Brushless direct current motor speed regulation control method based on BAS algorithm improved fuzzy PID

Abstract

The invention discloses a brushless direct current motor speed regulation control method based on BAS algorithm improved fuzzy PID. The initial parameters of the fuzzy PID controller are automatically optimized by using the global search capability of the BAS algorithm, so that the initial parameters are more suitable for the characteristics of a controlled object, and the control precision and the response speed are improved. Besides, the improved BAS algorithm can avoid generation of local optimal parameters, dynamically adjust proportion, integration and differential parameters of the fuzzy PID controller, overcome the defect that traditional PID parameters are fixed, enable the system to be in the optimal control state all the time, and further improve the control precision and the response speed.

Description

Technical Field

[0001] The present invention relates to the technical field of brushless DC motors, and specifically relates to a speed control method for brushless DC motors based on an improved fuzzy PID with the BAS algorithm. Background Art

[0002] In the fields of industrial automation and fluid control, as a key actuator, the control accuracy and dynamic response ability of a flow regulating valve directly affect the energy efficiency and stability of the system. Due to advantages such as high power density, low maintenance cost, and long service life, brushless DC motors have gradually become the preferred solution for the drive system of flow regulating valves. With the increasing demand for precision in intelligent manufacturing and precision fluid control, it is necessary to design a speed control method for brushless DC motors that suits the characteristics of flow regulating valves. By improving the control algorithm, a closed-loop speed regulation performance with high precision, strong robustness, and low energy consumption can be achieved.

[0003] The speed of a brushless DC motor is usually adjusted using PID control. This method is simple to operate and has a small computational load. However, this control method is prone to overshoot, difficult to adapt to system changes, and cannot meet the requirements of the motor for power control systems. Fuzzy PID control combines the accuracy of PID and the adaptability of fuzzy logic, and is good at dealing with nonlinear and uncertain systems. However, its design is complex, parameter tuning depends on experience, the computational load is large, and it may affect real-time performance.

[0004] CN113162491A discloses an optimization method for brushless DC motors based on an improved beetle antennae search algorithm, specifically disclosing the following steps: Step 1, construct a mathematical model of the speed and current double closed-loop speed regulation system of the brushless DC motor to be optimized, and build a simulation model of the brushless DC motor control system in Matlab / Simulink according to this model; Step 2, establish the initial value and random unit orientation of the beetle antennae search algorithm, decompose and assign the three-dimensional iteration position of the beetle antennae search algorithm to the three PID parameters, set the beetle update principle and termination condition in the improved beetle antennae search algorithm, and obtain the optimal control parameters of the motor using the improved beetle antennae search algorithm. An improved strategy of the BAS algorithm is provided for the problem of difficult to find the local optimal value during initialization, but the situation that the algorithm is prone to falling into the local optimal solution during optimization is not considered. Summary of the Invention

[0005] Aiming at the deficiencies of the prior art, the present invention provides a speed control method for brushless DC motors based on an improved fuzzy PID with the BAS algorithm. By optimizing the BAS algorithm, the control accuracy and response speed are improved, the system robustness and anti-interference ability are enhanced. The introduction of the BAS algorithm reduces the dependence on manual experience and lowers the debugging difficulty and maintenance cost of the control system.

[0006] To solve the problems of the prior art, the technical solutions adopted by the present invention are as follows:

[0007] A speed control method for a brushless DC motor based on an improved fuzzy PID of the BAS algorithm, comprising the following steps:

[0008] Step 1, construct a speed control device for a brushless DC motor, and achieve precise adjustment of the motor speed through a DSP controller;

[0009] Step 2, according to the speed control device for a brushless DC motor constructed in Step 1, assuming that the three-phase windings of the motor are completely symmetrical, that is, the resistance and inductance of each phase winding are the same; ignoring the slot effect, hysteresis loss and mechanical coupling effect between the rotor and the stator; when the magnetic field generated by the rotor is an ideal constant magnetic field and the air gap between the stator and the rotor is uniform, by establishing an equivalent circuit diagram of the brushless DC motor, the three-phase voltage balance equation is derived; at the same time, establish the motion equation and electromagnetic torque equation of the motor, so as to construct a complete mathematical model of the brushless DC motor to establish the mathematical model of the brushless DC motor;

[0010] Step 3: Combine the dynamic attenuation mechanism and the probability mutation strategy to optimize the BAS algorithm for global optimization to avoid the algorithm falling into local optimum;

[0011] Step 4: Input the parameters after global optimization in Step 3 into the fuzzy PID controller, establish a BAS algorithm-optimized fuzzy PID controller, output the optimal solution of the controller parameters through system simulation, and substitute it into the fuzzy PID controller, that is, obtain the speed control method for a brushless DC motor based on an improved fuzzy PID of the BAS algorithm

[0012]

[0013] As an improvement, in Step 1, the speed control device for a brushless DC motor includes a voltage module, a three-phase inverter, a decoder, a gate signal module, Hall components, a position sensor and a current sensor, and the Hall components include a Hall sensor, an encoder and a Schmitt trigger.

[0014] Further improvement is that in Step 1, the DSP controller uses the STM320F2807 chip, and the BAS algorithm is used to optimize the fuzzy PID control method to improve the motor control accuracy.

[0015] As an improvement, in Step 2, the mathematical model of the brushless DC motor is established as follows:

[0016] Q1 to Q6 in the equivalent circuit diagram of the brushless DC motor represent transistors, which constitute the core of the three-phase inverter, and the main function is to convert direct current into three-phase alternating current. Among them, each pair of transistors is responsible for controlling the current of one phase. According to the equivalent circuit diagram, the stator three-phase voltage balance equation can be obtained:

[0017]

[0018] Wherein, U a , U b , U c are the input voltages of each phase, i a , i b , i c are the currents of each phase, R is the resistance value of the stator winding, L is the self-inductance of each phase winding, M is the mutual inductance between two-phase windings, and e is the electromotive force of the three-phase winding;

[0019] The motion equation of the brushless DC motor is:

[0020]

[0021] Wherein, T e is the electromagnetic torque, T L is the load torque, B is the damping coefficient, and ω is the mechanical angular velocity;

[0022] The electromagnetic torque equation generated by the winding is:

[0023]

[0024] As an improvement, the steps of optimizing the BAS algorithm for global optimization in step 3 are as follows:

[0025] In the first step, when generating the initial population, set the initial position and parameters;

[0026] In the second step, normalize the direction of the longhorn beetle antennae, and randomly generate a direction vector to simulate the direction of the longhorn beetle antennae;

[0027] In the third step, determine the positions of the two antennae of the longhorn beetle and the distance between the two antennae;

[0028] In the fourth step, calculate the odor concentration sensed by the two antennae of the longhorn beetle according to the fitness function, and iterate the next position of the longhorn beetle. The fitness function f(x) selects the time integral error index:

[0029]

[0030] Among them, δ m is the search step length at time m, f(x r ), f(x l ) are the food concentrations sensed by the two antennae of the longhorn beetle.

[0031] sign[·] is the sign function, which is used to indicate the moving direction. If f(x r ) is less than f(x l ), then move in the x l direction, and vice versa;

[0032] Step 5: Establish the update rules for the longicorn beetle step length δ and the distance d between the two antennae

[0033] d m = d e ·d m-1 + 0.01 (9)

[0034] δ m = δ e ·δ m-1 (10);

[0035] Step 6: Introduce a probability constant and a random number p to measure the influence of the random direction on the algorithm. The value range of the probability constant is 0.1 - 0.3, the random number takes the value of 0.5, and the longicorn beetle position and search step length are iteratively updated with dynamic attenuation;

[0036] Step 7: Repeat the above steps. After reaching the maximum number of iterations of 100 times, output the optimal solution and its corresponding fitness error value.

[0037] As an improvement, in step 4, the speed control method of the brushless DC motor based on the improved BAS algorithm to improve the fuzzy PID is as follows:

[0038] Based on the working principle diagram of the fuzzy PID controller of the improved BAS algorithm, where r(t) is the rated speed, y(t) is the actual speed of the brushless DC motor, e is the deviation value between the target speed and the actual speed, and ec is the deviation change rate; the fuzzy PID controller fuzzifies the input signal, uses fuzzy inference to obtain the fuzzy output, and then obtains the actual control output through defuzzification. In addition, the BAS algorithm performs global optimization on the initial PID parameters K p0 、K i0 、K d0 to obtain the final PID parameter values by combining the two; the final PID parameter values are obtained according to the following formula:

[0039]

[0040] Further improved, the step of inputting the parameters after global optimization in step 3 into the fuzzy PID controller in step 4 is divided into the following steps:

[0041] Step S21: Improve the BAS algorithm and perform global optimization on the initial parameters of the fuzzy PID

[0042] The working principle of this algorithm is that when the longicorn beetle forages, it randomly explores the nearby area through two antennae, senses the concentration difference of food, and thus adjusts the moving direction to move towards the direction with a higher food concentration, gradually approaching the optimal solution in the search space;

[0043] Step S22: Design a fuzzy PID controller to obtain the best controller parameter values

[0044] The working process of the fuzzy PID controller can be elaborated in detail from the following steps:

[0045] (1) Fuzzification of system input: The fuzzy PID controller first converts the input signal into a fuzzy linguistic variable. After fuzzification, it is mapped to a fuzzy set by the membership function. Among them, the brushless DC motor speed control system is set as a two-input and three-output structure. The input quantities are the motor speed deviation e and the corresponding deviation change rate ec, and the output is the parameter correction amount ΔK p 、ΔK i 、ΔK d of the PID controller. The universes of discourse of each input and output quantity are [-6, 6];

[0046] (2) Fuzzy inference: According to the pre-designed fuzzy rule base, the fuzzified input is inferred and calculated. Among them, the fuzzy rule base is set as a seven-level fuzzy subset {NB, NM, NS, ZO, PS, PM, PB}, where N represents negative, P represents positive, B represents large, M represents medium, S represents small, ZO represents 0. In addition, the membership function adopts a triangular form;

[0047] (3) Defuzzification: The fuzzy output is converted into an accurate control signal through defuzzification to adjust the system output, and the values of K p 、K i 、K d are 20.6034, 6.03626, and 3.33983 respectively.

[0048] Beneficial effects

[0049] Compared with the prior art, the brushless DC motor speed control method based on the improved fuzzy PID by the BAS algorithm of the present invention has the following advantages:

[0050] 1. Improve control accuracy and response speed: In traditional fuzzy PID control, fuzzy rules and membership functions mostly rely on empirical settings and it is difficult to ensure optimality. The present invention utilizes the global search ability of the BAS algorithm to automatically optimize the initial parameters of the fuzzy PID controller, making it more suitable for the characteristics of the controlled object, thereby improving control accuracy and response speed. In addition, the improved BAS algorithm can avoid generating locally optimal parameters and dynamically adjust the proportional, integral, and differential parameters of the fuzzy PID controller, overcoming the disadvantage of fixed traditional PID parameters, so that the system is always in the best control state and further improving control accuracy and response speed.

[0051] 2. Enhance the system robustness and anti-interference ability: The brushless DC motor system has uncertainties such as non-linearity and parameter time-variation. The present invention introduces fuzzy control, which can effectively handle these uncertainties and enhance the system robustness. At the same time, the improved BAS algorithm has strong global search ability and local development ability, and can quickly adapt to system parameter changes and external interferences, so that the system maintains good control performance.

[0052] 3. Simplify the parameter tuning process and improve efficiency: The parameter tuning process of traditional fuzzy PID control is complex and requires a lot of experience and time. The present invention uses the BAS algorithm to automatically optimize the rules, simplifies the parameter tuning process, and improves the efficiency. The introduction of the BAS algorithm reduces the dependence on manual experience, and reduces the debugging difficulty and maintenance cost of the control system. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 It is the structure diagram of the flow regulating valve of the present invention. Among them, 100, brushless DC motor; 101, valve stem; 102, valve cavity; 103, valve seat;

[0054] Figure 2 It is the schematic diagram of the speed control device of the brushless DC motor of the present invention;

[0055] Figure 3 It is the speed control method of the brushless DC motor of the present invention;

[0056] Figure 4 It is the equivalent circuit diagram of the brushless DC motor of the present invention;

[0057] Figure 5 It is the schematic diagram of the improved fuzzy PID control of the BAS algorithm of the present invention;

[0058] Figure 6 It is the flow chart of the BAS algorithm of the present invention;

[0059] Figure 7 It is the simulation diagram of the brushless DC motor of the present invention;

[0060] Figure 8 It is the speed simulation curve of the brushless DC motor of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0061] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments.

[0062] As Figure 1As shown, the structure of the flow regulating valve includes a 100 brushless DC motor, a 101 valve stem, a 102 valve cavity, a 103 valve seat, etc. The brushless DC motor is connected to the valve stem. When the motor starts, it drives the valve stem to move linearly, thereby changing the opening of the flow regulating valve and controlling the flow rate. The magnitude of the flow rate depends on the rotational speed of the brushless DC motor. In order to improve the accuracy of flow regulation, it is necessary to improve the control accuracy of the rotational speed of the brushless DC motor. Therefore, a suitable control algorithm needs to be selected to achieve precise rotational speed regulation.

[0063] A speed control method for a brushless DC motor based on optimizing fuzzy PID control with the BAS algorithm, the flowchart is as shown in Figure 3, and it includes the following steps:

[0064] Step 1, construct a speed control device for the brushless DC motor, and achieve precise regulation of the motor speed through a DSP controller;

[0065] Among them, the composition of the speed control device for the brushless DC motor is as Figure 2 shown, and it is used for speed control of the brushless DC motor. Specifically, it includes a DSP controller, a voltage module, a three-phase inverter, a decoder, a gate signal module, Hall components, a position sensor, and a current sensor, and the Hall components include Hall sensors, encoders, and Schmitt triggers.

[0066] The DSP controller uses a STM320F2807 control chip, which is designed for real-time control, integrates a high-performance 32-bit CPU core and dedicated control peripherals, and is responsible for real-time processing of sensor data, executing control algorithms, and generating PWM signals.

[0067] The voltage module provides the DC bus voltage required by the inverter on the high-voltage side, and provides a 5V regulated power supply for the DSP controller, sensors, etc. on the low-voltage side.

[0068] The three-phase inverter consists of 6 power switch devices, MOSFET tubes, to form a full-bridge circuit, which converts direct current into three-phase variable-frequency alternating current to drive the motor. The said gate signal module uses a dedicated driver chip to achieve high-voltage and low-voltage isolation, and at the same time adds an RC buffer circuit to suppress switching noise.

[0069] The decoder converts the original signals of the Hall sensor or encoder into digital rotor position information, and uses a Schmitt trigger to eliminate signal jitter to ensure accurate commutation timing.

[0070] The position sensor uses an incremental encoder, which improves the control accuracy.

[0071] The control principle of the above brushless DC motor speed control device is as follows: After the motor windings are energized, the rotor is driven to rotate. The Hall components on the stator detect the rotor position in real time and output a square wave signal to the decoder; the decoder converts the square wave signal into digital commutation interval information and transmits it to the DSP controller. The DSP controller dynamically adjusts the duty cycle of the PWM signal through a control algorithm based on the difference between the target speed and the actual speed calculated by the Hall position sensor, and generates a corresponding drive command. The gate signal module outputs a low-voltage PWM signal through the DSP controller, converts the DC bus voltage into three-phase alternating current, and precisely controls the timing and amplitude of the winding current, thereby adjusting the rotor torque and speed. At the same time, the position sensor continuously feeds back the rotor position and speed information to the DSP controller to form a secondary closed loop, further optimizing the commutation timing and suppressing torque ripple.

[0072] Step 2, based on the brushless DC motor speed control device constructed in Step 1, assuming that the three-phase windings of the motor are completely symmetrical, that is, the resistance and inductance of each phase winding are the same; ignoring the slot effect, hysteresis loss, and mechanical coupling effect between the rotor and the stator; when the magnetic field generated by the rotor is an ideal constant magnetic field and the air gap between the stator and the rotor is uniform, establish a mathematical model of the brushless DC motor;

[0073] As Figure 4 shown, Q1 to Q6 in the equivalent circuit diagram of the brushless DC motor represent transistors, which constitute the core of the three-phase inverter. The main function is to convert direct current into three-phase alternating current. Among them, each pair of transistors (such as Q1 and Q4, Q2 and Q5, Q3 and Q6) is responsible for controlling the current of one phase.

[0074] According to the equivalent circuit diagram, the stator three-phase voltage balance equation can be obtained:

[0075]

[0076] In the formula, U a 、U b 、U c are the input voltages of each phase, i a 、i b 、i c are the currents of each phase, R is the resistance value of the stator winding, L is the self-inductance of each phase winding, M is the mutual inductance between two-phase windings, and e is the electromotive force of the three-phase winding.

[0077] The motion equation of the brushless DC motor is:

[0078]

[0079] In the formula, T e is the electromagnetic torque, T L is the load torque, B is the damping coefficient, and ω is the mechanical angular velocity.

[0080] The electromagnetic torque equation generated by the winding is as follows:

[0081]

[0082] The simulation models of numerous DC motors are as Figure 7 shown, which include a three-phase inverter bridge, a logic commutation module, a permanent magnet synchronous motor, and a display module. The logic commutation module receives the signals generated by the Hall devices to provide correct commutation signals to the three-phase inverter bridge. In addition, the three-phase inverter bridge selects switch devices of the Mosfe type.

[0083] Step 3: Optimize the BAS algorithm by combining the dynamic attenuation mechanism and the probabilistic mutation strategy for global optimization to avoid the algorithm falling into local optimum. The specific steps are as follows:

[0084] In the first step, when generating the initial population, set the initial position and parameters. Among them, the initial parameters include the random movement step size d 0 , the maximum number of iterations 100, and the population size 80;

[0085] In the second step, normalize the direction of the longhorn beetle antennae and randomly generate a direction vector to simulate the direction of the longhorn beetle antennae

[0086]

[0087] Among them, is the longhorn beetle orientation vector, and D is the dimension;

[0088] In the third step, determine the positions of the two antennae of the longhorn beetle and the distance between the two antennae;

[0089]

[0090] Among them, x l , x r are the positions of the left and right antennae of the longhorn beetle, x m is the centroid position of the longhorn beetle, is the distance between the two antennae of the longhorn beetle;

[0091] In the fourth step, calculate the odor concentration sensed by the two antennae of the longhorn beetle according to the fitness function and iterate the next position of the longhorn beetle. Among them, the fitness function f(x) selects the time integral error index:

[0092]

[0093] Among them, δ m is the search step size at time m, f(x r ), f(x l ) are the food concentrations sensed by the two antennae of the longhorn beetle. sign[·] is the sign function, which is used to indicate the movement direction. If f(x r ) is less than f(xl ) and then move in the x l direction, and vice versa;

[0094] Step 5: Establish the update rules for the longicorn beetle step size δ and the distance d between the two antennae;

[0095] d m = d e ·d m-1 + 0.01 (9)

[0096] δ m = δ e ·δ m-1 (10)

[0097] Step 6: Introduce the probability constant 0.1 - 0.3 and the random number p = 0.5 to measure the influence of the random direction on the algorithm, and adopt dynamic attenuation to iterate the longicorn beetle position and search step size;

[0098] Introduce the probability constant p δ and the random number p in the BAS algorithm to measure the influence of the random direction on the algorithm. When the longicorn beetle step size decreases to the length h and the random number is greater than p δ , it means that the longicorn beetle cannot find a smaller optimal value at the current step size, then update the step size, otherwise keep it unchanged.

[0099] In addition, in the traditional BAS algorithm, a fixed decreasing factor is usually adopted when updating and iterating the longicorn beetle position and search step size, which will cause the problem of too fast decrease in the early stage and too slow decrease in the later stage, reducing the search efficiency and accuracy. Therefore, dynamic attenuation is adopted:

[0100]

[0101] Step 7: Repeat the above steps. After reaching the maximum number of iterations of 100 times, output the optimal solution and its corresponding fitness error value.

[0102] Step 4: Input the parameters after global optimization in Step 3 into the fuzzy PID controller, establish the BAS algorithm to optimize the fuzzy PID controller, output the optimal solution of the controller parameters through system simulation, and substitute it into the fuzzy PID controller, that is, obtain the brushless DC motor speed control method based on the improved BAS algorithm for fuzzy PID;

[0103] Among them, the working principle of the fuzzy PID controller based on the improved BAS algorithm is as Figure 5 shown, where r(t) is the rated speed, y(t) is the actual speed of the brushless DC motor, e is the deviation value between the target speed and the actual speed, and ec is the deviation change rate; the fuzzy PID controller fuzzifies the input signal, uses fuzzy inference to obtain the fuzzy output, and then obtains the actual control output through defuzzification. In addition, the BAS algorithm is used for the initial parameters K of the PIDp0 , K i0 , K d0 Perform global optimization on them, combine the two to obtain the final PID parameter values; the final PID parameter values are obtained according to the following formula:

[0104]

[0105] Designing a fuzzy PID controller based on the improved BAS algorithm is divided into the following steps:

[0106] Step S21: Design an improved BAS algorithm, and the optimization steps are as Figure 6 shown, and perform global optimization on the initial parameters of the fuzzy PID; the working principle of this algorithm is that when longhorn beetles forage, they randomly explore the nearby area through two antennae, sense the concentration difference of food, and thus adjust the moving direction to move towards the direction with a large food concentration, gradually approaching the optimal solution in the search space.

[0107] Step S22: Design a fuzzy PID controller to obtain the optimal controller parameter values. The working process of the fuzzy PID controller can be elaborated in detail from the following steps:

[0108] (1) Fuzzification of the system input: The fuzzy PID controller first converts the input signal into a fuzzy linguistic variable. After fuzzification, it is mapped to a fuzzy set by the membership function. The brushless DC motor speed control system is set to a two-input and three-output structure. The input quantities are the motor speed deviation e and the corresponding deviation change rate ec, and the output is the parameter correction amount ΔK p , ΔK i , ΔK d , where the universes of discourse of each input and output quantity are [-6, 6].

[0109] (2) Fuzzy inference: Perform inference and calculation on the fuzzified input according to the pre-designed fuzzy rule base. The fuzzy rule base is set to a seven-level fuzzy subset {NB, NM, NS, ZO, PS, PM, PB}, where N represents negative, P represents positive, B represents large, M represents medium, S represents small, ZO represents 0. In addition, the membership function adopts a triangular form.

[0110] (3) Defuzzification: Convert the fuzzy output into an exact control signal through defuzzification, adjust the system output, and the obtained values of K p , K i , K d are 20.6034, 6.03626, and 3.33983 respectively.

[0111] As Figure 8As shown in the figure, the initial speed of the brushless DC motor is set to 1000 r / min. The PID control, fuzzy PID control, and BAS algorithm optimized fuzzy PID are applied to the speed control of the brushless DC motor, and the comparison curves of speed response are obtained. The PID control reaches near the rated speed at 0.15 s, showing overshoot. The required adjustment time is long and the stability is poor, and it is difficult to reach the set speed. The fuzzy PID controller reaches the rated speed at 0.1 s, greatly reducing the instability problem of the PID control. The BAS algorithm improved fuzzy PID control has a better adjustment effect. The system reaches the rated speed at 0.04 s after the motor runs. Compared with the PID control, the adjustment time is shortened by 73%, the response speed is the fastest, and there is no overshoot and oscillation phenomenon, enhancing the stability of the system.

[0112] In summary, for the brushless DC motor speed control method based on the BAS algorithm improved fuzzy PID of the present invention, the BAS algorithm is used to optimize the fuzzy PID control in the DSP controller. Compared with the PID algorithm and the fuzzy PID control, this control method effectively eliminates the overshoot phenomenon, and there is no obvious oscillation in the adjustment process. While ensuring fast response, it improves the dynamic performance and accuracy of the system, and has high application value.

Claims

1. A brushless DC motor speed control method based on BAS algorithm to improve fuzzy PID, characterized in that: The following steps are involved: Step 1: construct a brushless DC motor speed control device to achieve precise regulation of the motor speed through a DSP controller; Step 2, according to the brushless DC motor speed control device constructed in step 1, by establishing an equivalent circuit diagram of the brushless DC motor, deriving a three-phase voltage balance equation; at the same time, establishing the motion equation and electromagnetic torque equation of the motor, thereby constructing a complete brushless DC motor mathematical model; Step 3: Combine the dynamic attenuation mechanism and the probability mutation strategy to optimize the BAS algorithm for global optimization to avoid the algorithm falling into the local optimum; Step 4: Input the parameters after global optimization in step 3 into the fuzzy PID controller, establish the BAS algorithm to optimize the fuzzy PID controller, output the optimal solution of the controller parameters through system simulation, substitute it into the fuzzy PID controller, and obtain the brushless DC motor speed control method based on BAS algorithm to improve the fuzzy PID 2. According to claim 1, a brushless DC motor speed control method based on BAS algorithm improved fuzzy PID is characterized in that: In step 1, the brushless DC motor speed control device includes a voltage module, a three-phase inverter, a decoder, a gate signal module, a Hall element, a position sensor and a current sensor, and the Hall element includes a Hall sensor, an encoder and a Schmitt trigger.

3. The brushless DC motor speed control method based on BAS algorithm improved fuzzy PID according to claim 1, characterized in that: The DSP controller described in step 1 adopts the STM320F2807 chip, and uses the BAS algorithm to optimize the fuzzy PID control method to improve the motor control accuracy.

4. The brushless DC motor speed control method based on BAS algorithm improved fuzzy PID according to claim 1, characterized in that: In step 2, the equivalent circuit diagram of the brushless DC motor is established to derive the three-phase voltage balance equation; at the same time, the motion equation and electromagnetic torque equation of the motor are established to construct a complete mathematical model of the brushless DC motor.

5. The brushless DC motor speed control method based on BAS algorithm improved fuzzy PID according to claim 1, characterized in that: In step 3, the optimized BAS algorithm is used for global optimization, which includes initializing population parameters, normalizing the direction of beetle whiskers, determining the position and spacing of beetle whiskers, iteratively updating the position based on the fitness function, and constructing dynamic update rules for step size and spacing. The probability constant of 0.1-0.3 and the random number of 0.5 are introduced to enhance the search capability, and the maximum number of iterations is set to 100. Finally, the optimal solution and its corresponding fitness error value are output.

6. The brushless DC motor speed control method based on BAS algorithm improved fuzzy PID according to claim 5, characterized in that: The steps of global optimization of the optimized BAS algorithm described in step 3 are as follows: The first step is to set the initial position and parameters when generating the initial population; The second step is to normalize the direction of the beetle's antennae and randomly generate direction vectors to simulate the direction of the beetle's antennae. The third step is to determine the position of the two whiskers of the longhorn beetle and the distance between them; The fourth step is to calculate the odor concentration perceived by the two whiskers of the longhorn beetle according to the fitness function, iterate the next step position of the longhorn beetle, and the fitness function f(x) uses the time integral error indicator: Among them, δ m is the search step length at time m, f(x r )、f(x l ) is the food concentration sensed by the beetle’s two whiskers. sign[·] is the sign function used to indicate the direction of movement. If f(x r ) is less than f(x l ), then towards x l direction of movement and vice versa; Step 5: Establish the update rules for the beetle step length δ and the distance between the two whiskers d d m =d e ·d m-1 +0.01 (9) d m =d e ·d m-1 (10); Step 6: introduce probability constant and random number p to measure the influence of random direction on the algorithm. The probability constant is in the range of 0.1-0.3, the random number is 0.5, and dynamic attenuation is used to iterate the beetle position and search step length. Step 7: Repeat the above steps until the maximum number of iterations reaches 100, and then output the optimal solution and its corresponding fitness error value.

7. The brushless DC motor speed control method based on BAS algorithm improved fuzzy PID according to claim 1, characterized in that: In step 4, the brushless DC motor speed control method based on the improved fuzzy PID of the BAS algorithm takes the speed deviation and its change rate as input, first generates the fuzzy control quantity through fuzzy reasoning, and then obtains the actual control output through defuzzification. At the same time, the improved BAS algorithm is used to adjust the PID initial parameters. p , K i , K d Perform global optimization and finally obtain the optimized PID control parameters.

8. The brushless DC motor speed control method based on BAS algorithm improved fuzzy PID according to claim 7, characterized in that: In step 4, the input of the parameters after global optimization in step 3 into the fuzzy PID controller is divided into the following steps: Step S21: Improve the BAS algorithm and perform global optimization on the initial parameters of the fuzzy PID The algorithm uses two tentacles to randomly explore nearby areas when foraging, sense the concentration difference of food, and adjust the direction of movement to move towards the direction with high food concentration, gradually approaching the optimal solution in the search space. Step S22: Design a fuzzy PID controller and obtain the optimal controller parameter value (1) Fuzzification of system input: The fuzzy PID controller first converts the input signal into a fuzzy linguistic variable. After fuzzification, it is mapped to a fuzzy set by the membership function. The brushless DC motor speed control system is set to a two-input and three-output structure. The input is the motor speed deviation e and the corresponding deviation change rate ec. The output is the parameter correction value ΔK of the PID controller. p , ΔK i , ΔK d , the domain of each input and output quantity is [-6,6]; (2) Fuzzy reasoning: Reasoning and calculation of the fuzzified input is performed according to the pre-designed fuzzy rule base, where the fuzzy rule base is set to a seven-level fuzzy subset {NB, NM, NS, ZO, PS, PM, PB}, where N represents negative, P represents positive, B represents large, M represents medium, S represents small, and ZO represents 0. In addition, the membership function adopts a triangular form; (3) Defuzzification: The fuzzy output is converted into an accurate control signal through defuzzification, and the system output is adjusted to obtain K p , K i , K d The values ​​are 20.6034, 6.03626, and 3.33983 respectively.

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