阅读说明：本技术 一种bsrm模糊变参数转子振动主动控制方法 (BSRM fuzzy variable parameter rotor vibration active control method ) 是由 陈凌 于 2019-09-20 设计创作，主要内容包括：本发明公开了一种BSRM模糊变参数转子振动主动控制方法,包括：根据BSRM转子动力学方程及无源控制理论,推导BSRM机械子系统的端口受控哈密顿模型；构造自然互联注入阻尼控制器,推导满足闭环系统稳定性的无源控制律；采用复合控制器方式进行分段控制,在BSRM转子临界转速附近,运用模糊推理改变注入阻尼参数。本发明设计了BSRM转子机械子系统的无源控制器,可以抑制转子振动,使高速运行的BSRM较为平稳地通过临界转速,实现电机的平稳运行。(The invention discloses a BSRM fuzzy variable parameter rotor vibration active control method, which comprises the following steps: according to a BSRM rotor dynamic equation and a passive control theory, a port controlled Hamilton model of a BSRM mechanical subsystem is deduced; constructing a natural interconnection injection damping controller, and deriving a passive control law which meets the stability of a closed-loop system; and (3) performing segmented control by adopting a composite controller mode, and changing the injection damping parameters by using fuzzy reasoning near the critical rotating speed of the BSRM rotor. The invention designs the passive controller of the BSRM rotor mechanical subsystem, which can restrain the rotor from vibrating, so that the BSRM running at high speed can smoothly pass through the critical rotating speed, and the stable running of the motor is realized.)

1. A BSRM fuzzy variable parameter rotor vibration active control method is characterized by comprising the following steps:

constructing a mechanical subsystem port controlled Hamilton model according to the rotation information of the rotor;

constructing an injection damping controller according to a controlled Hamilton model of a mechanical subsystem port;

and changing parameters injected into the damping controller according to the rotation information of the rotor, and performing segmented control on the vibration of the rotor.

2. The active control method of BSRM rotor vibration with fuzzy parameters as claimed in claim 1, wherein: the rotation information of the rotor includes:

the displacement of the rotor in the radial horizontal and vertical directions, the suspension force of the rotor in the radial horizontal and vertical directions, the rotor mass, the critical rotating speed of the rotor, the axial rotating angular speed of the rotor, the torque of the rotor, the load torque of the rotor and the rotational inertia of the rotor.

3. The active control method of BSRM rotor vibration with fuzzy parameters as claimed in claim 1, wherein: the method for constructing the mechanical subsystem port-controlled Hamilton model comprises the following steps:

the method comprises the steps of obtaining rotation information of a rotor, and constructing a rotor dynamics equation according to the rotation information of the rotor, wherein the formula is as follows:

equation of rotor dynamics

In the formula (I), the compound is shown in the specification,

order toIs a state variable, x₁α is the displacement of the rotor in the radial horizontal α direction, x₂Beta is the displacement of the rotor in the radial direction perpendicular to the beta direction,

Deducing to obtain a system energy storage function according to the parameters and a rotor dynamics equation;

in the formula I₀Is the width of the air gap between the stator and the rotor, i.e. when x₂＝-l₀When the system potential energy is 0;

the port-controlled hamilton model of the mechanical subsystem thus obtained is:

in the formula (I), the compound is shown in the specification,

4. The active control method of BSRM rotor vibration with fuzzy parameters as claimed in claim 3, wherein: the construction method of the injection damping controller comprises the following steps:

determining a system expected balance point according to a port controlled Hamiltonian model of the mechanical subsystem:

in the above formula:

changing a system energy storage function by injecting a damping matrix, and obtaining the energy storage function according to a port controlled Hamilton model of a mechanical subsystem:

in the formula (I), the compound is shown in the specification,

from the desired balance point and the injection energy function:

and obtaining an injection damping controller according to the formula, a port controlled Hamilton model of the mechanical subsystem and system input variables:

5. the active control method of BSRM rotor vibration with fuzzy parameters as claimed in claim 1, wherein: the method for changing the parameters of the injection damping controller comprises the following steps:

taking the difference between the rotor rotation speed and the critical rotation speed of the rotor as the input of a preset fuzzy inference system;

the output obtained by the fuzzy inference system is the controller injection damping parameter.

6. The active control method of BSRM rotor vibration with fuzzy parameters as claimed in claim 1, wherein: the segmented control method comprises the following steps:

detecting a rotation speed of the rotor;

when the rotating speed of the rotor is in a critical rotating speed interval, the fuzzy reasoning is switched to adjust the injected damping parameters, and the rotor is controlled to rotate;

when the rotating speed of the rotor is outside the critical rotating speed interval, the damping parameters are fixedly injected through passive control, and the rotor is controlled to rotate.

Technical Field

The invention relates to a bearingless switched reluctance motor and the control field thereof, in particular to a BSRM fuzzy variable parameter rotor vibration active control method.

Background art:

the Bearingless Switched Reluctance Motor (BSRM) is a combination of a rapidly developed magnetic suspension technology and a Switched Reluctance Motor (SRM), has the advantages of simple and firm structure, low cost, wide speed regulation range, high operation reliability, high allowable rotating speed, low friction power consumption, no need of lubrication, long service life and the like, has outstanding advantages in the high-speed and ultrahigh-speed operation occasions, and is one of hot spots in the research field of high-speed motors.

The vibration problem of the rotor is very outstanding when the BSRM runs at high speed, the suspension stability of the rotor is directly influenced, and the improvement of the rotating speed of the motor is limited. Especially when the critical rotating speed is passed, the stator part and the rotor part in the motor are slightly collided and rubbed; the heavy motor can cause structural part damage, and the rotor is bent and deformed, so that the motor body is damaged.

Disclosure of Invention

The invention aims to provide a BSRM fuzzy variable parameter rotor vibration active control method to solve the defects that the stability of rotor suspension is not high and the rotating speed of a motor is limited in the prior art.

A BSRM fuzzy variable parameter rotor vibration active control method comprises the following steps:

constructing a mechanical subsystem port controlled Hamilton model according to the rotation information of the rotor;

constructing an injection damping controller according to a controlled Hamilton model of a mechanical subsystem port;

and changing parameters injected into the damping controller according to the rotation information of the rotor, and actively controlling the vibration of the rotor.

Further, the rotation information of the rotor includes:

the displacement of the rotor in the radial horizontal and vertical directions, the suspension force of the rotor in the radial horizontal and vertical directions, the rotor mass, the critical rotating speed of the rotor, the axial rotating angular speed of the rotor, the torque of the rotor, the load torque of the rotor and the rotational inertia of the rotor.

Further, the method for constructing the mechanical subsystem port-controlled Hamilton model comprises the following steps:

the method comprises the steps of obtaining rotation information of a rotor, and constructing a rotor dynamics equation according to the rotation information of the rotor, wherein the formula is as follows:

equation of rotor dynamics

In the formula (I), the compound is shown in the specification,

and

are respectively a rotationAcceleration of the child in radial horizontal alpha and vertical beta directions; f_αAnd F_βThe suspension forces of the rotor in the radial horizontal alpha direction and the vertical beta direction are respectively; m is the rotor mass; g is the acceleration of gravity;

is the rotor axial rotation angular acceleration; t is the torque of the rotor; t is_LIs the load torque; j is a function of_tIs the moment of inertia;

order to

Is a state variable, x₁α is the displacement of the rotor in the radial horizontal α direction, x₂Beta is the displacement of the rotor in the radial direction perpendicular to the beta direction,

for the amount of speed of change of displacement of the rotor in the radial horizontal alpha direction,

for the amount of speed of change of displacement of the rotor in the radial direction perpendicular to beta, x₅Omega is the axial rotation angular velocity of the rotor;

deducing to obtain a system energy storage function according to the parameters and a rotor dynamics equation;

in the formula I₀Is the width of the air gap between the stator and the rotor, i.e. when x₂＝-l₀When the system potential energy is 0;

the port-controlled hamilton model of the mechanical subsystem thus obtained is:

in the formula (I), the compound is shown in the specification,

is an interconnection matrix;is an external interconnection matrix;

is an input variable;

is a load matrix.

Further, the construction method of the injection damping controller comprises the following steps:

determining a system expected balance point according to a port controlled Hamiltonian model of the mechanical subsystem:

in the above formula:

and

the desired displacement amounts of the rotor in the radial horizontal alpha and vertical beta directions, respectively;and

the expected displacement change speed of the rotor in the radial horizontal alpha direction and the vertical beta direction respectively;

a desired angular velocity;

changing a system energy storage function by injecting a damping matrix, and obtaining the energy storage function according to a port controlled Hamilton model of a mechanical subsystem:

in the formula (I), the compound is shown in the specification,

injecting a damping matrix; wherein r is₁、r₂、r₃、r₄、r₅Injecting damping parameters; h_d(x)＝H(x)+H_a(x) As a function of the desired energy; h_a(x) As a function of the implant energy;

from the desired balance point and the injection energy function:

and obtaining an injection damping controller according to the formula, a port controlled Hamilton model of the mechanical subsystem and system input variables:

further, the method for changing the parameters of the injection damping controller comprises the following steps:

taking the difference between the rotor rotation speed and the critical rotation speed of the rotor as the input of a preset fuzzy inference system;

the output obtained by the fuzzy inference system is the controller injection damping parameter.

Further, the method for segment control comprises the following steps:

detecting a rotation speed of the rotor;

when the rotating speed of the rotor is in a critical rotating speed interval, the fuzzy reasoning is switched to adjust the injected damping parameters, and the rotor is controlled to rotate;

when the rotating speed of the rotor is outside the non-critical rotating speed interval, the damping parameters are fixedly injected through passive control, and the rotor is controlled to rotate.

The invention has the advantages that: the active control method for BSRM fuzzy variable parameter rotor vibration restrains rotor vibration, enables BSRM running at high speed to pass through critical rotating speed more stably, and achieves stable running of a motor.

Drawings

Fig. 1 is a schematic structural diagram of a BSRM of three-phase 12/8 structure in the present invention.

FIG. 2 is a schematic flow chart of the present invention.

FIG. 3 is a schematic diagram of a control system of the present invention.

FIG. 4 is a schematic diagram of the variation of the rotor vibration active control parameter according to the present invention.

Detailed Description

In order to make the technical means, the creation characteristics, the achievement purposes and the effects of the invention easy to understand, the invention is further described with the specific embodiments.

As shown in fig. 1 to 4, the study object of the embodiment of the present invention is a BSRM of a three-phase 12/8 structure, where α is a horizontal direction and β is a vertical direction. In the figure, only the main winding (W) of phase A is shown_ma) Alpha direction suspension winding (W)_sa1) And a beta direction levitation winding (W)_sa2)。W_maFormed by positive strings of 4 pole windings spaced 90 apart, and W_sa1、W_sa2The two-phase motor is formed by reversely connecting 2 suspension windings which are opposite in the radial directions of alpha and beta. W_maGenerating a bias magnetic flux, W_sa1The effects of strengthening (at the air gap a 1) and weakening (at the air gap a 3) are respectively generated for the bias magnetic flux, so that unbalanced magnetic pull force is generated. In the same way, by W_sa2To W_maThe enhancement of the bias field at gap a2 and the weakening at gap a4 also produce unbalanced magnetic pull forces. The magnetic pull force is decomposed in the horizontal and vertical directions, and the radial suspension force F in the horizontal direction can be obtained_αAnd vertical radial suspension force F_β。

The phase B and the phase C have the same winding structure, connection mode and suspension mechanism as the phase A. The stable suspension of the rotating shaft can be realized by utilizing the alternate conduction of the three-phase windings at intervals of 15 degrees and the negative feedback control of the rotor displacement.

The electromagnetic torque is tangential magnetic pull force generated by the main winding and the suspension winding together, and the rotation of the rotor is realized.

As shown in fig. 2, the embodiment of the present invention specifically includes the steps of:

and S101, deriving a port controlled Hamilton model of the BSRM mechanical subsystem according to a BSRM rotor dynamic equation and a passive control theory.

Specifically, step S101 includes the steps of:

s1011, according to BSRM rotor dynamics equation

In the formula (I), the compound is shown in the specification,

and

acceleration of the rotor in radial horizontal alpha and vertical beta directions respectively; f_αAnd F_βThe suspension forces of the rotor in the radial horizontal alpha direction and the vertical beta direction are respectively; m is the rotor mass; g is the acceleration of gravity;

is the rotor axial rotation angular acceleration; t is the torque of the rotor; t is_LIs the load torque; j is a function of_tIs the moment of inertia;

order to

Is a state variable, x₁α is the displacement of the rotor in the radial horizontal α direction, x₂Beta is the displacement of the rotor in the radial direction perpendicular to the beta direction,

for the amount of speed of change of displacement of the rotor in the radial horizontal alpha direction,for the amount of speed of change of displacement of the rotor in the radial direction perpendicular to beta, x₅ω is the rotor axial rotation angular velocity.

S1012, constructing a system energy storage function:

in the formula I₀Is the width of the air gap between the stator and the rotor, i.e. when x₂＝-l₀When the system potential energy is 0;

obtaining a port-controlled Hamilton model of a BSRM mechanical subsystem as

In the formula (I), the compound is shown in the specification,

is an interconnection matrix;

is an external interconnection matrix;

is an input variable;

is a load matrix.

S102, constructing a natural interconnection injection damping controller according to a port controlled Hamilton model of the BSRM mechanical subsystem to obtain a passive control law meeting the stability of a closed-loop system.

Specifically, step S102 includes the steps of:

s1021, determining the expected balance point of the system

Andthe desired displacement of the rotor in the radial horizontal alpha and vertical beta directions respectively,

andrespectively the desired displacement change speed of the rotor in the radial horizontal alpha and vertical beta directions,for a desired angular velocity, the BSRM is operated at a desired rotational speed and the rotor is in a non-eccentric state, i.e., a desired balance point when the displacement of the rotor in the radial horizontal and vertical directions is 0.

S1022, constructing a natural interconnection injection damping controller, and changing the original energy function of the system by adding a damping matrix, so that the closed-loop system meets the following requirements:

in the formula (I), the compound is shown in the specification,

to inject a damping dissipation matrix, r₁、r₂、r₃、r₄、r₅In order to inject damping parameters, specific parameters are obtained according to different rotors; h_d(x)＝H(x)+H_a(x) As a function of the desired energy, H_a(x) As a function of implant energy.

S1023, deducing according to passive theoretical integrability conditions and existence conditions of closed loop stable points:

the injection damping controller is as follows:

and S103, performing segmented control by adopting a composite controller mode according to the injection damping controller, and changing injection damping parameters by using fuzzy reasoning near the BSRM rotor critical rotating speed.

Specifically, step S103 includes the steps of:

s1031, according to the similarity of radial suspension force systems in the radial horizontal alpha and radial vertical beta directions of the BSRM rotor, r₁＝r₂，r₃＝r₄And the energy function of the closed loop system after damping injection is as follows:

wherein the system energy function and r₁、r₂Related to r₃、r₄、r₅Is irrelevant, but r₃、r₄、r₅The system convergence speed can be accelerated;

s1032, performing segmented control by adopting a composite controller mode, and switching fuzzy reasoning to determine r near the BSRM rotor critical rotating speed₃And r₄(ii) a The passive control law injection damping coefficients of other rotating speed intervals are fixed;

by using a self-adaptive neural network fuzzy inference system, the difference E between the rotor speed n and the critical speed omega is used as an input, and the output is used as a damping parameter r injected into the controller₃And r₄The specific control block diagram is shown in fig. 3, wherein α and β are displacement amounts of the rotor in radial horizontal α and vertical β directions, respectively; alpha is alpha^*And beta^*The desired displacement amounts of the rotor in the radial horizontal alpha and vertical beta directions, respectively; n is the rotor speed; n is^*A desired rotational speed; omega is the critical rotating speed of the rotor; e is the difference between the rotor speed n and the critical speed omega; r is₃And r₄And respectively injecting damping parameters in the radial horizontal alpha direction and the radial vertical beta direction of the rotor.

In the vicinity of the resonant frequency, the rotor vibration can be inhibited by changing the equivalent rigidity and damping coefficient of the BSRM suspension support. Using fuzzy reasoning to move through the system to the first orderAnd variable parameter control is realized in the process of critical rotating speed. The fuzzy control is adopted, so that the method has the advantages that qualitative and inaccurate control rules only need to be concluded based on experience, does not depend on a mathematical model of a controlled object, and is suitable for the situation that the flexible rotor dynamics high-dimensional model is difficult to model. In addition, according to the similarity of the radial suspension force system in the x and y directions and the PBC principle, the damping parameter r is injected₃＝r₄Changing only r₃And r₄The equivalent rigidity and damping coefficient of the suspension support can be adjusted, and the energy function of the system is not influenced.

This is further illustrated by the following specific examples:

the rotor mass m of a sample machine is 2.492kg, the critical rotating speed omega is 8600rpm, and the moment of inertia j is selected_t＝6.25369×10^-4kg·m². At rotor speed n<6000rpm and n>PBC injection damping parameters in 12000rpm interval (outside the critical rotation speed interval) are fixed, and are r₁＝r₂＝4×10^-6、r₃＝r₄＝150、r₅＝2.5×10⁶The corresponding suspension support equivalent stiffness coefficient K is 100.69N/mm, and the damping coefficient C is 0.93 N.s/mm; and switching the fuzzy reasoning to determine r in the range of n 6000-12000 rpm (within the critical speed range)₃And r₄。

Aiming at the vibration problem caused by resonance, in the process that the BSRM rotor passes through the first-order critical rotating speed, the fuzzy reasoning is adopted to carry out variable parameter control so as to realize active control of rotor vibration, and the variable parameter r₃And r₄As shown in fig. 4.

In the BSRM low-speed operation stage, when the rotating speed is less than 6000rpm, the parameter r₃And r₄Is a constant value; the rotation speed is in the range of 6000-12000 rpm, and the parameter r can be obtained according to fuzzy reasoning₃And r₄The change trend of gradually increasing, then basically maintaining stability and then gradually decreasing exists; when the rotating speed exceeds 12000rpm, the original parameter r₃And r₄The constant value can meet the requirement of active vibration control, so the constant value is adopted.

The fuzzy inference link takes the difference E between the rotor speed and the first-order critical speed as inputOutput as controller injected damping parameter r₃And r₄. The adaptive neural network fuzzy inference system provided in the MATLAB fuzzy toolbox is utilized, harmonic response analysis data learning, association and inference calculation are performed through a neural network technology, Sugeno type FIS is established by adopting a hybrid method, and fuzzy rules and membership function parameters are obtained. The selection uses double Gaussian membership function, the quantity of fuzzy subsets is 7, and the output quantity uses linear function.

It will be appreciated by those skilled in the art that the invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The embodiments disclosed above are therefore to be considered in all respects as illustrative and not restrictive. All changes which come within the scope of or equivalence to the invention are intended to be embraced therein.