A universal motor performance control system and method

Abstract

The present invention relates to the field of motor control systems, and specifically to a universal motor performance control system and method. A universal motor performance control system comprises: a motor driver module; an intelligent control module; a feedback regulation module; a user interface; and a safety protection module. The motor driver module is used to provide precise motor driving force; the intelligent control module comprises an adaptive control algorithm and an energy efficiency optimization algorithm to implement control decisions; the feedback regulation module comprises a sensor and a data processing unit to monitor the real-time state of the motor and provide feedback to the intelligent control module; the user interface allows the user to input commands and settings to adjust the performance of the motor; and the safety protection system is used to ensure the safe operation of the system under abnormal conditions.

Description

Technical Field

[0001] The invention provides a universal motor performance control system and method, which relates to the field of motor control systems. Background Art

[0002] The universal motor performance control system is an advanced control system that is widely used in various motor drive devices. Its purpose is to optimize the operating performance of the motor, including but not limited to speed, torque, energy efficiency and stability;

[0003] However, the motor control system of the prior art has the following shortcomings:

[0004] In traditional motor control systems, the lack of sufficient adaptability means that they cannot effectively respond to changing operating conditions. Such systems rely on preset fixed parameters, resulting in poor performance under load changes, environmental fluctuations or other dynamic conditions. For example, for rapidly changing loads or environmental conditions, traditional systems cannot adjust their control parameters in time to maintain optimal operation.

[0005] Existing technologies do not fully integrate energy efficiency optimization algorithms, or their energy efficiency management capabilities are limited. This leads to unnecessary waste of energy during motor operation, especially when full power operation is not required. Existing systems are unable to dynamically adjust power output according to actual working conditions, resulting in low energy efficiency, increased energy consumption and operating costs.

[0006] Traditional motor control systems lack a linkage mechanism that effectively combines performance requirements with energy efficiency goals. This leads to ignoring energy efficiency while pursuing maximum performance, or sacrificing necessary performance while focusing on energy efficiency. The lack of a comprehensive control strategy results in the system being inefficient in achieving optimal operating conditions and unable to meet both performance and energy-saving requirements. Summary of the invention

[0007] The present invention provides a universal motor performance control system and method, and the technical solutions adopted are as follows:

[0008] A universal motor performance control system, comprising:

[0009] Motor driver module;

[0010] Intelligent control module;

[0011] Feedback regulation module;

[0012] user interface;

[0013] Security protection module;

[0014] The motor driver module is used to provide precise motor driving force;

[0015] The intelligent control module includes an adaptive control algorithm and an energy efficiency optimization algorithm to implement control decisions;

[0016] The feedback adjustment module includes a sensor and a data processing unit, which are used to monitor the real-time state of the motor and feed back to the intelligent control module;

[0017] The user interface allows a user to input commands and settings to adjust the performance of the motor;

[0018] The safety protection system is used to ensure the safe operation of the system under abnormal circumstances;

[0019] The motor driver module includes a motor and a driver. The motor is an actuator that operates according to the instructions of the driver. The driver is used to directly control the speed, torque and rotation direction of the motor. The driver and controller:

[0020] The driver receives instructions from a controller of the intelligent control module, wherein the instructions are output based on an adaptive control algorithm and an energy efficiency optimization algorithm in the intelligent control module;

[0021] The controller receives motor data from the feedback system, wherein the motor data includes real-time speed and position information of the motor; and is used to adjust and optimize the control strategy to ensure that the motor operates as expected;

[0022] The user inputs control instructions and parameters through the interface, and the controller formulates and adjusts the control strategy accordingly;

[0023] The safety protection system monitors the operating status of the entire system, and when an abnormal situation is detected, including motor overheating or overload, the controller is immediately notified to take emergency measures, including reducing speed or stopping;

[0024] The motor driver module ensures precise motor drive force and provides appropriate speed and torque control for the motor. The intelligent control module uses adaptive control algorithms and energy efficiency optimization algorithms to automatically adjust the control strategy according to actual working conditions to achieve optimal control effects. The feedback regulation module provides motor operation data in a timely manner to ensure the accuracy of the control strategy. The user interface design allows operators to easily set and adjust system parameters. Finally, the addition of a safety protection system ensures safe operation of the system under abnormal conditions, prevents motor overheating or overloading, and enhances the reliability and safety of the system.

[0025] Preferably, the adaptive control algorithm comprises:

[0026] First, the speed and torque of the motor are monitored in real time;

[0027] Then, the actual output is compared with the expected target and the deviation is calculated;

[0028] Then, the PID control parameters are adjusted according to these deviations to achieve optimal control;

[0029] Finally, these parameters are continuously optimized iteratively to adapt to different working conditions;

[0030] The implementation of adaptive control algorithms, especially its real-time monitoring of motor speed and torque, deviation calculation and dynamic adjustment of PID parameters, brings significant flexibility and responsiveness to the motor control system. By comparing the actual output with the expected target, the system can detect and correct deviations in time, thereby maintaining the optimal state of motor operation. Continuous iterative optimization of PID parameters enables the system to adapt to different working conditions, such as load changes, environmental influences, etc., ensuring stability and high performance in various situations. This adaptive capability is essential for modern industrial applications with changing demands.

[0031] Preferably, the adaptive control algorithm is implemented by deviation calculation, and the deviation calculation formula includes:

[0032] p(t),i(t),d(t)=f(e(t),Δe(t));

[0033] Among them, p(t), i(t), d(t) are the proportional, integral, and differential control parameters that change with time, respectively, e(t) is the deviation between the target value and the actual value, and Δe(t) is the rate of change of the deviation.

[0034] In the adaptive control algorithm, the mentioned proportional-integral and differential control parameters that change over time are the core components of the PID (Proportional-Integral-Derivative) control strategy. These parameters are dynamically adjusted over time to adapt to the real-time operating conditions and performance requirements of the system. The following is a detailed explanation of the role of each parameter and how it changes over time:

[0035] P(t) directly affects the intensity of the system's response to the current error. Specifically, it determines the relationship between the control output and the current deviation (the difference between the target value and the actual value). A higher p(t) value will make the system react more quickly and strongly to the deviation, but too high a value may cause system instability and overshoot;

[0036] In adaptive control, p(t) is dynamically adjusted based on real-time feedback data, such as the size and duration of the deviation; for example, when the load suddenly increases, p(t) may need to be increased to quickly reduce the deviation.

[0037] i(t) is mainly aimed at small deviations that exist in the system for a long time. It adjusts the control output by accumulating long-term deviations, which helps to eliminate the steady-state error of the system. Under different operating conditions, i(t) can be adjusted to ensure long-term accuracy. For example, when there are small deviations in the system, the i(t) value can be appropriately increased to strengthen the correction of such long-term deviations.

[0038] d(t) focuses on the rate of change of the deviation, that is, how the deviation changes over time; it responds to rapid deviation changes, helps predict and mitigate future deviations, and thus improves system stability; when the dynamic characteristics of the system change (such as the response speed becomes faster or slower), d(t) can be adjusted to adapt to these changes. For example, in the case of rapid changes in deviation, increasing d(t) can help reduce overshoot and oscillation;

[0039] The core of the adaptive control algorithm is to adjust the PID parameters according to the real-time deviation e(t) and the deviation change rate Δe(t). In this way, the control of the motor is no longer static or based only on preset conditions, but dynamic and responsive in real time. This allows the control system to adapt more accurately to the actual working environment and needs, whether it is dealing with rapidly changing loads or changing operating conditions. Dynamic adjustment of PID parameters ensures the best performance of motor operation, improves energy efficiency, reduces maintenance costs, and extends the service life of the motor.

[0040] Preferably, the energy efficiency optimization algorithm includes:

[0041] Monitor the real-time energy consumption of the motor;

[0042] Analyze duty cycle and load conditions;

[0043] Adjust operating parameters to reduce ineffective power consumption;

[0044] Periodically evaluate and optimize control strategies;

[0045] Energy efficiency optimization algorithms bring significant energy savings to motor control systems by monitoring the real-time energy consumption of the motor, analyzing the duty cycle and load conditions, and adjusting operating parameters to reduce ineffective power consumption. This algorithm not only reduces the waste of electrical energy, but also optimizes the overall operating efficiency of the motor. Periodic evaluation and optimization of control strategies ensure long-term energy efficiency management, which is of great significance for cost savings and environmental protection.

[0046] Preferably, the energy efficiency optimization algorithm is specifically implemented through an energy efficiency optimization formula, and the energy efficiency optimization formula includes:

[0047] opt(t)=g(P(t),L(t),T(t));

[0048] opt(t) is the optimized power output, P(t) is the actual power consumption, L(t) and T(t) represent the load condition and time factor respectively;

[0049] The application of the energy efficiency optimization formula provides a quantitative and operational method for the energy efficiency optimization algorithm. Through this formula, the system can specifically calculate the energy efficiency under different load conditions and time factors.

[0050] The optimal power output opt(t) under the condition of . This makes energy efficiency optimization no longer based on general assumptions or fixed rules, but a process that can be dynamically adjusted according to actual conditions. The calculated opt(t) helps the system identify the most efficient way to use energy under specific operating conditions, thereby minimizing energy waste while maintaining motor performance. This method is particularly suitable for industrial applications where energy costs account for a significant proportion. It not only helps to reduce operating costs, but also helps to achieve a more sustainable production process.

[0051] Preferably, comprehensive control is achieved through adaptive control algorithms and energy efficiency optimization algorithms:

[0052] The adaptive control algorithm adjusts the control parameters according to the actual working state of the motor, and transmits these parameters and state data to the energy efficiency optimization algorithm;

[0053] The energy efficiency optimization algorithm adjusts the energy efficiency optimization strategy based on these data and energy consumption models, and feeds back to the adaptive control algorithm to further adjust the control strategy;

[0054] Combining adaptive control algorithms with energy efficiency optimization algorithms to achieve comprehensive control provides a highly optimized motor control strategy. This comprehensive control not only takes into account the immediate performance requirements of the motor (such as speed and torque control), but also takes into account the long-term energy efficiency. In this way, the system is able to balance performance and energy consumption under different operating conditions, ensuring that the motor can operate in an optimal manner in various situations. This flexibility and adaptability is essential to cope with complex and changing industrial environments, helping to improve the efficiency and reliability of the entire production process.

[0055] Preferably, the comprehensive control value is calculated by a comprehensive control formula, the comprehensive control formula comprising:

[0056] C(t)=h(p(t),i(t),d(t),opt(t));

[0057] Wherein, C(t) represents the comprehensive control value, which is composed of the PID control parameters p(t), i(t), d(t) in the adaptive control algorithm and the optimized power output opt(t) in the energy efficiency optimization algorithm;

[0058] The application of C(t) provides a unified decision-making criterion for motor control systems. This formula combines PID control parameters and energy efficiency optimization outputs, so that the adjustment of the control strategy is not based only on a single performance indicator, but on the overall optimal state of motor operation. Such comprehensive considerations help to optimize energy efficiency while maintaining high performance, thereby achieving a more economical and environmentally friendly operating mode. This comprehensive control method is particularly important for application scenarios that need to consider both performance and energy efficiency, such as high-energy-consuming industrial equipment.

[0059] Preferably, it includes:

[0060] Continuously monitor environmental and load changes, adjust p(t), i(t), d(t) and opt(t) in real time, and optimize integrated control through machine learning to adapt to changing working conditions;

[0061] By continuously monitoring environmental and load changes, adjusting PID parameters and energy efficiency optimization output in real time, the system is able to automatically adapt to various working conditions. Combined with the application of machine learning, this approach enables the control strategy to be continuously optimized over time, able to learn and adapt to complex and changing working environments. This improves the efficiency and reliability of the system and reduces dependence on human intervention, especially for industrial applications that require long-term stable operation. In this way, the motor control system not only performs well in the current working environment, but also evolves as conditions change, continuously providing optimal performance and energy efficiency.

[0062] A universal motor performance control method uses the universal motor performance control system.

[0063] The present invention has the following beneficial effects:

[0064] 1. Through the adaptive control algorithm, the system can monitor the speed and torque of the motor in real time, compare the actual output with the expected target, and calculate the deviation. Based on these deviations, the system dynamically adjusts the PID control parameters so that the motor can maintain the optimal state under various working conditions. This high degree of adaptability and responsiveness is essential to ensure that the motor maintains high performance in a changing working environment.

[0065] 2. The energy efficiency optimization algorithm reduces ineffective power consumption by monitoring real-time energy consumption and adjusting operating parameters according to the working cycle and load conditions. The application of this method, especially through the specific implementation of the formula, enables the motor to achieve maximum efficiency in energy use while maintaining the necessary performance. This energy-saving effect is particularly important today when energy costs are increasing, and has far-reaching significance for reducing operating costs and promoting sustainable production.

[0066] 3. The comprehensive control formula reflects the linkage effect of the adaptive control algorithm and the energy efficiency optimization algorithm. This formula not only takes into account the immediate performance requirements, but also takes into account the long-term energy efficiency goals, so that the control system can achieve the best balance between performance and energy efficiency under different operating conditions. This linkage mechanism improves the overall efficiency of the system, ensures that the motor can operate in the best way under various working conditions, and achieves dual optimization of performance and energy saving. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 , one A block diagram of a general motor performance control system. DETAILED DESCRIPTION

[0068] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0069] 1. Embodiment 1:

[0070] A universal motor performance control system, comprising:

[0071] Motor driver module;

[0072] Intelligent control module;

[0073] Feedback regulation module;

[0074] user interface;

[0075] Security protection module;

[0076] The motor driver module is used to provide precise motor driving force;

[0077] The intelligent control module includes an adaptive control algorithm and an energy efficiency optimization algorithm to implement control decisions;

[0078] The feedback adjustment module includes a sensor and a data processing unit, which are used to monitor the real-time state of the motor and feed back to the intelligent control module;

[0079] The user interface allows a user to input commands and settings to adjust the performance of the motor;

[0080] The safety protection system is used to ensure the safe operation of the system under abnormal circumstances;

[0081] The motor driver module includes a motor and a driver. The motor is an actuator that operates according to the instructions of the driver. The driver is used to directly control the speed, torque and rotation direction of the motor. The driver and controller:

[0082] The driver receives instructions from a controller of the intelligent control module, wherein the instructions are output based on an adaptive control algorithm and an energy efficiency optimization algorithm in the intelligent control module;

[0083] The controller receives motor data from the feedback system, wherein the motor data includes real-time speed and position information of the motor; and is used to adjust and optimize the control strategy to ensure that the motor operates as expected;

[0084] The user inputs control instructions and parameters through the interface, and the controller formulates and adjusts the control strategy accordingly;

[0085] The safety protection system monitors the operating status of the entire system, and when an abnormal situation is detected, including motor overheating or overload, the controller is immediately notified to take emergency measures, including reducing speed or stopping;

[0086] The motor driver module ensures precise motor drive force and provides appropriate speed and torque control for the motor. The intelligent control module uses adaptive control algorithms and energy efficiency optimization algorithms to automatically adjust the control strategy according to actual working conditions to achieve optimal control effects. The feedback regulation module provides motor operation data in a timely manner to ensure the accuracy of the control strategy. The user interface design allows operators to easily set and adjust system parameters. Finally, the addition of a safety protection system ensures safe operation of the system under abnormal conditions, prevents motor overheating or overloading, and enhances the reliability and safety of the system.

[0087] Specifically, the adaptive control algorithm includes:

[0088] First, the speed and torque of the motor are monitored in real time;

[0089] Then, the actual output is compared with the expected target and the deviation is calculated;

[0090] Then, the PID control parameters are adjusted according to these deviations to achieve optimal control;

[0091] Finally, these parameters are continuously optimized iteratively to adapt to different working conditions;

[0092] The implementation of adaptive control algorithms, especially its real-time monitoring of motor speed and torque, deviation calculation and dynamic adjustment of PID parameters, brings significant flexibility and responsiveness to the motor control system. By comparing the actual output with the expected target, the system can detect and correct deviations in time, thereby maintaining the optimal state of motor operation. Continuous iterative optimization of PID parameters enables the system to adapt to different working conditions, such as load changes, environmental influences, etc., ensuring stability and high performance in various situations. This adaptive capability is essential for modern industrial applications with changing demands.

[0093] Specifically, the adaptive control algorithm is implemented by deviation calculation, and the deviation calculation formula includes:

[0094] p(t),i(t),d(t)=f(e(t),Δe(t));

[0095] Among them, p(t), i(t), d(t) are the proportional, integral, and differential control parameters that change with time, e(t) is the deviation between the target value and the actual value, and Δe(t) is the rate of change of the deviation;

[0096] In the adaptive control algorithm, the mentioned proportional-integral and differential control parameters that change over time are the core components of the PID (Proportional-Integral-Derivative) control strategy. These parameters are dynamically adjusted over time to adapt to the real-time operating conditions and performance requirements of the system. The following is a detailed explanation of the role of each parameter and how it changes over time:

[0097] P(t) directly affects the intensity of the system's response to the current error. Specifically, it determines the relationship between the control output and the current deviation (the difference between the target value and the actual value). A higher p(t) value will make the system react more quickly and strongly to the deviation, but too high a value may cause system instability and overshoot;

[0098] In adaptive control, P(t) is dynamically adjusted based on real-time feedback data, such as the size and duration of the deviation; for example, when the load suddenly increases, p(t) may need to be increased to quickly reduce the deviation.

[0099] i(t) is mainly aimed at small deviations that exist in the system for a long time. It adjusts the control output by accumulating long-term deviations, which helps to eliminate the steady-state error of the system. Under different operating conditions, i(t) can be adjusted to ensure long-term accuracy. For example, when there are small deviations in the system, the i(t) value can be appropriately increased to strengthen the correction of such long-term deviations.

[0100] d(t) focuses on the rate of change of the deviation, that is, how the deviation changes over time; it responds to rapid deviation changes, helps predict and mitigate future deviations, and thus improves system stability; when the dynamic characteristics of the system change (such as the response speed becomes faster or slower), d(t) can be adjusted to adapt to these changes. For example, in the case of rapid changes in deviation, increasing d(t) can help reduce overshoot and oscillation;

[0101] The core of the adaptive control algorithm is to adjust the PID parameters according to the real-time deviation e(t) and the deviation change rate Δe(t). In this way, the control of the motor is no longer static or based only on preset conditions, but dynamic and responsive in real time. This allows the control system to adapt more accurately to the actual working environment and needs, whether it is dealing with rapidly changing loads or changing operating conditions. Dynamic adjustment of PID parameters ensures the best performance of motor operation, improves energy efficiency, reduces maintenance costs, and extends the service life of the motor.

[0102] Specifically, the energy efficiency optimization algorithm includes:

[0103] Monitor the real-time energy consumption of the motor;

[0104] Analyze duty cycle and load conditions;

[0105] Adjust operating parameters to reduce ineffective power consumption;

[0106] Periodically evaluate and optimize control strategies;

[0107] Energy efficiency optimization algorithms bring significant energy savings to motor control systems by monitoring the real-time energy consumption of the motor, analyzing the duty cycle and load conditions, and adjusting operating parameters to reduce ineffective power consumption. This algorithm not only reduces the waste of electrical energy, but also optimizes the overall operating efficiency of the motor. Periodic evaluation and optimization of control strategies ensure long-term energy efficiency management, which is of great significance for cost savings and environmental protection.

[0108] Specifically, the energy efficiency optimization algorithm is implemented through an energy efficiency optimization formula, and the energy efficiency optimization formula includes:

[0109] opt(t)=g(P(t),L(t),T(t));

[0110] opt(t) is the optimized power output, P(t) is the actual power consumption, L(t) and T(t) represent the load condition and time factor respectively;

[0111] The application of the energy efficiency optimization formula provides a quantitative and operational method for the energy efficiency optimization algorithm. Through this formula, the system can specifically calculate the optimal power output opt(t) under different load conditions and time factors. This makes energy efficiency optimization no longer based on general assumptions or fixed rules, but a process that can be dynamically adjusted according to actual conditions. The calculated opt(t) helps the system identify the most efficient way to use energy under specific working conditions, thereby minimizing energy waste while maintaining motor performance. This method is particularly suitable for industrial applications where energy costs account for a significant proportion. It not only helps to reduce operating costs, but also helps to achieve a more sustainable production process;

[0112] When monitoring energy consumption in real time, power sensors are used to monitor the power consumption of the motor in real time. The data collection frequency should be high enough to accurately capture the instantaneous changes in energy consumption.

[0113] The energy consumption data collected in real time is initially analyzed by a microprocessor or a dedicated data processing unit to identify energy consumption patterns and anomalies.

[0114] Analyze the motor's operating data, including load fluctuations, operating time length, and cycle energy consumption patterns to identify typical working cycles; use current and voltage sensors to monitor load conditions. Analyze current and voltage waveforms, as well as deviations from standard waveforms, to determine load changes and characteristics.

[0115] Set a fixed evaluation cycle, such as a comprehensive evaluation every week or month; the evaluation cycle can be adjusted according to the intensity and criticality of the motor's use; check the energy consumption data of the motor under various load conditions to identify any high energy consumption patterns or inefficiencies; compare the parameters adjusted in the previous evaluation cycle, analyze their impact on motor performance and energy consumption, and determine whether the adjustments have achieved the expected results; use data analysis tools, such as regression analysis, to extract insights from the operating data and adjust the optimization strategy based on these insights

[0116] The evaluation results are fed back to the energy efficiency optimization algorithm, and the parameters or logic of the algorithm are adjusted according to the evaluation results.

[0117] The specific calculation method of each variable in opt(t)=g(P(t), L(t), T(t)) is:

[0118] P(t): Real-time or near real-time monitoring of the motor's power consumption, recorded as time series data.

[0119] L(t): The load condition is inferred indirectly through the motor's load sensor or through the motor's operating parameters (such as speed and current).

[0120] T(t): Considers the motor's operating time and periodic variations, such as peak and valley periods.

[0121] Use a microprocessor, PLC or software module integrated in the motor controller to implement formula calculations and parameter adjustments; receive energy consumption, load and time data in real time, and perform rapid calculations and responses based on this.

[0122] Specifically, comprehensive control is achieved through adaptive control algorithms and energy efficiency optimization algorithms:

[0123] The adaptive control algorithm adjusts the control parameters according to the actual working state of the motor, and transmits these parameters and state data to the energy efficiency optimization algorithm;

[0124] The energy efficiency optimization algorithm adjusts the energy efficiency optimization strategy based on these data and energy consumption models, and feeds back to the adaptive control algorithm to further adjust the control strategy;

[0125] Combining adaptive control algorithms with energy efficiency optimization algorithms to achieve comprehensive control provides a highly optimized motor control strategy. This comprehensive control not only takes into account the immediate performance requirements of the motor (such as speed and torque control), but also takes into account the long-term energy efficiency. In this way, the system is able to balance performance and energy consumption under different operating conditions, ensuring that the motor can operate in an optimal manner in various situations. This flexibility and adaptability is essential to cope with complex and changing industrial environments, helping to improve the efficiency and reliability of the entire production process.

[0126] Specifically, the comprehensive control value is calculated by a comprehensive control formula, and the comprehensive control formula includes:

[0127] C(t)=h(p(t),i(t),d(t),opt(t));

[0128] Wherein, C(t) represents the comprehensive control value, which is composed of the PID control parameters p(t), i(t), d(t) in the adaptive control algorithm and the optimized power output opt(t) in the energy efficiency optimization algorithm;

[0129] The application of C(t) provides a unified decision-making criterion for motor control systems. This formula combines PID control parameters and energy efficiency optimization outputs, so that the adjustment of the control strategy is not based only on a single performance indicator, but on the overall optimal state of motor operation. Such comprehensive considerations help to optimize energy efficiency while maintaining high performance, thereby achieving a more economical and environmentally friendly operating mode. This comprehensive control method is particularly important for application scenarios that need to consider both performance and energy efficiency, such as high-energy-consuming industrial equipment.

[0130] Specifically, it includes:

[0131] Continuously monitor environmental and load changes, adjust p(t), i(t), d(t) and opt(t) in real time, and optimize integrated control through machine learning to adapt to changing working conditions;

[0132] By continuously monitoring environmental and load changes, adjusting PID parameters and energy efficiency optimization output in real time, the system is able to automatically adapt to various working conditions. Combined with the application of machine learning, this approach enables the control strategy to be continuously optimized over time, able to learn and adapt to complex and changing working environments. This improves the efficiency and reliability of the system and reduces dependence on human intervention, especially for industrial applications that require long-term stable operation. In this way, the motor control system not only performs well in the current working environment, but also evolves as conditions change, continuously providing optimal performance and energy efficiency.

[0133] A universal motor performance control method and a universal motor performance control system.

[0134] The beneficial effects of this embodiment are:

[0135] 1. Through the adaptive control algorithm, the system can monitor the speed and torque of the motor in real time, compare the actual output with the expected target, and calculate the deviation. Based on these deviations, the system dynamically adjusts the PID control parameters so that the motor can maintain the optimal state under various working conditions. This high degree of adaptability and responsiveness is essential to ensure that the motor maintains high performance in a changing working environment.

[0136] 2. Energy efficiency optimization algorithm reduces ineffective power consumption by monitoring real-time energy consumption and adjusting operating parameters according to working cycle and load conditions. The application of this method, especially through the specific implementation of the formula, enables the motor to achieve maximum efficiency in energy use while maintaining the necessary performance. This energy-saving effect is particularly important today when energy costs are increasing, and has far-reaching significance for reducing operating costs and promoting sustainable production.

[0137] 3. The comprehensive control formula reflects the linkage effect of the adaptive control algorithm and the energy efficiency optimization algorithm. This formula not only takes into account the immediate performance requirements, but also takes into account the long-term energy efficiency goals, so that the control system can achieve the best balance between performance and energy efficiency under different operating conditions. This linkage mechanism improves the overall efficiency of the system, ensures that the motor can operate in the best way under various working conditions, and achieves dual optimization of performance and energy saving.

[0138] 2. Embodiment 2:

[0139] A specific embodiment will be provided below, and an explanation will be given of how each calculated value actually works on a real universal motor performance control system.

[0140] Specific embodiment: Industrial conveyor belt motor control system

[0141] Preset usage scenario: Suppose we design a conveyor belt motor control system for an industrial production line. The system needs to automatically adjust the speed according to the load of items on the production line, while optimizing energy consumption to improve overall efficiency.

[0142] System Components:

[0143] Motor: A standard three-phase AC motor is used to drive the conveyor belt.

[0144] Motor driver: used to control the speed and torque of the motor.

[0145] Intelligent control module: includes adaptive control algorithm and energy efficiency optimization algorithm.

[0146] Feedback regulation module: includes speed and torque sensors to monitor the real-time status of the motor.

[0147] User Interface: Allows the operator to set target speed and monitor system status.

[0148] Safety protection system: used to monitor motor temperature and load to prevent overload and overheating.

[0149] Implementation steps and value substitution:

[0150] Adaptive control algorithm application:

[0151] Monitor the current speed of the motor, assuming it is 1500rpm, and the target speed is 2000rpm, so the deviation e(t) = 2000−1500 = 500rpm.

[0152] Assume that the initial PID parameters are Kp=0.1, Ki=0.01, Kd=0.05.

[0153] Adjust the PID parameters according to the deviation e(t) and the previous deviation change rate (assuming it is initially 0).

[0154] Energy efficiency optimization algorithm application:

[0155] Monitor the current motor energy consumption, assuming it is 5kW, analyze the load and duty cycle, and determine the optimized target power opt(t).

[0156] Assume that under the current load, the optimized power is 4.5kW.

[0157] Calculation of linkage control parameter C(t):

[0158] According to C(t)=h(p(t), i(t), d(t), opt(t)), the value of C(t) is calculated. Assume that C(t) integrates these parameters through a specific function, such as C(t)=0.3×p(t)+0.2×Ki(t)+0.1×d(t)+0.4×opt(t).

[0159] Substitute the values ​​and calculate C(t);

[0160] By the following logical rules:

[0161] Set a threshold. If C(t) exceeds the preset threshold, it can be judged that the speed needs to be increased; if C(t) is lower than another threshold, it can be judged that the energy consumption needs to be reduced;

[0162] If the contribution of opt(t) accounts for a large proportion in C(t) (i.e., the demand for energy efficiency optimization is high), and the contributions of Kp(t) and Kd(t) (reflecting the demand for speed control) are also high, it is judged that the speed needs to be increased and the energy consumption needs to be reduced.

[0163] If the calculation result of C(t) shows that the contribution of opt(t) is decreasing while the contributions of p(t) and d(t) are increasing, the operating parameters of the motor are adjusted to increase the speed and reduce the energy consumption.

Claims

1. A universal motor performance control system, characterized in that: include: Motor driver module; Intelligent control module; Feedback regulation module; user interface; Security protection module; The motor driver module is used to provide motor driving force; The intelligent control module includes an adaptive control algorithm and an energy efficiency optimization algorithm for implementing control decisions; The feedback adjustment module includes a sensor and a data processing unit, which are used to monitor the real-time state of the motor and feed back to the intelligent control module; The user interface is used for the user to input commands and settings to adjust the performance of the motor; The safety protection module is used to ensure the safe operation of the system under abnormal circumstances; The motor driver module includes a motor and a driver, the motor operates according to the instructions of the driver, and the driver is used to directly control the speed, torque and rotation direction of the motor; The driver receives instructions from a controller of the intelligent control module, wherein the instructions are output based on an adaptive control algorithm and an energy efficiency optimization algorithm in the intelligent control module; The controller receives motor data from a feedback system, wherein the motor data includes real-time speed and position information of the motor; The user inputs control instructions and parameters through the interface, and the controller formulates and adjusts the control strategy accordingly; The safety protection module monitors the operating status of the entire system, and when an abnormal situation is detected, the abnormal situation includes motor overheating or overload, the controller is immediately notified to take emergency measures, including reducing speed or stopping; The adaptive control algorithm includes: First, the speed and torque of the motor are monitored in real time; Then, the actual output is compared with the expected target and the deviation is calculated; Then, the PID control parameters are adjusted according to these deviations to achieve optimal control; Finally, these parameters are continuously optimized iteratively to adapt to different working conditions; The adaptive control algorithm is specifically implemented by deviation calculation, and the deviation calculation formula includes: p(t),i(t),d(t)=f(e(t),Δe(t)); Among them, p(t), i(t), d(t) are the proportional, integral, and differential control parameters that change with time, e(t) is the deviation between the target value and the actual value, and Δe(t) is the rate of change of the deviation; Energy efficiency optimization algorithms include: Monitor the real-time energy consumption of the motor; Analyze duty cycle and load conditions; Adjust operating parameters to reduce ineffective power consumption; Periodically evaluate and optimize control strategies; The energy efficiency optimization algorithm is specifically implemented through an energy efficiency optimization formula, and the energy efficiency optimization formula includes: opt(t)=g(P(t),L(t),T(t)); opt(t) is the optimized power output, P(t) is the actual power consumption, L(t) and T(t) represent the load condition and time factor respectively; Comprehensive control is achieved through adaptive control algorithms and energy efficiency optimization algorithms: The adaptive control algorithm adjusts the control parameters according to the actual working state of the motor, and transmits these parameters and state data to the energy efficiency optimization algorithm; The energy efficiency optimization algorithm adjusts the energy efficiency optimization strategy based on these data and energy consumption models, and feeds back to the adaptive control algorithm to further adjust the control strategy; The comprehensive control value is calculated by the comprehensive control formula, which includes: C(t)=h(p(t),i(t),d(t),opt(t)); Wherein, C(t) represents the comprehensive control value, which is composed of the PID control parameters p(t), i(t), d(t) in the adaptive control algorithm and the optimized power output opt(t) in the energy efficiency optimization algorithm; Also includes: Continuously monitor environmental and load changes, adjust p(t), i(t), d(t) and opt(t) in real time, and optimize comprehensive control through machine learning to adapt to changing working conditions.

2. A general motor performance control method, characterized in that: Use the universal motor performance control system described in claim 1.

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