deviation and deformation compensation amount are calculated, and the force control parameters are determined based on the clamping uniformity deviation and the deformation compensation amount.

[0037] Position closed-loop control is performed based on the deviation between the output displacement and the preset target displacement trajectory; feedforward compensation or overshoot suppression of motion speed is performed based on the output speed; and real-time identification and overload warning of the drive load are performed based on the output torque.

[0038] Then, by integrating the deformation compensation amount, the external environmental vibration, and the load disturbance amount, the position closed-loop control, the velocity feedforward compensation, and the force control parameters are comprehensively adjusted to generate the dynamic control command.

[0039] It should be noted that the clamping uniformity deviation is calculated by measuring the real-time clamping force distribution matrix F.real (m, n) and the ideal clamping force reference matrix F ideal The difference between (m, n) is used for quantification. Specifically:

[0040] First, calculate the norm deviation E of the matrix. norm =||F real -F ideal || F , where ||·|| F This represents the Frobenius norm, used to assess the overall force deviation.

[0041] Secondly, calculate the relative deviation δ(i,j) = |F_i| for each corresponding matrix element. real (i,j)-F ideal (i, j) | / F ideal (i,j), and count the number and location of nodes whose δ(i,j) exceeds a preset percentage (e.g., 10%) to form an uneven distribution map.

[0042] The deformation compensation amount Δ d The value is determined as follows: based on the micro-deformation d (unit: micro-strain με) measured by the first sensing unit, and combined with the material elastic modulus E and geometrical factor k of the elastic floating element, according to the formula Δ d The conversion is performed using k*d*E to obtain the amount of reverse adjustment required in the driving displacement to compensate for the deformation (in micrometers, μm).

[0043] The force control parameter is mainly the target fine-tuning torque T acting on the piezoelectric ceramic actuator in the dynamic control command. target and its adjustment frequency f adj .

[0044] Target fine-tuning torque T target From the basic torque T base Together with the correction amount ΔT based on the clamping uniformity deviation, T is determined: target =T base +Δ T Among them, Δ T According to E norm The non-uniformity distribution map is obtained by a lookup table method: a mapping table is pre-stored in the processor memory, which lists different E values. norm The extent and location of the main non-uniform regions are mapped to a specific Δ T Value sequence.

[0045] Adjust frequency f adj With the deformation compensation amount Δ d Related, when |Δ dWhen |Δ is large, a higher frequency (e.g., 500Hz) is used for rapid compensation; when |Δ d When the frequency is small, a lower frequency (such as 100Hz) is used for fine maintenance.

[0046] Regarding "position closed-loop control based on the deviation between the output displacement and the preset target displacement trajectory":

[0047] The position closed-loop control is implemented using a digital PID controller. The controller input is the output displacement x. act With the target displacement trajectory x ref (t) Deviation e in each control cycle (e.g., 1 ms) x (t) = x ref (t)-x act (t). The controller output is the servo motor speed command feedforward value v. ff Correction amount Δ v The calculation formula is as follows:

[0048] Δ v (t) = Kp x *e x (t) + Ki x *∫e x (t)dt+Kd x *de x (t) / dt,

[0049] Where Kp x Ki x Kd x These are the pre-tuned position loop PID parameters.

[0050] Regarding "feedforward compensation or overshoot suppression of motion speed based on the output speed":

[0051] Velocity feedforward compensation is to adjust the target displacement trajectory x ref (t) The target velocity v is obtained by differentiation. ref (t), then multiplied by a feedforward gain Kv ff This is directly superimposed on the speed command of the servo motor, i.e., v. ff (t) = Kv ff *v ref (t) is used to improve the tracking response speed. Overshoot suppression, on the other hand, is used when the output displacement x is detected. act Approaching the target position and the output speed v act If the value is still higher than the preset entry threshold, a deceleration phase trajectory is temporarily inserted, or the derivative gain Kd in the position loop PID controller is dynamically increased. x To dampen the tendency of excessively rapid movement.

[0052] Regarding "real-time identification and overload warning of the drive load based on the output torque":

[0053] Real-time identification of the drive load is achieved by establishing a simplified mechanical model of the servo motor drive system. During the constant speed or constant acceleration phase, the output torque τ is used as the basis for identification. m Motor current I m Given the known motor torque constant Kt and transmission system efficiency η, estimate the load torque τ referred to the motor shaft. load ≈(τ m -J*α) / (i*η), where J is the moment of inertia, α is the angular acceleration, and i is the deceleration ratio. By monitoring τ load Sudden changes or continuous increases can identify workpiece contact, jamming, and other states. Overload warnings are provided by setting a torque threshold τ. max Realize, when τ m Continue to exceed τ max A warning signal will be triggered when the predetermined time (e.g., 50ms) is reached.

[0054] Regarding "integrating the deformation compensation amount, the external environmental vibration, and the load disturbance amount to comprehensively adjust the position closed-loop control, velocity feedforward compensation, and force control parameters":

[0055] Comprehensive adjustment is the core of the adaptive control strategy, and it is executed sequentially within a total control cycle:

[0056] Environmental vibration fusion: Analysis of the environmental vibration spectrum V f If its main frequency f v If the frequency is within ±10% of the system's mechanical resonant frequency, then the output command Δ of the position closed-loop control will be... v (t) and velocity feedforward v ff (t) Apply a frequency of f v A band-stop filter is used to suppress resonance.

[0057] Load disturbance compensation: The real-time identified load disturbances (force and torque) are converted into additional compensation torque commands Δτ for the servo motor and / or the piezoelectric ceramic actuator using a Jacobian matrix. dist It is directly superimposed on their respective torque settings.

[0058] Deformation adaptation: The calculated deformation compensation amount Δ d Convert to target displacement trajectory x ref Real-time offset correction of (t), i.e., x ref '(t)=x ref (t) + Δ d This compensates for the structural deformation caused by the clamping force at its source.

[0059] Dynamic parameter adjustment: Based on the current fused motion state (stable, disturbed, high risk of resonance, etc.) and clamping stage (approaching, contacting, locking), an optimized set of Kp is dynamically selected and loaded from a pre-configured parameter set. x Ki x Kd x Kv ff The parameters and force control parameter mapping table are used to complete the comprehensive adjustment of the position closed-loop control, speed feedforward compensation and the force control parameters, and finally generate the dynamic control command for the drive execution module, which integrates position, speed, torque and high-frequency fine-tuning information.

[0060] Furthermore, in the above technical solution, refer to Figure 2 The core processing module is further configured to execute the adaptive control strategy comprising the following steps:

[0061] Step A1: Initialization phase, load the clamping target parameters, which include at least the target clamping force range, the target locking position threshold, and the maximum allowable deformation;

[0062] Step A2: In the learning and prediction phase, clamping process data similar to the current workpiece specifications from historical operation data is retrieved to predict the clamping force-displacement curve, which is then used as the feedforward input for the current control.

[0063] Step A3: Closed-loop adjustment stage. During the process of the drive execution module driving the motion component, the real-time clamping force distribution matrix is ​​continuously received, and its real-time error with the ideal clamping force reference matrix is ​​calculated. The force control parameters in the dynamic control command are fine-tuned online using a fuzzy PID control algorithm so that the real-time error converges to zero.

[0064] Step A4: State switching stage. When the real-time error continues to be lower than the stable threshold for a preset time and the output displacement enters the target locking position threshold, the clamping motion is determined to be completed, and the locking confirmation module is triggered to make the final state judgment.

[0065] It is important to know that the core processing module executes the adaptive control strategy through its internal strategy engine program block. This strategy engine runs in parallel with the aforementioned real-time control program and shares data. The specific implementation details of each stage are as follows:

[0066] Regarding step A1: Initialization phase

[0067] The clamping target parameters are stored in a configurable parameter file. The loading process includes:

[0068] Target clamping force range: a two-dimensional array [F]min F max The unit is Newton (N), which defines the range of permissible final locking force.

[0069] Target locking position threshold: a tolerance range [P] target -ΔP,P target +ΔP], where P target The theoretical locking position is defined in millimeters (mm), and ΔP is the allowable positional deviation in millimeters (mm).

[0070] Maximum allowable deformation Δ max The unit is micrometer (μm), used to limit excessive correction of deformation compensation during the control process to prevent mechanical overload.

[0071] During initialization, the system also sets the initial position of the servo motor to a safe "return to zero" position, and sets the output torque of the piezoelectric ceramic driver to zero. At the same time, it reads the initial environmental vibration spectrum from the sensing module as a reference.

[0072] Regarding step A2: the learning and prediction phase

[0073] This stage is achieved through a lightweight learning and prediction submodule:

[0074] Specification matching: The specification information of the current workpiece (such as code, size, material code) is obtained through external input (such as barcode scanning) or a preset program. The system searches the successful case database using the specification information as keywords and calculates the similarity with historical cases. The similarity is measured by the Euclidean distance of feature vectors (such as workpiece mass, estimated contact area, material hardness grade), and the K historical cases with the smallest distance (e.g., K=3) are selected as the similar case set.

[0075] Data extraction and preprocessing: From each similar case, extract the "driving displacement - array average clamping force" sequence pairs (x... i F avg，i All selected case sequences are time-normalized (or shift-normalized) to ensure a consistent number of data points.

[0076] Curve Prediction: A weighted average method is used to generate the predicted curve. The sequence data of each similar case are weighted according to the reciprocal of their similarity to the current workpiece specification (higher similarity results in greater weight). The force values ​​of all corresponding data points are then weighted and averaged to generate a predicted clamping force-displacement relationship curve F. pred (x).

[0077] Feedforward input generation: F pred(x) is converted into a feedforward control variable. Specifically, the planned macroscopic displacement trajectory x is converted into a feedforward control variable. ref (t) Substitute into F pred (x), to obtain the corresponding predicted clamping force sequence F pred (t). Then, based on the force-displacement stiffness model (which can be obtained by fitting historical data), the predicted force change ΔF is... pred Converted to incremental feedforward ΔI of the desired current of the servo motor ff (t), and the basic torque T of the piezoelectric ceramic actuator. base The preset value is then injected into the initial control command of the closed-loop adjustment stage in step A3 to improve the initial response speed and reduce the initial contact impact.

[0078] Regarding step A3: Closed-loop adjustment stage

[0079] The core of this stage is the fuzzy PID control algorithm's application to the force control parameters (mainly the target fine-tuning torque T). target Online fine-tuning.

[0080] Real-time error calculation: Calculate the real-time clamping force distribution matrix F in each control cycle (e.g., 1ms). real With F ideal The real-time error e(t). Here, e(t) is expressed as the Frobenius norm deviation E of the matrix. norm (t) or its rate of change ΔE norm (t) is the main input variable.

[0081] Fuzzification: The explicit input variables e(t) and Δe(t) (error rate of change) are transformed into fuzzy linguistic values ​​using a membership function. For example, the fuzzy universe of discourse for e(t) is {negative large (NB), negative small (NS), zero (ZO), positive small (PS), positive large (PB)}, and the universe of discourse for Δe(t) is similar. The membership function uses a triangular or Gaussian form.

[0082] Fuzzy inference: Inference is performed based on a pre-defined fuzzy rule base. The rule base is in the form of "IF e(t) is A AND Δe(t) is B, THEN ΔKp is C, ΔKi is D, ΔKd is E", where A and B are the input fuzzy sets, and C, D, and E are the output fuzzy sets (corresponding to the adjustment amounts of PID parameters Kp, Ki, and Kd). For example, "IF e(t) is PB AND Δe(t) is ZO, THEN ΔKp is PB, ΔKi is ZO, ΔKd is PS" means that when the error is large and does not change much, the proportional gain is increased significantly to quickly reduce the error, and the derivative gain is increased slightly to suppress possible overshoot.

[0083] Defuzzification and parameter update: The output fuzzy set obtained from inference is transformed into clear PID parameter adjustments ΔKp(t), ΔKi(t), and ΔKd(t) using defuzzification methods such as the centroid method. Then, the parameters used to calculate T are updated. target The PID controller parameters are: Kp'(t) = Kp0 + ΔKp(t), Ki'(t) = Ki0 + ΔKi(t), Kd'(t) = Kd0 + ΔKd(t), where Kp0, Ki0, and Kd0 are the initial parameters.

[0084] Force control parameter fine-tuning: Using the updated PID parameters, calculate the fine-tuning torque T on the target based on the real-time error e(t). target Correction amount ΔT fuzzy (t)=Kp'(t)*e(t)+Ki'(t)*∫e(t)dt+Kd'(t)*de(t) / dt. Ultimately, T target (t) = T base +ΔT(t)+ΔT fuzzy ΔT(t) is the correction amount obtained by looking up a table based on the clamping uniformity deviation (as mentioned above). In this way, the force control parameters can be finely adjusted online, driving e(t) to converge to zero.

[0085] Regarding step A4: State transition phase

[0086] Stability threshold ε stable : is a preset small positive value (e.g., corresponding to E) norm (5N).

[0087] Preset time T hold For example, 100ms indicates the length of time that the condition needs to be met continuously.

[0088] Switching logic: The system sets a counter and a flag. When E... norm (t) < ε stable And x act (t) falls into [P] target -ΔP,P target When the value is within the interval [+ΔP], the counter begins to accumulate; if any condition is not met during the accumulation process, the counter is reset to zero. The counter continues until its accumulated value reaches T. hold When the corresponding control cycle number is reached, the "clamping motion completed" flag is set.

[0089] State switching action: When the "clamping motion complete" flag is set, the strategy engine immediately sends a trigger signal to the locking confirmation module to start the final stable self-locking state judgment process (i.e., steps B1-B4). At the same time, the strategy engine itself suspends the active fine-tuning of the force control parameters (i.e., the fuzzy PID output of step A3 retains the last value or returns to zero), and transfers control to the locking confirmation and holding logic.

[0090] The drive execution module is communicatively connected to the core processing module and is used to receive the dynamic control command and drive the moving parts of the elastic floating clamping self-locking mechanism to generate corresponding adaptive motion.

[0091] Furthermore, in the above technical solution, refer to Figure 3 The drive execution module specifically includes:

[0092] The instruction parsing unit is used to parse the dynamic control instruction and decompose it into motion trajectory planning instructions and torque closed-loop setting values ​​for at least one servo motor or piezoelectric ceramic driver.

[0093] The composite drive unit includes a servo motor for achieving macroscopic displacement and a piezoelectric ceramic actuator for achieving microscopic clamping and high-frequency vibration suppression. The servo motor and the piezoelectric ceramic actuator work together. The servo motor performs positioning according to the motion trajectory planning command, and the piezoelectric ceramic actuator performs micro-amplitude high-frequency adjustment according to the torque closed-loop setting value.

[0094] It should be noted that the driver execution module implements its functions through a combination of hardware circuitry and embedded firmware, and the specific implementation methods of its internal units are as follows:

[0095] Regarding the instruction parsing unit:

[0096] This unit receives the dynamic control instructions from the core processing module. These instructions are typically encapsulated as a structured data packet, such as in binary or JSON format, containing the following key fields: {"motorCmd": {"targetPos": value1, "profileType": value2, ...}, "piezoCmd": {"targetTorque": value3, "freq": value4, ...}, "syncFlag": value5}. The instruction parsing unit is implemented by a dedicated microcontroller (such as an ARM Cortex-M series) or FPGA, and its internal firmware performs the following parsing process:

[0097] Packet unpacking and verification: First, verify the integrity and checksum of the data packet to ensure that the command is correct.

[0098] Field separation: According to the predefined protocol, the content of the "motorCmd" field in the data packet is extracted and converted into a specific command format that the servo motor driver can recognize, such as: target position (number of pulses or mm), motion curve mode (trapezoidal, S-shaped), maximum speed, acceleration, etc., which are combined to form the motion trajectory planning instruction.

[0099] Torque command generation: Simultaneously, the content of the "piezoCmd" field is extracted, and based on the "targetTorque" value (usually the voltage or current setting value) and the "freq" value (adjustment frequency), the corresponding analog voltage setting value or high-frequency PWM (pulse width modulation) control signal is generated as the torque closed-loop setting value.

[0100] Synchronization signal processing: If “syncFlag” indicates that coordinated action is required, the instruction parsing unit will generate a synchronization trigger signal to ensure that the actions of the servo motor and the piezoelectric ceramic driver start or switch at precise time points.

[0101] Regarding the aforementioned composite drive unit:

[0102] This unit includes a servo motor driver, a piezoelectric ceramic driver, and a power amplifier circuit on the hardware, all of which are controlled by the output signal of the instruction parsing unit.

[0103] The servo motor drive section includes a servo driver (such as an EtherCAT bus-type driver) and a matching servo motor. The motion trajectory planning command is sent to the servo driver via a high-speed fieldbus (such as EtherCAT) or a pulse direction interface. The servo driver operates in "position mode," where its internal position loop receives the target position sequence from the command parsing unit and simultaneously receives actual position feedback from the second sensing unit (encoder), completing closed-loop control and driving the machine to produce precise macroscopic displacement. Its output torque (current) is also limited by the internal torque loop, and this limit can be set by parameters in the command.

[0104] The piezoelectric ceramic drive section comprises a piezoelectric ceramic drive power supply (high-voltage amplifier) ​​and a piezoelectric ceramic actuator. The torque closed-loop setpoint (typically an analog voltage or digital command of 0-10V) is input to the piezoelectric drive power supply. Based on this setpoint, the piezoelectric drive power supply outputs a corresponding high voltage (e.g., 0-100V or higher) to the piezoelectric ceramic actuator, causing it to produce corresponding micro-elongation or contraction, thereby outputting a fine clamping force or high-frequency micro-motion. To achieve "high-frequency vibration suppression," this section can operate at higher frequency responses (e.g., >500Hz), receiving dynamic vibration compensation signals generated based on vibration spectrum analysis from the core processing module.

[0105] Collaborative Working Mechanism: Physically, both act on the same clamping action component through series or parallel mechanical structures. In terms of control timing, the servo motor typically performs rapid approach and main stroke positioning. Once it approaches the target position or contacts the workpiece, the piezoelectric ceramic actuator intervenes to perform fine force adjustment and high-frequency dynamic compensation. The instruction parsing unit dynamically allocates and coordinates the working modes and outputs of both according to different clamping stages (such as idle stroke, contact, loading, and holding), achieving seamless collaboration between "macro positioning" and "micro force control," that is, the servo motor and the piezoelectric ceramic actuator work together. For example, in the final locking stage, the servo motor may enter a low-stiffness "torque holding" mode to provide basic support, while the piezoelectric ceramic actuator performs high-frequency micro-adjustments to offset force fluctuations caused by environmental vibrations, jointly maintaining the stable self-locking state.

[0106] The locking confirmation module is communicatively connected to the sensing module and the core processing module. It is used to determine whether the mechanism has reached a preset stable self-locking state based on the physical state information, and to send a locking confirmation signal back to the core processing module.

[0107] Furthermore, in the above technical solution, refer to Figure 4 The locking confirmation module is configured to determine whether the stable self-locking state has been reached through the following steps:

[0108] Step B1: Monitor the physical state information fed back by the sensing module and extract the clamping force stability value F. s Clamping position stability value P s and environmental vibration spectrum V f ;

[0109] Step B2: Set the clamping force stabilization value F s With the preset lower limit of locking force F min and upper limit F max The results are compared, and the variance σ of the clamping force fluctuation within the preset time window is calculated. 2 ;

[0110] Step B3: When conditions F are met simultaneously min ≤F s ≤F max , σ 2 ≤σ 2 max And the environmental vibration spectrum V f When the amplitude of the main frequency is lower than the preset resonance risk threshold, a locking confirmation signal indicating that a stable self-locking state has been reached is generated and sent to the core processing module.

[0111] Step B4: After receiving the locking confirmation signal, the core processing module controls the drive execution module to stop active driving and causes the elastic floating clamping self-locking mechanism to enter a passive holding and locking state based on its own mechanical structure.

[0112] It should be noted that the locking confirmation module is implemented using an independently running monitoring thread or a dedicated microcontroller, and periodically (e.g., every 10ms) executes the judgment step. The specific implementation details are as follows:

[0113] Regarding step B1: Monitoring and Information Extraction

[0114] Clamping force stability value F s Extraction: Obtain the latest real-time clamping force distribution matrix F from the sensing module. real First, calculate the average value of all elements in the matrix to obtain an instantaneous average clamping force F. avg Then, for a length of T window F within a sliding time window (e.g., 200ms) avg The sequence is low-pass filtered (e.g., using a first-order Butterworth filter with a cutoff frequency of 5Hz) to remove high-frequency noise. The filtered output value is the current clamping force stability value F. s .

[0115] Clamping position stability value P s Extraction: Obtain the output displacement x from the sensing module act Similarly, for x act Within the same time window T window The sequence within is low-pass filtered (the cutoff frequency can be even lower, such as 2Hz), and the filtered output value is the stable value P of the clamping position at the current moment. s .

[0116] Environmental vibration spectrum V f Acquisition: Directly read the latest spectrum data processed by the third sensing unit using real-time Fourier transform (FFT). This spectrum is typically represented as an array, containing a series of frequency points and their corresponding amplitude values. The locking confirmation module focuses on the pre-calibrated system mechanical resonant frequency f. res Nearby (e.g., f) res The frequency band is ±5Hz.

[0117] Regarding step B2: Comparison and Variance Calculation

[0118] Force comparison: The extracted F s With the preset value F loaded from the parameter file min and F max Perform real-time comparisons. This is a simple numerical range determination.

[0119] Fluctuation variance σ 2 Calculation: Calculate the clamping force within the preset time window T window The variance within the window is used to quantify its stability. Specifically, suppose the window contains N F... avg The sampled value {F avg1 F avg2 F avgN};

[0120] First, calculate F within this window. avg Mean: μ F = ;

[0121] Then calculate the variance: σ 2 = , this σ 2 The value is an indicator representing the magnitude of the clamping force fluctuation. The preset upper limit of variance σ... max 2 Determined based on the allowable force fluctuation accuracy requirements of the workpiece.

[0122] Regarding step B3: Comprehensive Judgment and Signal Generation

[0123] This step involves a logical AND operation, requiring all three sub-conditions to be satisfied simultaneously:

[0124] Force range condition: F min ≤F s ≤F max ;

[0125] Stability condition for force: σ 2 ≤σ max 2 ;

[0126] Vibration safety conditions: in the environmental vibration spectrum V f In the process, find the frequency component with the largest amplitude, i.e., the dominant frequency f. dom and its amplitude A dom Determine A dom Is it below the preset resonance risk threshold A? thresh A thresh According to the system at f res The critical vibrational energy that could lead to clamping failure when resonance occurs nearby has been determined.

[0127] A stable self-locking state is finally determined only when all three conditions mentioned above are met within M consecutive judgment cycles (e.g., M=5, corresponding to a duration of 50ms). At this time, the locking confirmation module generates a digital pulse signal or sets a specific status register bit as the locking confirmation signal, and sends it to the core processing module via interrupt or polling.

[0128] Regarding step B4: State transition and retention

[0129] Response of the core processing module: After receiving the locking confirmation signal, the core processing module immediately terminates the current adaptive control strategy (such as the fuzzy PID adjustment in step A3) through its strategy engine.

[0130] Stop active drive: The core processing module sends a special "stop and hold" command to the command parsing unit of the drive execution module. After parsing the command, a "zero speed command" or "enable hold" command is sent to the servo driver to reduce its output torque to a level that is only sufficient to overcome static friction; at the same time, a command is sent to the piezoelectric ceramic drive power supply to set its output setpoint to zero or maintain it at an extremely low holding value.

[0131] Entering passive holding mode: In this mode, the elastic floating clamping self-locking mechanism mainly relies on its own mechanical structure (such as the preload of the elastic element, the wedge self-locking angle, or the friction pair) to passively maintain the clamping force, and the drive system no longer provides active adjustment force. The system switches from "active control mode" to "passive monitoring mode". The locking confirmation module and the sensing module continue to monitor the status at a lower frequency, but the core processing module no longer generates active dynamic control commands until a new release or re-clamping command is received.

[0132] The anomaly handling and optimization module is communicatively connected to the core processing module and the sensing module. It is used to monitor abnormal operating conditions during system operation and trigger corresponding adaptive adjustment or safety protection programs according to the anomaly type. At the same time, it optimizes the adaptive control strategy based on historical operating data.

[0133] Furthermore, in the above technical solution, the anomaly handling and optimization module includes:

[0134] The overload protection submodule, when the instantaneous clamping force detected by the sensing module exceeds the material safety limit, or the drive current of the drive execution module exceeds the rated value, immediately sends an emergency stop command to the core processing module and controls the drive execution module to perform a reverse release action;

[0135] The slip compensation submodule, when the locking confirmation module detects that the clamping force continues to decrease and exceeds the slip threshold again after issuing the locking confirmation signal, determines that the workpiece has slipped, reactivates the core processing module, and calls the incremental PID control algorithm dedicated to slip compensation to generate a compensatory clamping command and send it to the core processing module so that it can generate a new dynamic control command to drive the drive execution module to perform the compensatory clamping action;

[0136] The strategy optimization unit records the final stable clamping force distribution matrix, corresponding driving quantity, and environmental parameters during each successful clamping process, constructs a success case library, and optimizes and adjusts the parameters of the adaptive control strategy based on the success case library.

[0137] It should be noted that the exception handling and optimization module runs on the same hardware platform or a coprocessor as the core processing module, and is executed as a high-priority background task. The specific implementation methods of its sub-modules are as follows:

[0138] The overload protection submodule monitors two key signals in real time:

[0139] Instantaneous clamping force: The real-time clamping force distribution matrix F is obtained from the sensing module. real Calculate the peak value F of all its elements. peak ;

[0140] Drive current: The real-time effective value I of the motor phase current is directly read from the servo driver status register of the drive execution module. motor Or it can be obtained through sampling by a Hall sensor.

[0141] Overload detection logic:

[0142] Based on the material yield strength of the elastic floating element and the workpiece, when F peak >F safe When this occurs, overload protection is triggered;

[0143] According to the specifications of the servo motor and driver, when I motor >I rated If the duration exceeds the debounce time (e.g., 10ms), overload protection is triggered.

[0144] Execution of protective actions:

[0145] Once any condition is met, the submodule immediately sends an emergency stop command to the core processing module via a hardware interrupt or a highest-priority software message. This command contains emergency stop code. Simultaneously, the submodule directly takes over control of the drive execution module (or forwards it through the core processing module), sends a "rapid stop" command to the servo driver, and generates a preset "reverse release trajectory." Specifically, it controls the servo motor to stop its current motion at the maximum permissible deceleration, then reverses its motion according to a preset release distance and speed, while simultaneously forcing the output of the piezoelectric ceramic driver to zero. The entire emergency stop process has higher priority than any other control command.

[0146] The slip compensation submodule is activated and enters the monitoring state after the locking confirmation module issues a locking confirmation signal.

[0147] Slip detection:

[0148] Continuously monitor the stable value of the clamping force F s (The calculation method is the same as that of the locking confirmation module);

[0149] Calculate F s The descent slope k within a short time window (e.g., 100ms) slope ;

[0150] When F s From the initial value F when locking is confirmed init The drop exceeds the slip threshold ΔF slip (e.g. F) init (5%), and the downward slope k slope When the slope remains negative and exceeds the preset slope threshold, workpiece slippage is determined to have occurred.

[0151] Compensation action execution:

[0152] Reactivate the core processing module: Send a slip alarm and activation signal to the core processing module to exit the "passive monitoring mode" and re-enter the closed-loop adjustment preparation state;

[0153] Calling the incremental PID algorithm: This algorithm only calculates the increment of the control quantity Δu(k), and the formula is:

[0154] Δu(k)=Kp*[e(k)-e(k-1)]+Ki*e(k)+Kd*[e(k)-2*e(k-1)+e(k-2)],

[0155] Where, e(k) = F init -F s (k) represents the deviation between the current force and the initial locking force in the kth control cycle. k is an integer index that increments with the control cycle (e.g., the slip compensation algorithm is executed once every 5ms). Its physical meaning is the discrete time step. Kp, Ki, and Kd are parameters that are tuned separately for the slip compensation scenario. Their values ​​are usually smaller than the main control loop to achieve smooth compensation.

[0156] Generate compensation command: Use the output Δu(k) of the incremental PID algorithm as the compensation torque increment to generate a compensation clamping command. This command is essentially a desired additional torque value T. comp This instruction is then sent to the core processing module.

[0157] Integration and Execution: The core processing module will receive T comp As an additional force control target, it is integrated into the dynamic control command generation process to drive the drive execution module (mainly the piezoelectric ceramic actuator) to produce a fine, progressive compensated clamping action until Fs Restore to F init It has stabilized again in the vicinity.

[0158] The strategy optimization unit is responsible for data collection, case library management, and strategy parameter optimization.

[0159] Data recording and case library construction:

[0160] Each time a case is determined to be "successfully clamped" (i.e., steps A1-A4 and B1-B4 are completed normally), the unit automatically records a case.

[0161] One case contains the following data sequence:

[0162] Final stable clamping force distribution matrix F final ;

[0163] The corresponding driving quantities include the final position of the servo motor, the current curve of the entire motion process, and the final output torque of the piezoelectric ceramic actuator.

[0164] Environmental parameters: average environmental vibration spectrum characteristic value and load disturbance statistical value during the clamping process;

[0165] Performance metadata: specifications of the workpiece being clamped, total time to achieve locking, and total energy consumption.

[0166] All cases are categorized by workpiece specifications and stored in a database (success case library), which can be a file system or a lightweight database (such as SQLite).

[0167] Parameter optimization based on a case library:

[0168] Offline optimization: During system idle periods, the optimization unit analyzes the case library. For example, for a specific workpiece specification, it compiles all its successful cases.

[0169] Parameter optimization: For key parameters in the adaptive control strategy (such as the initial parameters Kp0, Ki0, Kd0 of the fuzzy PID, and the feedforward gain Kv) ff The optimization algorithm (such as gradient descent and genetic algorithm) is used to re-fine the data. The optimization goal is to make the comprehensive performance index of all historical cases (such as the weighted sum of total time and clamping force uniformity) optimal.

[0170] Model update: The optimized parameter set replaces the old preset parameter set. At the same time, the force-displacement stiffness model used in step A2 will also be refitted using the data from the new case to improve prediction accuracy.

[0171] Triggering retraining: When the system continuously detects the same type of abnormality (such as multiple slippages) or performance indicators (such as time consumption) that are significantly lower than the historical average level during the clamping process of the same specification of workpiece, the strategy optimization unit will mark the strategy of the workpiece of that specification as "to be optimized" and start the above-mentioned offline optimization process. The control strategy will be retrained using the latest case library (including failures and successes) of the workpiece to adapt to possible process changes or equipment wear.

[0172] Furthermore, in the above technical solution, the core processing module and the exception handling and optimization module are configured to collaboratively execute the following optimization steps:

[0173] Step C1: After each clamping task is completed, review the entire clamping process and extract key performance indicators, including the total time to reach a stable self-locking state, energy consumption, and the final score for clamping force uniformity.

[0174] Step C2: Compare the key performance indicators with the historical best case indicators. If the current indicators are better than the historical best, store the control parameter sequence and the corresponding perception data sequence of the current clamping process into the successful case library and mark it as a new best case.

[0175] Step C3: If the system detects the same type of abnormality or performance degradation when clamping the same specification workpiece multiple times in a row, the strategy retraining process is triggered, and the adaptive control strategy is further optimized using the updated success case library.

[0176] It should be noted that the core processing module and the exception handling and optimization module interact and coordinate through shared memory and message queues, and execute the optimization steps in the following specific manner:

[0177] Regarding step C1: Review and Key Performance Indicator Extraction

[0178] After each clamping task is completed (regardless of success or abnormal termination), the system automatically starts a low-priority review task. This task accesses the time-series data of the entire clamping process stored in the cache, including:

[0179] Sensing data sequence: such as F real (t), x act (t), τ m (t), V f (t) etc.

[0180] Control data sequences: such as the history of dynamic control commands, including the servo target trajectory x ref (t) and piezoelectric torque setpoint T target (t).

[0181] Event marker sequence: such as timestamps of key events like "contact start", "A4 stage trigger", and "lock confirmation".

[0182] Based on the above data, the following key performance indicators were calculated:

[0183] Total time T total The time difference between the issuance of the clamping command and the issuance of the locking confirmation signal by the locking confirmation module; if locking is not successful, the time is counted up to the last effective control action.

[0184] Energy consumption E total By controlling the servo motor drive current I motor (t) and voltage U motor The product of (t) is integrated to estimate the motor energy consumption, and the power consumption estimate of the piezoelectric ceramic actuator is accumulated (based on T). target (t) and its driving voltage), the formula is approximately:

[0185] E total ≈∫(I motor (t)*U motor (t))dt+k piezo *∫|T target (t)|dt,

[0186] Where k piezo Power coefficient;

[0187] Clamping force uniformity final score S uniform At the locking confirmation moment, take the final F. final The matrix scoring calculation takes into account both overall bias and local heterogeneity.

[0188] S uniform =w1*(1-E norm，final / E norm，max ) + w2*(1-N hotspot / N total ),

[0189] Among them, E norm，final It is the norm deviation at the final moment, E norm，max It is the maximum allowable norm deviation; N hotspot N is the number of nodes whose δ(i,j) exceeds a threshold (e.g., 15%). total This represents the total number of nodes; w1 and w2 are weighting coefficients, and w1 + w2 = 1, with a score S. uniform The closer to 1, the better the uniformity.

[0190] Regarding step C2: Case comparison and best case update

[0191] The system maintains a historical best case database, storing a current best case record for each type of workpiece specification, including the historical best T for that specification. total，best E total，best S uniform，best The index values ​​and their corresponding complete control parameter sequences and sensing data sequences.

[0192] Comparison logic: Once the clamping operation is determined to be "successful," the system will set the current (T) value as follows: total E total S uniform ) and the historical best index (T) of the corresponding workpiece specifications total，best E total，best S uniform，best A comparison was made. A weighted comprehensive evaluation method was used to determine whether it was "better": the comprehensive score Z was calculated. current =a*(T total，best / T total )+b*(E total，best / E total )+c*(S uniform / S uniform，best ), where a, b, and c are the weights of each indicator, and a+b+c=1, reflecting the emphasis on different performance characteristics. If Z current If the value is greater than 1, then the current indicator is considered to be better than the historical best.

[0193] Case storage: If the current result is better, the system will compress and format the complete control parameter sequence (including but not limited to: the predicted curve parameters used in step A2, the final parameter set of the fuzzy PID in step A3, the threshold parameters in step A4, etc.) and the corresponding sensing data sequence, and store them as a new data package in the successful case library, marking it as the new "optimal case" under the current specification. At the same time, the historical best index value of this specification will be updated.

[0194] Regarding step C3: Anomaly Detection and Policy Retraining Trigger

[0195] Anomaly and performance degradation monitoring: The anomaly handling and optimization module continuously statistically analyzes the clamping records of workpieces of the same specification, and defines two conditions to trigger retraining:

[0196] Triggering the frequency of the same type of abnormality: In N consecutive clamping operations (e.g., N=5), the number of times the same type of abnormality (e.g., "slippage") occurs exceeds a set threshold (e.g., 3 times).

[0197] Performance degradation trigger: The overall score Z is calculated based on M consecutive successful clamping attempts (e.g., M=10). current The average score is lower than the historical best case's overall score by a certain percentage (e.g., lower than 90%), or the key indicators (e.g., T) are lower than the historical best case's overall score. totalIt shows a statistically significant upward trend (e.g., using sliding window mean comparison or trend test).

[0198] Retraining process: When any condition is met, the system automatically triggers the policy retraining process, which calls the offline optimization function of the policy optimization unit;

[0199] Data preparation: Extract data from the success case library of all relevant cases (including new and old) for this specification of workpiece to form a training dataset;

[0200] Model parameter optimization: with the optimization objective (e.g., minimizing the average T) total or maximize the average S uniform Guided by the training dataset, and through optimization algorithms (such as particle swarm optimization or Bayesian optimization), the adjustable parameter set of the adaptive control strategy (e.g., membership function parameters of the fuzzy rule base, PID initial parameters (Kp0, Ki0, Kd0), feedforward gain Kv) is optimized. ff Search and iteratively update (the specific values ​​of the force control parameter mapping table, etc.);

[0201] Verification and Update: The performance of the optimized parameter set is verified using some reserved cases or through simulation. After confirming performance improvement or problem resolution, the new parameter set is updated online or offline to the configuration storage area of ​​the pre-stored clamping target parameters and adaptive control strategy of the core processing module. Subsequently, the clamping control of workpieces of this specification will be executed using the optimized strategy, thereby completing the re-optimization of the adaptive control strategy and achieving continuous improvement in system performance or adaptation to drift.

[0202] Three typical workpiece specifications were selected and tested under undisturbed, moderate vibration, and sudden load change conditions. The key performance indicators of the system are shown in the table below:

[0203]

[0204] The closer the clamping uniformity score is to 1, the better the uniformity; the total locking time includes the entire process from the issuance of the command to the confirmation of locking; the energy consumption is an estimated value of the total energy consumption of the servo motor and the piezoelectric ceramic driver.

[0205] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An intelligent control system for an elastic floating clamping self-locking mechanism, characterized in that, Includes the following modules: The sensing module is used to acquire physical state information of the clamping mechanism related to the clamping and locking states in real time; The core processing module is communicatively connected to the sensing module and is used to receive the physical state information and generate dynamic control commands based on the pre-stored clamping target parameters and adaptive control strategy. The drive execution module is communicatively connected to the core processing module and is used to receive the dynamic control command and drive the moving parts of the elastic floating clamping self-locking mechanism to generate corresponding adaptive motion. The locking confirmation module is communicatively connected to the sensing module and the core processing module. It is used to determine whether the mechanism has reached a preset stable self-locking state based on the physical state information, and to send a locking confirmation signal back to the core processing module. The anomaly handling and optimization module is communicatively connected to the core processing module and the sensing module. It is used to monitor abnormal operating conditions during system operation and trigger corresponding adaptive adjustment or safety protection programs according to the anomaly type. At the same time, it optimizes the adaptive control strategy based on historical operating data.

2. The intelligent control system for the elastic floating clamping self-locking mechanism according to claim 1, characterized in that, The sensing module specifically includes: The first sensing unit is arranged in an array on the elastic floating element of the clamping mechanism to measure the pressure distribution and micro-deformation of the contact area between the clamping surface and the workpiece, and outputs a real-time clamping force distribution matrix. The second sensing unit is located on the transmission link of the clamping mechanism and is used to measure the output displacement, output speed and output torque of the drive source. The third sensing unit is located on the workpiece bearing base and the clamping mechanism body, and is used to detect the amount of vibration and load disturbance in the external environment.

3. The intelligent control system for the elastic floating clamping self-locking mechanism according to claim 2, characterized in that, The core processing module is configured to perform the following operations: Receive the real-time clamping force distribution matrix, output displacement, output speed, output torque, external environmental vibration, and load disturbance from the sensing module; By comparing the real-time clamping force distribution matrix with the pre-stored ideal clamping force reference matrix, the clamping uniformity deviation and deformation compensation amount are calculated, and the force control parameters are determined based on the clamping uniformity deviation and the deformation compensation amount. Position closed-loop control is performed based on the deviation between the output displacement and the preset target displacement trajectory; feedforward compensation or overshoot suppression of motion speed is performed based on the output speed; and real-time identification and overload warning of the drive load are performed based on the output torque. Then, by integrating the deformation compensation amount, the external environmental vibration, and the load disturbance amount, the position closed-loop control, the velocity feedforward compensation, and the force control parameters are comprehensively adjusted to generate the dynamic control command.

4. The intelligent control system for the elastic floating clamping self-locking mechanism according to claim 1, characterized in that, The core processing module is further configured to execute the adaptive control strategy, which includes the following steps: Step A1: Initialization phase, load the clamping target parameters, which include at least the target clamping force range, the target locking position threshold, and the maximum allowable deformation; Step A2: In the learning and prediction phase, clamping process data similar to the current workpiece specifications from historical operation data is retrieved to predict the clamping force-displacement curve, which is then used as the feedforward input for the current control. Step A3: Closed-loop adjustment stage. During the process of the drive execution module driving the motion component, the real-time clamping force distribution matrix is ​​continuously received, and its real-time error with the ideal clamping force reference matrix is ​​calculated. The force control parameters in the dynamic control command are fine-tuned online using a fuzzy PID control algorithm so that the real-time error converges to zero. Step A4: State switching stage. When the real-time error continues to be lower than the stable threshold for a preset time and the output displacement enters the target locking position threshold, the clamping motion is determined to be completed, and the locking confirmation module is triggered to make the final state judgment.

5. The intelligent control system for the elastic floating clamping self-locking mechanism according to claim 1, characterized in that, The drive execution module specifically includes: The instruction parsing unit is used to parse the dynamic control instruction and decompose it into motion trajectory planning instructions and torque closed-loop setting values ​​for at least one servo motor or piezoelectric ceramic driver. The composite drive unit includes a servo motor for achieving macroscopic displacement and a piezoelectric ceramic actuator for achieving microscopic clamping and high-frequency vibration suppression. The servo motor and the piezoelectric ceramic actuator work together. The servo motor performs positioning according to the motion trajectory planning command, and the piezoelectric ceramic actuator performs micro-amplitude high-frequency adjustment according to the torque closed-loop setting value.

6. The intelligent control system for the elastic floating clamping self-locking mechanism according to claim 1, characterized in that, The locking confirmation module is configured to determine whether the stable self-locking state has been reached through the following steps: Step B1: Monitor the physical state information fed back by the sensing module and extract the clamping force stability value F. s Clamping position stability value P s and environmental vibration spectrum V f ; Step B2: Set the clamping force stabilization value F s With the preset lower limit of locking force F min and upper limit F max The results are compared, and the variance σ of the clamping force fluctuation within the preset time window is calculated. 2 ; Step B3: When conditions F are met simultaneously min ≤F s ≤F max , σ 2 ≤σ 2 max And the environmental vibration spectrum V f When the amplitude of the main frequency is lower than the preset resonance risk threshold, a locking confirmation signal indicating that a stable self-locking state has been reached is generated and sent to the core processing module. Step B4: After receiving the locking confirmation signal, the core processing module controls the drive execution module to stop active driving and causes the elastic floating clamping self-locking mechanism to enter a passive holding and locking state based on its own mechanical structure.

7. The intelligent control system for the elastic floating clamping self-locking mechanism according to claim 1, characterized in that, The anomaly handling and optimization module includes: The overload protection submodule, when the instantaneous clamping force detected by the sensing module exceeds the material safety limit, or the drive current of the drive execution module exceeds the rated value, immediately sends an emergency stop command to the core processing module and controls the drive execution module to perform a reverse release action; The slip compensation submodule, when the locking confirmation module detects that the clamping force continues to decrease and exceeds the slip threshold again after issuing the locking confirmation signal, determines that the workpiece has slipped, reactivates the core processing module, and calls the incremental PID control algorithm dedicated to slip compensation to generate a compensatory clamping command and send it to the core processing module so that it can generate a new dynamic control command to drive the drive execution module to perform the compensatory clamping action; The strategy optimization unit records the final stable clamping force distribution matrix, corresponding driving quantity, and environmental parameters during each successful clamping process, constructs a success case library, and optimizes and adjusts the parameters of the adaptive control strategy based on the success case library.

8. The intelligent control system for the elastic floating clamping self-locking mechanism according to claim 1, characterized in that, The core processing module and the exception handling and optimization module are configured to collaboratively execute the following optimization steps: Step C1: After each clamping task is completed, review the entire clamping process and extract key performance indicators, including the total time to reach a stable self-locking state, energy consumption, and the final score for clamping force uniformity. Step C2: Compare the key performance indicators with the historical best case indicators. If the current indicators are better than the historical best, store the control parameter sequence and the corresponding perception data sequence of the current clamping process into the successful case library and mark it as a new best case. Step C3: If the system detects the same type of abnormality or performance degradation when clamping the same specification workpiece multiple times in a row, the strategy retraining process is triggered, and the adaptive control strategy is further optimized using the updated success case library.

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