WO2020177269A1 - Bbmc异步电机调速系统控制参数自适应调整方法 - Google Patents

Bbmc异步电机调速系统控制参数自适应调整方法 Download PDF

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WO2020177269A1
WO2020177269A1 PCT/CN2019/098333 CN2019098333W WO2020177269A1 WO 2020177269 A1 WO2020177269 A1 WO 2020177269A1 CN 2019098333 W CN2019098333 W CN 2019098333W WO 2020177269 A1 WO2020177269 A1 WO 2020177269A1
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bbmc
output current
optimization
control parameters
optimal
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张小平
刘继
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湖南科技大学
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P23/00—Arrangements or methods for the control of AC motors characterised by a control method other than vector control
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P27/00—Arrangements or methods for the control of AC motors characterised by the kind of supply voltage
    • H02P27/04—Arrangements or methods for the control of AC motors characterised by the kind of supply voltage using variable-frequency supply voltage, e.g. inverter or converter supply voltage
    • H02P27/06—Arrangements or methods for the control of AC motors characterised by the kind of supply voltage using variable-frequency supply voltage, e.g. inverter or converter supply voltage using dc to ac converters or inverters
    • H02P27/08—Arrangements or methods for the control of AC motors characterised by the kind of supply voltage using variable-frequency supply voltage, e.g. inverter or converter supply voltage using dc to ac converters or inverters with pulse width modulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/10—Numerical modelling
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02M—APPARATUS FOR CONVERSION BETWEEN AC AND AC, BETWEEN AC AND DC, OR BETWEEN DC AND DC, AND FOR USE WITH MAINS OR SIMILAR POWER SUPPLY SYSTEMS; CONVERSION OF DC OR AC INPUT POWER INTO SURGE OUTPUT POWER; CONTROL OR REGULATION THEREOF
    • H02M3/00—Conversion of dc power input into dc power output
    • H02M3/02—Conversion of dc power input into dc power output without intermediate conversion into ac
    • H02M3/04—Conversion of dc power input into dc power output without intermediate conversion into ac by static converters
    • H02M3/10—Conversion of dc power input into dc power output without intermediate conversion into ac by static converters using discharge tubes with control electrode or semiconductor devices with control electrode
    • H02M3/145—Conversion of dc power input into dc power output without intermediate conversion into ac by static converters using discharge tubes with control electrode or semiconductor devices with control electrode using devices of a triode or transistor type requiring continuous application of a control signal
    • H02M3/155—Conversion of dc power input into dc power output without intermediate conversion into ac by static converters using discharge tubes with control electrode or semiconductor devices with control electrode using devices of a triode or transistor type requiring continuous application of a control signal using semiconductor devices only
    • H02M3/156—Conversion of dc power input into dc power output without intermediate conversion into ac by static converters using discharge tubes with control electrode or semiconductor devices with control electrode using devices of a triode or transistor type requiring continuous application of a control signal using semiconductor devices only with automatic control of output voltage or current, e.g. switching regulators
    • H02M3/158—Conversion of dc power input into dc power output without intermediate conversion into ac by static converters using discharge tubes with control electrode or semiconductor devices with control electrode using devices of a triode or transistor type requiring continuous application of a control signal using semiconductor devices only with automatic control of output voltage or current, e.g. switching regulators including plural semiconductor devices as final control devices for a single load
    • H02M3/1582—Buck-boost converters

Definitions

  • the invention relates to the technical field of power electronics, and in particular to an adaptive adjustment method for control parameters of a BBMC asynchronous motor speed regulation system.
  • Buck-Boost Matrix Converter (Buck-Boost Matrix Converter, BBMC) is a new type of power converter with high voltage transmission ratio and can directly output high-quality sine waves, suitable for use in asynchronous motor speed control systems.
  • BBMC Battery-Boost Matrix Converter
  • studies have shown that for the BBMC-based asynchronous motor speed control system, when the load it carries changes, the relevant control parameters of the speed control strategy need to be optimized and adjusted to make the BBMC output voltage harmonic distortion and other indicators To achieve the optimal coordination, so as to achieve the purpose of high-performance speed control of the speed control system.
  • the corresponding optimal control parameters are studied and determined, and then the change law between the optimal control parameters of the system and the actual output current of BBMC is studied. It is of great significance to adjust relevant control parameters in real time to achieve high-performance speed regulation of the system.
  • the present invention provides an adaptive adjustment method for the control parameters of a BBMC asynchronous motor speed regulation system with high accuracy and good operating effect.
  • an adaptive adjustment method for control parameters of a BBMC asynchronous motor speed regulation system which includes the following steps:
  • the control parameters controlled in a limited time are the optimization objects, and the BBMC output voltage harmonic distortion, capacitor voltage deviation signal and output current deviation signal are the optimization objectives, and the optimization objectives and optimization objects are established Mathematical model of time;
  • the adaptive wolf pack optimization algorithm is used to iteratively optimize the control parameters of the limited time control, so that the BBMC output voltage harmonic distortion, the capacitor voltage deviation signal and the output current deviation signal achieve the coordinated optimization, thereby obtaining a set of optimal Excellent finite time control parameters; adjust the actual output current of BBMC, repeat steps (2) and (3) to obtain n groups of optimal finite time control parameters;
  • the numerical fitting method is used to obtain the functional relationship between each optimal control parameter and the BBMC actual output current, and according to the obtained function
  • the relational formula can determine the optimal control parameters corresponding to any load of the speed control system.
  • the specific steps of establishing a mathematical model between the optimization target and the optimization target in the step (1) are:
  • u C is the capacitor voltage
  • i L is the inductor current
  • i 1 is the BBMC output current
  • u D is the BBMC DC side voltage
  • u DZ is the common terminal voltage of the stator winding of the asynchronous motor
  • L and C are the BBMC inverter stage inductances.
  • capacitance, R and L 1 are the equivalent resistance and equivalent inductance of the single-phase winding of the asynchronous motor
  • d is the duty cycle of the power switch in BBMC;
  • sat is the saturation function
  • ⁇ 2 u D i L -(u C +u D )i 1 -u D i Lref +(u Cref +u D )i 1ref
  • u Cref is the reference value of capacitor voltage
  • i Lref is the reference value of inductor current
  • i 1ref is the reference value of BBMC output current
  • K 1 , K 2 , ⁇ 1 , ⁇ 2 are finite time control parameters
  • ⁇ 2 2 ⁇ 1 /(1+ ⁇ 1 );
  • the harmonic distortion THD of the output voltage u is obtained as:
  • G ln(R/2)
  • T is the period of BBMC output voltage
  • ⁇ is the angular frequency of BBMC output voltage
  • the step of establishing a multi-objective optimization satisfaction function in the step (2) includes:
  • THD', ⁇ u C 'and ⁇ i' are the critical values of optimization target THD, ⁇ u C and ⁇ i respectively, c 1 , c 2 , and c 3 are satisfaction coefficients, and there are: c 1 >0, c 2 > 0, c 3 >0;
  • the specific steps of establishing a multi-objective optimization fitness function in the step (2) are:
  • the adaptive wolf pack algorithm is used to iteratively optimize the BBMC related control parameters.
  • the specific steps include:
  • Step 1 Use the selected actual output current of BBMC as the judgment reference value of the adaptive wolf pack algorithm
  • Step 2 Initialize the parameters; including: population size N, representing N groups of control parameters, the maximum number of iterations kmax , and multi-objective optimization fitness function f s representing the taste concentration of the wolf pack S(i);
  • Step 3 subjects are randomly arranged wolves direction and distance, the position of the i-th acquired wolves X i (K 1, K 2 , ⁇ 1);
  • Step 4 Obtain the taste concentration of the wolves to obtain a group of optimized objects; obtain corresponding THD, ⁇ u C and ⁇ i according to the optimized objects;
  • Step 5 Find the wolf pack individual with the highest taste concentration in the wolf pack as the optimal individual, and retain the taste concentration and position X m (K 1 , K 2 , ⁇ 1 ) of the optimal wolf pack individual;
  • Step 6 Eliminate N/10 wolves with a lower taste concentration in the wolf group, and randomly generate the same number of new wolves in the solution space to realize the update of the wolf group;
  • Step 7 Determine whether the maximum number of iterations is reached; if it is reached, output the optimal individual position X m (K 1 , K 2 , ⁇ 1 ), that is, output the optimal solution of the control parameters K 1 , K 2 , ⁇ 1 and enter Step 8; otherwise, after adding 1 to the number of iterations, return to step 3;
  • Step 8 Determine whether n sets of optimal control parameters have been obtained, if n sets of optimal control parameters have been obtained, go to step 9; otherwise, after changing the actual output current of BBMC at a certain interval, return to step 1;
  • Step 9 Output n groups of optimal control parameters and the corresponding actual output current of BBMC.
  • a numerical fitting method is used to obtain each optimal control parameter The functional relationship with the actual output current of BBMC; the numerical fitting method adopts the least square method; the functional relationship includes the functional relationship between the optimal control parameter K 1 and the actual output current i of BBMC, and the optimal control parameter The functional relationship between K 2 and the actual output current i of BBMC and the functional relationship between the optimal control parameter ⁇ 1 and the actual output current i of BBMC;
  • f K1 (i), f K2 (i) and f ⁇ 1 (i) represent the functions of the optimal control parameters K 1 , K 2 and ⁇ 1 respectively;
  • a 1 , A 2 , A 3 , A 4 , A 5 are the coefficients in the function f K1 (i);
  • B 1 , B 2 , B 3 , B 4 , and B 5 are the coefficients in the function f K2 (i);
  • C 1 , C 2 , C 3 , C 4 , C 5 , C 6 , C 7 , C 8 , C 9 , C 10 , C 11 , C 12 , C 13 , C 14 , C 15 are the coefficients of the function f ⁇ 1 (i); the coefficients are based on the smallest Two multiplication method and use Matlab analysis software to obtain;
  • the optimal control parameters corresponding to any load of the speed control system are determined.
  • the present invention is aimed at an asynchronous motor speed control system using Buck-Boost matrix converter (BBMC) as a frequency converter, and adopts a finite time control strategy for control.
  • the finite time control of various relevant control parameters is the optimization object , Take the BBMC output voltage harmonic distortion THD, capacitor voltage deviation signal ⁇ u c and output current deviation signal ⁇ i as the optimization targets, establish a mathematical model between the optimization target and the optimization object; then select a certain value for the speed control system Large and small loads, that is, when the actual output current of BBMC is a certain value, establish its multi-objective optimization satisfaction function and multi-objective optimization fitness function; then use the adaptive wolf pack algorithm to obtain a set of optimal control parameters for limited time control , Adjust the actual output current of BBMC according to a certain interval to obtain n groups of optimal control parameters for finite time control; finally, use the numerical fitting method to obtain the functional relationship between each optimal control parameter and the actual output current of BBMC; according to the
  • Figure 1 is a topological structure diagram of the speed regulation system in the present invention.
  • FIG. 2 is a flowchart of the present invention.
  • Figure 3 is a flowchart of the adaptive wolf pack optimization algorithm of the present invention.
  • Fig. 4 is a curve diagram of the optimal control parameter fitting curve of the limited time control of the present invention.
  • the speed control system uses BBMC as the frequency converter and three-phase asynchronous motor as its load motor.
  • the BBMC includes a rectifier stage and an inverter stage.
  • the rectifier stage is a three-phase PWM rectifier circuit, which rectifies the three-phase AC into a PWM-modulated DC voltage; and the inverter stage is a three-phase Buck-Boost inverter It is composed of three Buck-Boost DC/DC converters with the same structure.
  • a BBMC asynchronous motor speed control system control parameter adaptive adjustment method the steps are as follows:
  • the control parameters controlled in a limited time are the optimization objects, and the BBMC output voltage harmonic distortion, capacitor voltage deviation signal and output current deviation signal are the optimization objectives, and the optimization objectives and optimization objects are established Mathematical model of time.
  • u C is the capacitor voltage
  • i L is the inductor current
  • i 1 is the BBMC output current
  • u D is the BBMC DC side voltage
  • u DZ is the common terminal voltage of the stator winding of the asynchronous motor
  • L and C are the BBMC inverter stage inductances.
  • capacitance, R and L 1 are the equivalent resistance and equivalent inductance of the single-phase winding of the asynchronous motor
  • d is the duty cycle of the power switch in BBMC;
  • sat is the saturation function
  • ⁇ 2 u D i L -(u C +u D )i 1 -u D i Lref +(u Cref +u D )i 1ref
  • u Cref is the reference value of capacitor voltage
  • i Lref is the reference value of inductor current
  • i 1ref is the reference value of BBMC output current
  • K 1 , K 2 , ⁇ 1 , ⁇ 2 are finite time control parameters
  • ⁇ 2 2 ⁇ 1 /(1+ ⁇ 1 );
  • the harmonic distortion THD of the output voltage u is obtained as:
  • G ln(R/2)
  • T is the period of BBMC output voltage
  • ⁇ is the angular frequency of BBMC output voltage
  • THD', ⁇ u C 'and ⁇ i' are the critical values of optimization target THD, ⁇ u C and ⁇ i respectively, c 1 , c 2 , and c 3 are satisfaction coefficients, and there are: c 1 >0, c 2 > 0, c 3 >0;
  • the adaptive wolf pack optimization algorithm is used to iteratively optimize the control parameters of the limited time control, so that the BBMC output voltage harmonic distortion, the capacitor voltage deviation signal and the output current deviation signal achieve the coordinated optimization, thereby obtaining a set of optimal Optimal finite time control parameters; adjust the actual output current of BBMC, repeat steps (2) and (3) to obtain n groups of optimal finite time control parameters.
  • the adaptive wolf pack algorithm is used to iteratively optimize the relevant control parameters of BBMC.
  • the specific steps include:
  • Step 1 Use the selected actual output current of BBMC as the judgment reference value of the adaptive wolf pack algorithm
  • Step 2 Initialize the parameters; including: population size N, representing N groups of control parameters, the maximum number of iterations kmax , and multi-objective optimization fitness function f s representing the taste concentration of the wolf pack S(i);
  • Step 3 subjects are randomly arranged wolves direction and distance, the position of the i-th acquired wolves X i (K 1, K 2 , ⁇ 1);
  • Step 4 Obtain the taste concentration of the wolves to obtain a group of optimized objects; obtain corresponding THD, ⁇ u C and ⁇ i according to the optimized objects;
  • Step 5 Find the wolf pack individual with the highest taste concentration in the wolf pack as the optimal individual, and retain the taste concentration and position X m (K 1 , K 2 , ⁇ 1 ) of the optimal wolf pack individual;
  • Step 6 Eliminate N/10 wolves with a lower taste concentration in the wolf group, and randomly generate the same number of new wolves in the solution space to realize the update of the wolf group;
  • Step 7 Determine whether the maximum number of iterations is reached; if it is reached, output the optimal individual position X m (K 1 , K 2 , ⁇ 1 ), that is, output the optimal solution of the control parameters K 1 , K 2 , ⁇ 1 and enter Step 8; otherwise, after adding 1 to the number of iterations, return to step 3;
  • Step 8 Determine whether n sets of optimal control parameters have been obtained, if n sets of optimal control parameters have been obtained, go to step 9; otherwise, after changing the actual output current of BBMC at a certain interval, return to step 1;
  • Step 9 Output n groups of optimal control parameters and the corresponding actual output current of BBMC.
  • a numerical fitting method is used to obtain the functional relationship between each optimal control parameter and the BBMC actual output current.
  • the numerical fitting method adopts the least square method; the functional relationship includes the function relationship between the optimal control parameter K 1 and the actual output current i of BBMC, the function between the optimal control parameter K 2 and the actual output current i of BBMC Relations and functional relations between the optimal control parameter ⁇ 1 and the actual output current i of BBMC;
  • f K1 (i), f K2 (i) and f ⁇ 1 (i) represent the functions of the optimal control parameters K 1 , K 2 and ⁇ 1 respectively;
  • a 1 , A 2 , A 3 , A 4 , A 5 are the coefficients in the function f K1 (i);
  • B 1 , B 2 , B 3 , B 4 , and B 5 are the coefficients in the function f K2 (i);
  • C 1 , C 2 , C 3 , C 4 , C 5 , C 6 , C 7 , C 8 , C 9 , C 10 , C 11 , C 12 , C 13 , C 14 , C 15 are the coefficients of the function f ⁇ 1 (i); the coefficients are based on the smallest Two multiplication method and use Matlab analysis software to obtain;
  • the optimal control parameters corresponding to any load of the speed control system are determined.
  • Fig. 4 it is a fitting curve diagram of the optimal control parameters of the limited time control of the present invention.
  • P N 15kW
  • U N 380V
  • I N 30.1A.
  • the numerical fitting method is adopted to obtain the corresponding functional relationship; the numerical fitting method preferably adopts the least square method; the functional relationship includes the most preferably the control parameters K 1 and the functional relationship between BBMC actual output current, and the optimal control parameters K 2 BBMC functional relationship between the actual output current and the optimal control parameters ⁇ 1 and functional relationship between the actual output current BBMC, details as follows:
  • a 1 4.163 ⁇ 10 -5
  • a 2 -0.001382
  • a 5 0.02925.
  • the coefficients in the corresponding function relations of the above-mentioned optimal control parameters are obtained according to the least square method and using Matlab analysis software. According to the functional relationship between the above-mentioned optimal control parameters and the actual output current i of the BBMC, the control parameters can be adjusted in real time according to the actual load carried by the speed control system, so that the speed control system can achieve the best operating effect.

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Abstract

一种BBMC异步电机调速系统控制参数自适应调整方法，包括以下步骤：以有限时间控制的控制参数为优化对象，以Buck-Boost矩阵变换器(BBMC)输出电压谐波失真度、电容电压偏差信号和输出电流偏差信号为优化目标，建立优化目标和优化对象间的数学模型；建立多目标优化满意度函数与多目标优化适应度函数；采用自适应狼群优化算法对有限时间控制各控制参数进行迭代寻优，获得n组最优的有限时间控制参数；采用数值拟合方法获得各最优控制参数与BBMC实际输出电流间的函数关系式，根据所获得的函数关系式确定调速系统任意负载下所对应的最优控制参数。该方法能够根据所得函数关系式即可根据调速系统所带负载大小，也即BBMC实际输出电流大小实时调节各控制参数，从而使调速系统在任意负载下均能达到最佳的运行效果。

Description

BBMC异步电机调速系统控制参数自适应调整方法 技术领域
本发明涉及电力电子技术领域，特别涉及一种BBMC异步电机调速系统控制参数自适应调整方法。
背景技术
Buck-Boost矩阵变换器(Buck-Boost Matrix Converter,BBMC)是一种具有高电压传输比且可直接输出高品质正弦波的新型电力变换器，适合应用于异步电机调速系统中。然而研究表明，针对基于BBMC的异步电机调速系统，当其所带负载发生变化时，其调速控制策略的相关控制参数需作相应的优化调节，才能使BBMC输出电压谐波失真度等指标达到协同最优，从而达到调速系统的高性能调速控制的目的。因此根据调速系统所带负载的大小，即BBMC实际输出电流的大小，研究确定相应的最优控制参数，并进而研究系统最优控制参数与BBMC实际输出电流间的变化规律，对于根据负载变化实时调节相关控制参数以实现系统的高性能调速具有重要意义。
发明内容
为了解决上述技术问题，本发明提供一种精度高、运行效果好的BBMC异步电机调速系统控制参数自适应调整方法。
本发明解决上述问题的技术方案是：一种BBMC异步电机调速系统控制参数自适应调整方法，包括以下步骤：
(1)针对BBMC异步电机调速系统，以有限时间控制的控制参数为优化对象，以BBMC输出电压谐波失真度、电容电压偏差信号和输出电流偏差信号为 优化目标，建立优化目标和优化对象间的数学模型；
(2)针对调速系统任选某一大小负载，即BBMC实际输出电流为某一数值的情况下，建立多目标优化满意度函数与多目标优化适应度函数；
(3)采用自适应狼群优化算法对有限时间控制各控制参数进行迭代寻优，使BBMC输出电压谐波失真度、电容电压偏差信号及输出电流偏差信号达到协同最优，从而获得一组最优的有限时间控制参数；调节BBMC的实际输出电流，重复步骤(2)和(3)，获得n组最优的有限时间控制参数；
(4)根据所获得的n组有限时间最优控制参数以及相应的BBMC实际输出电流，采用数值拟合方法获得各最优控制参数与BBMC实际输出电流间的函数关系式，根据所获得的函数关系式即可确定调速系统任意负载下所对应的最优控制参数。
上述BBMC异步电机调速系统控制参数自适应调整方法，所述步骤(1)中建立优化目标和优化对象间的数学模型的具体步骤为：
1-1)以BBMC中电容电压u C、电感电流i L及输出电流i 1为系统控制变量，建立系统的状态微分方程：
Figure PCTCN2019098333-appb-000001
其中：u C为电容电压，i L为电感电流，i 1为BBMC输出电流，u D为BBMC直流侧电压，u DZ为异步电机定子绕组公共端电压，L和C分别为BBMC逆变级电感和电容，R和L 1分别为异步电机单相绕组的等效电阻与等效电感，d为BBMC中功率开关的占空比；
1-2)根据式(1)及有限时间控制原理，得BBMC中功率开关的占空比d表达式为：
Figure PCTCN2019098333-appb-000002
式中：sat为饱和函数，
Figure PCTCN2019098333-appb-000003
λ 2＝u Di L-(u C+u D)i 1-u Di Lref+(u Cref+u D)i 1ref，u Cref为电容电压参考值，i Lref为电感电流参考值，i 1ref为BBMC输出电流参考值，K 1、K 2、α 1、α 2为有限时间控制参数，α 2＝2α 1/(1+α 1)；
1-3)通过求解式(1)，得BBMC输出电压u和输出电流i 1的解析表达式分别为：
Figure PCTCN2019098333-appb-000004
Figure PCTCN2019098333-appb-000005
1-4)根据谐波失真度的定义，得到输出电压u的谐波失真度THD为：
Figure PCTCN2019098333-appb-000006
式中：
Figure PCTCN2019098333-appb-000007
G＝ln(R/2)，T为BBMC输出电压周期，ω为BBMC输出电压角频率；
1-5)由式(3)和式(4)所得输出电压u和输出电流i 1的解析表达式，得 某负载工况下电容电压u c与其对应理想状态下电压值U e的偏差△u c及输出电流i 1与其对应理想状态下电流值I e的偏差△i分别为：
Figure PCTCN2019098333-appb-000008
Figure PCTCN2019098333-appb-000009
上述BBMC异步电机调速系统控制参数自适应调整方法，所述步骤(2)中建立多目标优化满意度函数的步骤包括：
2-1-1)分别建立优化目标THD、Δu C及Δi的满意度函数，其中：
THD的满意度函数f 1如式(8)所示：
Figure PCTCN2019098333-appb-000010
Δu C的满意度函数f 2如式(9)所示：
Figure PCTCN2019098333-appb-000011
Δi的满意度函数f 3如式(10)所示：
Figure PCTCN2019098333-appb-000012
式中：THD'、Δu C'及Δi'分别为优化目标THD、Δu C及Δi的临界值，c 1、c 2、c 3为满意度系数，且有：c 1>0，c 2>0，c 3>0；
2-1-2)建立三个优化目标THD、Δu C及Δi的多目标优化满意度函数f，如 式(11)所示：
f＝k 1f 1+k 2f 2+k 3f 3    (11)
式中：k 1、k 2及k 3分别为优化目标THD、Δu C及Δi的权重系数，且k 1+k 2+k 3＝1。
上述BBMC异步电机调速系统控制参数自适应调整方法，所述步骤(2)中建立多目标优化适应度函数的具体步骤为：
2-2-1)判断任一优化目标的满意度与相应的满意度阈值的大小：当任一优化目标的满意度f j(j＝1,2,3)小于相应的满意度阈值M j(j＝1,2,3)时，则配置一个相应的动态惩罚因子b j；其中，所述满意度阈值分别为：
Figure PCTCN2019098333-appb-000013
Figure PCTCN2019098333-appb-000014
所述动态惩罚因子分别为：
Figure PCTCN2019098333-appb-000015
Figure PCTCN2019098333-appb-000016
否则，若所述优化目标的满意度f j(j＝1,2,3)大于或等于其对应的满意度阈值M j(j＝1,2,3)，则视其动态惩罚因子为b j＝1；
2-2-2)配置所述动态惩罚因子后，建立多目标优化适应度函数f s如式(12)所示：
f s＝k 1b 1f 1+k 2b 2f 2+k 3b 3f 3     (12)。
上述BBMC异步电机调速系统控制参数自适应调整方法，所述步骤(3)中采用自适应狼群算法对BBMC相关控制参数进行迭代寻优，具体步骤包括：
步骤1：将选取的BBMC实际输出电流作为自适应狼群算法的判定参考值；
步骤2：初始化参数；包括：种群规模N，表示N组控制参数、最大迭代次数k max，以多目标优化适应度函数f s表示狼群的味道浓度S(i)；
步骤3：配置狼群个体随机方向和距离，获取第i个狼群的位置X i(K 1,K 2,α 1)；
步骤4：获取狼群的味道浓度，得到一组所述优化对象；根据优化对象获得相应的THD、Δu C和Δi；
步骤5：在狼群群体中找出味道浓度最高的狼群个体作为最优个体，并保留最优狼群个体的味道浓度和位置X m(K 1,K 2,α 1)；
步骤6：淘汰狼群群体中味道浓度较小的N/10个狼群，并在解空间中随机生成相同数量的新狼群，实现狼群群体的更新；
步骤7：判断是否达到最大迭代次数；若达到，则输出最优个体位置X m(K 1,K 2,α 1)，即输出控制参数K 1,K 2,α 1的最优解，进入步骤8；否则，迭代次数加1后，返回步骤3；
步骤8：判断是否已获得n组最优控制参数，若已获得n组最优控制参数，则进入步骤9；否则，按一定间距改变BBMC的实际输出电流后，返回步骤1；
步骤9：输出n组最优控制参数以及相应的BBMC实际输出电流。
上述BBMC异步电机调速系统控制参数自适应调整方法，所述步骤(4)中根据所获得的n组最优控制参数以及相应的BBMC实际输出电流，采用数值拟合方法获得各最优控制参数与BBMC实际输出电流间的函数关系式；所述数值拟合方法采用最小二乘法；所述函数关系式包括最优控制参数K 1与BBMC实际输出电流i间的函数关系式、最优控制参数K 2与BBMC实际输出电流i间的函数关系式以及最优控制参数α 1与BBMC实际输出电流i间的函数关系式；
最优控制参数K 1与BBMC实际输出电流i间的函数关系式，如式(13)所示：
f K1(i)＝A 1i 4+A 2i 3+A 3i 2+A 4i+A 5    (13)
最优控制参数K 2与BBMC实际输出电流i间的函数关系式，如式(14)所示：
f K2(i)＝B 1i 4+B 2i 3+B 3i 2+B 4i+B 5    (14)
最优控制参数α 1与BBMC实际输出电流i间的函数关系式，如式(15)所示：
Figure PCTCN2019098333-appb-000017
式中：f K1(i)、f K2(i)和f α1(i)分别表示最优控制参数K 1、K 2和α 1的函数；A 1，A 2，A 3，A 4，A 5分别为函数f K1(i)中的系数；B 1，B 2，B 3，B 4，B 5分别为函数f K2(i)中的系数；C 1，C 2，C 3，C 4，C 5，C 6，C 7，C 8，C 9，C 10，C 11，C 12，C 13，C 14，C 15分别为函数f α1(i)的系数；所述各系数根据最小二乘法并利用Matlab分析软件获得；
根据所获得的函数关系式即确定调速系统任意负载下所对应的最优控制参数。
本发明的有益效果在于：本发明针对以Buck-Boost矩阵变换器(BBMC)为变频器的异步电机调速系统，采用有限时间控制策略进行控制，首先以有限时间控制各相关控制参数为优化对象，以BBMC输出电压谐波失真度THD、电容电压偏差信号△u c和输出电流偏差信号△i为优化目标，建立优化目标和优化对象间的数学模型；然后针对调速系统在任选某一大小负载，即BBMC实际输出电流为某一数值的情况下，建立其多目标优化满意度函数与多目标优化适应度函数；接着采用自适应狼群算法获得有限时间控制的一组最优控制参数，按一定间距调节BBMC的实际输出电流，获得n组有限时间控制的最优控制参数；最后采用数值拟合方法获得各最优控制参数与BBMC实际输出电流间的函数关系式；根据所得函数关系式即可根据调速系统所带负载大小，也即BBMC实际输出电流大小实时调节各控制参数，从而使调速系统在任意负载下均能达到最佳的运行效果。
附图说明
图1为本发明中调速系统拓扑结构图。
图2为本发明的流程图。
图3为本发明自适应狼群优化算法流程图。
图4为本发明有限时间控制最优控制参数拟合曲线图。
具体实施方式
下面结合附图和实施例对本发明作进一步的说明。
如图1所示，为本发明调速系统拓扑结构图。该调速系统以BBMC为变频器，以三相异步电机为其负载电机。其中BBMC包括整流级和逆变级两部分，其整流级为一个三相PWM整流电路，它将三相交流整流成PWM调制的直流电压；而逆变级则为一个三相Buck-Boost逆变器，它由三个结构相同的Buck-Boost DC/DC变换器构成。
如图2所示，一种BBMC异步电机调速系统控制参数自适应调整方法，步骤如下：
(1)针对BBMC异步电机调速系统，以有限时间控制的控制参数为优化对象，以BBMC输出电压谐波失真度、电容电压偏差信号和输出电流偏差信号为优化目标，建立优化目标和优化对象间的数学模型。
建立优化目标和优化对象间的数学模型的具体步骤为：
1-1)以BBMC中电容电压u C、电感电流i L及输出电流i 1为系统控制变量，建立系统的状态微分方程：
Figure PCTCN2019098333-appb-000018
其中：u C为电容电压，i L为电感电流，i 1为BBMC输出电流，u D为BBMC直流侧电压，u DZ为异步电机定子绕组公共端电压，L和C分别为BBMC逆变级电感和电容，R和L 1分别为异步电机单相绕组的等效电阻与等效电感，d为BBMC中功率开关的占空比；
1-2)根据式(1)及有限时间控制原理，得BBMC中功率开关的占空比d表达式为：
Figure PCTCN2019098333-appb-000019
式中：sat为饱和函数，
Figure PCTCN2019098333-appb-000020
λ 2＝u Di L-(u C+u D)i 1-u Di Lref+(u Cref+u D)i 1ref，u Cref为电容电压参考值，i Lref为电感电流参考值，i 1ref为BBMC输出电流参考值，K 1、K 2、α 1、α 2为有限时间控制参数，α 2＝2α 1/(1+α 1)；
1-3)通过求解式(1)，得BBMC输出电压u和输出电流i 1的解析表达式分别为：
Figure PCTCN2019098333-appb-000021
Figure PCTCN2019098333-appb-000022
Figure PCTCN2019098333-appb-000023
1-4)根据谐波失真度的定义，得到输出电压u的谐波失真度THD为：
Figure PCTCN2019098333-appb-000024
式中：
Figure PCTCN2019098333-appb-000025
G＝ln(R/2)，T为BBMC输出电压周期，ω为BBMC输出电压角频率；
1-5)由式(3)和式(4)所得输出电压u和输出电流i 1的解析表达式，得某负载工况下电容电压u c与其对应理想状态下电压值U e的偏差△u c及输出电流i 1与其对应理想状态下电流值I e的偏差△i分别为：
Figure PCTCN2019098333-appb-000026
Figure PCTCN2019098333-appb-000027
(2)针对调速系统任选某一大小负载，即BBMC实际输出电流为某一数值的情况下，建立多目标优化满意度函数与多目标优化适应度函数。
2-1)建立多目标优化满意度函数，具体步骤包括：
2-1-1)分别建立优化目标THD、Δu C及Δi的满意度函数，其中：
THD的满意度函数f 1如式(8)所示：
Figure PCTCN2019098333-appb-000028
Δu C的满意度函数f 2如式(9)所示：
Figure PCTCN2019098333-appb-000029
Δi的满意度函数f 3如式(10)所示：
Figure PCTCN2019098333-appb-000030
式中：THD'、Δu C'及Δi'分别为优化目标THD、Δu C及Δi的临界值，c 1、c 2、c 3为满意度系数，且有：c 1>0，c 2>0，c 3>0；
2-1-2)建立三个优化目标THD、Δu C及Δi的多目标优化满意度函数f，如式(11)所示：
f＝k 1f 1+k 2f 2+k 3f 3     (11)
式中：k 1、k 2及k 3分别为优化目标THD、Δu C及Δi的权重系数，且k 1+k 2+k 3＝1。
2-2)建立多目标优化适应度函数，具体步骤包括：
2-2-1)判断任一优化目标的满意度与相应的满意度阈值的大小：当任一优化目标的满意度f j(j＝1,2,3)小于相应的满意度阈值M j(j＝1,2,3)时，则配置一个相应的动态惩罚因子b j；其中，所述满意度阈值分别为：
Figure PCTCN2019098333-appb-000031
Figure PCTCN2019098333-appb-000032
所述动态惩罚因子分别为：
Figure PCTCN2019098333-appb-000033
Figure PCTCN2019098333-appb-000034
否则，若所述优化目标的满意度f j(j＝1,2,3)大于或等于其对应的满意度阈值M j(j＝1,2,3)，则视其动态惩罚因子为b j＝1；
2-2-2)配置所述动态惩罚因子后，建立多目标优化适应度函数f s如式(12)所示：
f s＝k 1b 1f 1+k 2b 2f 2+k 3b 3f 3      (12)。
(3)采用自适应狼群优化算法对有限时间控制各控制参数进行迭代寻优，使BBMC输出电压谐波失真度、电容电压偏差信号及输出电流偏差信号达到协同最优，从而获得一组最优的有限时间控制参数；调节BBMC的实际输出电流，重复步骤(2)和(3)，获得n组最优的有限时间控制参数。
采用自适应狼群算法对BBMC相关控制参数进行迭代寻优，结合图3，具体步骤包括：
步骤1：将选取的BBMC实际输出电流作为自适应狼群算法的判定参考值；
步骤2：初始化参数；包括：种群规模N，表示N组控制参数、最大迭代次数k max，以多目标优化适应度函数f s表示狼群的味道浓度S(i)；
步骤3：配置狼群个体随机方向和距离，获取第i个狼群的位置X i(K 1,K 2,α 1)；
步骤4：获取狼群的味道浓度，得到一组所述优化对象；根据优化对象获得相应的THD、Δu C和Δi；
步骤5：在狼群群体中找出味道浓度最高的狼群个体作为最优个体，并保留最优狼群个体的味道浓度和位置X m(K 1,K 2,α 1)；
步骤6：淘汰狼群群体中味道浓度较小的N/10个狼群，并在解空间中随机生成相同数量的新狼群，实现狼群群体的更新；
步骤7：判断是否达到最大迭代次数；若达到，则输出最优个体位置X m(K 1,K 2,α 1)，即输出控制参数K 1,K 2,α 1的最优解，进入步骤8；否则，迭代次数加1后，返回步骤3；
步骤8：判断是否已获得n组最优控制参数，若已获得n组最优控制参数，则进入步骤9；否则，按一定间距改变BBMC的实际输出电流后，返回步骤1；
步骤9：输出n组最优控制参数以及相应的BBMC实际输出电流。
(4)根据所获得的n组有限时间最优控制参数以及相应的BBMC实际输出电流，采用数值拟合方法获得各最优控制参数与BBMC实际输出电流间的函数关系式。所述数值拟合方法采用最小二乘法；所述函数关系式包括最优控制参数K 1与BBMC实际输出电流i间的函数关系式、最优控制参数K 2与BBMC实际输出电流i间的函数关系式以及最优控制参数α 1与BBMC实际输出电流i间的函数关系式；
最优控制参数K 1与BBMC实际输出电流i间的函数关系式，如式(13)所示：
f K1(i)＝A 1i 4+A 2i 3+A 3i 2+A 4i+A 5   (13)
最优控制参数K 2与BBMC实际输出电流i间的函数关系式，如式(14)所示：
f K2(i)＝B 1i 4+B 2i 3+B 3i 2+B 4i+B 5    (14)
最优控制参数α 1与BBMC实际输出电流i间的函数关系式，如式(15)所示：
Figure PCTCN2019098333-appb-000035
式中：f K1(i)、f K2(i)和f α1(i)分别表示最优控制参数K 1、K 2和α 1的函数；A 1，A 2，A 3，A 4，A 5分别为函数f K1(i)中的系数；B 1，B 2，B 3，B 4，B 5分别为函数f K2(i)中的系数；C 1，C 2，C 3，C 4，C 5，C 6，C 7，C 8，C 9，C 10，C 11，C 12，C 13，C 14，C 15分别为函数f α1(i)的系数；所述各系数根据最小二乘法并利用Matlab分析软件获得；
根据所获得的函数关系式即确定调速系统任意负载下所对应的最优控制参数。
如图4所示，为本发明有限时间控制最优控制参数拟合曲线图。设调速系统的额定功率、额定电压、额定电流分别为：P N＝15kW、U N＝380V、I N＝30.1A。在该调速系统额定负载范围内按一定间距取30组负载数据，即针对BBMC输出电流在其额定输出电流范围内按一定间距取30个输出电流数据，如取输出电流初值为1.4A，按0.4A的间距依次递增，取30个输出电流数据，并针对每个输出电流采用自适应狼群算法获得有限时间控制各最优控制参数，如表1所示。
表1 BBMC输出电流及相应的最优控制参数表
电流/A 1.4 1.8 2.2 2.6 3.0
K 1 0.05331 0.07200 0.09193 0.11297 0.13500
K 2 0.03554 0.04800 0.11288 0.25313 0.40003
α 1 0.12500 0.14285 0.16667 0.14285 0.12500
电流/A 3.4 3.8 4.2 4.6 5.0
K 1 0.15794 0.18169 0.20619 0.23136 0.25715
K 2 0.55294 0.71130 0.87460 1.04241 1.21435
α 1 0.11111 0.08333 0.07692 0.07142 0.07692
电流/A 5.4 5.8 6.2 6.6 7.0
K 1 0.28351 0.31039 0.33775 0.36555 0.39376
K 2 1.39007 1.56927 1.75167 1.93702 2.12511
α 1 0.08333 0.09090 0.16667 0.16667 0.14285
电流/A 7.4 7.8 8.2 8.6 9.0
K 1 0.42235 0.44216 0.46770 0.48816 0.50852
K 2 2.31573 2.49593 2.61804 2.75446 2.89015
α 1 0.11111 0.09090 0.08653 0.08142 0.07692
电流/A 9.4 9.8 10.2 10.6 11.0
K 1 0.52877 0.54892 0.56899 0.58897 0.60887
K 2 3.02515 3.15952 3.29329 3.42650 3.55919
α 1 0.07893 0.08111 0.11500 0.12285 0.14256
电流/A 11.4 11.8 12.2 12.6 13.0
K 1 0.62870 0.64846 0.66815 0.68778 0.70735
K 2 3.69137 3.82309 3.95436 4.08522 4.21568
α 1 0.16667 0.12500 0.10000 0.08142 0.07692
根据表1所获得的30组BBMC输出电流及相应的最优控制参数，采用数值拟合方法获得相应的函数关系式；所述数值拟合方法优选采用最小二乘法；所述函数关系式包括最优控制参数K 1与BBMC实际输出电流间的函数关系式、最优控制参数K 2与BBMC实际输出电流间的函数关系式以及最优控制参数α 1与BBMC实际输出电流间的函数关系式，具体如下：
1)最优控制参数K 1与BBMC实际输出电流i间的函数关系式，如式(13)所示：
f K1(i)＝A 1i 4+A 2i 3+A 3i 2+A 4i+A 5   (13)
式中：A 1＝4.163×10 -5，A 2＝-0.001382，A 3＝0.01484，A 4＝0.00125，A 5＝0.02925。
2)最优控制参数K 2与BBMC实际输出电流i间的函数关系式，如式(14)所示：
f K2(i)＝B 1i 4+B 2i 3+B 3i 2+B 4i+B 5     (14)
式中：B 1＝0.0004296，B 2＝-0.01408，B 3＝0.1537，B 4＝-0.2423，B 5＝0.0802。
3)最优控制参数α 1与BBMC实际输出电流i间的函数关系式，如式(15)所示：
Figure PCTCN2019098333-appb-000036
式中：C 1＝0.1448，C 2＝11.29，C 3＝1.84，C 4＝0.07538，C 5＝6.413，C 6＝0.4914，C 7＝0.07578，C 8＝2.162，C 9＝1.123，C 10＝0.08371，C 11＝2.678，C 12＝4.058，C 13＝0.09212，C 14＝7.344，C 15＝1.663。
上述各最优控制参数相应函数关系式中的系数为根据最小二乘法并利用Matlab分析软件而获得。根据上述各最优控制参数与BBMC实际输出电流i间的函数关系式，即可根据调速系统所带实际负载的大小实时调节各控制参数，使调速系统达到最佳的运行效果。

Claims (6)

  1. 一种BBMC异步电机调速系统控制参数自适应调整方法，包括以下步骤：
    (1)针对BBMC异步电机调速系统，以有限时间控制的控制参数为优化对象，以BBMC输出电压谐波失真度、电容电压偏差信号和输出电流偏差信号为优化目标，建立优化目标和优化对象间的数学模型；
    (2)针对调速系统任选某一大小负载，即BBMC实际输出电流为某一数值的情况下，建立多目标优化满意度函数与多目标优化适应度函数；
    (3)采用自适应狼群优化算法对有限时间控制各控制参数进行迭代寻优，使BBMC输出电压谐波失真度、电容电压偏差信号及输出电流偏差信号达到协同最优，从而获得一组最优的有限时间控制参数；调节BBMC的实际输出电流，重复步骤(2)和(3)，获得n组最优的有限时间控制参数；
    (4)根据所获得的n组有限时间最优控制参数以及相应的BBMC实际输出电流，采用数值拟合方法获得各最优控制参数与BBMC实际输出电流间的函数关系式，根据所获得的函数关系式即可确定调速系统任意负载下所对应的最优控制参数。
  2. 根据权利要求1所述的BBMC异步电机调速系统控制参数自适应调整方法，其特征在于，所述步骤(1)中建立优化目标和优化对象间的数学模型的具体步骤为：
    1-1)以BBMC中电容电压u C、电感电流i L及输出电流i 1为系统控制变量，建立系统的状态微分方程：
    Figure PCTCN2019098333-appb-100001
    其中：u C为电容电压，i L为电感电流，i 1为BBMC输出电流，u D为BBMC直流侧电压，u DZ为异步电机定子绕组公共端电压，L和C分别为BBMC逆变级电感和电容，R和L 1分别为异步电机单相绕组的等效电阻与等效电感，d为BBMC中功率开关的占空比；
    1-2)根据式(1)及有限时间控制原理，得BBMC中功率开关的占空比d表达式为：
    Figure PCTCN2019098333-appb-100002
    式中：sat为饱和函数，
    Figure PCTCN2019098333-appb-100003
    λ 2＝u Di L-(u C+u D)i 1-u Di Lref+(u Cref+u D)i 1ref，u Cref为电容电压参考值，i Lref为电感电流参考值，i 1ref为BBMC输出电流参考值，K 1、K 2、α 1、α 2为有限时间控制参数，α 2＝2α 1/(1+α 1)；
    1-3)通过求解式(1)，得BBMC输出电压u和输出电流i 1的解析表达式分别为：
    Figure PCTCN2019098333-appb-100004
    Figure PCTCN2019098333-appb-100005
    Figure PCTCN2019098333-appb-100006
    1-4)根据谐波失真度的定义，得到输出电压u的谐波失真度THD为：
    Figure PCTCN2019098333-appb-100007
    式中：
    Figure PCTCN2019098333-appb-100008
    G＝ln(R/2)，T为BBMC输出电压周期，ω为BBMC输出电压角频率；
    1-5)由式(3)和式(4)所得输出电压u和输出电流i 1的解析表达式，得某负载工况下电容电压u c与其对应理想状态下电压值U e的偏差△u c及输出电流i 1与其对应理想状态下电流值I e的偏差△i分别为：
    Figure PCTCN2019098333-appb-100009
    Figure PCTCN2019098333-appb-100010
  3. 根据权利要求1所述的BBMC异步电机调速系统控制参数自适应调整方法，其特征在于，所述步骤(2)中建立多目标优化满意度函数的步骤包括：
    2-1-1)分别建立优化目标THD、Δu C及Δi的满意度函数，其中：
    THD的满意度函数f 1如式(8)所示：
    Figure PCTCN2019098333-appb-100011
    Δu C的满意度函数f 2如式(9)所示：
    Figure PCTCN2019098333-appb-100012
    Δi的满意度函数f 3如式(10)所示：
    Figure PCTCN2019098333-appb-100013
    式中：THD'、Δu C'及Δi'分别为优化目标THD、Δu C及Δi的临界值，c 1、c 2、c 3为满意度系数，且有：c 1>0，c 2>0，c 3>0；
    2-1-2)建立三个优化目标THD、Δu C及Δi的多目标优化满意度函数f，如式(11)所示：
    f＝k 1f 1+k 2f 2+k 3f 3      (11)
    式中：k 1、k 2及k 3分别为优化目标THD、Δu C及Δi的权重系数，且k 1+k 2+k 3＝1。
  4. 根据权利要求3所述的BBMC异步电机调速系统控制参数自适应调整方法，其特征在于，所述步骤(2)中建立多目标优化适应度函数的具体步骤为：
    2-2-1)判断任一优化目标的满意度与相应的满意度阈值的大小：当任一优化目标的满意度f j(j＝1,2,3)小于相应的满意度阈值M j(j＝1,2,3)时，则配置一个相应的动态惩罚因子b j；其中，所述满意度阈值分别为：
    Figure PCTCN2019098333-appb-100014
    Figure PCTCN2019098333-appb-100015
    所述动态惩罚因子分别为：
    Figure PCTCN2019098333-appb-100016
    Figure PCTCN2019098333-appb-100017
    否则，若所述优化目标的满意度f j(j＝1,2,3)大于或等于其对应的满意度阈值M j(j＝1,2,3)，则视其动态惩罚因子为b j＝1；
    2-2-2)配置所述动态惩罚因子后，建立多目标优化适应度函数f s如式(12)所示：
    f s＝k 1b 1f 1+k 2b 2f 2+k 3b 3f 3       (12)。
  5. 根据权利要求4所述的BBMC异步电机调速系统控制参数自适应调整方法，其特征在于，所述步骤(3)中采用自适应狼群算法对BBMC相关控制参数进行迭代寻优，具体步骤包括：
    步骤1：将选取的BBMC实际输出电流作为自适应狼群算法的判定参考值；
    步骤2：初始化参数；包括：种群规模N，表示N组控制参数、最大迭代次数k max，以多目标优化适应度函数f s表示狼群的味道浓度S(i)；
    步骤3：配置狼群个体随机方向和距离，获取第i个狼群的位置X i(K 1,K 2,α 1)；
    步骤4：获取狼群的味道浓度，得到一组所述优化对象；根据优化对象获得相应的THD、Δu C和Δi；
    步骤5：在狼群群体中找出味道浓度最高的狼群个体作为最优个体，并保留最优狼群个体的味道浓度和位置X m(K 1,K 2,α 1)；
    步骤6：淘汰狼群群体中味道浓度较小的N/10个狼群，并在解空间中随机生成相同数量的新狼群，实现狼群群体的更新；
    步骤7：判断是否达到最大迭代次数；若达到，则输出最优个体位置X m(K 1,K 2,α 1)，即输出控制参数K 1,K 2,α 1的最优解，进入步骤8；否则，迭代次数加1后，返回步骤3；
    步骤8：判断是否已获得n组最优控制参数，若已获得n组最优控制参数，则进入步骤9；否则，按一定间距改变BBMC的实际输出电流后，返回步骤1；
    步骤9：输出n组最优控制参数以及相应的BBMC实际输出电流。
  6. 根据权利要求5所述的BBMC异步电机调速系统控制参数自适应调整方法，其特征在于，所述步骤(4)中根据所获得的n组最优控制参数以及相应的BBMC实际输出电流，采用数值拟合方法获得各最优控制参数与BBMC实际输出电流间的函数关系式；所述数值拟合方法采用最小二乘法；所述函数关系式包括最优控制参数K 1与BBMC实际输出电流i间的函数关系式、最优控制参数K 2与BBMC实际输出电流i间的函数关系式以及最优控制参数α 1与BBMC实际输出电流i间的函数关系式；
    最优控制参数K 1与BBMC实际输出电流i间的函数关系式，如式(13)所示：
    f K1(i)＝A 1i 4+A 2i 3+A 3i 2+A 4i+A 5       (13)
    最优控制参数K 2与BBMC实际输出电流i间的函数关系式，如式(14)所示：
    f K2(i)＝B 1i 4+B 2i 3+B 3i 2+B 4i+B 5      (14)
    最优控制参数α 1与BBMC实际输出电流i间的函数关系式，如式(15)所示：
    Figure PCTCN2019098333-appb-100018
    式中：f K1(i)、f K2(i)和f α1(i)分别表示最优控制参数K 1、K 2和α 1的函数；A 1，A 2，A 3，A 4，A 5分别为函数f K1(i)中的系数；B 1，B 2，B 3，B 4，B 5分别为函数f K2(i)中的系数；C 1，C 2，C 3，C 4，C 5，C 6，C 7，C 8，C 9，C 10，C 11，C 12，C 13，C 14，C 15分别为函数f α1(i)的系数；所述各系数根据最小二乘法并利用Matlab分析软件获得；
    根据所获得的函数关系式即确定调速系统任意负载下所对应的最优控制参数。
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