A high-precision linear vibration feedback motor system

Abstract

The application discloses a high-precision linear vibration feedback motor system and relates to the technical field of precise electromagnetic driving, which comprises the following steps: a moving-magnet linear motor with a Halbach array magnetic circuit structure is adopted as a core driving module, and a plurality of physical field sensing units are integrated; a dynamic parameter tracking module is adopted to collect spring stiffness coefficients, damping coefficients and magnetic force constants in real time when the motor is running, and a dynamic displacement model is established based on a recursive least square method; a double closed-loop control architecture is adopted, a magnetic field oriented control is adopted in an inner loop, and model predictive control is adopted in an outer loop; an aging prediction unit is adopted, when the cumulative vibration frequency is greater than a predetermined threshold, a temperature rise compensation algorithm is activated; an adaptive calibration engine is adopted, a sweep excitation signal is injected when the system is started, and individual difference parameters are automatically compensated according to resonance peak offset; a digital twin mapping unit is adopted to establish a real-time mapping relationship between motor physical parameters and a virtual model, and model parameters are dynamically corrected by comparing actual displacement and model displacement.

Description

Technical Field

[0001] This invention relates to the field of precision electromagnetic drive technology, specifically to a high-precision linear vibration feedback motor system. Background Technology

[0002] Traditional eccentric rotary motors (ERMs) suffer from slow response (hundreds of milliseconds), low control precision, and large size, failing to meet the demands of modern devices for fast and precise tactile feedback. Linear motors, on the other hand, reduce mechanical conversion losses through direct linear motion, resulting in higher energy efficiency and millisecond-level response times.

[0003] The pursuit of immersive tactile experiences in consumer electronics such as smartphones and wearable devices, as well as the demand for precision control in the medical and industrial fields, have driven the development of high-precision linear vibration feedback technology. For example, the Chinese linear vibration motor market is projected to reach 12 billion yuan in 2025, with a compound annual growth rate of 21%, driven primarily by new energy vehicles and 3C electronics.

[0004] Advances in piezoelectric actuation and micro / nano manufacturing technologies have significantly improved accuracy and reliability. For example, the open-loop resolution of piezoelectric motors can reach 0.05 μm, and the closed-loop repeatability can reach 0.1 μm. Adaptive algorithms and dynamic calibration technologies have effectively solved the problem of inconsistent vibration caused by individual differences in motors.

[0005] However, existing technologies still have problems that need to be solved: control accuracy and dynamic adaptability. The limitation of static models is that traditional control relies on factory-preset static displacement models. After long-term use, the accuracy decreases due to spring fatigue and material aging, requiring dynamic correction. Individual differences are that the physical parameters of motors in the same batch vary significantly, and consistency needs to be improved through calibration technology; otherwise, the user experience will be significantly different. Summary of the Invention

[0006] To address the aforementioned technical problems, a high-precision linear vibration feedback motor system is provided. This technical solution solves the problems of insufficient implicit feature extraction capability and difficulty in multi-source data fusion.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0008] A high-precision linear vibration feedback motor system includes:

[0009] Core drive module: A moving magnet linear motor with a Halbach array magnetic circuit structure, integrating a multi-physics sensing unit, including a piezoelectric thin film force sensor, a temperature sensor, and a triaxial accelerometer;

[0010] Dynamic parameter tracking module: Real-time acquisition of reed stiffness coefficient, damping coefficient, and magnetic constant during motor operation; establishes dynamic displacement model based on recursive least squares method; adopts dual closed-loop control architecture, with the inner loop using field-oriented control and the outer loop based on model predictive control; aging prediction unit: activates temperature rise compensation algorithm when the cumulative number of vibrations exceeds a predetermined threshold.

[0011] Adaptive calibration engine: Injects a sweep frequency excitation signal when the system starts up, and automatically compensates for individual differences in parameters based on the resonant peak offset; Digital twin mapping unit: Establishes a real-time mapping relationship between motor physical parameters and virtual model, and dynamically corrects model parameters by comparing actual displacement with model displacement; Batch parameter cloud synchronization mechanism: Encrypts and uploads the resonant frequency and quality factor values ​​of production line tests to the blockchain, and automatically downloads the calibration benchmark parameter set when the same batch of equipment is powered on.

[0012] Preferably, the core driver module specifically includes:

[0013] Halbach array magnetic circuit structure: adopts an asymmetric NNSNS magnetic pole topology, optimizes the magnetic circuit through laser-assisted magnetic domain orientation technology, selects N52H grade neodymium iron boron permanent magnets, and performs titanium plating on the surface to enhance corrosion resistance, combined with a 3D printed titanium alloy skeleton, and laser-assisted magnetic domain orientation technology.

[0014] Preferably, the core driver module specifically includes:

[0015] Piezoelectric thin film force sensor: embedded in the contact surface between the mover and the reed, dynamic range detection; using polyvinylidene fluoride piezoelectric material, response bandwidth DC-1kHz, real-time feedback of electromagnetic force-mechanical force coupling error;

[0016] Distributed temperature monitoring network: at the coil winding, monitor copper loss temperature rise and establish a temperature rise model; on the permanent magnet surface, detect the risk of irreversible demagnetization; at the bearing node, capture the accumulation of frictional heat;

[0017] The three-axis MEMS accelerometer is embedded at a predetermined angle and uses wavelet denoising algorithm to extract micro-vibration signals; it also uses FPGA hardware-level timestamps and sets a temperature compensation matrix.

[0018] Preferably, the core driver module specifically includes:

[0019] A real-time data fusion engine is constructed, with piezoelectric film force signals providing electromagnetic drive force feedback, temperature network triggering magnetic attenuation compensation, and triaxial accelerometers detecting multidimensional vibration coupling; signal transmission uses differential shielded cables, and the outer shell is covered with a nanocrystalline alloy shielding layer; mechanical vibration isolation uses silicone rubber damping rings.

[0020] Preferably, the dynamic parameter tracking module specifically includes:

[0021] Spring stiffness coefficient detection: A combination of laser micro-displacement interferometer and strain gauge is used for detection, and the stiffness value is inverted through the frequency response function; an environmental compensation mechanism is used to correct the stiffness in real time based on the temperature-stiffness relationship model;

[0022] Online identification of damping coefficient: Vibration attenuation analysis method, inject pulse excitation, collect free vibration attenuation curves, extract instantaneous damping ratio using Hilbert transform; nonlinear compensation, establish velocity-damping relationship matrix;

[0023] Magnetic constant drift tracking: dual-channel Hall array, multi-point distributed magnetic flux detection, based on temperature-remanence decay model; combined with dynamic compensation strategy, demagnetization detection is automatically performed after a predetermined number of vibrations.

[0024] Preferably, the dynamic parameter tracking module specifically includes:

[0025] Recursive least squares dynamic displacement model: parameter update mechanism, sliding time window design; introduction of weighted forgetting factor, focusing on recent data for high-temperature conditions; dynamic model architecture:

[0026]

[0027] In the formula, Dynamic displacement is the amount of displacement that changes over time, reflecting the system's response position after being subjected to an excitation force. The excitation force is a force signal that varies with time and acts on the system to cause displacement; The stiffness of the system reflects the structure's ability to resist deformation and may change over time. The imaginary unit is used in the formula to represent the phase characteristics of the damping term; Angular frequency, measured in radians per second, describes the speed of vibration. The linear damping coefficient reflects the energy dissipation characteristics of the system during vibration. This is a velocity-dependent nonlinear damping term, representing the nonlinear relationship between damping force and velocity v, used to more accurately describe the damping characteristics of complex systems. is the material fatigue factor, used to consider the impact of material performance degradation under cyclic loading on the dynamic behavior of the system; N is the number of cyclic loads, recording the number of cyclic loads experienced by the material, used to assess the degree of fatigue damage to the material.

[0028] Preferably, the dynamic parameter tracking module specifically includes:

[0029] Dual closed-loop control architecture: inner loop field-oriented control, harmonic suppression technology, high-frequency injection method for rotor position detection, dead zone compensation algorithm; outer loop model predictive control, prediction time domain optimization, setting rolling time domain length and objective function weights; real-time solver, QP problem solving based on FPGA hardware;

[0030] Aging prediction and temperature rise compensation: A three-level early warning mechanism is used for cumulative vibration frequency threshold management; the temperature rise compensation algorithm is based on a multi-physics coupling model.

[0031]

[0032] In the formula, This refers to the temperature of the coil, typically measured in degrees Celsius. This is the initial temperature, i.e., the reference temperature of the coil when it is not energized and not in operation; Thermal resistance reflects the degree to which the coil and its heat sink impede the transfer of heat. Electric current, measured in amperes; The resistance of the coil is expressed in ohms. This refers to the power generated by mechanical friction, measured in watts;

[0033] Dynamic current limiting adjusts the maximum current based on real-time temperature rise.

[0034] Preferably, the adaptive calibration engine specifically includes:

[0035] Wideband sweep frequency excitation and signal generation: sinusoidal sweep frequency signal is generated using direct digital synthesis technology; resonant peak detection: the peak point of the resonant frequency is located by analyzing the acceleration response through FFT and the resonant peak offset is dynamically tracked.

[0036] The parameter compensation algorithm calculates the stiffness compensation formula and damping matching rules, and dynamically adjusts the damping ratio based on the coupling relationship between the resonance peak offset and temperature.

[0037] Self-checking and fault-tolerance mechanisms, calibration data hash verification, and automatic switching to safe mode under abnormal operating conditions.

[0038] Preferably, the adaptive calibration engine specifically includes:

[0039] Multi-dimensional physical modeling, the virtual model architecture is a multibody dynamics model, and an electromagnetic-mechanical coupled field finite element model; real-time data stream, stiffness, damping, and magnetic force are periodically synchronized, and the model update rate is strictly synchronized with the control system clock.

[0040] Residual-driven parameter correction is based on displacement error analysis; Kalman filter fusion combines historical data and real-time observations to dynamically optimize model weight coefficients.

[0041] Predictive maintenance function, based on digital twin comparative analysis, provides early warning of bearing wear and magnet demagnetization, with life prediction error below a predetermined proportion, in compliance with ISO 13374-3 standard.

[0042] Preferably, the adaptive calibration engine specifically includes:

[0043] The blockchain batch parameter cloud synchronization mechanism includes encrypted parameters such as resonant frequency, quality factor Q value, and batch feature fingerprint in the production line data on-chain process. The blockchain architecture includes a private chain built on Hyperledger Fabric, data block generation interval is less than or equal to a predetermined time, and smart contracts automatically verify data legality.

[0044] The device-side synchronization strategy includes a power-on self-test process that involves obtaining the device's unique ID, downloading the same batch of benchmark parameter groups from the blockchain, and comparing local parameters with cloud benchmarks. The dynamic calibration rules activate factory mode recalibration if the parameter standard deviation exceeds a predetermined threshold, and support OTA incremental updates.

[0045] For security and privacy protection, data transmission uses the national standard SM4 encryption, federated learning technology enables anonymous parameter aggregation, and physical anti-tamper design is implemented.

[0046] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0047] This invention proposes a real-time multiphysics sensing network that captures instantaneous changes in key parameters such as stiffness, damping, and magnetic force on a millisecond-level timescale, and dynamically corrects control commands by combining them with a self-evolving displacement model. This closed-loop "sensing-modeling-control" system solves the accuracy degradation problem caused by parameter drift in traditional systems.

[0048] A three-tiered architecture—frequency sweep incentive, digital twin, and blockchain—is introduced: Wideband frequency sweep signals are used to quickly locate device characteristic fingerprints; digital twin mapping enables precise "one device, one model" adaptation; and blockchain technology constructs a batch-level parameter knowledge base, allowing new equipment to obtain production line-level optimized parameters upon startup. This not only eliminates individual performance fluctuations but also significantly reduces the networking and debugging costs of high-end equipment.

[0049] Dynamic synergistic optimization of the electromagnetic-mechanical-thermal coupled field is achieved through a real-time simulation model: magnetic field orientation control compensates for magnetic force attenuation caused by temperature rise, silicone rubber damping rings suppress interference from high-frequency vibrations on sensors, and a multi-node temperature monitoring network predictively adjusts power output. This multi-domain synergy enables the system to maintain stable output even under extreme environments such as high temperature, high humidity, and strong vibration.

[0050] An aging prediction model coupling vibration frequency, temperature, and stress is constructed. Digital twin comparisons provide early warnings of potential faults such as bearing wear and magnet demagnetization. Blockchain technology is integrated to synchronize maintenance strategies across devices. This three-tiered "prediction-early warning-prevention" protection system upgrades traditional passive maintenance to proactive health management, significantly extending equipment lifespan. Attached Figure Description

[0051] Figure 1 This is an internal framework diagram of a high-precision linear vibration feedback motor system. Detailed Implementation

[0052] The following description is intended to disclose the invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious variations will occur to those skilled in the art.

[0053] Reference Figure 1 As shown, a high-precision linear vibration feedback motor system includes:

[0054] Core drive module: A moving magnet linear motor with a Halbach array magnetic circuit structure, integrating a multi-physics sensing unit, including a piezoelectric thin film force sensor, a temperature sensor, and a triaxial accelerometer;

[0055] Dynamic parameter tracking module: Real-time acquisition of reed stiffness coefficient, damping coefficient, and magnetic constant during motor operation; establishes dynamic displacement model based on recursive least squares method; adopts dual closed-loop control architecture, with the inner loop using field-oriented control and the outer loop based on model predictive control; aging prediction unit: activates temperature rise compensation algorithm when the cumulative number of vibrations exceeds a predetermined threshold.

[0056] Adaptive calibration engine: Injects a sweep frequency excitation signal when the system starts up, and automatically compensates for individual differences in parameters based on the resonant peak offset; Digital twin mapping unit: Establishes a real-time mapping relationship between motor physical parameters and virtual model, and dynamically corrects model parameters by comparing actual displacement with model displacement; Batch parameter cloud synchronization mechanism: Encrypts and uploads the resonant frequency and quality factor values ​​of production line tests to the blockchain, and automatically downloads the calibration benchmark parameter set when the same batch of equipment is powered on.

[0057] It should be noted that the system achieves a leap in electromagnetic drive efficiency through the Halbach array magnetic circuit, and in conjunction with the multi-physics sensing unit, the piezoelectric sensor has a resolution of 0.001N and the triaxial accelerometer has a noise density of 25μg / √Hz, capturing micro-mechanical signals in real time.

[0058] The recursive least squares model of the dynamic parameter tracking module iteratively updates parameters such as reed stiffness and damping at a frequency of 1kHz. Together with the dual closed-loop control architecture, the inner loop FOC suppresses harmonics to 1.5% and the outer loop MPC predicts the displacement trajectory in 5ms, forming a real-time feedback chain to ensure displacement accuracy of ±0.5μm under all working conditions.

[0059] The adaptive calibration engine eliminates batch differences through blockchain-encrypted synchronization, compressing the standard deviation of the resonant frequency from 15% to 1.8%. Combined with digital twin mapping, it enables model self-evolution, with residual-driven correction rate >98%. The three form a closed loop of "perception-decision-calibration".

[0060] The core driver module specifically includes:

[0061] Halbach array magnetic circuit structure: adopts an asymmetric NNSNS magnetic pole topology, optimizes the magnetic circuit through laser-assisted magnetic domain orientation technology, selects N52H grade neodymium iron boron permanent magnets, and performs titanium plating on the surface to enhance corrosion resistance, combined with a 3D printed titanium alloy skeleton, and laser-assisted magnetic domain orientation technology;

[0062] Piezoelectric thin film force sensor: embedded in the contact surface between the mover and the reed, dynamic range detection; using polyvinylidene fluoride piezoelectric material, response bandwidth DC-1kHz, real-time feedback of electromagnetic force-mechanical force coupling error;

[0063] Distributed temperature monitoring network: at the coil winding, monitor copper loss temperature rise and establish a temperature rise model; on the permanent magnet surface, detect the risk of irreversible demagnetization; at the bearing node, capture the accumulation of frictional heat;

[0064] The triaxial MEMS accelerometer is embedded at a predetermined angle and uses wavelet denoising algorithm to extract micro-vibration signals; it uses FPGA hardware-level timestamps and sets a temperature compensation matrix.

[0065] A real-time data fusion engine is constructed, with piezoelectric film force signals providing electromagnetic drive force feedback, temperature network triggering magnetic attenuation compensation, and triaxial accelerometers detecting multidimensional vibration coupling; signal transmission uses differential shielded cables, and the outer shell is covered with a nanocrystalline alloy shielding layer; mechanical vibration isolation uses silicone rubber damping rings.

[0066] It should be noted that the Halbach array magnetic circuit structure is as follows:

[0067] The asymmetric magnetic pole topology employs an NNSNS arrangement, and the magnetic circuit is optimized through laser-assisted magnetic domain orientation technology to achieve 98.7% magnetic flux uniformity. The magnets are made of N52H grade neodymium iron boron with a remanence of 1.45T and a coercivity of 1114kA / m. The surface is treated with titanium plating to resist salt spray corrosion for 96 hours without failure. The 3D-printed titanium alloy skeleton has a density of 4.5g / cm³. With the topology optimization design, a thrust density of 3.8N / cm³ is achieved within a thickness of 1.5mm, while suppressing eddy current losses to <5W / kg (100Hz operating condition).

[0068] Piezoelectric thin film force sensor:

[0069] Micro-force sensing and dynamic coupling compensation: A 0.2 mm thick polyvinylidene fluoride (PVDF) piezoelectric film is embedded in the mover-reed interface to achieve micro-force detection of 0.001 to 10 N (resolution 0.1 mN), meeting the sensing requirements of vascular pulsation (0.01 N level);

[0070] The DC-1kHz wideband response covers the entire operating frequency band and compensates for displacement deviations caused by reed deformation by real-time feedback of electromagnetic-mechanical coupling errors. It adopts a charge integrating amplifier circuit to maintain a signal-to-noise ratio of >80dB under 50dB ambient noise, ensuring reliability in medical / industrial scenarios.

[0071] Distributed temperature monitoring network:

[0072] Multi-node thermal management strategy, coil winding monitoring, platinum resistance temperature sensor, real-time acquisition of copper loss temperature rise, and establishment of copper loss model:

[0073]

[0074] In the formula, This refers to the temperature rise of the coil, which is the difference between the coil temperature and the ambient temperature, usually expressed in degrees Celsius. Thermal resistance, usually measured in °C / W, reflects the degree to which the coil and its heat sink impede heat transfer. , which is the current, measured in amperes (A), and represents the magnitude of the current flowing through the coil; The resistance is AC, measured in ohms (Ω), taking into account the resistance characteristics of the coil under AC current. It is the iron loss coefficient, used to quantify the characteristics of heat generation from iron core losses in a coil; Frequency, measured in Hertz (Hz), represents the frequency of alternating current.

[0075] Permanent magnet protection, infrared thermopile non-contact temperature measurement, demagnetization compensation algorithm activated when magnet temperature > 80℃:

[0076]

[0077] In the formula, is the magnetic flux density of a permanent magnet at temperature T, usually expressed in tesla; This is the initial magnetic flux density, i.e., the magnetic flux density at the reference temperature of 20°C; The current temperature is in degrees Celsius.

[0078] Bearing node early warning: MEMS thermocouple array captures friction hotspots, and combined with vibration spectrum analysis, provides early warning of wear failures up to 500 hours in advance.

[0079] Triaxial MEMS accelerometer:

[0080] Anti-interference micro-vibration detection employs a 30° tilted embedded mounting, reducing inter-axis crosstalk to <0.1% through mechanical structure decoupling;

[0081] Key performance characteristics:

[0082] Measuring range ±50g (expandable to ±200g impact protection); noise density 18μg / √Hz; wavelet denoising algorithm (Db4 wavelet basis) extracts effective signals at the 0.1m / s² level;

[0083] Temperature drift suppression, FPGA hardware timestamp synchronization accuracy <10ns; temperature compensation matrix:

[0084]

[0085] In the formula, , , The correction amount after temperature compensation is the result after adjusting the original measurement value through the temperature compensation matrix, which is used to more accurately reflect the actual changes in physical quantities. , , The values ​​represent the original measurements before temperature compensation and may be affected by temperature variations. T is the inter-axis coupling correction matrix, used to correct the coupling relationship between different axes, ensuring that the measurements on each axis are independent and accurate. It is a mathematical tool that uses matrix operations to eliminate inter-axis interference. , , These are zero-bias temperature functions used to describe the effect of temperature changes on the measured value. These functions adjust the zero bias of the measured value based on the temperature t to compensate for the error caused by temperature drift; t is the temperature variable, representing the current temperature value, which is a key factor affecting the zero bias of the measured value.

[0086] Multiphysics data fusion engine:

[0087] Real-time collaborative control architecture, guaranteed signal transmission, differential shielded cable (impedance 120Ω±1%), nanocrystalline alloy shielding layer (permeability 1.2×10⁻⁶). 5 (Attenuation >40dB@10MHz);

[0088] Mechanical vibration isolation design, silicone rubber damping ring (loss factor 0.25), vibration transmissibility: <5%@100Hz (automotive conditions), <10%@500Hz (industrial scenarios).

[0089] Fusion control logic: Pedal power signal → electromagnetic drive force feedforward compensation (delay <50μs);

[0090] Temperature data → Dynamically adjust MPC control weights (increase displacement error weight by 20% for every 10℃ temperature rise);

[0091] Acceleration signal → Actively suppresses triaxial coupled vibration (cancellation rate > 92%).

[0092] The dynamic parameter tracking module specifically includes:

[0093] Spring stiffness coefficient detection: A combination of laser micro-displacement interferometer and strain gauge is used for detection, and the stiffness value is inverted through the frequency response function; an environmental compensation mechanism is used to correct the stiffness in real time based on the temperature-stiffness relationship model;

[0094] Online identification of damping coefficient: Vibration attenuation analysis method, inject pulse excitation, collect free vibration attenuation curves, extract instantaneous damping ratio using Hilbert transform; nonlinear compensation, establish velocity-damping relationship matrix;

[0095] Magnetic constant drift tracking: dual-channel Hall array, multi-point distributed magnetic flux detection, based on temperature-remanence decay model; combined with dynamic compensation strategy, demagnetization detection is automatically performed after a predetermined number of vibrations;

[0096] Recursive least squares dynamic displacement model: parameter update mechanism, sliding time window design; introduction of weighted forgetting factor, focusing on recent data for high-temperature conditions; dynamic model architecture:

[0097]

[0098] In the formula, Dynamic displacement is the amount of displacement that changes over time, reflecting the system's response position after being subjected to an excitation force. The excitation force is a force signal that varies with time and acts on the system to cause displacement; The stiffness of the system reflects the structure's ability to resist deformation and may change over time. The imaginary unit is used in the formula to represent the phase characteristics of the damping term; Angular frequency, measured in radians per second, describes the speed of vibration. The linear damping coefficient reflects the energy dissipation characteristics of the system during vibration. This is a velocity-dependent nonlinear damping term, representing the nonlinear relationship between damping force and velocity v, used to more accurately describe the damping characteristics of complex systems. is the material fatigue factor, used to consider the impact of material performance degradation under cyclic loading on the dynamic behavior of the system; N is the number of cyclic loading cycles, which records the number of cyclic loading cycles the material has experienced and is used to assess the degree of fatigue damage to the material.

[0099] Dual closed-loop control architecture: inner loop field-oriented control, harmonic suppression technology, high-frequency injection method for rotor position detection, dead zone compensation algorithm; outer loop model predictive control, prediction time domain optimization, setting rolling time domain length and objective function weights; real-time solver, QP problem solving based on FPGA hardware;

[0100] Aging prediction and temperature rise compensation: A three-level early warning mechanism is used for cumulative vibration frequency threshold management; the temperature rise compensation algorithm is based on a multi-physics coupling model.

[0101]

[0102] In the formula, This refers to the temperature of the coil, typically measured in degrees Celsius. This is the initial temperature, i.e., the reference temperature of the coil when it is not energized and not in operation; Thermal resistance reflects the degree to which the coil and its heat sink impede the transfer of heat. Electric current, measured in amperes; The resistance of the coil is expressed in ohms. This refers to the power generated by mechanical friction, measured in watts;

[0103] Dynamic current limiting adjusts the maximum current based on real-time temperature rise.

[0104] It should be noted that the spring stiffness coefficient is tested as follows:

[0105] The composite sensing technology employs a laser micro-displacement interferometer (wavelength 632.8nm, resolution 0.1μm) and a micro-strain gauge (range ±5000με) working in tandem to retrieve stiffness values ​​through the frequency response function. Within a frequency sweep range of 20–500Hz, the dynamic stiffness error is calculated to be <1.5% by fitting the slope of the peak point of the frequency response curve using least squares fitting.

[0106] The temperature-stiffness compensation model establishes a nonlinear relationship based on experimental data:

[0107]

[0108] In the formula,

[0109] When the temperature rises from -40℃ to 150℃, the stiffness fluctuation rate after compensation is <±2% (>15% without compensation).

[0110] Online damping coefficient identification mechanism:

[0111] Pulse excitation and attenuation analysis was performed by injecting a force pulse with a pulse width of 0.1 ms and an amplitude of 5 N, and acquiring free vibration signals with a sampling rate of 1000 Hz. The envelope slope was extracted using Hilbert transform, and the instantaneous damping ratio ζ was calculated with an accuracy of ±0.002.

[0112] Magnetic constant drift tracking system:

[0113] A dual-channel Hall array layout with 16 points in a ring (2mm spacing) detects the spatial gradient of magnetic flux density and constructs an attenuation model based on temperature sensor data; a demagnetization self-test strategy is implemented every 10... 4 The demagnetization detection is triggered by secondary vibration, and the residual magnetism attenuation rate is measured by reverse current pulse (amplitude 5A, pulse width 10ms).

[0114] Recursive least squares dynamic displacement model:

[0115] Sliding time window and forgetting factor, window optimization: window length 50ms (covering 3 typical oscillation cycles), parameters are updated every 5ms to balance real-time performance and data continuity;

[0116] Abnormal operating condition handling: When the model residual > 5μm, a three-level fault tolerance mechanism is triggered.

[0117] Short-term: Increases the forgetting factor to 0.99, suppressing mutation interference;

[0118] Mid-term: Use historical parameter database for interpolation compensation;

[0119] Long-term: Activate the digital twin mapping unit to reconstruct the model.

[0120] Dual closed-loop control architecture collaboration:

[0121] The system employs inner-loop field-oriented control (FOC), harmonic suppression technology, and high-frequency signal injection (25kHz carrier frequency) to detect rotor position, combined with a phase-locked loop (PLL) to achieve an angle error of <0.05°. A dead-zone compensation algorithm, through pre-distortion voltage correction, reduces current harmonic distortion (THD) from 5% to 1.2%.

[0122] Dynamic response metrics: current loop bandwidth of 3kHz, torque response time <0.3ms;

[0123] Outer loop model predictive control (MPC) is used, with prediction time-domain optimization and a rolling time-domain length of 5ms (covering system mechanical delay). The objective function weights are set to displacement error: velocity error: energy consumption = 6:3:1, prioritizing positioning accuracy.

[0124] The real-time solver, based on an FPGA parallel computing architecture, decomposes the quadratic programming (QP) problem into 8 threads for processing, with a single solution taking less than 8μs, meeting the 1kHz control cycle requirement.

[0125] Synergy between Aging Prediction and Thermal Management:

[0126] Three-level early warning mechanism, threshold settings:

[0127] Number of vibrations N Compensation action <![CDATA[N>10 5 ]]> Linear temperature rise compensation (ΔI_max=5%) <![CDATA[N>5×10 5 ]]> Activate nonlinear fatigue correction (ηN^0.33 terms) <![CDATA[N>10 6 ]]> Forced derating operation (power limited to 70%)

[0128] The adaptive calibration engine specifically includes:

[0129] Wideband sweep frequency excitation and signal generation: sinusoidal sweep frequency signal is generated using direct digital synthesis technology; resonant peak detection: the peak point of the resonant frequency is located by analyzing the acceleration response through FFT and the resonant peak offset is dynamically tracked.

[0130] The parameter compensation algorithm calculates the stiffness compensation formula and damping matching rules, and dynamically adjusts the damping ratio based on the coupling relationship between the resonance peak offset and temperature.

[0131] Self-checking and fault-tolerance mechanisms, calibration data hash verification, and automatic switching to safe mode under abnormal operating conditions;

[0132] Multi-dimensional physical modeling, the virtual model architecture is a multibody dynamics model, and an electromagnetic-mechanical coupled field finite element model; real-time data stream, stiffness, damping, and magnetic force are periodically synchronized, and the model update rate is strictly synchronized with the control system clock.

[0133] Residual-driven parameter correction is based on displacement error analysis; Kalman filter fusion combines historical data and real-time observations to dynamically optimize model weight coefficients.

[0134] Predictive maintenance function, based on digital twin comparative analysis, provides early warning of bearing wear and magnet demagnetization, with life prediction error below a predetermined proportion, in compliance with ISO 13374-3 standard;

[0135] The blockchain batch parameter cloud synchronization mechanism includes encrypted parameters such as resonant frequency, quality factor Q value, and batch feature fingerprint in the production line data on-chain process. The blockchain architecture includes a private chain built on Hyperledger Fabric, data block generation interval is less than or equal to a predetermined time, and smart contracts automatically verify data legality.

[0136] The device-side synchronization strategy includes a power-on self-test process that involves obtaining the device's unique ID, downloading the same batch of benchmark parameter groups from the blockchain, and comparing local parameters with cloud benchmarks. The dynamic calibration rules activate factory mode recalibration if the parameter standard deviation exceeds a predetermined threshold, and support OTA incremental updates.

[0137] For security and privacy protection, data transmission uses the national standard SM4 encryption, federated learning technology enables anonymous parameter aggregation, and physical anti-tamper design is implemented.

[0138] It should be noted that precision signal generation and resonance tracking are involved.

[0139] DDS sweep frequency technology uses a 32-bit direct digital synthesizer to generate a 20–500Hz sinusoidal sweep frequency signal with a frequency resolution of 0.001Hz and a total harmonic distortion of <0.3%. It avoids mechanical shock caused by frequency jumps through continuous phase switching technology.

[0140] Dynamic location of resonance peaks; 2048-point FFT analysis of triaxial acceleration response (sampling rate 5kHz); Gaussian fitting algorithm to extract resonance frequency peaks with an accuracy of ±0.05Hz; real-time calculation of resonance peak offset.

[0141] Multiphysics Parameter Compensation Algorithm

[0142] Intelligent damping matching, constructing a three-dimensional mapping table of temperature-frequency-damping:

[0143] Δf (Hz) T (°C) Damping ratio ζ 0–1 -40~85 0.02–0.04 >5 >100 0.12–0.15

[0144] Self-checking and fault-tolerant layered defense ensure data trustworthiness: SHA-256 hash verification calibration parameters, error rate <10⁻ 9 When switching to safety mode, if the residual exceeds the limit (>10μm), switch to the ISO 13849 PLd safety level, and the backup PID controller takes over momentarily (delay <100μs).

[0145] Digital twin-driven dynamic mapping:

[0146] Multi-scale virtual model construction, multi-body dynamics model, including 15 degrees of freedom (6 rigid bodies + 9 elastic modes), can simulate micro-deformation at the 0.1μm level;

[0147] Electromagnetic-mechanical coupled field, transient finite element model with 500,000 meshes, real-time calculation of the coupling effect of Lorentz force and magnetic resistance;

[0148] The core algorithm for predictive maintenance provides early warning of bearing wear. If the 3×BPFO (ball passing the outer ring frequency) component in the vibration envelope spectrum increases by more than 8dB, an early warning can be given 500 hours in advance (with a confidence level of 95%).

[0149] Blockchain-enabled batch collaboration networks:

[0150] Production line data on-chain architecture:

[0151] Encryption parameters Technical Specifications <![CDATA[Resonant frequency f0]]> Accuracy ±0.1Hz, ±0.01Hz after temperature drift compensation. Quality factor Q Range 10–200, resolution 0.1 Batch fingerprints 256-bit feature vector (generated by PCA dimensionality reduction)

[0152] Blockchain performance: Hyperledger Fabric 3.0 private chain, block generation interval of 0.8 seconds (5 times faster than in 2023), smart contracts automatically verify data range (if the Q value exceeds the limit, it will be rejected).

[0153] Device-side synchronous optimization strategy, three-step self-test upon startup: PUF chip generates device DNA; blockchain queries benchmark parameters of the same batch; local parameter comparison;

[0154] Dynamic calibration rules: Over-tolerance recalibration, factory mode is activated when the deviation is >3% (accuracy is restored to 99.8%); OTA incremental update, differential compression technology makes data packets ≤35KB;

[0155] End-to-end security protection, national standard SM4 encryption, and a 256-bit key resistant to quantum computing; federated learning privacy protection, local training of model gradients (without transmitting raw data), and global aggregation to update baseline parameters (GDPR compliant).

[0156] It features a physical anti-tamper design with a self-destruct mechanism triggered by both photosensitive and pressure sensors, with a response time of 7ms.

[0157] In summary, the advantages of this invention are as follows:

[0158] This invention achieves millisecond-level parameter tracking and dynamic correction through real-time multiphysics sensing and a self-calibration model. During long-term operation, the system continuously optimizes core parameters such as stiffness and damping, solving the accuracy degradation problem caused by parameter drift in traditional equipment and reducing manual calibration work.

[0159] To address the challenge of performance variation among motors in the same batch, this system integrates frequency scanning fingerprint recognition with blockchain cloud synchronization technology. Upon power-on, the equipment automatically downloads production line-level precision parameter benchmarks, achieving precise matching of "one model per machine" through digital twin technology. This mechanism reduces individual differences to a negligible range, significantly lowering the network debugging costs for high-end equipment.

[0160] Breaking down the traditional design barriers of electromagnetic, mechanical, and thermal management, cross-domain synergy is achieved through real-time simulation of electromagnetic-mechanical-thermal coupled fields. Magnetic field control compensates for temperature rise and magnetic decay, silicone rubber damping suppresses high-frequency vibration, and a temperature network provides predictive power adjustment, enabling the system to maintain nanometer-level stable output even under extreme environments such as high temperature, strong vibration, and vacuum.

[0161] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention. The scope of protection claimed by the appended claims and their equivalents is defined.

Claims

1. A high precision linear oscillating feedback motor system, characterized by, Comprise: Core drive module: Halbach array magnetic circuit structure of moving magnet linear motor, integrated multi-physical field sensing unit, including piezoelectric film force sensor, temperature sensor, three-axis accelerometer; Dynamic parameter tracking module: real-time acquisition of motor running reed stiffness coefficient, damping coefficient, magnetic force constant, based on recursive least squares method, dynamic displacement model is established; Double closed loop control architecture, inner loop uses magnetic field oriented control, outer loop based on model predictive control; Aging prediction unit, when the cumulative vibration frequency is greater than the predetermined threshold, the temperature rise compensation algorithm is activated; Adaptive calibration engine: injects a sweep excitation signal when the system starts, automatically compensates for individual difference parameters according to the resonance peak shift; Digital twin mapping unit, establish real-time mapping relationship between motor physical parameters and virtual model, compare actual displacement and model displacement, dynamically correct model parameters; Batch parameter cloud synchronization mechanism, the resonant frequency and quality factor value tested on the production line are encrypted and uploaded to the blockchain, and the same batch of equipment automatically downloads the calibration reference parameter group when starting The dynamic parameter tracking module specifically comprises: Recursive least square dynamic displacement model: parameter updating mechanism, sliding time window design; introduce the weighted forgetting factor, high temperature working condition focuses on recent data; dynamic model architecture: In the formula, is the dynamic displacement, which is the displacement amount changing with time, reflecting the response position of the system after being subjected to the excitation force; is the excitation force, which is the force signal changing with time, acting on the system to cause displacement; is the stiffness of the system, reflecting the ability of the structure to resist deformation, and may change with time; is the imaginary unit, used in the formula to represent the phase characteristic of the damping term; is the angular frequency, with the unit of radian per second, describing the speed of vibration; is the linear damping coefficient, reflecting the energy dissipation characteristics of the system in the vibration process; is the speed-dependent nonlinear damping term, representing the nonlinear relationship between the damping force and the speed v, used to more accurately describe the damping characteristics in complex systems; is the material fatigue factor, used to consider the influence of the performance degradation of the material under cyclic loading on the dynamic behavior of the system; N is the number of cyclic loads, recording the number of cyclic loads experienced by the material, used to evaluate the degree of fatigue damage of the material.

2. A high precision linear resonant seismic motor system according to claim 1, wherein, The core drive module specifically comprises: Halbach array magnetic circuit structure: adopt asymmetric N-N-S-N-S magnetic pole topology, optimize the magnetic circuit through laser-assisted magnetic domain orientation technology, select N52H grade neodymium iron boron permanent magnet, surface titanium plating treatment to enhance corrosion resistance, cooperate with 3D printing titanium alloy framework, combined with laser-assisted magnetic domain orientation technology.

3. A high precision linear resonant seismic motor system according to claim 2, wherein, The core drive module specifically comprises: Piezoelectric film force sensor: embedded in the contact surface of the mover and the reed, dynamic range detection; Adopt polyvinylidene fluoride piezoelectric material, response bandwidth DC-1kHz, real-time feedback electromagnetic force-mechanical force coupling error; Distributed temperature monitoring network: coil winding, monitor copper loss temperature rise, establish temperature rise model; Permanent magnet surface, detect irreversible demagnetization risk; Bearing node, capture friction heat accumulation; Three-axis MEMS accelerometer, embedded at a certain angle, combined with wavelet denoising algorithm to extract micro-vibration signals; Use FPGA hardware level timestamp, set temperature compensation matrix.

4. A high precision linear resonant seismic motor system according to claim 3, wherein, The core drive module specifically comprises: Build real-time data fusion engine, piezoelectric film force signal provides electromagnetic driving force feedback, temperature network triggers magnetic force attenuation compensation, three-axis accelerometer detects multi-dimensional vibration coupling; Signal transmission uses differential shielded cable, the shell covers a nanocrystalline alloy shielding layer; Mechanical vibration isolation uses silicone rubber damping ring.

5. A high precision linear resonant seismic motor system according to claim 4, wherein, The dynamic parameter tracking module specifically comprises: Reed stiffness coefficient detection: composite detection of laser micro-displacement interferometer and strain gauge, stiffness value is obtained through frequency response function inversion; Environmental compensation mechanism, real-time correction based on temperature-stiffness relationship model; Damping coefficient online identification: vibration attenuation analysis method, inject pulse excitation, collect free vibration decay curve, use Hilbert transform to extract instantaneous damping ratio; Nonlinear compensation, establish speed-damping relationship matrix; Magnetic force constant drift tracking: double-channel Hall array, multi-point distributed magnetic flux detection, based on temperature-remnant magnetization decay model; Combined with dynamic compensation strategy, automatically execute demagnetization detection after a certain number of vibrations.

6. A high precision linear resonant seismic motor system according to claim 1, wherein, The dynamic parameter tracking module specifically includes: Double-loop control architecture: inner loop field-oriented control, harmonic suppression technology, high-frequency injection method to detect rotor position, dead zone compensation algorithm; outer loop model predictive control, prediction time domain optimization, set rolling time domain length and objective function weight; real-time solver, FPGA-based hardware to solve QP problem; Aging prediction and temperature rise compensation: cumulative vibration frequency threshold management, three-level early warning mechanism; temperature rise compensation algorithm based on multi-physical field coupling model: In the formula, T is the temperature of the coil, in degrees Celsius; T0 is the initial temperature, that is, the reference temperature of the coil when it is not powered on and not working; R is the thermal resistance, which reflects the degree of hindering heat transfer of the coil and its heat sink; I is the current, in amperes; R is the resistance of the coil, in ohms; P is the mechanical friction power, in watts, the heat generated by mechanical friction; Dynamic current limiting, adjust the maximum current according to the real-time temperature rise.

7. A high precision linear resonant seismic motor system according to claim 6, wherein, The adaptive calibration engine specifically includes: Wideband sweep excitation, signal generation, using direct digital synthesis technology to generate sinusoidal sweep signal; resonance peak detection, FFT analysis of acceleration response, locate the resonance frequency peak point, dynamic tracking of resonance peak shift; Parameter compensation algorithm, calculate stiffness compensation formula, damping matching rule, dynamically adjust the damping ratio according to the coupling relationship between resonance peak shift and temperature; Self-checking and fault-tolerant mechanism, calibration data hash check, abnormal working condition automatically switches to safety mode.

8. A high precision linear resonant seismic motor system according to claim 7, wherein, The adaptive calibration engine specifically includes: Multi-dimensional physical modeling, virtual model architecture as a multi-body dynamics model, electromagnetic-mechanical coupling field finite element model; real-time data flow, periodically synchronize stiffness, damping, magnetic force, model update rate strictly synchronized with control system clock; Residual driven parameter correction, based on displacement error analysis; Kalman filter fusion, combined with historical data and real-time observation value, dynamically optimize model weight coefficient; Predictive maintenance function, based on digital twin comparative analysis, early warning of bearing wear and magnet demagnetization, life prediction error less than a certain proportion, in line with ISO 13374-3 standard.

9. A high precision linear resonant seismic motor system according to claim 8, wherein, The adaptive calibration engine specifically includes: Blockchain batch parameter cloud synchronization mechanism, encrypted parameters including resonance frequency, quality factor Q value, batch characteristic fingerprint in production line data chaining process, blockchain architecture includes private chain based on Hyperledger Fabric, data block generation interval less than or equal to a certain time, smart contract automatically verifies data legality; Device synchronization strategy, power-on self-test process includes obtaining device unique ID, downloading same batch reference parameter group from blockchain, comparing local parameters with cloud reference; dynamic calibration rule, if the parameter standard deviation is greater than a certain threshold, activate factory mode recalibration, support OTA incremental update; Security and privacy protection, data transmission uses national encryption SM4 encryption, federated learning technology realizes parameter anonymous aggregation, physical anti-disassembly design.

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