Permanent magnet motor robust prediction position control method with low observation noise characteristic

Abstract

The invention discloses a permanent magnet motor robust prediction position control method with a low observation noise characteristic, and belongs to the technical field of permanent magnet motors. The method comprises the following steps: S1, constructing a total disturbance mathematical model of the surface-mounted permanent magnet motor position control system under the action of considering parameters and load disturbance, and on the basis, constructing a position control system prediction model considering disturbance and linear constraint conditions; s2, constructing a low-noise characteristic observer, and observing disturbance and disturbance differential information; s3, performing constraint compensation on a prediction model and a position control system based on disturbance observation; s4, constructing a constrained convex optimization model of the robust position control system; and S5, solving a constraint-containing optimization model based on a Hildreth method. The method is easy to implement, the robustness of the prediction position control strategy under the action of parameter and load disturbance can be enhanced, and compared with an algorithm model using motor nominal parameters, the steady-state positioning precision of the prediction position control strategy under the action of parameter disturbance and load disturbance can be improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of permanent magnet motors, and in particular relates to a robust predictive position control method for a permanent magnet motor with low observation noise characteristics. Background Art

[0002] Position servo control is a typical application form of permanent magnet motors in high-end equipment manufacturing scenarios such as CNC machine tools, robotic arms, and simulation turntables, aiming to achieve fast position following or high-precision positioning. The traditional control strategy is a cascade three-closed-loop structure control scheme based on magnetic field oriented control, which can achieve decoupled independent control of current, speed, and position. However, cascade multi-loop control requires that the inner loop bandwidth is much faster than the outer loop, which limits the dynamic response of position servo control; linear control schemes that rely on frequency domain analysis are difficult to consider the impact of factors such as dead zone and nonlinearity on the control system, and mismatch between electrical parameters and mechanical parameters will lead to deterioration of control performance. The position servo system is a typical high-order multivariable system and a multi-constrained control system. Model predictive control has good adaptability to multivariable control and system constraints, so it has good application prospects in position servo control. It can be used to realize integrated control of position, speed, and current, eliminate control loops, and improve position dynamic response, that is, predictive position control strategy. Current research focuses on the application and implementation of model predictive control strategy in position servo control. However, model predictive control relies on accurate mathematical models, so it is insufficient in terms of parameter immunity. Currently, there is no research specifically targeting robustness improvement solutions for predictive position control.

[0003] At present, there are two main technical routes to improve the robustness of model predictive control strategies: one is a data-based solution, which typically includes data-driven and neural network-based solutions. The control process can be separated from the model, but this type of solution relies on historical data or training data, is more complex to implement, and has poor versatility; the other type of solution is a model-based solution that improves control robustness by improving the accuracy of the model. The solution based on various observers is a typical method to improve model accuracy. It is relatively simple to implement, but this solution faces the contradiction between observation bandwidth and observation noise. In addition, current research on the robustness of predictive control generally does not consider the robustness of constraints, so it is not easy to improve the impact of parameter disturbances on the position dynamic process. Summary of the invention

[0004] The purpose of the present invention is to solve the parameter robustness problem faced by model predictive control in position servo applications, fully consider the parameter interference resistance of the position control system constraints, and improve the noise problem faced when the observer is applied to model predictive control, thereby proposing a permanent magnet motor robust predictive position control method with low observation noise characteristics.

[0005] In the present invention, the low observation noise refers to having better noise suppression characteristics under the same observer bandwidth.

[0006] To achieve the above object, the technical solution adopted by the present invention is:

[0007] A method for robust predictive position control of a permanent magnet motor with low observation noise characteristics, the method comprising the following steps:

[0008] S1: Construct a mathematical model of the total disturbance of the surface-mounted permanent magnet motor position control system under the influence of parameter and load disturbance. On this basis, construct a prediction model of the position control system considering disturbance and linear constraints of the position system.

[0009] S2: Construct a low-noise characteristic observer to observe disturbances and disturbance differential information;

[0010] S3: Prediction model and constraint compensation of position control system based on disturbance observation;

[0011] S4: Construct a constrained convex optimization model for a robust position system;

[0012] S5: Solving constrained optimization models based on the Hildreth method.

[0013] Furthermore, in step S1, the total disturbance mathematical model of the surface-mounted permanent magnet motor position system under the effect of parameter and load disturbance is constructed, and on this basis, a position control system prediction model and linear constraints of the position control system considering disturbance are constructed; the following steps are included:

[0014] S11: Construct a total disturbance mathematical model of the surface-mounted permanent magnet motor position control system considering parameter and load disturbance effects; as shown in the following formula (2):

[0015]

[0016] Among them, i d ,i q ,u d ,u q are d-axis current, q-axis current, d-axis voltage and q-axis voltage respectively, L s is the motor inductance, λ f is the flux linkage, θ is the motor electrical angle, ω e is the electrical angular velocity, J and p are the moment of inertia and pole pair number respectively, and the disturbance f d ,f q ,f ω They are the total disturbance of d-axis current, q-axis current and speed caused by parameter changes. The disturbance at time k is expressed as f d,k ,f q,k ,f ω,k, whose expression is:

[0017]

[0018] Where: R s , B are the motor resistance and friction coefficient, ΔR s , ΔL s , Δλ f , ΔJ and ΔB are the parameter changes of the corresponding variables respectively; i d,k ,i q,k ,u d,k ,u q,k are the d-axis current, q-axis current, d-axis voltage and q-axis voltage at time k respectively; ω e,k ,θ k , T l They are the electrical angular velocity at time k, the electrical angle at time k and the load torque;

[0019] S12: Construct a prediction model for the position control system considering disturbances;

[0020] The forward Euler discretization method shown in the following equation (4) is used to discretize equation (2):

[0021]

[0022] Among them: x represents the state quantity, T s is the discrete time step, k represents the kth moment;

[0023] The prediction model of the position control system is expressed as:

[0024]

[0025] Where: x k ,y k is the state variable matrix and output variable matrix, A m , B m , C m is the position control system matrix, ε k is the perturbation matrix, u k is the control voltage at time k, and the state quantity with subscript k represents the state value at time k. The definition of related quantities is as follows:

[0026]

[0027] S13: Construct linear constraints for position control systems considering parameter disturbances;

[0028] The position control system needs to consider the dq axis current constraint I dmax ,I qmax , speed constraint ω maxand dq axis voltage constraint u dmax ,u qmax , expressed as:

[0029]

[0030] The truncated Taylor expansion method shown in formula (7) is used to take the order N j =1 and N j =2 Discretize the current and speed system in equation (2):

[0031]

[0032] Combined with equation (6), the current and speed constraints are derived as follows:

[0033]

[0034] Speed ​​Constraint

[0035] Where: is the first-order differential of the speed disturbance;

[0036] The voltage constraint uses a rectangular approximation scheme:

[0037]

[0038] Where: ε is the rectangular coefficient, 0≤ε≤1, V dc is the bus voltage.

[0039] Furthermore, in step S2, the low noise characteristic observer is constructed to observe the disturbance and the disturbance differential information; the specific construction process is:

[0040] The current and speed dynamic model in equation (2) is regarded as the disturbance model as shown below:

[0041]

[0042] Where: x1 is the state variable, representing i in formula (2) d ,i q ,ω e ; x2 is the state variable, representing the total disturbance f in equation (2) d ,f q ,f ω ; u is the control input, representing u in equation (2) d ,u q ,i q ; b0 is the gain before input, f (1) is the first-order differential of the disturbance;

[0043] Based on the disturbance model (11), the low-noise observer is designed as follows:

[0044]

[0045] Where: l1, l2 are observer gains, m3 is the gain coefficient, which is used to adjust the observation effect of disturbance and disturbance observation information, τ is the first-order low-pass filter coefficient, which is used to adjust the noise suppression performance; x e Input error The filter value of .

[0046] Furthermore, in step S3, the prediction model based on disturbance observation and position control system constraint compensation are specifically:

[0047] The designed observer formula (12) is used to apply the d-axis current equation, q-axis current equation, and speed dynamic equation in formula (2) respectively;

[0048] The corresponding disturbance observers are designed as follows:

[0049]

[0050] Where: the superscript ^ represents the estimated value of the corresponding variable, x ed 、x eq 、x eω are the error filtering values ​​between the actual and observed values ​​of the d-axis current, q-axis current and speed, respectively, d , τ q , τ ω is the filter coefficient of the corresponding variable, are the first-order differentials of the total disturbance of d-axis current, q-axis current and speed respectively, m d3 、m q3 、m ω3 Adjust the gain for the disturbed observations, l d1 , l d2 , l q1 , l q2 , l ω1 , l ω2 They are observer gains respectively, and their values ​​are:

[0051]

[0052] Where: d ,ω q and ω s They are the d-axis current observer bandwidth, q-axis current observer bandwidth and speed observer bandwidth respectively;

[0053] Using the obtained disturbance information f d ,f q ,fω and perturbation differential information Observed value It is used to compensate the prediction model (5) and the position control system constraints (8) and (9).

[0054] Furthermore, in step S4, the robust position control system includes a constrained convex optimization model; the specific construction process is:

[0055] Consider the prediction time domain N p When , the value function of position control is designed as follows:

[0056]

[0057] stx j+1 =A m x j +B m u j +ε j

[0058] Where: y r,j+1 where j=k,k+1,…,k+N p -1,y r,j+1 is the target reference value, that is: Q and R are the corresponding weight coefficient matrices, respectively: k id ,k θ ,k u are the d-axis current weight coefficient, position weight coefficient and control weight coefficient respectively; in order to smooth the control input, a smoothing factor a∈(0,1) is introduced, then the target reference value at different times It is expressed as:

[0059]

[0060] Where: i dref,j+1 ,θ ref,j+1 where j=k,k+1,…,k+N p -1,i dref,j+1 ,θ ref,j+1 is the given value of d-axis current and position at different times. For the sake of simplicity, in the whole prediction time domain N p Take the same value within;

[0061] definition: For the sake of simplicity of calculation, assume that: Take time k as the initial state and output y at different times j+1 =C m x j+1 , j=k,k+1,…,k+N p-1, determined by the following formula:

[0062] Y=Yx k +Γυ+Ωε k (15)

[0063] in:

[0064]

[0065] Where I is the identity matrix.

[0066] Based on equations (8), (9), (10) and (13), a constrained convex optimization model for the robust position control system is constructed. The solution of this model is regarded as a linear quadratic programming problem. In order to simplify the solution, only the constraints at time k are considered, and the constrained convex optimization model is obtained as follows:

[0067]

[0068] Where: and is the estimated value of the disturbance at time k.

[0069] Furthermore, in step S5, the constrained optimization model based on the Hildreth method is solved; specifically:

[0070] Based on the Hildreth method, the optimization model shown in equation (16) is solved. First, when the position control system constraints are not considered, the unconstrained solution υ of the position control system is n Solve according to the following formula:

[0071]

[0072] Unconstrained solution n It is expressed as:

[0073]

[0074] Where: symbol represents the Kronecker product, Y r is the reference value matrix, expressed as: definition And F = -Γ T Θ(Y r -Υx k -Ωε k ), then the optimal solution containing constraints is c (k) is expressed as:

[0075] υ c =-E -1 (F+M T λ un) (19)

[0076] Where: un is the Lagrange multiplier;

[0077] Based on formula (19), the optimal solution v at time k is used c,k Applied to position control systems to achieve robust predictive position control,

[0078]

[0079] Where: c,k is the control voltage when considering the constraints of the position control system, u cd,k for u c,k The d-axis component, u cq,k for u c,k The q-axis component of .

[0080] The beneficial effects of the present invention over the prior art are as follows: the present invention provides a disturbance compensation scheme for predictive position control strategy with low observation noise, which has better suppression performance for high-frequency noise under the same bandwidth. The feature of the present invention is that a robust predictive position control scheme is realized, and the compensation model simultaneously considers the prediction model and constraint conditions of the position control system, thereby having a certain compensation effect on both dynamic performance and steady-state performance. The integrated scheme eliminates the series loop, and the position, speed and current of the motor are controlled by one controller, which has better dynamic performance.

[0081] The method of the present invention is simple to implement, and can enhance the robustness of the predicted position control strategy under parameter and load disturbances. Compared with the algorithm model using the nominal parameters of the motor (without considering the parameters when the parameters change), the steady-state positioning accuracy of the predicted position control strategy under parameter disturbances and load disturbances can be improved. The method of the present invention is of great significance to the practical application of the predicted position control strategy in the field of high-end equipment manufacturing. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] Figure 1 It is an overall control block diagram of the robust predictive position control method of a permanent magnet motor with low observation noise characteristics of the present invention;

[0083] Figure 2 This is a schematic diagram of the low noise observer structure. DETAILED DESCRIPTION

[0084] Specific implementation method 1: This implementation method discloses a method for robust predictive position control of a permanent magnet motor with low observation noise characteristics, the method comprising the following steps:

[0085] S1: Construct a mathematical model of the total disturbance of the surface-mounted permanent magnet motor position control system under the influence of parameter and load disturbance. On this basis, construct a position control system prediction model and linear constraints of the position control system considering disturbance.

[0086] S2: Construct a low-noise characteristic observer (to improve the noise suppression performance of the observer), observe the disturbance and the differential information of the disturbance (observe the disturbance and the differential term of the speed and current dynamic equation);

[0087] S3: Prediction model based on disturbance observation and position control system constraint compensation (compensate the position control system prediction model based on real-time observed disturbances, and modify the position control system constraints using disturbance and disturbance differential information);

[0088] S4: Construct a constrained convex optimization model for a robust position control system (use the modified prediction model and constraints to construct a constrained convex optimization model for a position control system, including the value function and constraints of integrated control);

[0089] S5: Solving the constrained optimization model based on the Hildreth method (using the Hildreth method to solve the modified constrained convex optimization model online, and the output voltage is used for the position control system).

[0090] Furthermore, in step S1, the total disturbance mathematical model of the surface-mounted permanent magnet motor position control system under the effect of parameter and load disturbance is constructed, and on this basis, a position control system prediction model and linear constraints of the position control system considering disturbance are constructed; the following steps are included:

[0091] S11: Construct a total disturbance mathematical model of the surface-mounted permanent magnet motor position control system considering parameter and load disturbance effects; as shown in the following formula (2):

[0092]

[0093] Among them, i d ,i q ,u d ,u q are d-axis current, q-axis current, d-axis voltage and q-axis voltage respectively, L s is the motor inductance, λ f is the flux linkage, θ is the motor electrical angle, ω e is the electrical angular velocity, J and p are the moment of inertia and pole pair number respectively, and the disturbance f d ,f q ,f ω They are the total disturbance of d-axis current, q-axis current and speed caused by parameter changes. The disturbance at time k is expressed as f d,k ,fq,k ,f ω,k , whose expression is:

[0094]

[0095] Where: R s , B are the motor resistance and friction coefficient, ΔR s , ΔL s , Δλ f , ΔJ and ΔB are the parameter changes of the corresponding variables respectively; i d,k ,i q,k ,u d,k ,u q,k are the d-axis current, q-axis current, d-axis voltage and q-axis voltage at time k respectively; ω e,k ,θ k , T l They are the electrical angular velocity at time k, the electrical angle at time k and the load torque;

[0096] S12: Construct a prediction model for the position control system considering disturbances;

[0097] The forward Euler discretization method shown in the following equation (4) is used to discretize equation (2):

[0098]

[0099] Among them: x represents the state quantity, T s is the discrete time step, k represents the kth moment;

[0100] The prediction model of the position control system is expressed as:

[0101]

[0102] Where: x k ,y k is the state variable matrix and output variable matrix, A m , B m , C m is the position control system matrix, ε k is the perturbation matrix, u k is the control voltage at time k, and the state quantity with subscript k represents the state value at time k. The definition of related quantities is as follows:

[0103]

[0104] S13: Construct linear constraints for position control systems considering parameter disturbances;

[0105] The position control system needs to consider the dq axis current constraint I dmax ,I qmax, speed constraint ω max and dq axis voltage constraint u dmax ,u qmax , expressed as:

[0106]

[0107] The truncated Taylor expansion method shown in formula (7) is used to take the order N j =1 and N j =2 Discretize the current and speed system in equation (2):

[0108]

[0109] Combined with equation (6), the current and speed constraints are derived as follows:

[0110]

[0111] Where: is the first-order differential of the speed disturbance;

[0112] The voltage constraint uses a rectangular approximation scheme:

[0113]

[0114] Where: ε is the rectangular coefficient, 0≤ε≤1, V dc is the bus voltage.

[0115] Furthermore, in step S2, the low noise characteristic observer is constructed to observe the disturbance and the disturbance differential information; the specific construction process is:

[0116] The current and speed dynamic model in equation (2) is regarded as the disturbance model as shown below:

[0117]

[0118] Where: x1 is the state variable, representing i in formula (2) d ,i q ,ω e ; x2 is the state variable, representing the total disturbance f in equation (2) d ,f q ,f ω ; u is the control input, representing u in equation (2) d ,u q ,i q ; b0 is the gain before input, f (1) is the first-order differential of the disturbance;

[0119] Based on the disturbance model (11), the low-noise observer is designed as follows:

[0120]

[0121] Where: l1, l2 are observer gains, m3 is the gain coefficient, which is used to adjust the observation effect of disturbance and disturbance observation information, τ is the first-order low-pass filter coefficient, which is used to adjust the noise suppression performance; x e Input error The filter value of .

[0122] Furthermore, in step S3, the prediction model based on disturbance observation and position control system constraint compensation are specifically:

[0123] The designed observer formula (12) is used to apply the d-axis current equation, q-axis current equation, and speed dynamic equation in formula (2) respectively;

[0124] The corresponding disturbance observers are designed as follows:

[0125]

[0126] Where: the superscript ^ represents the estimated value of the corresponding variable, x ed 、x eq 、x eω are the error filtering values ​​between the actual and observed values ​​of the d-axis current, q-axis current and speed, respectively, d , τ q , τ ω is the filter coefficient of the corresponding variable, are the first-order differentials of the total disturbance of d-axis current, q-axis current and speed respectively, m d3 、m q3 、m ω3 Adjust the gain for the disturbed observations, l d1 , l d2 , l q1 , l q2 , l ω1 , l ω2 They are observer gains respectively, and their values ​​are:

[0127]

[0128] Where: d ,ω q and ω s They are the d-axis current observer bandwidth, q-axis current observer bandwidth and speed observer bandwidth respectively;

[0129] Using the obtained disturbance information f d ,f q ,f ω and perturbation differential information Observed value It is used to compensate the prediction model (5) and the position control system constraints (8) and (9).

[0130] Furthermore, in step S4, the robust position control system includes a constrained convex optimization model; the specific construction process is:

[0131] Consider the prediction time domain N p When , the value function of position control is designed as follows:

[0132]

[0133] Where: y r,j+1 where j=k,k+1,…,k+N p -1,y r,j+1 is the target reference value, that is: Q and R are the corresponding weight coefficient matrices, respectively: k id ,k θ ,k u are the d-axis current weight coefficient, position weight coefficient and control weight coefficient respectively; in order to smooth the control input, a smoothing factor a∈(0,1) is introduced, then the target reference value at different times It is expressed as:

[0134]

[0135] Where: i dref,j+1 ,θ ref,j+1 where j=k,k+1,…,k+N p -1,i dref,j+1 ,θ ref,j+1 is the given value of d-axis current and position at different times. For the sake of simplicity, in the whole prediction time domain N p Take the same value within;

[0136] definition: For the sake of simplicity of calculation, assume that: Take time k as the initial state and output y at different times j+1 =C m x j+1 , j=k,k+1,…,k+N p -1, determined by the following formula:

[0137] Y=Yx k +Γυ+Ωε k (15)

[0138] in:

[0139]

[0140] Where I is the identity matrix.

[0141] Based on equations (8), (9), (10) and (13), a constrained convex optimization model for the robust position control system is constructed. The solution of this model is regarded as a linear quadratic programming problem. In order to simplify the solution, only the constraints at time k are considered, and the constrained convex optimization model is obtained as follows:

[0142]

[0143]

[0144] Where: and is the estimated value of the disturbance at time k.

[0145] Furthermore, in step S5, the constrained optimization model based on the Hildreth method is solved; specifically:

[0146] Based on the Hildreth method, the optimization model shown in equation (16) is solved. First, when the position control system constraints are not considered, the unconstrained solution υ of the position control system is n Solve according to the following formula:

[0147]

[0148] Unconstrained solution n It is expressed as:

[0149]

[0150] Where: symbol represents the Kronecker product, Y r is the reference value matrix, expressed as: definition And F = -Γ T Θ(Y r -Υx k -Ωε k ), then the optimal solution containing constraints is c (k) is expressed as:

[0151] υ c =-E -1 (F+M T λ un )(19)

[0152] Where: un is the Lagrange multiplier;

[0153] Based on formula (19), the optimal solution v at time k is used c,kApplied to position control systems to achieve robust predictive position control,

[0154]

[0155] Where: c,k is the control voltage when considering the constraints of the position control system, u cd,k for u c,k The d-axis component, u cq,k for u c,k The q-axis component of .

[0156] Figure 1 illustrate: Figure 1 The proposed overall control block diagram is an integrated control structure of current, speed and position, eliminating the multi-loop control structure in the traditional control scheme. The current and speed measurement information is used as the input of the prediction model, position control system constraints and disturbance observer. The low-noise disturbance observer is used to observe the model disturbance information caused by parameter mismatch and load change. The observed disturbance is used to compensate and modify the prediction model and constraints of the position control system. The modified prediction model and constraints constitute a constrained convex optimization model, which is solved by the Hildreth method to obtain the constrained optimal solution υ c,k , and then modulated and output to the inverter through the space vector pulse width modulation algorithm (SVPWM), ensuring excellent positioning performance even when there is parameter mismatch or load disturbance.

[0157] Figure 2 illustrate: Figure 2 The low-noise observer structure designed for predictive position control strategy is applicable to first-order dynamic models such as current and speed, namely:

[0158]

[0159] The structure of the observer can be considered to be composed of a third-order generalized proportional integral observer embedded in a first-order low-pass filter with a time constant of τ. The filter can be used to adjust the noise suppression performance of the observer, and m3 is used to adjust the disturbance and disturbance differential information observation value. By embedding the filter and adjusting the filter time constant, the noise suppression characteristics of the observer in the high frequency band can be improved. At the same time, the third-order generalized proportional integral observer can be used to simultaneously observe the disturbance term x2 and the disturbance differential term f in the first-order position control system. (1) (correspond Figure 2 middle ).

[0160] The above are only preferred specific implementation modes of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical solutions and inventive concepts of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for robust predictive position control of a permanent magnet motor with low observation noise characteristics, characterized in that: The method comprises the following steps: S1: Construct a mathematical model of the total disturbance of the surface-mounted permanent magnet motor position control system under the influence of parameter and load disturbance. On this basis, construct a position control system prediction model and linear constraints of the position control system considering disturbance. S2: Construct a low-noise characteristic observer to observe disturbances and disturbance differential information; S3: Prediction model and constraint compensation of position control system based on disturbance observation; S4: Construct a constrained convex optimization model for a robust position control system; S5: Solving constrained optimization models based on the Hildreth method.

2. The method for robust predictive position control of a permanent magnet motor with low observation noise characteristics according to claim 1, characterized in that: In step S1, a total disturbance mathematical model of the surface-mounted permanent magnet motor position control system under the influence of parameter and load disturbance is constructed, and on this basis, a position control system prediction model and linear constraints of the position control system under the influence of disturbance are constructed; The following steps are involved: S11: Construct a total disturbance mathematical model of the surface-mounted permanent magnet motor position control system considering parameter and load disturbance effects; as shown in the following formula (2): Among them, i d ,i q ,u d ,u q are d-axis current, q-axis current, d-axis voltage and q-axis voltage respectively, L s is the motor inductance, λ f is the flux linkage, θ is the motor electrical angle, ω e is the electrical angular velocity, J and p are the moment of inertia and pole pair number respectively, and the disturbance f d ,f q ,f ω They are the total disturbance of d-axis current, q-axis current and speed caused by parameter changes. The disturbance at time k is expressed as f d,k ,f q,k ,f ω,k , whose expression is: Where: R s , B are the motor resistance and friction coefficient, ΔR s , ΔL s , Δλ f , ΔJ and ΔB are the parameter changes of the corresponding variables respectively; i d,k ,i q,k ,u d,k ,u q,k are the d-axis current, q-axis current, d-axis voltage and q-axis voltage at time k respectively; ω e,k ,θ k , T l are the electrical angular velocity at time k, the electrical angle at time k and the load torque; S12: Construct a prediction model for the position control system considering disturbances; The forward Euler discretization method shown in the following equation (4) is used to discretize equation (2): Among them: x represents the state quantity, T s is the discrete time step, k represents the kth moment; The prediction model of the position control system is expressed as: Where: x k ,y k is the state variable matrix and output variable matrix, A m , B m , C m is the position control system matrix, ε k is the perturbation matrix, u k is the control voltage at time k, and the state quantity with subscript k represents the state value at time k. The definition of related quantities is as follows: S13: Construct linear constraints for position control systems considering parameter disturbances; The position control system needs to consider the dq axis current constraint I dmax ,I qmax , speed constraint ω max and dq axis voltage constraint u dmax ,u qmax , expressed as: The truncated Taylor expansion method shown in formula (7) is used to take the order N j =1 and N j =2 Discretize the current and speed system in equation (2): Combined with equation (6), the current and speed constraints are derived as follows: Current Constraint Speed ​​Constraint Where: is the first-order differential of the speed disturbance; The voltage constraint uses a rectangular approximation scheme: Where: ε is the rectangular coefficient, 0≤ε≤1, V dc is the bus voltage.

3. The method for robust predictive position control of a permanent magnet motor with low observation noise characteristics according to claim 2, characterized in that: In step S2, the low noise characteristic observer is constructed to observe the disturbance and the disturbance differential information; the specific construction process is: The current and speed dynamic model in equation (2) is regarded as the disturbance model as shown below: Where: x1 is the state variable, representing i in formula (2) d ,i q ,ω e ; x2 is the state variable, representing the total disturbance f in equation (2) d ,f q ,f ω ; u is the control input, representing u in equation (2) d ,u q ,i q ; b0 is the gain before input, f (1) is the first-order differential of the disturbance; Based on the disturbance model (11), the low-noise observer is designed as follows: Where: l1, l2 are observer gains, m3 is the gain coefficient, which is used to adjust the observation effect of disturbance and disturbance observation information, τ is the first-order low-pass filter coefficient, which is used to adjust the noise suppression performance; x e Input error The filter value of .

4. The method for robust predictive position control of a permanent magnet motor with low observation noise characteristics according to claim 3, characterized in that: In step S3, the prediction model based on disturbance observation and position control system constraint compensation are specifically: The designed observer formula (12) is used to apply the d-axis current equation, q-axis current equation, and speed dynamic equation in formula (2) respectively; The corresponding disturbance observers are designed as follows: Where: the superscript ^ represents the estimated value of the corresponding variable, x ed 、x eq 、x eω are the error filtering values ​​between the actual and observed values ​​of the d-axis current, q-axis current and speed, respectively, d , τ q , τ ω is the filter coefficient of the corresponding variable, are the first-order differentials of the total disturbance of d-axis current, q-axis current and speed respectively, m d3 、m q3 、m ω3 Adjust the gain for the disturbed observations, l d1 , l d2 , l q1 , l q2 , l ω1 , l ω2 They are observer gains respectively, and their values ​​are: Where: d ,ω q and ω s They are the d-axis current observer bandwidth, q-axis current observer bandwidth and speed observer bandwidth respectively; Using the obtained disturbance information f d ,f q ,f ω and perturbation differential information Observed value It is used to compensate the prediction model (5) and the position control system constraints (8) and (9).

5. The method for robust predictive position control of a permanent magnet motor with low observation noise characteristics according to claim 4, characterized in that: In step S4, the robust position control system includes a constrained convex optimization model; the specific construction process is: Consider the prediction time domain N p When , the value function of position control is designed as follows: Where: y r,j+1 where j=k,k+1,…,k+N p -1,y r,j+1 is the target reference value, that is: Q and R are the corresponding weight coefficient matrices, respectively: k id ,k θ ,k u are the d-axis current weight coefficient, position weight coefficient and control weight coefficient respectively; in order to smooth the control input, a smoothing factor a∈(0,1) is introduced, then the target reference value at different times It is expressed as: Where: i dref,j+1 ,θ ref,j+1 where j=k,k+1,…,k+N p -1,i dref,j+1 ,θ ref,j+1 is the given value of d-axis current and position at different times. For the sake of simplicity, in the whole prediction time domain N p Take the same value within; definition: For the sake of simplicity of calculation, assume that: Take time k as the initial state and output y at different times j+1 =C m x j+1 , j=k,k+1,…,k+N p -1, determined by the following formula: Y=γx k +Y+Oh k (15) in: Where I is the identity matrix. Based on equations (8), (9), (10) and (13), a constrained convex optimization model for the robust position control system is constructed. The solution of this model is regarded as a linear quadratic programming problem. In order to simplify the solution, only the constraints at time k are considered, and the constrained convex optimization model is obtained as follows: Where: and is the estimated value of the disturbance at time k.

6. The method for robust predictive position control of a permanent magnet motor with low observation noise characteristics according to claim 5, characterized in that: In step S5, the constrained optimization model based on the Hildreth method is solved; specifically: Based on the Hildreth method, the optimization model shown in equation (16) is solved. First, when the position control system constraints are not considered, the unconstrained solution υ of the position control system is n Solve according to the following formula: Unconstrained solution n It is expressed as: Where: symbol represents the Kronecker product, Y r is the reference value matrix, expressed as: definition And F = -Γ T Θ(Y r -γx k -Ωε k ), then the optimal solution containing constraints is c (k) is expressed as: u c =-E -1 (F+M T l un ) (19) Where: un is the Lagrange multiplier; Based on formula (19), the optimal solution v at time k is used c,k Applied to position control systems to achieve robust predictive position control, Where: c,k is the control voltage when considering the constraints of the position control system, u cd,k for u c,k The d-axis component, u cq,k for u c,k The q-axis component of .

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