Dynamic response enhanced magnetic disturbance suppression system and method

Abstract

The invention discloses a dynamic response enhanced magnetic disturbance suppression system and method. The system comprises an AD acquisition unit, a magnetic field sensor, a sliding mode observer, a self-adaptive extended linear quadratic regulation controller and a DA output unit. Wherein the adaptive extended linear quadratic regulation controller comprises an adaptive calculation module and an error extended linear quadratic regulator (eLQR); the AD acquisition unit is used for acquiring an analog signal output by the magnetic field sensor and converting the analog signal into a digital signal; the sliding mode observer is used for estimating external magnetic field disturbance and system unmodeled dynamics in real time; the self-adaptive calculation module is used for carrying out self-adaptive calculation on a cost function of an error expansion linear quadratic regulator by using a fuzzy immune regulation method, namely FIS, in combination with a disturbance observation value, and the eLQR after parameter updating generates an optimal control quantity; and the DA output unit is used for converting the control quantity into an analog voltage signal and driving a magnetic compensation coil to generate a reverse compensation magnetic field through a current source.

Description

Technical Field

[0001] The present invention belongs to the technical field of active magnetic compensation of magnetic shielding devices, and in particular relates to a dynamic response enhanced magnetic disturbance suppression system and method. Background Art

[0002] As a fundamental physical quantity, magnetic field has demonstrated significant application value in the biomedical field, particularly in magnetocardiography and magnetoencephalography. The intensity of magnetocardiography signals is typically on the order of femtotesla (fT), only one billionth of the Earth's magnetic field, while magnetoencephalography signals can be as low as sub-femtotesla. Directly measuring such weak signals in the face of complex electromagnetic interference from the Earth's magnetic field and external interference is virtually impossible. Current mainstream approaches utilize passive magnetic shielding and active magnetic compensation to create a near-zero magnetic field environment to meet the needs of magnetocardiography and magnetoencephalography.

[0003] Passive magnetic shielding devices rely on a multilayer structure of high-permeability materials (such as Permalloy) and high-conductivity materials (such as aluminum). While they can significantly attenuate external magnetic fields, their internal residual magnetism still struggles to reach the threshold for direct measurement of cardiocerebral magnetism (typically required to be below 1 nT). Furthermore, passive shielding devices require high machining precision, are expensive to manufacture, and are unable to dynamically suppress time-varying disturbances (such as magnetic field fluctuations caused by traffic vibrations). Therefore, an active magnetic compensation system is needed to further suppress residual magnetism and random interference while reducing reliance on the performance of the passive shielding layer.

[0004] Existing active magnetic compensation systems mostly use PID closed-loop control or a combination of disturbance observers (such as extended state observers). However, when dealing with sudden large disturbances, there are problems such as slow response speed, complex parameter adjustment, and insufficient robustness. Traditional PID control is sensitive to nonlinear and time-varying disturbances. When dealing with short-term impulse disturbances or dynamic frequency-varying disturbances, the suppression effect is significantly reduced. The system based on the disturbance observer has limited convergence speed under sudden disturbances, which can easily lead to overshoot or instability. In addition, the coupling design of the observer and the controller in the existing technology lacks global optimization, making it difficult to take into account both dynamic response and steady-state accuracy. In response to the above problems, there is an urgent need for a new control strategy that can quickly suppress sudden large disturbances while ensuring system stability. Summary of the Invention

[0005] In order to solve the problems existing in the prior art, the present invention provides a dynamic response enhanced magnetic disturbance suppression system and method, which achieves better interference suppression effect in the face of short-term large-amplitude impulse interference through the strong robustness of the sliding mode observer and the global optimization characteristics of the error expansion linear quadratic regulator. At the same time, the fuzzy immune regulation method is used to adjust the control coefficient in real time, realizing the adaptive optimization of the system to external interference, improving the system stability, robustness and dynamic performance, and providing a high-precision near-zero magnetic environment for extremely weak magnetic signal detection scenarios such as magnetic cardiography measurement.

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

[0007] A dynamic response enhanced magnetic disturbance suppression system, comprising: an AD acquisition unit, a magnetic field sensor, a sliding mode observer, an adaptive extended linear quadratic regulation controller, and a DA output unit;

[0008] The adaptive extended linear quadratic regulator controller includes an adaptive calculation module and an error extended linear quadratic regulator (eLQR);

[0009] The AD acquisition unit is used to collect the analog signal output by the magnetic field sensor and convert it into a digital signal;

[0010] The sliding mode observer is used to estimate the external magnetic field disturbance and the unmodeled dynamics of the system in real time;

[0011] The adaptive calculation module is used to use the fuzzy immune regulation method (FIS) combined with the disturbance observation value to adaptively calculate the cost function of the error expansion linear quadratic regulator, and the eLQR after updating the parameters generates the optimal control quantity;

[0012] The DA output unit is used to convert the control quantity into an analog voltage signal, and drive the magnetic compensation coil to generate a reverse compensation magnetic field through a current source.

[0013] Preferably, the constructed sliding mode observer is:

[0014]

[0015] in, is the state observation value, L is the observer gain matrix, v is the disturbance observation value, k c is the disturbance gain, sat is the saturation function, phi is the preprocessing coefficient, x represents the state vector, u represents the input vector, and y represents the output vector; A is the system matrix, which describes the dynamic relationship between states; B is the input matrix, which describes how the control input affects the state, and C is the output matrix, which describes how the state affects the output.

[0016] Preferably, the adaptive calculation module includes: a fuzzy immune feedback unit, an immune parameter dynamic adjustment unit, and a fuzzy immune adaptive calculation unit;

[0017] The fuzzy immune feedback unit is used to construct a fuzzy rule base, based on the system disturbance d(t) and the rate of change Set the fuzzy input variables and the corresponding membership functions; map the fuzzy input variables to the control input u(t) through the bio-immune feedback mechanism, and generate the immune gain adjustment signal;

[0018] The immune parameter dynamic adjustment unit is used to calculate the immune gain adjustment factor λ(t) in real time based on preset fuzzy rules using the Mamdani fuzzy inference method; compare the immune gain adjustment factor λ(t) with a preset threshold, limit the dynamic range through a saturation function, and generate an immune feedback parameter;

[0019] The fuzzy immune adaptive calculation unit is used to dynamically update the parameter α(t) according to the immune feedback parameter λ(t) and the observed disturbance d(t) by the following formula:

[0020] α(t)=α0×λ(t)×ρ

[0021] Among them, α0 is the reference control gain, and ρ is the preset adjustment coefficient.

[0022] Preferably, the cost function of the error expansion linear quadratic regulator is:

[0023]

[0024] Among them, x e is the system state error, x is the state vector, u is the input vector, the Q matrix determines the weight of each state, the R matrix is ​​used to balance the costs between different control inputs, and the parameter α and the adjustment matrices Q and R are updated in real time by the fuzzy immune adjustment module according to the disturbance.

[0025] The present invention also provides a dynamic response enhanced magnetic disturbance suppression method, which is implemented using the aforementioned dynamic response enhanced magnetic disturbance suppression system, and comprises:

[0026] Step S1, obtaining an ambient magnetic field simulation signal;

[0027] Step S2: Building a sliding mode observer based on the environmental magnetic field simulation signal and the system control output to observe the system state value and the size of the external disturbance;

[0028] Step S3: performing adaptive calculation according to the size of the external disturbance and iteratively updating the parameters of the error expansion linear quadratic regulator cost function;

[0029] Step S4: The output result of the error expansion linear quadratic regulator after parameter update is sent from the DA output unit to the magnetic compensation coil via the current source for magnetic field compensation.

[0030] Preferably, the constructed sliding mode observer is:

[0031]

[0032] in, is the state observation value, L is the observer gain matrix, v is the disturbance observation value, k cis the disturbance gain, sat is the saturation function, phi is the preprocessing coefficient, x represents the state vector, u represents the input vector, and y represents the output vector; A is the system matrix, which describes the dynamic relationship between states; B is the input matrix, which describes how the control input affects the state, and C is the output matrix, which describes how the state affects the output.

[0033] Preferably, step S3 includes:

[0034] Step S31: Calculate the system error signal d(t) and its rate of change Perform fuzzy membership preprocessing and generate fuzzy input variables through a preset triangular membership function;

[0035] Step S32: Based on the fuzzy rule base, the immune regulation factor λ(t) is calculated using the Mamdani fuzzy reasoning method, and threshold filtering and normalization processing are combined;

[0036] Step S33: Dynamically fuse the processed immune feedback parameter λ(t) with the reference control gain α0, and iteratively update the control gain α(t):

[0037] α(t)=α0×ρ×λ(t)

[0038] Wherein, ρ is the preset adjustment coefficient.

[0039] Preferably, the cost function of the error expansion linear quadratic regulator is:

[0040]

[0041] Among them, x e is the system state error, x is the state vector, u is the input vector, the Q matrix determines the weight of each state, the R matrix is ​​used to balance the costs between different control inputs, and the parameter α and the adjustment matrices Q and R are updated in real time by the fuzzy immune adjustment module according to the disturbance.

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] (1) Aiming at the problem of active magnetic compensation control in extremely weak magnetic fields, the present invention proposes an adaptive control method based on a sliding mode observer and an extended linear quadratic regulation controller (eLQR). This method can estimate the external magnetic field disturbance and the unmodeled state of the system in real time, and accurately establish a zero magnetic environment by adaptively adjusting the parameters of the LQR, thereby improving the compensation effect.

[0044] (2) The sliding mode observer designed in this invention can accurately estimate the magnetic field state and external disturbances, overcoming the inability of traditional methods to specifically address large impulsive disturbances. By combining the fuzzy immune calculation method with the dynamic adjustment of the state error term coefficient in the eLQR cost function, the control parameters can be adaptively optimized according to the disturbance size, ensuring that the system maintains optimal performance under different operating conditions.

[0045] (3) Compared with traditional PID controllers or other linear control methods, the present invention combines the sliding mode observer with the adaptive eLQR control, so that the system can respond to external magnetic field disturbances more accurately, while improving the stability and robustness of the system, reducing the overshoot of the system and accelerating the response speed, thereby enhancing the dynamic performance of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] 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.

[0047] Figure 1 Schematic diagram of the structure of a dynamic response enhanced magnetic disturbance suppression system for a magnetic shielding device according to an embodiment of the present invention;

[0048] Figure 2 This is a calculation flow chart of the adaptive calculation module based on the fuzzy immune regulation method in an embodiment of the present invention. DETAILED DESCRIPTION

[0049] 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.

[0050] 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.

[0051] Example 1

[0052] like Figure 1As shown, the present invention provides a dynamic response enhanced magnetic disturbance suppression system for dealing with short-duration, high-amplitude impulse interference, improving system stability, robustness, and dynamic performance, and is particularly suitable for construction in near-zero magnetic environments. The system includes: an AD acquisition unit, a magnetic field sensor, a sliding mode observer, an adaptive extended linear quadratic regulation controller, a DA output unit, a high-precision current source, and a magnetic compensation coil; the adaptive extended linear quadratic regulation controller includes an adaptive calculation module and an error extended linear quadratic regulator (eLQR). The system uses an STM32 chip as the MCU for control and calculation.

[0053] Among them, the AD acquisition unit collects the analog signal output by the magnetic field sensor and converts it into a digital signal; a sliding mode observer is constructed to estimate the external magnetic field disturbance and the unmodeled dynamics of the system in real time; the adaptive calculation module uses the fuzzy immune regulation method (FIS) combined with the disturbance observation value to adaptively calculate the cost function of the extended linear quadratic control controller, and the eLQR after updating the parameters generates the optimal control quantity; the control quantity is converted into an analog voltage signal by the DA output unit, and the magnetic compensation coil is driven by a high-precision current source to generate a reverse compensation magnetic field.

[0054] In an embodiment of the present invention, a sliding mode observer (SMO) is constructed to estimate the external magnetic field disturbance and the system state in real time, and the estimation results are fed back to the extended linear quadratic controller (eLQR). The sliding mode observer is a robust estimation tool that can accurately estimate the magnetic field state and external disturbances. In this step, the magnetic field signal and the output signal of the controller are used as input signals, and the system state and the disturbance estimate are used as output signals, which are fed back to the eLQR controller to generate the optimal control quantity and achieve more accurate magnetic field compensation. The design of the sliding mode observer includes the following steps:

[0055] Step 10: Establish the model of the forward channel of the magnetic field compensation control system, as shown below:

[0056] y=G f ×G r ×K a

[0057] Among them, y represents the output vector, G f is the transfer function of the current source filter in the system, G r is the system coil transfer function, K a is the amplification factor of the current source and coil link.

[0058] Among them, G f and G r Considered as a first-order inertial link:

[0059]

[0060] Among them, τ1 and τ2 are the time constants of the filter link and the coil link, and s represents the complex frequency domain variable in the Laplace transform.

[0061] The simplified system state equation is:

[0062]

[0063] Where x represents the state vector, represents the derivative with respect to x, u represents the input vector, and y represents the output vector. A is the system matrix, describing the dynamic relationship between states. B is the input matrix, describing how control inputs affect states. C is the output matrix, describing how states affect outputs. All of these matrices can be derived from a known forward pass model.

[0064] Step 20: Build a sliding mode observer and select the sliding surface as shown below:

[0065] δ=y-Cx

[0066] Where δ is the sliding surface. Based on the sliding surface and the system state space equation, the sliding mode observer formula is as follows:

[0067]

[0068] in, is the state observation value, is the observed value of x The derivative of L is the observer gain matrix, v is the disturbance observation value, k c is the disturbance gain, sat is the saturation function, and phi is the preprocessing coefficient.

[0069] In this embodiment, the adaptive calculation module includes: a fuzzy immune feedback unit, an immune parameter dynamic adjustment unit, and a fuzzy immune adaptive calculation unit;

[0070] Fuzzy immune feedback unit is used to build a fuzzy rule base based on the system disturbance d(t) and the rate of change Set the fuzzy input variables and the corresponding membership functions; map the fuzzy input variables to the control input u(t) through the bio-immune feedback mechanism, and generate the immune gain adjustment signal;

[0071] An immune parameter dynamic adjustment unit is used to calculate the immune gain adjustment factor λ(t) in real time based on preset fuzzy rules using the Mamdani fuzzy inference method; compare the immune gain adjustment factor λ(t) with a preset threshold, limit the dynamic range through a saturation function, and generate an immune feedback parameter;

[0072] The fuzzy immune adaptive calculation unit is used to dynamically update the parameter α(t) according to the immune feedback parameter λ(t) and the observed disturbance d(t) through the following formula:

[0073] α(t)=α0×λ(t)×ρ

[0074] Among them, α0 is the reference control gain, and ρ is the preset adjustment coefficient.

[0075] As an implementation method of an embodiment of the present invention, an extended linear quadratic controller (eLQR) is constructed to perform adaptive control based on the magnetic field disturbance and system state estimated by a sliding mode observer. The eLQR controller is an optimization control method that can adjust parameters based on the system's state error and control input to achieve optimal control. In this step, the system state value and state error estimated by the sliding mode observer are used as input signals. The eLQR dynamically adjusts the coefficients in the cost function based on the estimated disturbance signal to ensure that the system maintains the best compensation effect under different disturbance conditions.

[0076] The cost function used by the extended linear quadratic controller is shown below:

[0077]

[0078] where x e is the system state error, x is the state vector, and u is the input vector. The Q matrix determines the weight of each state, and the R matrix is ​​used to balance the costs between different control inputs. The parameter α and the control matrices Q and R are updated in real time by the fuzzy immune control module based on disturbances.

[0079] Introducing an error term into the cost function accurately balances the system's state tracking accuracy with the energy consumption of the control input. By increasing the weight of the state error term, the system's focus on state tracking is strengthened, allowing the controller to prioritize reducing the error between the system output and the desired value, thereby improving the system's dynamic response and stability. Furthermore, appropriately adjusting the weight of the state error term can effectively address the effects of external disturbances and enhance the system's robustness. With this term, the eLQR controller can make real-time adjustments based on the system's actual state and error, optimizing control effectiveness and ensuring rapid system convergence and optimal control under varying operating conditions.

[0080] As an implementation method of the present invention, an adaptive calculation module of the fuzzy immune regulation algorithm is constructed to adjust the coefficients of the extended linear quadratic controller cost function in real time according to the size of the system observation disturbance to perform optimal control. The calculation process of the adaptive calculation module is as follows: Figure 2As shown in Figure 1, the fuzzy immune regulation method combines the advantages of fuzzy logic and immune algorithms, and can dynamically adjust the control strategy based on the system's input and output signals and disturbance information. In this step, the system's external disturbance estimate is used as input, and the current disturbance state of the system is judged through fuzzy inference rules. The control parameters are adaptively adjusted in combination with the immune algorithm to optimize the system's response performance. This method not only enhances the system's adaptive ability, but also improves the control system's robustness to complex environments and nonlinear disturbances, helping to achieve more accurate and stable control effects. The design of the adaptive calculation module includes the following steps:

[0081] Step 10: Construct a fuzzy immune rule base. The input variables include the normalized disturbance amplitude d(t) and its rate of change. Both are mapped to the interval 0, 1. The disturbance amplitude is divided into three fuzzy sets of "small", "medium" and "large", using left triangle, symmetric triangle and right triangle membership functions respectively. The mathematical expressions are:

[0082]

[0083] Among them, μ s (x), μ M (x), μ L (x) are three fuzzy sets: “small”, “medium” and “large” respectively.

[0084] The disturbance change rate is also divided into three fuzzy sets: "slow," "medium," and "fast," with the membership function defined identically to the amplitude. The output variable is the immune gain adjustment factor λ(t), which has a value range of (0.1, 5.0) corresponding to different levels of disturbance suppression.

[0085] Step 20: Design fuzzy immune feedback rules and defuzzification. Based on the combination of disturbance amplitude and change rate, design a fuzzy rule base containing nine rules to cover all input states. For example, when the disturbance amplitude is "large" and the change rate is "fast", the output strong inhibition gain λ(t) = 5.0; when the disturbance amplitude is "small" and the change rate is "slow", the output weak inhibition gain λ(t) = 0.1. The Mamdani minimum-maximum inference method is used to calculate the activation of each rule, and the center of gravity method is used to defuzzify the accurate λ(t). The calculation formula is:

[0086]

[0087] where μ i is the rule activation degree, ω i is the corresponding output value.

[0088] Step 30: Dynamically update the parameters of the eLQR cost function and introduce the error term observation into the traditional LQR cost function in For the predicted output obtained by observing the state quantity, an extended quadratic cost function is constructed:

[0089]

[0090] Among them, x represents the state vector, u represents the input vector, the Q matrix determines the weight of each state, the R matrix is ​​used to balance the costs between different control inputs, and the parameter α and the adjustment matrices Q and R are updated in real time by the fuzzy immune adjustment module according to the disturbance.

[0091] The weight matrices α, Q, and R are dynamically adjusted according to λ(t), the error term weight α = α0·λ(t)ρ, the state weight matrix Q(t) = Q0·λ(t)·ρ, and the control weight matrix R(t) = R0·λ(t)·ρ, where α0, Q0, and R0 are the initial parameters of each matrix, respectively.

[0092] Step 40: Stability verification and parameter constraints, through Lyapunov function:

[0093]

[0094] Among them, x e is the system error, P is the Ricatti matrix, to ensure the stability of the closed-loop system, it is required Solve the improved Riccati equation:

[0095] (A+BK(t)) T P+P(A+BK(t))+Q(t)=0

[0096] Where K(t) is the control gain matrix updated in real time.

[0097] To prevent parameter mutations, the value range of λ(t) is limited to 0.1, 5.0, and the out-of-bounds situation is handled by a saturation function.

[0098] In this embodiment, the dynamic response enhanced magnetic disturbance suppression system includes a forward integration module for eliminating the steady-state error of the system.

[0099] Example 2

[0100] The present invention proposes a dynamic response enhanced magnetic disturbance suppression method, which is implemented using a dynamic response enhanced magnetic disturbance suppression system described in Example 1. It has a good interference suppression effect on short-term impulse interference and low-frequency disturbances, and realizes stable control of the magnetic field in the magnetic shielding device.

[0101] The method comprises:

[0102] Step S1, obtaining an ambient magnetic field simulation signal;

[0103] Step S2: Building a sliding mode observer based on the environmental magnetic field simulation signal and the system control output to observe the system state value and the size of the external disturbance;

[0104] Step S3: performing adaptive calculation according to the size of the external disturbance and iteratively updating the parameters of the error expansion linear quadratic regulator cost function;

[0105] Step S4: The output result of the error expansion linear quadratic regulator after parameter update is sent from the DA output unit to the magnetic compensation coil via the current source for magnetic field compensation.

[0106] In this embodiment, the constructed sliding mode observer is:

[0107]

[0108] in, is the state observation value, L is the observer gain matrix, v is the disturbance observation value, k c is the disturbance gain, sat is the saturation function, phi is the preprocessing coefficient, x represents the state vector, u represents the input vector, and y represents the output vector; A is the system matrix, which describes the dynamic relationship between states; B is the input matrix, which describes how the control input affects the state, and C is the output matrix, which describes how the state affects the output.

[0109] In this embodiment, step S3 includes:

[0110] Step S31: Calculate the system error signal d(t) and its rate of change Perform fuzzy membership preprocessing and generate fuzzy input variables through a preset triangular membership function;

[0111] Step S32: Based on the fuzzy rule base, the immune regulation factor λ(t) is calculated using the Mamdani fuzzy reasoning method, and threshold filtering and normalization processing are combined;

[0112] Step S33: Dynamically fuse the processed immune feedback parameter λ(t) with the reference control gain α0, and iteratively update the control gain α(t):

[0113] α(t)=α0×ρ×λ(t)

[0114] Wherein, ρ is the preset adjustment coefficient.

[0115] In this embodiment, the cost function of the error expansion linear quadratic regulator is:

[0116]

[0117] Among them, x eis the system error, x represents the state vector, u represents the input vector, the Q matrix determines the weight of each state, and the R matrix is ​​used to balance the costs between different control inputs. The parameter α and the adjustment matrices Q and R are updated in real time by the fuzzy immune adjustment module according to the disturbance.

[0118] In this embodiment, the dynamic response enhanced magnetic disturbance suppression method includes a forward integration module for eliminating the steady-state error of the system.

[0119] The present invention proposes a dynamic response-enhanced magnetic disturbance suppression method that solves the magnetic compensation problem when facing short-term, high-amplitude impulse-type interference in extremely weak magnetic fields. This method accurately estimates external magnetic field disturbances and the unmodeled state of the system through a sliding mode observer, and dynamically adjusts the eLQR cost function in combination with a fuzzy immune calculation method, thereby achieving adaptive optimization of the system to external disturbances. Compared with traditional PID controllers and other linear control methods, this method can more accurately respond to external magnetic field disturbances, improve system stability, robustness, and dynamic performance, reduce overshoot, and accelerate response speed. It is particularly suitable for compensation tasks in extremely weak magnetic field environments.

[0120] The embodiments described above are merely descriptions of preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by persons skilled in the art should fall within the scope of protection defined by the claims of the present invention.

Claims

1. A dynamic response enhanced magnetic disturbance suppression system, characterized in that: The system includes: an AD acquisition unit, a magnetic field sensor, a sliding mode observer, an adaptive extended linear quadratic regulation controller, and a DA output unit; The adaptive extended linear quadratic regulator controller includes an adaptive calculation module and an error extended linear quadratic regulator (eLQR); The AD acquisition unit is used to collect the analog signal output by the magnetic field sensor and convert it into a digital signal; The sliding mode observer is used to estimate the external magnetic field disturbance and the unmodeled dynamics of the system in real time; The adaptive calculation module is used to use the fuzzy immune regulation method (FIS) combined with the disturbance observation value to adaptively calculate the cost function of the error expansion linear quadratic regulator, and the eLQR after updating the parameters generates the optimal control quantity; The DA output unit is used to convert the control quantity into an analog voltage signal, and drive the magnetic compensation coil to generate a reverse compensation magnetic field through a current source.

2. The system according to claim 1, wherein: The constructed sliding mode observer is: in, is the state observation value, L is the observer gain matrix, v is the disturbance observation value, k c is the disturbance gain, sat is the saturation function, phi is the preprocessing coefficient, x represents the state vector, u represents the input vector, and y represents the output vector; A is the system matrix, which describes the dynamic relationship between states; B is the input matrix, which describes how the control input affects the state, and C is the output matrix, which describes how the state affects the output.

3. The system according to claim 1, wherein: The adaptive calculation module includes: a fuzzy immune feedback unit, an immune parameter dynamic adjustment unit, and a fuzzy immune adaptive calculation unit; The fuzzy immune feedback unit is used to construct a fuzzy rule base, based on the system disturbance d(t) and the rate of change Set the fuzzy input variables and the corresponding membership functions; map the fuzzy input variables to the control input u(t) through the bio-immune feedback mechanism, and generate the immune gain adjustment signal; The immune parameter dynamic adjustment unit is used to calculate the immune gain adjustment factor λ(t) in real time based on preset fuzzy rules using the Mamdani fuzzy inference method; compare the immune gain adjustment factor λ(t) with a preset threshold, limit the dynamic range through a saturation function, and generate an immune feedback parameter; The fuzzy immune adaptive calculation unit is used to dynamically update the parameter α(t) according to the immune feedback parameter λ(t) and the observed disturbance d(t) by the following formula: α(t)=α0×λ(t)×ρ Among them, α0 is the reference control gain, and ρ is the preset adjustment coefficient.

4. The system according to claim 1, wherein: The cost function of the error expansion linear quadratic regulator is: Among them, x e is the system state error, x is the state vector, u is the input vector, the Q matrix determines the weight of each state, the R matrix is ​​used to balance the costs between different control inputs, and the parameter α and the adjustment matrices Q and R are updated in real time by the fuzzy immune adjustment module according to the disturbance.

5. A dynamic response enhanced magnetic disturbance suppression method, the method being implemented by using a dynamic response enhanced magnetic disturbance suppression system according to any one of claims 1 to 4, characterized in that: The method comprises: Step S1, obtaining an ambient magnetic field simulation signal; Step S2: Building a sliding mode observer based on the environmental magnetic field simulation signal and the system control output to observe the system state value and the size of the external disturbance; Step S3: performing adaptive calculation according to the size of the external disturbance and iteratively updating the parameters of the error expansion linear quadratic regulator cost function; Step S4: The output result of the error expansion linear quadratic regulator after parameter update is sent from the DA output unit to the magnetic compensation coil via the current source for magnetic field compensation.

6. The method according to claim 5, wherein The constructed sliding mode observer is: in, is the state observation value, L is the observer gain matrix, v is the disturbance observation value, k c is the disturbance gain, sat is the saturation function, phi is the preprocessing coefficient, x represents the state vector, u represents the input vector, and y represents the output vector; A is the system matrix, which describes the dynamic relationship between states; B is the input matrix, which describes how the control input affects the state, and C is the output matrix, which describes how the state affects the output.

7. The method according to claim 5, wherein Step S3 includes: Step S31: Calculate the system error signal d(t) and its rate of change Perform fuzzy membership preprocessing and generate fuzzy input variables through a preset triangular membership function; Step S32: Based on the fuzzy rule base, the immune regulation factor λ(t) is calculated using the Mamdani fuzzy reasoning method, and threshold filtering and normalization processing are combined; Step S33: Dynamically fuse the processed immune feedback parameter λ(t) with the reference control gain α0, and iteratively update the control gain α(t): α(t)=α0×ρ×λ(t) Wherein, ρ is the preset adjustment coefficient.

8. The method according to claim 5, wherein The cost function of the error expansion linear quadratic regulator is: Among them, x e is the system state error, x is the state vector, u is the input vector, the Q matrix determines the weight of each state, the R matrix is ​​used to balance the costs between different control inputs, and the parameter α and the adjustment matrices Q and R are updated in real time by the fuzzy immune adjustment module according to the disturbance.

Images