An improved EKF disturbance observer anti-saturation integral sliding mode adaptive control method

Abstract

The present invention belongs to the field of control theory and control engineering. The method described in the present invention includes designing a PMSM mathematical model and introducing a cross-covariance matrix based on the traditional EKF disturbance observer implementation process; accurately predicting the disturbance torque during motor operation using different parameters in the system noise covariance matrix; and designing a fuzzy adaptive EKF disturbance observer to predict the system disturbance torque, thereby obtaining an improved EKF disturbance observer. The present invention designs an improved EKF disturbance observer that accurately observes sudden and continuous disturbances in real time, introduces the disturbance observation values ​​into an improved integral sliding mode controller, and effectively improves the overall motor system's ability to resist load disturbances.

Description

Technical Field

[0001] The present invention belongs to the field of control theory and control engineering, and in particular relates to an improved EKF disturbance observation anti-saturation integral sliding mode adaptive control method. Background Art

[0002] Since the operating conditions of electric vehicles and high-power door and window openers are relatively complex, the electric vehicle drive system needs to overcome the internal parameter perturbations caused by changes in the external environment and the sudden changes in external loads to ensure the anti-interference ability and global robustness of the overall system. Therefore, the research on disturbance observers is particularly necessary. In 1978, Japanese scholar K. Ohnishi first proposed the concept of disturbance observers. The core idea is to use the difference between the actual output of the controlled object and the nominal model output as an equivalent disturbance and compensate it to the control input, thereby eliminating the influence of external disturbances on the system control performance. Figure 2 As shown. Currently, the more common disturbance observers include extended state observers, sliding mode observers, and Lumberg observers. One literature proposes an improved sliding mode control strategy that takes into account sliding mode chattering and disturbance compensation. This strategy chooses the integral sliding surface, but it ignores the Windup phenomenon caused by the integral link, making it difficult for the dynamic performance of the motor startup phase of high-power door and window openers to achieve the ideal state; some scholars have designed a load torque observer based on sliding mode control, but the control system still uses the traditional PI controller, which is slightly insufficient when facing internal parameter perturbations; some literature proposes to use the Lumberg observer to observe load disturbances in real time, but the Lumberg observer does not effectively consider the impact of sensor noise in the closed-loop link of the system; there are also proposed disturbance observer designs based on extended Kalman, but the traditional extended Kalman observer ignores the correlation between process noise and measurement noise.

[0003] Through model simulation, this paper discovered its shortcomings in dealing with the influence of external unknown disturbances, and therefore proposed an improved EKF disturbance observer. Through simulation comparison and analysis with the Lumberg observer and the traditional EKF observer, its rapidity and stability in load disturbance observation were demonstrated; then it was integrated into the improved integral sliding mode controller, and simulation comparison was carried out with the control strategy when disturbance observation was not introduced, verifying that the scheme in this chapter has better anti-interference ability. Summary of the Invention

[0004] In light of the aforementioned existing problems, the present invention was proposed. To address the speed fluctuations and controller instability caused by external load torque disturbances and internal parameter perturbations during the operation of high-power window and door motors, as well as the difficulty of the traditional extended Kalman observer in simultaneously ensuring both dynamic and steady-state performance, this paper designs an improved EKF disturbance observer based on the Lumberg observer. The observed disturbance torque is then incorporated into an improved integral sliding mode controller, effectively reducing speed and torque ripple and improving the system's anti-interference performance.

[0005] Therefore, an improved EKF disturbance observer anti-saturation integral sliding mode adaptive control method is provided.

[0006] To solve the above technical problems, the present invention provides the following technical solution: an improved EKF disturbance observation anti-saturation integral sliding mode adaptive control method, comprising:

[0007] A mathematical model of permanent magnet synchronous motor is established to improve the motor output torque and speed regulation range of high-power door and window openers. Lyapunov analysis is performed after the stability conditions are met. A sliding mode controller based on a new reaching law is designed to solve the speed overshoot and system chattering of the sliding mode controller, and the discrete switching bandwidth is analyzed. An anti-saturation integral sliding mode controller based on a new nonlinear integral sliding surface is designed to improve the saturation phenomenon of the integral link during the startup of the motor of the high-power door and window opener. Combined with the new reaching law, an improved integral sliding mode control is obtained to control its output torque. A PMSM mathematical model is designed, and the mutual covariance matrix is ​​introduced according to the traditional EKF disturbance observer implementation process. During the operation of the motor of the high-power door and window opener, the disturbance torque is accurately predicted by different parameters in the system noise covariance matrix. A fuzzy adaptive EKF disturbance observer is designed to predict the system disturbance torque, and an improved EKF disturbance observer is obtained.

[0008] As a preferred solution of the improved EKF disturbance observation anti-saturation integral sliding mode adaptive control method described in the present invention, the mathematical model of the permanent magnet synchronous motor includes that the electromagnetic torque of the PMSM mainly consists of two parts, the first part is the torque generated between the q-axis current component and the permanent magnet magnetic field, and the second part is the reluctance torque generated by the rotor salient pole effect. The mathematical model of the PMSM in the dq coordinate system is:

[0009]

[0010] The stability condition is satisfied by using sliding mode control. The sliding mode surface s divides the state space of the system into two parts: s(x)>0 and s(x)<0:

[0011]

[0012] When the system meets the reachability condition, existence condition and stability condition, sliding mode control can be performed, where x∈R n is the state space variable, f(x, t) and g(x, t) are nonlinear vector functions, and u(t) is the input variable. s is the stator resistance, u d 、u q is the stator voltage, i d 、i q is the stator voltage, d, q axis components, p is the differential operator, ψ d , ψ q is the stator flux d, the flux component of the q axis.

[0013] As a preferred solution of the improved EKF disturbance observation anti-saturation integral sliding mode adaptive control method described in the present invention, the sliding mode controller based on the new reaching law includes using a piecewise reaching law to design the controller, and the reaching law expression is:

[0014]

[0015] Among them, k=k1+k2, k1>0, k2>0, m>0, 1>λ>0, 1>α>0, p>0, δ>0, sat(s) is the saturation function, σ is the boundary layer, m, λ, α and δ are simulation model parameters, p is the differential operator, and the amount of chattering during the switching process is reduced by feedback control within the boundary layer, while outside the boundary layer, the sign function value is still used to quickly approach the sliding surface outside the boundary.

[0016] When the sliding mode system is far away from the sliding surface, that is, in the stage of |s|>δ, by adjusting the values ​​of λ and m, the system trajectory is quickly approached to the sliding surface. At the same time, the saturation function sat(s) enables the system to maintain anti-bounce capability while maintaining a faster approach speed. When the sliding mode system approaches the sliding surface, that is, in the stage of |s|<δ, |s|→0, At the same time, the weights of k1 and k2 are adjusted to reduce the high-frequency chattering of the traditional sliding mode.

[0017] The analysis of discrete switching bandwidth includes the problem of system chattering of the sliding mode controller, and the analysis of discrete switching bandwidth When the state trajectory of the system approaches the sliding surface, the new reaching law is discretized and the switching bandwidth of the new reaching law is expressed as Δ=k1|s| α T s , where T s is the system sampling period, is the discrete switching bandwidth. Δ decreases as |s| decreases, and |s|→0, suppressing chattering.

[0018] As a preferred embodiment of the improved EKF disturbance observation anti-windup integral sliding mode adaptive control method of the present invention, the anti-windup integral sliding mode controller based on the novel nonlinear integral sliding mode surface includes using a novel nonlinear function f(s) to limit the action time of the integral link when the speed error is large, and to accelerate the adjustment speed of the controller when the speed error is small:

[0019]

[0020]

[0021] Among them, k1 and k2 are adjustable coefficients. K1 determines the rising speed of f(s) in the small error amplification area, and k2 determines the width of the amplification area. Set k1 = 0.1 and k2 = 12.3. When x=0.1, a new nonlinear integral sliding surface is designed using f(s). The difference between the output torque of the sliding mode controller and the given torque limit value, as well as the difference between the speed error and the speed error limit value, is used as linear negative feedback to compensate for large errors in the integral link and achieve limit saturation:

[0022]

[0023] Where η is the feedback gain, e sat It is the maximum output of the error limiting link.

[0024] As a preferred solution of the improved EKF disturbance observation anti-saturation integral sliding mode adaptive control method described in the present invention, the PMSM mathematical model includes the mathematical model of the PMSM in the dq coordinate system:

[0025]

[0026] The introduction of the cross-covariance matrix includes adding a cross-covariance matrix M to the improved EKF disturbance observer considering the correlation between process and measurement noise while keeping the control matrix A, control matrix B, and observation matrix G unchanged. k :

[0027] M=[-5e-4 0]

[0028] Among them, R s is the stator resistance, i a 、i b 、i c is the three-phase current, ψ a , ψ b , ψ c is the three-phase flux linkage.

[0029] As a preferred solution of the improved EKF disturbance observation anti-saturation integral sliding mode adaptive control method described in the present invention, the improved EKF includes the following expression:

[0030]

[0031] The system state matrix A, control matrix B, and observation matrix G are:

[0032]

[0033] G=[1 0]

[0034] Among them, K k is the Kalman gain calculation, is the covariance estimate, Error covariance correction, T s is the system sampling period, B m is the damping coefficient, J is the moment of inertia, and k is the adjustable coefficient.

[0035] The accuracy prediction of the disturbance torque by different parameters in the system noise covariance matrix includes the accuracy prediction of the disturbance torque by different parameters q1 and q2 in the system noise covariance matrix Q during the operation of the motor of a high-power door and window opener: when q1 increases, the steady-state accuracy of the torque prediction improves, but the corresponding dynamic following ability will decrease; when q1 decreases, the dynamic following performance improves, but a certain overshoot will occur; when q2 increases, the dynamic following ability of the torque improves, but the corresponding steady-state accuracy will decrease; when q2 decreases, the steady-state accuracy of the torque prediction improves, but the dynamic following ability will decrease.

[0036] As a preferred solution of the improved EKF disturbance observation anti-saturation integral sliding mode adaptive control method described in the present invention, the fuzzy adaptive EKF disturbance observer includes the implementation process of the improved EKF disturbance observer: according to the influence of the covariance matrix parameters on the torque prediction, the change scheme of the disturbance torque of the motor system of the high-power door and window opener and the current state of the system, the parameter values ​​in the covariance matrix are adaptively configured to obtain the empirical values ​​of the disturbance torque prediction of different parameters in dynamic and steady states: after comparing the actual current value iq with the given value, the difference E and the difference change EC are used as input signals for fuzzy processing to obtain fuzzy quantities, and the fuzzy quantities and fuzzy control rules are combined and fuzzy reasoning is performed according to the empirical values. Finally, the inferred fuzzy quantities are defuzzified and converted into precise parameters q1 and q2.

[0037] The fuzzy quantity obtained by the fuzzification processing includes performing a domain transformation on the input quantity after quantization processing. The fuzzy domain of variables E and EC is divided into five fuzzy subsets {negative large, negative small, zero, positive small, positive large}, namely {NB, NS, ZO, PS, PB}. In order to cope with the load mutation moment, a triangular membership function is adopted. The fuzzy adaptive controller can adjust the covariance matrix parameters in real time: if the difference between the given value and the actual value of the q-axis current is large, the covariance matrix parameters are adjusted to quickly reduce the deviation. If the difference is small, the stability of the disturbance observer is considered.

[0038] The prediction of the system disturbance torque includes establishing an improved EKF state equation in the dq coordinate system based on the PMSM mathematical model and the EKF, which is expressed as follows:

[0039]

[0040] At the same time, the EKF algorithm discretizes the PMSM nonlinear system, and the state and prediction equations after discretization are expressed as:

[0041]

[0042] Where x = [ω m T L ] T , u=T e , y=ω m , T e is the electromagnetic torque, ω m is the mechanical angular velocity, v is the velocity, T L is the rotation gain.

[0043] Another object of the present invention is to provide an improved EKF disturbance observation anti-saturation integral sliding mode adaptive control method system, which can effectively improve the overall motor system's ability to resist load disturbances by addressing speed fluctuations and controller instability caused by external load torque disturbances, internal parameter perturbations, etc. in the motor operation process of high-power door and window openers, as well as the problem that traditional extended Kalman observers are difficult to simultaneously ensure the dynamic and steady-state performance of the observer.

[0044] As a preferred solution of the improved EKF disturbance observation anti-saturation integral sliding mode adaptive control system described in the present invention, it includes: a permanent magnet synchronous motor mathematical model construction module, a new reaching law sliding mode controller construction module, a nonlinear integral sliding mode surface construction module, a cross-covariance matrix module, an improved EKF disturbance observer module, an accuracy prediction module and an improved EKF disturbance observer module.

[0045] The permanent magnet synchronous motor mathematical model building module models the permanent magnet synchronous motor to obtain a mathematical model of the permanent magnet synchronous motor, providing a basis for subsequent controller design.

[0046] The sliding mode controller building module of the novel reaching law makes the dynamic behavior of the system approach and enter the sliding mode surface, thereby realizing rapid tracking and control of the system state.

[0047] The nonlinear integral sliding surface building module controls the integral of the system state to achieve precise control of the speed and position physical quantities of the permanent magnet synchronous motor.

[0048] The cross-covariance matrix module calculates and updates the cross-covariance matrix between the system state variables and the observation variables;

[0049] The improved EKF disturbance observer module improves the traditional EKF disturbance observer by introducing a new observation model or an observation noise model.

[0050] The accuracy prediction module predicts the system state estimation accuracy within a future period of time based on the current system state estimation value and observation value.

[0051] The improved EKF disturbance observer module introduces fuzzy logic control theory on the basis of the improved EKF disturbance observer, performs fuzzy processing on the system state estimation, and outputs the final system state estimation value.

[0052] A computer device includes a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, the steps of the method described in an improved EKF disturbance observation anti-windup integral sliding mode adaptive control are implemented.

[0053] A computer-readable storage medium stores a computer program thereon, wherein when the computer program is executed by a processor, the computer program implements the steps of the method described in an improved EKF disturbance observation anti-windup integral sliding mode adaptive control.

[0054] The beneficial effects of the present invention are as follows: based on the structure of the Lumberg observer, an improved EKF disturbance observer is designed, which improves the dynamic following capability and steady-state performance of the disturbance observation, and introduces the disturbance observation value into the improved integral sliding mode controller, which effectively improves the control system's ability to resist external unknown disturbances. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive efforts. Among them:

[0056] Figure 1 A flowchart of an improved EKF disturbance observation anti-saturation integral sliding mode adaptive control method provided by an embodiment of the present invention.

[0057] Figure 2 An improved EKF disturbance observation anti-saturation integral sliding mode adaptive control method provided by an embodiment of the present invention is provided with a graph of the f(s) function under different k1 and k2 (a) for different k1 and (b) for different k2.

[0058] Figure 3 A graph of the nonlinear function f(s) of an improved EKF disturbance observer anti-saturation integral sliding mode adaptive control method provided by one embodiment of the present invention.

[0059] Figure 4 A block diagram of an anti-windup integral sliding mode controller based on a nonlinear sliding mode surface is provided for an improved EKF disturbance observer anti-windup integral sliding mode adaptive control method according to an embodiment of the present invention.

[0060] Figure 5 A state diagram of the sliding mode switching surface of an improved EKF disturbance observation anti-saturation integral sliding mode adaptive control method provided by one embodiment of the present invention.

[0061] Figure 6 An embodiment of the present invention provides an improved EKF disturbance observation anti-saturation integral sliding mode adaptive control method based on a new reaching law ISMC simulation module.

[0062] Figure 7 An embodiment of the present invention provides an improved EKF disturbance observation anti-saturation integral sliding mode adaptive control method and an ISMC simulation module based on a novel nonlinear integral sliding mode surface.

[0063] Figure 8 An embodiment of the present invention provides an improved EKF disturbance observation anti-saturation integral sliding mode adaptive control method for disturbance sudden increase speed response.

[0064] Figure 9 An embodiment of the present invention provides an improved EKF disturbance observation anti-saturation integral sliding mode adaptive control method for the disturbance sudden increase electromagnetic torque response.

[0065] Figure 10An embodiment of the present invention provides an improved EKF disturbance observation anti-saturation integral sliding mode adaptive control method for the three-phase current response to a sudden disturbance increase.

[0066] Figure 11 An embodiment of the present invention provides an improved EKF disturbance observation anti-saturation integral sliding mode adaptive control method for disturbance sudden increase d and q axis current responses.

[0067] Figure 12 An improved EKF disturbance observation anti-saturation integral sliding mode adaptive control method provided by an embodiment of the present invention is L d , L q Speed ​​response waveform when parameters are perturbed.

[0068] Figure 13 The invention provides an improved EKF disturbance observation anti-saturation integral sliding mode adaptive control method provided by one embodiment of the present invention, which shows a speed response waveform when the flux parameter is perturbated.

[0069] Figure 14 A reaching law discrete switching bandwidth diagram of an improved EKF disturbance observation anti-saturation integral sliding mode adaptive control method provided by one embodiment of the present invention.

[0070] Figure 15 An embodiment of the present invention provides a novel reaching law integral sliding mode controller block diagram of an improved EKF disturbance observation anti-saturation integral sliding mode adaptive control method.

[0071] Figure 16 An embodiment of the present invention provides an improved EKF disturbance observation anti-saturation integral sliding mode adaptive control method based on the traditional disturbance observer principle framework.

[0072] Figure 17 An improved integral sliding mode control block diagram based on an improved EKF disturbance observer is provided for an improved EKF disturbance observer anti-windup integral sliding mode adaptive control method according to one embodiment of the present invention.

[0073] Figure 18 An improved EKF disturbance observer anti-saturation integral sliding mode adaptive control method provided by an embodiment of the present invention has a disturbance observer diagram (a) showing load startup and (b) showing a sudden load change.

[0074] Figure 19 An improved EKF disturbance observation anti-saturation integral sliding mode adaptive control method provided by an embodiment of the present invention shows speed waveforms before and after the introduction of disturbance observation (a) without the introduction of disturbance observation (b) with the introduction of disturbance observation.

[0075] Figure 20An embodiment of the present invention provides an improved EKF disturbance observation anti-saturation integral sliding mode adaptive control method. Te waveforms before and after the introduction of disturbance observation (a) are without the introduction of disturbance observation and (b) with the introduction of disturbance observation.

[0076] Figure 21 An embodiment of the present invention provides an improved EKF disturbance observation anti-saturation integral sliding mode adaptive control method. The three-phase current waveforms before and after the introduction of disturbance observation (a) are without the introduction of disturbance observation (b) with the introduction of disturbance observation.

[0077] Figure 22 An embodiment of the present invention provides an improved EKF disturbance observation anti-saturation integral sliding mode adaptive control method. The disturbance observation diagram (a) is a triangle wave disturbance observation and (b) is an observation error.

[0078] Figure 23 An improved EKF disturbance observation anti-saturation integral sliding mode adaptive control method provided by an embodiment of the present invention has speed waveforms before and after the introduction of disturbance observation (a) without the introduction of disturbance observation (b) with the introduction of disturbance observation.

[0079] Figure 24 An embodiment of the present invention provides an improved EKF disturbance observation anti-saturation integral sliding mode adaptive control method. The influence of different parameters in Q on torque prediction (a) is the influence of q1 on torque prediction (b) is the influence of q2 on torque prediction. DETAILED DESCRIPTION

[0080] To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, but not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in this field without making creative efforts should fall within the scope of protection of the present invention.

[0081] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0082] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it individually or selectively refer to an embodiment that is mutually exclusive of other embodiments.

[0083] The present invention is described in detail with reference to schematic diagrams. For ease of illustration, cross-sectional views of device structures may be partially enlarged and not to scale when describing embodiments of the present invention. Furthermore, the schematic diagrams are merely illustrative and should not limit the scope of the present invention. Furthermore, in actual production, the three-dimensional dimensions of length, width, and depth should be included.

[0084] In the description of the present invention, it should be noted that the terms "upper, lower, inner, and outer" and other references to orientations or positional relationships are based on the orientations or positional relationships shown in the accompanying drawings and are intended solely to facilitate and simplify the description of the present invention. They are not intended to indicate or imply that the devices or components referred to must have, be constructed, or operate in a specific orientation, and therefore should not be construed as limitations on the present invention. Furthermore, the terms "first, second, or third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0085] In this disclosure, unless otherwise specified or limited, the terms "mounted," "connected," and "connected" should be interpreted broadly. For example, they may refer to fixed, removable, or integral connections. They may also refer to mechanical, electrical, or direct connections, indirect connections through an intermediary, or internal communication between two components. Those skilled in the art will understand the specific meanings of these terms in this disclosure.

[0086] Example 1

[0087] Reference Figure 1-Figure 5 、 Figure 14-15 , which is the first embodiment of the present invention, provides an improved EKF disturbance observation anti-saturation integral sliding mode adaptive control method, comprising:

[0088] S1: Develop a mathematical model for a permanent magnet synchronous motor to improve its output torque and speed regulation range. Once stability requirements are met, perform a Lyapunov analysis. Design a sliding mode controller based on a novel reaching law to address speed overshoot and system chattering. Analyze the discrete switching bandwidth.

[0089] Furthermore, the mathematical model of the permanent magnet synchronous motor includes the electromagnetic torque of the PMSM, which is mainly composed of two parts. The first part is the torque generated between the q-axis current component and the permanent magnet magnetic field, and the second part is the reluctance torque generated by the rotor salient pole effect. The mathematical model of the PMSM in the dq coordinate system is:

[0090]

[0091] The stator voltage equation in the dq axis coordinate system is:

[0092]

[0093] In the dq coordinate system, the motor flux equation is as follows:

[0094]

[0095] The electromagnetic torque equation of permanent magnet synchronous motor is expressed as:

[0096]

[0097] The mechanical motion equation of the permanent magnet synchronous motor is:

[0098]

[0099] Among them, R s is the stator resistance, u d 、u q is the stator voltage, i d 、i q is the stator voltage, d, q axis components, p is the differential operator, ψ d , ψ q is the stator flux d, the flux component of the q axis, ψ f is the permanent magnet flux, n p is the number of pole pairs of the permanent magnet synchronous motor, T e is the electromagnetic torque, L d 、L d is the shaft equivalent inductance, J is the moment of inertia, B is the viscosity coefficient, T L is the load torque, ω e is the rotor electrical angular velocity, ω m is the mechanical angular velocity, satisfying ω e =ω m p n , d is the simulation model parameter.

[0100] Furthermore, the stability condition is satisfied by using sliding mode control such as Figure 5 , the sliding surface s divides the state space of the system into two parts: s(x)>0 and s(x)<0:

[0101]

[0102] When the system meets the reachability condition, existence condition and stability condition, sliding mode control can be performed, where x∈R n is the state space variable, f(x, t) and g(x, t) are nonlinear vector functions, and u(t) is the input variable.

[0103] Among them, the reachability condition ensures that a point in the state space can reach the sliding surface or its vicinity within a finite time and is expressed as:

[0104]

[0105] The existence condition ensures that the sliding trajectory always points to the sliding surface and ensures that the points in the sliding area can become stop points, which can be expressed as:

[0106]

[0107] It should be noted that in order to better design the sliding surface of the control system, the state error equation of the system is first defined:

[0108]

[0109] in, is the expected value of the target speed, ω rel is the actual mechanical speed of the motor. To avoid the high-frequency noise introduced in the traditional sliding mode process and the steady-state error in the sliding mode process, the present invention adds an integral link for the state error, which is specifically defined as follows:

[0110]

[0111] Among them, e ω0 represents the initial value of the speed error, and c is an integral adjustable constant. By adjusting the value of c, we can ensure that the system can move on the sliding surface at time t = 0, thereby improving the robustness of the system in the global range. Let the sliding surface defined and differentiate it, we can get:

[0112]

[0113] Speed ​​error in time constant is an exponential and gradually approaches 0, so a suitable sliding mode coefficient can ensure the dynamic performance during the sliding mode process and achieve a balance between the stabilization time and the vibration amplitude.

[0114] It should be noted that the controller is designed using the piecewise reaching law, and the reaching law expression is:

[0115]

[0116] Among them, k=k1+k2, k1>0, k2>0, m>0, 1>λ>0, 1>α>0, p>0, δ>0, sat(s) is the saturation function, σ is the boundary layer, m, λ, α and δ are simulation model parameters, p is the differential operator, and the amount of chattering during the switching process is reduced by feedback control within the boundary layer, while outside the boundary layer, the sign function value is still used to quickly approach the sliding surface outside the boundary.

[0117] When the sliding mode system is far away from the sliding surface, that is, in the stage of |s|>δ, by adjusting the values ​​of λ and m, the system trajectory is quickly approached to the sliding surface. At the same time, the saturation function sat(s) enables the system to maintain anti-bounce capability while maintaining a faster approach speed. When the sliding mode system approaches the sliding surface, that is, in the stage of |s|<δ, |s|→0, At the same time, the weights of k1 and k2 are adjusted to reduce the high-frequency chattering of the traditional sliding mode.

[0118] It should also be noted that the problem of system chattering of the sliding mode controller is analyzed from the perspective of discrete switching bandwidth:

[0119]

[0120] When the state trajectory of the system approaches the sliding surface, the new reaching law is discretized to obtain:

[0121]

[0122] s(n+1)-s(n)={-k1|s| α sat[s(n)]-k2s(n)}T s

[0123] The switching bandwidth of the new reaching law is expressed as:

[0124]

[0125] Δ=k1|s| α T s

[0126] Among them, T s is the system sampling period, is the discrete switching bandwidth. Δ decreases as |s| decreases, and |s|→0, suppressing chattering.

[0127] In the traditional method, the bandwidth of the traditional reaching law is a constant, Δ = kT s When the system approaches the sliding surface, the control system cannot gradually approach zero, but at -kT s and kT s There is constant chattering between Figure 14 As shown; In order to verify that the new reaching law designed in this paper can meet the stability required by sliding mode control, the Lyapunov equation is defined as:

[0128]

[0129] According to the defined Lyapunov equation, it can be seen that the integral sliding mode controller meets the stability requirements as follows Figure 15 shown.

[0130] S2: Based on the sliding mode controller with a new reaching law, an anti-saturation integral sliding mode controller based on a new nonlinear integral sliding surface is designed to improve the saturation phenomenon of the integral link during the startup of the motor of a high-power door and window opener. Combined with the new reaching law, an improved integral sliding mode controller is obtained to control its output torque.

[0131] Furthermore, a new convergence law is designed to reduce the steady-state error and high-frequency chattering problems existing in traditional sliding mode control. However, in actual PMSM control systems, the speed has a certain limit range. If there is a large error between the actual speed and the target value, the integral link will cause the sliding mode surface to suddenly increase and produce a large impact, which will deteriorate the transient performance. The output of the sliding mode controller designed in this invention is the torque limit value. The optimal value is found after the given electromagnetic torque is optimized by MTPA. In order to protect the entire control system, torque limitation is required.

[0132]

[0133] in, is the optimal electromagnetic torque (the superscript * means the best).

[0134] To prevent high-power window and door motors from maintaining maximum current and causing prolonged speed overshoot, an anti-saturation link is necessary in the integral sliding mode controller. A new nonlinear function f(s) is used to limit the duration of the integral link when the speed error is large, while accelerating the controller's adjustment speed when the speed error is small.

[0135]

[0136] Among them, k1 and k2 are adjustable coefficients, such as Figure 2 , k1 determines the rising speed of f(s) in the small error amplification area, k2 determines the width of the amplification area, set k1 = 0.1, k2 = 12.3, x=0.1, such as Figure 3 , a new nonlinear integral sliding surface is designed using f(s). The difference between the output torque of the sliding mode controller and the given torque limit value, as well as the difference between the speed error and the speed error limit value, is used as linear negative feedback to compensate for large errors in the integral link and achieve limit saturation:

[0137]

[0138] Taking the derivative of s, we get

[0139]

[0140] Where η is the feedback gain, e sat It is the maximum output of the error limiting link.

[0141] It should be noted that the output torque of the improved integral sliding mode controller combined with the new nonlinear integral sliding mode surface is:

[0142]

[0143] Among them, B m is the damping coefficient, J is the moment of inertia, k is the adjustable coefficient, T e is the electromagnetic torque, ω m is the mechanical angular velocity.

[0144] S3: Design the PMSM mathematical model and introduce the cross-covariance matrix according to the traditional EKF disturbance observer implementation process.

[0145] Furthermore, the PMSM mathematical model includes a PMSM mathematical model in the dq coordinate system:

[0146]

[0147] Among them, R s is the stator resistance, i a 、i b 、i c is the three-phase current, p is the differential operator, ψ a , ψ b , ψ c is the three-phase flux linkage.

[0148] It should be noted that when electric vehicles and high-power door and window openers operate under complex working conditions, they are often accompanied by external unknown load disturbances, which will seriously affect the robust performance of the entire system. For this reason, this paper designs an improved EKF disturbance observer. By accurately observing the load disturbance and integrating it into the improved integral sliding mode controller designed above, the system can reduce the speed fluctuation and torque pulsation problems caused by the disturbance.

[0149] The state space equations of a discrete stochastic system are calculated as follows:

[0150] x k =f(x k-1 ,u k-1 )+w k-1 w k ~(0,Q k )

[0151] y k =h(x k )+v k v k ~(0,R k )

[0152] Use Taylor series to transform the equation of state at x kThe following is expanded nearby:

[0153] x k =Fx k-1 +Bu k-1 +w k-1

[0154] y k =Hx k +v k

[0155] The traditional EKF implementation process is as follows, and the prior state estimation is expressed as:

[0156]

[0157] The covariance matrix estimate is expressed as:

[0158]

[0159] The Kalman gain calculation is expressed as:

[0160]

[0161] The state estimation correction expression is:

[0162]

[0163] Error covariance matrix correction

[0164]

[0165] The introduction of the cross-covariance matrix includes the assumption that there is no correlation between process noise and measurement noise while the control matrix A, control matrix B, and observation matrix G remain unchanged. However, in actual motor applications, the appearance of any complex working condition will affect the dynamic performance of the motor of the high-power door and window opener and the accuracy of the sensor sampling data. Considering the correlation between process and measurement noise, the improved EKF disturbance observer adds a cross-covariance matrix M. k :

[0166] M=[-5e-4 0]

[0167] Among them, x k is the state variable at time k, Q k 、R k w k 、v k The covariance matrix, w k 、v k are process and measurement noise respectively, which are zero-mean white noises that conform to normal distribution. F and H are f(x k-1, u k-1, ) and h(xk ) in x k The first derivative at .

[0168] The improved EKF includes the following expressions:

[0169]

[0170] The system state matrix A, control matrix B, and observation matrix G are

[0171]

[0172] G=[1 0]

[0173] When f(x k )=Ax k 、g(x k )=Gx k , the Jacobian matrix is ​​as follows:

[0174]

[0175] Among them, K k is the Kalman gain calculation, is the covariance estimate, Error covariance correction, T s is the system sampling period, B m is the damping coefficient, J is the moment of inertia, and k is the adjustable coefficient.

[0176] S4: Accurately predict the disturbance torque during the operation of the motor of a high-power door and window closer using different parameters in the system noise covariance matrix.

[0177] Furthermore, the impact of different parameters q1 and q2 in the system noise covariance matrix Q on the disturbance torque prediction accuracy during the operation of the motor of a high-power door and window opener was studied. As q1 increases, the steady-state accuracy of the torque prediction increases, but the corresponding dynamic tracking capability decreases. On the other hand, as q1 decreases, the dynamic tracking performance improves, but a certain amount of overshoot occurs. As q2 increases, the dynamic tracking capability of the torque improves, but there is a certain loss of steady-state accuracy. As q2 decreases, the steady-state accuracy of the torque prediction increases, but the dynamic tracking capability decreases.

[0178] It should be noted that if the torque prediction is to be able to maintain the accuracy in steady state while following the given disturbance as quickly as possible, it is necessary to abandon the fixed noise covariance matrix and adaptively configure the parameter values ​​in the covariance matrix according to the current state of the system, so as to achieve accurate prediction of the disturbance torque of the PMSM system.

[0179] It should also be noted that the speed change of the motor of a high-power door and window opener can usually reflect the operating status of the motor. However, due to the existence of mechanical inertia, the speed feedback has a certain delay. q As a current loop, it has a fast response speed. During the motor startup phase, when the load suddenly increases at 0.1s, and when the load suddenly decreases at 0.3s, i q Both can quickly reflect the changing trend of torque. Therefore, this paper chooses to use the error between the q-axis given current and the actual value and the error trend to judge the changing state of the disturbance torque in the motor system.

[0180] Based on the influence of covariance matrix parameters on torque prediction and the change scheme of judging the disturbance torque of the motor system described above, the empirical values ​​of different parameters for disturbance torque prediction in dynamic and steady-state aspects were obtained, and a fuzzy adaptive EKF disturbance observer was designed using a fuzzy rule controller to estimate the system disturbance torque.

[0181] S5: Design an EKF disturbance observer based on fuzzy adaptation to predict the system disturbance torque and obtain an improved EKF disturbance observer.

[0182] Furthermore, the implementation process of the improved EKF disturbance observer is as follows: after comparing the actual current value iq with the given value, the difference E and the difference change EC are used as input signals for fuzzy processing to obtain fuzzy quantities. Combining the fuzzy quantities and fuzzy control rules, fuzzy reasoning is performed based on empirical values. Finally, the fuzzy quantities obtained by reasoning are defuzzified and converted into precise parameters q1 and q2.

[0183] The fuzzy quantity obtained by the fuzzification processing includes performing a domain transformation on the input quantity after quantization processing. The fuzzy domain of variables E and EC is divided into five fuzzy subsets {negative large, negative small, zero, positive small, positive large}, namely {NB, NS, ZO, PS, PB}. In order to cope with the load mutation moment, a triangular membership function is adopted. The fuzzy adaptive controller can adjust the covariance matrix parameters in real time: if the difference between the given value and the actual value of the q-axis current is large, the covariance matrix parameters are adjusted to quickly reduce the deviation. If the difference is small, the stability of the disturbance observer is considered, which not only ensures the control accuracy, but also can adjust the coverage of the membership function domain, so that the disturbance observer observation value reaches the optimal value.

[0184] It should be noted that if the difference between the given value and the actual value of the q-axis current is large, the covariance matrix parameters should be adjusted to quickly reduce the deviation as the main purpose; if the difference is small, more consideration should be given to the stability of the disturbance observer. Based on the above requirements, this paper establishes fuzzy rules for q1 and q2 as shown in the table:

[0185] Table 1 Fuzzy rules table of q1

[0186]

[0187] Table 2 Fuzzy rules table of q2

[0188]

[0189]

[0190] It should also be noted that based on the PMSM mathematical model and EKF, the state equation of the improved EKF is chosen to be expressed in the dq coordinate system as follows:

[0191]

[0192] At the same time, the EKF algorithm discretizes the PMSM nonlinear system, and the state and prediction equations after discretization are expressed as:

[0193]

[0194] Where x = [ω m T L ] T , u=T e , y=ω m , T e is the electromagnetic torque, ω m is the mechanical angular velocity, v is the velocity, T L is the rotation gain.

[0195] Example 2

[0196] Reference Figure 6-Figure 13 、 Figures 17-24 , which is an embodiment of the present invention, provides an improved EKF disturbance observation anti-saturation integral sliding mode adaptive control method. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through experiments.

[0197] A simulation experiment was conducted on the design of an anti-saturation integral controller based on a new reaching law and a nonlinear integral sliding surface. According to the principles in the embodiment, the parameters of the new reaching law simulation model designed in this paper are:

[0198] k=100,k1=200,k2=80,d=10,c=40,λ=0.6,α=0.2,η=15 in the new nonlinear integral sliding surface.

[0199] Figure 6 For the ISMC simulation module using the new reaching law, Figure 7 The present invention adopts a new type of nonlinear integral sliding surface and a new ISMC simulation module. Figure 8 and Figure 9, electromagnetic torque response such as Figure 10 and Figure 11 , the three-phase current response is as follows Figure 12 and Figure 13 Conduct simulation comparative analysis.

[0200] (1) Speed, torque, and current waveforms at the moment of sudden disturbance

[0201] from Figure 8 (a) and (b) show that during the startup phase, the speed waveform overshoot of the ISMC based on the new reaching law is 149 rpm, and it reaches the target speed in about 0.1 s. Compared with the traditional PI control, the speed overshoot is reduced by about 50 rpm; while the speed overshoot of the ISMC based on the new nonlinear integral sliding surface is 0 during the startup phase, and it reaches the target speed in about 0.015 s. Compared with the ISMC based on the new reaching law, the speed overshoot is reduced by 149 rpm, and the time is shortened by about 0.085 s.

[0202] from Figures 9 to 11 It can be seen that in the startup phase, the T of ISMC based on the new reaching law is e The maximum is about 22 N·m, and the maximum three-phase current is about 150 A. When a load disturbance of 5 N·m is suddenly added at 0.2 s, the torque overshoot of the ISMC based on the new reaching law is 0; the maximum Te of the ISMC based on the new nonlinear integral sliding surface is less than 10 N·m in the startup phase, and the maximum three-phase current is about 65 A, which is about 65 A lower than that of the ISMC based on the new reaching law. When a sudden disturbance is added at 0.2 s, there is no obvious overshoot of current and torque.

[0203] (2) Speed ​​waveform when internal parameters are perturbed

[0204] In order to verify the anti-disturbance capability of the integral sliding mode controller designed in this paper when the dq axis inductance parameters and permanent magnet flux linkage are perturbed, simulation comparative analysis is carried out under different parameters, such as Figure 12 and Figure 13 shown.

[0205] from Figure 12 (a) It can be seen that when Ld and Lq change, the maximum speed of the ISMC based on the new reaching law will fluctuate by about 300 rpm during the startup phase, which is about 100 rpm lower than that of PI; while the ISMC based on the new nonlinear integral sliding surface, such as Figure 12 As shown in (b), the speed fluctuates by about 25 rpm during the startup phase, which is about 275 rpm lower than that of the ISMC based on the new reaching law. Figure 13(a) It can be seen that when the permanent magnet flux is perturbed by 0.8 times and 0.5 times respectively, the maximum speed fluctuation of the ISMC at the startup stage based on the new reaching law will be about 350 rpm, which is about 50 rpm lower than that of PI; while the ISMC based on the new nonlinear integral sliding surface is as follows Figure 13 As shown in (b), the maximum fluctuation is only 25 rpm, which is about 325 rpm lower than that of the ISMC based on the new reaching law. The above simulation experiments confirm the effectiveness and superiority of the scheme proposed in this paper.

[0206] In order to verify the anti-disturbance performance of the improved integral sliding mode controller based on the improved EKF disturbance observer, according to the principle framework Figure 17 A simulation model was built, and ISMC without introducing disturbance observation was used for comparative analysis.

[0207] Set the motor's torque to 10 N·m and its maximum speed to 1000 rpm. After the motor starts and stabilizes with a load of 5 N·m, increase the load to 10 N·m in 0.15s. After the system stabilizes, reduce the load to 5 N·m in 0.3s.

[0208] from Figure 18 The disturbance observation results show that when the motor starts with load, the EKF disturbance observer can quickly track the given torque within 0.01s; at the same time, when the load suddenly increases at 0.15s and decreases at 0.3s, the disturbance observer can quickly converge to the given value, and there is no obvious fluctuation during the convergence process.

[0209] from Figure 19 As can be seen from (a) and (b), during the motor load startup phase, the motor system without disturbance observation reaches the target speed in about 0.06s, while the motor system with disturbance observation can quickly reach the target speed in only 0.02s, and there is no speed overshoot in either system. When the load disturbance suddenly increases at 0.15s and suddenly decreases at 0.3s, the motor system without disturbance observation has a speed fluctuation of nearly 8rpm and takes about 0.04s to recover. Although the motor system with disturbance observation also has a speed error of less than 7rpm when the load suddenly increases or decreases, it can recover quickly in only about 0.015s.

[0210] from Figure 20 As can be seen from (a) and (b), the electromagnetic torque Te without the introduction of disturbance observation has obvious pulsation from 0.15s to 0.3s and the maximum range is about ±3N·m; while the electromagnetic torque Te after the introduction of disturbance observation can stably follow the changes of load disturbance, and there is no obvious oscillation in the process, and the maximum range of fluctuation is only between ±1N·m.

[0211] from Figure 21It can be seen that in the period of 0.15s to 0.3s when the load suddenly increases or decreases, the three-phase current without the introduction of the disturbance observer has obvious distortion, and iq fluctuates by about ±10A; after the introduction of the disturbance observer, the three-phase current is a stable sine wave without obvious distortion.

[0212] To verify the anti-interference ability of the ISMC based on the improved EKF disturbance observer to continuous disturbances, a triangular wave with a period of 0.2s and an amplitude of 5N·m was added as a load disturbance. The speed, torque, and current waveforms were analyzed and compared with the ISMC without the introduction of the disturbance observer.

[0213] from Figure 22 As can be seen from (a) and (b), the EKF disturbance observer can quickly follow the given disturbance, and the observation error is stable within 0.04 N·m.

[0214] from Figure 23 (a) and (b) show that after adding a continuous triangular wave disturbance load, the motor speed without the disturbance observer has an overshoot of about 8 rpm in the startup phase, and returns to the target value after 0.05 s. At the same time, the speed fluctuates by about 5 rpm during operation. After the disturbance observer is introduced, there is no speed overshoot in the startup phase, which is 8 rpm lower than before, and it only takes 0.02 s to recover to stability, which is 0.03 s lower than before. At the same time, during operation, the speed fluctuation is only 0.15 rpm, which is about 4.85 rpm lower than before. When the continuous triangular wave disturbance is introduced, the improved EKF reduces the disturbance observation error by nearly 20% compared with the traditional EKF. After the continuous triangular wave disturbance is introduced, it can still quickly and continuously follow the given value. The observation error is reduced by about 72.8% compared with the Romberg method, by about 24% compared with the traditional EKF, and by about 4.2% compared with the EKF that considers the correlation between process and measurement noise.

[0215] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0216] Example 3

[0217] The third embodiment of the present invention is different from the first two embodiments in that:

[0218] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0219] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0220] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0221] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0222] Example 4

[0223] The fourth embodiment of the present invention provides an improved EKF disturbance observation anti-saturation integral sliding mode adaptive control system, including a permanent magnet synchronous motor mathematical model construction module, a new reaching law sliding mode controller construction module, a nonlinear integral sliding mode surface construction module, a cross-covariance matrix module, an improved EKF disturbance observer module, an accuracy prediction module and an improved EKF disturbance observer module.

[0224] The permanent magnet synchronous motor mathematical model construction module models the permanent magnet synchronous motor and obtains its mathematical model, providing a foundation for subsequent controller design. The sliding mode controller construction module with a novel reaching law enables the system's dynamic behavior to approach and enter the sliding mode surface, thereby achieving rapid tracking and control of the system state. The nonlinear integral sliding mode surface construction module implements integral control of the system state to achieve precise control of the speed and position physical quantities of the permanent magnet synchronous motor. The cross-covariance matrix module calculates and updates the cross-covariance matrix between the system state variables and the observed variables. The improved EKF disturbance observer module improves the traditional EKF disturbance observer by introducing a new observation model or observation noise model. The accuracy prediction module predicts the accuracy of the system state estimate for a future period based on the current system state estimate and observation values. Building on the improved EKF disturbance observer, the improved EKF disturbance observer module incorporates fuzzy logic control theory to fuzzify the system state estimate and output the final system state estimate.

[0225] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An improved EKF disturbance observation anti-saturation integral sliding mode adaptive control method, characterized by: include, A mathematical model of a permanent magnet synchronous motor was established to improve the motor output torque and speed regulation range of high-power door and window openers. After the stability conditions were met, a Lyapunov analysis was performed. A sliding mode controller based on a novel reaching law was designed to address speed overshoot and system chattering of the sliding mode controller, and the discrete switching bandwidth was analyzed. An anti-saturation integral sliding mode controller based on a new nonlinear integral sliding mode surface is designed to improve the integral link saturation phenomenon during the startup of the motor of a high-power door and window opener. An improved integral sliding mode controller is obtained by combining it with a new reaching law to control its output torque. Design the PMSM mathematical model and introduce the cross-covariance matrix according to the traditional EKF disturbance observer implementation process; During the operation of the motor of a high-power door and window closer, the disturbance torque is accurately predicted by using different parameters in the system noise covariance matrix. An EKF disturbance observer based on fuzzy adaptive method is designed to predict the system disturbance torque, and an improved EKF disturbance observer is obtained. The mathematical model of the permanent magnet synchronous motor includes the electromagnetic torque of the PMSM. The electromagnetic torque of the PMSM mainly consists of two parts. The first part is the torque generated between the q-axis current component and the permanent magnet magnetic field, and the second part is the reluctance torque generated by the rotor salient pole effect. The mathematical model of the PMSM in the dq coordinate system is: The stability condition is satisfied by using sliding mode control, where the sliding surface s divides the state space of the system into two parts: s(x)>0 and s(x)<0: When the system meets the reachability condition, existence condition and stability condition, sliding mode control can be performed, where x∈R n is the state space variable, f(x, t) and g(x, t) are nonlinear vector functions, u(t) is the input variable, and R s is the stator resistance, u d 、u q is the stator voltage in the d and q axis components, i d 、i q is the stator current in the d and q axis components, p is the differential operator, ψ d , ψ q is the stator flux component in the d and q axes, ω e is the rotor electrical angular velocity; The sliding mode controller based on the novel reaching law includes designing the controller by adopting a piecewise reaching law. The reaching law expression is: Wherein, k=k1+k2, k1>0, k2>0, m>0, 1>λ>0, 1>α>0, p>0, δ>0, sat(s) is the saturation function, σ is the boundary layer, m, λ, α and δ are simulation model parameters, p is the differential operator, and the chattering amount during the switching process is reduced by feedback control within the boundary layer, while the sign function value is still used outside the boundary layer to quickly approach the sliding surface. When the sliding mode system is far away from the sliding surface, that is, in the stage of |s|>δ, by adjusting the values ​​of λ and m, the system trajectory is quickly approached to the sliding surface. At the same time, the saturation function sat(s) enables the system to maintain anti-bounce capability while maintaining a faster approach speed. When the sliding mode system approaches the sliding surface, that is, in the stage of |s|<δ, |s|→0, At the same time, the weights of k1 and k2 are adjusted to reduce the high-frequency chattering of the traditional sliding mode; The analysis of discrete switching bandwidth includes analyzing the system chattering problem of the sliding mode controller from the discrete switching bandwidth. When the state trajectory of the system approaches the sliding surface, the new reaching law is discretized and the switching bandwidth of the new reaching law is expressed as Δ=k1|s| α T s , where T s is the system sampling period, is the discrete switching bandwidth. Δ decreases as |s| decreases, and |s|→0, suppressing chattering.

2. The improved EKF disturbance observer anti-saturation integral sliding mode adaptive control method according to claim 1, characterized in that: The anti-saturation integral sliding mode controller based on the novel nonlinear integral sliding mode surface includes using a novel nonlinear function f(s) to limit the action time of the integral link when the speed error is large, and to speed up the adjustment speed of the controller when the speed error is small: Among them, k1 and k2 are adjustable coefficients. K1 determines the rising speed of f(s) in the small error amplification area, and k2 determines the width of the amplification area. Set k1 = 0.1 and k2 = 12.

3. A new nonlinear integral sliding surface is designed using f(s). The difference between the output torque of the sliding mode controller and the given torque limit value, as well as the difference between the speed error and the speed error limit value, is used as linear negative feedback to compensate for large errors in the integral link and achieve limit saturation: Where η is the feedback gain, e sat is the maximum output of the error limiting link, e ω represents the speed error, c is an integral adjustable constant, is the optimal electromagnetic torque, T e is the electromagnetic torque.

3. The improved EKF disturbance observer anti-windup integral sliding mode adaptive control method according to claim 2, characterized in that: The mathematical model of the PMSM in the three-phase stationary coordinate system is: The introduction of the cross-covariance matrix includes adding a cross-covariance matrix M to the improved EKF disturbance observer considering the correlation between process and measurement noise while keeping the state matrix A, control matrix B, and observation matrix G unchanged. k : M k =[-5e-40] Among them, R s is the stator resistance, i a 、i b 、i c is the three-phase current, ψ a , ψ b , ψ c is the three-phase magnetic flux, and e is a natural constant.

4. The improved EKF disturbance observer anti-windup integral sliding mode adaptive control method according to claim 3, characterized in that: The improved EKF expression is as follows: The system state matrix A, control matrix B, and observation matrix G are: G＝[1 0] Among them, K k is the Kalman gain calculation, is the covariance estimate, Error covariance correction, T s is the system sampling period, B m is the damping coefficient, J is the moment of inertia, Q k 、R k w k 、v k The covariance matrix, w k 、v k are process and measurement noise, Q k-1 w k-1 The covariance matrix, G k is the observation matrix at time k; The accuracy prediction of the disturbance torque by different parameters in the system noise covariance matrix includes the accuracy prediction of the disturbance torque by different parameters q1 and q2 in the system noise covariance matrix Q during the operation of the motor of a high-power door and window opener: when q1 increases, the steady-state accuracy of the torque prediction improves, but the corresponding dynamic following ability will decrease; when q1 decreases, the dynamic following performance improves, but a certain overshoot will occur; when q2 increases, the dynamic following ability of the torque improves, but the corresponding steady-state accuracy will decrease; when q2 decreases, the steady-state accuracy of the torque prediction improves, but the dynamic following ability will decrease.

5. The improved EKF disturbance observer anti-windup integral sliding mode adaptive control method according to claim 4, characterized in that: The fuzzy adaptive EKF disturbance observer includes an improved EKF disturbance observer, which is implemented by: judging the change scheme of the disturbance torque of the motor system of the high-power door and window closer and the current state of the system based on the influence of the covariance matrix parameters on the torque prediction, and adaptively configuring the parameter values ​​in the covariance matrix, thereby obtaining empirical values ​​of the disturbance torque prediction in dynamic and steady states for different parameters; After comparing the actual current value with the given value, the difference E and the difference change EC are used as input signals for fuzzy processing to obtain fuzzy quantities. Combining the fuzzy quantities with fuzzy control rules, fuzzy reasoning is performed based on empirical values. Finally, the fuzzy quantities obtained by reasoning are defuzzified and converted into precise parameters q1 and q2. The fuzzy quantity obtained by the fuzzification process is to perform a domain transformation on the input quantity after quantization processing. The fuzzy domain of variables E and EC is divided into five fuzzy subsets {negative large, negative small, zero, positive small, positive large}, namely {NB, NS, ZO, PS, PB}. When the load is in a sudden change moment, a triangular membership function is adopted, and the covariance matrix parameters are adjusted in real time through a fuzzy adaptive controller. If the difference between the given value and the actual value of the q-axis current is large, the covariance matrix parameters are adjusted to quickly reduce the deviation. If the difference is small, the stability of the disturbance observer is considered. The prediction of the system disturbance torque includes establishing an improved EKF state equation in the dq coordinate system based on the PMSM mathematical model and EKF, which is expressed as: At the same time, the EKF algorithm discretizes the PMSM nonlinear system, and the state and prediction equations after discretization are expressed as: In which, let x=[ω m T L ] T , let u=T e , let y = ω m , T e is the electromagnetic torque, ω m is the mechanical angular velocity, v is the measurement noise, T L is the rotation gain; Substitute x, u, and y into the state space equation of the discrete random system as follows: x k =f(x k-1 ,u k-1 )+w k-1 w k ~(0,Q k ) y k =h(x k )+v k v k ~(0,R k ) Among them, Q k 、R k w k 、v k The covariance matrix, w k 、v k are process and measurement noise, respectively.

6. A system using the improved EKF disturbance observer anti-windup integral sliding mode adaptive control method according to any one of claims 1 to 5, characterized in that: It includes permanent magnet synchronous motor mathematical model building module, new reaching law sliding mode controller building module, nonlinear integral sliding mode surface building module, cross-covariance matrix module, improved EKF disturbance observer module, accuracy prediction module and improved EKF disturbance observer module; The permanent magnet synchronous motor mathematical model building module models the permanent magnet synchronous motor to obtain a mathematical model of the permanent magnet synchronous motor, providing a basis for subsequent controller design; The sliding mode controller building module of the novel reaching law makes the dynamic behavior of the system approach and enter the sliding mode surface, thus achieving rapid tracking and control of the system state; The nonlinear integral sliding surface building module controls the integral of the system state to achieve precise control of the speed and position physical quantities of the permanent magnet synchronous motor; The cross-covariance matrix module calculates and updates the cross-covariance matrix between the system state variables and the observation variables; The improved EKF disturbance observer module improves the traditional EKF disturbance observer by introducing a new observation model or observation noise model; The accuracy prediction module predicts the system state estimation accuracy within a future period of time based on the current system state estimation value and observation value; The improved EKF disturbance observer module introduces fuzzy logic control theory on the basis of the improved EKF disturbance observer, performs fuzzy processing on the system state estimation, and outputs the final system state estimation value.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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