Motor speed regulation control method based on FUZZY-PID-BANGBANG coupling algorithm

Abstract

The invention belongs to the technical field of motor speed regulation control, and particularly relates to a motor speed regulation control method based on a FUZZY-PID-BANGBANG coupling algorithm. In order to quickly and accurately control the rotating speed of the motor, a control mode is intelligently selected by setting a switching threshold: in the initial starting stage of motor speed regulation control, when the absolute value of the error of the rotating speed of the motor is greater than or equal to the switching threshold, a BANGBANG control algorithm is adopted, and limit torque is output to realize quick dynamic response; in the later adjustment stage of motor speed regulation control, when the absolute value of the motor rotating speed error is smaller than a switching threshold value, a parameter self-adaption FUZZY PID control algorithm is switched to, the algorithm takes the rotating speed error and the error change rate as input, the correction amount of PID controller parameters is output, and then a motor torque instruction signal is obtained.

Description

Technical Field

[0001] This invention belongs to the field of motor speed control technology, specifically relating to a motor speed control method based on the FUZZY-PID-BANGBANG coupling algorithm. Background Technology

[0002] Motor speed control is a crucial technology in modern industrial automation and intelligent manufacturing, widely used in various motor-driven mechanical equipment. For example, in the shift control of dedicated hybrid transmissions in hybrid passenger vehicles, the motor can not only operate in torque mode to drive the vehicle (or achieve regenerative braking), but also in speed mode to assist in shifting. Motor-assisted shifting has become a key research direction for improving the shifting quality of hybrid transmissions. Given a target speed, the research on motor speed control algorithms is a critical control problem for motor-assisted shifting.

[0003] PID (Proportional Integral-Derivative) control algorithms are fundamental to industrial control, possessing advantages such as simple principle, wide adaptability, and high reliability, and are widely used in motor speed control algorithms. However, their dynamic response is poor, and they lack adaptive capability, resulting in unsatisfactory control performance for time-varying and nonlinear systems; challenges remain in achieving high precision, high response speed, and system robustness.

[0004] Existing motor speed control methods mostly rely on traditional PID control or other single control methods, which cannot maintain high efficiency under different loads, operating conditions, or motor characteristics. FUZZY PID control and BANGBANG control are two classic algorithms; however, while BANGBANG control offers a fast response, it suffers from large steady-state jitter and poor accuracy. FUZZY PID control, on the other hand, has limited parameter adjustment effectiveness and a slow response during the large error phase of startup. Therefore, combining the advantages of both to achieve a "fast, accurate, and stable" control effect has become a pressing technical problem in this field. Summary of the Invention

[0005] This invention provides a motor speed control method based on the FUZZY-PID-BANGBANG coupling algorithm to address the above-mentioned problems.

[0006] To achieve the above objectives, the present invention employs the following technical solution:

[0007] This invention provides a motor speed control method based on the FUZZY-PID-BANGBANG coupled algorithm, comprising the following steps:

[0008] Step 1, obtain the target speed of the motor ( ) and actual speed ( ), and calculate the error e between the target speed and the actual speed of the motor and its absolute value |e|;

[0009] Step 2: Compare the absolute value |e| with the preset switching thresholds for the two types of control algorithm modal decisions. Compare;

[0010] Step 3, if If |e|, then the BANGBANG control algorithm is used to generate the motor torque command signal;

[0011] Step 4, if If |e| is true, then a parameter-adaptive fuzzy PID control algorithm is used to generate the motor torque command signal; the parameter-adaptive fuzzy PID control algorithm calculates and outputs the correction amount for the PID controller parameters in real time based on the error e and the error change rate ec using a fuzzy control algorithm. , and This leads to the motor torque command signal.

[0012] Furthermore, the switching threshold It is 10% of the target rotational speed.

[0013] Furthermore, the BANGBANG control algorithm can achieve optimal control time during the initial start-up phase of the motor, and its control rules are as follows:

[0014] If the error e > 0, it means that the actual speed is lower than the target speed. Then, the maximum value of the motor torque is output to accelerate the motor.

[0015] If the error e < 0, it means that the actual speed is lower than the target speed. In this case, the minimum value of the output motor torque will be used to decelerate the motor.

[0016] Furthermore, the parameter adaptive FUZZY PID control algorithm aims to tune the three multiplier factors of the PID control algorithm online, establishing a system with e and ec as input variables, and... , and This is a two-input, three-output FUZZY control algorithm model with output variables. The model fuzzifies the input variables e and ec, performs fuzzy inference based on membership functions and fuzzy rules, and obtains the correction amount of the multiplier factor for the three control links of the PID control algorithm after defuzzification. , and Thus achieving and Online self-adjustment, specifically including:

[0017] The error e and the rate of change of error ec are used as the input variables of the parameter adaptive FUZZY PID control algorithm.

[0018] The fuzzy control algorithm performs inference based on a preset fuzzy rule base, and the output variable is the adaptive correction value of the three multiplier factors of the PID control algorithm. , and ;

[0019] According to the formula , , Calculate real-time PID control parameters. , , The initial values ​​of the three multiplier factors are given, and the final motor torque command signal is calculated using real-time PID control parameters.

[0020] Furthermore, the input variables of the fuzzy control algorithm are the error e and the error rate of change ec, both of which have a fuzzy universe of discourse of [-6, 6], and are divided into seven fuzzy subsets {NL, NM, NS, ZO, PS, PM, PL}, corresponding to the linguistic terms {negative large, negative medium, negative small, zero, positive small, positive medium, positive large}; the output variable is... , and The fuzzy domains of all of them are [-3, 3], and they are divided into the same seven fuzzy subsets.

[0021] Furthermore, in the fuzzy subsets of the error e and the error change rate ec, NL and PL adopt Gaussian membership functions, while NM, NS, ZO, PS, and PM adopt triangular membership functions; the output variable , and All fuzzy subsets are assigned a triangular membership function.

[0022] Furthermore, the fuzzy rule base contains multiple IF-THEN rules. The antecedent of the rule is a combination of fuzzy subsets of the error e and the error change rate ec, and the consequent of the rule is output. , and A fuzzy subset of , which has the following form:

[0023] If e is A and ec is B, then is C, is D, is E;

[0024] Where A and B are input fuzzy subsets, and C, D, and E are output fuzzy subsets.

[0025] Furthermore, the fuzzy rule base is constructed based on the following design principles:

[0026] The fuzzy rule base contains 7 × 7 = 49 rules, corresponding to all combinations of the 7 fuzzy subsets of the input variable error e and the error change rate ec. The design principles include:

[0027] (a) When |e| is large, prioritize adjusting the proportional parameter. To quickly eliminate errors;

[0028] (b) Error change rate ec-dominant stage: When ec is large, the control output... Take a larger value to suppress overshoot;

[0029] (c) Steady-state stage: When both |e| and ec are small, control output Choose an appropriate value to eliminate steady-state error.

[0030] An example of the fuzzy rule base is as follows:

[0031] When e is NL and ec is ZO, then For ZO, For PL, For PS;

[0032] When e is ZO and ec is PL, then For NL, For ZO, For PS.

[0033] Compared with the prior art, the present invention has the following advantages:

[0034] 1. Speed: By fully utilizing the time-optimal characteristics of the BANGBANG control algorithm during the large error phase, the rise time and peak time of the system are greatly shortened.

[0035] 2. Stability: Switching to FUZZY PID control during the small error phase, and effectively suppressing overshoot through its parameter adaptability.

[0036] 3. Intelligence and robustness: By adjusting PID parameters online through a specific fuzzy rule base, the system can quickly adapt to changes in system parameters, enhancing its adaptability to different operating conditions. Attached Figure Description

[0037] Figure 1 This is a flowchart of the control method of the present invention;

[0038] Figure 2 This is a schematic diagram of the motor speed control principle of the present invention;

[0039] Figure 3 The diagrams are membership function diagrams, where (a) is a membership function diagram of the input variable and (b) is a membership function diagram of the output variable.

[0040] Figure 4 This is a comparison chart of the results of the embodiments of the present invention and different motor speed control algorithms. Detailed Implementation

[0041] To further illustrate the technical solution of the present invention, the present invention will be further described below through embodiments.

[0042] like Figure 1 and Figure 2 As shown in the figure, a motor speed control method based on the FUZZY-PID-BANGBANG coupled algorithm in this embodiment includes the following steps:

[0043] Step 1, obtain the target speed of the motor ( ) and actual speed ( ), and calculate the error e between the target speed and the actual speed of the motor and its absolute value |e|.

[0044] Step 2: Compare the absolute value |e| with the preset switching thresholds for the two types of control algorithm modal decisions. Compare and switch thresholds Set to 10% of the target speed.

[0045] Step 3: During the initial startup phase of the motor, the speed error is relatively large. If |e|, then the BANGBANG control algorithm is used to generate the motor torque command signal to achieve optimal control time during the initial start-up phase of the motor. The control rules are designed as follows:

[0046] If the error e > 0, it means that the actual speed is lower than the target speed, and the maximum value of the output motor torque is then obtained.

[0047] If the error e < 0, it means that the actual speed is lower than the target speed, and the output motor torque is the minimum value.

[0048] Step 4, in the later adjustment stage of motor speed control, at this time If |e|, then the adaptive FUZZY PID control algorithm is used to generate the motor torque command signal; the adaptive FUZZY PID control algorithm, through fuzzy control algorithm, calculates and outputs the correction amount of the PID controller parameters in real time based on the error e and the error change rate ec. , and The specific design process is as follows:

[0049] 1. Based on actual operational variations, and with the goal of online tuning of the three multiplier factors in the PID control algorithm, the fuzzy control algorithm uses a preset fuzzy rule base to infer and establish an adaptive correction value for the three multiplier factors, with e and ec as input variables. , and This paper presents a two-input, three-output FUZZY control algorithm model with a variable output. The initial values ​​of the multiplier factor for the PID control algorithm are obtained based on empirical formulas using the Ziegler-Nichols parameter tuning method. , and The determined parameters are =0.013, =0.02, =0.5.

[0050] Calculate the real-time PID control parameters using the following formula:

[0051] ;

[0052] Then, the final motor torque command signal is calculated using real-time PID control parameters.

[0053] 2. The input variables of the fuzzy control algorithm are the error e and the rate of change of error ec, both of which have a fuzzy universe of discourse of [-6, 6], divided into seven fuzzy subsets {NL, NM, NS, ZO, PS, PM, PL}, corresponding to the linguistic terms {negative large, negative medium, negative small, zero, positive small, positive medium, positive large}; the output variable is... , and The fuzzy domains of all of them are [-3, 3], and they are divided into the same seven fuzzy subsets.

[0054] 3. For example Figure 3 As shown, in the fuzzy subsets of the error e and the error change rate ec, NL and PL adopt Gaussian membership functions, while NM, NS, ZO, PS, and PM adopt triangular membership functions; the output variable , and All fuzzy subsets are assigned a triangular membership function.

[0055] 4. The fuzzy rule base contains multiple IF-THEN rules. The antecedent of the rule is a fuzzy subset combination based on the error e and the error change rate ec, and the consequent of the rule is output. , and A fuzzy subset of the form: IF e is A and ec is B, THEN is C, is D, is E, where A and B are input fuzzy subsets, and C, D, and E are output fuzzy subsets. It is constructed based on the following design principles:

[0056] Based on long-term manual operation experience, the fuzzy rule base contains 7×7=49 rules, and its complete rules are shown in Table 1 below. These rules correspond to all combinations of the 7 fuzzy subsets of the input variable error e and the error change rate ec. The design principles include:

[0057] (a) When |e| is large, prioritize adjusting the proportional parameter. To quickly eliminate errors;

[0058] (b) Error change rate ec-dominant stage: When ec is large, the control output... Take a larger value to suppress overshoot;

[0059] (c) Steady-state stage: When both |e| and ec are small, control output Choose an appropriate value to eliminate steady-state error.

[0060] Table 1 , and Fuzzy logic rules

[0061]

[0062] The target speed of the motor is set, and five methods—PID algorithm, parameter adaptive FUZZY PID algorithm, BANGBANG control algorithm, PID-BANGBANG coupled algorithm, and parameter adaptive FUZZY-PID-BANGBANG coupled optimization control—are applied to the motor speed control, resulting in a comparison curve of the speed response. Figure 4 (and the corresponding control performance indicators).

[0063] Table 2 Control performance indicators of five types of motor speed regulation algorithms

[0064]

[0065] Depend on Figure 4As shown in Table 2, when the motor speed control system adopts the parameter adaptive FUZZY-PID-BANGBANG coupled optimization control algorithm, the rise time of the motor speed regulation process is 0.22s, the peak time is 0.56s, the maximum overshoot is 12.3%, and the settling time is 1.01s. For this coupled optimization control algorithm, the BANGBANG control algorithm is used in the initial start-up stage of motor speed regulation. Therefore, compared with the parameter adaptive FUZZY PID algorithm, the rise time and peak time are reduced by 43.6% and 31.7%, respectively. In the later adjustment stage of motor speed regulation, the parameter adaptive FUZZY PID control algorithm is used. Compared with the BANGBANG control algorithm, the maximum overshoot is reduced by 79.6%. Compared with the PID-BANGBANG coupled control algorithm, the peak time, settling time, and maximum overshoot are reduced by 18.8%, 35.7%, and 57.6%, respectively.

[0066] In summary, the motor speed control method based on the FUZZY-PID-BANGBANG coupled algorithm of this invention exhibits superior steady-state and dynamic response compared to the other three algorithms during motor speed regulation. It effectively resolves the contradiction between speed and accuracy inherent in a single control algorithm, achieving both rapid start-up and reduced oscillations while maintaining stability during speed regulation; thus achieving a control effect that balances speed, stability, and accuracy throughout the entire active speed regulation process of the motor.

[0067] The foregoing has shown and described the main features and advantages of the present invention. It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, it is intended that all variations falling within the meaning and scope of equivalents of the claims be included within the present invention.

[0068] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A motor speed control method based on a FUZZY-PID-BANGBANG coupled algorithm, characterized in that, Includes the following steps: Step 1: Obtain the target speed and actual speed of the motor, and calculate the error e between the target speed and the actual speed and its absolute value |e|. Step 2: Compare the absolute value |e| with the preset switching thresholds for the two types of control algorithm modal decisions. Compare; Step 3, if If |e|, then the BANGBANG control algorithm is used to generate the motor torque command signal; Step 4, if If |e|, then the adaptive FUZZY PID control algorithm is used to generate the motor torque command signal; the adaptive FUZZY PID control algorithm, through fuzzy control algorithm, calculates and outputs the correction amount of the PID controller parameters in real time based on the error e and the error change rate ec. , and This leads to the motor torque command signal.

2. The motor speed control method based on the FUZZY-PID-BANGBANG coupled algorithm according to claim 1, characterized in that, The switching threshold It is 10% of the target rotational speed.

3. The motor speed control method based on the FUZZY-PID-BANGBANG coupled algorithm according to claim 1, characterized in that, The control rules of the BANGBANG control algorithm are as follows: If the error e > 0, it means that the actual speed is lower than the target speed, and the maximum value of the output motor torque is then obtained. If the error e < 0, it means that the actual speed is lower than the target speed, and the output motor torque is the minimum value.

4. The motor speed control method based on the FUZZY-PID-BANGBANG coupled algorithm according to claim 1, characterized in that, The parameter adaptive FUZZY PID control algorithm includes: The error e and the rate of change of error ec are used as the input variables of the parameter adaptive FUZZY PID control algorithm. The fuzzy control algorithm performs inference based on a preset fuzzy rule base, and the output variable is the adaptive correction value of the three multiplier factors of the PID control algorithm. , and ; According to the formula , , Calculate real-time PID control parameters. , , The initial values ​​of the three multiplier factors are given, and the final motor torque command signal is calculated using real-time PID control parameters.

5. A motor speed control method based on a FUZZY-PID-BANGBANG coupled algorithm according to claim 4, characterized in that, The input variables of the fuzzy control algorithm are error e and error rate of change ec, and their fuzzy universe of discourse is [-6, 6], which is divided into seven fuzzy subsets {NL, NM, NS, ZO, PS, PM, PL}, corresponding to the linguistic terms {negative large, negative medium, negative small, zero, positive small, positive medium, positive large}. Output variables , and The fuzzy domains of all of them are [-3, 3], and they are divided into the same seven fuzzy subsets.

6. The motor speed control method based on the FUZZY-PID-BANGBANG coupled algorithm according to claim 5, characterized in that, In the fuzzy subset of the error e and the error change rate ec, NL and PL adopt Gaussian membership functions, while NM, NS, ZO, PS, and PM adopt triangular membership functions; the output variable , and All fuzzy subsets are assigned a triangular membership function.

7. A motor speed control method based on a FUZZY-PID-BANGBANG coupled algorithm according to claim 6, characterized in that, The fuzzy rule base contains multiple IF-THEN rules. The antecedent of the rule is a combination of fuzzy subsets based on the error e and the rate of change of error ec, and the consequent of the rule is the output. , and A fuzzy subset of , which has the following form: IF e is A and ec is B, THEN is C, is D, is E; Where A and B are input fuzzy subsets, and C, D, and E are output fuzzy subsets.

8. A motor speed control method based on the FUZZY-PID-BANGBANG coupled algorithm according to claim 7, characterized in that, The fuzzy rule base is constructed based on the following design principles: The fuzzy rule base contains 7 × 7 = 49 rules, corresponding to all combinations of the 7 fuzzy subsets of the input variable error e and the error change rate ec. The design principles include: (a) When |e| is large, prioritize adjusting the proportional parameter. To quickly eliminate errors; (b) Error change rate ec-dominant stage: When ec is large, the control output... Take a larger value to suppress overshoot; (c) Steady-state stage: When both |e| and ec are small, control output Choose an appropriate value to eliminate steady-state error.

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