A state parameter self-learning servo system and model algorithm

Abstract

The application provides a state parameter self-learning servo system and a model algorithm, the system is composed of a servo control unit and a monitoring management unit; the servo control unit and the monitoring management unit are connected through a strong real-time bus; the servo control unit is used for real-time sending of servo control data and state data of the servo system; the monitoring management unit is used for collecting state data and parameter algorithm self-learning, and the monitoring management unit deploys a self-learning model algorithm, collects state data of servo control of the servo control unit in real time, calculates servo control adjustment parameters, sends the servo control adjustment parameters to the servo control unit, and realizes self-adaptive adjustment of the servo system; through the state parameter self-learning servo system and the model algorithm, the adaptive capacity of the servo system in complex mechanical and electrical systems and complex environment applications can be improved by optimizing the setting of the system structure, so that the servo system control parameters can be self-adaptively adjusted, and the high-precision and intelligent control level of the system is ensured.

Description

Technical Field

[0001] This invention relates to the field of servo systems and servo control technology, and more specifically, to a state parameter self-learning servo system and model algorithm. Background Technology

[0002] A servo control system is used to drive a mechanism to move to a designated position according to a tracked command. Specifically, it relies on the difference between the command and the status feedback to adjust the control components and actuators. Servo control systems offer advantages such as high precision, high stability, and strong adaptability, and are widely used in equipment control across various industries, including shipbuilding, machine tools, and robotics. In complex electromechanical systems operating under conditions of high temperature, high humidity, high sea states, strong magnetic fields, and multiple operating conditions, the time-varying effects of electromechanical parameters and nonlinear disturbance torques affect high precision and high dynamic response performance. The servo control system actively senses changes in the environment, learns its own status parameters, and adaptively adjusts its control parameters, which is essential for ensuring high-precision and intelligent control of equipment.

[0003] Currently, servo control in servo systems mostly employs the classic PID control algorithm, integrating intelligent algorithms such as optimal control and genetic algorithms. Based on the weighting of steady-state and dynamic performance indicators (rise time, settling time, maximum overshoot, steady-state error, etc.) in the fitness parameters, the optimal PID control parameters are adjusted to achieve self-tuning of state parameters and adaptive parameter adjustment. However, this algorithm suffers from limitations in generality and slow convergence speed, and cannot automatically adapt to changes in environmental conditions. AC servo drives in the servo control field possess self-tuning algorithms. These algorithms identify parameters using a servo control motor model or record command and speed output data, then solve for specific values ​​using an open-loop approximation model of the servo control speed, calculating the speed loop proportional coefficient and inertia time coefficient, etc., to achieve self-tuning of the speed loop control parameters. However, this method is limited to the internal speed loop self-tuning of the AC servo drive and cannot adaptively adjust the position loop of the servo system. Therefore, researching how to solve the problem of poor adaptability of traditional servo systems in complex electromechanical systems and complex environments, and their inability to adaptively adjust servo system control parameters, is of great significance.

[0004] Patent publication CN116500137A proposes a cantilever servo control system based on circumferential gripping force detection. This system includes a cantilever lifting unit, a feed mechanism operation pressure acquisition unit, and a wireless pressure signal feedback control unit. The cantilever lifting unit is used for the lifting, raising, or horizontal movement of the feed mechanism. A circumferential array sensor is connected to the acquisition module, which communicates in real-time with the pressure acquisition controller via a wireless Bluetooth module. The pressure acquisition controller receives and processes the pressure-sensing voltage signal data from the circumferential array sensor. This system can be used for cantilever servo control in hollow shaft ultrasonic flaw detection equipment, assisting single-person operation of the feed mechanism's raising / lowering / horizontal movement. However, because this patent relies on fixed fuzzy rules and BP neural network parameter tuning, it lacks self-learning and online update capabilities, and cannot achieve separate deployment of monitoring and control. Summary of the Invention

[0005] In view of this, the present invention aims to propose a state parameter self-learning servo system and model algorithm to solve the problems of poor adaptability of traditional servo systems in complex electromechanical systems and complex environments, and the inability to adaptively adjust the control parameters of the servo system in the prior art. In this way, by optimizing the system structure, the adaptability of the servo system in complex electromechanical systems and complex environments can be improved, enabling adaptive adjustment of the control parameters of the servo system, thereby ensuring the high precision and intelligent control level of the system.

[0006] To achieve the above objectives, the technical solution of the present invention is implemented as follows:

[0007] This invention relates to a state parameter self-learning servo system and its model algorithm. The state parameter self-learning servo system consists of a servo control unit and a monitoring and management unit. The servo control unit and the monitoring and management unit are connected via a strong real-time bus. The servo control unit is used for real-time transmission of servo control data and state data of the servo system. The monitoring and management unit is used for collecting state data and parameter algorithm self-learning. The monitoring and management unit deploys a self-learning model algorithm, collects the state data of servo control from the servo control unit in real time, calculates the servo control adjustment parameters, sends them to the servo control unit, and realizes adaptive adjustment of the servo system.

[0008] Furthermore, the strong real-time bus uses a programmable logic controller (FPGA) to implement a custom communication protocol.

[0009] Furthermore, the servo control unit consists of a real-time servo module, a real-time drive module, and a motor load; one end of the real-time servo module and the real-time drive module are connected to the monitoring and management unit via a strong real-time bus, the other end of the real-time servo module is connected to the real-time drive module, and the other end of the real-time servo module is connected to the sensor; the other end of the real-time drive module is connected to the motor load.

[0010] Furthermore, the real-time servo module, real-time drive module, motor load, and sensor are all configured with n units, where n is a positive integer.

[0011] Furthermore, each of the aforementioned real-time servo module, real-time drive module, motor load, and sensor constitutes a subsystem; the various subsystems are connected through a real-time bus.

[0012] Furthermore, both the real-time servo module and the real-time drive module adopt embedded controller MCU and programmable controller FPGA. The controller MCU and programmable controller FPGA are connected and integrated to form the main control chip.

[0013] Furthermore, both the real-time servo module and the real-time drive module are configured with a real-time bus interface and a general-purpose function interface, respectively; both the real-time bus interface and the general-purpose function interface are connected to the main control chip for communication.

[0014] Furthermore, the monitoring and management unit includes a display control module and a learning management module; the display control module is connected to the real-time servo module in real-time via a strong real-time bus; the learning management module is connected to the real-time drive module in real-time via a strong real-time bus.

[0015] A state parameter self-learning model algorithm is applied to the aforementioned state parameter self-learning servo system; the algorithm includes the following steps:

[0016] Step 1: Establish a self-learning model for state parameters: Establish the inertia disturbance parameter model and the motor state parameter model respectively;

[0017] Step 2: Adjusting Control Parameters for the Controlled Object Model: Based on the established self-learning model of state parameters and the calculated state data, obtain the controlled object model of the servo system; establish performance evaluation indicators such as stability, speed and responsiveness of the servo system, adjust the PI control parameters of the controllers corresponding to the position loop, velocity loop and current loop of the controlled object model, and test whether the output of the servo system meets the optimal performance indicators.

[0018] Furthermore, step one includes:

[0019] Step S11: Establish the inertia disturbance parameter model:

[0020] The influence of any one or more load disturbance torques in high sea state swaying, impact, and vibration, as well as any one or more nonlinear factors in friction torque, transmission chain idling, and load torque, are combined and integrated into disturbance d, and then the motion equation of the servo system inertial load is established.

[0021] Input to obtain the motor q-axis current i in real time qThe operating status data, composed of the feedback rotational speed ω, is used to calculate the moment of inertia J and the disturbance torque T using a corresponding learning algorithm. d The estimated value.

[0022] Compared with existing technologies, the state parameter self-learning follow-up system and model algorithm described in this invention have the following advantages:

[0023] By optimizing the system structure, the adaptability of the servo system to complex electromechanical systems and complex environments can be improved, enabling adaptive adjustment of the servo system control parameters and thus ensuring high precision and intelligent control of the system. Attached Figure Description

[0024] The accompanying drawings, which constitute a part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings:

[0025] Figure 1 This is a schematic diagram of the overall system structure;

[0026] Figure 2 This is a schematic diagram showing the connection relationship between the components of the real-time servo module and the real-time drive module.

[0027] Figure 3 This is a block diagram of the self-learning of state parameters for a servo system model.

[0028] Figure 4 This is a block diagram of the speed loop self-tuning model.

[0029] Explanation of reference numerals in the attached diagram: 1. Follow-up control unit; 11. Real-time servo module; 12. Real-time drive module; 121. Control loop; 1211. Extended PWM interface; 1212. Extended AD interface; 122. Drive loop; 1221. IGBT power drive loop; 13. Motor load; 14. Sensor; 100. Main control chip; 101. Real-time bus interface; 102. General function interface; 1021. Digital input / output interface; 1022. CAN interface; 1023. Ethernet interface; 1024. RS485 / 422 interface; 1025. Position and speed feedback interface; 2. Monitoring and management unit; 21. Display control module; 22. Learning management module. Detailed Implementation

[0030] The inventive concepts of this disclosure will be described below using terminology commonly used by those skilled in the art to communicate the essence of their work to others skilled in the art. However, these inventive concepts may be embodied in many different forms and should not be construed as limited to the embodiments described herein.

[0031] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other.

[0032] The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0033] To address the problem of poor adaptability of traditional servo systems in complex electromechanical systems and environments, and their inability to adaptively adjust control parameters, this embodiment proposes a state parameter self-learning servo system and its model algorithm. This state parameter self-learning servo system consists of a servo control unit 1 and a monitoring and management unit 2. The servo control unit 1 and the monitoring and management unit 2 are connected via a strong real-time bus. The servo control unit 1 is used for real-time transmission of servo control data and state data of the servo system. The monitoring and management unit 2 is used for collecting state data and performing parameter self-learning. Specifically, the monitoring and management unit 2 deploys a self-learning model algorithm, collects the servo control state data from the servo control unit 1 in real time, calculates the servo control adjustment parameters, sends them to the servo control unit 1, and enables adaptive adjustment of the servo system.

[0034] The servo control unit 1 consists of a real-time servo module 11, a real-time drive module 12, and a motor load 13. One end of both the real-time servo module 11 and the real-time drive module 12 are connected to the monitoring and management unit 2 via a high-real-time bus. The other end of the real-time servo module 11 is connected to the real-time drive module 12, and the other end of the real-time servo module 11 is connected to the sensor 14. The other end of the real-time drive module 12 is connected to the motor load 13.

[0035] By configuring the components within the servo control unit 1, the servo control and adjustment functions of the position loop, speed loop, and current loop of the servo system can be completed. It can also send real-time status data from the servo control unit 1 and receive control commands and adjustment parameters from the monitoring and management unit 2.

[0036] The system comprises n real-time servo modules 11, 12, 13 motor loads, and 14, where n is a positive integer. Each of these modules forms a subsystem. The subsystems are connected via a real-time bus. One end of the first real-time servo module 11 and the nth real-time drive module 12 is connected to the monitoring and management unit 2 via a strong real-time bus. The other end of the first real-time servo module 11 is connected sequentially to the other ends of the first, second, and nth real-time drive modules 12. Each real-time servo module 11 is connected to its corresponding sensor 14, and each real-time drive module 12 is connected to its corresponding motor load 13.

[0037] By coordinating different subsystems, the scalability of the servo control unit 1 can be improved, while also ensuring its operational stability and security. This contributes to enhancing the operational reliability of the servo control unit 1.

[0038] Specifically, both the real-time servo module 11 and the real-time drive module 12 employ an embedded MCU and a programmable logic controller (FPGA). The MCU and FPGA are connected and integrated to form the main control chip 100. Furthermore, both the real-time servo module 11 and the real-time drive module 12 are configured with a real-time bus interface 101 and a general-purpose function interface 102, respectively. Both the real-time bus interface 101 and the general-purpose function interface 102 are communicatively connected to the main control chip 100.

[0039] In addition, the general-purpose function interface 102 includes a digital input / output interface 1021, a CAN interface 1022, an Ethernet interface 1023, an RS485 / 422 interface 1024, and a position / speed feedback interface 1025. The digital input / output interface 1021, CAN interface 1022, Ethernet interface 1023, and RS485 / 422 interface 1024 are all bidirectionally connected to the main control chip 100. The position / speed feedback interface 1025 is unidirectionally connected to the main control chip 100; the position / speed feedback interface 1025 is used to feed back position and speed information to the main control chip 100.

[0040] It is important to note that, such as Figure 2As shown, the real-time drive module 12 consists of a control loop 121 and a drive loop 122. The controller MCU, programmable logic controller FPGA, real-time bus interface 101, and general function interface 102 are all located within the control loop 121. In addition to the above components, the control loop 121 also includes an extended PWM interface 1211 and an extended AD interface 1212. One end of both the extended PWM interface 1211 and the extended AD interface 1212 is unidirectionally connected to the main control chip 100; the other end of the extended PWM interface 1211 is connected to the IGBT power drive loop 1221 within the drive loop 122; the other end of the extended AD interface 1212 is connected to a sensor 14 within the drive loop 122. Preferably, the sensor 14 includes a voltage sensor or a current sensor. The extended PWM interface 1211 is used to receive instructions from the main control chip 100 and drive the IGBT power drive loop 1221 to perform corresponding actions. The extended AD interface 1212 is used to transmit the acquired voltage and current information to the main control chip 100. The programmable controller FPGA is customized to connect to the real-time bus interface 101, the extended PWM interface 1211, and the extended AD interface 1212, thereby enabling the main control chip 100 to access data from the current sensor and / or voltage sensor, so that the follow-up control unit 1 can adapt to the high-speed processing performance requirements of the current loop.

[0041] Through the coordinated configuration of the components within the real-time drive module 12, the real-time drive module 12 can perform current loop control via a complex programmable logic controller (FPGA). The FPGA expands PWM and AD interfaces to connect the power drive circuit and any one or more sensors 14 for current and voltage. Strong real-time communication is achieved via a real-time bus interface, enabling high response of the current loop, short-cycle status data, and strong real-time interaction between the current loop and the monitoring and management unit 2.

[0042] The monitoring and management unit 2 includes a display control module 21 and a learning management module 22. The display control module 21 and the learning management module 22 are two functionally independent modules. The display control module 21 is connected in real-time to the real-time servo module 11 of the servo control unit 1 via a strong real-time bus. The learning management module 22 is connected in real-time to the real-time drive module 12 of the servo control unit 1 via a strong real-time bus. The display control module 21 is used for the two traditional monitoring functions of sending servo system commands and displaying status. The learning management module 22 is used for status data acquisition and parameter learning and tuning. Preferably, the display control module 21 and the learning management module 22 are two hardware modules. Alternatively, the display control module 21 and the learning management module 22 can be integrated into the same hardware module. The hardware module embeds a real-time CPU module, thereby enabling the monitoring and management unit 2 to have status data storage and computational processing resources, and to support real-time communication between the servo control unit 1 and the monitoring and management unit 2 after accessing the real-time bus interface within the servo control unit 1. In this embodiment, display control and learning management are two software functions, set on the hardware module as required, forming the display control module 21 and the learning management module 22.

[0043] By employing two software functions, each implemented on a separate hardware module, the monitoring and management unit 2 can achieve the aforementioned functions. Alternatively, the two software function modules can share a single hardware module to implement the functions. This approach enhances the flexibility of the monitoring and management unit 2's structure, allowing for appropriate replacements based on different application scenarios and reducing system costs.

[0044] The high-real-time bus uses a programmable logic controller (FPGA) for a custom communication protocol, supporting not only Powerlink or EtherCAT high-real-time industrial Ethernet buses, but also other high-speed, low-jitter, high-real-time protocol buses. The high-real-time bus connection adopts a linear topology, with monitoring and management unit 2 acting as the bus master, and the real-time servo module 11 and real-time drive module 12 of the servo control unit 1 acting as bus slaves. The servo control unit 1 can be expanded to n bus slaves. These n bus slaves include a series of servo control modules (n real-time servo modules 11) and a series of drive control modules (n real-time drive modules 12), controlling n motor loads 13 as controlled objects, achieving self-learning and servo control of multiple servo subsystem status parameters such as elevation and orientation.

[0045] Preferred, such as Figure 1As shown, the first real-time servo module 11 receives position commands from the display control module 21 within the monitoring and management unit 2 via the strong real-time bus, and collects the current position information of the first sensor 14. After PID calculation in the position loop, it sends speed control commands to the first real-time drive module 12. The real-time drive module 12 controls the movement of the first motor load 13 through the speed loop and current loop. The power IGBT switching frequency of the real-time drive module is typically 5KHz-10KHz, and the current loop features high processing frequency, short cycle, and fast response speed. The current loop is required to receive adjustment parameters with a cycle period of 5KHz-10KHz, an interface data transmission cycle period of 0.1ms-0.2ms, and a calculation processing and data transmission interface capability of up to 0.1ms. The first real-time servo module 11 and the first real-time drive module 12 respectively send the current position, speed, current, and voltage status parameters to the learning management module 22 of the monitoring and management unit 2 via the strong real-time bus, and receive the self-learning servo control adjustment parameters from the learning management module 22.

[0046] The robust real-time bus adopts the EtherCAT real-time industrial Ethernet bus, with a linear topology master-slave structure and a data transmission cycle of less than 1ms. The monitoring and management unit 2 uses an embedded industrial control integrated PC (IPC) as the real-time bus master, connected to the real-time bus via a standard Ethernet interface 1023. It actively sends control commands and EtherCAT data frames for adjusting parameters. The real-time servo module 11 and real-time drive module 12 act as bus slaves, each with two EtherCAT real-time bus interfaces 101 (input and output). They read EtherCAT data frames at high speed through the input interface, dynamically process the inserted status data, and output it to the next bus slave node.

[0047] A state parameter self-learning model algorithm is applied to the aforementioned state parameter self-learning servo system. The algorithm includes the following steps:

[0048] Step 1: Establish a self-learning model for state parameters: Based on the traditional three-loop control model, establish an inertia disturbance parameter model and a motor state parameter model respectively.

[0049] Step 2: Adjusting Control Parameters for the Controlled Object Model: Based on the established self-learning model of state parameters and the calculated state data, obtain the controlled object model of the servo system. Establish performance evaluation indicators such as stability, speed, and responsiveness of the servo system, adjust the PI control parameters of the controllers corresponding to the position loop, velocity loop, and current loop of the controlled object model, and test whether the servo system output meets the optimal performance indicators.

[0050] In step one, the three loops refer to the position loop, velocity loop, and current loop. Step one includes: Step S11: Establishing the inertia disturbance parameter model:

[0051] The influence of any one or more load disturbance torques in high sea state swaying, impact, and vibration, as well as any one or more nonlinear factors in friction torque, transmission chain idling, and load torque, are combined and integrated into disturbance d, and then the motion equation of the servo system inertia load is established.

[0052] Input to obtain the motor q-axis current i in real time q The operating status data, composed of the feedback rotational speed ω, is used to calculate the moment of inertia J and the disturbance torque T using a corresponding learning algorithm. d The estimated value.

[0053] Step one includes: Step S12: Establishing the motor state parameter model requires using a motor state equation model, inputting the voltages (u) of the motor's d-axis and q-axis. d u q ) and current (i d i q ), and feedback speed ω operating status data, to calculate any one or more motor state parameters among flux linkage, resistance R and inductance L.

[0054] By setting up the aforementioned method, it becomes possible to analyze the data required for the self-learning of state parameters in a servo system, including the data volume and data transmission cycle. It also addresses whether the determined servo system control structure, hardware, and communication interface support the required data volume and data transmission cycle for state parameter self-learning. Furthermore, it enables the servo system to use the servo control unit 1 as the controlled object, with the real-time servo module 11, real-time drive module 12, motor, and load forming the actual controlled object's position loop, speed loop, and current loop. The learning management module 22 of the monitoring and management unit 2 deploys the self-learning model of the controlled object's state parameters, including the inertia disturbance parameter model and the motor state parameter model. The controlled object model solves for the controlled object's state parameters by collecting actual controlled object data, and optimizes and adjusts the control parameters of the position loop, speed loop, and current loop based on the performance evaluation output of the servo system test.

[0055] Specifically, step S11 includes:

[0056] Step S111: Construct the inertia disturbance parameter model of the controlled object based on the dynamic model.

[0057] Step S112: Combine any one or more of the load torque and disturbance torque as the integrated disturbance torque T. d And simplify the inertia disturbance parameter model.

[0058] Step S113: Construct state equations for different angular velocities.

[0059] Step S114: The inertia disturbance parameter model collects the actual rotational speed ω and i of the controlled object's servo control unit 1 based on the state equations for different angular velocities. q Value, current i q The electromagnetic torque T is obtained by combining the value and the torque coefficient. e The model parameter learning and identification methods, such as extended sliding mode observer and variable period least squares method, are adopted, and the learning data ω and i are input. q The value can be used to obtain the estimated values ​​of each state equation within the state equation for different angular velocities. This leads to the inertia value J. This refers to the estimated value of the ideal expected output value r of the feedback system. This refers to the disturbance torque T. d The estimated value.

[0060] In step S111, the inertia disturbance parameter model is as follows: ;

[0061] In the formula: J is the moment of inertia, T e For electromagnetic torque, T L Load torque, T Ld For load disturbance torque, T c sign(ω) m Nonlinear factors The product of the system's moment of inertia and the motor's acceleration is given by ω. m This represents the angular velocity of the motor. It refers to the differential point of angular velocity, representing the angular acceleration of the motor.

[0062] In step S112, the simplified inertia disturbance parameter model is as follows: .

[0063] In step S113, the state equations for different angular velocities are:

[0064] ;

[0065] In the formula: r = 1 / J, d1 and d2 are r and T respectively. d The time derivative. This refers to the motor's angular velocity ω m1 The differential point corresponds to the angular acceleration of the motor; This refers to the motor's angular velocity ω m2 The differential point corresponds to the angular acceleration of the motor; This refers to the differential point of the integrated disturbance torque; T e1 This refers to the motor's angular velocity ω m1 At that time, the corresponding electromagnetic torque; T e2 This refers to the motor's angular velocity ω m2 At that time, the corresponding electromagnetic torque; It is the differential of r.

[0066] Step S12 includes:

[0067] Step S121: Construct a motor state parameter model using the motor state equation model;

[0068] Step S122: Based on the motor state parameter model, collect the actual speed ω and current i of the controlled object servo control unit 1. d i q Value, and voltage u d u q The value is calculated, and the parameter motor inductance L is solved separately. s Motor resistance R s and motor flux linkage ψ f The state estimate.

[0069] In step S121, the formula for the motor state parameter model is:

[0070] ;

[0071] In the formula: u d u q For the d-axis and q-axis voltages of the stator, i d i q L represents the d-axis and q-axis currents of the stator. s For stator inductance, R s P is the stator resistance. n It is an extreme logarithm.

[0072] By setting the steps in Step One, a self-learning model of state parameters can be established, laying the groundwork for adjusting the control parameters of the controlled object model in Step Two. This helps improve the accuracy and reliability of system operation.

[0073] Step two includes:

[0074] Step S21: Obtain the controlled object model of the servo system based on the self-learning model of the state parameters and the calculated state parameters.

[0075] Step S22: The controller parameters of the current loop, speed loop and position loop are tuned using the three frequency domain evaluation indicators corresponding to stability, speed and responsiveness: phase margin, open-loop cutoff frequency and closed-loop bandwidth.

[0076] By combining steps one and two, the problem of traditional servo systems having poor adaptability in complex electromechanical systems and complex environments, and being unable to adaptively adjust the control parameters of the servo system, can be solved, thereby improving the high precision and intelligent control level of equipment.

[0077] Specifically, taking speed loop tuning as an example, the speed loop tuning model has the current loop closed loop as the inner loop and consists of a motor model, a PI controller, and a filter. When performing speed loop tuning, the current loop model can be considered as 1, and the filtering stage can be ignored to obtain the speed loop tuning model.

[0078] like Figure 4 As shown, the transfer function G of the PI controller in the speed loop model ps Open-loop transfer function G of velocity loop so and closed-loop transfer function G sc They are respectively:

[0079] ;

[0080] ;

[0081] In the formula, k ps k is the proportional gain parameter of the PI controller within the transfer function. is For the integral gain parameter of the PI controller within the transfer function; K t 1 / Js is the torque current coefficient; 1 / Js is the integral element of the transfer function corresponding to the motor and load, J is the system moment of inertia, where 1 / s is the integration factor; and s is the variable.

[0082] Based on the open-loop transfer function G so List the formulas for phase margin and open-loop cutoff frequency, and set the phase margin γ. s and open-loop cutoff frequency ω so The values ​​are as follows. The formulas for phase margin and open-loop cutoff frequency are as follows:

[0083] ;

[0084] In the formula, K ps K represents the proportional gain parameter of the PI controller within the frequency formula. is Here, j represents the integral gain parameter of the PI controller within the frequency formula; j is the imaginary unit.

[0085] After simplifying the listed formulas for phase margin and open-loop cutoff frequency, we obtain the simplified formulas as follows:

[0086] ;

[0087] In the formula, u takes the value tan(γ). s ).

[0088] Based on the velocity loop closed-loop function G sc The formula for the magnitude of the closed-loop function is as follows:

[0089] .

[0090] Substituting the simplified formula into the formula for the magnitude of the closed-loop function, we obtain the closed-loop bandwidth ω. sc With open-loop cutoff frequency ω so formula:

[0091] .

[0092] The closed-loop bandwidth ω sc With open-loop cutoff frequency ω so Substituting the formulas into the position margin and open-loop cutoff frequency formulas, we obtain the speed loop PI controller adjustment parameter K. is K ps Phase margin γ with performance evaluation index s and closed-loop bandwidth ω sc The formula is given by γ, where u is determined by the phase margin γ. s Determine, simultaneously determine, the inertia J and the torque current coefficient K t The state parameters of the controlled object model are obtained from step one.

[0093] Speed ​​loop PI controller adjustment parameter K is K ps Phase margin γ with performance evaluation index s and closed-loop bandwidth ω sc The formula is:

[0094] ;

[0095] In the formula, ψ is .

[0096] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A state parameter self-learning follower system, characterized in that, The system consists of a follow-up control unit (1) and a monitoring and management unit (2); the follow-up control unit (1) and the monitoring and management unit (2) are connected through a strong real-time bus; the follow-up control unit (1) is used to send servo control data and status data of the follow-up system in real time; the monitoring and management unit (2) is used to collect status data and parameter algorithm self-learning, and the monitoring and management unit (2) deploys self-learning model algorithm, collects the status data of servo control of the follow-up control unit (1) in real time, calculates the servo control adjustment parameters, sends them to the follow-up control unit (1), and realizes the adaptive adjustment of the follow-up system.

2. The state parameter self-learning follow-up system according to claim 1, characterized in that, The high-real-time bus uses a programmable logic controller (FPGA) to implement a custom communication protocol.

3. The state parameter self-learning follow-up system according to claim 1, characterized in that, The follow-up control unit (1) consists of a real-time servo module (11), a real-time drive module (12), and a motor load (13). One end of the real-time servo module (11) and the real-time drive module (12) are connected to the monitoring and management unit (2) via a strong real-time bus. The other end of the real-time servo module (11) is connected to the real-time drive module (12), and the other end of the real-time servo module (11) is connected to the sensor (14). The other end of the real-time drive module (12) is connected to the motor load (13).

4. The state parameter self-learning follow-up system according to claim 3, characterized in that, The real-time servo module (11), real-time drive module (12), motor load (13), and sensor (14) are each configured with n units, where n is a positive integer.

5. A state parameter self-learning follow-up system according to claim 4, characterized in that, Each of the real-time servo module (11), real-time drive module (12), motor load (13), and sensor (14) constitutes a subsystem; the subsystems are connected to each other via a real-time bus.

6. A state parameter self-learning follow-up system according to claim 3, characterized in that, The real-time servo module (11) and the real-time drive module (12) both adopt embedded controller MCU and programmable controller FPGA. The controller MCU is connected to the programmable controller FPGA, and the controller MCU and programmable controller FPGA are integrated to form the main control chip (100).

7. A state parameter self-learning follow-up system according to claim 6, characterized in that, The real-time servo module (11) and the real-time drive module (12) are each configured with a real-time bus interface (101) and a general function interface (102); the real-time bus interface (101) and the general function interface (102) are both connected to the main control chip (100) for communication.

8. A state parameter self-learning follow-up system according to claim 3, characterized in that, The monitoring and management unit (2) includes a display control module (21) and a learning management module (22); the display control module (21) is connected to the real-time servo module (11) in real time via a strong real-time bus; the learning management module (22) is connected to the real-time drive module (12) in real time via a strong real-time bus.

9. A state parameter self-learning model algorithm, characterized in that, The algorithm is applied to a state parameter self-learning servo system according to any one of claims 1-8; the algorithm includes the following steps: Step 1: Establish a self-learning model for state parameters: Establish the inertia disturbance parameter model and the motor state parameter model respectively; Step 2: Adjusting Control Parameters for the Controlled Object Model: Based on the established self-learning model of state parameters and the calculated state data, obtain the controlled object model of the servo system; establish performance evaluation indicators such as stability, speed and responsiveness of the servo system, adjust the PI control parameters of the controllers corresponding to the position loop, velocity loop and current loop of the controlled object model, and test whether the output of the servo system meets the optimal performance indicators.

10. The state parameter self-learning model algorithm according to claim 9, characterized in that, Step one includes: Step S11: Establish the inertia disturbance parameter model: The influence of any one or more load disturbance torques in high sea state swaying, impact, and vibration, as well as any one or more nonlinear factors in friction torque, transmission chain idling, and load torque, are combined and integrated into disturbance d, and then the motion equation of the servo system inertial load is established. Input to obtain the motor q-axis current i in real time q The operating status data, composed of the feedback rotational speed ω, is used to calculate the moment of inertia J and the disturbance torque T using a corresponding learning algorithm. d The estimated value.

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