CN114527641A - Brushless direct current motor fuzzy control method based on artificial electric field algorithm optimization - Google Patents
Brushless direct current motor fuzzy control method based on artificial electric field algorithm optimization Download PDFInfo
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Abstract
The invention discloses a brushless direct current motor fuzzy control method based on artificial electric field algorithm optimization, which comprises the following steps: building a dynamic model of the brushless direct current motor; constructing a fuzzy PID controller based on the brushless direct current motor dynamic model; based on an artificial electric field algorithm, optimizing the quantization factor and the scale factor in the fuzzy PID controller to obtain the optimized quantization factor and scale factor; and calculating parameter adjustment quantity according to the optimized quantization factor and the optimized scale factor, outputting a control variable by a PID regulator, and controlling the rotating speed of the motor. By using the invention, the stability and the anti-interference performance of the rotating speed control of the brushless direct current motor can be improved. The brushless direct current motor fuzzy control method based on the artificial electric field algorithm optimization can be widely applied to the field of motor control.
Description
Technical Field
The invention relates to the field of motor control, in particular to a brushless direct current motor fuzzy control method based on artificial electric field algorithm optimization.
Background
At present, for a brushless direct current motor fuzzy PID controller, online adjustment of gains of three parameters of PID is realized in a fixed universe of discourse. The input variable fuzzification quantization factors ke and key of the method both take fixed values, and when the error of a control system gradually approaches to zero. The fuzzy rule division on the initially given larger input domain of discourse is rough, the control precision is not high, and in addition, the parameter selection of the existing fuzzy control method has the defect of excessively depending on expert experience and engineering experience.
Disclosure of Invention
In order to solve the above technical problems, an object of the present invention is to provide a brushless dc motor fuzzy control method based on artificial electric field algorithm optimization, which can improve stability and anti-interference performance of the brushless dc motor speed control.
The technical scheme adopted by the invention is as follows: a brushless direct current motor fuzzy control method based on artificial electric field algorithm optimization comprises the following steps:
building a brushless direct current motor dynamic model;
constructing a fuzzy PID controller based on the brushless direct current motor dynamic model;
based on an artificial electric field algorithm, optimizing the quantization factor and the scale factor in the fuzzy PID controller to obtain the optimized quantization factor and scale factor;
and calculating parameter adjustment quantity according to the optimized quantization factor and the optimized scale factor, outputting a control variable by a PID regulator, and controlling the rotating speed of the motor.
Further, the step of building a dynamic model of the brushless dc motor specifically includes:
building a dynamic model of the brushless direct current motor according to the mathematical model of the brushless direct current motor based on the simulation platform;
the dynamic model of the brushless direct current motor adopts rotating speed and current double closed-loop control.
Further, the step of constructing the fuzzy PID controller based on the brushless dc motor dynamic model specifically includes:
selecting a fuzzy PID controller with double input and three output;
acquiring a rotating speed error and an error rate of the motor, and converting the rotating speed error and the error from a basic domain to a fuzzy domain through a quantization factor to obtain the rotating speed error and the error rate after fuzzy conversion;
and carrying out fuzzy reasoning and deblurring processing on the rotation speed error and the error rate after the fuzzy conversion by combining a preset fuzzy rule to obtain a fuzzy output variable.
Further, the step of optimizing the quantization factor and the scale factor in the fuzzy PID controller based on the artificial electric field algorithm to obtain the optimized quantization factor and scale factor specifically includes:
initializing the parameters of the artificial electric field algorithm and constructing a fitness function by taking the quantization factor and the scale factor as optimization targets;
calculating a current coulomb constant;
acquiring the position of the optimal fitness value of the charge, and calculating the electric quantity of the charge, the coulomb force applied to the charge and the acceleration of the charge by combining a fitness function;
speed and location of the update charge;
and returning to the current coulomb constant calculation step until the out-of-loop condition is met, and outputting the optimized quantization factor and scale factor.
Further, the fitness function formula is expressed as follows:
in the above formula, the first and second carbon atoms are,
d represents the dimension for the location of charge i in the k dimension.
Further, the calculation formula of the acceleration of the electric charge is expressed as follows:
in the above-mentioned formula, the compound has the following structure,
representing the acceleration of the charge i in d-dimension at time t, Qi(t) represents the charge i coulombic,
representing the electric field strength of the charge i in d-dimension at time t, Mi(t) represents the mass of the charge.
Further, the update formula of the velocity and position of the charge is expressed as follows:
in the above formula, the first and second carbon atoms are,
representing the velocity of charge i in the d dimension at time t,
representing the position of the charge i in d-dimension at time t, randiRepresents [0,1 ]]Random number in between.
Further, the step of calculating a parameter adjustment amount according to the optimized quantization factor and the optimized scale factor, outputting a control variable by a PID regulator, and controlling the motor rotation speed specifically includes:
transmitting the optimized quantization factor and the optimized scale factor to a simulation platform, assigning the quantization factor and the optimized scale factor to a fuzzy controller, and calculating a parameter adjustment quantity by combining the initial quantization factor and the scale factor;
the fuzzy controller outputs the parameter adjustment quantity to the PID regulator, and the PID regulator outputs a control variable;
and correspondingly controlling the rotating speed of the motor according to the control variable.
The method has the beneficial effects that: the invention can quickly find out the global optimal solution based on the Artificial Electric Field Algorithm (AEFA), and adopts the artificial electric field algorithm to optimize the quantization factor and the scale factor on line, thereby effectively improving the response speed, the anti-interference capability and the stability of the rotating speed control and basically not generating overshoot.
Drawings
FIG. 1 is a flow chart of the steps of a brushless DC motor fuzzy control method based on the optimization of an artificial electric field algorithm according to the present invention;
FIG. 2 is a schematic flow chart of optimization based on an artificial electric field algorithm according to an embodiment of the present invention;
FIG. 3 is a frame structure diagram of a brushless DC motor fuzzy control method based on an artificial electric field algorithm optimization according to an embodiment of the present invention;
FIG. 4 is a fuzzy rule table in accordance with an embodiment of the present invention;
FIG. 5 is a schematic flow chart of an artificial electric field algorithm according to an embodiment of the present invention;
FIG. 6 is a graph of iteration times for an objective function when an artificial electric field algorithm is used in accordance with an embodiment of the present invention;
Detailed Description
The invention is described in further detail below with reference to the figures and the specific embodiments. The step numbers in the following embodiments are provided only for convenience of illustration, the order between the steps is not limited at all, and the execution order of each step in the embodiments can be adapted according to the understanding of those skilled in the art.
Referring to fig. 1,2 and 3, the invention provides a brushless direct current motor fuzzy control method based on artificial electric field algorithm optimization, which comprises the following steps:
s1, building a brushless direct current motor dynamic model;
specifically, a dynamic model of the brushless direct current motor is built based on an MATLAB/simulink simulation platform, and the rotating speed and current double closed-loop control is adopted: the inner loop is a current loop and the outer loop is a speed loop.
S2, constructing a fuzzy PID controller based on the brushless direct current motor dynamic model;
s2.1, selecting a double-input and three-output fuzzy PID controller;
s2.2, acquiring a rotating speed error and an error rate of the motor, and converting the rotating speed error and the error from a basic domain to a fuzzy domain through a quantization factor to obtain the rotating speed error and the error rate after fuzzy conversion;
specifically, the rotation speed error e and the error rate ec are converted from a basic domain to a fuzzy domain through quantization factors Ke and Kec, the initial domain of the fuzzy rule is [ -3,3], and a triangular membership function is adopted.
And S2.3, combining a preset fuzzy rule, and performing fuzzy reasoning and deblurring processing on the rotation speed error and the error rate after the fuzzy conversion to obtain a fuzzy output variable.
Specifically, the fuzzy rule is to divide the input variable and the output variable into 7 stages: { NB, NM, NS, ZE, PS, PM, PB }, i.e. the correspondence indicates { negative large, negative medium, negative small, zero, positive small, positive medium, positive large }, whereby 49 fuzzy rules can be obtained. During the experiment, a double-input and three-output fuzzy controller is selected, and a fuzzy rule table refers to fig. 4. And (3) combining a fuzzy rule table, and performing fuzzy reasoning and deblurring processing on the rotation speed error e and the error rate ec after the fuzzy conversion to obtain fuzzy output variables delta Kp ', delta Ki ' and delta Kd '.
S3, optimizing the quantization factor and the scale factor in the fuzzy PID controller based on the artificial electric field algorithm to obtain the optimized quantization factor and scale factor;
s3.1, initializing parameters of an artificial electric field algorithm and constructing a fitness function:
specifically, the parameters for initializing the artificial electric field algorithm are set in the d-dimensional search space, N charges are initialized, and the position of the ith charge is as follows:
1,2, …, N, wherein,
is the position of the charge i in the d-dimension, and N is the total number of charges.
The fitness function is formulated as follows:
s3.2, calculating a current coulomb constant;
calculating a current coulomb constant K (t), wherein the coulomb constant is a function of the current iteration times and the system iteration times, and decreases exponentially, and the expression is
Wherein K0And expressing an initial value of coulomb constant iteration, expressing an adjusting parameter by alpha, controlling the change speed of the coulomb constant, expressing the current iteration times by iter, and expressing the maximum iteration times by maxim.
S3.3, acquiring the position of the optimal fitness value of the charge, and calculating the electric quantity of the charge, the coulomb force borne by the charge and the acceleration of the charge by combining a fitness function;
position of best fitness value of the charge i at time t is obtained:
The electric quantity of each charge is calculated by a fitness function:
where bst (t) min (fit)j(t))，wst(t)＝max(fitj(t))，fitj(t) is the fitness value of charge j at time t.
Calculating the quantity of charge Q of the updated chargei(t)：
Calculating the coulomb force suffered by each charge in the d-dimension
Wherein R isij(t)＝||Xi(t),Xj(t)||2Is the Euclidean distance between charge i and charge j, randjIs [0,1 ]]A random number in between.
Calculating the electric field intensity of the charge i in the d-th dimension
Calculating the acceleration of the charge i at time t
S3.4, speed of updating electric charge
And position
And S3.5, returning to the step of initializing parameters until the condition of jumping out of the cycle is met, and outputting the optimized quantization factor and scale factor.
Specifically, referring to fig. 5 and 6, the control parameters of the system, i.e., the total number N of charges used for optimization, the maximum iteration step number max _ it of the whole optimization process, the dimension D of the problem, and the maximum tolerance R of the objective function, are input in advance. Judging whether the change of the fitness function value is smaller than a maximum tolerance value R or whether the iteration times reach a maximum value max _ it, if so, ending the optimization process, and returning to the optimized parameters; if not, returning to the current coulomb constant calculation step to continue iteration.
And S4, calculating parameter adjustment according to the optimized quantization factor and the scale factor, outputting a control variable by a PID regulator, and controlling the rotating speed of the motor.
S4.1, transmitting the optimized quantization factor and the optimized scale factor to a simulation platform, assigning values to a fuzzy controller, and calculating parameter adjustment quantity by combining the initial quantization factor and the scale factor;
s4.2, the fuzzy controller outputs the parameter adjustment quantity to the PID regulator, and the PID regulator outputs a control variable;
and S4.3, correspondingly controlling the rotating speed of the motor according to the control variable.
Referring to fig. 3, the optimized parameter adjustment quantities Δ Kp, Δ Ki and Δ Kd are superimposed with the initial quantization factors Kp, Ki and the scaling factor Kd to control the current loop, Is output after passing through the amplitude limiting module Is used as the input of the current regulator, and then Is driven by PWM to generate six paths of waveforms to control the BLDCM motor.
Compared with the control results of the fuzzy pid control after the common pid control, the fuzzy controller pid control and the artificial electric field algorithm optimization, the control results are shown in the following table. And after the system is stabilized, giving a fixed value of interference to the system, and observing the interference resistance of the system.
| Control strategy | Adjusting time/s | Steady state error | Overshoot amount/% | Disturbance amount/%) |
| AEFA parameter optimization control | 0.503s | 0.088‰ | 0.3％ | 1.1％ |
| Fuzzy control | 0.506s | 0.105‰ | 0.6％ | 1.5％ |
| PID control | 0.544s | 1.961‰ | 1.6％ | 3.8％ |
The adjusting time, the steady-state error and the overshoot of the motor are all improved under the parameter optimization control of the artificial electric field algorithm, and the superiority of the control algorithm is embodied.
The result shows that the invention can carry out good optimization control on the brushless DC motor, so that the motor has better anti-interference performance and dynamic characteristic. The problem that the fuzzy rule of the current fuzzy PID controller of the brushless direct current motor is mostly obtained according to expert experience, and the online adjustment of control parameters is poor is effectively solved, the system performance is effectively improved, the anti-interference performance and the robustness are enhanced while the real-time performance of the system is kept, and the precision, the response speed and the stability of the rotating speed control are ensured.
A brushless DC motor fuzzy control device based on artificial electric field algorithm optimization:
at least one processor;
at least one memory for storing at least one program;
when executed by the at least one processor, the at least one program causes the at least one processor to implement a brushless dc motor fuzzy control method optimized based on an artificial electric field algorithm as described above.
The contents in the above method embodiments are all applicable to the present apparatus embodiment, the functions specifically implemented by the present apparatus embodiment are the same as those in the above method embodiments, and the advantageous effects achieved by the present apparatus embodiment are also the same as those achieved by the above method embodiments.
A storage medium having stored therein instructions executable by a processor, the storage medium comprising: the processor-executable instructions, when executed by the processor, are for implementing a brushless dc motor fuzzy control method optimized based on an artificial electric field algorithm, as described above.
The contents in the above method embodiments are all applicable to the present storage medium embodiment, the functions specifically implemented by the present storage medium embodiment are the same as those in the above method embodiments, and the advantageous effects achieved by the present storage medium embodiment are also the same as those achieved by the above method embodiments.
While the preferred embodiments of the present invention have been illustrated and described, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (8)
1. The brushless direct current motor fuzzy control method based on artificial electric field algorithm optimization is characterized by comprising the following steps:
building a brushless direct current motor dynamic model;
constructing a fuzzy PID controller based on the brushless direct current motor dynamic model;
optimizing the quantization factor and the scale factor in the fuzzy PID controller based on an artificial electric field algorithm to obtain the optimized quantization factor and scale factor;
and calculating parameter adjustment quantity according to the optimized quantization factor and the optimized scale factor, outputting a control variable by a PID regulator, and controlling the rotating speed of the motor.
2. The brushless direct current motor fuzzy control method based on artificial electric field algorithm optimization according to claim 1, wherein the step of building the dynamic model of the brushless direct current motor specifically comprises:
building a dynamic model of the brushless direct current motor according to a mathematical model of the brushless direct current motor based on the simulation platform;
the dynamic model of the brushless direct current motor adopts rotating speed and current double closed-loop control.
3. The brushless direct current motor fuzzy control method based on artificial electric field algorithm optimization according to claim 2, wherein the step of constructing the fuzzy PID controller based on the brushless direct current motor dynamic model specifically comprises:
selecting a fuzzy PID controller with double input and three output;
acquiring a rotating speed error and an error rate of the motor, and converting the rotating speed error and the error from a basic domain to a fuzzy domain through a quantization factor to obtain the rotating speed error and the error rate after fuzzy conversion;
and carrying out fuzzy reasoning and deblurring processing on the rotation speed error and the error rate after the fuzzy conversion by combining a preset fuzzy rule to obtain a fuzzy output variable.
4. The brushless direct current motor fuzzy control method based on artificial electric field algorithm optimization according to claim 3, wherein the step of optimizing the quantization factor and the scale factor in the fuzzy PID controller based on the artificial electric field algorithm to obtain the optimized quantization factor and scale factor specifically comprises:
initializing the parameters of the artificial electric field algorithm and constructing a fitness function by taking the quantization factor and the scale factor as optimization targets;
calculating a current coulomb constant;
acquiring the position of the optimal fitness value of the charge, and calculating the electric quantity of the charge, the coulomb force borne by the charge and the acceleration of the charge by combining a fitness function;
speed and location of the update charge;
and returning to the current coulomb constant calculation step until the out-of-loop condition is met, and outputting the optimized quantization factor and scale factor.
5. The brushless direct current motor fuzzy control method based on artificial electric field algorithm optimization of claim 4, characterized in that the fitness function formula is expressed as follows:
in the above formula, the first and second carbon atoms are,
representing the position of charge i in the k-th dimension at time t and D representing the dimension.
6. The fuzzy control method for the brushless direct current motor based on the artificial electric field algorithm optimization of claim 5, wherein the calculation formula of the acceleration of the electric charge is expressed as follows:
in the above formula, the first and second carbon atoms are,
representing the acceleration of the charge i in d-dimension at time t, Qi(t) represents the charge i coulombic,
representing the electric field strength of the charge i in d-dimension at time t, Mi(t) represents the mass of the charge.
7. The fuzzy control method for the brushless DC motor based on the optimization of the artificial electric field algorithm according to claim 6, wherein the update formula of the speed and the position of the charge is expressed as follows:
in the above formula, the first and second carbon atoms are,
representing the velocity of charge i in the d dimension at time t,
representing the position of the charge i in d-dimension at time t, randiRepresents [0,1 ]]Random number in between.
8. The fuzzy control method for brushless DC motor based on artificial electric field algorithm optimization of claim 7,
the step of calculating parameter adjustment according to the optimized quantization factor and the optimized scale factor, outputting a control variable by a PID regulator, and controlling the rotating speed of the motor specifically comprises the following steps:
transmitting the optimized quantization factor and the optimized scale factor to a simulation platform, assigning the quantization factor and the optimized scale factor to a fuzzy controller, and calculating a parameter adjustment quantity by combining the initial quantization factor and the scale factor;
the fuzzy controller outputs the parameter adjustment quantity to the PID regulator, and the PID regulator outputs a control variable;
and correspondingly controlling the rotating speed of the motor according to the control variable.
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