High-precision dynamic calibration algorithm for industrial CT

Abstract

The invention discloses a high-precision dynamic calibration algorithm of an industrial CT (Computed Tomography), and relates to the technical field of industrial CT. The algorithm comprises the following steps: constructing a state space model containing kinematic factors (machining errors, assembly errors and the like) and dynamic factors (mass, friction force and the like); model predictive control (MPC) is combined with a dynamic compensation algorithm (disturbance observation, end effect compensation and friction force fitting) to achieve real-time correction, a laser interferometer (the resolution is smaller than or equal to 1 nm) and a grating encoder (the precision is + / -2 microns) are used for collecting data, and parameters are optimized through PSNR evaluation and Simulink simulation. Compared with a traditional PID algorithm, the MPC supports multivariable optimization, nonlinear model adaptation and constraint embedding, the dynamic positioning precision (micron level), the response speed and the image quality (PSNR is larger than or equal to 45 dB) are remarkably improved, the method is suitable for dynamic detection of materials such as coal and rocks, artifacts and measurement errors are effectively reduced, and an efficient and reliable dynamic calibration scheme is provided for an industrial CT system.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial CT, and in particular to a high-precision dynamic calibration algorithm for industrial CT. Background Art

[0002] Existing industrial CT (Computed Tomography) systems, when performing dynamic scanning, suffer from mechanical defects such as kinematic factors—machining errors, mechanical tolerances, assembly errors, reducer accuracy, reducer backlash, and scale line errors; or dynamic factors—mass, inertia tensor, friction, and flexible transmission. This can lead to large errors between actual theoretical and measured values, resulting in the following problems:

[0003] Image blur: The accuracy of the motion system determines the spatial resolution of CT images. If the motion system is not accurate enough, the image will be unclear and have tails, making it difficult to identify the internal structure of the object.

[0004] Measurement Error: Poor motion accuracy can lead to positional deviations during scanning, which in turn affects the accuracy of measurement results. This error can manifest as inaccurate dimensional measurements and incorrect positioning. For example, the calculation of SODSDD makes it difficult to accurately assess the object being measured.

[0005] Reconstruction Issues: In CT scanning, data reconstruction relies on accurate scan data. Poor motion accuracy can lead to inaccurate data, which in turn affects the quality of data reconstruction. This can cause distortion and artifacts in the reconstructed image, reducing image quality.

[0006] Impact analysis: In industrial CT scanning, it is often necessary to analyze the scan results to assess the internal structure, defects, etc. of the object. If the motion accuracy is poor, it may lead to inaccurate analysis results and even mislead the analyst's judgment.

[0007] In view of this, this application is hereby filed. Summary of the Invention

[0008] The purpose of the present invention is to provide a high-precision dynamic calibration algorithm for industrial CT to solve the problems mentioned in the above background technology.

[0009] To solve the above technical problems, the present invention provides a high-precision dynamic calibration algorithm for industrial CT, comprising the following steps:

[0010] Step 1: Based on the dynamic characteristics and kinematic constraints of the industrial CT motion control system, a state-space model containing kinematic and dynamic factors is constructed. The state-space model includes the dynamic equations of the mechanical system, the state equations of the electrical system, and the state equations of the control system.

[0011] Step 2: Using the model predictive control (MPC) algorithm, the system's state sequence and input sequence in the prediction time domain are predicted based on the discretized state space model. A high-order fitting algorithm is used to perform polynomial fitting on the calibration parameters, and a dynamic compensation algorithm is used to perform real-time correction on the motion platform.

[0012] Step 3: Use high-precision sensors to collect the command position data and actual position data of the CT control system in real time. After filtering and denoising preprocessing, they are used for model verification and parameter optimization.

[0013] Step 4: Compare the experimental data with the model prediction results, adjust the high-order fitting algorithm parameters to approximate the actual dynamic characteristics of the system, and verify the calibration accuracy through third-party sensors and CT scanning results; through step-by-step model construction, prediction compensation, data acquisition and iterative optimization, the positioning accuracy and response speed of industrial CT dynamic scanning are systematically improved, effectively compensating for mechanical errors and dynamic interference, suitable for dynamic internal structure detection of materials such as coal and rock, and significantly improving the clarity of CT images and detection reliability.

[0014] Furthermore, in step one, kinematic factors include mechanical tolerances, processing errors, assembly errors, reducer precision errors, etc., and dynamic factors include mass, inertia tensor, friction and external disturbances; clarifying the specific types of kinematic factors and dynamic factors makes the model construction more comprehensive and accurate, and can more accurately describe the system characteristics, providing a reliable basis for subsequent control and compensation, and improving the adaptability of the algorithm to complex industrial environments.

[0015] Furthermore, in step one, the model uses the zero-order hold method to model the mechanical system and the forward discrete Euler method to model the electrical system; the zero-order hold method and the forward discrete Euler method are used to discretize the mechanical system and the electrical system respectively, which can accurately convert the continuous system into a discrete system, retain the dynamic characteristics of the system, improve the prediction accuracy of the model, and provide a more accurate mathematical model for subsequent control and compensation.

[0016] Furthermore, in step 2, the dynamic compensation algorithm includes designing an extended state observer to estimate disturbances and friction, using an IP controller to compensate for the end-effect error of the linear motor, and modeling the friction curve through a Gaussian fitting method; through the combination of multiple dynamic compensation strategies, the influence of external disturbances and system nonlinear factors on the CT scanning accuracy is effectively suppressed, the system's anti-interference ability and dynamic response performance are improved, the positioning error and jitter during movement are reduced, and the image reconstruction quality is improved.

[0017] Furthermore, in the step 2, the objective function of the model predictive control (MPC) dynamically adjusts the weight parameters of displacement, speed, and current according to the scanning scenario, increases the displacement weight in the spiral CT / bias scanning scenario, increases the speed weight in the ordinary CT / DR scanning scenario, and increases the current weight in the low-fever scenario; the weight parameters of the MPC objective function are dynamically adjusted according to different scanning scenarios, so that the algorithm can adapt to different scanning requirements, while ensuring the scanning accuracy, optimizing the energy consumption and heat generation of the system, extending the service life of the equipment, and improving the adaptability and flexibility of the system.

[0018] Furthermore, in step three, the high-precision sensor includes a laser interferometer with a resolution of not less than 1 nm and a linear phase grating encoder with a grating pitch of not more than 8 μm and an accuracy level of ±2 μm; the use of a high-resolution laser interferometer and a high-precision linear phase grating encoder for data acquisition ensures the accuracy and real-time performance of position measurement, provides reliable data support for subsequent model verification and parameter optimization, and further improves the accuracy and reliability of the calibration algorithm.

[0019] Furthermore, in step four, the image reconstruction quality is evaluated by the peak signal-to-noise ratio (PSNR), and the weight parameters are optimized based on Simulink simulation; by evaluating the image reconstruction quality by PSNR and verifying the system response performance by Simulink simulation, the effect of the calibration algorithm can be intuitively evaluated, problems can be discovered and solved in a timely manner, and the weight parameters can be optimized, thereby further improving the quality of CT images and the performance of the system, and ensuring the effectiveness and reliability of the algorithm.

[0020] The high-precision dynamic calibration algorithm for industrial CT is used to improve positioning accuracy, response speed, and in-place stability during dynamic scanning, meeting the needs of internal structure detection of coal, rock, lithium batteries, CPUs, PCB boards, composite materials, metal materials, etc. under dynamic loading conditions. Integrating the algorithm into the industrial CT system achieves a comprehensive improvement in positioning accuracy, response speed, and stability during dynamic scanning, providing an efficient and reliable technical solution for dynamic material performance testing and industrial non-destructive testing, with significant engineering application value.

[0021] Compared with the prior art, the present invention has the following beneficial effects:

[0022] 1. Improved Image Clarity: The precision of the motion system determines the spatial resolution of CT images. Improving motion accuracy significantly reduces positional deviation and jitter during scanning, ensuring more accurate data collection. This results in clearer, more detailed images, enabling inspectors to more accurately observe the object being measured.

[0023] 2. Improved measurement accuracy: In industrial CT scanning, accurate measurement is paramount. Improving motion accuracy can reduce measurement errors, making dimensional measurements and positioning more accurate. This is crucial for assessing an object's internal structure and defects, as well as for quality control.

[0024] 3. Optimizing Data Reconstruction: Data reconstruction is a critical step in industrial CT scanning. Improving motion precision ensures more accurate and complete data acquisition, thereby enhancing data reconstruction. This helps generate more realistic and accurate 3D images, providing strong support for subsequent analysis and evaluation.

[0025] 4. Enhanced Analysis Accuracy: In industrial CT scanning, analysis of scan results is often required to assess an object's internal structure and defects. Improving motion precision can enhance CT scanning accuracy, enabling engineers to more accurately determine an object's internal structure and defects. This can subsequently improve product quality, increase production efficiency, and reduce production costs.

[0026] 5. Reduce artifacts and distortion: Poor motion accuracy can cause problems such as artifacts and distortion during scanning. Improving motion accuracy can reduce the occurrence of these problems, thereby generating more realistic and reliable images and data. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 Schematic diagram of laser interferometer linear measurement in the high-precision dynamic calibration algorithm for industrial CT;

[0028] Figure 2 Schematic diagram of feedback control consisting of observer, external disturbance, and measured object model in the high-precision dynamic calibration algorithm for industrial CT.

[0029] Figure 3 This is a schematic diagram of the dynamic calibration process consisting of mechanical error, dynamic calibration instructions, and compensator in the high-precision dynamic calibration algorithm for industrial CT.

[0030] Figure 4 This is a comparison chart of friction force feedback value and fitting value in the high-precision dynamic calibration algorithm of industrial CT. DETAILED DESCRIPTION

[0031] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0032] See also Figure 1-Figure 4 This invention provides a technical solution: a high-precision dynamic calibration algorithm for industrial CT. First, a mathematical model of the system is established based on the dynamic characteristics and kinematic constraints of the motion control system. Then, an MPC algorithm is used to predict the system's future behavior. A high-order fitting algorithm is used to perform polynomial fitting on the calibration parameters. This algorithm combines model prediction with compensation algorithms to accurately describe the system's dynamic characteristics.

[0033] A dynamic compensation algorithm is introduced to perform real-time correction of the industrial CT system's motion platform, ensuring the accuracy of measurement results. Experiments cover motion control under different motion axes, positions, and directions, ensuring the reliability and repeatability of experimental results.

[0034] During the dynamic calibration experiment, a high-precision sensor (laser interferometer) was used to collect the actual and commanded position data of the CT control system's operating position in real time. The collected data was preprocessed through filtering and denoising before being used for subsequent comparisons and calculations.

[0035] The experimental data was compared with the simulation results predicted by the calibration algorithm to evaluate the accuracy and applicability of the model. Based on the analysis results, the high-order fitting algorithm was further optimized to improve the calibration accuracy. At the same time, through an iterative optimization process, the actual dynamic characteristics of the system were gradually approached.

[0036] The model parameters and calibration compensation values ​​are applied to the industrial CT control system, and the results are verified using third-party high-precision sensors and actual industrial CT scanning effects.

[0037] The control system is further debugged and optimized based on the test results to ensure the accuracy and reliability of the industrial CT system in detecting changes in the internal structure of the tested samples during actual scanning and detection.

[0038] The results achieved: 1. Improved the positioning accuracy and response speed of industrial CT systems in both static and dynamic environments, enabling precise measurement of samples. 2. Enhanced the stability and robustness of industrial CT systems. The application of dynamic compensation algorithms enables the industrial CT system to maintain stable measurement performance under complex working conditions and reduces sensitivity to external interference. 3. Provided reliable technical support for optimizing industrial CT scanning results and image reconstruction algorithms. 4. Improved the efficiency of data acquisition and processing in industrial CT scanning, shortening testing cycles.

[0039] Example 1:

[0040] The high-precision dynamic calibration algorithm for industrial CT includes the following steps:

[0041] Step 1: According to the dynamic characteristics and kinematic constraints of the industrial CT motion control system, a state space model containing kinematic factors and dynamic factors is constructed. The state space model includes the dynamic equations of the mechanical system, the state equations of the electrical system, and the state equations of the control system; the kinematic factors include mechanical tolerances, processing errors, assembly errors, reducer precision errors, etc., and the dynamic factors include mass, inertia tensor, friction and external disturbances; the specific types of kinematic factors and dynamic factors are clarified to make the model construction more comprehensive and accurate, and to more accurately describe the system characteristics, provide a reliable foundation for subsequent control and compensation, and improve the adaptability of the algorithm to complex industrial environments; the model uses the zero-order hold method to model the mechanical system and the forward discrete Euler method to model the electrical system; the zero-order hold method and the forward discrete Euler method are used to discretize the mechanical system and the electrical system respectively, which can accurately convert the continuous system into a discrete system, retain the dynamic characteristics of the system, improve the prediction accuracy of the model, and provide a more accurate mathematical model for subsequent control and compensation;

[0042] Step 2: A model predictive control (MPC) algorithm is used to predict the system's state sequence and input sequence within the prediction time domain based on a discretized state-space model. A high-order fitting algorithm is used to perform polynomial fitting of the calibration parameters, and a dynamic compensation algorithm is used to perform real-time correction of the motion platform. The dynamic compensation algorithm includes designing an extended state observer to estimate disturbances and friction, using an IP controller to compensate for the linear motor's end-effect errors, and modeling the friction curve using a Gaussian fitting method. This combination of multiple dynamic compensation strategies effectively suppresses the effects of external disturbances and system nonlinearities on CT scanning accuracy, improves the system's anti-interference capability and dynamic response performance, reduces positioning error and jitter during motion, and enhances image reconstruction quality. The MPC objective function dynamically adjusts the weighting parameters of displacement, velocity, and current based on the scanning scenario. The weighting of the MPC objective function is increased for spiral CT / offset scanning scenarios, velocity for standard CT / DR scanning scenarios, and current for low-heat scenarios. Dynamic adjustment of the MPC objective function weights based on different scanning scenarios enables the algorithm to adapt to different scanning requirements. While ensuring scanning accuracy, it optimizes the system's energy consumption and heat generation, extending the equipment's service life and improving the system's adaptability and flexibility.

[0043] Step 3: Use high-precision sensors to collect the command position data and actual position data of the CT control system in real time. After filtering and denoising preprocessing, they are used for model verification and parameter optimization. The high-precision sensors include a laser interferometer with a resolution of not less than 1nm and a linear phase grating encoder with a grating pitch of not more than 8μm and an accuracy level of ±2μm. The use of high-resolution laser interferometers and high-precision linear phase grating encoders for data acquisition ensures the accuracy and real-time performance of position measurement, providing reliable data support for subsequent model verification and parameter optimization, thereby further improving the accuracy and reliability of the calibration algorithm.

[0044] Step 4: Compare the experimental data with the model prediction results, adjust the high-order fitting algorithm parameters to approximate the actual dynamic characteristics of the system, and verify the calibration accuracy through third-party sensors and CT scanning effects; evaluate the image reconstruction quality through peak signal-to-noise ratio (PSNR), and optimize the weight parameters based on Simulink simulation; Evaluating the image reconstruction quality through PSNR and verifying the system response performance through Simulink simulation can intuitively evaluate the effect of the calibration algorithm, timely discover and solve problems, optimize the weight parameters, further improve the quality of CT images and system performance, and ensure the effectiveness and reliability of the algorithm; through step-by-step model construction, prediction compensation, data acquisition and iterative optimization, the positioning accuracy and response speed of industrial CT dynamic scanning are systematically improved, kinematic factors and dynamic interference are effectively compensated, and it is suitable for dynamic internal structure detection of coal, rock, lithium batteries, CPUs, PCB boards, composite materials, metal materials, etc., significantly improving the clarity of CT images and detection reliability.

[0045] The high-precision dynamic calibration algorithm for industrial CT is used to improve positioning accuracy, response speed, and in-place stability during dynamic scanning, meeting the needs of internal structure detection of coal, rock, lithium batteries, CPUs, PCB boards, composite materials, metal materials, etc. under dynamic loading conditions. Integrating the algorithm into the industrial CT system achieves a comprehensive improvement in positioning accuracy, response speed, and stability during dynamic scanning, providing an efficient and reliable technical solution for dynamic material performance testing and industrial non-destructive testing, with significant engineering application value.

[0046] It should be noted here that:

[0047] Derivation of MPC algorithm

[0048] 1. Establishment of dynamic model of motion axis:

[0049]

[0050] in:

[0051] x: linear displacement;

[0052] m: load mass;

[0053] f f : Friction (may include static friction, Coulomb friction and viscous friction);

[0054] fd: external disturbance (such as load change);

[0055] Ft: electromagnetic thrust.

[0056] Ft=k*iq;

[0057] k: thrust constant;

[0058] iq: q-axis current (linearly related to thrust).

[0059] 2. State Space Representation

[0060] Defining state variables

[0061] Input u=[vd,vq]^T,

[0062] The output is the displacement x,

[0063] The state equation is:

[0064]

[0065] 3. Establish a discrete model

[0066] Mechanical system:

[0067] Using the zero-order hold method,

[0068] Assume that the thrust Ft and friction force are constant within the sampling period Ts:

[0069]

[0070]

[0071] Electrical system:

[0072] Use forward discrete Euler:

[0073] Id(k+1)=id(k)+Ts / Ld(Vd(k)-Rs*id(k)+We(k)Lqiq(k)),

[0074]

[0075] 4. Model prediction extension

[0076] Predict the state sequence X(k) and input sequence U(k) in the time domain Np,

[0077] Contains mechanical and electrical status:

[0078]

[0079] A and B are constructed recursively from the discretization model.

[0080] D contains the cumulative effect of external disturbances.

[0081] 5. Objective function design

[0082] Displacement tracking, thrust fluctuation, and current constraints need to be optimized simultaneously:

[0083]

[0084] Among them, Qx, Qv, Qi are the weights of displacement, velocity and current,

[0085] Δv(k)=v(k)-v(k-1) is used to smooth the voltage input.

[0086] The physical meaning of the weight matrix:

[0087] Qx: The weight of the displacement tracking error, reflecting the requirement for positioning accuracy.

[0088] Qv: The weight of the velocity error, which affects the motion smoothness and anti-disturbance ability.

[0089] Qi: Weight of current (thrust) error, related to energy efficiency and motor heating.

[0090] Based on the principle of control target priority

[0091] CT spiral scanning / offset scanning scenarios:

[0092] Qx must be much larger than Qv and Qi (Qx=10 3 , Qv=10 1 , Qi=1), ensuring that the position tracking error is minimized.

[0093] Ordinary CT or DR scanning scene,

[0094] Increase Q v To suppress speed fluctuations (e.g. Qv = 10 2 ,Qx=10 1 ),

[0095] Low fever scenarios are required:

[0096] Increase Qi to limit the current amplitude (for example Qi = 10, Qx = 10 2 ).

[0097] Normalize the state and control variables to avoid numerical problems caused by dimension differences:

[0098]

[0099] The initial weight value can be set as:

[0100]

[0101] Dynamic adjustment strategy:

[0102] Iterative parameter adjustment method:

[0103] 1. Initially set Qx = 1, Qv = 0.1, Qi = 0.01;

[0104] 2. Gradually increase Qx until the position error meets the standard;

[0105] 3. Adjust Qv to suppress speed overshoot;

[0106] 4. Finally adjust Qi to balance current consumption.

[0107] Frequency domain analysis method:

[0108] Through the open-loop frequency response function, the weights are adjusted to ensure that the CT scanning system is within the tracking bandwidth of more than 10 Hz).

[0109] Special considerations for industrial CT scenarios

[0110] (1) Scanning trajectory characteristics

[0111] Ordinary CT or DR scanning mode:

[0112] During the uniform speed-dwell phase, an extremely high Qx is required to ensure positioning accuracy; during the uniform speed scanning phase, Qv is increased.

[0113] Spiral CT scanning mode:

[0114] Qx and Qv need to be balanced, and the ratio of Qx:Qv≈10:1 needs to be adjusted.

[0115] (2) Anti-disturbance requirements

[0116] Vibration suppression: Increase Qv to enhance damping (Qv = 2Qx).

[0117] Load mutation: Appropriately reduce Qi to allow instantaneous overcurrent (Qi reduced by 50%).

[0118] (3) Influence of motor parameters

[0119] Thrust constant Kf:

[0120] Qi should be compared with kf 2 Inversely proportional, use a high thrust motor.

[0121] Mechanical resonant frequency Fres:

[0122] If Fres is close to the control bandwidth, Qv needs to be increased to suppress resonance.

[0123] Steps for determining weights in actual projects

[0124] 1. Simulation verification:

[0125] Build a model in Simulink and test the step / sine response; observe the overshoot, settling time, and current peak, and adjust the weight.

[0126] 2. Experimental calibration:

[0127] Fixed Qi = 1, scan (Qx, Qv) combination;

[0128] Select the parameters that minimize the location RMSE.

[0129] 3. Online Adaptation:

[0130] Design rules:

[0131] is the position error Qx(k)=Qx+α|ex(k)|; ex is the position error;

[0132] Dynamically adjust weights to adapt to different scanning stages.

[0133] The following are recommended values:

[0134]

[0135] 6. Adjustment of Constraints

[0136] Electrical constraints

[0137] Current amplitude limit: √(i 2 d+i 2 q)≤Imax;

[0138] Voltage amplitude limit: √(v 2 d+v 2 q)≤Vmax;

[0139] Mechanical restraint;

[0140] Speed ​​Limit:

[0141] Displacement range: Xmin≤X≤Xmax;

[0142] Thrust limit: |Ft|≤|Fmax|.

[0143] 7. Arrangement of special issues

[0144] The first is the end effect of the linear motor. The end effect will appear at the end of the linear motor stator because there will be a tiny gap at the stator joint. The magnetic flux distribution here is different from the magnetic flux distribution in the middle part. Not only is the magnetic field weak, but it will also be distorted. Current fluctuations will occur here. An IP controller can be designed here to compensate for the current fluctuations here.

[0145] End compensation effect:

[0146] Ft=kf*iq+ΔFend(x);

[0147] like Figure 4 As shown, the second is friction compensation (Gaussian fitting method)

[0148] The friction curve fitting method is straight line fitting.

[0149] The dependent variable is the command torque, and the independent variable is the command speed, which is related to the direction of the command speed.

[0150] myfittype=fittype('b*sign(x)'dependent',{'y'},"independent',{'x'}....

[0151] 'coefficients',{'b'})

[0152] myfit=fit(cmdvel,cmdTog,myfittype,"startPoint',0)

[0153] fitFriction:(myfit,b+0.000)*sign(cmdVel)

[0154] lab=sprintf('%0.3f*sign(qdot)',myfit.b):

[0155] 8. Adjustment of control framework

[0156] Thrust-current decoupling: Maximizing the thrust / current ratio by controlling Id=0;

[0157] Disturbance Observer: Design an extended state observer (ESO) to estimate Fd and friction in real time;

[0158] Parameter adaptation: Online identification of m and kf to cope with load changes.

[0159] Experimental data comparison basis:

[0160] Comparison method:

[0161] 1. Use PSNR (peak signal-to-noise ratio) method to discuss

[0162] Principle: The degree of distortion is measured by pixel-level error.

[0163] Advantages: Simple calculation, suitable for quick evaluation.

[0164] Disadvantages: Low correlation with human eye perception, unable to distinguish between structural distortion and noise.

[0165] Application scenario: Preliminary evaluation of image compression and denoising algorithms.

[0166] Image difference corresponding to the value: The typical peak signal-to-noise ratio value in image and video compression is between 30dB and 50dB, and the higher the better.

[0167] The PSNR is close to 50dB, which means that the compressed image has only a very small error.

[0168] The PSNR is greater than 30dB, and the human eye can hardly detect the difference between the compressed and original images.

[0169] When PSNR is between 20dB and 30dB, the human eye can perceive the difference in images.

[0170] The PSNR is between 10dB and 20dB. The human eye can still see the original structure of the image and intuitively judge that there is not much difference between the two images.

[0171] When PSNR is lower than 10dB, it is difficult for humans to judge with the naked eye whether two images are the same or whether one image is the compressed result of another image.

[0172] Due to the use of the MPC algorithm of image model + motion model, each discrete motion point is within the pixel block, and there is almost no pixel error. At this time, the signal-to-noise ratio is in a perfect state.

[0173] The positioning sensor uses SUPRADU with HEIDENHAIN steel matrix.

[0174] Linear Phase Grating:

[0175] Pitch: 8um.

[0176] Linear expansion coefficient: 10*10^(-6)K^(-1).

[0177] Diagnostic interface: 1VPP analog subdivision;

[0178] Signal period: 4um;

[0179] Cut-off frequency: ≥250kHz;

[0180] Accuracy grade: ±2um.

[0181] The laser interferometer used for calibration is Renishaw laser interferometer.

[0182] Laser resolution 1nm.

[0183] like Figure 1 As shown: Using more accurate measuring instruments and verification CT equipment to improve the overall confidence of the equipment.

[0184] To sum up: As follows:

[0185] characteristic MPC PID Multivariable control Support MIMO, global optimization Requires independent design, high coupling risk Constraint processing Explicitly incorporate optimizations Dependence on external logic Dynamic response Proactive optimization, adaptive lag Passive feedback, hysteresis sensitive Nonlinear / time-varying systems Support nonlinear models, online adaptation Performance is limited and frequent parameter adjustments are required Optimization goal Multi-objective comprehensive optimization Only error minimization Computational complexity High (need to solve optimization problems online) Very low (fixed calculation)

[0186] The traditional PID algorithm is commonly used in the market now. Compared with the traditional PID algorithm,

[0187] MPC naturally supports multivariable systems (MIMO) and global optimization to avoid coupling; PID requires independent design of multiple controllers and is susceptible to interference between variables;

[0188] MPC directly embeds constraints (such as the safe range of current and speed) in the optimization to avoid the risk of exceeding the limit. PID requires external logic to handle constraints, which can easily lead to integral saturation.

[0189] MPC uses dynamic models to predict future states and adjust control actions in advance, significantly improving the response of hysteresis systems. PID relies only on current / historical error feedback, which limits its performance in hysteresis scenarios.

[0190] MPC can handle nonlinear and time-varying models and supports online updates (such as real-time changes and adjustments to position and image effects). PID relies on linear assumptions and has fixed parameters, requiring frequent parameter adjustments in complex scenarios.

[0191] MPC can comprehensively optimize multiple objectives such as tracking accuracy, energy consumption, and economic cost; PID only pursues error minimization.

[0192] MPC combines prediction with state estimation (such as designing a Kalman filter) to actively compensate for measurable disturbances; PID relies on integral action and responds slowly to sudden disturbances.

Claims

1. High-precision dynamic calibration algorithm for industrial CT, characterized by: The following steps are involved: Step 1: Based on the dynamic characteristics and kinematic constraints of the industrial CT motion control system, a state-space model containing kinematic and dynamic factors is constructed. The state-space model includes the dynamic equations of the mechanical system, the state equations of the electrical system, and the state equations of the control system. Step 2: Using a model predictive control algorithm, the system's state sequence and input sequence in the prediction time domain are predicted based on a discretized state space model. A high-order fitting algorithm is used to perform polynomial fitting on the calibration parameters, and a dynamic compensation algorithm is used to perform real-time corrections on the motion platform. Step 3: Use high-precision sensors to collect the command position data and actual position data of the CT control system in real time. After filtering and denoising preprocessing, they are used for model verification and parameter optimization. Step 4: Compare the experimental data with the model prediction results, adjust the high-order fitting algorithm parameters to approximate the actual dynamic characteristics of the system, and verify the calibration accuracy through third-party sensors and CT scanning results.

2. The high-precision dynamic calibration algorithm for industrial CT according to claim 1, characterized in that: In the step 1, the kinematic factors include mechanical tolerance, processing error, assembly error, and reducer precision error, and the dynamic factors include mass, inertia tensor, friction, and external disturbance.

3. The high-precision dynamic calibration algorithm for industrial CT according to claim 1, characterized in that: In the step 1, the model uses the zero-order hold method to model the mechanical system, and uses the forward discrete Euler method to model the electrical system.

4. The high-precision dynamic calibration algorithm for industrial CT according to claim 1, characterized in that: In the second step, the dynamic compensation algorithm includes designing an extended state observer to estimate disturbance and friction, using an IP controller to compensate for the end-effect error of the linear motor, and modeling the friction curve through a Gaussian fitting method.

5. The high-precision dynamic calibration algorithm for industrial CT according to claim 1, characterized in that: In step 2, the objective function of the model predictive control dynamically adjusts the weight parameters of displacement, velocity, and current according to the scanning scenario. The displacement weight is increased in the spiral CT / offset scanning scenario, the velocity weight is increased in the ordinary CT / DR scanning scenario, and the current weight is increased in the low fever scenario.

6. The high-precision dynamic calibration algorithm for industrial CT according to claim 1, characterized in that: In the step three, the high-precision sensor includes a laser interferometer with a resolution of not less than 1 nm and a linear phase grating encoder with a grating pitch of not more than 8 μm and an accuracy level of ±2 μm.

7. The high-precision dynamic calibration algorithm for industrial CT according to claim 1, characterized in that: In the step 4, the image reconstruction quality is evaluated by the peak signal-to-noise ratio, and the weight parameters are optimized based on Simulink simulation.

8. The high-precision dynamic calibration algorithm for industrial CT according to any one of claims 1 to 7, characterized in that: It is used to improve the positioning accuracy, response speed and positioning stability during dynamic scanning, and meet the internal structure detection needs of coal, rock, lithium batteries, CPU, PCB boards, composite materials and metal materials under dynamic loading conditions.

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