Intelligent control system for high-efficiency direct-current brushless motor

Abstract

The invention discloses an intelligent control system for a high-efficiency direct-current brushless motor, and belongs to the related technical field of motor control systems. The motor comprises: a motor body having a stator and a rotor; the Hall effect sensor is used for detecting the position of the rotor; the controller is electrically connected with the Hall effect sensor and is used for receiving a rotor position signal; the current sensor is used for detecting the phase current of the motor; the voltage sensor is used for detecting the phase voltage of the motor; the signal processing unit is electrically connected with the current sensor and the voltage sensor; the control algorithm module is electrically connected with the signal processing unit; the driving circuit is electrically connected with the controller and the motor main body; the detection module is electrically connected with the controller and is used for detecting a system state and carrying out threshold judgment; the system has good adaptability and intelligent monitoring and adjusting functions, so that the operation efficiency and stability of the motor are improved, and the requirements of different application scenes are met.

Description

Technical Field

[0001] The present invention belongs to the technical field related to motor control systems, and in particular relates to an intelligent control system for a high-efficiency brushless DC motor. Background Art

[0002] Brushless DC motors (BLDC motors) are widely used in industrial automation, household appliances, electric vehicles and other fields due to their high efficiency, low noise and long life. However, there are still some problems in the existing brushless DC motor control system in practical applications, mainly including the following aspects:

[0003] Traditional control systems are usually designed based on fixed control parameters and are difficult to adapt to different operating environments and load conditions. For example, under extreme conditions such as high temperature, high humidity or high load, the operating characteristics of the motor will change significantly, and a control system with fixed parameters is difficult to ensure stable operation of the motor.

[0004] Most existing control systems rely on preset thresholds and simple feedback mechanisms, lacking intelligent monitoring and adjustment capabilities. When the motor's operating status is abnormal, it is difficult for the system to identify and adjust in time, which may cause motor damage or system failure.

[0005] In terms of energy efficiency optimization, traditional control systems often have difficulty achieving real-time energy efficiency optimization. Due to the lack of comprehensive monitoring and intelligent adjustment of the motor's operating status, the motor's energy efficiency performance is poor under partial load or non-rated conditions, resulting in energy waste.

[0006] In view of the above problems, there is an urgent need for an intelligent control system that can efficiently detect and control the operating status of a brushless DC motor. Summary of the invention

[0007] In view of the above problems, the present invention provides an intelligent control system for a high-efficiency brushless DC motor, which is used to solve the problems in the prior art that the intelligent control system of the motor has slow response capability and adaptability, and the monitoring and adjustment functions need to be improved in intelligence.

[0008] An intelligent control system for a high-efficiency brushless DC motor, comprising:

[0009] a motor body having a stator and a rotor;

[0010] Hall effect sensor to detect the position of the rotor;

[0011] a controller electrically connected to the Hall effect sensor and configured to receive a rotor position signal;

[0012] Current sensor, used to detect the phase current of the motor;

[0013] A voltage sensor is used to detect the phase voltage of the motor;

[0014] a signal processing unit, which is electrically connected to the current sensor and the voltage sensor and is used to process the detection signal;

[0015] A control algorithm module, which is electrically connected to the signal processing unit and is used to execute a control algorithm;

[0016] A driving circuit, which is electrically connected to the controller and the motor body and is used to drive the motor;

[0017] A detection module, which is electrically connected to the controller and is used to detect the system state and perform threshold judgment;

[0018] The controller generates a driving signal through the control algorithm module according to the rotor position signal detected by the Hall effect sensor and the current and voltage signals processed by the signal processing unit, and controls the driving circuit to drive the motor body.

[0019] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention provides a complete intelligent control system framework, ensuring that the system can efficiently detect and control the operating state of the brushless DC motor.

[0020] As a further improvement of the above solution, the control algorithm module includes:

[0021] A speed control algorithm for generating a speed control signal according to an error between a target speed and an actual speed;

[0022] a current control algorithm for generating a current control signal according to an error between a target current and an actual current;

[0023] A voltage control algorithm is used to generate a voltage control signal according to the error between the target voltage and the actual voltage.

[0024] The technical effect of the above improvements is: by introducing speed control algorithm, current control algorithm and voltage control algorithm, the system can generate corresponding control signals according to the error between the target value and the actual value, thereby achieving precise control and improving the operating efficiency and stability of the motor.

[0025] As a further improvement of the above scheme, the speed control algorithm adopts the following formula:

[0026]

[0027] Among them, S is the speed control signal, α, β, γ, δ are proportional, integral, differential and sine coefficients respectively, V t is the target speed, V a is the actual speed.

[0028] The technical effect of the above improvement is: the formula of the speed control algorithm is described in detail to ensure that the system can generate a speed control signal according to the error between the target speed and the actual speed, thereby achieving precise speed control and improving the dynamic response performance of the motor.

[0029] As a further improvement of the above solution, the current control algorithm adopts the following formula:

[0030]

[0031] Where I is the current control signal, ò, ζ, η, θ are the proportional, integral, differential and cosine coefficients respectively, I t is the target current, I a is the actual current.

[0032] The technical effect of the above improvement is: the formula of the current control algorithm is described in detail to ensure that the system can generate a current control signal according to the error between the target current and the actual current, thereby achieving precise current control and protecting the motor from overcurrent damage.

[0033] As a further improvement of the above solution, the voltage control algorithm adopts the following formula:

[0034]

[0035] Where V is the voltage control signal, ι, κ, λ, μ are the proportional, integral, differential and tangent coefficients respectively, V t is the target voltage, V a is the actual voltage.

[0036] The technical effect of the above improvement is: the formula of the voltage control algorithm is described in detail to ensure that the system can generate a voltage control signal according to the error between the target voltage and the actual voltage, thereby achieving precise voltage control and ensuring that the motor runs under stable voltage conditions.

[0037] As a further improvement of the above solution, the control algorithm module further includes:

[0038] Adaptive control algorithm, used to dynamically adjust control parameters according to operating status to optimize system performance and ensure stable operation;

[0039] The adaptive control algorithm monitors the running status of the motor in real time and adjusts control parameters such as proportional, integral, differential and sine coefficients to cope with different load and environmental changes, thereby improving the response speed and stability of the system.

[0040] The technical effects of the above improvements are: the adaptive control algorithm monitors the operating status of the motor in real time, dynamically adjusts the control parameters, improves the response speed and stability of the system, and ensures that the motor can operate stably under different load and environmental conditions.

[0041] As a further improvement of the above solution, the detection module performs threshold determination based on the following formula:

[0042]

[0043] Where T is the threshold judgment signal, ρ, σ, τ, υ are the coefficients of current ratio, voltage ratio, current change rate and voltage change rate respectively, I t is the target current, I a is the actual current, V t is the target voltage, V a is the actual voltage.

[0044] The technical effect of the above improvement is: providing a threshold judgment formula for the detection module, ensuring that the system can accurately detect the operating status of the motor, and promptly identify and respond to abnormal situations to protect the safety of the motor and the system.

[0045] As a further improvement of the above solution, the adjustment method after judging the threshold value includes:

[0046] Detection anomaly: When the detection module calculates the threshold through the formula and the judgment signal exceeds the preset range, the system will identify the abnormal state.

[0047] Feedback adjustment: The controller will make feedback adjustments through the control algorithm module according to the detected abnormal state. The specific steps are as follows:

[0048] Speed ​​control: If the speed deviates from the target value, the speed control algorithm will generate a new speed control signal according to the formula to adjust the motor speed.

[0049] Current control: If the current deviates from the target value, the current control algorithm generates a new current control signal to adjust the motor phase current.

[0050] Voltage control: If the voltage deviates from the target value, the voltage control algorithm generates a new voltage control signal to adjust the phase voltage of the motor.

[0051] Adaptive Adjustment: The adaptive control algorithm dynamically adjusts control parameters based on the current operating status to optimize system performance and ensure stable operation.

[0052] Drive adjustment: The drive signal generated by the controller will be transmitted to the drive circuit, and the drive circuit will drive the motor body according to the adjusted control signal to bring its operating parameters back to a reasonable range.

[0053] The technical effect of the above improvement is: describing a method for adjustment based on threshold judgment, including abnormality detection, feedback adjustment, adaptive adjustment and drive adjustment, ensuring that the system can make timely adjustments after detecting an abnormal state, so that the motor parameters return to a reasonable range and ensure stable operation of the system.

[0054] As a further improvement of the above solution, the feedback adjustment further includes:

[0055] Multi-level feedback control: If the system still fails to achieve the expected effect after primary feedback adjustment, secondary or multi-level feedback adjustment will be carried out to gradually optimize the control signal to ensure that the motor parameters return to a reasonable range;

[0056] Real-time monitoring and adjustment: The system continuously monitors the motor's operating status and adjusts the control signal in real time to ensure that the motor always operates in the best condition under different working conditions.

[0057] The technical effect of the above improvement is: further supplementing the feedback adjustment method, including multi-level feedback control and real-time monitoring and adjustment, to ensure that when the system still fails to achieve the expected effect after primary feedback adjustment, it can perform secondary or multi-level feedback adjustment, gradually optimize the control signal, and ensure that the motor parameters return to a reasonable range.

[0058] As a further improvement of the above solution, the adaptive adjustment further includes:

[0059] Parameter self-learning: The system uses machine learning algorithms to automatically learn and optimize control parameters based on historical operating data and current status, thereby improving the system's adaptive capabilities;

[0060] Environmental adaptation: The system dynamically adjusts control parameters according to changes in the external environment to ensure that the motor can operate stably under various environmental conditions.

[0061] The technical effects of the above improvements are: further supplementing the adaptive adjustment methods, including parameter self-learning and environmental adaptation. The system uses machine learning algorithms to automatically learn and optimize control parameters based on historical operating data and current status, thereby improving the system's adaptability and dynamically adjusting control parameters according to changes in the external environment to ensure that the motor can operate stably under various environmental conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 This is a component diagram of the intelligent control system of the present invention.

[0063] Figure 2 It is a schematic diagram of the detection process of the present invention. DETAILED DESCRIPTION

[0064] In order to enable those skilled in the art to better understand the technical solution, the present invention is described in detail below in conjunction with embodiments. The description in this section is only exemplary and explanatory and should not have any limiting effect on the scope of protection of the present invention.

[0065] An intelligent control system for a high-efficiency brushless DC motor, comprising:

[0066] a motor body having a stator and a rotor;

[0067] Hall effect sensor to detect the position of the rotor;

[0068] a controller, electrically connected to the Hall effect sensor, for receiving a rotor position signal;

[0069] Current sensor, used to detect the phase current of the motor;

[0070] A voltage sensor is used to detect the phase voltage of the motor;

[0071] a signal processing unit, which is electrically connected to the current sensor and the voltage sensor and is used to process the detection signal;

[0072] A control algorithm module, which is electrically connected to the signal processing unit and is used to execute a control algorithm;

[0073] A driving circuit, which is electrically connected to the controller and the motor body and is used to drive the motor;

[0074] A detection module, which is electrically connected to the controller and is used to detect the system state and perform threshold judgment;

[0075] The controller generates a driving signal through the control algorithm module according to the rotor position signal detected by the Hall effect sensor and the current and voltage signals processed by the signal processing unit, and controls the driving circuit to drive the motor body.

[0076] The control algorithm module includes:

[0077] A speed control algorithm for generating a speed control signal according to an error between a target speed and an actual speed;

[0078] a current control algorithm for generating a current control signal according to an error between a target current and an actual current;

[0079] A voltage control algorithm is used to generate a voltage control signal according to the error between the target voltage and the actual voltage.

[0080] The speed control algorithm uses the following formula:

[0081]

[0082] Among them, S is the speed control signal, α, β, γ, δ are proportional, integral, differential and sine coefficients respectively, V t is the target speed, V a is the actual speed.

[0083] Specifically, the proportional control term is used to quickly respond to speed errors. Its expression is:

[0084] P = α·(V t -V a )

[0085] Among them, P is the proportional control term, α is the proportional coefficient, V t is the target speed, V a is the actual speed.

[0086] The integral control term is used to eliminate the steady-state error. Its expression is:

[0087]

[0088] Among them, I is the integral control term, β is the integral coefficient, is the integral of the velocity error.

[0089] The differential control term is used to predict the error change trend and provide advance correction. Its expression is:

[0090]

[0091] Where D is the differential control term, γ is the differential coefficient, is the derivative of the velocity error. The sinusoidal control term is used to handle nonlinear errors, and its expression is:

[0092] Sine = δ·sin(V t -V a )

[0093] Among them, Sine is the sinusoidal control top, δ is the sinusoidal coefficient, sin(V t -V a ) is the sine function of the speed error. Adding the above items together, we get the comprehensive control signal:

[0094]

[0095] The following parameters and conditions are set for the specific numerical examples:

[0096] Target speed V t : 3000RPM

[0097] Actual speed V a ): 2900RPM

[0098] Proportional coefficient α: 1.0

[0099] Integration coefficient β: 0.1

[0100] Differential coefficient γ: 0.01

[0101] Sine coefficient δ: 0.001

[0102] Calculate various control signals:

[0103] Proportional Controls:

[0104] P = 1.0 (3000-2900) = 100

[0105] Integral control item (set the integral time to 1 second):

[0106]

[0107] Derivative control item (set speed change rate to 100RPM / s):

[0108]

[0109] Sine control term:

[0110] Sine=0.001·sin(3000-2900)≈0.001·sin(100)≈0.001·0.84=0.00084

[0111] Comprehensive control signal(S):

[0112] S=100+10+1+0.00084≈111.00084

[0113] Through the above calculation, the speed control signal S obtained is 111.00084, which is used to adjust the speed of the motor to make it close to the target speed.

[0114] The current control algorithm uses the following formula:

[0115]

[0116] Where I is the current control signal, ò, ζ, η, θ are the proportional, integral, differential and cosine coefficients respectively, I t is the target current, I a is the actual current.

[0117] Specifically, proportional control (P):

[0118] The proportional control term is used to quickly respond to current errors. Its expression is:

[0119] P=ò·(It -I a )

[0120] Among them, P is proportional control, ò is the proportional coefficient, I t is the target current, I a is the actual current.

[0121] The integral control term is used to eliminate the steady-state error. Its expression is:

[0122]

[0123] Where I is the integral control term, ζ is the integral coefficient, is the integral of the current error.

[0124] The differential control term is used to predict the error change trend and provide advance correction. Its expression is:

[0125]

[0126] Where D is the differential control term, η is the differential coefficient, is the derivative of the current error.

[0127] The cosine control term is used to deal with nonlinear errors. Its expression is:

[0128] Cosine = θ·cos(I t -I a )

[0129] Among them, Cosine is the cosine control term, θ is the cosine coefficient, cos(I t -I a ) is the cosine function of the current error.

[0130] Adding the above items together, we get the comprehensive control signal:

[0131]

[0132] The following parameters and conditions are set for the specific numerical examples:

[0133] Target current I t : 10A

[0134] Actual current I a : 9.5A

[0135] Proportional coefficient: 1.0

[0136] Integration coefficient ζ: 0.1

[0137] Differential coefficient η: 0.01

[0138] Cosine coefficient θ: 0.001

[0139] Calculate various control signals:

[0140] Proportional Controls:

[0141] P = 1.0·(10-9.5) = 0.5

[0142] Integral control item (set the integral time to 1 second):

[0143]

[0144] Derivative control term (assuming the current change rate is 0.5A / s):

[0145]

[0146] Cosine Control Term:

[0147] Cosine=0.001·cos(10-9.5)≈0.001·cos(0.5)≈0.001·0.877=0.000877

[0148] Integrated control signal (I):

[0149] I=0.5+0.05+0.005+0.000877≈0.555877

[0150] Through the above calculation, the current control signal (I) obtained is 0.555877, which is used to adjust the phase current of the motor to make it close to the target current.

[0151] The voltage control algorithm uses the following formula:

[0152]

[0153] Where V is the voltage control signal, ι, κ, λ, μ are the proportional, integral, differential and tangent coefficients respectively, V t is the target voltage, V a is the actual voltage.

[0154] Specifically, the proportional control term is used to quickly respond to voltage errors. Its expression is:

[0155] P = ι·(V t -V a )

[0156] Among them, P is the proportional control term, ι is the proportional coefficient, V t is the target voltage, V a is the actual voltage.

[0157] The integral control term is used to eliminate the steady-state error. Its expression is:

[0158]

[0159] Among them, I is the integral control term, κ is the integral coefficient, is the integral of the voltage error.

[0160] The differential control term is used to predict the error change trend and provide advance correction. Its expression is:

[0161]

[0162] Where D is the differential control term, λ is the differential coefficient, is the derivative of the voltage error.

[0163] The tangent control term is used to deal with nonlinear errors. Its expression is:

[0164] Tangent=μ·tan(V t -V a )

[0165] Among them, Tangent tangent control term, μ is the tangent coefficient, tan(V t -V a ) is the tangent function of the voltage error. Adding the above items together, we get the comprehensive control signal:

[0166]

[0167] The specific numerical examples assume the following parameters and conditions:

[0168] Target voltage V t : 48V

[0169] Actual voltage V a : 47.5V

[0170] Proportional coefficient ι: 1.0

[0171] Integration coefficient κ: 0.1

[0172] Differentiation coefficient λ: 0.01

[0173] Tangent coefficient μ: 0.001

[0174] Calculate various control signals:

[0175] Proportional Controls:

[0176] P = 1.0·(48-47.5) = 0.5

[0177] Integral control term (assuming the integration time is 1 second):

[0178]

[0179] Derivative control term (assuming the voltage change rate is 0.5V / s):

[0180]

[0181] Tangent Control:

[0182] Tangent=0.001·tan(48-47.5)≈0.001·tan(0.5)≈0.001·0.546=0.000546

[0183] Comprehensive control signal (V):

[0184] V=0.5+0.05+0.005+0.000546≈0.555546

[0185] Through the above calculation, the voltage control signal (V) obtained is 0.555546, which is used to adjust the phase voltage of the motor to make it close to the target voltage.

[0186] The detection module performs threshold determination based on the following original formula:

[0187]

[0188] Where T is the threshold judgment signal, ρ, σ, τ, υ are the coefficients of current ratio, voltage ratio, current change rate and voltage change rate respectively, I t is the target current, I a is the actual current, V t is the target voltage, V a is the actual voltage.

[0189] Current ratio term:

[0190] The current ratio term is used to measure the ratio of the actual current to the target current. Its expression is:

[0191]

[0192] Where ρ is the current ratio coefficient, I t is the target current, I a is the actual current.

[0193] Voltage ratio term:

[0194] The voltage ratio term is used to measure the ratio of the actual voltage to the target voltage. Its expression is:

[0195]

[0196] Where σ is the voltage ratio coefficient, Vt is the target voltage, V a is the actual voltage.

[0197] Current rate of change term:

[0198] The current change rate term is used to measure the change rate of the actual current. Its expression is:

[0199]

[0200] Where τ is the current change rate coefficient, is the actual current change rate. Voltage change rate term:

[0201] The voltage change rate term is used to measure the rate of change of the actual voltage. Its expression is:

[0202]

[0203] Where, υ is the voltage change rate coefficient, is the rate of change of the actual voltage. Add the above items together to get the comprehensive threshold judgment signal:

[0204]

[0205] The specific numerical examples assume the following parameters and conditions:

[0206] Target current I t : 10A

[0207] Actual current I a : 9.5A

[0208] Target voltage V t : 48V

[0209] Actual voltage V a : 47.5V

[0210] Current change rate 0.5A / s

[0211] Voltage change rate 0.5V / s

[0212] Current ratio coefficient ρ: 1.0

[0213] Voltage ratio coefficient σ: 1.0

[0214] Current change rate coefficient τ: 0.1

[0215] Voltage change rate coefficient υ: 0.1

[0216] Calculate various threshold judgment signals:

[0217] Current ratio term:

[0218]

[0219] Voltage ratio term:

[0220]

[0221] Current rate of change term:

[0222] Current Rate of Change=0.1·0.5=0.05

[0223] Voltage change rate term:

[0224] Voltage Rate of Change=0.1·0.5=0.05

[0225] Comprehensive threshold judgment signal (T):

[0226] T=0.95+0.9896+0.05+0.05≈2.0396

[0227] Through the above calculation, the threshold judgment signal T obtained is 2.0396, which is used to judge whether the running state of the motor is within a reasonable range.

[0228] The default value of the system default is 2.5:

[0229] If (T) is less than the preset value (2.5), it means that the motor's operating status is within a reasonable range.

[0230] If (T) is greater than or equal to this preset value, it means that the motor may be in an abnormal state and needs to be adjusted or checked. Therefore, the reasonable range preset by the system is (T<2.5), and the calculated (T=2.0396) is within the reasonable range.

[0231] The adjustment methods based on the threshold judgment include:

[0232] Detection anomaly: When the detection module calculates the threshold through the formula and the judgment signal exceeds the preset range, the system will identify the abnormal state.

[0233] Feedback adjustment: The controller will make feedback adjustments through the control algorithm module according to the detected abnormal state. The specific steps are as follows:

[0234] Speed ​​control: If the speed deviates from the target value, the speed control algorithm will generate a new speed control signal according to the formula to adjust the motor speed.

[0235] Current control: If the current deviates from the target value, the current control algorithm generates a new current control signal to adjust the motor phase current.

[0236] Voltage control: If the voltage deviates from the target value, the voltage control algorithm generates a new voltage control signal to adjust the phase voltage of the motor.

[0237] Adaptive Adjustment: The adaptive control algorithm dynamically adjusts control parameters based on the current operating status to optimize system performance and ensure stable operation.

[0238] Drive adjustment: The drive signal generated by the controller will be transmitted to the drive circuit, and the drive circuit will drive the motor body according to the adjusted control signal to bring its operating parameters back to a reasonable range.

[0239] The feedback adjustment further includes:

[0240] Multi-level feedback control: If the system still fails to achieve the expected effect after primary feedback adjustment, secondary or multi-level feedback adjustment will be carried out to gradually optimize the control signal to ensure that the motor parameters return to a reasonable range;

[0241] Real-time monitoring and adjustment: The system continuously monitors the motor's operating status and adjusts the control signal in real time to ensure that the motor always operates in the best condition under different working conditions.

[0242] The adaptive adjustment further comprises:

[0243] Parameter self-learning: The system uses machine learning algorithms to automatically learn and optimize control parameters based on historical operating data and current status, thereby improving the system's adaptive capabilities;

[0244] Environmental adaptation: The system dynamically adjusts control parameters according to changes in the external environment to ensure that the motor can operate stably under various environmental conditions.

[0245] It should be noted that, in this article, the terms: include, contain and any other variants are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. Specific examples are used in this article to illustrate the principle and implementation of the technical solution of the present invention. The above examples are only used to help understand the method of the present invention and its core idea. The above is only a preferred implementation of the present invention. It should be pointed out that due to the limitations of textual expression and the objective existence of infinite specific structures, for ordinary technicians in this technical field, without departing from the principle of the present invention, several improvements, modifications or changes can be made, and the above technical features can also be combined in an appropriate manner; these improvements, modifications, changes or combinations, or the direct application of the concept and technical solution of the present invention to other occasions without improvement, should be regarded as the protection scope of the present invention.

Claims

1. An intelligent control system for a high-efficiency brushless DC motor, characterized in that: include: a motor body having a stator and a rotor; Hall effect sensor to detect the position of the rotor; a controller electrically connected to the Hall effect sensor and configured to receive a rotor position signal; Current sensor, used to detect the phase current of the motor; A voltage sensor is used to detect the phase voltage of the motor; a signal processing unit, which is electrically connected to the current sensor and the voltage sensor and is used to process the detection signal; A control algorithm module, which is electrically connected to the signal processing unit and is used to execute a control algorithm; A driving circuit, which is electrically connected to the controller and the motor body and is used to drive the motor; A detection module, which is electrically connected to the controller and is used to detect the system state and perform threshold judgment; The controller generates a driving signal through the control algorithm module according to the rotor position signal detected by the Hall effect sensor and the current and voltage signals processed by the signal processing unit, and controls the driving circuit to drive the motor body.

2. The intelligent control system for a high-efficiency brushless DC motor according to claim 1, characterized in that: in, The control algorithm module includes: A speed control algorithm for generating a speed control signal according to an error between a target speed and an actual speed; a current control algorithm for generating a current control signal according to an error between a target current and an actual current; A voltage control algorithm is used to generate a voltage control signal according to the error between the target voltage and the actual voltage.

3. The intelligent control system for a high-efficiency brushless DC motor according to claim 2, characterized in that: in, The speed control algorithm uses the following formula: Among them, S is the speed control signal, α, β, γ, δ are proportional, integral, differential and sine coefficients respectively, V t is the target speed, V a is the actual speed.

4. The intelligent control system for a high-efficiency brushless DC motor according to claim 2, characterized in that: in, The current control algorithm uses the following formula: Where I is the current control signal, ò, ζ, η, θ are the proportional, integral, differential and cosine coefficients respectively, I t is the target current, I a is the actual current.

5. An intelligent control system for a high-efficiency brushless DC motor according to any one of claim 2, characterized in that: in, The voltage control algorithm uses the following formula: Where V is the voltage control signal, ι, κ, λ, μ are the proportional, integral, differential and tangent coefficients respectively, V t is the target voltage, V a is the actual voltage.

6. An intelligent control system for a high-efficiency brushless DC motor according to any one of claims 2 to 5, characterized in that: in, The detection module performs threshold determination based on the following original formula: Where T is the threshold judgment signal, ρ, σ, τ, υ are the coefficients of current ratio, voltage ratio, current change rate and voltage change rate respectively, I t is the target current, I a is the actual current, V t is the target voltage, V a is the actual voltage.

7. The intelligent control system for a high-efficiency brushless DC motor according to claim 6, characterized in that: The adjustment methods based on the threshold judgment include: Detection of abnormality: When the detection module calculates the threshold through the formula and the judgment signal exceeds the preset range, the system identifies the abnormal state; Feedback adjustment: The controller performs feedback adjustment through the control algorithm module according to the detected abnormal state; the specific steps are as follows: Speed ​​control: If the speed deviates from the target value, the speed control algorithm generates a new speed control signal according to the formula to adjust the motor speed; Current control: If the current deviates from the target value, the current control algorithm generates a new current control signal to adjust the motor phase current; Voltage control: If the voltage deviates from the target value, the voltage control algorithm generates a new voltage control signal to adjust the phase voltage of the motor; Adaptive adjustment: The adaptive control algorithm dynamically adjusts the control parameters according to the current operating status to optimize system performance and ensure stable operation; Drive adjustment: The drive signal generated by the controller is transmitted to the drive circuit, and the drive circuit drives the motor body according to the adjusted control signal to bring its operating parameters back to a reasonable range.

8. The intelligent control system for a high-efficiency brushless DC motor according to claim 7, characterized in that: in, The feedback adjustment further includes: Multi-level feedback control: If the system still fails to achieve the expected effect after primary feedback adjustment, secondary or multi-level feedback adjustment will be carried out to gradually optimize the control signal to ensure that the motor parameters return to a reasonable range; Real-time monitoring and adjustment: The system continuously monitors the motor's operating status and adjusts the control signal in real time to ensure that the motor always operates in the best condition under different working conditions.

9. The intelligent control system for a high-efficiency brushless DC motor according to claim 7, characterized in that: in, The adaptive adjustment further comprises: Parameter self-learning: The system uses machine learning algorithms to automatically learn and optimize control parameters based on historical operating data and current status, thereby improving the system's adaptive capabilities; Environmental adaptation: The system dynamically adjusts control parameters according to changes in the external environment to ensure that the motor can operate stably under various environmental conditions.

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