Permanent magnet synchronous motor proportional integral funnel control method based on error symbol robust integral unknown dynamic estimator

Abstract

The invention discloses a permanent magnet synchronous motor proportional integral funnel control method based on an error symbol robust integral unknown dynamic estimator, and the method comprises the steps: firstly building a mathematical model of a permanent magnet synchronous motor, and converting the mathematical model into a state equation form based on lumped uncertainty; secondly, designing an unknown dynamic estimator to estimate lumped uncertainty based on an error symbol robust integration (RISE) technology, and providing feed-forward compensation; a novel nonlinear function is provided to construct an improved funnel function, a funnel function-based PI error compensation mechanism is developed through an improved funnel variable, and the funnel function-based PI error compensation mechanism is incorporated into a control design to ensure that a steady-state tracking error reaches zero; secondly, designing a PI funnel controller combined with an RISE unknown dynamic estimator, and applying the PI funnel controller to a permanent magnet synchronous motor system; the provided control method not only can effectively compensate unknown system dynamics, but also can improve transient and steady-state performance. And finally, the stability of the closed system under the control strategy is ensured by using the Lyapunov stability theory.

Description

Technical Field

[0001] The invention relates to a permanent magnet synchronous motor proportional integral funnel control method based on an error sign robust integral unknown dynamic estimator, belonging to the technical field of permanent magnet synchronous motor proportional integral funnel control. Background Art

[0002] In recent years, permanent magnet synchronous motors (PMSM) have been widely used in industrial processes such as drones, robots, and electric vehicles due to their high control accuracy. Among the control methods of permanent magnet synchronous motors, the traditional proportional-integral-derivative (PID) control is widely used due to its simple structure and easy implementation. However, with the increasing demand for permanent magnet synchronous motors in industrial production processes, traditional PID control cannot meet the requirements of high-performance control. Therefore, improving the control performance of permanent magnet synchronous motors has become an urgent problem to be solved, and a variety of new control methods have emerged, such as adaptive control, sliding mode control, and disturbance compensation control. At the same time, due to the approximation ability of intelligent control technologies such as neural networks or fuzzy logic control, they have also been used in permanent magnet synchronous motor control. The above control schemes can improve tracking performance.

[0003] However, the existence of unknown disturbances in permanent magnet synchronous motors will reduce the control performance of PMSM. In order to compensate for the influence of unknown disturbances, an effective method is to design a disturbance observer to observe the unknown disturbances and incorporate its estimated value into the controller design to suppress the unknown disturbances. Observers that have been applied in engineering include: linear uncertainty and disturbance estimator, extended state observer, unknown system dynamic estimator and generalized proportional integral observer. Although the above control method based on disturbance observer improves the control performance of the system from different aspects, it has certain limitations because it is sensitive to measurement noise. Therefore, a new robust control scheme called robust integral of the sign of the error (RISE) was developed to suppress bounded disturbances. This control technology has been applied: for example, CN117920444A proposed a solid waste sand crusher material level control method based on robust error sign integral and CN110673473B proposed an error sign integral robust adaptive control method for a two-axis coupled tank gun system.

[0004] Furthermore, while maintaining the stability of the permanent magnet synchronous motor, its excessive transient performance and slow convergence speed will also affect the control performance. Traditional control methods cannot pre-design transient performance and can only ensure its transient performance by adjusting the tracking error. In order to solve this problem, funnel control (FC) uses the barrier function number to quantitatively specify the transient and steady-state performance to ensure that the tracking error is within the predefined boundaries. In addition, as a proportional control, FC depends on the structural characteristics of the control object, making it relatively simple to apply in engineering. So far, this method has been successfully applied in some fields, such as CN114499306B proposed an adaptive funnel dynamic surface control method for permanent magnet synchronous motors with time lag and disturbances and CN110673473B proposed a dynamic pseudo-inverse compensation control method for nonlinear systems with unknown hysteresis.

[0005] There are still some problems with the existing technology. For example, although FC control can improve the control performance, due to continuous disturbances, parameter uncertainty and other reasons, the steady-state error can only converge to a region near zero, but not to zero; the disturbance observer is designed using RISE technology to estimate the dynamic characteristics of the unknown system, but the sign is not continuous on each coordinate axis. Summary of the invention

[0006] The present invention provides a proportional integral funnel control method for a permanent magnet synchronous motor based on an error sign robust integral unknown dynamic estimator. First, a mathematical model of the permanent magnet synchronous motor is established and converted into a state equation form based on lumped uncertainty. Then, an unknown dynamic estimator is designed based on the error sign robust integral (RISE) technology to estimate the lumped uncertainty and provide feedforward compensation. A new type of nonlinear function is proposed to construct an improved funnel function. Then, a PI error compensation mechanism based on the funnel function is developed through the improved funnel variable and incorporated into the control design to ensure that the steady-state tracking error reaches zero. Then, a PI funnel controller combined with the RISE unknown dynamic estimator is designed and applied to a permanent magnet synchronous motor system. The proposed control method can not only effectively compensate for the unknown system dynamics, but also improve the transient and steady-state performance. Finally, the Lyapunov stability theory is used to ensure the stability of the closed system under the control strategy.

[0007] The present invention is achieved through the following technical solutions:

[0008] The present invention proposes a permanent magnet synchronous motor proportional integral funnel control method based on an error sign robust integral unknown dynamic estimator, comprising the following steps:

[0009] S1. Establish a mathematical model of a permanent magnet synchronous motor; transform the mathematical model of the permanent magnet synchronous motor into a state equation form according to the selected state variables; consider lumped uncertainty, rewrite the state equation form, and obtain a state equation based on lumped uncertainty;

[0010] S2, design an error sign based robust integral unknown dynamic estimator to estimate the lumped uncertainty;

[0011] S3, incorporating the improved funnel function into the error transformation function design to design a new error transformation function;

[0012] S4. Based on the selected state variables, according to the new error transfer function and the lumped uncertainty estimate, a proportional-integral funnel controller based on the RISE unknown dynamic estimator is established to realize the control of the permanent magnet synchronous motor.

[0013] Furthermore, the error sign-based robust integral unknown dynamic estimator is specifically:

[0014]

[0015] In the formula, Represents the aggregate uncertainty The estimated value of; k0, m1 are design parameters; Defined as is a set of real numbers, e1 represents the observation error of the first state variable x1, and e2 represents the observation error of the second state variable x2.

[0016] Furthermore, the S3 is specifically:

[0017] The funnel function is defined according to the proposed nonlinear function to obtain an improved funnel function;

[0018] The improved funnel function is incorporated into the error transformation function design to design a new error transformation function.

[0019] The new error conversion function η(t) is defined as:

[0020]

[0021] Where z(t) is the tracking error; ρ(t) represents the improved funnel function; α is a constant.

[0022] Furthermore, the S4 is specifically:

[0023] defining a tracking error of a first state variable;

[0024] Based on the new error transformation function, a first error variable is obtained according to the tracking error of the first state variable;

[0025] According to the first error variable, design the first proportional integral error variable;

[0026] Design the intermediate control law based on the first proportional-integral error variable;

[0027] According to the intermediate control law, the error surface is defined;

[0028] Based on the new error transformation function and the error surface, the second error variable is obtained;

[0029] According to the second error variable, design the second proportional integral error variable;

[0030] According to the second error variable, the second proportional-integral error variable and the lumped uncertainty estimate, a proportional-integral funnel controller based on the RISE unknown dynamics estimator is defined.

[0031] Furthermore, the proportional-integral funnel controller based on the RISE unknown dynamic estimator u Defined as:

[0032]

[0033] Where b0 is the nominal value of b, b = K t / J,K t is the torque constant, J is the moment of inertia; κ2 and c2 are positive constants, s2 is the second proportional integral error variable, and η2 is the second error variable; Represents the aggregate uncertainty Estimated value of; g2 = α2H2, z2 is the error surface, ρ2 represents the improved funnel function; α2 is a constant.

[0034] The beneficial effects of the present invention are:

[0035] Compared with the prior art, for permanent magnet synchronous motors with unknown system dynamics, the present invention proposes a proportional integral funnel control method for permanent magnet synchronous motors based on an error sign robust integral unknown dynamic estimator. On the one hand, an error sign robust integral unknown dynamic estimator is designed using the RISE technology to estimate the unknown system dynamics. In the design of the estimator, Instead of sign(e), dir(e) is discontinuous only when e=0, while the sign is discontinuous on each coordinate axis; on the other hand, the error transformation function constructed by the improved funnel function is incorporated into the design of the PI funnel controller, which not only ensures the transient behavior, but also achieves zero tracking error and intermediate error. The comparison shows that the permanent magnet synchronous motor proportional integral funnel control based on the error sign robust integral unknown dynamic estimator proposed in the present invention has a better control performance than PI control and traditional non-singular terminal sliding mode control (NTMSC). BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 It is a process principle diagram of a permanent magnet synchronous motor proportional integral funnel control method based on an error sign robust integral unknown dynamic estimator according to the present invention;

[0037] Figure 2 A servo mechanism experimental device according to an embodiment of the present invention;

[0038] Figure 3 This is a PMSM speed control experiment structure diagram of the present invention;

[0039] Figure 4 It is a schematic diagram comparing the speed response simulation effects of three controllers involved in the embodiments of the present invention under the desired trajectory of ω=200r / min when the load is disturbed;

[0040] Figure 5 It is a schematic diagram comparing the speed response simulation effects of three controllers involved in the embodiments of the present invention under the desired trajectory of ω=600r / min when the load is moving.

[0041] Figure 6 It is a schematic diagram comparing the speed response simulation effects of three controllers involved in the embodiments of the present invention under the expected trajectory of ω=1000r / min when the load is disturbed. DETAILED DESCRIPTION

[0042] In order to make the purpose, technical scheme and advantages of the embodiments of the present invention clearer, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. It should be noted that the embodiments in this application and the features in the embodiments can be combined with each other arbitrarily without conflict.

[0043] Example 1: Figure 1-6As shown, according to a first aspect of an embodiment of the present invention, a method for proportional-integral funnel control of a permanent magnet synchronous motor based on an error sign robust integral unknown dynamic estimator is provided, comprising the following steps:

[0044] S1. Establish a mathematical model of the permanent magnet synchronous motor; transform the mathematical model of the permanent magnet synchronous motor into a state equation form according to the selected state variables; consider the lumped uncertainty, rewrite the state equation form, and obtain the state equation based on the lumped uncertainty; initialize the system state, the process is as follows:

[0045] S1.1. The mathematical model of permanent magnet synchronous motor can be described as follows:

[0046]

[0047] In the formula, i d and i q are the d-axis and q-axis stator currents respectively; and Respectively represent i d 、i q and the first-order derivative of ω; ω is the angular velocity; L is the stator inductance; R is the stator resistance; n p is the number of magnetic poles; ψ f is the rotor flux; K t is the torque constant; J is the moment of inertia; B is the viscous friction coefficient; u d and u q are the d-axis and q-axis stator voltages respectively; T L is the load torque.

[0048] In general, the d-axis current is i d =0, torque constant K t =1.5n p ψ f ; Then we can get the following formula:

[0049]

[0050] S1.2. Select state variables The mathematical model of the permanent magnet synchronous motor is transformed into the state equation form:

[0051]

[0052] Wherein, x1 represents the first state variable, and x2 represents the second state variable; represents the set of real numbers; a=-B / J, b=K t / J,d=-T L / J and u=i q , considering the lumped uncertainty, formula (3) can be rewritten as:

[0053]

[0054] in, is the lumped uncertainty; a0, b0 are the nominal values ​​of a and b, d0 is the nominal value of d; Δa0, Δb0 and Δd0 are the uncertainties of the parameters respectively.

[0055] Assumption 1: Aggregate uncertainty and its first and second derivatives satisfy and Where δ1 and δ2 are positive constants.

[0056] S2. Design an error sign-based robust integral unknown dynamic estimator to estimate the lumped uncertainty:

[0057] For the state equation (4) based on lumped uncertainty, the state observer is designed as:

[0058]

[0059] Where F is a positive constant; χ(t) is the state observer gain, i.e., the state observer input to be constructed; Represents the aggregate uncertainty An estimated value of Respectively represent the estimated values ​​of the first state variable x1 and the second state variable x2;

[0060] The state observer observation error is defined as follows:

[0061]

[0062] From equations (4), (5) and (6), we can see that the error dynamic equation of the state observer is:

[0063]

[0064] In the formula, They represent the first-order derivatives of e1 and e2 respectively; e1 represents the observation error of the first state variable x1, and e2 represents the observation error of the second state variable x2. represents the unknown dynamics error;

[0065] Then, a robust integral unknown dynamics estimator based on the error sign is designed:

[0066]

[0067] Among them, k0 and m1 are design parameters. Defined as Defined as:

[0068]

[0069] In (8), we can find the signal The unknown dynamic estimate of needs to be identified. However, when only the velocity signal is available, the signal Based on the sliding control theory, a low-pass filter is constructed to obtain the signal

[0070] Among them, τ>0 is the filtering time constant.

[0071] The stability of the designed unknown dynamic estimator is proved by using Lyapunov stability. The specific steps are as follows:

[0072] For the convenience of analysis, the coordinate transformation ζ=T1e,ζ=[ζ1,ζ2] is introduced T , T1 is defined as:

[0073]

[0074] We can get:

[0075]

[0076] Then, taking the derivative of the above formula, we can get:

[0077]

[0078] The observer gain χ(t) is defined as:

[0079] χ(t)=β1e1(t) (14)

[0080] Where β1 is a positive constant. Define auxiliary filter variables r1 and for:

[0081]

[0082] From (8) we can see The time derivative of is:

[0083]

[0084] By calculating the time derivative of (15) through (13), we can obtain:

[0085]

[0086] in, Substituting (16) into (17), we can obtain:

[0087]

[0088] In order to analyze the convergence, an auxiliary function P(t) is constructed as follows:

[0089]

[0090] Among them, δ h and N(σ) is defined as:

[0091]

[0092] in, e1(0), e2(0), express e1, e2, The initial value of .

[0093] For the following analysis, several parameter ranges are given:

[0094]

[0095] Where β2>0.

[0096] According to equations (5) and (8), the estimation error can be guaranteed and can converge to zero, so that ζ1, ζ2, r2 and On the compact set Φ, and the control gain m1 satisfies:

[0097] m1>β1+β2 (22)

[0098] The proof is as follows:

[0099] Define vector v = [ζ1, ζ2, r1] T and Defining the Lyapunov function for:

[0100]

[0101] It satisfies the following inequality:

[0102]

[0103] in and is a continuous positive definite function. The time derivative of , and substituting (15), (19) and (20) into (24) yields:

[0104]

[0105] For formula (25), add Subtract We can get:

[0106]

[0107] Based on (15), we have:

[0108]

[0109] According to formulas (11) and (13) and the definition When ζ1=0, we can get and

[0110] Then, (27) can be written as:

[0111]

[0112] where γ1 is chosen to satisfy the first equation in (21). In addition, satisfy where λ(||v||)||v|| is a positive, globally reversible, non-decreasing function. From Young's inequality we get (26) can be expressed as:

[0113]

[0114] Using inequalities where γ2 is as shown in (21). Then, (29) can be expressed as:

[0115]

[0116] β1 can be selected to satisfy (21). The last term in (30) can be completely squared to obtain:

[0117]

[0118] in set up For Domain Positive function on :

[0119]

[0120] definition for:

[0121]

[0122] Then the errors ζ1, ζ2, r1 and the unknown dynamic estimator error can converge to zero, that is:

[0123]

[0124] S3. Design a new error conversion function. The specific steps are as follows:

[0125] Using Funnel control can improve transient and steady-state performance, however, the traditional funnel controller is only applicable to systems with a relative degree of 1 or 2. In order to relax this limitation, a new error transfer function is designed in this step.

[0126] Define tracking error z1(t)=x1(t)-y d (t), it should be ensured to be within the specified funnel boundary Θ(t):

[0127]

[0128] Among them, y d (t) represents the desired angular velocity; is the boundary of the funnel, and the positive function Θ(t) is selected according to the initial condition z1(0) to satisfy |z1(0)|<Θ(t). x1 is a variable about time, and x1(t) is x1.

[0129] Then the traditional funnel function It can be defined as:

[0130]

[0131] In order to relax the restrictions of Funnel control, a new nonlinear function ψ is proposed θ (t), has the following properties:

[0132] 1)ω θ (0) = 1; ψ θ (t)=0;t≥θ

[0133] 2) 0<t<θ

[0134] 3) is continuous in the interval [0,∞);

[0135] where θ is a constant and ψ θ (t) is defined as:

[0136]

[0137] On this basis, the improved funnel function can be defined as:

[0138]

[0139] in, The value is taken under the condition of |z1(0)|<ρ(0). Then the improved preset boundary of the present invention can be defined as:

[0140]

[0141] A new error transformation function η(t) is defined as:

[0142]

[0143] Where z(t) is the tracking error; ρ(t) represents the improved funnel function.

[0144] S4. Design Based on the RISE disturbance observer, design the proportional integral funnel control; analyze its stability through the Lyapunov function. The specific design steps are as follows:

[0145] S4.1 Design of a proportional-integral funnel controller based on the RISE unknown dynamics estimator:

[0146] Define the tracking error of the first state variable x1:

[0147] z1(t)=x1(t)-y d (t) (41)

[0148] Based on the error transformation function (40), according to the tracking error of the first state variable, the first error variable is obtained as:

[0149]

[0150] in, ε1 and α1 are constants, ψ θ (t) is a nonlinear function.

[0151] Taking the derivative of η1(t) we get:

[0152]

[0153] in, And g1=a1H1.

[0154] The first proportional-integral error variable is:

[0155] s1=η1+c1∫0 t η1(σ)dσ (44)

[0156] Among them, c1 is a positive constant. Then the intermediate control law v1 is designed as:

[0157]

[0158] Among them, κ1 is a design parameter.

[0159] The error surface z2(t) is defined as:

[0160] z2(t)=x2-v1 (46)

[0161] Based on the error transformation function (40), the second error variable is obtained:

[0162]

[0163] in ε2 and a2 is a constant.

[0164] Taking the derivative of η2(t) we get:

[0165]

[0166] in, And g2 = α2H2. The second proportional integral error variable is:

[0167] s2=η2+c2∫0 t η2(σ)dσ (49)

[0168] Where c2 is a positive constant.

[0169] Then, the proportional-integral funnel controller based on the RISE unknown dynamics estimator is u Defined as:

[0170]

[0171] Among them, κ2 is a positive constant.

[0172] S4.2 Select Lyapunov function and prove the stability of the system

[0173] Considering the system (4), the intermediate control law (45) and the actual controller (50) are designed. If the initial condition |z i (0)|<ρ i (0), i = 1, 2, then the design can ensure that all signals in the closed-loop system are bounded and the tracking error z1(t) can be kept within the predefined boundaries (39).

[0174] The proof of system stability will be carried out in two steps, respectively proving that the tracking error z i (t), convergence of i=1,2.

[0175] The first step is to prove the stability of z1(t), and choose the first Lyapunov function as:

[0176]

[0177] The derivative of V1 is:

[0178]

[0179] Substituting the intermediate control law (45) into (52), we can obtain:

[0180]

[0181] in, is a concentrated dynamics. For the above analysis, under |z1(t)|<ρ1(t), h1 is in [0, t max ). Then we can define:

[0182]

[0183] Then, (53) can be written as:

[0184]

[0185] For t<t max , V1<0. If |s1| satisfies the following conditions:

[0186]

[0187] Based on the above analysis, according to (42), we can conclude that:

[0188]

[0189] Therefore, the tracking error z1(t) has a predefined bound |z1(t)|<ρ1, and the intermediate control law v1 is bounded.

[0190] The second step is to prove the stability of z2(t) and choose the second Lyapunov function:

[0191]

[0192] According to (48) and (49), the derivative of (58) can be obtained as:

[0193]

[0194] Substituting (4) and (50) into (59), we obtain:

[0195]

[0196] in, is a concentrated dynamics. For the above analysis, under |z2(t)|<ρ2(t), h2 is in [0, t max ). Then we can define

[0197]

[0198] Then, (60) can be written as:

[0199]

[0200] For t<t max , V2<0. If |s2| satisfies the following conditions:

[0201]

[0202] Based on the above analysis, according to (47), we can conclude that:

[0203]

[0204] Therefore, the tracking error z2(t) has a predefined bound |z2(t)|<ρ2, and the controller u is bounded.

[0205] Compared with the prior art, the present invention proposes a proportional integral funnel control method for permanent magnet synchronous motors with unknown system dynamics (lumped uncertainty) based on an error sign robust integral unknown dynamic estimator. The method first designs an unknown dynamic estimator using the RISE technology to estimate the lumped uncertainty. In the designed unknown dynamic estimator, Instead of sign(e), dir(e) is discontinuous only when e=0, while the sign function is discontinuous on each coordinate axis. The improved funnel function is incorporated into the error transformation function design, which not only ensures the transient behavior but also achieves zero tracking error. The comparison shows that the proposed permanent magnet synchronous motor proportional integral funnel control based on error sign robust integral unknown dynamic estimator has superior control performance compared with PI control and traditional non-singular terminal sliding mode control (NTMSC).

[0206] In order to demonstrate the effectiveness of the funnel controller (45), (50) and RISE estimator (8) proposed in the present invention, a 130MB, 150A non-salient-pole PMSM drive system is used as a test bench ( Figure 2 ), the PMSM speed control experimental structure diagram is as follows Figure 3 As shown. The control algorithm is implemented in LINKS-RT. The sampling frequency is 10KHz. The basic sampling time of Simulink is 0.1ms. The sampling period of the current loop is 0.1ms, and the sampling period of the speed loop is 10ms. The proposed control algorithm is compiled into target code and downloaded to the target computer through RT-SIM software for running. In the experiment, the moment of inertia of the load can be adjusted by adding additional load components. The system parameters are listed in Table 1.

[0207] Table 1 Permanent magnet synchronous motor parameter data

[0208]

[0209] The conventional non-singular terminal sliding mode NTSMC and the widely used PI control method are compared with the permanent magnet synchronous motor proportional integral funnel control method PI-FC-RISE based on the error sign robust integral unknown dynamic estimator of the present invention. Its sliding surface is Where p>q. The PI control gain is selected as k p =0.4,k i =0.05. The parameters of the traditional non-singular terminal sliding mode NTSMC are defined as p=7, q=3, α=70, β=1.2; the parameters of the permanent magnet synchronous motor proportional integral funnel control method PI-FC-RISE based on the error sign robust integral unknown dynamic estimator are selected as c1=110, c2=80, κ1=4, κ2=1.2, β1=2, β2=16, k0=3, m1=1.2.

[0210] Figure 4-Figure 6 The simulation results of speed tracking performance, tracking error and control input of different controllers under load torque TL = 2N·m when starting at speeds of 200r / min, 600r / min and 1000r / min are shown respectively. It can be seen that the PI-FC-RIES control method developed by the present invention has a small overshoot, fast dynamic response and set time compared with PI and NTSMC.

[0211] According to the second aspect of the embodiment of the present invention, a proportional integral funnel control system of a permanent magnet synchronous motor based on an error sign robust integral unknown dynamic estimator is provided, comprising a first module for executing S1: establishing a mathematical model of a permanent magnet synchronous motor; transforming the mathematical model of the permanent magnet synchronous motor into a state equation form according to the selected state variables; considering the lumped uncertainty, rewriting the state equation form to obtain a state equation based on the lumped uncertainty; a second module for executing S2: designing an error sign robust integral unknown dynamic estimator to estimate the lumped uncertainty; a third module for executing S3: incorporating the improved funnel function into the error transformation function design to design a new error conversion function; a fourth module for executing S4: based on the selected state variables, according to the new error conversion function and the lumped uncertainty estimate, establishing a proportional integral funnel controller based on the RISE unknown dynamic estimator to achieve control of the permanent magnet synchronous motor. Although the system described in the above embodiment is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceived. For the parts not described in detail in each module, please refer to the relevant description of this embodiment.

[0212] According to a third aspect of an embodiment of the present invention, a processor is provided, which is used to run a program, wherein the program, when running, executes a permanent magnet synchronous motor proportional integral funnel control method based on an error sign robust integral unknown dynamic estimator as described in any one of the above.

[0213] The specific implementation modes of the present invention are described in detail above in conjunction with the accompanying drawings, but the present invention is not limited to the above implementation modes, and various changes can be made within the knowledge scope of ordinary technicians in this field without departing from the purpose of the present invention.

Claims

1. A proportional-integral funnel control method for a permanent magnet synchronous motor based on an error-sign robust integral unknown dynamic estimator, characterized in that: The following steps are involved: S1. Establishing a mathematical model of a permanent magnet synchronous motor; transforming the mathematical model of the permanent magnet synchronous motor into a state equation form according to the selected state variables; Considering the lumped uncertainty, the state equation is rewritten to obtain the state equation based on the lumped uncertainty; S2, design an error sign based robust integral unknown dynamic estimator to estimate the lumped uncertainty; S3, incorporating the improved funnel function into the error transformation function design to design a new error transformation function; S4. Based on the selected state variables, according to the new error transfer function and the lumped uncertainty estimate, a proportional-integral funnel controller based on the RISE unknown dynamic estimator is established to realize the control of the permanent magnet synchronous motor.

2. The method for controlling a permanent magnet synchronous motor proportional integral funnel based on an error sign robust integral unknown dynamic estimator according to claim 1, characterized in that: The error sign-based robust integral unknown dynamic estimator is specifically: In the formula, Represents the aggregate uncertainty The estimated value of; k0, m1 are design parameters; Defined as is a set of real numbers, e1 represents the observation error of the first state variable x1, and e2 represents the observation error of the second state variable x2.

3. The method for controlling a permanent magnet synchronous motor proportional integral funnel based on an error sign robust integral unknown dynamic estimator according to claim 1, characterized in that: The S3 is specifically: The funnel function is defined according to the proposed nonlinear function to obtain an improved funnel function; The improved funnel function is incorporated into the error transformation function design to design a new error transformation function.

4. The method for controlling a permanent magnet synchronous motor proportional integral funnel based on an error sign robust integral unknown dynamic estimator according to claim 3 is characterized in that: The new error conversion function η(t) is defined as: Where z(t) is the tracking error; ρ(t) represents the improved funnel function; α is a constant.

5. The method for controlling a permanent magnet synchronous motor proportional integral funnel based on an error sign robust integral unknown dynamic estimator according to claim 1, characterized in that: The S4 is specifically: defining a tracking error of a first state variable; Based on the new error transformation function, a first error variable is obtained according to the tracking error of the first state variable; According to the first error variable, design the first proportional integral error variable; Design the intermediate control law based on the first proportional-integral error variable; According to the intermediate control law, the error surface is defined; Based on the new error transformation function and the error surface, the second error variable is obtained; According to the second error variable, design the second proportional integral error variable; According to the second error variable, the second proportional-integral error variable and the lumped uncertainty estimate, a proportional-integral funnel controller based on the RISE unknown dynamics estimator is defined.

6. The method for proportional-integral funnel control of a permanent magnet synchronous motor based on an error-sign robust integral unknown dynamic estimator according to claim 5, characterized in that: The proportional-integral funnel controller u based on the RISE unknown dynamics estimator is defined as: Where b0 is the nominal value of b, b = K t / J,K t is the torque constant, J is the moment of inertia; κ2 and c2 are positive constants, s2 is the second proportional integral error variable, and η2 is the second error variable; Represents the aggregate uncertainty Estimated value of; g2 = α2H2, z2 is the error surface, ρ2 represents the improved funnel function; α2 is a constant.

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