A system-integrated brushless actuator and intelligent control method of motion parameters thereof

Abstract

The present invention relates to the field of brushless push rod technology, and specifically to a system-integrated brushless push rod and a motion parameter intelligent control method thereof, comprising: a brushless motor push rod module, which uses a brushless motor as a drive, and converts the rotational motion of the brushless motor into the linear motion of the push rod through a transmission mechanism; an intelligent controller module, which includes a motion controller and a motor driver, the motion controller sends a control signal, and the motor driver drives the power output of the brushless motor according to the control signal; a sensor module, which includes a position sensor, which is used to monitor the linear displacement of the push rod or the rotation angle of the brushless motor, and feeds back the monitoring results to the motion controller. The system-integrated brushless push rod device in the present invention is smaller in size, can be embedded in a structure to ensure aesthetics, reduce wiring, effectively reduce the difficulty of assembly and maintenance, can quickly adjust the position according to user requirements, improve the operating experience, and reduce noise in the use scenario.

Description

Technical Field

[0001] The present invention relates to the technical field of brushless push rods, and in particular to a system-integrated brushless push rod and a motion parameter intelligent control method thereof. Background Art

[0002] At present, there is a wide range of application demands for linear actuators in the home furnishing industry, such as electric sofas, adjustable seats and other fields. Such products usually need to have flexible posture adjustment functions to meet the comfort needs of users in different usage scenarios. For example, electric sofas need to be able to adjust multiple positions such as backrest angle and leg support height to ensure that users can adjust their posture comfortably.

[0003] Traditional linear actuators usually use brushed motors or low-precision stepper motors as drivers, and convert the motor's rotational motion into linear motion of the actuator through a mechanical transmission mechanism. In the design of such linear actuators, due to the limitations of the motor's own structural characteristics and control methods, position errors, vibrations or noise are often easily generated during the movement process, thus affecting the product's user experience and performance. The control method of traditional linear actuators usually adopts simple open-loop control or low-precision closed-loop control. Under different working conditions used by different users, it is difficult for traditional actuators to achieve accurate and smooth adjustment, resulting in slow system response speed and poor accuracy.

[0004] The information disclosed in this background technology section is only intended to deepen the understanding of the overall background technology of the present invention, and should not be regarded as acknowledging or suggesting in any form that the information constitutes the prior art already known to those skilled in the art. Summary of the invention

[0005] The present invention provides a system-integrated brushless push rod and a motion parameter intelligent control method thereof, thereby effectively solving the problems pointed out in the background technology.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is:

[0007] A system-integrated brushless actuator, comprising:

[0008] The brushless motor push rod module uses a brushless motor as a drive and converts the rotational motion of the brushless motor into the linear motion of the push rod through a transmission mechanism;

[0009] An intelligent controller module, comprising a motion controller and a motor driver, wherein the motion controller sends a control signal, and the motor driver drives the power output of the brushless motor according to the control signal;

[0010] The sensor module includes a position sensor, which is used to monitor the linear displacement of the push rod or the rotation angle of the brushless motor and feed back the monitoring result to the motion controller.

[0011] Furthermore, the sensor module also includes a speed sensor for monitoring the movement speed of the push rod and feeding back a speed signal to the motion controller.

[0012] Furthermore, the motion controller adopts a position and speed dual closed-loop control algorithm, including a position control loop and a speed control loop that are nested and both adopt PID control;

[0013] The position control loop converts the position error into a speed command, and transmits the speed command to the speed control loop for execution;

[0014] The speed control loop converts the speed error into the control signal.

[0015] Furthermore, the PID control formula of the position control loop is as follows:

[0016]

[0017] Among them, u(t) is the output speed command, Kp(t) is the proportional gain, K i (t) is the integral gain, K d (t) is the differential gain, K p (t)·e(t) is a proportional control term, which generates a speed command output proportional to the error, K i (t) ∫0 t e(τ)dτ is the integral control term, which generates a speed command output proportional to the accumulated error. It is a differential control term, which generates a speed command output proportional to the error change trend;

[0018] A 输出 (t) is the adaptive control output, A 输出 (t) = K*Ga*f(system state), where K is the dimension adjustment coefficient, the unit is the same as the unit of the speed command, Ga is the adaptive gain coefficient, and f(system state) is an adaptive function calculated according to the real-time working conditions of the system, which include load and friction; the proportional gain, integral gain and differential gain are calculated according to A 输出 (t) Adjustment.

[0019] Furthermore, the method for determining the adaptive gain coefficient Ga includes:

[0020] Setting different working conditions, and testing each working condition to obtain different adaptive gain coefficients;

[0021] Constructing an adaptive gain coefficient table;

[0022] According to the current working conditions, the corresponding adaptive gain coefficient Ga is obtained from the adaptive gain coefficient table.

[0023] Further, obtaining a corresponding adaptive gain coefficient Ga in the adaptive gain coefficient table according to the current working condition includes:

[0024] A1: Determine whether there is a working condition that completely corresponds to the current working condition in the adaptive gain coefficient table. If so, directly select the corresponding adaptive gain coefficient G a , if not, proceed to step A2;

[0025] A2: Determine four points distributed in a quadrilateral in the gain coefficient table, the quadrilateral includes the current working condition inside, and obtain the corresponding Ga, load and friction for each point, the quadrilateral is a rectangle or a parallelogram;

[0026] A3: Interpolate Ga in the load direction 1-2 and Ga 3-4 , the formula is as follows:

[0027] Ga 1-2 =Ga1+(Ga2-Ga1)*(L-L1) / (L2-L1); Ga 3-4 =Ga3+(Ga4-Ga3)*(L-L3) / (L4-L3);

[0028] Wherein, L is the current load, L1, L2, L3 and L4 are the loads corresponding to the four points respectively, L1 and L2 are arranged from small to large and have the same corresponding friction coefficient value, L3 and L4 are arranged from small to large and have the same corresponding friction coefficient value, Ga1, Ga2, Ga3 and Ga4 are the adaptive gain coefficients corresponding to the four points respectively;

[0029] A4: Calculate the corresponding adaptive gain coefficient value, the formula is as follows:

[0030] Ga=Ga 1-2 +(Ga 3-4 -Ga 1-2 )*( μ -μ1) / ( μ3 -μ1);

[0031] in, μ is the current friction coefficient value, μ1 and μ3 They are the friction coefficient values ​​corresponding to L1 and L3, respectively, and are arranged from small to large.

[0032] Furthermore, the formula of the adaptive function is:

[0033] f(system state)=α1*L norm +α2*μnorm ;

[0034] Among them, L norm and μ norm are respectively the normalized current load and current friction coefficient values, α1 and α2 are respectively the load weight coefficient and friction weight coefficient, and the determination methods of α1 and α2 include:

[0035] Collect experimental data under different loads and friction coefficient values, and record position error, velocity error, and response time;

[0036] Define the objective function J(α1,α2) about α1 and α2 according to the experimental data;

[0037] Use the least squares method to solve the objective function J(α1,α2) and find the optimal α1 and α2.

[0038] Further, the proportional gain, integral gain and differential gain are based on A 输出 (t) Regulation, specifically:

[0039] K p (t) = K p,0 +β p ·A 输出 (t)

[0040] K i (t) = K i，0 +β i ·A 输出 (t)

[0041] K d (t) = K d，0 +β d ·A 输出 (t);

[0042] Among them, K p,0 , K i,0 , K d,0 The initial proportional, integral and differential gain values ​​set for the system; β p , β i , β d is the sensitivity coefficient of proportional, integral and differential gain to adaptive gain, which is determined by:

[0043] Definition about β p , β i , β d The objective function J(β p , β i , β d )for:

[0044]

[0045] ΔK p (t) = β p ·A 输出 (t)

[0046] ΔK i (t) = β i ·A 输出 (t)

[0047] ΔK d (t) = β d ·A 输出 (t);

[0048] Among them, e p (t) is the position error, e v (t) is the position error, λ p ,λ i ,λ d is the penalty factor, which is used to balance the weight between error and gain change and is set by the system;

[0049] Use the least squares method to solve the objective function J(β p , β i , β d ), find the optimal sensitivity coefficient combination β p , β i , β d .

[0050] Furthermore, the motor driver drives the power output of the brushless motor according to the control signal, including:

[0051] The motor driver adopts an electronic commutation circuit, which measures the stator voltage and current, estimates the amplitude and phase of the back electromotive force, derives the angle of the rotor to determine the current rotor position of the brushless motor, and energizes the stator coil according to the set commutation sequence to form a rotating magnetic field to drive the rotor to rotate.

[0052] A method for intelligently controlling motion parameters of a system-integrated brushless actuator as described above comprises:

[0053] The speed sensor monitors the movement speed of the push rod in real time, and feeds the monitoring result back to the motion controller;

[0054] And, monitoring the linear displacement of the push rod or the rotation angle of the brushless motor in real time through the position sensor, and feeding back the monitoring result to the motion controller;

[0055] The motion controller calculates the real-time position error and speed error respectively, and converts the position error into a speed command through a position control loop, and transmits the speed command to the speed control loop for execution, and converts the speed error into the control signal through the speed control loop;

[0056] The control signal is fed back to the motor driver to drive the brushless motor to rotate a required angle, and the required angle is converted into a corresponding linear displacement of the push rod.

[0057] The technical solution of the present invention can achieve the following technical effects:

[0058] The system-integrated brushless actuator in the present invention integrates core components such as brushless motors, transmission mechanisms, sensors, controllers, and drivers into a modular system, thereby making the device smaller in size. When used in the home furnishing industry, it can be embedded in structures such as sofas to ensure aesthetics, meet the requirements of lightweight and compact design, reduce wiring, and effectively reduce the difficulty of assembly and maintenance.

[0059] Through its high-precision rotation control, the brushless motor can make the push rod perform linear motion quickly and smoothly, thereby achieving rapid and accurate product posture adjustment. After sending the command, the brushless motor responds immediately and quickly drives the screw and other transmission mechanisms to make adjustments, ensuring that the applied home products can be quickly adjusted to the required position according to the user's requirements, improving the operating experience and reducing noise in the use scenario. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0061] Figure 1 This is a frame diagram of a system-integrated brushless actuator;

[0062] Figure 2 Optimized frame diagram for system-integrated brushless actuator;

[0063] Figure 3 It is the framework diagram of the double closed-loop control algorithm;

[0064] Figure 4 is a flow chart of a method for determining an adaptive gain coefficient Ga;

[0065] Figure 5 The present invention is a flow chart of the intelligent control method of motion parameters of a system-integrated brushless actuator. DETAILED DESCRIPTION

[0066] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0067] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items.

[0068] Embodiment 1

[0069] like Figure 1 As shown, a system-integrated brushless push rod includes: a brushless motor push rod module, which uses a brushless motor as a drive and converts the rotational motion of the brushless motor into linear motion of the push rod through a transmission mechanism; an intelligent controller module, which includes a motion controller and a motor driver, the motion controller sends a control signal, and the motor driver drives the power output of the brushless motor according to the control signal; a sensor module, which includes a position sensor, which is used to monitor the linear displacement of the push rod or the rotation angle of the brushless motor, and feed back the monitoring result to the motion controller.

[0070] In this embodiment, the brushless motor may specifically include a stator, a rotor and a built-in Hall sensor or a sensorless algorithm for commutation. Compared with the traditional brushed motor, the brushless motor has higher efficiency, longer service life and better control accuracy. The rated power, rated voltage and torque output capacity of the brushless motor are matched according to the load capacity of the push rod in this embodiment; during the design process, the transmission mechanism can specifically adopt a screw transmission mechanism, and the output speed and thrust of the push rod are controlled by adjusting the pitch of the screw and the reduction ratio of the motor.

[0071] The system-integrated brushless actuator in this embodiment integrates core components such as brushless motors, transmission mechanisms, sensors, controllers, and drivers into a modular system, thereby making the device smaller. Taking the application in electric sofas as an example, it can be embedded in the structure of the sofa to ensure aesthetics, meet the requirements of lightweight and compact design, reduce wiring, and effectively reduce the difficulty of assembly and maintenance.

[0072] Taking the application scenario of electric sofas as an example, when the user adjusts the posture of the sofa, the brushless motor can make the sofa push rod move linearly quickly and smoothly through its high-precision rotation control, thereby achieving rapid and accurate posture adjustment; after sending instructions through the remote control or other control device, the brushless motor responds immediately and quickly drives the screw and other transmission mechanisms to make adjustments, ensuring that the electric sofa can be quickly adjusted to the desired position according to the user's requirements, improving the operating experience and reducing noise in the usage scenario.

[0073] In the specific use process, the speed of the push rod may be affected by factors such as load changes and friction, resulting in speed errors. As a preferred embodiment of the above, Figure 2 As shown, the sensor module also includes a speed sensor for monitoring the movement speed of the push rod and feeding back the speed signal to the motion controller. In this preferred solution, by adding a speed sensor, the system can monitor the movement speed of the push rod in real time and feed it back to the motion controller, thereby realizing speed closed-loop control. The speed sensor can help the system accurately perceive the deviation between the actual speed and the set speed. The motion controller makes precise adjustments based on the feedback speed signal to ensure that the push rod runs smoothly at the desired speed; the application of the speed sensor can ensure that the push rod remains stable during acceleration and deceleration to avoid sudden speed changes.

[0074] As a preferred embodiment of the above, Figure 3 As shown, the motion controller adopts a position and speed dual closed-loop control algorithm, including a nested position control loop and a speed control loop both of which use PID control; the position control loop converts the position error into a speed command, and transmits the speed command to the speed control loop for execution; the speed control loop converts the speed error into a control signal.

[0075] In this preferred embodiment, through a two-stage closed-loop system, the movement of the push rod can be controlled more accurately and smoothly to ensure that the final target position and the speed of the movement process are in line with expectations; specifically, the task of the position control loop is to calculate the position error based on the given position command and the actual position feedback of the push rod. The position error represents the deviation between the current actual position of the push rod and the target position. In order to correct this deviation, the position control loop will convert the error into a speed command and pass the speed command to the speed control loop; the position control loop focuses on the final position of the push rod. No matter how the speed of the push rod fluctuates during the movement of the push rod, the purpose of the position control loop is to ensure that the push rod can eventually reach the target position accurately. The motion trajectory of the push rod can be continuously adjusted through position feedback until the position error is minimized. The output of the position control loop is a dynamic speed command, which does not directly control the final action of the push rod, but provides the target speed for the speed control loop.

[0076] The task of the speed control loop is to ensure that the push rod runs at the target speed based on the speed command from the position control loop. The speed control loop will compare the difference between the target speed and the actual speed and calculate the speed error. This speed error is used to generate a control signal for the brushless motor to adjust the drive current of the motor. The focus of the speed control loop is to ensure that the push rod maintains a stable and smooth speed during movement. Even if the load changes or the external environment interferes, the speed control loop maintains the dynamic response of the system through feedback adjustment to avoid too fast or too slow situations. By adjusting the speed error, the system can correct the speed output of the motor in real time to ensure that the movement of the push rod is smooth and smooth. The control signal output by the speed control loop is often the motor drive signal, which directly affects the torque and speed of the motor, and ultimately controls the linear motion of the push rod.

[0077] As a preferred embodiment of the above, the PID control formula of the position control loop is as follows:

[0078]

[0079] Among them, u(t) is the output speed command, K p (t) is the proportional gain, K i (t) is the integral gain, K d (t) is the differential gain, K p (t)·e(t) is a proportional control term, which generates a speed command output proportional to the error, K i (t) ∫0 t e(τ)dτ is the integral control term, which generates a speed command output proportional to the accumulated error. It is a differential control term, which generates a speed command output proportional to the error change trend;

[0080] A 输出 (t) is the adaptive control output, A 输出 (t) = K*Ga*f(system state), where K is the dimension adjustment coefficient, and the unit is the same as the speed command unit, Ga is the adaptive gain coefficient, and f(system state) is an adaptive function calculated according to the real-time working conditions of the system, including load and friction; the proportional gain, integral gain and differential gain are calculated according to A 输出 (t) Adjustment.

[0081] In this preferred solution, adaptive control output is introduced to enhance the traditional PID control, solving the problem that fixed parameters cannot cope with the load changes and friction changes that occur during system operation in actual applications, thereby improving the robustness of the system and improving the control accuracy. f (system state) can accurately reflect the dynamic characteristics of the system by calculating the system load and friction in real time, avoiding the inability of traditional PID control to accurately consider the impact of load and friction on the system, and adjusting the control strategy according to the real-time system state, so that the controller can maintain a good control effect under various load and friction conditions, avoiding the phenomenon of system overshoot, undershoot or oscillation caused by a single parameter setting in traditional PID control, thereby improving the accuracy and stability of the system, so that the control system is always in the optimal control state, and achieving better control performance.

[0082] In different usage scenarios, such as the weight borne by the electric sofa and the resistance encountered when moving, the load and friction conditions may vary greatly. In this preferred solution, the complex load and friction model is selected to enable the control system to effectively control the movement of the push rod under different conditions; in this preferred solution, by A 输出 (t) can be adjusted directly, which is equivalent to adding compensation items on the basis of PID control to deal with dynamic errors caused by changes in load and friction in actual working conditions, reducing the hysteresis effect caused by relying on integral item adjustment in traditional PID control. In complex usage scenarios, errors under specific conditions can be quickly corrected without changing the overall control strategy. Changes in load and friction are often nonlinear in actual applications. Through A 输出 (t) It can compensate for the system deviation caused by nonlinearity, making the system response more in line with expectations.

[0083] The proportional gain, integral gain and differential gain are based on A 输出 (t) adjustment can make the parameters of the PID controller no longer fixed, but adjusted in real time according to the system status, so as to achieve optimal control under different working conditions. When the load and friction force change greatly, the dynamic adjustment of the PID controller parameters can avoid the system instability caused by parameter mismatch in traditional PID control and improve the system stability. This indirect adjustment method can make the system respond quickly when facing large errors, make the error converge faster, and improve the dynamic performance of the overall system.

[0084] In summary, in this preferred solution, direct regulation is used to quickly respond to external changes, and indirect regulation is used to adjust control parameters in the long term. The combination of the two regulation methods enables the system to be dynamically adjusted under different time scales and conditions, thereby improving the adaptability and flexibility of the system. Through the synergistic effect of the two regulation methods, the system can remain stable in both the short and long term, and improve control accuracy, which can better cope with control requirements under complex working conditions.

[0085] As a preferred embodiment of the above, Figure 4 As shown, the method for determining the adaptive gain coefficient Ga includes:

[0086] Different working conditions are set, and different adaptive gain coefficients are obtained for each working condition test; an adaptive gain coefficient table is constructed; and the corresponding adaptive gain coefficient Ga is obtained from the adaptive gain coefficient table according to the current working condition.

[0087] During the implementation process, different working conditions are set up for testing according to various working conditions that the control system may face, such as load changes, friction changes, etc.; under each working condition, the corresponding optimal adaptive gain coefficient Ga is obtained through experiments or simulation tests. This coefficient can be adjusted according to the characteristics of each working condition to ensure that the system has the best control performance under this working condition.

[0088] As a further optimization method, the corresponding adaptive gain coefficient Ga is obtained from the adaptive gain coefficient table according to the current working conditions, including:

[0089] A1: Determine whether there is a working condition in the adaptive gain coefficient table that completely corresponds to the current working condition. If so, directly select the corresponding adaptive gain coefficient Ga, which helps to reduce the amount of calculation. If not, execute step A2;

[0090] A2: Determine four points distributed in a quadrilateral in the gain coefficient table. The quadrilateral includes the current working condition inside. Obtain the corresponding Ga, load and friction for each point. The quadrilateral is a rectangle or a parallelogram. In the implementation process, it is preferred to reduce the area of ​​the quadrilateral to the minimum while meeting the above requirements.

[0091] A3: Interpolate Ga in the load direction 1-2 and Ga 3-4 , the formula is as follows:

[0092] Ga 1-2 =Ga1+(Ga2-Ga1)*(L-L1) / (L2-L1); Ga 3-4 =Ga3+(Ga4-Ga3)*(L-L3) / (L4-L3);

[0093] These two coefficients are the linear interpolation results along the load direction, preparing for the subsequent interpolation calculation in the friction direction, where L is the current load, L1, L2, L3 and L4 are the loads corresponding to the four points, L1 and L2 are arranged from small to large and have the same corresponding friction coefficient values, L3 and L4 are arranged from small to large and have the same corresponding friction coefficient values, Ga1, Ga2, Ga3 and Ga4 are the adaptive gain coefficients corresponding to the four points;

[0094] A4: Calculate the corresponding adaptive gain coefficient value, the formula is as follows:

[0095] Ga=Ga 1-2 +(Ga 3-4 -Ga 1-2 )*(μ-μ1) / (μ3-μ1);

[0096] Among them, μ is the current friction coefficient value, μ1 and μ3 are the friction coefficient values ​​corresponding to L1 and L3 respectively, and they are arranged from small to large. This interpolation method can take both load and friction factors into account at the same time to obtain a more accurate Ga.

[0097] This preferred solution introduces a method for dynamically obtaining the adaptive gain coefficient Ga according to the current working condition, and through linear interpolation calculation, the optimal gain coefficient under the current working condition can be accurately obtained when the load and friction force change, which can effectively reduce the position error and speed error of the push rod during the actual movement process. When the load and friction force change, the traditional fixed gain control strategy is often difficult to cope with, and the Ga obtained by interpolation calculation can timely compensate for the system error caused by the load change and friction force change, so that the push rod can more accurately follow the target position and speed.

[0098] In this preferred solution, the difference method can achieve a smooth transition of the adaptive gain coefficient between different working conditions, reducing the problem of control parameter jumps caused by small changes in load or friction coefficient. This smooth transition can reduce the possibility of system oscillation, thereby improving the overall stability of the control system. In addition, this method enables the system to achieve precise control even when the density of midpoints in the adaptive gain coefficient table is low. This is because the interpolation method can make inferences between two points, reducing the control error caused by the lack of density of midpoints in the gain coefficient table. In the application scenario of brushless push rods, the relationship between load and friction coefficient may be nonlinear, and the interpolation method can achieve a smooth transition between nonlinear data, thereby improving the performance of the system in nonlinear control.

[0099] As a preferred embodiment of the above embodiment, the formula of the adaptive function is:

[0100] f(system state)=α1*L norm +α2*μ norm ;

[0101] Among them, L norm and μ norm are respectively the normalized current load and current friction coefficient values, α1 and α2 are respectively the load weight coefficient and friction weight coefficient, and the determination methods of α1 and α2 include:

[0102] Collect experimental data under different loads and friction coefficient values, and record position error, velocity error, and response time;

[0103] Define the objective function J(α1,α2) about α1 and α2 according to the experimental data;

[0104] Use the least squares method to solve the objective function J(α1,α2) and find the optimal α1 and α2.

[0105] In the above preferred scheme, the influence ratios of load and friction coefficient on system performance are controlled respectively, so as to adjust the system more flexibly. The objective function is constructed using experimental data, which can truly reflect the performance of the system under different loads and friction coefficients, and help to find the optimal weight coefficient that is more in line with practical applications. The least squares method can find the optimal solution of the weight coefficient in the experimental data, thereby effectively reducing errors and improving the overall control accuracy and stability of the system.

[0106] In the specific implementation process, the objective function is used to measure the performance indicators of the system under different weight coefficient combinations. The performance indicators in this embodiment include position error, speed error and response time. The least square method is used to optimize the weight coefficient of the adaptive function, which can significantly improve the accuracy of the model and the reflection of the system state. In the implementation process, the most important position error can be selected as the optimization target to maximize the position accuracy and ensure that it can be accurately adjusted to the user's desired position in the electric sofa application scenario; or a multi-objective optimization strategy based on position accuracy priority can be adopted. In the designed comprehensive objective function, the position error weight can be set to the maximum, and the speed error and response time weights are second, so that while ensuring the position accuracy, a smoother speed control and a reasonable response time can be obtained; the implementation of the least square method can adopt the existing method, which will not be repeated here.

[0107] As a preferred embodiment of the above, the proportional gain, the integral gain and the differential gain are calculated according to A. 输出 (t) Regulation, specifically:

[0108] K p (t) = K p，0 +β p ·A 输出 (t)

[0109] K i (t) = K i，0 +β i ·A 输出 (t)

[0110] K d (t) = K d，0 +β d .A 输出 (t);

[0111] Among them, K p,0 , K i,0 , K d,0 The initial proportional, integral and differential gain values ​​set for the system; β p , β i , β d is the sensitivity coefficient of proportional, integral and differential gain to adaptive gain, which is determined by:

[0112] Definition about β p , β i , β d The objective function J(β p , β i , β d )for:

[0113]

[0114] ΔK p (t) = β p ·A 输出 (t)

[0115] ΔK i (t) = β i ·A 输出 (t)

[0116] ΔK d (t) = β d ·A 输出 (t);

[0117] Among them, e p (t) is the position error, e v (t) is the position error, λ p ,λ i ,λ d is the penalty factor, which is used to balance the weight between error and gain change and is set by the system;

[0118] Use the least squares method to solve the objective function J(β p , β i , β d ), find the optimal sensitivity coefficient combination β p , β i , β d .

[0119] In the above preferred scheme, by adaptively adjusting the PID gain value, the system can quickly adjust the gain value when the load and friction coefficient change, thereby improving the accuracy and response speed of the system. The gain change is controlled by the penalty factor to prevent system instability caused by drastic fluctuations in the gain value, thereby improving the overall stability and robustness of the system. The optimization scheme can adjust the PID gain value in real time according to changes in the system state, thereby improving the system's adaptability to the external environment under complex working conditions, such as load changes and changes in the friction coefficient.

[0120] As a preferred embodiment of the above, the motor driver drives the power output of the brushless motor according to the control signal, including:

[0121] The motor driver uses an electronic commutation circuit, which measures the stator voltage and current, estimates the amplitude and phase of the back electromotive force, derives the angle of the rotor to determine the current rotor position of the brushless motor, and energizes the stator coil according to the set commutation sequence to form a rotating magnetic field to drive the rotor to rotate.

[0122] In this embodiment, the functions of the electronic commutation circuit specifically include:

[0123] Measuring stator voltage and current: The driver monitors the voltage and current signals on the motor stator in real time through sensors. These signals are a direct reflection of the motor's operating status and can provide necessary electrical information.

[0124] Estimation of back EMF: Back EMF is the voltage generated by electromagnetic induction when the motor rotates. Its amplitude and phase are directly related to the speed and position of the rotor. The electronic commutation circuit uses the measured stator voltage and current information to estimate the amplitude and phase of the back EMF through mathematical algorithms, such as filtering or derivation based on the measured values.

[0125] According to the derived rotor position, the electronic commutation circuit will execute a specific commutation sequence, which determines the order and timing of the stator coils to be energized in order to form a suitable rotating magnetic field. By energizing the stator coils in sequence, the generated rotating magnetic field can effectively drive the rotor to rotate. The frequency and phase of the rotating magnetic field are directly related to the operating state of the motor, and different speeds and torque outputs can be achieved by adjusting the power supply parameters. Through the above process, the motor driver can adjust the power output of the motor according to the user's control signal, so that the brushless motor can respond quickly and maintain efficient operation under different working conditions.

[0126] Embodiment 2

[0127] A method for intelligently controlling motion parameters of a brushless actuator integrated with the system as above, such as Figure 5 As shown, including:

[0128] S1: monitor the movement speed of the push rod in real time through the speed sensor, and feed the monitoring result back to the motion controller; and monitor the linear displacement of the push rod or the rotation angle of the brushless motor in real time through the position sensor, and feed the monitoring result back to the motion controller;

[0129] S2: The motion controller calculates the real-time position error and speed error respectively, and converts the position error into a speed command through the position control loop, and transmits the speed command to the speed control loop for execution, and converts the speed error into a control signal through the speed control loop;

[0130] S3: Feedback the control signal to the motor driver to drive the brushless motor to rotate the required angle, and the required angle is converted into the corresponding linear displacement of the push rod.

[0131] The technical effects achieved by this embodiment are as described in the above embodiments and will not be repeated here.

[0132] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A system-integrated brushless push rod motion parameter intelligent control method, using a brushless motor as a drive, converting the rotational motion of the brushless motor into the linear motion of the push rod through a transmission mechanism, the method comprising: The speed sensor monitors the movement speed of the push rod in real time and feeds the monitoring results back to the motion controller; and, monitoring the linear displacement of the push rod or the rotation angle of the brushless motor in real time through a position sensor, and feeding back the monitoring result to the motion controller; The motion controller calculates the real-time position error and speed error respectively. The motion controller adopts a position and speed double closed-loop control algorithm, including a position control loop and a speed control loop that are nested and both use PID control. The position error is converted into a speed command through the position control loop, and the speed command is passed to the speed control loop for execution. In addition, the speed error is converted into a control signal through the speed control loop. The control signal is fed back to the motor driver to drive the brushless motor to rotate the required angle, and the required angle is converted into the corresponding linear displacement of the push rod. The characteristics are: The PID control formula of the position control loop is as follows: ； Among them, u(t) is the output speed command, K p (t) is the proportional gain, K i (t) is the integral gain, K d (t) is the differential gain, It is a proportional control term, which generates a speed command output proportional to the error. is an integral control term, which generates a speed command output proportional to the accumulated error. It is a differential control term, which generates a speed command output proportional to the error change trend; A 输出 (t) is the adaptive control output, A 输出 (t) = K*Ga*f(system state), where K is the dimension adjustment coefficient, the unit is the same as the speed command unit, Ga is the adaptive gain coefficient, and f(system state) is an adaptive function calculated according to the real-time working conditions of the system, including load and friction; the proportional gain, integral gain and differential gain are calculated according to A 输出 (t) regulation; The formula of the adaptive function is: f(system state)=α1*L norm +α2*μ norm ; Among them, L norm and μ norm are the normalized current load coefficient value and current friction coefficient value, α1 and α2 are the load weight coefficient and friction weight coefficient, respectively; Proportional gain, integral gain and differential gain are based on A 输出 (t) Regulation, specifically: ； Among them, K p,0 , K i,0 , K d,0 The initial proportional, integral and differential gain values ​​set for the system; β p , β i , β d is the sensitivity coefficient of proportional, integral and differential gain to adaptive gain.

2. The motion parameter intelligent control method of the system-integrated brushless actuator according to claim 1, characterized in that: Methods for determining α1 and α2 include: Collect experimental data under different loads and friction coefficient values, and record position error, velocity error, and response time; Define the objective function J(α1,α2) about α1 and α2 according to the experimental data; Use the least squares method to solve the objective function J(α1,α2) and find the optimal α1 and α2; β p , β i , β d The determination method is: Definition about β p , β i , β d The objective function J (β p , β i , β d )for: ； Among them, e p (t) is the position error, e v (t) is the speed error, λ p ,λ i ,λ d is the penalty factor, which is used to balance the weight between error and gain change and is set by the system; Use the least squares method to solve the objective function J(β p , β i , β d ), find the optimal sensitivity coefficient combination β p , β i , β d .

3. The motion parameter intelligent control method of the system-integrated brushless actuator according to claim 1, characterized in that: The method for determining the adaptive gain coefficient Ga includes: Setting different working conditions, and testing each working condition to obtain different adaptive gain coefficients; Constructing an adaptive gain coefficient table; Obtaining the corresponding adaptive gain coefficient Ga in the adaptive gain coefficient table according to the current working conditions includes: A1: Determine whether there is an operating condition in the adaptive gain coefficient table that completely corresponds to the current operating condition. If so, directly select the corresponding adaptive gain coefficient Ga. If not, execute step A2; A2: Determine four points distributed in a quadrilateral in the gain coefficient table, the quadrilateral includes the current working condition inside, and obtain the corresponding Ga, load and friction for each point, the quadrilateral is a rectangle or a parallelogram; A3: Interpolate Ga in the load direction 1-2 and Ga 3-4 , the formula is as follows: <h2 style=";text-align:left;direction:ltr">Ga<h2 style=";text-align:left;direction:ltr"> 1-2 <h2 style=";text-align:left;direction:ltr"> =Ga1+(Ga2-Ga1)*(L-L1) / (L2-L1);Ga<h2 style=";text-align:left;direction:ltr"> 3-4 <h2 style=";text-align:left;direction:ltr"> =Ga3+(Ga4-Ga3)*(L-L3) / (L4-L3); Among them, L is the current load, L1, L2, L3 and L4 are the loads corresponding to the four points, L1 and L2 are arranged from small to large and have the same corresponding friction coefficient value, L3 and L4 are arranged from small to large and have the same corresponding friction coefficient value, Ga1, Ga2, Ga3 and Ga4 are the adaptive gain coefficients corresponding to the four points; A4: Calculate the corresponding adaptive gain coefficient value, the formula is as follows: Ga=Ga 1-2 +(Ga 3-4 -Ga 1-2 )*(μ-μ1) / (μ3-μ1); Among them, μ is the current friction coefficient value, μ1 and μ3 are the friction coefficient values ​​corresponding to L1 and L3 respectively, and they are arranged from small to large.

4. The motion parameter intelligent control method of the system-integrated brushless actuator according to claim 1, characterized in that: The motor driver drives the power output of the brushless motor according to the control signal, including: The motor driver uses an electronic commutation circuit, which measures the stator voltage and current, estimates the amplitude and phase of the back electromotive force, derives the angle of the rotor to determine the current rotor position of the brushless motor, and energizes the stator coil according to the set commutation sequence to form a rotating magnetic field to drive the rotor to rotate.

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