A control method for adjusting the tunneling posture of a shield machine

Abstract

The present invention discloses a control method for adjusting the tunneling posture of a shield machine, which specifically relates to the field of shield machine posture control. The method is used to solve the problem that the posture deviation of the shield machine caused by changes in the underground environment during the tunneling process cannot be corrected in a timely and effective manner. The method calculates the membership of horizontal deviation and vertical deviation and their change rates, extracts high-frequency and low-frequency features, generates independent phase signals, and uses multi-dimensional data fusion and high-dimensional exponential integration methods to calculate the judgment coefficient, so as to dynamically adjust the weight of the fuzzy control rule library and realize adaptive optimization of PID gain parameters; generates a total control output signal based on the updated PID parameters, accurately adjusts the thrust of the partitioned oil cylinder, ensures that the posture adjustment of the shield machine under complex environmental conditions is fast, accurate and stable, improves the accuracy of tunneling direction control and construction safety, and further enhances the overall posture correction capability of the shield machine through dynamic adjustment of the rolling angle.

Description

Technical Field

[0001] The present invention relates to the field of shield machine posture control, and more specifically, to a control method for adjusting the tunneling posture of a shield machine. Background Art

[0002] During underground tunnel excavation using a shield machine, precise control of the machine's advance along the designed axis is crucial. Due to the complexity of the underground rock formations and the presence of obstacles such as underground pipelines, the shield machine often faces the challenge of real-time attitude adjustment during excavation. Currently, shield machines utilize positioning and guidance systems such as a total station mounted on the machine body and a prism at the center of the shield head to measure and monitor key attitude parameters, such as shield head deviation, shield tail deviation, advance trend, roll angle, and pitch angle, to ensure accuracy and safety during construction.

[0003] However, traditional attitude adjustment methods are primarily based on PID control, which achieves precise control within a certain range by adjusting the proportional, integral, and differential gain parameters. However, under the nonlinear, multivariable, and complex environmental conditions of shield machines, the response speed and control effectiveness of traditional PID control often fail to meet requirements. This is especially true in scenarios where geological conditions change frequently or tunneling trajectory adjustments are frequently made. The system struggles to dynamically adapt to environmental changes, resulting in delayed attitude adjustment or insufficient control accuracy. In these situations, achieving dynamic parameter adjustment and real-time optimization has become a key issue that urgently needs to be addressed in attitude control technology.

[0004] In order to solve the above problems, a technical solution is now provided. Summary of the Invention

[0005] To overcome the aforementioned shortcomings of the prior art, embodiments of the present invention provide a control method for adjusting the tunneling posture of a shield machine. This method utilizes a fuzzy-PID-based adaptive control method, combined with multidimensional posture data acquired by a laser positioning and guidance system and a total station, to achieve precise control of the shield machine's posture during tunneling, significantly improving its adaptability to complex nonlinear and multivariable environments. Through membership calculation, frequency domain feature extraction, phase signal processing, and multidimensional data fusion, a judgment coefficient for dynamically adjusting the PID gain is generated. This further optimizes the weight distribution of the fuzzy control rule base, ensuring that the proportional, integral, and differential gain parameters are updated in real time based on the actual tunneling state. Furthermore, through defuzzification, the fuzzy control output is converted into a precise gain adjustment value, thereby dynamically optimizing the thrust distribution of the propulsion cylinder and the cutterhead reversal adjustment process. This achieves high-precision correction of the shield machine's roll angle, pitch angle, and tunneling deviation, meeting the high-precision and high-reliability requirements of tunneling, and addressing the aforementioned issues raised in the background art.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A control method for adjusting the tunneling posture of a shield machine, comprising the steps of:

[0008] S1, using the laser positioning guidance system and total station to measure the various operating data of the shield machine in real time and convert the data into the original numerical set;

[0009] S2, divide the horizontal deviation and vertical deviation and their change rates into seven subsets: negative large, negative medium, negative small, zero, positive small, positive medium, and positive large, and calculate the membership degree;

[0010] S3 performs a fifth-order fast Fourier transform on the horizontal error to extract high-frequency and low-frequency features, processes the vertical error to generate a phase signal, fuses the two-directional signals, and calculates the judgment coefficient;

[0011] S4, adjusting the weight of the fuzzy control rule base based on the judgment coefficient to generate a PID gain adjustment amount;

[0012] S5, adding the defuzzified gain adjustment amount to the current PID gain to dynamically update the PID parameters;

[0013] S6, uses the updated PID parameters to calculate the total control output and transmits it to the partition cylinder actuator to adjust the posture;

[0014] S7, adjust the cylinder thrust according to the total control output.

[0015] In a preferred embodiment, step S1 includes the following contents:

[0016] A laser scanner is used to monitor the positions of the shield head and tail of the shield machine in real time; the omnidirectional measurement capability of the total station is used to obtain the tunneling trend angle, roll angle and pitch angle of the shield machine in three-dimensional space; the three-dimensional spatial posture data is converted into a complex domain representation; the complex domain data is subjected to multi-resolution analysis using wavelet transform to extract posture change features at different frequency levels; the extracted high-dimensional feature data is standardized; the standardized data is mapped to the interval [0,1]; and the processed standardized and normalized data is organized into a multi-dimensional original numerical set.

[0017] In a preferred embodiment, step S2 includes the following:

[0018] Horizontal deviation and vertical deviation Defined as the angular deviation between the current posture of the shield machine and the design axis; horizontal deviation change rate and vertical deviation rate of change Defined as the change in horizontal deviation and vertical deviation per unit time;

[0019] Set the range of horizontal deviation, vertical deviation, horizontal deviation change rate and vertical deviation change rate to [-90°, +90°];

[0020] Divide the interval into seven subsets: negative large , negative 、Negative small ,zero , Zhengxiao ,middle Zhengda ;

[0021] Design a triangular membership function for each subset, whose peak is located at the center of the subset and the basis covers the boundaries of adjacent subsets; define the center point of each subset as and the boundary points are ;

[0022] For any input value , which is in the subset The membership degree in Determined by the following triangular membership functions:

[0023] ;

[0024] Specific formula description:

[0025] When the input value is in the left half of the subset When , the membership increases linearly from 0 to 1;

[0026] When the input value is in the right half of the subset When , the membership decreases linearly from 1 to 0;

[0027] When the input value is not within the interval of the subset, the membership is 0;

[0028] For each input value, a vectorized calculation method is used to calculate its membership in the seven subsets: ;

[0029] Use nonlinear weighting factors The membership degree of each subset is adjusted as follows: ; Among them, the weighting factor Determined by the following composite function: ;parameter , , is a preset constant;

[0030] To ensure that the sum of all subset memberships is 1, the adjusted memberships are normalized and then organized into a membership matrix, where each row corresponds to an input value and each column corresponds to a subset.

[0031] In a preferred embodiment, step S3 includes the following contents:

[0032] S31. Calculate the membership values ​​of the obtained horizontal deviation and vertical deviation and their change rates in each membership subset; combine the corresponding membership information with the original error value, and perform a weighted transformation on each error value: , ;in and is the final membership value obtained after normalizing the horizontal deviation and the vertical deviation in step S2, and Indicates the index of the membership subset;

[0033] S32. Perform a fifth-order fast Fourier transform on the weighted horizontal error, map it from the time domain to the frequency domain, and recursively perform a five-layer decomposition; set the first-layer fast Fourier transform output to ,right After frequency band separation and nonlinear phase correction, a new time domain signal is obtained, and fast Fourier transform is performed again until the fifth-order decomposition is completed;

[0034] S33. Performing dual nonlinear phase modulation on the weighted vertical error; first, mapping the weighted vertical error to the complex domain: ;in is a nonlinear phase function dynamically calculated based on actual geological parameters. , , , is a preset constant; the same type of nonlinear phase modulation is applied to the phase modulated signal to obtain a more complex complex phase signal ;

[0035] S34. Perform multidimensional data interleaving and fusion on the complex plane of the horizontal error composite spectrum extracted by the fifth-order fast Fourier transform and the complex phase signal obtained by dual nonlinear phase modulation of the vertical error. This process uses multidimensional complex space interpolation to decompose the horizontal error composite spectrum and the complex phase signal into several sub-bands and sub-phase units, which are then cross-arranged on the complex plane.

[0036] Perform high-dimensional exponential integral calculation on the fusion matrix to obtain the judgment coefficient for judgment .

[0037] In a preferred embodiment, step S4 includes the following contents:

[0038] Assume that the rule base contains rules, each rule is as follows: ;in is the error, is the error change rate, and It is a subset of the seven subsets: negative large, negative medium, negative small, zero, positive small, positive medium, and positive large. , , are the fuzzy output subsets for proportional, integral, and differential gain adjustment respectively;

[0039] Define each rule The weight factor as follows: Where Is a preset constant used to reflect the rules Base sensitivity to gain adjustments; It is a nonlinear index used to enhance the weighting effect at high complexity;

[0040] Based on the known error and error change rate membership, each rule is matched with its antecedents, and the minimum operation is used to achieve fuzzy synthesis of the rule antecedents: ;in and are the membership degrees of error and error change rate under the corresponding subsets respectively;

[0041] Then, As the cut-set height of the subsequent output fuzzy set, the fuzzy subsets of the proportional, integral, and differential gain adjustment are cut, and the weight factor is combined Vertically enlarge the membership function of the corresponding cut-set posterior: ;in is the original membership function of the corresponding rule output subset;

[0042] Perform maximum aggregation on the weighted output fuzzy sets of all rules and synthesize the corresponding gain adjustment fuzzy outputs into three final fuzzy sets;

[0043] The maximum membership method is used to defuzzify the final fuzzy set; the point with the maximum membership is found in the domain of the gain adjustment amount, and the value of this point is output as the final PID gain adjustment amount.

[0044] In a preferred embodiment, step S5 includes the following contents:

[0045] Use nonlinear mapping functions to perform complex nonlinear amplification or compression on the gain adjustment amount;

[0046] The following nonlinear mapping methods are defined:

[0047] (1) Update of proportional gain: ;

[0048] (2) Update of integral gain: ;

[0049] (3) Update of differential gain: ;

[0050] in , , are the proportional, integral, and differential gain adjustment values ​​respectively;

[0051] Through nonlinear mapping, the proportional, integral, and differential gain adjustments are integrated with the judgment coefficients and their respective function mappings to finally obtain new PID parameters. , , .

[0052] In a preferred embodiment, step S6 includes the following contents:

[0053] According to the PID control logic, three correction quantities are calculated in sequence:

[0054] The proportional correction is achieved by multiplying the current error value by the updated proportional gain, reflecting the direct impact of the current deviation;

[0055] The integral correction is based on the historical error accumulation and is combined with a nonlinear kernel function to amplify the impact of persistent deviations.

[0056] The differential correction depends on the error change rate and is combined with a smoothing function to limit the risk of over-adjustment caused by sudden changes.

[0057] After calculating the basic control output, the attitude compensation parameters are added and generated through the five-dimensional exponential convolution formula to dynamically balance the asymmetric distribution of the thrust between the cylinder groups; finally, the total control output is obtained.

[0058] In a preferred embodiment, step S7 includes the following contents:

[0059] The thrust of the four groups of cylinders of the shield machine is adjusted using the obtained total control output signal. By calculating the posture adjustment information in the total control output signal, the thrust adjustment amount of each group of cylinders is allocated, and it is ensured that the extension amount of each group of cylinders is always within the preset safety threshold range.

[0060] The technical effects and advantages of the control method for adjusting the tunneling posture of a shield machine according to the present invention are as follows:

[0061] The present invention uses a fuzzy PID-based adaptive control method, combined with multi-dimensional posture data obtained by a laser positioning and guidance system and a total station, to achieve precise control of the shield machine's posture during excavation, significantly improving its adaptability to complex nonlinear and multivariable environments. Through membership calculation, frequency domain feature extraction, phase signal processing, and multi-dimensional data fusion, a judgment coefficient for dynamically adjusting the PID gain is generated, further optimizing the weight distribution of the fuzzy control rule base to ensure that the proportional, integral, and differential gain parameters are updated in real time according to the actual excavation status. At the same time, the fuzzy control output is converted into a precise gain adjustment through defuzzification processing, thereby dynamically optimizing the thrust distribution of the propulsion cylinder and the cutterhead reversal adjustment process, achieving high-precision correction of the shield machine's roll angle, pitch angle, and excavation deviation, meeting the high-precision and high-reliability requirements of tunnel excavation. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 The present invention is a flow chart of a control method for adjusting the tunneling posture of a shield machine. DETAILED DESCRIPTION

[0063] 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.

[0064] Example 1: Figure 1 The present invention provides a control method for adjusting the tunneling posture of a shield machine, comprising:

[0065] S1, using the laser positioning guidance system and total station to measure the various operating data of the shield machine in real time and convert the data into the original numerical set;

[0066] S2, divide the horizontal deviation and vertical deviation and their change rates into seven subsets: negative large, negative medium, negative small, zero, positive small, positive medium, and positive large, and calculate the membership degree;

[0067] S3 performs a fifth-order fast Fourier transform on the horizontal error to extract high-frequency and low-frequency features, processes the vertical error to generate a phase signal, fuses the two-directional signals, and calculates the judgment coefficient;

[0068] S4, adjusting the weight of the fuzzy control rule base based on the judgment coefficient to generate a PID gain adjustment amount;

[0069] S5, adding the defuzzified gain adjustment amount to the current PID gain to dynamically update the PID parameters;

[0070] S6, uses the updated PID parameters to calculate the total control output and transmits it to the partition cylinder actuator to adjust the posture;

[0071] S7, adjust the cylinder thrust according to the total control output.

[0072] During the tunneling process, accurate real-time measurement and acquisition of the shield machine's multi-dimensional posture data are essential for achieving automatic posture adjustment. The data captured by the high-precision sensor system not only reflects the shield machine's current operating status but also provides the necessary information support for subsequent posture adjustments. The core of step S1 is the use of laser positioning and guidance systems and total station technology to collect the shield machine's key posture parameters in real time and convert them into a set of raw values ​​suitable for subsequent processing. This process involves complex data acquisition and conversion technologies to ensure data accuracy and real-time performance, laying a solid foundation for the efficient operation of the overall control system.

[0073] Step S1 includes the following contents:

[0074] 1. Multi-sensor data acquisition:

[0075] Laser Positioning and Guidance System: A high-precision laser scanner is used to monitor the position of the shield head and tail of the shield machine in real time. The laser positioning and guidance system transmits a laser beam, receives the reflected signal, and calculates the deviation of the shield head and tail relative to the predetermined design axis.

[0076] Total Station: Utilizing its omnidirectional measurement capabilities, the total station acquires the shield machine's three-dimensional tunneling trend angle, roll angle, and pitch angle. Through precise angle and distance measurements, the total station updates the shield machine's spatial position and attitude changes in real time.

[0077] 2. Data synchronization and timestamp marking:

[0078] To ensure data synchronization, all sensor data are transmitted to the central processing unit via a high-frequency data bus (such as EtherCAT or CAN bus).

[0079] Each set of data is accompanied by an accurate timestamp, which allows for time alignment of different sensor data during subsequent data processing, eliminating errors caused by transmission delays.

[0080] 3. High-dimensional data fusion and preprocessing:

[0081] Data fusion algorithm: An adaptive Kalman filter is used to fuse multi-source data from the laser positioning guidance system and the total station. The Kalman filter uses prediction and update steps to filter out sensor noise and improve the signal-to-noise ratio of the data.

[0082] Outlier detection and correction: An outlier detection method based on principal component analysis is used to identify and eliminate possible sensor abnormal data to ensure data reliability.

[0083] 4. Complex number field conversion and feature extraction:

[0084] Complex Domain Conversion: Converts 3D spatial pose data into complex domain representation for subsequent frequency domain analysis and phase processing. This primarily involves representing the shield head deviation and shield tail deviation as complex numbers.

[0085] Feature extraction: Wavelet transforms are applied to complex domain data for multi-resolution analysis, extracting posture change features at different frequency levels. These features include low-frequency trend components and high-frequency fluctuation components, corresponding to the shield machine's large-scale posture adjustment and subtle posture correction requirements, respectively.

[0086] 5. Data standardization and normalization processing:

[0087] To meet the input requirements of the subsequent fuzzy control algorithm, the extracted high-dimensional feature data is standardized. Using the Z-score standardization method, each dimension of the data is converted to a standard normal distribution with a mean of zero and a standard deviation of one, eliminating the impact of different dimensions and numerical ranges on the control system.

[0088] Min-Max normalization is further applied to map the standardized data to the interval [0,1] to ensure that the data is within a uniform scale, which is convenient for the membership calculation and rule matching of the fuzzy controller.

[0089] 6. Generation and output of original value set:

[0090] The processed, standardized and normalized data are organized into a multidimensional raw numerical set, including key parameters such as shield head deviation, shield tail deviation, tunneling trend angle, roll angle, and pitch angle. These parameters are stored in vector form and serve as input for the subsequent fuzzy control algorithm.

[0091] The generated raw value set is transmitted to the central control unit via a high-speed data bus interface (such as PCIe or Ethernet interface) to ensure the real-time and integrity of the data.

[0092] Step S1 uses high-precision real-time measurement from a laser positioning and guidance system and a total station, combined with data fusion and processing techniques, to generate a high-quality raw set of multidimensional pose data. Complex number field conversion, feature extraction, standardization, and normalization ensure data accuracy and consistency, providing a reliable data foundation for the subsequent fuzzy PID control algorithm. This process not only improves data acquisition accuracy and efficiency but also provides solid technical support for the shield machine's automatic posture adjustment in complex geological environments.

[0093] During the tunneling process of a shield machine, real-time monitoring and accurate assessment of its attitude deviation are crucial to ensuring construction quality and safety. Horizontal and vertical deviations, as well as their rates of change, are important indicators for measuring the attitude of a shield machine. To achieve precise attitude control, these deviation data must be systematically divided and refined. The core of step S2 is to subdivide the horizontal and vertical deviations, as well as their rates of change, into seven subsets within the range of -90 degrees to 90 degrees, and through a complex membership calculation method, ensure that the membership calculation of each input value in each subset is accurate and innovative. This process provides a high-quality input foundation for subsequent fuzzy control, enhancing the response sensitivity and adjustment accuracy of the control system.

[0094] Step S2 includes the following contents:

[0095] 1. Definition and preparation of deviation and its rate of change:

[0096] Horizontal deviation and vertical deviation It is defined as the angular deviation between the current posture of the shield machine and the design axis, in degrees.

[0097] Horizontal deviation change rate and vertical deviation rate of change It is defined as the change in horizontal deviation and vertical deviation per unit time, with the unit being degree / second.

[0098] 2. Interval division:

[0099] The range of horizontal deviation, vertical deviation, horizontal deviation change rate and vertical deviation change rate is set to [-90°, +90°].

[0100] Divide the interval into seven subsets: negative large , negative 、Negative small ,zero , Zhengxiao ,middle Zhengda .

[0101] 3. Triangular membership function design:

[0102] A unique triangular membership function is designed for each subset, with its peak located at the subset center and its basis covering the boundaries of adjacent subsets.

[0103] Define the center point of each subset and boundary points as follows:

[0104]

[0105] 4. Membership calculation formula:

[0106] For any input value (represents one of horizontal deviation, vertical deviation, horizontal deviation change rate, and vertical deviation change rate), which is in the subset The membership degree in Determined by the following triangular membership functions:

[0107] ;

[0108] Specific formula description:

[0109] When the input value is in the left half of the subset When , the membership increases linearly from 0 to 1.

[0110] When the input value is in the right half of the subset When , the membership decreases linearly from 1 to 0.

[0111] When the input value is not within the interval of the subset, the membership is 0.

[0112] 5. Membership calculation formula:

[0113] For each input value, calculate its membership in the seven subsets: ; Use vectorized calculation method to ensure efficient processing of membership calculation of multiple input values.

[0114] 6. Membership adjustment mechanism:

[0115] In order to improve the accuracy of membership calculation, a nonlinear weighting factor is used The membership degree of each subset is adjusted as follows: ; Among them, the weighting factor Determined by the following composite function: ;parameter , , It is a preset constant used to adjust the nonlinear change of membership and ensure flexible adjustment of membership under extreme deviation conditions.

[0116] 7. Membership adjustment mechanism:

[0117] To ensure that the sum of all subset memberships is 1, the adjusted memberships are normalized: ; This processing ensures that the relative proportional relationship of the membership degree remains unchanged, while meeting the requirements of the fuzzy control algorithm for input data.

[0118] 8. Output membership matrix:

[0119] The normalized membership Organized into a membership matrix where each row corresponds to an input value and each column corresponds to a subset.

[0120] The membership matrix serves as the basic input for subsequent fuzzy reasoning and control rule matching, ensuring that the control system can make decisions based on accurate membership information.

[0121] Step S2 divides the horizontal and vertical deviations, as well as their rates of change, within the range of -90 degrees to 90 degrees into seven precise subsets. Using a complex triangular membership function combined with a nonlinear weighting mechanism, the degree of membership of each input value within each subset is calculated. This process not only improves the accuracy and adaptability of the membership calculation but also, through complex mathematical formulas and multidimensional data processing methods, ensures that the control system can make accurate attitude adjustment decisions based on high-quality input data. This innovative processing logic enables the system to demonstrate greater sensitivity and responsiveness in complex geological environments, significantly enhancing the level of automated and intelligent control during shield machine excavation.

[0122] In the shield machine's automatic attitude control system, accurately determining whether the weights of the proportional gain, integral gain, and differential gain need to be adjusted is a key step in achieving precise control. Through the first two steps, the membership information and corresponding error values ​​of the multi-dimensional attitude data are obtained. On this basis, step S3 interweaves and fuses the two-directional information in a multidimensional space by performing high-order frequency domain analysis on the horizontal error and nonlinear phase modulation on the vertical error. The fused signal is then subjected to a high-dimensional exponential integral operation, ultimately obtaining a judgment coefficient that can quantify the complexity of attitude adjustment. This judgment coefficient provides a direct and accurate basis for subsequent PID parameter optimization and dynamic weight adjustment.

[0123] Step S3 includes the following contents:

[0124] S31. Input data and preprocessing:

[0125] After completing step S2, the membership values ​​of the obtained horizontal deviation and vertical deviation and their change rates in each membership subset are combined with the original error values ​​(including horizontal error and vertical error) obtained in step S1, and a weighted transformation is performed on each error value: , ;in and The final membership value obtained after normalizing the horizontal deviation and vertical deviation in step S2 is obtained by weighting these memberships. and Provide more accurate input data basis for subsequent frequency domain analysis and phase modulation, and Indicates the index of the member subset.

[0126] S32. Fifth-order Fast Fourier Transform Analysis of Horizontal Error:

[0127] The weighted horizontal error is decomposed by the fifth-order fast Fourier transform, mapped from the time domain to the frequency domain, and recursively decomposed into five layers. The output of the first layer of fast Fourier transform is set to ,right After frequency band separation and nonlinear phase correction, a new time domain signal is obtained, and the fast Fourier transform is performed again until the fifth-order decomposition is completed. The process can be expressed as: ;in represents the fast Fourier transform operator, is the initial time domain signal, The signal is restored by the last inverse fast Fourier transform. Through five-order iterations, each order performs nonlinear mapping on the phase and amplitude characteristics (for example, using exponentially nested nonlinear phase correction of the frequency components), thereby obtaining horizontal error composite spectrum data containing multi-level frequency domain characteristics. .

[0128] The use of a five-layer fast Fourier transform to decompose horizontal errors in the shield machine attitude control system is based on the need for in-depth analysis of the signal's frequency domain characteristics. The five-layer decomposition can effectively capture multi-level frequency components from low frequency to high frequency, ensuring that the system can identify both slowly changing trend errors and rapidly changing transient disturbances. In addition, the five-layer decomposition achieves an optimal balance between spectral resolution and computational complexity, providing sufficient frequency domain details to support accurate judgment coefficient calculations while avoiding the computational resource consumption and real-time processing delays caused by too many layers. Through this multi-level frequency domain analysis method, the system can comprehensively and meticulously understand and evaluate the dynamic behavior of the shield machine in complex geological environments, thereby providing high-quality input data for subsequent phase modulation and data fusion, and ultimately achieving accurate dynamic optimization of the PID controller parameters.

[0129] S33. Double nonlinear phase modulation of vertical error:

[0130] Double nonlinear phase modulation is performed on the weighted vertical error. First, the weighted vertical error is mapped to the complex domain: ;in is a nonlinear phase function dynamically calculated based on actual geological parameters. , , , is a preset constant. This phase function nonlinearly depends on the square term of the vertical error value and the sine transform, ensuring that different phase offset characteristics are presented under different error ranges.

[0131] Repeat the process of step S33 twice, that is, apply the same nonlinear phase modulation to the phase modulated signal again to obtain a more complex complex phase signal. .

[0132] S34. Multi-dimensional data interweaving and fusion:

[0133] The horizontal error composite spectrum extracted by the fifth-order fast Fourier transform and the complex phase signal obtained by double nonlinear phase modulation of the vertical error are interleaved and fused in the complex plane. This process uses multidimensional complex space interpolation to decompose the horizontal error composite spectrum and complex phase signal into several sub-bands and sub-phase units, which are then cross-arranged in the complex plane: ;in is the multidimensional frequency and phase joint domain, represents tensor concatenation operations over complex fields, is a nonlinear weighting factor to ensure that the two-directional data form a complementary structure in the multidimensional space, so that the final fusion matrix At the same time, the frequency domain and phase characteristics of horizontal and vertical errors are retained.

[0134] in, It is the frequency component index used to index the frequency domain features extracted after the weighted horizontal error is subjected to the fifth-order fast Fourier transform; It is a phase component index used to index the independent phase signal generated by processing the weighted vertical error through the dual nonlinear phase modulation algorithm.

[0135] 5. High-dimensional exponential integral calculation judgment coefficient:

[0136] Perform high-dimensional exponential integral calculation on the fusion matrix to obtain the judgment coefficient for judgment .

[0137] Define the high-dimensional exponential integral as: ;in is the complex modulus value of the corresponding unit in the fusion matrix, represents the natural logarithm, is a nonlinear distribution coefficient related to geological parameters, cutterhead speed, and propulsion speed, which emphasizes the influence of specific frequency and phase components in a weighted manner. is a normalization factor used to map the exponential integration result to a reasonable range (e.g., 0 to 1).

[0138] Based on the data from the previous two steps, step S3 performs multi-level transformation and fusion processing on the horizontal and vertical errors and their membership relationships. A fifth-order fast Fourier transform is used to extract the multi-frequency characteristics of the horizontal error. Dual nonlinear phase modulation is then used to enhance the phase characteristics of the vertical error signal. The two are interwoven and fused in a multidimensional space. Finally, a judgment coefficient is calculated through high-dimensional exponential integration. This judgment coefficient accurately captures the multidimensional complexity of the attitude error, providing solid data and decision-making support for subsequent fuzzy rule weight adjustment and PID gain adaptive control.

[0139] After processing through the previous steps, the system has obtained the precise membership and judgment coefficients of the multidimensional posture data. As a core indicator reflecting the complexity of shield machine posture adjustment, the judgment coefficient has a critical impact on the subsequent weight allocation of PID gain parameters. The goal of step S4 is to fully utilize this judgment coefficient to redistribute the weights of the outputs of each rule in the Mandelbrot-type fuzzy control rule base. This ensures that under highly complex operating conditions, the influence of specific rules on the gain adjustment process is more prominent; under less complex operating conditions, a relatively balanced rule effect is maintained. Through this weight adjustment and fuzzy inference process, the fuzzy output is ultimately defuzzified into a clear PID gain adjustment using the maximum membership method.

[0140] Step S4 includes the following contents:

[0141] 1. Mandelbrot fuzzy control rule base structure:

[0142] Assume that the rule base contains rules, each rule is as follows:

[0143] ;in is the error, is the error change rate, and It is a subset of the seven subsets: negative large, negative medium, negative small, zero, positive small, positive medium, and positive large. , , They are the fuzzy output subsets of proportional, integral and differential gain adjustment amounts respectively.

[0144] 2. Calculation of weight adjustment factor:

[0145] In general, each fuzzy rule has the same weight in Manderly-type reasoning. To achieve adaptive adjustment, define each rule The weight factor as follows: Where Is a preset constant used to reflect the rules Base sensitivity to gain adjustments; is a nonlinear index used to enhance the weighted effect at high complexity. When the judgment coefficient is small, The value is low, When it is close to 1, it means that each rule basically maintains its initial weight; when the judgment coefficient increases and reaches a higher level, the rule weight factor increases rapidly, allowing specific rules to play a greater role in high-complexity states.

[0146] 3. Mandelbrot-type fuzzy reasoning process:

[0147] Based on the known error and error change rate membership (from step S2), antecedent matching is performed on each rule, and the fuzzy synthesis of rule antecedents is achieved using the minimum operation: ;in and are the membership degrees of error and error change rate in the corresponding subsets respectively.

[0148] Then, As the cut-set height of the fuzzy set output as the consequent, the fuzzy subsets of the proportional, integral, and differential gain adjustment quantities are cut (i.e., the fuzzy reasoning steps in the Mandelbrot-type reasoning), and the weight factor is combined with Vertically enlarge the membership function of the corresponding cut-set posterior: ;in is the original membership function of the corresponding rule output subset. Through this operation, the output fuzzy set of each rule is weighted and deformed according to the judgment coefficient and its own matching degree.

[0149] 4. Fuzzy output synthesis and aggregation:

[0150] Perform maximum aggregation on the weighted output fuzzy sets of all rules and synthesize the corresponding gain adjustment fuzzy outputs into three final fuzzy sets: , , ; Through maximum aggregation, the weighted influence of all rule outputs is integrated into the overall fuzzy result.

[0151] 5. Defuzzification to obtain PID gain adjustment:

[0152] The maximum membership method is used to classify the final fuzzy set , , This method searches for the point with the largest membership within the domain of the gain adjustment value, and outputs the value of this point as the final PID gain adjustment value: , , ; obtained , , They are the precise values ​​of proportional, integral and differential gain adjustments respectively.

[0153] Step S4, guided by the judgment coefficients, redistributes the weights of the Mandelbrot-type fuzzy control rule base and generates a fuzzy output of the gain adjustment through a Mandelbrot-type fuzzy inference process. This fuzzy output is then converted into a clear PID gain adjustment value using the maximum membership method. This process allows the control system to adaptively adjust the relative weights of the proportional, integral, and differential gains under complex operating conditions, improving the accuracy and real-time performance of attitude adjustments and providing a highly reliable data foundation for the dynamic updating and execution of PID controller parameters in subsequent steps.

[0154] In the previous steps, the system extracted precise membership distributions and judgment coefficients from the multidimensional posture data, and obtained the corresponding proportional gain adjustments, integral gain adjustments, and differential gain adjustments through fuzzy reasoning and defuzzification. At this point, the basic parameters of the PID controller (proportional gain, integral gain, and differential gain) have not yet been actually updated, and these adjustments are needed to dynamically correct the existing PID parameters. The core of step S5 is the nonlinear superposition of the gain adjustments obtained from the previous defuzzification step with the current PID parameters to achieve adaptive updates of the PID parameters. By introducing complex mapping and transformation methods, the gain updates are made more flexible and targeted in their response to posture control, thereby achieving more precise posture adjustment under complex operating conditions.

[0155] Step S5 includes the following contents:

[0156] 1. Nonlinear mapping function design:

[0157] While simply adding the gain adjustment to the old parameters can achieve an update, it doesn't reflect the system's resilience to complex environments. Therefore, a nonlinear mapping function is used to perform complex nonlinear amplification or compression on the gain adjustment, enabling flexible control of parameter adjustments.

[0158] The following nonlinear mapping methods are defined:

[0159] (1) Update of proportional gain: ;

[0160] This formula means:

[0161] When the judgment coefficient is close to 1, the adjustment is exponentially amplified, and when the judgment coefficient is low, the change is gentle, so that the adjustment range of the proportional gain is significantly enhanced under highly complex working conditions.

[0162] Mapping the gain adjustment to a sinusoidal relationship in the [-1,1] interval ensures a sensitive response to small adjustments and an oscillation suppression effect for excessive adjustments.

[0163] (2) Update of integral gain: ;

[0164] This formula means:

[0165] is a Sigmoid function, when When is positive and large, the update amplitude approaches 1, otherwise it approaches 0, so the adjustment of the integral gain has asymmetric and nonlinear characteristics. When it is positive, the integral gain increases faster; when the judgment coefficient is low or When negative, the update amplitude is limited.

[0166] (3) Update of differential gain: ;

[0167] This formula means: When the judgment coefficient is higher, it will have a stronger influence on the differential gain, thereby suppressing rapid error changes more timely under complex working conditions.

[0168] when It may have a slight inhibitory effect when it becomes larger to ensure that the adjustment of the differential gain is not over-amplified, so that the system maintains stability while suppressing high-frequency oscillations.

[0169] 2. Output after parameter update:

[0170] Through the above nonlinear mapping, the proportional, integral, and differential gain adjustment amounts are integrated with the judgment coefficients and their respective complex function mappings to finally obtain the new PID parameters. , , .

[0171] These new parameters will replace the original PID parameters and be used in the proportional, integral, and differential calculation processes in the subsequent step S6.

[0172] Explanation of parameter changes: In complex geological environments or conditions with drastic attitude changes, the judgment coefficient is relatively large. The combined operation of exponential and trigonometric functions will moderately amplify the gain parameter, thereby increasing the controller's sensitivity to sudden deviations. When the operating conditions are relatively simple and the judgment coefficient is relatively small, the gain update is compressed and passivated, allowing the system to maintain a relatively stable control strategy.

[0173] Step S5 implements a complex, nonlinear superposition of the defuzzified gain adjustment with the existing PID parameters and introduces a judgment coefficient as a flexible amplification or suppression factor, achieving refined dynamic adjustment of the proportional, integral, and differential gains. This process enables the system to automatically adapt and optimize its control strategy in tunnel construction environments of varying complexity, enabling the PID controller to demonstrate greater accuracy and flexibility in subsequent operations, laying a solid foundation for high-precision automated control of shield machine posture.

[0174] During the tunneling process, precise control of the shield machine's posture is crucial for ensuring construction quality and equipment stability. Posture deviations can cause directional deviations, uneven force distribution, and other issues, impacting tunneling efficiency and construction safety. Therefore, dynamically generating control outputs through a PID controller to adjust the proportional gain, integral gain, and differential gain values, and adjusting the thrust of the partitioned cylinders and correcting posture deviations are key solutions to this problem. Step S6 aims to use the updated PID parameters to perform comprehensive error calculations, generate a high-precision total control output, and directly drive the partitioned cylinder actuators to complete posture correction.

[0175] Step S6 includes the following contents:

[0176] 1. Determination of input data:

[0177] Collect the proportional gain, integral gain and differential gain values ​​after dynamic update in step S5. The current error value and error change rate provided by steps S1 and S2 are recorded as and , which serve as the calculation inputs for this step.

[0178] 2. Decomposition of error calculation:

[0179] According to the PID control logic, three correction quantities are calculated in sequence:

[0180] ;

[0181] Item 1 Indicates the proportional correction amount of the current error, which is used to reflect the error amplitude in real time;

[0182] The proportional correction is achieved by multiplying the current error value by the updated proportional gain, reflecting the direct impact of the current deviation;

[0183] Item 2 represents the integral correction amount, where the integral kernel function A third-order nonlinear weight function is used, which is: By performing nonlinear amplification on the accumulation of historical errors, the ability to compensate for continuous error deviation is enhanced;

[0184] The integral correction is based on the historical error accumulation and is combined with a nonlinear kernel function to amplify the impact of persistent deviations.

[0185] Item 3 Represents the differential correction, where the differential function is the hyperbolic tangent function: ; Smooth the output at high error change rates to avoid over-regulation caused by sudden changes;

[0186] The differential correction depends on the error change rate and is combined with a smoothing function to limit the risk of over-adjustment caused by sudden changes.

[0187] 3. High-dimensional error compensation:

[0188] After calculating the basic control output Then, add the attitude compensation parameters , which is generated by the five-dimensional exponential convolution formula and is used to dynamically balance the asymmetric distribution of thrust between cylinder groups:

[0189] ;

[0190] Represents the residual errors in five directions;

[0191] is the direction-related error sensitivity coefficient, which is determined through the previous attitude optimization experiment;

[0192] is the exponential decay factor;

[0193] is the normalization constant.

[0194] The final total control output is: ;

[0195] The control quantity after error correction and compensation is transmitted to the partition cylinder actuator for thrust adjustment.

[0196] By combining dynamically adjusted PID parameters with nonlinear calculations and high-dimensional error compensation techniques, step S6 generates a precise overall control output, ensuring flexible and efficient thrust regulation. This enables precise correction of multi-directional attitude deviations, providing a stable mechanical foundation and optimization guidance for step S7.

[0197] The core of shield machine posture control lies in achieving precise correction through dynamic thrust adjustment of grouped cylinders. Due to the complex construction environment, the shield machine's posture may deviate due to uneven force or changing geological conditions. Posture adjustment requires not only real-time sensing of error signals but also the coordinated control of parameters such as the cylinder thrust increment and cutterhead roll angle, while also building an experience database to optimize subsequent control processes. S7 is a key link in the entire process, ensuring that posture deviations can be rapidly corrected through multi-dimensional joint control when the thrust threshold is limited.

[0198] Step S7 includes the following contents:

[0199] Step S7 uses the total control output signal obtained from step S6 to precisely adjust the thrust of the shield machine's four groups of cylinders. By calculating the posture adjustment information in the total control output signal, the thrust adjustment amount of each group of cylinders is allocated, and the extension amount of each group of cylinders is ensured to always be within the preset safety threshold range. When the extension amount of a group of cylinders exceeds the set threshold, its incremental output is immediately limited, and negative feedback control is used to apply adjustments to the cylinders in the opposite direction to quickly correct the posture deviation. At the same time, to correct the posture problems caused by insufficient cylinder adjustment, the roll angle is optimized by precisely controlling the reversing cutterhead to ensure the stability and accuracy of the shield machine's posture. During each control process, the system stores key data such as thrust adjustment amount, extension amount, and cutterhead reversal angle in the experience database, and optimizes the subsequent control strategy with the help of data analysis, realizing dynamic learning from historical experience to further improve the adaptability and robustness of the control.

[0200] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0201] The above description is merely illustrative of certain exemplary embodiments of the present invention. It goes without saying that those skilled in the art will be able to modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and description are illustrative in nature and should not be construed as limiting the scope of protection of the claims.

[0202] It should be noted that, in this document, if there are relational terms such as first and second, etc., they are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprises", "includes" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article or device. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or device that includes the element.

[0203] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A control method for adjusting the tunneling posture of a shield machine, characterized in that: Including steps: S1, using the laser positioning guidance system and total station to measure the various operating data of the shield machine in real time and convert the data into the original numerical set; S2, divide the horizontal deviation and vertical deviation and their change rates into seven subsets: negative large, negative medium, negative small, zero, positive small, positive medium, and positive large, and calculate the membership degree; Step S2 includes the following contents: Horizontal deviation and vertical deviation Defined as the angular deviation between the current posture of the shield machine and the design axis; horizontal deviation change rate and vertical deviation rate of change Defined as the change in horizontal deviation and vertical deviation per unit time; Set the range of horizontal deviation, vertical deviation, horizontal deviation change rate and vertical deviation change rate to [-90°, +90°]; Divide the interval into seven subsets: negative large , negative 、Negative small ,zero , Zhengxiao ,middle Zhengda ; Design a triangular membership function for each subset, whose peak is located at the center of the subset and the basis covers the boundaries of adjacent subsets; define the center point of each subset as and the boundary points are ; For any input value , which is in the subset The membership degree in Determined by the following triangular membership functions: ； Specific formula description: When the input value is in the left half of the subset When , the membership increases linearly from 0 to 1; When the input value is in the right half of the subset When , the membership decreases linearly from 1 to 0; When the input value is not within the interval of the subset, the membership is 0; For each input value, a vectorized calculation method is used to calculate its membership in the seven subsets: ; Use nonlinear weighting factors The membership degree of each subset is adjusted as follows: ; Among them, the weighting factor Determined by the following composite function: ;parameter , , is a preset constant; To ensure that the sum of all subset memberships is 1, the adjusted memberships are normalized and then organized into a membership matrix, where each row corresponds to an input value and each column corresponds to a subset. S3 performs a fifth-order fast Fourier transform on the horizontal error to extract high-frequency and low-frequency features, processes the vertical error to generate a phase signal, fuses the two-directional signals, and calculates the judgment coefficient; S4, adjusting the weight of the fuzzy control rule base based on the judgment coefficient to generate a PID gain adjustment amount; S5, adding the defuzzified gain adjustment amount to the current PID gain to dynamically update the PID parameters; S6, uses the updated PID parameters to calculate the total control output and transmits it to the partition cylinder actuator to adjust the posture; S7, adjust the cylinder thrust according to the total control output.

2. A control method for adjusting the tunneling posture of a shield machine according to claim 1, characterized in that: Step S1 includes the following contents: A laser scanner is used to monitor the shield head and tail positions of the shield machine in real time. The omnidirectional measurement capability of the total station is used to obtain the shield machine's tunneling trend angle, roll angle, and pitch angle in three dimensions. Convert the three-dimensional spatial pose data into a complex domain representation; apply wavelet transform to perform multi-resolution analysis on the complex domain data to extract pose change features at different frequency levels; The extracted high-dimensional feature data is standardized; the standardized data is mapped to the [0,1] interval; and the processed standardized and normalized data is organized into a multi-dimensional original numerical set.

3. The control method for adjusting the tunneling posture of a shield machine according to claim 1, characterized in that: Step S3 includes the following contents: S31. Calculate the membership values ​​of the obtained horizontal deviation and vertical deviation and their change rates in each membership subset; combine the corresponding membership information with the original error value, and perform a weighted transformation on each error value: , ;in and is the final membership value obtained after normalizing the horizontal deviation and the vertical deviation in step S2, and Indicates the index of the membership subset; S32. Perform a fifth-order fast Fourier transform on the weighted horizontal error, map it from the time domain to the frequency domain, and recursively perform a five-layer decomposition; set the first-layer fast Fourier transform output to ,right After frequency band separation and nonlinear phase correction, a new time domain signal is obtained, and fast Fourier transform is performed again until the fifth-order decomposition is completed; S33. Performing dual nonlinear phase modulation on the weighted vertical error; first, mapping the weighted vertical error to the complex domain: ;in is a nonlinear phase function dynamically calculated based on actual geological parameters. , , , is a preset constant; the same type of nonlinear phase modulation is applied to the phase modulated signal to obtain a more complex complex phase signal ; S34. Perform multidimensional data interleaving and fusion on the complex plane of the horizontal error composite spectrum extracted by the fifth-order fast Fourier transform and the complex phase signal obtained by dual nonlinear phase modulation of the vertical error. This process uses multidimensional complex space interpolation to decompose the horizontal error composite spectrum and the complex phase signal into several sub-bands and sub-phase units, which are then cross-arranged on the complex plane. Perform high-dimensional exponential integral calculation on the fusion matrix to obtain the judgment coefficient for judgment .

4. A control method for adjusting the tunneling posture of a shield machine according to claim 3, characterized in that: Step S4 includes the following contents: Assume that the rule base contains rules, each rule is as follows: ;in is the error, is the error change rate, and It is a subset of the seven subsets: negative large, negative medium, negative small, zero, positive small, positive medium, and positive large. , , are the fuzzy output subsets for proportional, integral, and differential gain adjustment respectively; Define each rule The weight factor as follows: Where Is a preset constant used to reflect the rules Base sensitivity to gain adjustments; It is a nonlinear index used to enhance the weighting effect at high complexity; Based on the known error and error change rate membership, each rule is matched with its antecedents, and the minimum operation is used to achieve fuzzy synthesis of the rule antecedents: ;in and are the membership degrees of error and error change rate under the corresponding subsets respectively; Then, As the cut-set height of the subsequent output fuzzy set, the fuzzy subsets of the proportional, integral, and differential gain adjustment are cut, and the weight factor is combined Vertically enlarge the membership function of the corresponding cut-set posterior: ;in is the original membership function of the corresponding rule output subset; Perform maximum aggregation on the weighted output fuzzy sets of all rules and synthesize the corresponding gain adjustment fuzzy outputs into three final fuzzy sets; The maximum membership method is used to defuzzify the final fuzzy set; the point with the maximum membership is found in the domain of the gain adjustment amount, and the value of this point is output as the final PID gain adjustment amount.

5. The control method for adjusting the tunneling posture of a shield machine according to claim 4, characterized in that: Step S5 includes the following contents: Use nonlinear mapping functions to perform complex nonlinear amplification or compression on the gain adjustment amount; The following nonlinear mapping methods are defined: (1) Update of proportional gain: ; (2) Update of integral gain: ; (3) Update of differential gain: ; in , , are the proportional, integral, and differential gain adjustment values ​​respectively; Through nonlinear mapping, the proportional, integral, and differential gain adjustments are integrated with the judgment coefficients and their respective function mappings to finally obtain new PID parameters. , , .

6. The control method for adjusting the tunneling posture of a shield machine according to claim 5, characterized in that: Step S6 includes the following contents: According to the PID control logic, three correction quantities are calculated in sequence: The proportional correction is achieved by multiplying the current error value by the updated proportional gain, reflecting the direct impact of the current deviation; The integral correction is based on the historical error accumulation and is combined with a nonlinear kernel function to amplify the impact of persistent deviations. The differential correction depends on the error change rate and is combined with a smoothing function to limit the risk of over-adjustment caused by sudden changes. After calculating the basic control output, the attitude compensation parameters are added and generated through the five-dimensional exponential convolution formula to dynamically balance the asymmetric distribution of the thrust between the cylinder groups; finally, the total control output is obtained.

7. The control method for adjusting the tunneling posture of a shield machine according to claim 6, characterized in that: Step S7 includes the following contents: The thrust of the four groups of cylinders of the shield machine is adjusted using the obtained total control output signal. By calculating the posture adjustment information in the total control output signal, the thrust adjustment amount of each group of cylinders is allocated, and it is ensured that the extension amount of each group of cylinders is always within the preset safety threshold range.

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