External magnetic field active disturbance rejection control method and system based on Kalman filtering

Abstract

The invention discloses an external magnetic field active-disturbance-rejection control method and system based on Kalman filtering, and the method comprises the steps: firstly, building an external coil control system to compensate a residual magnetic field in a magnetic shielding cabin measured by a three-axis fluxgate sensor, and enabling the residual magnetic field to be reduced to a normal working range of Quspin; then starting a Quspin sensor, performing data filtering and fusion on the fluxgate sensor and the Quspin sensor through a Kalman filter, and inputting a fusion value and a control quantity into an extended state observer to realize online estimation of a state and total disturbance; and performing feedback control of related linear state errors on the total disturbance estimator so as to compensate a residual magnetic field and magnetic field disturbance in the magnetic shielding cabin. The magnetic field interference resistance of the active magnetic compensation device can be enhanced, the control precision of an external coil control system on the magnetic field in the cabin is effectively improved, and the cost of the magnetic shielding cabin is reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of active magnetic compensation system control, and in particular relates to an external magnetic field self-anti-disturbance control method and system based on Kalman filtering. Background Art

[0002] Magnetoencephalography and magnetocardiography are non-invasive, non-contact imaging technologies that can measure weak magnetic field signals of the human body, providing a very unique non-invasive window for humans to understand the functions of the heart and brain and diagnose diseases. However, the magnetic field amplitude generated by the human heart is about 10-100pT, and the signal strength of the neural magnetic field at the scalp is usually in the range of 50-500fT, which is 20 to 24 orders of magnitude smaller than the Earth's magnetic field and is extremely weak.

[0003] The measurement of extremely weak magnetic fields relies on highly sensitive magnetic field sensors, mainly including superconducting quantum interferometers and optically pumped atomic magnetometers. Among them, optically pumped atomic magnetometers do not require a low-temperature environment and can measure magnetic field signals of the order of nT or even fT, with a measurement sensitivity of up to However, its principle determines that it can only work in a relatively low magnetic field environment and has a small dynamic measurement range. In order to achieve high-sensitivity measurement of cardiac and brain magnetic signals, it is necessary to construct a near-zero magnetic field environment with low residual magnetic field, low magnetic field gradient and low residual magnetic field drift through magnetic shielding technology.

[0004] Magnetic shielding technology is mainly divided into two categories according to the different magnetic field shielding principles: passive magnetic shielding and active magnetic compensation. The internal magnetic field environment of passive shielding is stable and has strong tolerance to external magnetic field interference. The disadvantage is that it is expensive, cannot effectively compensate for uneven magnetic fields and random interference magnetic fields, and still has a certain static residual magnetic field. Due to various external activities, multi-source magnetic field disturbances will inevitably occur, such as magnetic field changes caused by vehicles, electrical equipment and ground vibrations. If magnetic field interference is not suppressed, it will affect the uniformity of the magnetic field distribution inside the shielding device. Therefore, it is necessary to combine active shielding technology to actively compensate for external disturbance signals in real time, reduce the magnetic field noise in the internal space, and reduce the cost of passive shielding. Active magnetic compensation technology uses an energized coil to generate a magnetic field of the same magnitude and opposite direction as the interference magnetic field. Combined with a closed-loop negative feedback control mode, it can achieve compensation for environmental residual magnetism and magnetic field gradients, and can effectively suppress both low-frequency and high-frequency magnetic fields.

[0005] Existing active magnetic compensation methods mostly use PID closed-loop control methods, but their control effect is not good for systems with strong nonlinearity, time-varying, and uncertain structural parameters; for unknown controlled objects, the active disturbance rejection control method can use the extended state observer to expand the nonlinearity and uncertainty into a new state variable, regard it as the total disturbance of the system, and compensate it at the control input end, thereby improving the anti-interference ability of the external coil control system and quickly and effectively eliminating the interfering magnetic field.

[0006] The external coil magnetic compensation system mainly uses a three-axis fluxgate sensor to measure the residual magnetic field in the magnetic shielding device. However, due to the limitation of the sensor sensitivity, the control accuracy of the system is low. The Kalman filter method can be used to fuse the Quspin sensor with higher sensitivity and smaller dynamic measurement range with the three-axis fluxgate sensor to obtain more accurate magnetic field measurement values, thereby improving the accuracy of the external coil control system. Summary of the invention

[0007] In order to solve the problems of unknown external disturbances from multiple sources and high passive shielding costs in the magnetic compensation process, the present invention proposes an external magnetic field self-anti-disturbance control method and system based on Kalman filtering. A more accurate residual magnetic field measurement value in the magnetic shielding cabin is obtained by Kalman filtering, and the estimated total disturbance is feedback controlled in combination with the self-anti-disturbance control method. At the same time, the residual magnetic field and magnetic field disturbance in the magnetic shielding cabin are compensated, thereby improving the anti-interference ability and control accuracy of the magnetic compensation coil control system and reducing the cost of the magnetic shielding cabin.

[0008] To achieve the above object, the present invention provides the following solutions:

[0009] A Kalman filter-based external magnetic field self-disturbance rejection control method comprises the following steps:

[0010] Step 1: Build an external coil control system to compensate for the residual magnetic field in the magnetic shielding cabin measured by the three-axis fluxgate sensor, and reduce the residual magnetism to the normal working range of the Quspin sensor;

[0011] Step 2: Turn on the Quspin sensor, filter and fuse the data of the fluxgate sensor and the Quspin sensor through the Kalman filter, and obtain the optimal estimate of the magnetic field;

[0012] Step 3: Input the optimal estimated value of the magnetic field and the control quantity into the extended state observer to realize the online estimation of the state and the total disturbance and obtain the total disturbance estimation;

[0013] Step 4: Feedback control of the linear state error is performed on the total disturbance estimate to compensate for the residual magnetic field and magnetic field disturbance in the magnetic shielding cabin.

[0014] Preferably, the external coil control system includes a three-axis fluxgate sensor, a signal conditioning circuit, an external controller, a voltage-controlled current source, and an external Helmholtz coil.

[0015] Preferably, the method of filtering and fusing data of the fluxgate sensor and the Quspin sensor by using a Kalman filter to obtain the optimal estimate of the magnetic field includes:

[0016] According to the state transfer equation The three-axis fluxgate sensor measurement value is equivalent to the prior estimate obtained by the system state space state equation in the Kalman filter, and the Quspin sensor measurement value is equivalent to the measurement value obtained by the measurement equation in the Kalman filter, thereby establishing the posterior estimate Among them, A and H are equivalent to the observation matrices of the three-axis fluxgate sensor measurement value and the Quspin sensor magnetic field measurement value, respectively, and X k-1 , Z k are equivalent to the magnetic field measurement values ​​of the three-axis fluxgate sensor and the Quspin sensor at time k, B is the control input matrix, U k is the control input, w k 、v k are the observation noise of the magnetic field measurement values ​​of the three-axis fluxgate sensor and the Quspin sensor, respectively, k is the magnetic field value in the magnetic shielding cabin at time k; is the prior estimate, is the a posteriori estimate obtained by data fusion based on the prior estimate and the measured value, K k is the Kalman gain matrix at time k;

[0017] According to the prediction equation and the correction equation Design a Kalman filter; where, is the prior estimation error covariance matrix at time k, P k is the a posteriori estimation error covariance matrix, R and Q represent the observation noise covariance matrices of the three-axis fluxgate sensor and Quspin sensor, respectively, and I is the identity matrix;

[0018] Update the Kalman gain matrix in real time to filter and fuse the data of the three-axis fluxgate sensor and Quspin sensor to obtain the optimal magnetic field estimate

[0019] Preferably, the method of inputting the optimal estimated value of the magnetic field and the control amount into the extended state observer to realize the online estimation of the state and the total disturbance includes:

[0020] Mathematical model of magnetic shielding cabin based on frequency sweeper The state space equation of the system is obtained Where: G(s) is the transfer function of the magnetic shielding cabin system, K is the system gain, T is the time constant; F is the state transfer matrix, G is the control input matrix, C is the output matrix, D is the input-output matrix, x is the state variable of the system, x is the rate of change of the state variable of the system, u is the control input, f is the unknown disturbance, and y is the output of the system;

[0021] Based on the state space equation of the system Building a Second-Order Extended State Observer Where: z is the state variable of the observer, is the rate of change of the observer state variable, L is the observer error feedback gain matrix;

[0022] The optimal magnetic field estimate obtained by fusing Kalman data As system output The control variable u of the external controller is input into the extended state observer to obtain the online reconstruction of the system residual magnetic field and the total disturbance.

[0023] Preferably, the method for performing feedback control on the total disturbance estimate related to the linear state error comprises:

[0024] Design of linear state error feedback control law based on the characteristics of magnetic shielding cabin system Among them, k p is the controller gain parameter, u 0 is the initial control input, is the disturbance estimate, b 0 is the compensation coefficient;

[0025] The extended state observer and the state error feedback control law are combined to form a first-order active disturbance rejection control system.

[0026] The output control voltage signal of the system is converted into a compensation current through a voltage-controlled current source;

[0027] The compensation current flows into the Helmholtz coil to generate a magnetic field signal that cancels out the original interfering magnetic field.

[0028] The present invention also provides an external magnetic field self-disturbance rejection control system based on Kalman filtering, comprising: a building module, a fusion module, an estimation module and a feedback control module;

[0029] The building module is used to build an external coil control system to compensate for the residual magnetic field in the magnetic shielding cabin measured by the three-axis fluxgate sensor, and reduce the residual magnetism to the normal working range of the Quspin sensor;

[0030] The fusion module is used to turn on the Quspin sensor, filter and fuse the data of the fluxgate sensor and the Quspin sensor through the Kalman filter, and obtain the optimal estimate of the magnetic field;

[0031] The estimation module is used to input the optimal estimation value of the magnetic field and the control amount into the extended state observer, realize the online estimation of the state and the total disturbance, and obtain the total disturbance estimation;

[0032] The feedback control module is used to perform feedback control on the linear state error of the total disturbance estimation, so as to compensate for the residual magnetic field and magnetic field disturbance in the magnetic shielding cabin.

[0033] Preferably, the external coil control system includes a three-axis fluxgate sensor, a signal conditioning circuit, an external controller, a voltage-controlled current source, and an external Helmholtz coil.

[0034] Preferably, the process of filtering and fusing data of the fluxgate sensor and the Quspin sensor through a Kalman filter to obtain the optimal estimate of the magnetic field includes:

[0035] According to the state transfer equation The three-axis fluxgate sensor measurement value is equivalent to the prior estimate obtained by the system state space state equation in the Kalman filter, and the Quspin sensor measurement value is equivalent to the measurement value obtained by the measurement equation in the Kalman filter, thereby establishing the posterior estimate Among them, A and H are equivalent to the observation matrices of the three-axis fluxgate sensor measurement value and the Quspin sensor magnetic field measurement value, respectively, and X k-1 , Z k are equivalent to the magnetic field measurement values ​​of the three-axis fluxgate sensor and the Quspin sensor at time k, B is the control input matrix, U k is the control input, w k 、v k are the observation noise of the magnetic field measurement values ​​of the three-axis fluxgate sensor and the Quspin sensor, respectively, k is the magnetic field value in the magnetic shielding cabin at time k; is the prior estimate, is the a posteriori estimate obtained by data fusion based on the prior estimate and the measured value, K k is the Kalman gain matrix at time k;

[0036] According to the prediction equation and the correction equation Design a Kalman filter; where, is the prior estimation error covariance matrix at time k, P k is the a posteriori estimation error covariance matrix, R and Q represent the observation noise covariance matrices of the three-axis fluxgate sensor and Quspin sensor, respectively, and I is the identity matrix;

[0037] Update the Kalman gain matrix in real time to filter and fuse the data of the three-axis fluxgate sensor and Quspin sensor to obtain the optimal magnetic field estimate

[0038] Preferably, the optimal estimated value of the magnetic field and the control amount are input into the extended state observer to achieve online estimation of the state and the total disturbance, including:

[0039] Mathematical model of magnetic shielding cabin based on frequency sweeper The state space equation of the system is obtained Where: G(s) is the transfer function of the magnetic shielding cabin system, K is the system gain, T is the time constant; F is the state transfer matrix, G is the control input matrix, C is the output matrix, D is the input and output matrix, x is the state variable of the system, is the rate of change of the system state variables, u is the control input, f is the unknown disturbance, and y is the output of the system;

[0040] Based on the state space equation of the system Building a Second-Order Extended State Observer Where: z is the state variable of the observer, is the rate of change of the observer state variable, L is the observer error feedback gain matrix;

[0041] The optimal magnetic field estimate obtained by fusing Kalman data As system output The control variable u of the external controller is input into the extended state observer to obtain the online reconstruction of the system residual magnetic field and the total disturbance.

[0042] Preferably, the process of performing feedback control on the total disturbance estimate related to the linear state error includes:

[0043] Design of linear state error feedback control law based on the characteristics of magnetic shielding cabin system Among them, k p is the controller gain parameter, u 0 is the initial control input, is the disturbance estimate, b 0 is the compensation coefficient;

[0044] The extended state observer and the state error feedback control law are combined to form a first-order active disturbance rejection control system.

[0045] The output control voltage signal of the system is converted into a compensation current through a voltage-controlled current source;

[0046] The compensation current flows into the Helmholtz coil to generate a magnetic field signal that cancels out the original interfering magnetic field.

[0047] Compared with the prior art, the present invention has the following beneficial effects:

[0048] 1. The present invention introduces a Kalman filter to fuse data between a three-axis fluxgate sensor and a Quspin sensor that have certain complementary characteristics in terms of magnetic field measurement range and sensitivity, thereby improving the control accuracy of the external coil compensation system and reducing the passive shielding cost of the magnetic shielding device;

[0049] 2. The present invention adopts an anti-disturbance control method, introduces an extended state observer to reconstruct the state and total disturbance of the system, and combines the state error feedback control law to perform feedback control on the estimated total disturbance; compared with the traditional PID control method, this method can improve the anti-interference ability of the external coil control system and realize real-time compensation of static residual magnetism and dynamic magnetic field fluctuations inside the magnetic shielding cabin. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] 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 creative labor.

[0051] Figure 1 This is a flow chart of an external magnetic field self-disturbance rejection control method based on Kalman filtering according to an embodiment of the present invention;

[0052] Figure 2 Schematic diagram of a magnetic field auto-disturbance rejection control system based on Kalman filtering according to an embodiment of the present invention;

[0053] Figure 3 The present invention is a control system block diagram of an external magnetic field self-disturbance rejection control method based on Kalman filtering according to an embodiment of the present invention. DETAILED DESCRIPTION

[0054] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0055] 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.

[0056] Embodiment 1

[0057] like Figure 1 As shown, the present invention proposes an external magnetic field self-disturbance rejection control method based on Kalman filtering, which specifically includes the following steps:

[0058] Step 1: Place the three-axis fluxgate sensor inside the magnetic shielding cabin. The external controller generates a control quantity according to the difference between the measured value of the fluxgate sensor and the target value. The output control voltage signal is converted into a control current through a voltage-controlled current source. The current flows into the Helmholtz coil to generate a magnetic field signal that is equal in magnitude and opposite in direction to the original interference magnetic field, thereby compensating for the residual magnetic field and magnetic field disturbance in the magnetic shielding cabin and reducing the residual magnetism to a range within which the Quspin sensor can work normally.

[0059] Step 2, turn on the Quspin sensor, and perform data filtering and fusion on the magnetic field measurement values ​​of the three-axis fluxgate sensor and the Quspin sensor through the Kalman filter to obtain the optimal estimate of the magnetic field;

[0060] Step 3: Input the optimal estimated value of the magnetic field and the control variable into the extended state observer to achieve online estimation of the state and total disturbance;

[0061] Step 4: Calculate the control quantity for the total disturbance estimate according to the linear state error feedback control law, input the control quantity into the voltage-controlled current source to generate an external coil compensation current, and reduce the residual magnetic field and magnetic field fluctuation in the magnetic shielding cabin.

[0062] The external coil control system in step 1 includes a three-axis fluxgate sensor, a Quspin sensor, a signal conditioning circuit, an external controller, a voltage-controlled current source, and an external Helmholtz coil;

[0063] Combined with the magnetic shielding cabin and the external coil control system, it can form Figure 2 The schematic diagram of the magnetic field self-disturbance rejection control system shown in the figure, the three-axis fluxgate sensor and the Quspin sensor are placed inside the magnetic shielding cabin, and the signal conditioning circuit, the external controller, the external voltage-controlled current source, and the external Helmholtz coil are placed outside the magnetic shielding cabin; the three-axis fluxgate sensor is connected to the input end of the external controller through the signal conditioning circuit, the analog output of the Quspin sensor is connected to the input end of the external controller, the output end of the external controller is connected to the external voltage-controlled current source, and the external voltage-controlled current source is connected to the external Helmholtz coil.

[0064] The three-axis fluxgate sensor and Quspin sensor are used to measure the residual magnetic field and magnetic field disturbance; the external controller generates a control signal according to the return value of the magnetic field sensor; the external voltage-controlled current source is used to generate an external compensation current; and the external compensation coil is used to generate a compensation magnetic field.

[0065] Furthermore, the Kalman filter in step 2 performs data filtering and fusion on the magnetic field measurement values ​​of the fluxgate sensor and the Quspin sensor, specifically including:

[0066] According to the state transfer equation The three-axis fluxgate sensor measurement value is equivalent to the prior estimate obtained by the system state space state equation in the Kalman filter, and the Quspin sensor measurement value is equivalent to the measurement value obtained by the measurement equation in the Kalman filter, thereby establishing the posterior estimate Among them, A and H are equivalent to the observation matrices of the three-axis fluxgate sensor measurement value and the Quspin sensor magnetic field measurement value, respectively, and X k-1 , Z k are equivalent to the magnetic field measurement values ​​of the three-axis fluxgate sensor and the Quspin sensor at time k, B is the control input matrix, U k is the control input, w k 、v k are the observation noise of the magnetic field measurement values ​​of the three-axis fluxgate sensor and the Quspin sensor, respectively, k is the magnetic field value in the magnetic shielding cabin at time k; is the prior estimate, is the a posteriori estimate obtained by data fusion based on the prior estimate and the measured value, K k is the Kalman gain matrix at time k;

[0067] The Kalman filter is based on the prediction equation and the correction equation Recursively estimate the system state and continuously optimize the state estimate as new observations arrive. The prediction process mainly uses the state equation to establish a priori estimates of the current state, and timely forward-calculates the values ​​of the current state variables and error covariance estimates to construct a priori estimates for the next time state; the correction process is responsible for feedback, using the measurement equation to establish an improved a posteriori estimate of the current state based on the prior estimates of the prediction process and the current measured variables. Among them, is the prior estimation error covariance matrix at time k, P k is the a posteriori estimation error covariance matrix, R and Q represent the observation noise covariance matrices of the three-axis fluxgate sensor and Quspin sensor respectively, and I is the unit matrix; according to the above Formula, continuously update the Kalman gain matrix K k This enables real-time iteration and optimization of the data fusion values ​​of the three-axis fluxgate sensor and the Quspin sensor, improves the measurement accuracy of the magnetic field sensor, and obtains the optimal magnetic field estimation value.

[0068] Furthermore, in step 3, the optimal estimated value of the magnetic field and the control amount are input into the extended state observer to realize the online estimation of the state and the total disturbance, which specifically includes:

[0069] Mathematical model of magnetic shielding cabin based on frequency sweeper The state space equation of the magnetic shielding cabin system is obtained Based on this, we build a second-order extended state observer Where: G(s) is the transfer function of the magnetic shielding cabin system, K is the system gain, T is the time constant; F is the state transfer matrix, G is the control input matrix, C is the output matrix, D is the input and output matrix, x is the state variable of the system, is the rate of change of the system state variable, u is the control input, f is the unknown disturbance, y is the output of the system; z is the state variable of the observer, is the rate of change of the observer state variable, L is the observer error feedback gain matrix; the optimal magnetic field estimate obtained after Kalman data fusion is As the system output optimal estimate The control quantity u of the external controller is input into the extended state observer, and the real-time estimation of the system residual magnetic field and the total disturbance is realized according to the above second-order extended state observer formula.

[0070] Furthermore, the step 4 calculates the control amount according to the linear state error feedback control law for the total disturbance estimate, and inputs the control amount into the voltage-controlled current source to generate the external coil compensation current, specifically including:

[0071] Design of linear state error feedback control law based on the characteristics of magnetic shielding cabin system Among them, k p is the controller gain parameter, u 0 is the initial control input, is the disturbance estimate, b 0 is the compensation coefficient; a first-order active anti-disturbance control system is constructed by combining a second-order extended state observer and a state error feedback control law. The output control voltage signal of the system is input into a voltage-controlled current source and converted into a compensation current according to a specific voltage-to-current ratio. The compensation current flows into the Helmholtz coil to generate a magnetic field signal that is equal in magnitude and opposite in direction to the interference magnetic field in the environment, thereby reducing the residual magnetic field and magnetic field disturbance in the magnetic shielding cabin.

[0072] Combining the above-mentioned Kalman filter, first-order anti-disturbance controller and magnetic shielding cabin controlled system, a Figure 3The control system block diagram is shown. The controlled object model of the magnetic shielding cabin system is obtained by a frequency sweeper, and the measured values ​​of the fluxgate sensor and the Quspin sensor are input into the Kalman filter as the system output measured values. The two sensor data are fused to obtain the optimal magnetic field estimate. The extended state observer receives the control quantity of the system and the system output quantity estimated by the Kalman filter, and inputs the estimated value of the system output state and the estimated value of the disturbance state into the error feedback controller. The feedback controller compares the output state of the system with the reference state, generates the control quantity that compensates the system state deviation and disturbance, and acts on the magnetic shielding cabin system, thereby realizing effective and real-time compensation for static residual magnetism and dynamic magnetic field interference in the magnetic shielding cabin.

[0073] The present invention proposes an external magnetic field self-disturbance rejection control method based on Kalman filtering. By introducing the Kalman filter, a three-axis fluxgate sensor and a Quspin sensor with certain complementary characteristics in magnetic field measurement range and sensitivity are fused for data, which can reduce the passive shielding cost of a magnetic shielding device and improve the control accuracy of an external coil compensation system. In addition, an self-disturbance rejection control method is adopted, an extended state observer is introduced to reconstruct the state and total disturbance of the system, and the estimated total disturbance is feedback controlled in combination with a state error feedback control law. Compared with the traditional PID control method, the anti-disturbance ability of the external coil control system can be improved, and real-time compensation of static residual magnetism and dynamic magnetic field fluctuations inside a magnetic shielding cabin can be realized.

[0074] Embodiment 2

[0075] The present invention also provides an external magnetic field self-disturbance rejection control system based on Kalman filtering, comprising: a building module, a fusion module, an estimation module and a feedback control module;

[0076] The module is used to build an external coil control system to compensate for the residual magnetic field in the magnetic shielding cabin measured by the three-axis fluxgate sensor, and reduce the residual magnetism to the normal working range of the Quspin sensor;

[0077] The fusion module is used to turn on the Quspin sensor, filter and fuse the data of the fluxgate sensor and the Quspin sensor through the Kalman filter, and obtain the optimal estimate of the magnetic field;

[0078] The estimation module is used to input the optimal estimation value of the magnetic field and the control quantity into the extended state observer, realize the online estimation of the state and the total disturbance, and obtain the total disturbance estimation;

[0079] The feedback control module is used to perform feedback control on the linear state error of the total disturbance estimation, so as to compensate for the residual magnetic field and magnetic field disturbance in the magnetic shielding cabin.

[0080] In this embodiment, the external coil control system includes a three-axis fluxgate sensor, a signal conditioning circuit, an external controller, a voltage-controlled current source, and an external Helmholtz coil.

[0081] In this embodiment, the process of filtering and fusing the data of the fluxgate sensor and the Quspin sensor through the Kalman filter to obtain the optimal estimated value of the magnetic field includes:

[0082] According to the state transfer equation The three-axis fluxgate sensor measurement value is equivalent to the prior estimate obtained by the system state space state equation in the Kalman filter, and the Quspin sensor measurement value is equivalent to the measurement value obtained by the measurement equation in the Kalman filter, thereby establishing the posterior estimate Among them, A and H are equivalent to the observation matrices of the three-axis fluxgate sensor measurement value and the Quspin sensor magnetic field measurement value, respectively, and X k-1 , Z k are equivalent to the magnetic field measurement values ​​of the three-axis fluxgate sensor and the Quspin sensor at time k, B is the control input matrix, U k is the control input, w k 、v k are the observation noise of the magnetic field measurement values ​​of the three-axis fluxgate sensor and the Quspin sensor, respectively, k is the magnetic field value in the magnetic shielding cabin at time k; is the prior estimate, is the a posteriori estimate obtained by data fusion based on the prior estimate and the measured value, K k is the Kalman gain matrix at time k;

[0083] According to the prediction equation and the correction equation Design a Kalman filter; where, is the prior estimation error covariance matrix at time k, P k is the a posteriori estimation error covariance matrix, R and Q represent the observation noise covariance matrices of the three-axis fluxgate sensor and Quspin sensor, respectively, and I is the identity matrix;

[0084] Update the Kalman gain matrix in real time to filter and fuse the data of the three-axis fluxgate sensor and Quspin sensor to obtain the optimal magnetic field estimate

[0085] In this embodiment, the optimal estimated value of the magnetic field and the control variable are input into the extended state observer, and the process of realizing online estimation of the state and the total disturbance includes:

[0086] Mathematical model of magnetic shielding cabin based on frequency sweeper The state space equation of the system is obtained Where: G(s) is the transfer function of the magnetic shielding cabin system, K is the system gain, T is the time constant; F is the state transfer matrix, G is the control input matrix, C is the output matrix, D is the input and output matrix, x is the state variable of the system, is the rate of change of the system state variables, u is the control input, f is the unknown disturbance, and y is the output of the system;

[0087] Based on the state space equation of the system Building a Second-Order Extended State Observer Where: z is the state variable of the observer, is the rate of change of the observer state variable, L is the observer error feedback gain matrix;

[0088] The optimal magnetic field estimate obtained by fusing Kalman data As system output The control variable u of the external controller is input into the extended state observer to obtain the online reconstruction of the system residual magnetic field and the total disturbance.

[0089] In this embodiment, the process of performing feedback control on the linear state error on the total disturbance estimate includes:

[0090] Design of linear state error feedback control law based on the characteristics of magnetic shielding cabin system Among them, k p is the controller gain parameter, u 0 is the initial control input, is the disturbance estimate, b 0 is the compensation coefficient;

[0091] The extended state observer and the state error feedback control law are combined to form a first-order active disturbance rejection control system.

[0092] The output control voltage signal of the system is converted into a compensation current through a voltage-controlled current source;

[0093] The compensation current flows into the Helmholtz coil to generate a magnetic field signal that cancels out the original interfering magnetic field.

[0094] The embodiments described above are only descriptions of the preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. A Kalman filter-based external magnetic field self-disturbance rejection control method, characterized in that: The following steps are involved: Step 1: Build an external coil control system to compensate for the residual magnetic field in the magnetic shielding cabin measured by the three-axis fluxgate sensor, and reduce the residual magnetism to the normal working range of the Quspin sensor; Step 2: Turn on the Quspin sensor, filter and fuse the data of the fluxgate sensor and the Quspin sensor through the Kalman filter, and obtain the optimal estimate of the magnetic field; Step 3: Input the optimal estimated value of the magnetic field and the control quantity into the extended state observer to realize the online estimation of the state and the total disturbance and obtain the total disturbance estimation; Step 4: Feedback control of the linear state error is performed on the total disturbance estimate to compensate for the residual magnetic field and magnetic field disturbance in the magnetic shielding cabin.

2. The external magnetic field auto-disturbance rejection control method based on Kalman filtering according to claim 1 is characterized in that: The external coil control system includes a three-axis fluxgate sensor, a signal conditioning circuit, an external controller, a voltage-controlled current source, and an external Helmholtz coil.

3. The external magnetic field auto-disturbance rejection control method based on Kalman filtering according to claim 1 is characterized in that: The method of filtering and fusing the fluxgate sensor and Quspin sensor data through the Kalman filter to obtain the optimal estimate of the magnetic field includes: According to the state transfer equation The three-axis fluxgate sensor measurement value is equivalent to the prior estimate obtained by the system state space state equation in the Kalman filter, and the Quspin sensor measurement value is equivalent to the measurement value obtained by the measurement equation in the Kalman filter, thereby establishing the posterior estimate Among them, A and H are equivalent to the observation matrices of the three-axis fluxgate sensor measurement value and the Quspin sensor magnetic field measurement value, respectively, and X k-1 , Z k are equivalent to the magnetic field measurement values ​​of the three-axis fluxgate sensor and the Quspin sensor at time k, B is the control input matrix, U k is the control input, w k 、v k are the observation noise of the magnetic field measurement values ​​of the three-axis fluxgate sensor and the Quspin sensor, respectively, k is the magnetic field value in the magnetic shielding cabin at time k; is the prior estimate, is the a posteriori estimate obtained by data fusion based on the prior estimate and the measured value, K k is the Kalman gain matrix at time k; According to the prediction equation and the correction equation Design a Kalman filter; where, is the prior estimation error covariance matrix at time k, P k is the a posteriori estimation error covariance matrix, R and Q represent the observation noise covariance matrices of the three-axis fluxgate sensor and Quspin sensor, respectively, and I is the identity matrix; Update the Kalman gain matrix in real time to filter and fuse the data of the three-axis fluxgate sensor and Quspin sensor to obtain the optimal magnetic field estimate 4. The external magnetic field auto-disturbance rejection control method based on Kalman filtering according to claim 1 is characterized in that: The method of inputting the optimal estimated value of the magnetic field and the control quantity into the extended state observer to realize the online estimation of the state and the total disturbance includes: Mathematical model of magnetic shielding cabin based on frequency sweeper The state space equation of the system is obtained Where: G(s) is the transfer function of the magnetic shielding cabin system, K is the system gain, T is the time constant; F is the state transfer matrix, G is the control input matrix, C is the output matrix, D is the input and output matrix, x is the state variable of the system, is the rate of change of the system state variables, u is the control input, f is the unknown disturbance, and y is the output of the system; Based on the state space equation of the system Building a Second-Order Extended State Observer Where: z is the state variable of the observer, is the rate of change of the observer state variable, L is the observer error feedback gain matrix; The optimal magnetic field estimate obtained by fusing Kalman data As system output The control variable u of the external controller is input into the extended state observer to obtain the online reconstruction of the system residual magnetic field and the total disturbance.

5. The external magnetic field auto-disturbance rejection control method based on Kalman filtering according to claim 1, characterized in that: The method of performing feedback control on the total disturbance estimator with respect to the linear state error includes: Design of linear state error feedback control law based on the characteristics of magnetic shielding cabin system Among them, k p is the controller gain parameter, u0 is the initial control input, is the disturbance estimate, b0 is the compensation coefficient; The extended state observer and the state error feedback control law are combined to form a first-order active disturbance rejection control system. The output control voltage signal of the system is converted into a compensation current through a voltage-controlled current source; The compensation current flows into the Helmholtz coil to generate a magnetic field signal that cancels out the original interfering magnetic field.

6. An external magnetic field self-disturbance rejection control system based on Kalman filtering, characterized in that: include: Building module, fusion module, estimation module and feedback control module; The building module is used to build an external coil control system to compensate for the residual magnetic field in the magnetic shielding cabin measured by the three-axis fluxgate sensor, and reduce the residual magnetism to the normal working range of the Quspin sensor; The fusion module is used to turn on the Quspin sensor, filter and fuse the data of the fluxgate sensor and the Quspin sensor through the Kalman filter, and obtain the optimal estimate of the magnetic field; The estimation module is used to input the optimal estimation value of the magnetic field and the control amount into the extended state observer, realize the online estimation of the state and the total disturbance, and obtain the total disturbance estimation; The feedback control module is used to perform feedback control on the linear state error of the total disturbance estimation, so as to compensate for the residual magnetic field and magnetic field disturbance in the magnetic shielding cabin.

7. The Kalman filter-based external magnetic field auto-disturbance rejection control system according to claim 6, characterized in that: The external coil control system includes a three-axis fluxgate sensor, a signal conditioning circuit, an external controller, a voltage-controlled current source, and an external Helmholtz coil.

8. The Kalman filter-based external magnetic field auto-disturbance rejection control system according to claim 6, characterized in that: The process of filtering and fusing the fluxgate sensor and Quspin sensor data through the Kalman filter to obtain the optimal estimate of the magnetic field includes: According to the state transfer equation The three-axis fluxgate sensor measurement value is equivalent to the prior estimate obtained by the system state space state equation in the Kalman filter, and the Quspin sensor measurement value is equivalent to the measurement value obtained by the measurement equation in the Kalman filter, thereby establishing the posterior estimate Among them, A and H are equivalent to the observation matrices of the three-axis fluxgate sensor measurement value and the Quspin sensor magnetic field measurement value, respectively, and X k-1 , Z k are equivalent to the magnetic field measurement values ​​of the three-axis fluxgate sensor and the Quspin sensor at time k, B is the control input matrix, U k is the control input, w k 、v k are the observation noise of the magnetic field measurement values ​​of the three-axis fluxgate sensor and the Quspin sensor, respectively, k is the magnetic field value in the magnetic shielding cabin at time k; is the prior estimate, is the a posteriori estimate obtained by data fusion based on the prior estimate and the measured value, K k is the Kalman gain matrix at time k; According to the prediction equation and the correction equation Design a Kalman filter; where, is the prior estimation error covariance matrix at time k, P k is the a posteriori estimation error covariance matrix, R and Q represent the observation noise covariance matrices of the three-axis fluxgate sensor and Quspin sensor, respectively, and I is the identity matrix; Update the Kalman gain matrix in real time to filter and fuse the data of the three-axis fluxgate sensor and Quspin sensor to obtain the optimal magnetic field estimate 9. The Kalman filter-based external magnetic field auto-disturbance rejection control system according to claim 6, characterized in that: The optimal estimated value of the magnetic field and the control quantity are input into the extended state observer to realize the online estimation process of the state and the total disturbance. Mathematical model of magnetic shielding cabin based on frequency sweeper The state space equation of the system is obtained Where: G(s) is the transfer function of the magnetic shielding cabin system, K is the system gain, T is the time constant; F is the state transfer matrix, G is the control input matrix, C is the output matrix, D is the input and output matrix, x is the state variable of the system, is the rate of change of the system state variables, u is the control input, f is the unknown disturbance, and y is the output of the system; Based on the state space equation of the system Building a Second-Order Extended State Observer Where: z is the state variable of the observer, is the rate of change of the observer state variable, L is the observer error feedback gain matrix; The optimal magnetic field estimate obtained by fusing Kalman data As system output The control variable u of the external controller is input into the extended state observer to obtain the online reconstruction of the system residual magnetic field and the total disturbance.

10. The Kalman filter-based external magnetic field auto-disturbance rejection control system according to claim 6, characterized in that: The process of feedback control of the total disturbance estimator with respect to the linear state error includes: Design of linear state error feedback control law based on the characteristics of magnetic shielding cabin system Among them, k p is the controller gain parameter, u0 is the initial control input, is the disturbance estimate, b0 is the compensation coefficient; The extended state observer and the state error feedback control law are combined to form a first-order active disturbance rejection control system. The output control voltage signal of the system is converted into a compensation current through a voltage-controlled current source; The compensation current flows into the Helmholtz coil to generate a magnetic field signal that cancels out the original interfering magnetic field.

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