A method for constructing a PI improved adaptive fuzzy controller for permanent magnet synchronous motor

Abstract

The application discloses a kind of permanent magnet synchronous motor PI improved adaptive fuzzy controller construction method, belong to permanent magnet synchronous motor intelligent control technical field.Permanent magnet synchronous motor control system structure is constructed, its speed outer ring uses PI improved adaptive fuzzy controller;The overall structure of PI improved adaptive fuzzy controller is designed, and the PI fuzzy controller without adaptive mechanism is built;Select error e and its normalized acceleration r v (k) as observation, build adaptive mechanism with global adaptive adjustment characteristics, while deducing the clear calculation formula of r v (k), design improved adaptive adjustment law;Finally, controller parameter optimization is carried out, and the PI improved adaptive fuzzy controller design of PMSM is completed.The PI improved adaptive fuzzy controller of permanent magnet synchronous motor and its self-adjusting law design method are proposed in the application, with the characteristics of faster response speed, smaller overshoot, stronger anti-load disturbance capability, etc.

Description

Technical Field

[0001] This invention relates to the field of intelligent control technology for permanent magnet synchronous motors, and is particularly suitable for control applications of permanent magnet synchronous motor drive equipment that require fast response speed, small overshoot, and strong resistance to load disturbances. Background Technology

[0002] Permanent magnet synchronous motors (PMSMs) are characterized by their small size, high efficiency, low torque ripple, high power density, and ease of maintenance, making them a popular choice for speed control systems. However, for high-order, nonlinear, strongly coupled, and multivariable PMSM systems, traditional PID control strategies cannot meet the requirements for high-performance dynamic decoupling control. Fuzzy control, as a nonlinear mapping mechanism, is characterized by its independence from the mathematical model of the controlled object and has become a research hotspot in the field of PMSM control.

[0003] Literature and patent searches reveal that domestic and international research on fuzzy controllers primarily focuses on the integration of fuzzy controllers with classical PID controllers, adaptive adjustment of fuzzy controller parameters, design of fuzzy controller structure and adaptive laws, and the fusion of fuzzy control with other advanced control algorithms. Given its simple design, strong nonlinearity, and ease of DSP implementation (lookup table method), adaptive fuzzy controllers are well-suited for speed control of permanent magnet synchronous motors. Currently, the widely studied adaptive fuzzy PID controllers essentially use fuzzy control algorithms to tune PID parameters online. During the control process, the PID controller still bears the primary control task; it is not a pure fuzzy controller and therefore cannot fully leverage the inherent advantages of fuzzy control. As a two-dimensional fuzzy controller, the PI-like fuzzy controller does not have fixed proportional and integral gains and exhibits stronger nonlinearity. Adding adaptive adjustment characteristics to the PI-like fuzzy controller further enhances its superior control capabilities. However, adaptive adjustment typically uses the errors e and ec as the selected input observations, limiting adjustments to a specific operating point of the system, which has certain limitations. To further improve the dynamic and static performance of permanent magnet synchronous motor systems, selecting appropriate adaptive observation variables, improving the controller structure and adaptive law, and designing a permanent magnet synchronous motor-like PI-based improved adaptive fuzzy controller algorithm with adaptive global adjustment capability has significant theoretical and technical application value. Summary of the Invention

[0004] The purpose of this invention is to provide a method for constructing a PI-like improved adaptive fuzzy controller for permanent magnet synchronous motors. The permanent magnet synchronous motor control system using the PI-like improved adaptive fuzzy controller has the characteristics of fast response speed, smaller overshoot, and strong resistance to load disturbances.

[0005] To achieve the above objectives, this invention provides a method for constructing a PI adaptive fuzzy controller for permanent magnet synchronous motors, comprising the following steps:

[0006] S1. Construct the control system structure for the permanent magnet synchronous motor, and adopt a PI-like improved adaptive fuzzy controller for its outer speed loop.

[0007] S2. Design the overall structure of a PI-like improved adaptive fuzzy controller, and build a PI-like fuzzy controller without an adaptive mechanism, selecting the error e and its normalized acceleration r. v (k) is used as an observation to construct an adaptive mechanism with global adaptive adjustment characteristics, and r is derived. v ( k The clear calculation formula and design improvement adaptive adjustment rules;

[0008] S3. Optimize the controller parameters and complete the design of a PI-like improved adaptive fuzzy controller for PMSM.

[0009] Preferably, in step S1, the specific steps for constructing the permanent magnet synchronous motor control system structure are as follows:

[0010] A dual-loop control method is adopted to construct the control system structure of the permanent magnet synchronous motor. In this structure, the "speed error" is fed into the PI-like adaptive fuzzy controller in the outer loop of the speed control, and its "normalized acceleration dynamic variable" is obtained through real-time calculation. The output of the adaptive fuzzy controller is the q-axis torque current setpoint in the rotor flux orientation dq coordinate system. The current is dynamically adjusted in the dq coordinate system. The d-axis electromagnetic current closed loop and the q-axis torque current closed loop adopt ordinary PI regulators.

[0011] Preferably, in step S2, the specific steps for designing the overall structure of the PI-based improved adaptive fuzzy controller are as follows:

[0012] S21. A two-dimensional incremental PI fuzzy controller is used as the main speed controller.

[0013] S22. Connect a dual-input single-output auxiliary fuzzy controller in parallel with the incremental PI fuzzy controller in step S21 as a self-adjusting mechanism to construct a "relative rate observer".

[0014] S23. Adaptive adjustment of the output increment of fuzzy controller 1 by the self-adjustment factor α of the auxiliary fuzzy controller in step S22.

[0015] Preferably, in step S21, the incremental PI fuzzy controller adopts a dual-input single-output structure, and its input quantity e N and △e N The error e of rotational speed and the rate of change of error Δe are respectively derived from the corresponding quantization factor G.e G Δe The result after multiplication is the output quantity, which is the control increment Δu. N .

[0016] Preferably, in step S22, the "relative rate observer" takes the error change rate Δe as input and its output is the normalized (relative) error acceleration r. v (k);

[0017] r v (k) and e N Together they serve as the input to the auxiliary fuzzy controller; the output α of the auxiliary fuzzy controller... N With output factor G α The self-adjusting factor α is obtained after multiplication.

[0018] Preferably, in step S23, the self-adjustment factor α output by the auxiliary fuzzy controller is used to adaptively adjust the output increment of the incremental PI fuzzy controller. Specifically, this is achieved by adjusting the output quantity Δu of the incremental PI fuzzy controller. N Its output scaling factor G Δu After multiplication, it needs to be multiplied by (1+α) to obtain the total output U of the speed ring incremental PI adaptive fuzzy controller.

[0019] Preferably, in step S21, the specific steps for constructing the incremental PI fuzzy controller are as follows:

[0020] First, a fuzzy set is selected based on the actual speed control accuracy of the permanent magnet synchronous motor;

[0021] Seven levels were selected as fuzzy sets: negative large NB, negative medium NM, negative small NS, zero ZO, positive small PS, positive medium PM, and positive large PB; the input quantity e of fuzzy controller 1 was defined. N Δe N and output quantity Δu N The fuzzy set is: {NB, NM, NS, ZO, PS, PM, PB}. The membership function shapes are all symmetrical, uniformly distributed, and semi-overlapping triangles, and the normalized universe of discourse is [-1,1].

[0022] Select the quantization factor G of the incremental PI fuzzy controller e G Δe and scaling factor G Δu The reference formula for selecting relevant factors is:

[0023]

[0024] Among them, [e] min ,e max ]、[Δemin ,Δe max ] and [Δu min ,Δu max These represent the dynamic range of input and output variables during actual system operation. In the process of speed control of a permanent magnet synchronous motor, the upper and lower limits of the system's dynamic range are equal. max =-e min Equation (1) simplifies to:

[0025]

[0026] Secondly, the output variable Δu is designed based on the desired waveform of the system's dynamic response and the characteristics of the classic incremental PI controller. N Fuzzy rule table;

[0027] Finally, the fuzzy output is obtained according to Mamdani's min-max inference method, and then the fuzzy output is obtained after centroid defuzzification. The calculation formula is as follows:

[0028]

[0029] Preferably, in step S22, the normalized error acceleration r, which contains information about the system response speed, is... v (k) and speed error e N As two inputs to the auxiliary fuzzy controller, the normalized error acceleration r v The computational design for (k) is shown below:

[0030] Define the normalized error acceleration r v (k) The formula is as follows:

[0031]

[0032] In the formula, de(k) and dde(k) are the error velocity and error acceleration of the system at time k, respectively, and their definitions are as follows:

[0033] de(k)=Δe=e(k)-e(k-1) (5)

[0034] dde(k)=de(k)-de(k-1) (6)

[0035] The definition of de(m) in the formula is:

[0036]

[0037] Substituting equation (7) into equation (4), we get r v The clarification formula for (k) is:

[0038]

[0039] From equation (8), we can see that r v (k) A quantitative description of the system's response speed is provided, and its variation pattern is as follows:

[0040] When the system's response speed is fast enough, r v The value of (k) is close to 1;

[0041] When the system response speed is slow, r v The value of (k) is close to -1;

[0042] When the system rises or falls at a certain speed, r v The value of (k) approaches 0;

[0043] When de(k) and de(k-1) are both positive or both negative, and the values ​​of |de(k)| and |de(k-1)| are not both 0, r v The range of (k) is [-1, 1].

[0044] Preferably, in step S23, the specific steps for designing the adaptive adjustment law and the fuzzy rule table of the self-adjustment factor α are as follows:

[0045] Based on the observed error e and normalized error acceleration r at the input of the auxiliary fuzzy controller v The changing pattern of (k) divides the system into different response stages;

[0046] When the auxiliary fuzzy controller performs real-time adaptive adjustment of the output of the incremental PI fuzzy controller, the fuzzy rule table can be determined according to the following rules:

[0047] When the system error is large and the response speed is fast, increasing the output of the auxiliary fuzzy controller will increase the total output U of the controller in the positive direction, and the system will quickly approach the set value.

[0048] When the system error is small and the response speed is constant, adjust the output of the auxiliary fuzzy controller to reduce the total output U of the controller and avoid excessive overshoot.

[0049] When the system speed begins to drop and the response speed is slow, the total output U of the controller should be further reduced to quickly approach the steady state.

[0050] When the system falls back to near the set point and the response speed remains constant, the output of the auxiliary fuzzy controller is finely adjusted to increase the total output U of the controller in advance, thereby reducing the reverse overshoot.

[0051] When the system falls back excessively, regardless of the response speed, adjust the output of the auxiliary fuzzy controller to continuously increase the total output U of the controller, thereby suppressing overshoot and accelerating the response speed.

[0052] Based on the above adaptive rules, a fuzzy rule table was designed.

[0053] Preferably, in step S23, the auxiliary fuzzy controller in the PI-like improved adaptive fuzzy controller has its output variable and its output factor G. α The variation pattern and range of the self-adjustment factor α obtained after multiplication are determined by the two input quantities of the auxiliary fuzzy controller, the fuzzy rules and inference methods used, and its output factor G. α Joint decision;

[0054] The magnitude of the self-adjustment factor α dynamically affects the degree of adaptive adjustment of the system. The algorithm for adjusting the total system output U(k) is designed as follows:

[0055] At the current time k in the system, the output of fuzzy controller 1 without a self-adjusting mechanism is:

[0056] ΔU(k1)=Δu N (k)*G Δu (9)

[0057] When introducing a self-adjusting mechanism, the following applies:

[0058] ΔU(k)=Δu N (k)*G Δu (1+α(k)) (10)

[0059] In this design, the outputs of the two fuzzy controllers both vary within the range of [-1, 1], and α(k) varies within the range of [-G]. α G α The range of ΔU(k) ​​is [ΔU(k1)*(1-G] α ),ΔU(k1)*(1+G α )];

[0060] Furthermore, based on the structure of the PI-like fuzzy controller, we can see that:

[0061] U(k)=U(k-1)+ΔU(k) ​​(11)

[0062] Therefore, substituting equation (11) into equation (10), we can obtain the expression for the total output U(k) of the control system at time k as follows:

[0063] U(k)=U(k-1)+Δu N (k)*G Δu (1+α(k)) (12)

[0064] Therefore, the present invention provides a method for constructing a permanent magnet synchronous motor-type PI-based improved adaptive fuzzy controller with the above-described structure, which has the following beneficial effects:

[0065] (1) This invention uses an adaptive mechanism that can make global adjustments to the control system to adjust the output variables of a PI-like fuzzy controller, and proposes a design method for the self-adjustment law of a PI-like (incremental) adaptive fuzzy controller for permanent magnet synchronous motors. The permanent magnet synchronous motor control system using a PI-like adaptive fuzzy controller has the characteristics of fast response speed, smaller overshoot, and stronger resistance to load disturbances.

[0066] (2) The technology of this invention has certain reference value for the design of PI-like adaptive fuzzy controllers for other AC motors, and can be extended to other types of advanced adaptive fuzzy control systems.

[0067] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0068] Figure 1 This is a schematic diagram of the principle of an adaptive fuzzy control system for a permanent magnet synchronous motor using a PI adaptive fuzzy controller construction method for a permanent magnet synchronous motor according to the present invention.

[0069] Figure 2 This is a schematic diagram illustrating the principle of an incremental PI-based adaptive fuzzy controller based on the construction method of a permanent magnet synchronous motor PI-based adaptive fuzzy controller according to the present invention. Detailed Implementation

[0070] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0071] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0072] Example

[0073] This invention provides a method for constructing a PI-based improved adaptive fuzzy controller for permanent magnet synchronous motors, comprising the following steps:

[0074] S1. Construct a PI adaptive fuzzy control system for a permanent magnet synchronous motor. The control system structure for the permanent magnet synchronous motor is constructed using a dual-closed-loop control method, as follows: Figure 1 As shown. Figure 1 The block diagram shows the structure of a PMSM control system employing an improved adaptive PI-type fuzzy controller. The system startup process uses i... d In the 0-control mode, both inner current loops are designed as PI controls, while the outer speed loop uses a PI-like modified adaptive fuzzy controller to improve the system's dynamic and static performance. Specifically, the "speed error" is fed into the PI-like modified adaptive fuzzy controller in the outer speed loop, where its "normalized acceleration dynamic variable" is calculated in real time. The adaptive fuzzy controller outputs the q-axis torque current setpoint in the rotor flux orientation dq coordinate system. The current is dynamically adjusted in the dq coordinate system, with the d-axis electromagnetic current closed loop and the q-axis torque current closed loop using ordinary PI controllers.

[0075] S2. Construct a PI-like adaptive fuzzy speed controller. Figure 2 As shown ( Figure 2 The structure diagram of the improved PI-like adaptive fuzzy controller is shown. The overall control structure employs two fuzzy controllers in parallel; fuzzy controller 1 plays the dominant control role, and fuzzy controller 2 is the auxiliary controller, capable of adjusting the output of the former in real time. The incremental PI-like adaptive fuzzy controller is used as a speed controller, and the specific steps are as follows:

[0076] First, a two-dimensional incremental PI fuzzy controller (fuzzy controller 1) is used as the main speed controller; this controller adopts a dual-input single-output structure, and its input quantity e N and △e N The error e of rotational speed and the rate of change of error Δe are respectively derived from the corresponding quantization factor G. e G Δe The result after multiplication is the output quantity, which is the control increment Δu. N .

[0077] Then, a dual-input single-output auxiliary fuzzy controller (fuzzy controller 2) is connected in parallel with fuzzy controller 1 as a self-adjusting mechanism. To improve the global adaptive characteristics of the controller, a "relative rate observer" is constructed to observe the relative speed of the system response in real time. The "relative rate observer" takes the error change rate Δe as input and its output is the normalized (relative) error acceleration r. v (k); r v (k) and e NBoth are used as inputs to fuzzy controller 2; the output α of fuzzy controller 2 is... N With output factor G α The self-adjusting factor α is obtained after multiplication.

[0078] Finally, the self-adjustment factor α output by fuzzy controller 2 is used to adaptively adjust the output increment of fuzzy controller 1. Specifically, this is achieved by adjusting the output quantity Δu of fuzzy controller 1. N Its output scaling factor G Δu After multiplication, it needs to be multiplied by (1+α) again to obtain the total output U of the speed ring type (incremental) PI adaptive fuzzy controller.

[0079] (1) Construct a class (incremental) PI fuzzy controller (fuzzy controller 1).

[0080] First, a fuzzy set is selected based on the actual speed control accuracy of the permanent magnet synchronous motor;

[0081] Seven levels are selected as fuzzy sets: negative large (NB), negative medium (NM), negative small (NS), zero (ZO), positive small (PS), positive medium (PM), and positive large (PB); the input quantity e of fuzzy controller 1 is defined. N Δe N and output quantity Δu N The fuzzy set is: {NB, NM, NS, ZO, PS, PM, PB}. The membership functions are all symmetrical, uniformly distributed, and semi-overlapping triangles. The normalized universe of discourse is [-1, 1].

[0082] Then, the quantization factor G of fuzzy controller 1 is selected. e G Δe and scaling factor G Δu The reference formula for selecting relevant factors is:

[0083]

[0084] Among them, [e] min ,e max ]、[Δe min ,Δe max ] and [Δu min ,Δu max [e] represents the dynamic range of the input and output variables during actual system operation. Considering that the upper and lower limits of the system's dynamic range are equal during speed control of the permanent magnet synchronous motor, i.e., e max =-e min Therefore, equation (1) can be simplified to:

[0085]

[0086] Secondly, the output variable Δu is designed based on the desired waveform of the system's dynamic response and the characteristics of the classic incremental PI controller. N The fuzzy rule table is shown in Table 1.

[0087] Table 1 shows the output Δu of the PI-like improved adaptive fuzzy controller. N The fuzzy rule table allows for fine-tuning of 49 fuzzy rules in practical applications to obtain a more ideal system dynamic response waveform.

[0088] Table 1 Δu N Fuzzy rule table

[0089]

[0090] Table 1 shows the output variable Δu when the error e and the error rate of change Δe are taken with different linguistic values ​​after fuzzification. N The fuzzy rule table determines the magnitude of the controller's current output increment, which in turn affects the controller's total output.

[0091] Finally, the fuzzy output is obtained according to Mamdani's min-max inference method, and then the fuzzy output is obtained after centroid defuzzification. The calculation formula is as follows:

[0092]

[0093] (2) Construct an improved PI-like adaptive fuzzy controller adaptive mechanism (fuzzy controller 2).

[0094] The normalized error acceleration r contains information about the speed of the system response. v (k) and speed error e N As two input variables of the self-adjusting mechanism (fuzzy controller 2), it dynamically identifies each stage of the system response process; the definition of the fuzzy subsets of the input and output variables of fuzzy controller 2, the shape of the membership function, the partitioning of the universe of discourse, and the methods of inference and defuzzification are consistent with those of fuzzy control 1; the construction steps of fuzzy controller 2 are similar to those of the classic incremental fuzzy PI controller, the main difference being that it requires "normalizing the error acceleration r". v The calculation of (k)” and the design of the adaptive adjustment law are as follows:

[0095] 1) Calculate the normalized error acceleration r v (k).

[0096] First, define the normalized error acceleration r. v (k) The formula is as follows:

[0097]

[0098] In the formula, de(k) and dde(k) are the error velocity and error acceleration of the system at time k, respectively, and their definitions are as follows:

[0099] de(k)=Δe=e(k)-e(k-1) (5)

[0100] dde(k)=de(k)-de(k-1) (6)

[0101] The definition of de(m) in the formula is:

[0102]

[0103] Then, substituting equation (7) into equation (4), we get r. v The clarification formula for (k) is:

[0104]

[0105] From equation (8), we can see that r v (k) can provide a quantitative description of the system's response speed, and its variation pattern is as follows:

[0106] ① When the system's response speed is fast enough, r v The value of (k) is close to 1;

[0107] ② When the system response speed is very slow, r v The value of (k) is close to -1;

[0108] ③ When the system rises or falls at a certain speed, r v The value of (k) approaches 0;

[0109] ④ When de(k) and de(k-1) are both positive or both negative, and the values ​​of |de(k)| and |de(k-1)| are not both 0, r v The range of (k) is [-1, 1], and these two limitations need to be taken into account when the system is actually running.

[0110] 2) Design an adaptive adjustment law and, based on this, design a fuzzy rule table for the self-adjustment factor α.

[0111] First, based on the observed error (i.e., rotational speed error) e and the normalized error acceleration r at the input of the self-adjusting mechanism... v The changing pattern of (k) divides the system into different response stages.

[0112] Then, appropriate fuzzy rules are formulated for different system response stages based on expert knowledge and experience. Figure 2When fuzzy controller 2 adaptively adjusts the output of fuzzy controller 1 in real time, the fuzzy rule table can be determined according to the following rules:

[0113] ①When the system error is large and the response speed is fast, increase the output of fuzzy controller 2 to make the total output U of the controller increase in the positive direction, and the system will quickly approach the set value;

[0114] ② When the system error is small and the response speed is constant, adjust the output of fuzzy controller 2 to reduce the total output U of the controller and avoid excessive overshoot;

[0115] ③ When the system speed begins to drop and the response speed is slow, the total output U of the controller should be further reduced to quickly approach the steady state;

[0116] ④ When the system falls back to near the set point and the response speed is constant, fine-tune the output of fuzzy controller 2 to increase the total output U of the controller in advance and reduce the reverse overshoot;

[0117] ⑤ When the system falls back excessively, regardless of the response speed, adjust the output of fuzzy controller 2 to continuously increase the total output U of the controller, suppress overshoot and speed up the response.

[0118] Finally, based on the above adaptive rules, a fuzzy rule table as shown in Table 2 was designed.

[0119] Table 2 is a fuzzy rule table for the self-adjustment factor α, which can be corrected according to the actual response waveform of the permanent magnet synchronous motor control system so that the system obtains the correct adjustment action at a certain response stage.

[0120] Table 2. Fuzzy rule table for self-adjusting factor α

[0121]

[0122]

[0123] For example, the linguistic variable for the error e corresponding to rule ③ is NS, and the normalized error acceleration is r. v If the linguistic variable for (k) is NM or NS, then the fuzzy output should be PM or PB. Meanwhile, in Figure 2 In the above, when the self-adjustment factor α takes a positive value, and the output Δu of fuzzy controller 1 is... N A negative value means that the total output U of the current controller will decrease at a faster rate, allowing the system to quickly reach a steady state.

[0124] 3) Optimize controller parameters and complete the design of a PI-like improved adaptive fuzzy controller for the PMSM. Design an algorithm to adjust the self-adjusting factor α to the total system output U(k). The fuzzy controller 2 in the PI-like adaptive fuzzy controller has its output variable related to its output factor G. α The variation pattern and range of the self-adjustment factor α obtained after multiplication are determined by the two input quantities of fuzzy controller 2, the fuzzy rules used, and its output factor G. α The self-adjustment factor α is jointly determined; its magnitude dynamically affects the degree of adaptive adjustment of the system, and the algorithm for adjusting the total system output U(k) is designed as follows:

[0125] First, at the current time k in the system, the output of fuzzy controller 1 without a self-adjusting mechanism is:

[0126] ΔU(k1)=Δu N (k)*G Δu (9)

[0127] Secondly, when introducing a self-adjusting mechanism:

[0128] ΔU(k)=Δu N (k)*G Δu (1+α(k)) (10)

[0129] Where, the range of α(k) is [-G α G α The range of ΔU(k) ​​is [ΔU(k1)*(1-G] α ),ΔU(k1)*(1+G α Furthermore, based on the structure of a PI-like fuzzy controller, it can be seen that:

[0130] U(k)=U(k-1)+ΔU(k) ​​(11)

[0131] Therefore, substituting equation (11) into equation (10), we can obtain the expression for the total output U(k) of the control system at time k as follows:

[0132] U(k)=U(k-1)+Δu N (k)*G Δu (1+α(k)) (12)

[0133] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for constructing a permanent magnet synchronous motor-based PI-modified adaptive fuzzy controller, characterized in that, Includes the following steps: S1. Construct the control system structure for the permanent magnet synchronous motor, and adopt a PI-like improved adaptive fuzzy controller for its outer speed loop. S2. Design the overall structure of a PI-like improved adaptive fuzzy controller, and build a PI-like incremental fuzzy controller without an adaptive mechanism, selecting the rotational speed error. e and its normalized error acceleration r v ( k As an observation, an adaptive mechanism with global adaptive adjustment characteristics is constructed, and the following is derived: r v ( k The clear calculation formula and design improvement adaptive adjustment rules; In step S2, the specific steps for designing the overall structure of the PI-based improved adaptive fuzzy controller are as follows: S21. A two-dimensional incremental PI fuzzy controller is used as the main speed controller. S22. Connect a dual-input single-output auxiliary fuzzy controller in parallel with the incremental PI fuzzy controller in step S21 as an adaptive mechanism to construct a relative rate observer. S23. Adaptively adjust the output of the incremental PI fuzzy controller by using the self-adjustment factor α of the auxiliary fuzzy controller in step S22. In step S21, the incremental PI fuzzy controller adopts a dual-input single-output structure, and its input quantity... e N and △ e N respectively by speed error e and the rate of change of error Δ e With corresponding quantification factors G e , G Δe The result after multiplication is the output quantity, which is the control increment Δ. u N ; In step S22, the relative rate observer uses the error change rate Δ e The input is the normalized error acceleration, and the output is the normalized error acceleration. r v ( k ); r v ( k )and e N Together they serve as inputs to the auxiliary fuzzy controller; Output of the auxiliary fuzzy controller α N With output factor G α The self-adjustment factor is obtained after multiplication. α ; In step S23, the output of the incremental PI fuzzy controller is adaptively adjusted by the self-adjustment factor α. Specifically, the output of the incremental PI fuzzy controller is... u N With scaling factor G Δu After multiplying, it needs to be multiplied by (1+α) again to obtain the total output U of the outer loop of the speed; S3. Optimize the controller parameters and complete the design of a PI-like improved adaptive fuzzy controller for the permanent magnet synchronous motor.

2. The method for constructing a permanent magnet synchronous motor-type PI-based improved adaptive fuzzy controller according to claim 1, characterized in that: In step S1, the specific steps for constructing the permanent magnet synchronous motor control system structure are as follows: The permanent magnet synchronous motor control system structure is constructed using a dual closed-loop control method, wherein: the output of the incremental PI fuzzy controller is the rotor flux orientation. dq In the coordinate system q Shaft torque current setpoint; current in dq Dynamic adjustment is performed within the coordinate system. d Shaft-type electromagnetic current closed loop and q The shaft torque current closed loop uses a common PI regulator.

3. The method for constructing a permanent magnet synchronous motor-like PI-based improved adaptive fuzzy controller according to claim 1, characterized in that: In step S21, the specific steps for constructing the incremental PI fuzzy controller are as follows: First, a fuzzy set is selected based on the actual speed control accuracy of the permanent magnet synchronous motor; Seven levels were selected as fuzzy sets: negative large (NB), negative medium (NM), negative small (NS), zero (ZO), positive small (PS), positive medium (PM), and positive large (PB); the input quantities of the incremental PI fuzzy controller were defined. e N , e N and output u N The fuzzy set is: {NB, NM, NS, ZO, PS, PM, PB}. The membership function shapes are all symmetrical, uniformly distributed, and semi-overlapping triangles, and the normalized universe of discourse is [-1,1]. Selecting the quantization factor for an incremental PI fuzzy controller G e , G Δe and scaling factor G Δu The reference formula for selecting relevant factors is: （1） Among them [ e min , e max ]、[Δ e min ,Δ e max ] and [Δ u min ,Δ u max These represent the dynamic range of input and output variables during actual system operation. In the process of speed control of a permanent magnet synchronous motor, the upper and lower limits of the system's dynamic range are equal. Equation (1) simplifies to: （2） Secondly, the output quantity Δ is designed based on the desired waveform of the system's dynamic response and the characteristics of the classic incremental PI controller. u N Fuzzy rule table; Finally, the fuzzy output is obtained according to Mamdani's min-max inference method, and then the fuzzy output is obtained after centroid defuzzification. The calculation formula is as follows: （3）。 4. The method for constructing a permanent magnet synchronous motor-like PI-based improved adaptive fuzzy controller according to claim 1, characterized in that: In step S22, the normalized error acceleration r v The computational design for (k) is shown below: Define normalized error acceleration r v ( k The formula is as follows: (4) In the formula de ( k )and dde ( k ) are respectively the system in k The error velocity and error acceleration at time t are defined as follows: (5) (6) In the formula de ( m The definition of ) is: (7) Substituting equation (7) into equation (4), we get r v ( k The formula for clarification: (8) From equation (8), we can see that r v ( k The system response speed is quantitatively described, and its variation pattern is as follows: When the system's response speed is fast enough r v ( k The value of ) is close to 1; When the system response speed is slow r v ( k The value of ) is close to -1; When the system rises or falls at a certain speed r v ( k The value of ) approaches 0; when de ( k )and de ( k -1) takes the value of either "both positive" or "both negative", and | de ( k )| and | de ( k -1)| is not both 0. r v ( k The range of variation is [-1, 1].

5. The method for constructing a permanent magnet synchronous motor-like PI-based improved adaptive fuzzy controller according to claim 1, characterized in that: In step S23, an adaptive adjustment law is designed, and a fuzzy rule table for the self-adjustment factor α is designed accordingly. The specific steps are as follows: According to the input of the auxiliary fuzzy controller e N and normalized error acceleration r v ( k The changing patterns of the system can be used to divide the system into different response stages; When the auxiliary fuzzy controller performs real-time adaptive adjustment of the output of the incremental PI fuzzy controller, the fuzzy rule table is determined according to the following rules: When the system error is large and the response speed is fast, increasing the output of the auxiliary fuzzy controller will increase the total output U of the controller in the positive direction, and the system will quickly approach the set value. When the system error is small and the response speed is constant, adjust the output of the auxiliary fuzzy controller to reduce the total output U of the controller and avoid excessive overshoot. When the system speed begins to drop and the response speed is slow, the total output U of the controller should be further reduced to quickly approach the steady state. When the system falls back to near the set point and the response speed remains constant, the output of the auxiliary fuzzy controller is finely adjusted to increase the total output U of the controller in advance, thereby reducing the reverse overshoot. When the system falls back excessively, regardless of the response speed, adjust the output of the auxiliary fuzzy controller to continuously increase the total output U of the controller, thereby suppressing overshoot and accelerating the response speed. Based on the above adaptive rules, a fuzzy rule table was designed.

6. The method for constructing a permanent magnet synchronous motor-like PI-based improved adaptive fuzzy controller according to claim 1, characterized in that: In step S23, the output of the auxiliary fuzzy controller and its output factor are... G α The self-adjustment factor obtained after multiplication α The changing patterns and ranges are determined by the two input quantities of the auxiliary fuzzy controller, the fuzzy rules and inference methods used, and its output factor. G α Joint decision; The magnitude of the self-adjustment factor α dynamically affects the degree of adaptive adjustment of the system. The algorithm for adjusting the total output U(k) is designed as follows: In the current system k At any given time, the output of the incremental PI fuzzy controller, without an adaptive mechanism, has: (9) When introducing an adaptive mechanism, we have: (10) Since the output range of the two fuzzy controllers involved in the design process is [-1, 1], therefore α ( k The range of variation is [- G α , G α ], Δ U ( k The range of variation is [Δ U ( k 1)*(1- G α ), Δ U ( k 1)*(1+ G α )]; Furthermore, based on the structure of the incremental PI fuzzy controller, we can see that: (11) Substituting equation (10) into equation (11), we can obtain... k Total output at time step U ( k The expression is: (12)。

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