An active magnetic compensation control system and method based on magnetic field multi-source disturbance suppression

Abstract

This invention belongs to the technical field of active magnetic field compensation systems and discloses an active magnetic compensation control system and method based on multi-source magnetic field disturbance suppression. The system comprises: an ADC acquisition unit, a DAC output unit, a controller, a high-precision current source, a magnetic compensation coil, a magnetic field sensor, a TD-Smith lead predictor, an enhanced model-assisted extended state observer (AMESO), and a filter equivalent tuning method. The technical solution of this invention significantly improves the real-time estimation accuracy of external magnetic field disturbances, suppresses the impact of multi-source magnetic field disturbances on the internal magnetic field of a magnetic shielding device, and improves the uniformity and stability of the internal magnetic field of the magnetic shielding device. It is suitable for achieving high-precision control of extremely weak magnetic fields.

Description

Technical Field

[0001] The present invention relates to the technical field of active magnetic field compensation systems, and in particular to an active magnetic compensation control system and method based on magnetic field multi-source disturbance suppression. Background Art

[0002] Magnetic fields are widely used in everyday life, and precise magnetic field measurement is becoming increasingly important in fields such as geophysics, materials science, and bioinformatics research. The measurement and research of cardiac and brain magnetic signals are particularly in-depth. Cardiac and brain magnetic signal intensities range from tens of fT to tens of pT, while the magnetic field strength in the geomagnetic environment is approximately 50,000 nT, significantly greater than these signals. Furthermore, environmental interference, such as elevators, CT equipment, and subway and high-speed train traffic, generates magnetic fields in the nT or even uT range. Therefore, magnetic shielding devices are required to shield the geomagnetic field from external interference, and active magnetic compensation control systems are designed to further suppress the impact of magnetic interference on the magnetic field within the shielding device.

[0003] Due to the requirements of cardio-cerebral magnetic field measurements, the magnetic field strength within the magnetic shielding device must reach the pT or even fT level. Therefore, high-precision magnetic field sensors are required for control and measurement, and OPM magnetic field sensors are generally used. The structure and principle of OPM magnetic field sensors result in time lag in their output signals, which affects the stability and control effectiveness of active magnetic compensation control systems. Traditional Smith predictors are prone to instability due to model mismatch, and time lag significantly weakens the interference observation capability of the extended state observer. Furthermore, control system circuit noise, 1 / f noise, and sensor measurement noise are unavoidable, as is power frequency interference caused by the power supply method. When the control gain is large, these interferences can enter the system through the control channel and affect the measurement. Therefore, an active magnetic compensation control system and method based on multi-source magnetic field disturbance suppression is of great significance for suppressing multi-source disturbances and providing an extremely weak magnetic measurement environment for cardio-cerebral magnetic field measurements. Summary of the Invention

[0004] In order to solve the above technical problems, the present invention provides an active magnetic compensation control system and method based on multi-source magnetic field disturbance suppression, which can effectively suppress multi-source magnetic field disturbances in the magnetic shielding device and provide an extremely weak magnetic field environment for cardio-cerebral magnetic measurement.

[0005] The present invention provides an active magnetic compensation control system based on magnetic field multi-source disturbance suppression, the system comprising: an ADC acquisition unit, a DAC output unit, a controller, a high-precision current source, a magnetic compensation coil, a magnetic field sensor, a TD-Smith lead predictor, and an enhanced model-assisted extended state observer (AMESO).

[0006] Preferably, the system as a whole is a closed-loop structure, the enhanced model assisted extended state observer AMESO output z1 is used for negative feedback of closed-loop control, the reference value of the controller is 0, and the error between the controller and the feedback z1 of the enhanced model assisted extended state observer AMESO is used to design the control law to obtain the controller output; the disturbance estimation output z1 of the enhanced model assisted extended state observer AMESO is n+1 Feedforward to the controller output to obtain the final control output.

[0007] Preferably, the magnetic compensation coil is the actuator of the system, which is used to convert the control current into a magnetic field; the final control output is converted into an analog voltage signal through a DAC output unit, and current is applied to the magnetic compensation coil through a high-precision current source to compensate for multi-source disturbances in the magnetic field, so that the magnetic field disturbance measured by the magnetic field sensor is close to 0, providing an extremely weak magnetic environment for cardio-cerebral magnetic measurement.

[0008] The present invention also provides an active magnetic compensation control method based on multi-source magnetic field disturbance suppression, which is applied to any of the above systems and includes the following steps:

[0009] The ADC acquisition unit collects the signal with time delay output by the magnetic field sensor;

[0010] The signal with time delay output by the magnetic field sensor is estimated by the TD-Smith advance predictor, wherein the TD advance device performs advance correction on the delayed interference signal;

[0011] The model-assisted extended state observer MESO is tuned using the filter equivalent tuning method to obtain the enhanced model-assisted extended state observer AMESO.

[0012] The external magnetic field disturbance is estimated by the enhanced model-assisted extended state observer (AMESO) and fed forward to the controller output to obtain the final control output.

[0013] The final control output passes through the DAC output unit and the high-precision current source to apply current to the magnetic compensation coil, thereby suppressing multi-source magnetic field disturbances including time lag, external magnetic field disturbances, power frequency interference, 1 / f noise and model mismatch.

[0014] Preferably, the TD advance device includes a TD tracking differentiator and an advance compensation polynomial, and the TD-Smith advance predictor estimates the signal containing time delay output by the magnetic field sensor, including:

[0015] A basic Smith predictor is established based on the nominal time delay τ0 of the magnetic field sensor and the nominal model G0(s) of the system;

[0016] The TD advancer order n and the tracking factor r are selected according to the system noise level and the model mismatch degree, and the TD tracking differentiator is used to obtain the 1st to nth order differentials of the disturbance signal with time delay.

[0017] The nth-order lead compensation polynomial is used to combine 1st to nth-order differential polynomials to approximate the lead compensation of time lag. The form of the lead compensation polynomial is:

[0018]

[0019] Where s is the Laplace operator, k is the indicator variable of the summation function, k! is the factorial of k, and k! = k × (k-1) × (k-2) × ... × 1;

[0020] The disturbance corrected by the TD advancer and the system output estimated by Smith are added together and then passed to the enhanced model-assisted extended state observer (AMESO).

[0021] Preferably, the filter equivalent tuning method is used to tune the model-assisted extended state observer MESO to obtain the enhanced model-assisted extended state observer AMESO, which includes:

[0022] According to the n-order nominal model G0(s), an n+1-order model-assisted extended state observer is established;

[0023] The gain matrix of the n+1 order model-assisted extended state observer is obtained by using the bandwidth tuning method as L0=[β1 β2 …β n β n+1 ] T ;

[0024] According to the design principle of low-pass filter in disturbance observer, Chebyshev type I filter is selected as the frequency domain equivalent filter of model-assisted extended state observer.

[0025] Choose an appropriate maximum passband attenuation value α max The selection principle is that if the high-frequency measurement noise is large, the maximum passband attenuation value α is increased. max To improve the observer performance, reduce the maximum passband attenuation value α max ;

[0026] According to the determined α max Calculate the passband ripple coefficient

[0027] Select the filter order N+1, which is the same as the order of the model-assisted extended state observer, and select the filter cutoff frequency ω b , which is the same as the observation bandwidth of the model-assisted extended state observer;

[0028] Calculate the N+1 order Chebyshev polynomial P(ω)=cos[(N+1)arccos(ω)], |ω|≤1;

[0029] According to the transfer function of the Chebyshev filter, find the equation The solution s1,s2…s in the left half plane N ,s N+1 , and calculate the polynomial f(s)=(s-s1)(s-s2)…(sS N+1 )=s N+1 +a1s N +…+a N s+a N+1 The coefficients are recorded as correction coefficients a1, a2…a N ,a N+1 ;

[0030] The obtained correction coefficient is used to correct the observer gain matrix to be L = [a1β1 a2β2 … a N β n α N+1 β n+1 ] T , the adjustment is completed.

[0031] Compared with the prior art, the present invention has the following beneficial effects:

[0032] 1. The present invention uses pure time delay to characterize the time lag characteristics of the magnetic field sensor output signal, which has higher modeling accuracy for the actual system. The TD-Smith advance predictor is used to predict the interference signal containing time lag, which improves the poor robustness of the traditional Smith prediction method.

[0033] 2. The enhanced model-assisted extended state observer designed in the present invention takes model information into account and uses the output of the TD-Smith advance predictor for estimation, thereby improving the accuracy and real-time performance of disturbance estimation and effectively improving the magnetic field disturbance compensation capability of the active magnetic compensation system.

[0034] 3. The present invention utilizes the filter equivalent tuning method to obtain an enhanced model-assisted extended state observer. Compared with the traditional bandwidth tuning method, it has better suppression capability for high-frequency interference and can improve the signal-to-noise ratio of the measured signal. At the same time, due to the reduction of multi-source disturbances, especially high-frequency noise, it can further increase the system bandwidth, improve the system response speed and robustness.

[0035] 4. The present invention can improve the disturbance suppression capability of the magnetic shielding device, realize real-time estimation and suppression of environmental interference, improve the magnetic field stability inside the magnetic shielding device, and improve the signal-to-noise ratio of the measured cardio-cerebral magnetic signals. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solution of the present invention, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0037] Figure 1 Schematic diagram of the structure of an active magnetic compensation control system and method based on magnetic field multi-source disturbance suppression for a magnetic shielding device according to an embodiment of the present invention;

[0038] Figure 2 This is a control block diagram of an active magnetic compensation control system and method based on magnetic field multi-source disturbance suppression for a magnetic shielding device according to an embodiment of the present invention;

[0039] Figure 3 This is a flow chart of a filter equivalent tuning method for an active magnetic compensation control system and method based on magnetic field multi-source disturbance suppression for a magnetic shielding device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0040] 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.

[0041] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present disclosure should have the usual meanings understood by people with ordinary skills in the field to which the present disclosure belongs. The "first", "second" and similar words used in the embodiments of the present disclosure do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative position relationships. When the absolute position of the described object changes, the relative position relationship may also change accordingly.

[0042] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0043] Example 1

[0044] The embodiment of the present invention provides Figure 1 As shown, it is a structural schematic diagram of an active magnetic compensation control system based on magnetic field multi-source disturbance suppression provided by an embodiment of the present invention, including an ADC acquisition unit, a DAC output unit, a controller, a high-precision current source, a magnetic compensation coil, a magnetic field sensor, a TD-Smith lead predictor, an enhanced model-assisted extended state observer (AMESO) and a filter equivalent tuning method.

[0045] Among them, the ADC acquisition unit acquires the output signal of the magnetic field sensor. Due to the characteristics of the magnetic field sensor itself, the output signal of the ADC acquisition unit has an inertia link plus time lag characteristic; the TD-Smith advance predictor includes a Smith predictor and a TD advance device, wherein the TD advance device includes a TD tracking differentiator and a lead compensation polynomial. The signal containing time lag output by the magnetic field sensor is predicted by the TD-Smith advance predictor to obtain a time-lag-free signal of the disturbance and magnetic field, wherein the TD advance device performs advance correction on the lagging interference signal; the filter equivalent tuning method is used to adjust the model-assisted extended state observer. The AMESO is tuned and estimated by ADAMS. The AMESO estimates the external magnetic field disturbance and feeds forward to the controller output to obtain the final control output (the disturbance estimation signal Zn+1 and the magnetic field estimation signal Z1, Z1 is used as feedback and the difference between it and the target value is used to obtain the error that enters the controller to calculate the control quantity. The final control output is given to the high-precision current source through the DAC output unit, and the control current is obtained through the high-precision current source. The control current is passed through the magnetic compensation coil to obtain the compensation magnetic field, thereby suppressing multi-source disturbances of the magnetic field, including time lag, external magnetic field disturbance, power frequency interference, 1 / f noise and model mismatch.

[0046] like Figure 2 As shown in FIG, a control block diagram of an active magnetic compensation control system based on multi-source disturbance suppression of a magnetic field provided by an embodiment of the present invention; Figure 3 The figure shows a flow chart of a filter equivalent tuning method for an active magnetic compensation control system based on magnetic field multi-source disturbance suppression provided by an embodiment of the present invention. Figure 2 , Figure 3 The specific implementation method is described.

[0047] In the control block diagram, the reference value is 0; C(s) is the controller; G p (s) is the actual model without time delay; G0(s) is the nominal model without time delay; τ pis the time lag of the actual model; τ0 is the time lag of the nominal model; the small box with dotted lines is the TD advance device, TD is the tracking differentiator, and T3(s) is the third-order lead compensation polynomial; the large box with dotted lines is the TD-Smith lead predictor; AMESO is the second-order enhanced model-assisted extended state observer; β1, β2 are the observer gains; a1, a2 are the correction coefficients.

[0048] As an implementation manner of the embodiment of the present invention, the controller C(s) is a linear feedback control law controller, including but not limited to a PID controller.

[0049] As an implementation method of the embodiment of the present invention, the time-delay-free nominal model G0(s) is fitted into a first-order inertial link by frequency sweeping. The model includes but is not limited to a first-order inertial link.

[0050] As an implementation manner of the embodiment of the present invention, the lead compensation polynomial includes but is not limited to a third-order lead compensation polynomial T3(s).

[0051] As an implementation method of the embodiment of the present invention, the AMESO design steps are as follows: Figure 3 As shown, the MESO design steps are as follows:

[0052] ① When time delay is ignored and the model is matched, the input-output relationship of the system is: The inverse Laplace transform yields:

[0053] ② Order Then the system state equation is

[0054] ③ Order C=[1 0], then the above system state equation can be written as

[0055] ④ According to the above system state equation, MESO is obtained as Model Information It is included in the system matrix A, which can estimate the disturbance more accurately, where L0 is the observer gain matrix and the appropriate observation bandwidth ω is selected. o Setting L0 yields

[0056] As an implementation method of the embodiment of the present invention, the AMESO is obtained by further tuning the MESO using the filter equivalent tuning method, and the tuning steps are as follows:

[0057] ①Choose Chebyshev type I filter as the frequency domain equivalent filter of model-assisted extended state observer;

[0058] ② Select the appropriate passband maximum attenuation value α according to the interference level max , according to α max Calculate the passband ripple coefficient

[0059] ③ Select the filter order 2, which is the same as the order of the model-assisted extended state observer, and select the filter cutoff frequency ω b , and the observation bandwidth ω of the model-assisted extended state observer o same;

[0060] ④ Calculate the second-order Chebyshev polynomial P(ω)=cos[2arccos(ω)]=2ω 2 -1,|ω|≤1;

[0061] ⑤Find the equation The solution S1, S2 in the left half plane, and the calculation of the polynomial f(S) = (S-s1)(s-S2) = s 2 The coefficient of +a1s+a2 is recorded as the correction coefficient a1,a2;

[0062] ⑥Use the correction coefficient obtained in the previous step to correct the observer gain matrix to L = [a1β1a2β2] T , complete the tuning and AMESO design.

[0063] As an implementation method of an embodiment of the present invention, the entire system has a closed-loop structure. The reference value of the PID controller is 0. The error between the reference value and the feedback z1 of the MESO is used to design a control law to obtain the controller output u0. The disturbance estimation output z2 of the MESO is fed forward to the controller output to obtain the final control output u.

[0064] In summary, the present invention proposes an active magnetic compensation control system based on multi-source disturbance suppression of the magnetic field. The TD-Smith advance predictor is used to correct the interference and predict the signal. By enhancing the model-assisted extended state observer and the filter equivalent tuning method, the magnetic field disturbance can be estimated and compensated more accurately and in real time while taking into account the bandwidth. The TD advance predictor can improve the robustness of Smith and reduce the requirements for model accuracy. This method can improve the stability and robustness of the system, reduce the limitations of multi-source disturbances such as time delay, power frequency interference, and circuit noise on the observer bandwidth, thereby further increasing the bandwidth and improving the response speed, significantly improving the real-time estimation accuracy and compensation suppression capability of external magnetic field disturbances, improving the magnetic field stability inside the magnetic shielding device, and providing an extremely weak magnetic environment for cardio-cerebral magnetic measurement.

[0065] Example 2

[0066] Based on the same inventive concept, corresponding to any of the above-mentioned embodiments and methods, the present disclosure further provides an active magnetic compensation control method based on multi-source magnetic field disturbance suppression, and the method is applied to any of the above-mentioned systems, comprising the following steps:

[0067] The ADC acquisition unit collects the signal with time delay output by the magnetic field sensor;

[0068] The signal with time delay output by the magnetic field sensor is estimated by the TD-Smith advance predictor, wherein the TD advance device performs advance correction on the delayed interference signal;

[0069] The model-assisted extended state observer MESO is tuned using the filter equivalent tuning method to obtain the enhanced model-assisted extended state observer AMESO.

[0070] The external magnetic field disturbance is estimated by the enhanced model-assisted extended state observer (AMESO) and fed forward to the controller output to obtain the final control output.

[0071] The final control output passes through the DAC output unit and the high-precision current source to apply current to the magnetic compensation coil, thereby suppressing multi-source magnetic field disturbances including time lag, external magnetic field disturbances, power frequency interference, 1 / f noise and model mismatch.

[0072] In this embodiment, the TD advance device includes a TD tracking differentiator and an advance compensation polynomial. The TD-Smith advance predictor estimates the signal containing time delay output by the magnetic field sensor, including:

[0073] A basic Smith predictor is established based on the nominal time delay τ0 of the magnetic field sensor and the nominal model G0(s) of the system;

[0074] The TD advancer order n and the tracking factor r are selected according to the system noise level and the model mismatch degree, and the TD tracking differentiator is used to obtain the 1st to nth order differentials of the disturbance signal with time delay.

[0075] The nth-order lead compensation polynomial is used to combine 1st to nth-order differential polynomials to approximate the lead compensation of time lag. The form of the lead compensation polynomial is:

[0076]

[0077] In the above formula, s is the Laplace operator, k is the indicator variable of the summation function, k! is the factorial of k, and k! = k × (k-1) × (k-2) × ... × 1. The disturbance corrected by the TD predictor and the system output estimated by the Smith predictor are summed and then passed to the enhanced model-assisted extended state observer (AMESO).

[0078] In this embodiment, the filter equivalent tuning method is used to tune the model-assisted extended state observer MESO to obtain the enhanced model-assisted extended state observer AMESO, which includes:

[0079] According to the n-order nominal model G0(s), an n+1-order model-assisted extended state observer is established;

[0080] The gain matrix of the n+1 order model-assisted extended state observer is obtained by using the bandwidth tuning method as L0=[β1 β2 …β n β n+1 ] T ;

[0081] The bandwidth tuning method is used to obtain the n+1 order model-assisted extended state observer gain matrix L0, which is defined as L0 = [β1 β2 … β n β n+1 ] T , where [β1 β2 … β n β n+1 ] T is the observer gain coefficient;

[0082] According to the design principle of low-pass filter in disturbance observer, Chebyshev type I filter is selected as the frequency domain equivalent filter of model-assisted extended state observer.

[0083] Choose an appropriate maximum passband attenuation value α max The selection principle is that if the high-frequency measurement noise is large, the maximum passband attenuation value α is increased. max To improve the observer performance, reduce the maximum passband attenuation value α max ;

[0084] According to the determined α max Calculate the passband ripple coefficient

[0085] Select the filter order N+1, which is the same as the order of the model-assisted extended state observer, and select the filter cutoff frequency ω b , which is the same as the observation bandwidth of the model-assisted extended state observer;

[0086] Calculate the N+1 order Chebyshev polynomial P(ω)=cos[(N+1)arccos(ω)], |ω|≤1;

[0087] According to the transfer function of the Chebyshev filter, find the equation Where j is a complex operator, find the solution s1, s2…s in the left half plane of the equation N ,s N+1 , and calculate the polynomial f(s)=(s-s1)(s-s2)…(ssN+1 )=S N +1 +a1s N +…+a N s+a N+1 The coefficients are recorded as correction coefficients a1, a2…a N ,a N+1 ;

[0088] The obtained correction coefficient is used to correct the observer gain matrix to be L = [a1β1 a2β2 … a N β n a N+1 β n+1 ] T , the adjustment is completed.

[0089] The method of the above embodiment is used to be applied to a corresponding active magnetic compensation control system based on multi-source disturbance suppression of the magnetic field in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be repeated here.

[0090] The embodiments of the present disclosure are intended to cover all such substitutions, modifications, and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present disclosure should be included in the scope of protection of the present disclosure.

Claims

1. An active magnetic compensation control system based on multi-source disturbance suppression of magnetic field, characterized in that: The system includes: an ADC acquisition unit, a DAC output unit, a controller, a high-precision current source, a magnetic compensation coil, a magnetic field sensor, a TD-Smith advance predictor, and an enhanced model-assisted extended state observer (AMESO); The overall system is a closed-loop structure. The output z1 of the enhanced model-assisted extended state observer AMESO is used for negative feedback of the closed-loop control. The reference value of the controller is 0. The error between the controller and the feedback z1 of the enhanced model-assisted extended state observer AMESO is used to design the control law to obtain the controller output. The disturbance estimation output z1 of the enhanced model-assisted extended state observer AMESO is n+1 Feed forward to the controller output to obtain the final control output; The voltage control quantity output by the controller is converted into an output control current by a high-precision current source. The magnetic compensation coil is the actuator of the system, used to convert the control current into a magnetic field. The final control output is converted into an analog voltage signal by the DAC output unit. The current is applied to the magnetic compensation coil by a high-precision current source to compensate for the multi-source disturbance of the magnetic field. The magnetic field disturbance measured by the magnetic field sensor is close to zero, providing an extremely weak magnetic environment for cardio-cerebral magnetic measurement. The ADC acquisition unit acquires the output signal of the magnetic field sensor. Due to the characteristics of the magnetic field sensor itself, the output signal of the ADC acquisition unit has an inertial link plus time lag characteristic. The TD-Smith lead predictor includes a Smith predictor and a TD lead. The TD lead includes a TD tracking differentiator and a lead compensation polynomial. The TD-Smith lead predictor estimates the signal with time lag output by the magnetic field sensor to obtain a time-lag-free signal of the disturbance and magnetic field. The TD lead performs lead correction on the lagging disturbance signal. The model-assisted extended state observer is tuned using the filter equivalent tuning method to obtain the AMESO. The AMESO estimates the external magnetic field disturbance and feeds forward to the controller output to obtain the final control output. The final control output is fed to a high-precision current source through a DAC output unit, and the control current is obtained through the high-precision current source. The control current passes through the magnetic compensation coil to obtain a compensation magnetic field, thereby suppressing multi-source magnetic field disturbances including time lag, external magnetic field disturbances, power frequency interference, 1 / f noise, and model mismatch.

2. An active magnetic compensation control method based on multi-source magnetic field disturbance suppression, the method being applied to the system according to claim 1, characterized in that: The following steps are involved: The ADC acquisition unit collects the signal with time delay output by the magnetic field sensor; The signal with time delay output by the magnetic field sensor is estimated by the TD-Smith advance predictor, wherein the TD advance device performs advance correction on the delayed interference signal; The model-assisted extended state observer MESO is tuned using the filter equivalent tuning method to obtain the enhanced model-assisted extended state observer AMESO. The external magnetic field disturbance is estimated by the enhanced model-assisted extended state observer (AMESO) and fed forward to the controller output to obtain the final control output. The final control output passes through the DAC output unit and the high-precision current source to apply current to the magnetic compensation coil, thereby suppressing multi-source magnetic field disturbances including time lag, external magnetic field disturbances, power frequency interference, 1 / f noise and model mismatch.

3. The method according to claim 2, characterized in that The TD advance device includes a TD tracking differentiator and an advance compensation polynomial. The TD-Smith advance predictor estimates the signal containing time delay output by the magnetic field sensor, including: According to the nominal hysteresis of the magnetic field sensor Establish a basic Smith predictor with the system nominal model G0(s); The TD advancer order n and the tracking factor r are selected according to the system noise level and the model mismatch degree, and the TD tracking differentiator is used to obtain the 1st to nth order differentials of the disturbance signal with time delay. The nth-order lead compensation polynomial is used to combine the 1st to nth-order differential polynomials to approximate the lead compensation of the time lag. The form of the lead compensation polynomial is: ； Where s is the Laplace operator, k is the indicator variable of the summation function, k! is the factorial of k, and k! = k × (k-1) × (k-2) × ... × 1; The disturbance corrected by the TD advancer and the system output estimated by Smith are added together and then passed to the enhanced model-assisted extended state observer (AMESO).

4. The method according to claim 3, characterized in that The model-assisted extended state observer MESO is tuned using the filter equivalent tuning method to obtain the enhanced model-assisted extended state observer AMESO, which includes: According to the n-order nominal model G0(s), an n+1-order model-assisted extended state observer is established; The gain matrix of the n+1 order model-assisted extended state observer is obtained by using the bandwidth tuning method: ; According to the design principle of low-pass filter in disturbance observer, Chebyshev type I filter is selected as the frequency domain equivalent filter of model-assisted extended state observer. Choose the appropriate maximum passband attenuation value The selection principle is that if the high-frequency measurement noise is large, the maximum attenuation value of the passband is increased. To improve the observer performance, reduce the maximum attenuation value of the passband ; According to the determined Calculate the passband ripple coefficient ; Select the filter order N+1, which is the same as the order of the model-assisted extended state observer, and select the filter cutoff frequency , which is the same as the observation bandwidth of the model-assisted extended state observer; Calculates the N+1 order Chebyshev polynomial ; According to the transfer function of the Chebyshev filter, find the equation Left half plane solution , where j is a complex operator and computes the polynomial The coefficient is recorded as the correction coefficient ; The obtained correction coefficient is used to correct the observer gain matrix: , the adjustment is completed.

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