CN104980069A - Multipurpose optimization method for double closed-loop speed governing system of brushless DC motor - Google Patents

Abstract

The invention relates to a multipurpose optimization method for a double closed-loop speed governing system of a brushless DC motor. Based on the quantum genetic algorithm, the multipurpose optimization for the double closed-loop speed governing system of the brushless DC motor can be realized. In this way, the problem in the prior art that the manual setting operation is time-consuming, labor-consuming and large in error can be effectively solved. Meanwhile, the prematurity and the local convergence ability of conventional algorithms are poor can also be overcome. In addition, a multipurpose fitness function is designed. Therefore, the damage to components caused by the large amount of controlled variables can be effectively prevented, so that an ideal speed governing system can be obtained.

Description

A kind of brshless DC motor double loop speed-regulating system Multipurpose Optimal Method

Technical field

The present invention relates to a kind of brshless DC motor double loop speed-regulating system Multipurpose Optimal Method, belong to electric machine speed regulation optimisation technique field.

Background technology

Brshless DC motor (Brushless Direct Current Motor, BLDCM) there is the advantages such as simple, reliable, the easy to maintenance and mechanical commutator of structure, particularly along with power electronic technology and novel permanent magnetic Materials are with ripe rapidly, brshless DC motor has been widely used in industrial production.

It is double loop speed-regulating system that current Speed Regulation Systems of BLDCM uses the most typical, and wherein double loop speed-regulating system is controlled by speed control and current controller; Simple, the widely used PI controller of the normal selection principle of double loop speed-regulating system middle controller.Because controller parameters setting directly affects the control performance of brshless DC motor double loop speed-regulating system, therefore the size of PI parameter value is particularly important.

Brshless DC motor double loop speed-regulating system is adjusted by artificial experience the parameter size of speed and current controller usually, but this method not only wastes time and energy but also control system poor performance.In order to obtain that error is little, fast response time and the strong double loop speed-regulating system of antijamming capability, advanced intelligent algorithm can be adopted the optimization of brshless DC motor double loop speed-regulating system.But a lot of intelligent algorithm often there will be the phenomenon such as " precocity " and local convergence capabilities difference, and optimum results is unsatisfactory.

When traditional intelligence algorithm is to PI parameter optimization, usually be all the deviate only considering some control rings, the result drawn not is optimal solution, and from the maintenance angle of components and parts, consider that the output variable of controller is also very important, otherwise be easy to cause component damage, thus strengthen maintenance cost.Therefore, design the adjust controller parameter of double loop speed-regulating system of an advanced optimized algorithm and multiple target fitness function method and just seem particularly important.

Summary of the invention

Technical problem to be solved by this invention is to provide one and improves for existing brshless DC motor double loop speed-regulating system, introduce rotating speed deviation and current deviation, adopt quantum genetic algorithm effectively can improve the brshless DC motor double loop speed-regulating system Multipurpose Optimal Method of brshless DC motor work speed governing operating efficiency.

The present invention is in order to solve the problems of the technologies described above by the following technical solutions: the present invention devises a kind of brshless DC motor double loop speed-regulating system Multipurpose Optimal Method, comprises the steps:

Step 001. sets up brshless DC motor double loop speed-regulating system, wherein, comprise speed ring, electric current loop and brshless DC motor, speed ring comprises speed control, electric current loop comprises current controller, the output variable of electric current loop inbound pacing ring, as the input current value of electric current loop, enters step 002;

Step 002. is for the proportionality coefficient kp of proportional-plus-integral controller in speed control ₁with integration time constant ki ₁, setting span; And for the proportionality coefficient kp of proportional-plus-integral controller in current controller ₂with integration time constant ki ₂, setting span; And initialization iterations m=1, enter step 003;

Step 003. is according to proportionality coefficient kp ₁, integration time constant ki ₁, proportionality coefficient kp ₂with integration time constant ki ₂span, produce the controller parameter of preset group number immediately, wherein, each group controller parameter comprises proportionality coefficient kp respectively ₁, integration time constant ki ₁, proportionality coefficient kp ₂with integration time constant ki ₂value, and enter step 004;

Each group controller parameter is substituted into the proportional-plus-integral controller in corresponding speed controller by step 004. respectively, and the proportional-plus-integral controller in current controller; Meanwhile, set a default rated speed for brshless DC motor, control brshless DC motor and start working, and enter step 005;

Step 005. corresponds respectively to each group controller parameter, obtain the actual speed in the brshless DC motor course of work and actual current value respectively, and rotating speed deviation v (t) obtained respectively between brshless DC motor actual speed and default rated speed, and brshless DC motor actual current value and electric current loop receive current deviation e (t) between input current value, and enter step 006;

Step 006. corresponds respectively to each group controller parameter, according to following fitness function f, obtains the fitness function value f corresponding to each group controller parameter respectively;

$f=\frac{1}{{\∫}_0^{\mathrm{\∞}}\[{\mathrm{\ω}}_{1}t\left|v\left(t\right)\right|+{\mathrm{\ω}}_{2}t\left|e\left(t\right)\right|\]dt}$

Wherein, f is fitness function value, and t is the running time of brshless DC motor, ω ₁, ω ₂the absolute value integration item of the absolute value integration item and current deviation e (t) that are respectively rotating speed deviation v (t) is multiplied by the running time of brshless DC motor after t respectively, the speed control weight obtained and current controller weight, and 0 < ω ₁, ω ₂< 1, ω ₁+ ω ₂=1, enter step 007;

Step 007. judges whether iterations m equals default total iterations, is enter step 008; Otherwise enter step 009;

Step 008. obtains the maximum adaptation degree functional value in fitness function value corresponding to each group controller parameter, and obtain a group controller parameter corresponding to this maximum adaptation degree functional value, this group controller parameter is optimal controller parameter, by the proportionality coefficient kp in optimal controller parameter ₁, integration time constant ki ₁, proportionality coefficient kp ₂with integration time constant ki ₂substitute into proportional-plus-integral controller in proportional-plus-integral controller in speed control and current controller respectively, realize the control for brshless DC motor double loop speed-regulating system, optimization method terminates;

Step 009., for each group controller parameter, carries out quantum coding by such as drag respectively, namely obtains proportionality coefficient kp ₁, integration time constant ki ₁, proportionality coefficient kp ₂with integration time constant ki ₂corresponding quantum coding is $\left[\begin{array}{cccc}{\mathrm{\&alpha;}}_1& {\mathrm{\&alpha;}}_2& {\mathrm{\&alpha;}}_3& {\mathrm{\&alpha;}}_4\\ {\mathrm{\&beta;}}_1& {\mathrm{\&beta;}}_2& {\mathrm{\&beta;}}_3& {\mathrm{\&beta;}}_4\end{array}\right];$

|j ₁〉＝α ₁|0〉+β ₁|1〉；

|j ₂〉＝α ₂|0〉+β ₂|1〉；

|j ₃〉＝α ₃|0〉+β ₃|1〉；

|j ₄〉＝α ₄|0〉+β ₄|1〉；

Wherein, | j ₁> represents proportionality coefficient kp ₁expression state in quantum mechanics, | j ₂> represents integration time constant ki ₁expression state in quantum mechanics, | j ₃> represents proportionality coefficient kp ₂expression state in quantum mechanics, | j ₄> represents integration time constant ki ₂expression state in quantum mechanics; α _irepresent | the probability of 0 >, β _irepresent | the probability of 1 >, and | α _i| ²+ | β _i| ²=1, i={1,2,3,4}; Enter step 010;

Step 010. obtains the maximum adaptation degree functional value in fitness function value corresponding to each group controller parameter, and obtain a group controller parameter corresponding to this maximum adaptation degree functional value, and obtain this quantum coding corresponding to group controller parameter, as the optimum quantum coding of current iteration, then according to following formula, and in conjunction with existing quantum anglec of rotation selection strategy, respectively the direction of the quantum coding corresponding to all the other each group controller parameters towards the optimum quantum coding of this current iteration is developed, upgrade each group of quantum coding obtained except the optimum quantum coding of current iteration,

$\left[\begin{array}{c}{\mathrm{\&alpha;}}_i^{\&prime;}\\ {\mathrm{\&beta;}}_i^{\&prime;}\end{array}\right]=\left[\begin{array}{cc}{\mathrm{cos\&theta;}}_i& -{\mathrm{sin\&theta;}}_i\\ {\mathrm{sin\&theta;}}_i& {\mathrm{cos\&theta;}}_i\end{array}\right]\left[\begin{array}{c}{\mathrm{\&alpha;}}_i\\ {\mathrm{\&beta;}}_i\end{array}\right]$

Wherein, θ _ifor the anglec of rotation of Quantum rotating gate, α ' _ifor α _iupdated value after evolution, β ' _ifor β _iupdated value after evolution, uses α ' respectively _iwith β ' _ivalue go to upgrade corresponding α _iand β _ivalue; Enter step 011;

Step 011. is according to upgrading each group of quantum coding obtained, obtain this respectively group quantum coding institute distinguish each group controller parameter of correspondence, and the group controller parameter corresponding to this each group controller parameter and the optimum quantum coding of current iteration is formed the controller parameter of preset group number, the value of iterations m is added 1, upgrade for iterations m, and return step 004.

As a preferred technical solution of the present invention: the preset group number of described controller parameter is 40 groups, described default total iterations is 30.

As a preferred technical solution of the present invention: in described step 002, for the proportionality coefficient kp of proportional-plus-integral controller in speed control ₁with integration time constant ki ₁, set span as (0,2.3).

As a preferred technical solution of the present invention: in described step 002, for the proportionality coefficient kp of proportional-plus-integral controller in current controller ₂with integration time constant ki ₂, set span as (0,12).

As a preferred technical solution of the present invention: in described step 006, ω ₁=ω ₂=0.5.

A kind of brshless DC motor double loop speed-regulating system Multipurpose Optimal Method of the present invention adopts above technical scheme compared with prior art, there is following technique effect: a kind of brshless DC motor double loop speed-regulating system Multipurpose Optimal Method of the present invention's design, the multiple-objection optimization for brshless DC motor double loop speed-regulating system is realized based on quantum genetic algorithm, can efficiently solve manually adjusts the problem such as to waste time and energy, error is large, and overcomes the difficulty such as algorithm " precocity " and local convergence capabilities difference in the past; Design multiple target fitness function in addition, can effectively prevent controlled quentity controlled variable excessive and cause components and parts to damage, be conducive to obtaining good governing system.

Accompanying drawing explanation

Fig. 1 is the schematic diagram of brshless DC motor double loop speed-regulating system;

Fig. 2 is fitness function iteration schematic diagram in Application Example of the present invention;

Fig. 3 is optimal controller parameter implementation result schematic diagram in the embodiment of the present invention.

Embodiment

Below in conjunction with Figure of description, the specific embodiment of the present invention is described in further detail.

Designed by the present invention, a kind of brshless DC motor double loop speed-regulating system Multipurpose Optimal Method, in the middle of actual application, specifically comprises the steps:

Step 001. as shown in Figure 1, based on MATLAB/SIMULINK platform, set up brshless DC motor double loop speed-regulating system, wherein, comprise speed ring, electric current loop and, brshless DC motor, voltage inverter, pulse-width modulation and commutation, angular velocity detection, position probing, speed calculate and the module such as current detecting, speed ring comprises speed control, electric current loop comprises current controller, the output variable of electric current loop inbound pacing ring, as the input current value of electric current loop, enters step 002.

Wherein, in this embodiment, the parameter of selected brshless DC motor is as follows: stator phase winding resistance R=0.6 Ω, stator phase winding self-induction L=0.205e-3H, moment of inertia J=9.25e-6kgm ², damping coefficient B=1e-9Nms/rad, number of pole-pairs p=8.

Step 002. is for the proportionality coefficient kp of proportional-plus-integral controller in speed control ₁with integration time constant ki ₁, set span as (0,2.3); And for the proportionality coefficient kp of proportional-plus-integral controller in current controller ₂with integration time constant ki ₂, set span as (0,12); And initialization iterations m=1, enter step 003.

Step 003. is according to proportionality coefficient kp ₁, integration time constant ki ₁, proportionality coefficient kp ₂with integration time constant ki ₂span, produce the controller parameter of 40 groups immediately, wherein, each group controller parameter comprises proportionality coefficient kp respectively ₁, integration time constant ki ₁, proportionality coefficient kp ₂with integration time constant ki ₂value, and enter step 004.

Each group controller parameter is substituted into the proportional-plus-integral controller in corresponding speed controller by step 004. respectively, and the proportional-plus-integral controller in current controller; Meanwhile, set a default rated speed for brshless DC motor, control brshless DC motor and start working, and enter step 005.

Step 005. corresponds respectively to each group controller parameter, obtain the actual speed in the brshless DC motor course of work and actual current value respectively, and rotating speed deviation v (t) obtained respectively between brshless DC motor actual speed and default rated speed, and brshless DC motor actual current value and electric current loop receive current deviation e (t) between input current value, and enter step 006.

Step 006. corresponds respectively to each group controller parameter, according to following fitness function f, obtains the fitness function value f corresponding to each group controller parameter respectively.

$f=\frac{1}{{\&Integral;}_0^{\mathrm{\&infin;}}\&lsqb;{\mathrm{\&omega;}}_{1}t\left|v\left(t\right)\right|+{\mathrm{\&omega;}}_{2}t\left|e\left(t\right)\right|\&rsqb;dt}$

Wherein, f is fitness function value, and t is the running time of brshless DC motor, ω ₁, ω ₂the absolute value integration item of the absolute value integration item and current deviation e (t) that are respectively rotating speed deviation v (t) is multiplied by the running time of brshless DC motor after t respectively, the speed control weight obtained and current controller weight, and 0 < ω ₁, ω ₂< 1, ω ₁+ ω ₂=1, in this embodiment, ω ₁=ω ₂=0.5, enter step 007.

Step 007. judges whether iterations m equals 30, is, enters step 008; Otherwise enter step 009.

Step 008. obtains the maximum adaptation degree functional value in fitness function value corresponding to each group controller parameter, and obtain a group controller parameter corresponding to this maximum adaptation degree functional value, this group controller parameter is optimal controller parameter, by the proportionality coefficient kp in optimal controller parameter ₁, integration time constant ki ₁, proportionality coefficient kp ₂with integration time constant ki ₂substitute into proportional-plus-integral controller in proportional-plus-integral controller in speed control and current controller respectively, realize the control for brshless DC motor double loop speed-regulating system, optimization method terminates.

Step 009., for each group controller parameter, carries out quantum coding by such as drag respectively, namely obtains proportionality coefficient kp ₁, integration time constant ki ₁, proportionality coefficient kp ₂with integration time constant ki ₂corresponding quantum coding is $\left[\begin{array}{cccc}{\mathrm{\&alpha;}}_1& {\mathrm{\&alpha;}}_2& {\mathrm{\&alpha;}}_3& {\mathrm{\&alpha;}}_4\\ {\mathrm{\&beta;}}_1& {\mathrm{\&beta;}}_2& {\mathrm{\&beta;}}_3& {\mathrm{\&beta;}}_4\end{array}\right].$

|j ₁〉＝α ₁|0〉+β ₁|1〉；

|j ₂〉＝α ₂|0〉+β ₂|1〉；

|j ₃〉＝α ₃|0〉+β ₃|1〉；

|j ₄〉＝α ₄|0〉+β ₄|1〉；

Wherein, | j ₁> represents proportionality coefficient kp ₁expression state in quantum mechanics, | j ₂> represents integration time constant ki ₁expression state in quantum mechanics, | j ₃> represents proportionality coefficient kp ₂expression state in quantum mechanics, | j ₄> represents integration time constant ki ₂expression state in quantum mechanics; α _irepresent | the probability of 0 >, β _irepresent | the probability of 1 >, and | α _i| ²+ | β _i| ²=1, i={1,2,3,4}; Enter step 010.

Step 010. obtains the maximum adaptation degree functional value in fitness function value corresponding to each group controller parameter, and obtain a group controller parameter corresponding to this maximum adaptation degree functional value, and obtain this quantum coding corresponding to group controller parameter, as the optimum quantum coding of current iteration, then according to following formula, and in conjunction with existing quantum anglec of rotation selection strategy, respectively the direction of the quantum coding corresponding to all the other each 39 group controller parameters towards the optimum quantum coding of this current iteration is developed, upgrade each group of quantum coding obtained except the optimum quantum coding of current iteration.

$\left[\begin{array}{c}{\mathrm{\&alpha;}}_i^{\&prime;}\\ {\mathrm{\&beta;}}_i^{\&prime;}\end{array}\right]=\left[\begin{array}{cc}{\mathrm{cos\&theta;}}_i& -{\mathrm{sin\&theta;}}_i\\ {\mathrm{sin\&theta;}}_i& {\mathrm{cos\&theta;}}_i\end{array}\right]\left[\begin{array}{c}{\mathrm{\&alpha;}}_i\\ {\mathrm{\&beta;}}_i\end{array}\right]$

Wherein, existing quantum anglec of rotation selection strategy, come from " quantum Men Biaoge: in " 30 analyses of cases of MATLAB intelligent algorithm " publishing house of Shi Feng-2011-BJ University of Aeronautics & Astronautics p82, as shown in table 1 below:

In table 1, delta is the size of the anglec of rotation, generally gets 0.01 π; x _irepresent current i-th bit data; b _irepresent the i-th bit data that current fitness function is maximum, f (x) represents fitness function value, Δ θ _iwith s (α _i, β _i) represent θ respectively _isize and direction of rotation; θ _ifor the anglec of rotation of Quantum rotating gate, the size and Orientation of the anglec of rotation can be chosen according to upper table 1, α ' _ifor α _iupdated value after evolution, β ' _ifor β _iupdated value after evolution, uses α ' respectively _iwith β ' _ivalue go to upgrade corresponding α _iand β _ivalue; Enter step 011.

Step 011. is according to upgrading each group of quantum coding obtained, obtain this respectively group quantum coding institute distinguish each group controller parameter of correspondence, and the group controller parameter corresponding to this each group controller parameter and the optimum quantum coding of current iteration is formed the controller parameter of 40 groups, the value of iterations m is added 1, upgrade for iterations m, and return step 004.

Through the application of the invention described above method for designing, after completing 30 iteration, namely enter in step 008, obtain the maximum adaptation degree functional value in fitness function value corresponding to each group controller parameter, and obtain a group controller parameter corresponding to this maximum adaptation degree functional value, this group controller parameter is optimal controller parameter, because in the present invention, fitness function is as follows:

$f=\frac{1}{{\&Integral;}_0^{\mathrm{\&infin;}}\&lsqb;{\mathrm{\&omega;}}_{1}t\left|v\left(t\right)\right|+{\mathrm{\&omega;}}_{2}t\left|e\left(t\right)\right|\&rsqb;dt}$

Wherein, for target function, therefore, in order to obtain the maximum adaptation degree functional value in fitness function value corresponding to each group controller parameter, namely need to obtain minimum target functional value, as shown in Figure 2, in the present embodiment, minimum target functional value 2.8833e-5, i.e. this minimum target functional value 2.8833e-5, corresponding fitness function value f is maximum adaptation degree functional value, the group controller parameter that this maximum adaptation degree functional value is corresponding, is optimal controller parameter, this optimal controller parameter logistic coefficient k p ₁=2.2877, integration time constant ki ₁=0.2656, proportionality coefficient kp ₂=3.3071 and integration time constant ki ₂=5.9474, optimal controller parameter is substituted into respectively proportional-plus-integral controller in proportional-plus-integral controller in speed control and current controller, carry out the control for brshless DC motor double loop speed-regulating system, as shown in Figure 3, obviously can find out that start-up time is very short, and during 0.2s strengthen rotating speed time the rise time shorter.

By reference to the accompanying drawings embodiments of the present invention are explained in detail above, but the present invention is not limited to above-mentioned execution mode, in the ken that those of ordinary skill in the art possess, can also makes a variety of changes under the prerequisite not departing from present inventive concept.

Claims (5)

1. a brshless DC motor double loop speed-regulating system Multipurpose Optimal Method, is characterized in that, comprises the steps:

Step 001. sets up brshless DC motor double loop speed-regulating system, wherein, comprise speed ring, electric current loop and brshless DC motor, speed ring comprises speed control, electric current loop comprises current controller, the output variable of electric current loop inbound pacing ring, as the input current value of electric current loop, enters step 002;

Step 002. is for the proportionality coefficient kp of proportional-plus-integral controller in speed control ₁with integration time constant ki ₁, setting span; And for the proportionality coefficient kp of proportional-plus-integral controller in current controller ₂with integration time constant ki ₂, setting span; And initialization iterations m=1, enter step 003;

Step 003. is according to proportionality coefficient kp ₁, integration time constant ki ₁, proportionality coefficient kp ₂with integration time constant ki ₂span, produce the controller parameter of preset group number immediately, wherein, each group controller parameter comprises proportionality coefficient kp respectively ₁, integration time constant ki ₁, proportionality coefficient kp ₂with integration time constant ki ₂value, and enter step 004;

Each group controller parameter is substituted into the proportional-plus-integral controller in corresponding speed controller by step 004. respectively, and the proportional-plus-integral controller in current controller; Meanwhile, set a default rated speed for brshless DC motor, control brshless DC motor and start working, and enter step 005;

Step 005. corresponds respectively to each group controller parameter, obtain the actual speed in the brshless DC motor course of work and actual current value respectively, and rotating speed deviation v (t) obtained respectively between brshless DC motor actual speed and default rated speed, and brshless DC motor actual current value and electric current loop receive current deviation e (t) between input current value, and enter step 006;

Step 006. corresponds respectively to each group controller parameter, according to following fitness function f, obtains the fitness function value f corresponding to each group controller parameter respectively;

$f=\frac{1}{{\&Integral;}_0^{\mathrm{\&infin;}}\&lsqb;{\mathrm{\&omega;}}_{1}t\left|v\left(t\right)\right|+{\mathrm{\&omega;}}_{2}t\left|e\left(t\right)\right|\&rsqb;dt}$

Wherein, f is fitness function value, and t is the running time of brshless DC motor, ω ₁, ω ₂the absolute value integration item of the absolute value integration item and current deviation e (t) that are respectively rotating speed deviation v (t) is multiplied by the running time of brshless DC motor after t respectively, the speed control weight obtained and current controller weight, and 0 < ω ₁, ω ₂< 1, ω ₁+ ω ₂=1, enter step 007; Step 007. judges whether iterations m equals default total iterations, is enter step 008; Otherwise enter step 009;

Step 008. obtains the maximum adaptation degree functional value in fitness function value corresponding to each group controller parameter, and obtain a group controller parameter corresponding to this maximum adaptation degree functional value, this group controller parameter is optimal controller parameter, by the proportionality coefficient kp in optimal controller parameter ₁, integration time constant ki ₁, proportionality coefficient kp ₂with integration time constant ki ₂substitute into proportional-plus-integral controller in proportional-plus-integral controller in speed control and current controller respectively, realize the control for brshless DC motor double loop speed-regulating system, optimization method terminates;

Step 009., for each group controller parameter, carries out quantum coding by such as drag respectively, namely obtains proportionality coefficient kp ₁, integration time constant ki ₁, proportionality coefficient kp ₂with integration time constant ki ₂corresponding quantum coding is $\left[\begin{array}{cccc}{\mathrm{\&alpha;}}_1& {\mathrm{\&alpha;}}_2& {\mathrm{\&alpha;}}_3& {\mathrm{\&alpha;}}_4\\ {\mathrm{\&beta;}}_1& {\mathrm{\&beta;}}_2& {\mathrm{\&beta;}}_3& {\mathrm{\&beta;}}_4\end{array}\right];$

|j ₁>＝α ₁|0>+β ₁|1>；

|j ₂>＝α ₂|0>+β ₂|1>；

|j ₃>＝α ₃|0>+β ₃|1>；

|j ₄>＝α ₄|0>+β ₄|1>；

Wherein, | j ₁> represents proportionality coefficient kp ₁expression state in quantum mechanics, | j ₂> represents integration time constant ki ₁expression state in quantum mechanics, | j ₃> represents proportionality coefficient kp ₂expression state in quantum mechanics, | j ₄> represents integration time constant ki ₂expression state in quantum mechanics; α _irepresent | the probability of 0>, β _irepresent | the probability of 1>, and | α _i| ²+ | β _i| ²=1, i={1,2,3,4}; Enter step 010;

Step 010. obtains the maximum adaptation degree functional value in fitness function value corresponding to each group controller parameter, and obtain a group controller parameter corresponding to this maximum adaptation degree functional value, and obtain this quantum coding corresponding to group controller parameter, as the optimum quantum coding of current iteration, then according to following formula, and in conjunction with existing quantum anglec of rotation selection strategy, respectively the direction of the quantum coding corresponding to all the other each group controller parameters towards the optimum quantum coding of this current iteration is developed, upgrade each group of quantum coding obtained except the optimum quantum coding of current iteration,

$\left[\begin{array}{c}{\mathrm{\&alpha;}}_i^{\&prime;}\\ {\mathrm{\&beta;}}_i^{\&prime;}\end{array}\right]=\left[\begin{array}{cc}{\mathrm{cos\&theta;}}_i& -{\mathrm{sin\&theta;}}_i\\ {\mathrm{sin\&theta;}}_i& {\mathrm{cos\&theta;}}_i\end{array}\right]\left[\begin{array}{c}{\mathrm{\&alpha;}}_i\\ {\mathrm{\&beta;}}_i\end{array}\right]$

Wherein, θ _ifor the anglec of rotation of Quantum rotating gate, α _i' be α _iupdated value after evolution, β _i' be β _iupdated value after evolution, uses α respectively _i' and β _i' value go to upgrade corresponding α _iand β _ivalue; Enter step 011;

Step 011. is according to upgrading each group of quantum coding obtained, obtain this respectively group quantum coding institute distinguish each group controller parameter of correspondence, and the group controller parameter corresponding to this each group controller parameter and the optimum quantum coding of current iteration is formed the controller parameter of preset group number, the value of iterations m is added 1, upgrade for iterations m, and return step 004.

2. a kind of brshless DC motor double loop speed-regulating system Multipurpose Optimal Method according to claim 1, is characterized in that: the preset group number of described controller parameter is 40 groups, and described to preset total iterations be 30.

3. a kind of brshless DC motor double loop speed-regulating system Multipurpose Optimal Method according to claim 1, is characterized in that: in described step 002, for the proportionality coefficient kp of proportional-plus-integral controller in speed control ₁with integration time constant ki ₁, set span as (0,2.3).

4. a kind of brshless DC motor double loop speed-regulating system Multipurpose Optimal Method according to claim 1, is characterized in that: in described step 002, for the proportionality coefficient kp of proportional-plus-integral controller in current controller ₂with integration time constant ki ₂, set span as (0,12).

5. a kind of brshless DC motor double loop speed-regulating system Multipurpose Optimal Method according to claim 1, is characterized in that: in described step 006, ω ₁=ω ₂=0.5.