阅读说明：本技术 一种抑制永磁同步电机转矩脉动的控制系统及算法 (Control system and algorithm for inhibiting torque ripple of permanent magnet synchronous motor ) 是由 王旭 黄巧亮 于 2020-11-26 设计创作，主要内容包括：本发明涉及计算机算法技术领域,具体地说,是一种抑制永磁同步电机转矩脉动的控制系统及算法,该算法基于脑情感控制器的模型参考自适应用于无位置传感器永磁同步电机的控制,与传统无位置传感器模型参考自适应永磁同步电机的控制相比该算法可以根据电机当前工作状态,实时变更自适应律来达到更加准确快速的估计转子位子,从而有效的抑制被控电机的转矩脉动。该算法进一步改进还可以进行永磁同步电机参数的在线辨识,进一步优化对永磁同步电机的控制。(The invention relates to the technical field of computer algorithms, in particular to a control system and an algorithm for inhibiting torque pulsation of a permanent magnet synchronous motor. The algorithm is further improved, and the parameters of the permanent magnet synchronous motor can be identified on line, so that the control of the permanent magnet synchronous motor is further optimized.)

1. A control system for inhibiting torque pulsation of a permanent magnet synchronous motor adopts a model reference self-adaptive position-free sensor vector control strategy based on a brain emotion controller, and is characterized by comprising a speed loop controller, a current loop controller and a model reference self-adaptive controller based on brain emotion control; the speed loop adopts the traditional sliding mode control to improve the anti-interference capability of the system, and the two current loops adopt the traditional PI control.

2. A control algorithm for inhibiting torque pulsation of a permanent magnet synchronous motor is characterized by comprising three steps:

the first step is as follows: constructing a vector control strategy of the permanent magnet synchronous motor without the position sensor as a frame, wherein a speed loop is controlled by a sliding mode, and a current loop is controlled by a PI (proportional integral) mode;

the second step is that: constructing a model reference adaptive controller based on a brain emotion controller;

the third step: and optimizing the model reference adaptive control according to the adaptive law obtained by the emotional controller to obtain the more accurate position and speed of the permanent magnet synchronous motor rotor so as to form closed-loop control.

3. The control algorithm for suppressing the torque ripple of the permanent magnet synchronous motor according to claim 2, wherein in the second step, the emotional controller is designed as follows:

step 1: the design process of the controller starts with selecting input parameters of the controller, namely sensory signals;

step 2: processing the output of sensory signals in the sensory cortex, amygdala and OFC;

and step 3: amygdala and OFC have an association of emotional cues;

and 4, step 4: the final output of the controller is based on the amygdala and the OFC output;

and 5: adjusting the output of the AG with the help of the OFC;

step 6: the AG and OFC signals are processed in E, where the process will stop if the generated speed matches the response required by the controlled object, otherwise go to step 1 and then start over.

4. Control algorithm for suppressing torque ripple of permanent magnet synchronous motor according to claim 3, characterized in that the step 1 sensory signal (S)_i) Is selected according to the following equation:

f＝(G₁+G₂)x+∫G₃u_c，

wherein the sensory signal comprises x and u_cIs an input variable.

5. The control algorithm for suppressing torque ripple of permanent magnet synchronous motor according to claim 4, wherein said step 2 analyzes the sensory signal in the Sensory Cortex (SC) to utilize the following equation:

improvement of S_iThe signal, SC signal, is further processed in the amygdala and OFC to make signal conditioning and produce faster controller output, i.e., emotional response.

6. The control algorithm for suppressing the torque ripple of the PMSM according to claim 5, wherein the emotional clues of the amygdala and OFC in step 3 are modeled by the following equations:

h＝A∫x+Bu_c+C|xu_c|。

7. the control algorithm for suppressing the torque ripple of the permanent magnet synchronous motor according to claim 5, wherein the step 4: the final output of the controller is based on the amygdala and OFC outputs, and the Amygdala (AG) is designed using the following equation:

ΔV_i＝αmax(0，EC-V_i-1)SC_i

the maximum term in the equation makes the output of the amygdala monotonic, i.e., never decreasing or always high.

8. The control algorithm for suppressing torque ripple of PMSM according to claim 7, wherein said step 5 adjusts the output of AG with the help of OFC, which uses the equation

O＝W_iS_i

The model contains among others sensory signals, sensory cortex, emotional cues and learning rate β.

Technical Field

The invention relates to the technical field of computer algorithms, in particular to a control system and an algorithm for inhibiting torque pulsation of a permanent magnet synchronous motor, which are a Model Reference Adaptive (MRAS) control position-sensing-free control algorithm for controlling the permanent magnet synchronous motor based on brain emotion optimization, can effectively inhibit the torque pulsation and can be applied to a ship electric propulsion system and an electric automobile power system.

Background

For a permanent magnet synchronous motor, the influence of the difference of the design of the motor body and the selection of the control strategy of the motor body on the torque ripple of the motor is great. Generally, when the same permanent magnet synchronous motor is controlled respectively, the torque ripple of the position sensor control strategy is smaller than that of the position sensor-free control strategy. However, there is a position sensor control strategy that mounts mechanical sensors (hall position sensors, rotary encoders, etc.) on the motor shaft to obtain position and speed information of the permanent magnet motor. However, after the mechanical sensor is introduced, the mechanical sensor not only increases the rotational inertia of the rotating shaft of the motor, reduces the mechanical performance of the rotor, but also increases the size and volume of the motor. Meanwhile, after the mechanical sensor is installed, on one hand, the connection of peripheral hardware circuits is increased, so that the system is easy to interfere and unstable, on the other hand, the cost is increased, particularly for a low-power permanent magnet motor driving system, the proportion of the cost of the sensor in the whole system is large, and in some special application occasions, the reliability of the system after the mechanical sensor is installed is greatly reduced under the influence of a working environment. Therefore, it is very important to research a position sensorless control strategy and a motor pulsation suppression control algorithm of the permanent magnet synchronous motor. Compared with the traditional model reference adaptive position sensorless control algorithm, the model reference adaptive position sensorless control algorithm based on brain emotion control has higher position estimation precision on the motor rotor, so that the pulsation of the torque can be further inhibited.

Disclosure of Invention

Aiming at the problem of overlarge torque ripple in the control of the existing permanent magnet synchronous motor without the position sensor, the invention provides a control system and an algorithm capable of inhibiting the torque ripple of the permanent magnet synchronous motor without the position sensor.

The invention adopts the following specific technical scheme:

a control system for inhibiting torque pulsation of a permanent magnet synchronous motor adopts a position-sensorless vector control strategy based on brain emotion controller model reference self-adaptation, and comprises a speed loop controller, a current loop controller and a model reference self-adaptation controller based on brain emotion control; the speed loop adopts the traditional sliding mode control to improve the anti-interference capability of the system, and the two current loops adopt the traditional PI control. The estimation of the position and the speed of the rotor adopts model reference self-adaptive control based on a brain feeling controller, and the estimation method can improve the torque pulsation problem in the control process of the permanent magnet synchronous motor without the position sensor.

A control algorithm for inhibiting torque ripple of a permanent magnet synchronous motor is designed by adopting a model reference adaptive control strategy based on a brain feeling controller, and the control principle is as follows:

sensory Signal S_iCan be expressed as a function of f:

S_i＝f(e，u_p，u_c) (1) processing S with the g function_iTo achieve an organoleptic skin layer:

SC＝g(S_i) (2)

the g function is expressed as:

the almond kernel and OFC learning model is designed as follows:

A＝V_iS_i (4)

ΔV_i＝αmax(0，EC-V_i-1)SC_i (5)

O＝W_iS_i (6)

ΔW_i＝β(E^l-EC)SC_i (7)

E^l＝A_i-O_i (8)

EC＝h(e，u_c，u_p) (9)

the output of the controller is an emotion signal E, which is derived as:

E＝A-O (10)

wherein u is_pIs the controlled object output, and u_cIs the controller output. A and O are the outputs of the amygdala and OFC,e is the controller output, V_iAnd W_iIs the gain of amygdala and OFC. α and β are the learning rates of amygdala and OFC, respectively, while the Δ sign represents the change in weight. E^lIs the result of the controller obtained from the outputs of the amygdala and OFC. The mood controller is used herein in the adaptive mechanism of MRAS technology and as a speed controller in a vector controlled PMSM driver. The selection of functions for sensory signals and emotional cues of the emotional controller is different for the adaptive mechanism and the tempo controller.

Sensory signals and emotional cue functions for the adaptive mechanism to estimate rotor speed are obtained as follows:

f＝(G₁+G₂)x+∫G₃u_c (11)

h＝A∫x+Bu_c+C|xu_c| (12)

wherein the x value is obtained from the tunable model and the reference model. G1, G2 and G3 are gain values of the sensory signal function, and A, B and C are gain values of the emotional cue function, respectively.

Estimating motor parameters for adaptive mechanismsThe sensory signal and emotional cue functions of (a):

wherein the x value is obtained from the tunable model and the reference model. G_1RAnd G_2RRespectively, the gain value of the sensory signal function, A_RAnd B_RRespectively, the gain values of the emotional cue functions.

Sensory and emotional cue functions for motion signals of adaptive mechanisms for estimating motor parameters

To obtain:

f＝G_1Lx+G_2Lu_c (14)

h＝A_Lx+B_Lu_c (15)

wherein the x value is obtained from the tunable model and the reference model. G_1LAnd G_2LRespectively, the gain value of the sensory signal function, A_LAnd BL are the gain values of the emotional cue functions, respectively.

The sensory signal and emotional cue function of the tempo controller can be obtained by:

f＝K₁e+K₂u_p+K₃∫u_cdt (16)

h＝ae+b|eu_c|+cY_P (17)

where e is a value obtained from a reference value and an actual speed, u_pIs output by the control object, and u_cIs the controller output. K₁，K₂And K₃Is the gain value of the function of the sensory signal, and a, b, and c are the gain values of the function of the emotional cues. The design process of the controller is the same as the estimation process of the adaptive mechanism, which is the only difference used to estimate speed, motor parameters, and control the PMSM driver is the choice of sensory and emotional cue responses.

The structure of the MRAS contains a reference model and can be adjusted by an adaptive mechanism. The reference model and the adjustable model are designed using motor equations that are required to drive the mathematical model. In addition, the reference model and the tunable model are used to develop an adaptive mechanism using the Lyapunov design method.

Mathematical modeling of the permanent magnet synchronous motor:

T_e＝T_L+J_mPω_r+B_mω_r (20)

equation (18) above can be rewritten in terms of the currents used in MRAS, with the reference and tunable models being designed in terms of the currents and voltages of the motor. The errors of the reference model and the adjustable model are used as inputs to an adaptive mechanism to estimate the rotor speed and identify motor parameters.

When phi is_f＝L_mi_fr，L_d＝L_q＝L，R_d＝R_qWhen R.

The output of the MRAS structure, the reference model and the adjustable model are used to design an adaptive mechanism to estimate the velocity. It is important to ensure that the system is stable and that the estimator will converge to the actual value of the adaptation mechanism. In general, ω_rIs a variable and therefore for the purpose of deriving the adaptive mechanism, the model is a linear time-varying system. However, initially ω will be_rThe constant parameters considered as a model are valid. The Lyapunov function method is established to design an adaptive mechanism to estimate the speed and position of the rotor.

The motor equations (21) and (22) can be expressed as:

equation (23) above is a reference model and is expressed as a state space model

For adjustable modelsExpressed as a regulating parameter

The state space model (25) of the equation can be expressed as

Equation (23) can be written as

Equation (25) can be written as

From equations (27) - (30)

From equations (31) and (32)

Equation (33) above is expressed in state space as follows

Model (35) derives an adaptation law to estimate ω_r. Consider the following Lyapunov function.

Where tr (P) denotes the trace of matrix P, K is the gain parameter, and P ═ P^T> 0 is chosen as the solution to the equation, as follows.

PA+A^TP＝-Q (36)

Where P is positive, the derivative of V is given by

By X in equation (37)_eInstead of the formerBecome into

To make it possible toIs negative, select F₁As

Under equilibrium conditionsAnd X_e0 is an adaptation law

Estimating the angle of the motor rotor:

δ＝∫ω_rdt (39)

the variable K in equation (38) will speed up or slow down the speed adaptation law depending on the desired response. The variable K is obtained using a brain mood controller.

The invention has the beneficial effects that: compared with the traditional control of the position sensor-free model reference self-adaptive permanent magnet synchronous motor, the algorithm can change the self-adaptive law in real time according to the current working state of the motor to achieve more accurate and rapid estimation of the rotor position, so that the torque pulsation of the controlled motor is effectively inhibited. The algorithm is further improved, and the parameters of the permanent magnet synchronous motor can be identified on line, so that the control of the permanent magnet synchronous motor is further optimized.

Drawings

FIG. 1 is an overall structural view of the present invention.

Fig. 2 is a schematic diagram of a position sensorless model reference adaptive permanent magnet synchronous motor control.

Fig. 3 is a conventional model reference adaptation structure.

FIG. 4 modified model reference adaptation.

FIG. 5 is a brain emotion control adaptive law optimization principle.

FIG. 6 shows a process of designing an emotional controller.

Fig. 7 is a simulation comparison diagram.

Fig. 8 is a symbol definition table related to the present invention.

Detailed Description

For the purpose of enhancing the understanding of the present invention, the present invention will be described in further detail with reference to the accompanying drawings and examples, which are provided for the purpose of illustration only and are not intended to limit the scope of the present invention.

The control structure of the traditional model reference self-adaptive position sensorless permanent magnet synchronous motor is shown in fig. 2, and the basic structure of the MRAS in fig. 2 is shown in fig. 3, and comprises three parts, namely a reference model, an adjustable model and a parameter self-adaptation law. The structure of the improved MRAS is shown in fig. 4, and the improved MRAS uses an emotional controller when determining the parameter adaptation law. The structure of the emotional controller is shown in fig. 5.

Example (b): a control system for suppressing torque ripple of a permanent magnet synchronous motor adopts a model reference self-adaptive position-free sensor vector control strategy based on a brain emotion controller, and comprises a speed loop controller, a current loop controller and a model reference self-adaptive controller based on brain emotion control, which are shown in figure 1. The speed loop adopts the traditional sliding mode control to improve the anti-interference capability of the system, and the two current loops adopt the traditional PI control. The estimation of the position and the speed of the rotor adopts model reference self-adaptive control based on a brain feeling controller, and the estimation method can improve the torque pulsation problem in the control process of the permanent magnet synchronous motor without the position sensor.

In the embodiment, a model reference adaptive control strategy based on a brain feeling controller is adopted for designing the controller, and the control principle is as follows:

sensory Signal S_iCan be expressed as a function of f:

S_i＝f(e，u_p，u_c) (1)

processing S with the g function_iTo achieve an organoleptic skin layer:

SC＝g(S_i) (2)

the g function is expressed as:

the almond kernel and OFC learning model is designed as follows:

A＝V_iS_i (4)

ΔV_i＝αmax(0，EC-V_i-1)SC_i (5)

O＝W_iS_i (6)

ΔW_i＝β(E^l-EC)SC_i (7)

E^l＝A_i-O_i (8)

EC＝h(e，u_c，u_p) (9)

the output of the controller is an emotion signal E, which is derived as:

E＝A-O (10)

wherein u is_pIs the controlled object output, and u_cIs the controller output. A and O are the outputs of the almond kernel and OFC, E is the controller output, V_iAnd W_iIs the gain of amygdala and OFC. α and β are the learning rates of amygdala and OFC, respectively, while the Δ sign represents the change in weight. E^lIs the result of the controller obtained from the outputs of the amygdala and OFC. The emotion controller is used in the adaptive mechanism of MRAS technology and willWhich acts as a speed controller in a vector controlled PMSM driver. The selection of functions for sensory signals and emotional cues of the emotional controller is different for the adaptive mechanism and the tempo controller.

Sensory signals and emotional cue functions for the adaptive mechanism to estimate rotor speed are obtained as follows

f＝(G₁+G₂)x+∫G₃u_c (11)

h＝A∫x+Bu_c+C|xu_c| (12)

Wherein the x value is obtained from the tunable model and the reference model. G1, G2 and G3 are gain values of the sensory signal function, and A, B and C are gain values of the emotional cue function, respectively.

Sensory signals and emotional cue functions for the adaptive mechanism to estimate rotor speed are obtained as follows:

f＝(G₁+G₂)x+∫G₃u_c (11)

h＝A∫x+Bu_c+C|xu_c| (12)

wherein the x value is obtained from the tunable model and the reference model. G1, G2 and G3 are gain values of the sensory signal function, and A, B and C are gain values of the emotional cue function, respectively.

Estimating motor parameters for adaptive mechanismsThe sensory signal and emotional cue functions of (a):

wherein the x value is obtained from the tunable model and the reference model. G_1RAnd G_2RRespectively, the gain value of the sensory signal function, A_RAnd B_RRespectively, the gain values of the emotional cue functions.

Sensory and emotional cue functions for motion signals of adaptive mechanisms for estimating motor parameters

To obtain:

f＝G_1Lx+G_2Lu_c (14)

h＝A_Lx+B_Lu_c (15)

wherein the x value is obtained from the tunable model and the reference model. G_1LAnd G_2LRespectively, the gain value of the sensory signal function, A_LAnd BL are the gain values of the emotional cue functions, respectively.

The sensory signal and emotional cue function of the tempo controller can be obtained by:

f＝K₁e+K₂u_p+K₃∫u_cdt (16)

h＝ae+b|eu_c|+cY_P (17)

where e is a value obtained from a reference value and an actual speed, u_pIs output by the control object, and u_cIs the controller output. K₁，K₂And K₃Is the gain value of the function of the sensory signal, and a, b, and c are the gain values of the function of the emotional cues. The design process of the controller is the same as the estimation process of the adaptive mechanism, which is the only difference used to estimate speed, motor parameters, and control the PMSM driver is the choice of sensory and emotional cue responses.

The structure of the MRAS contains a reference model and can be adjusted by an adaptive mechanism. The reference model and the adjustable model are designed using motor equations that are required to drive the mathematical model. In addition, the reference model and the tunable model are used to develop an adaptive mechanism using the Lyapunov design method.

Mathematical modeling of the permanent magnet synchronous motor:

T_e＝T_L+J_mPω_r+B_mω_r (20)

equation (18) above can be rewritten in terms of the currents used in MRAS, with the reference and tunable models being designed in terms of the currents and voltages of the motor. The errors of the reference model and the adjustable model are used as inputs to an adaptive mechanism to estimate the rotor speed and identify motor parameters.

When phi is_f＝L_mi_fr，L_d＝L_q＝L，R_d＝R_qWhen R.

The MRAS architecture shown in fig. 4, the outputs of the reference model and the tunable model are used to design an adaptive mechanism to estimate the velocity. It is important to ensure that the system is stable and that the estimator will converge to the actual value of the adaptation mechanism. In general, ω_rIs a variable and therefore for the purpose of deriving the adaptive mechanism, the model is a linear time-varying system. However, initially ω will be_rThe constant parameters considered as a model are valid. The Lyapunov function method is established to design an adaptive mechanism to estimate the speed and position of the rotor.

The motor equations (21) and (22) can be expressed as:

equation (23) above is a reference model and is expressed as a state space model

For adjustable modelsExpressed as a regulating parameter

The state space model (25) of the equation can be expressed as

Equation (23) can be written as

Equation (25) can be written as

From equations (27) - (30)

From equations (31) and (32)

Equation (33) above is expressed in state space as follows

Model (35) derives an adaptation law to estimate ω_r. Consider the following Lyapunov function.

Where tr (P) denotes the trace of matrix P, K is the gain parameter, and P ═ P^T> 0 is chosen as the solution to the equation, as follows.

PA+A^TP＝-Q (36)

Where P is positive, the derivative of V is given by

By X in equation (37)_eInstead of the formerBecome into

To make it possible toIs negative, select F₁As

Under equilibrium conditionsAnd X_e0 is an adaptation law

Estimating the angle of the motor rotor:

δ＝∫ω_rdt (39)

the variable K in equation (38) will speed up or slow down the speed adaptation law depending on the desired response. The variable K is obtained using a brain mood controller.

The implementation method of the control algorithm in the control of the permanent magnet synchronous motor comprises three steps:

the first step is as follows: the construction method is characterized in that a position-sensor-free permanent magnet synchronous motor vector control strategy is used as a frame, wherein a speed loop is controlled by a sliding mode, and a current loop is controlled by a PI.

The second step is that: constructing a model reference adaptive controller based on the emotional controller, wherein the emotional controller is designed by the following steps:

step 1: the design process of the controller begins with selecting an input parameter of the controller, i.e., a sensory signal.

Step 2: the output of sensory signals was processed in the sensory cortex, amygdala and OFC.

And step 3: the amygdala and OFC have an association of emotional cues, which are reward signals according to the desired output response.

And 4, step 4: the final output of the controller is based on the amygdala and the OFC output.

And 5: the output of the AG is adjusted with the help of the OFC.

Step 6: AG and OFC signals were processed in E to generate emotional responses using equation (10). Namely rotor speed and equation (38) and rotor position and equation (39). If the generated speed matches the response required by the controlled object, the process will stop here, otherwise go to step 1 and then start again.

The third step: and optimizing the model reference adaptive control according to the adaptive law obtained by the emotional controller to obtain the more accurate position and speed of the permanent magnet synchronous motor rotor so as to form closed-loop control.

The invention adopts a Simulink simulation mode to verify the effect of the algorithm in the control of the three-phase permanent magnet synchronous motor. As shown in fig. 6, the motor pulsation using the algorithm of the present invention is much smaller than that of the conventional algorithm, and the pulsation gradually decreases with the passage of time.

The meaning of the symbols in the above formula is shown in fig. 8.

The foregoing illustrates and describes the principles, general features, and advantages of the present invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.