Load inertia identification method, device, electronic equipment and system

Abstract

The application provides a load inertia identification method, device, electronic equipment and system. The method comprises the following steps: obtaining an input control instruction, a motor speed and a load speed of a mechanical transmission system in a previous state; predicting a current system predicted state of the mechanical transmission system according to the input control instruction, the motor speed, the load speed and a state prediction equation of the mechanical transmission system; obtaining a current motor speed measurement value and a current load speed measurement value of the mechanical transmission system; determining a current optimal estimated state of the mechanical transmission system according to the current system predicted state, the current motor speed measurement value and the current load speed measurement value; and determining load inertia information of the mechanical transmission system in the current state according to the current optimal estimated state. The application can improve the accuracy of load inertia identification in the inertia identification process.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of automatic control, in particular to a load inertia identification method and device, electronic equipment and system. BACKGROUND

[0002] Servomechanism is a feedback control system used to accurately follow or reproduce a process. Servo motor is a kind of auxiliary motor indirect speed change device used to control the operation of mechanical elements in a servo system. Servo system mainly relies on pulses to position. When a servo motor receives a pulse, it will rotate an angle corresponding to the pulse, so as to realize displacement. The servo motor itself has the function of sending pulses, so the servo motor will send a corresponding number of pulses every time it rotates an angle. The pulses sent by the servo motor form a response or a closed loop with the pulses received by the servo motor. In this way, the servo system can know how many pulses are sent to the servo motor and how many pulses are received back at the same time, so as to control the rotation of the motor and realize positioning. During the operation of the motor, the system load torque and rotational inertia change with the change of the operating condition of the motor. In order to improve the dynamic anti-disturbance performance of the servo system, the control parameters need to be adjusted accordingly to make the operating characteristics of the servo system optimal. Therefore, the identification of rotational inertia is particularly important. SUMMARY

[0003] The purpose of the embodiments of the present application is to provide a load inertia identification method, device, electronic equipment and system, which can accurately identify the rotational inertia of the load end of the system.

[0004] The first aspect of the embodiments of the present application provides a load inertia identification method, which comprises: obtaining an input control instruction, a motor speed and a load speed of a mechanical transmission system to be measured at a previous state; predicting a current system predicted state of the mechanical transmission system according to the input control instruction, the motor speed, the load speed and a state prediction equation of the mechanical transmission system; obtaining a current motor speed measurement value and a current load speed measurement value of the mechanical transmission system; determining a current optimal estimated state of the mechanical transmission system according to the current system predicted state, the current motor speed measurement value and the current load speed measurement value; and determining load inertia information of the mechanical transmission system at the current state according to the current optimal estimated state.

[0005] In an embodiment, the method further comprises the step of establishing the state prediction equation of the mechanical transmission system: determining a state equation and an output equation of the mechanical transmission system according to double inertia parameters and a mechanical motion equation of the mechanical transmission system; and determining the state prediction equation of the mechanical transmission system according to the state equation and the output equation of the mechanical transmission system.

[0006] In one embodiment, the input control instruction is an electromagnetic torque instruction of the motor; the method further comprises determining the state equation and the output equation of the mechanical transmission system by using the following formula:

[0007]

[0008] y=Cx

[0009] wherein,

[0010] x=[ω m ω l T w T l M l ] T

[0011] y=[ω m ω l ] T

[0012] u=T e =K e ·I q

[0013]

[0014]

[0015]

[0016] wherein, k represents a period in which a previous state of the mechanical transmission system is located, x is a state variable of the mechanical transmission system, y is an output of the mechanical transmission system, ω m is a motor speed of the mechanical transmission system, ω l is a load speed of the mechanical transmission system, T w is a transmission shaft torque of the mechanical transmission system, T l is a load torque of the mechanical transmission system, M l is an inverse of a load inertia J2 of the mechanical transmission system, K e is a proportional factor, I q is a q-axis current of the motor of the mechanical transmission system, and f(x) represents a prediction function of the kth period of the mechanical transmission system, and k is an integer.

[0017] In one embodiment, the method of predicting the current system prediction state of the mechanical transmission system according to the input control instruction, the motor speed, the load speed, and the state prediction equation of the mechanical transmission system comprises:

[0018] The current system prediction state is calculated by using the following formula:

[0019]

[0020]

[0021]

[0022] wherein,

[0023]

[0024]

[0025] wherein, k represents a period in which a previous state of the mechanical transmission system is located, k+1 represents a period in which a current state of the mechanical transmission system is located, is a system state of the mechanical transmission system in the kth period, is a prediction function of the system state of the mechanical transmission system in the kth period, is a system prediction state of the mechanical transmission system in the (k+1)th period, T s is a sampling period of the mechanical transmission system, is a prediction output state of the mechanical transmission system in the (k+1)th period, u(k) is an input control instruction of the mechanical transmission system in the kth period, is a noise covariance of the mechanical transmission system in the kth period, is a prediction noise covariance of the mechanical transmission system in the (k+1)th period, Q(k) is a covariance of a system error of the mechanical transmission system in the kth period, J1 is a moment of inertia of a motor rotor of the mechanical transmission system, M l is an inverse of a load inertia J2 of the mechanical transmission system.

[0026] In an embodiment, the current optimal estimation of the mechanical transmission system is determined according to the current system prediction state, the current motor speed measurement value and the current load speed measurement value, comprising:

[0027] The Kalman gain of the mechanical transmission system is calculated by using the following formula:

[0028]

[0029] The current optimal estimation is calculated by using the following formula:

[0030]

[0031] The error mean square of the current optimal estimation is calculated by using the following formula:

[0032]

[0033] wherein k represents a period in which a previous state of the mechanical transmission system is located; k+1 represents a period in which a current state of the mechanical transmission system is located; K(k+1) represents the Kalman gain of the mechanical transmission system in the (k+1)th period; is a predicted noise covariance of the mechanical transmission system in the (k+1)th period; R(k) is a covariance of a measurement error of the mechanical transmission system in the kth period; is an optimal estimated state of the mechanical transmission system in the (k+1)th period; is a system predicted state of the mechanical transmission system in the (k+1)th period; is a predicted output state of the mechanical transmission system in the (k+1)th period; y(k+1) is an output state measurement value of the mechanical transmission system in the (k+1)th period, which is determined based on the current motor speed measurement value and the current load speed measurement value; is an optimal estimated error mean square of the mechanical transmission system in the (k+1)th period.

[0034] In an embodiment, the method further comprises: outputting the load inertia information of the mechanical transmission system.

[0035] A second aspect of the embodiments of the present application provides an inertia identification device, comprising: a first acquisition module, configured to acquire an input control instruction, a motor speed and a load speed of a mechanical transmission system to be measured in a previous state; a prediction module, configured to predict a current system predicted state of the mechanical transmission system according to the input control instruction, the motor speed, the load speed and a state prediction equation of the mechanical transmission system; a second acquisition module, configured to acquire a current motor speed measurement value and a current load speed measurement value of the mechanical transmission system; a calculation module, configured to determine a current optimal estimated state of the mechanical transmission system according to the current system predicted state, the current motor speed measurement value and the current load speed measurement value; and a determination module, configured to determine load inertia information of the mechanical transmission system in a current state according to the current optimal estimated state.

[0036] In an embodiment, the device further comprises an establishment module, configured to: determine a state equation and an output equation of the mechanical transmission system according to double inertia parameters and a mechanical motion equation of the mechanical transmission system; and determine the state prediction equation of the mechanical transmission system according to the state equation and the output equation of the mechanical transmission system.

[0037] In one embodiment, the establishing module is configured to determine the state equation and the output equation of the mechanical transmission system by using the following formulas:

[0038]

[0039] y = Cx

[0040] wherein,

[0041] x = [ω m ω l T w T l M l ] T

[0042] y = [ω m ω l ] T

[0043] u = T e = K e ·I q

[0044]

[0045]

[0046]

[0047] wherein, k represents a period in which a previous state of the mechanical transmission system is located, x is a state variable of the mechanical transmission system, y is an output of the mechanical transmission system, ω m is a motor speed of the mechanical transmission system, ω l is a load speed of the mechanical transmission system, T w is a transmission shaft torque of the mechanical transmission system, T l is a load torque of the mechanical transmission system, M l is an inverse of a load inertia J2 of the mechanical transmission system, u is an input control variable carried by the input control instruction, T e is a motor electromagnetic torque, K e is a proportional factor, I q is a q-axis current of the motor of the mechanical transmission system, and f(x) represents a prediction function of the kth period of the mechanical transmission system, and k is an integer.

[0048] In one embodiment, the prediction module is configured to calculate the current system prediction state by using the following formulas:

[0049]

[0050]

[0051]

[0052] wherein,

[0053]

[0054] wherein, k represents a period in which a previous state of the mechanical transmission system is located, k+1 represents a period in which a current state of the mechanical transmission system is located, is a system state of the mechanical transmission system in the kth period, is a prediction function of the system state of the mechanical transmission system in the kth period, is a predicted state of the mechanical transmission system in the (k+1)th period, T s is a sampling period of the mechanical transmission system, is a predicted output state of the mechanical transmission system in the (k+1)th period, u(k) is an input control instruction of the mechanical transmission system in the kth period, is a noise covariance of the mechanical transmission system in the kth period, is a predicted noise covariance of the mechanical transmission system in the (k+1)th period, Q(k) is a covariance of a system error of the mechanical transmission system in the kth period, J1 is a moment of inertia of a motor rotor of the mechanical transmission system, M l is an inverse of a load inertia J2 of the mechanical transmission system.

[0055] In an embodiment, the calculation module is configured to calculate the Kalman gain of the mechanical transmission system by using the following formula:

[0056]

[0057] The current optimal estimation is calculated by using the following formula:

[0058]

[0059] The error mean square of the current optimal estimation is calculated by using the following formula:

[0060]

[0061] wherein, k represents a period in which a previous state of the mechanical transmission system is located; k+1 represents a period in which a current state of the mechanical transmission system is located; K(k+1) represents the Kalman gain of the mechanical transmission system in the (k+1)th period; is a predicted noise covariance of the mechanical transmission system in the (k+1)th period; R(k) is a covariance of measurement errors of the mechanical transmission system in the kth period; is an optimal estimated state of the mechanical transmission system in the (k+1)th period; is a system predicted state of the mechanical transmission system in the (k+1)th period; is a predicted output state of the mechanical transmission system in the (k+1)th period; y(k+1) is an output state measurement value of the mechanical transmission system in the (k+1)th period determined based on the current motor speed measurement value and the current load speed measurement value; is an optimal estimated error mean square of the mechanical transmission system in the (k+1)th period.

[0062] In an embodiment, the apparatus further includes an output module configured to output the load inertia information of the mechanical transmission system.

[0063] A third aspect of an embodiment of the present application provides an electronic device, including a memory configured to store a computer program, and a processor configured to execute the computer program to implement the method of the first aspect of the present application or any of the embodiments thereof.

[0064] A fourth aspect of an embodiment of the present application provides a load inertia identification system, including a mechanical transmission system, a servo driver connected to the mechanical transmission system and configured to drive the mechanical transmission system to operate, and an inertia identifier connected to the mechanical transmission system and the servo driver and configured to identify load inertia information of the mechanical transmission system.

[0065] The load inertia identification method, apparatus, electronic device, and system provided by the present application predict the current system state of the mechanical transmission system based on the input control instruction, the motor speed, the load speed, and the state prediction equation of the mechanical transmission system in the previous state. Then, the current optimal estimated state of the mechanical transmission system is determined based on the current system predicted state of the mechanical transmission system and the current motor speed measurement value and the current load speed measurement value of the mechanical transmission system. Since the motor speed and the load speed of the mechanical transmission system are considered respectively, the load inertia information of the mechanical transmission system in the current state can be determined based on the current optimal estimated state of the mechanical transmission system. Compared with the traditional method of regarding the motor and the load as one inertia parameter, the load inertia of the system can be more accurately identified. BRIEF DESCRIPTION OF DRAWINGS

[0066] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiments of the present application will be briefly introduced as follows. It should be understood that the following drawings only show some of the embodiments of the present application, and therefore should not be regarded as a limitation on the scope. For those of ordinary skill in the art, other related drawings can also be obtained without creative labor on the basis of these drawings.

[0067] Figure 1 A structural schematic diagram of an electronic device according to an embodiment of the present application;

[0068] Figure 2A A schematic diagram of a load inertia identification system model according to an embodiment of the present application;

[0069] Figure 2B A schematic diagram of a double-inertia mechanical transmission device according to an embodiment of the present application;

[0070] Figure 3 A flowchart of a load inertia identification method according to an embodiment of the present application;

[0071] Figure 4 A flowchart of a load inertia identification method according to an embodiment of the present application;

[0072] Figure 5 A schematic diagram of mechanical arm servo motion data waveform according to an embodiment of the present application;

[0073] Figure 6 A structural schematic diagram of a load inertia identification device according to an embodiment of the present application.

[0074] Icon: 1-electronic device; 10-bus; 11-processor; 12-memory; 2-identification system; 3-double-inertia mechanical transmission device; 110-servo driver; 120-mechanical transmission system; 130-load mechanism; 140-inertia identifier; 150-motor; 160-transmission mechanism; 600-load inertia identification device; 601-first acquisition module; 602-prediction module; 603-second acquisition module; 604-computation module; 605-determination module. DETAILED DESCRIPTION

[0075] The technical solutions in the embodiments of the present application will be described below in combination with the drawings in the embodiments of the present application. In the description of the present application, the terms “first”, “second”, etc. are only used for differentiation and description, and cannot be understood as indicating or implying relative importance.

[0076] Please refer to Figure 1 which is a structural schematic diagram of an electronic device 1 provided by an embodiment of the present application, comprising: at least one processor 11 and a memory 12, Figure 1Taking a processor as an example, the processor 11 and the memory 12 are connected via a bus 10. The memory 12 stores instructions that can be executed by the processor 11. The instructions are executed by the processor 11 to enable the electronic device 1 to perform all or part of the process of the method in the following embodiments.

[0077] In one embodiment, the electronic device 1 may be a mobile phone, tablet computer, laptop computer, desktop computer, or other similar device.

[0078] like Figure 2A The diagram shown is a schematic representation of a load inertia identification system 2 according to an embodiment of this application. The load inertia identification system 2 includes a servo driver 110, a mechanical transmission system 120, a load mechanism 130, and an inertia identifier 140.

[0079] In this embodiment, a permanent magnet synchronous motor is used as the servo driver 110. The servo driver 110 is connected to the load mechanism 130 via a mechanical transmission system 120. An EKF extended Kalman observer is used as the inertia identifier 140.

[0080] In this embodiment, the current sampling circuit detects the three-phase current i of the motor in the three-phase stationary coordinate system using a Hall sensor. a i b i c After Clark transformation (3s / 2s), the current value i is converted into a stationary two-phase coordinate system. α i β The error between the given rotational speed in the outer speed loop and the feedback speed obtained by the differential of the position sensor is then adjusted by the outer speed loop controller, outputting the given torque current in the rotor rotating coordinate system and the current value i in the stationary two-phase coordinate system. α i β And the electromagnetic angle θ1 output by the rotor-side position sensor.

[0081] In one embodiment, the current sampling circuit can also detect the three-phase current i of the motor in the three-phase stationary coordinate system using other sampling methods. a i b i c .

[0082] In this embodiment, the current value i in the stationary two-phase coordinate system α i β After Park transformation (2s / 2r), the excitation current i is calculated using a two-phase feedback method in the rotor rotating coordinate system. d_fdb And feedback calculation of torque current i q_fdb Given the excitation current i d_ref With feedback calculation of excitation current i d_fdbIn comparison, after adjustment by the current regulator, the given output voltage u of the two-phase rotating coordinate system along the d-axis is obtained. d .

[0083] In this embodiment, given torque current i q_ref With feedback calculation of torque current i q_fdb After comparison and adjustment by the current regulator, the q-axis given output voltage u of the two-phase rotating coordinate system is obtained. q Two-phase given output voltage u in a rotating coordinate system d with u q After the Park inverse transformation (2r / 2s), it is converted into a two-phase voltage u in a stationary two-phase coordinate system. α with u β After adjustment by the PWM generation module, a PWM wave is generated, which drives the servo driver 110 after passing through the three-phase power inverter circuit. Known parameters include the torsional stiffness K of the mechanical transmission system 120, the moment of inertia J1 of the servo driver 110 rotor, and the feedback calculated torque current i. q_fdb Feedback speed with servo driver 110 side 130-side feedback speed of load mechanism As the input to the inertia identifier 140, the output is the load-side rotational inertia J2 of the inertia identification system 2.

[0084] Please see Figure 2B This is a schematic diagram of a dual-inertia mechanical transmission device 3 abstracted from the load inertia identification system 2 in one embodiment of this application. The dual-inertia mechanical transmission device 3 includes a motor 150, a transmission mechanism 160, and a load mechanism 130. In the diagram, J1 is the moment of inertia of the rotor on the side of motor 150, a basic parameter of the motor, obtained by testing by the manufacturer of motor 150 and marked in the user manual of motor 150; C1 is the damping coefficient of motor 150, obtained by testing by the manufacturer of motor 150 and marked in the user manual of motor 150; T... e The electromagnetic torque of motor 150 is obtained by calculating the q-axis current I through the acquisition of the three-phase current of motor 150 by sensors. q Approximately, the q-axis current I of motor 150 q Its electromagnetic torque T e It is directly proportional, and its proportionality factor is K. e θ1 is the electromagnetic rotation angle output by the rotor-side position sensor of the motor (T). w The torque of the transmission mechanism 160 is T, which will be generated when the transmission mechanism 160 undergoes torsional deformation. w C w C is the damping coefficient of the transmission mechanism 160. Since the transmission mechanism 160 will be lubricated, therefore C... wVery small, provided by the manufacturer of the transmission mechanism 160, sometimes can be set to 0; K is the torsional stiffness coefficient, a constant value or parameter table determined by the manufacturer of the transmission mechanism 160, given in the manual of the transmission mechanism 160, the torsional stiffness coefficient K represents the flexibility of the motor 150 and the load mechanism 130 connected in the double-inertia mechanical transmission device 3; θ2 is the load rotation angle output by the load mechanism 130 side position sensor; J2 is the equivalent rotational inertia of the load mechanism 130, which needs to be identified; C2 is the damping coefficient of the load mechanism 130, because the load mechanism 130 will be lubricated, so C2 is very small, provided by the manufacturer of the load mechanism 130, sometimes can be set to 0; T l is the torque on the load mechanism 130 side.

[0085] In this embodiment, the motor 150, the transmission mechanism 160 and the load mechanism 130 form a typical double-inertia mechanical transmission device 3. The transmission mechanism 160 connects the motor 150 and the load mechanism 130, has a certain torsional stiffness K and a damping coefficient C w When the transmission mechanism 160 is twisted and deformed, a torque Tw will be generated. The electromagnetic torque T e and the transmission mechanism 160 side torque T w act on the rotating shaft of the motor 150 with rotational inertia J1 and damping coefficient C1. The equivalent rotational inertia of the load mechanism 130 side is J2, the damping coefficient is C2, and the transmission mechanism 160 torque T w and the load torque T l jointly determine the load side rotation speed ω l . Among them, the motor 150 side rotation speed the load mechanism 130 side rotation speed

[0086] The above parameters and their meanings are combined to establish a system of differential equations, as shown in equation (1).

[0087]

[0088] In the formula, J1 is the rotational inertia of the motor 150 side rotor, θ1 is the electromagnetic rotation angle, T e is the electromagnetic torque of the motor 150, C1 is the damping coefficient of the motor 150, T w is the torque of the transmission mechanism 160, J2 is the equivalent rotational inertia of the load mechanism 130, θ2 is the load mechanism 130 side rotation angle, C2 is the damping coefficient of the load mechanism 130, T l is the torque on the load mechanism 130 side, C w is the damping coefficient of the transmission mechanism 160, K is the torsional stiffness coefficient of the transmission mechanism 160, ω m is the motor 150 side rotation speed, ωl The load mechanism operates at a speed of 130 on one side.

[0089] Since the dual-inertia mechanical transmission device 3 is lubricated, the damping coefficient in the dual-inertia mechanical transmission device 3 is very small and can be set to 0; in order to perform inertia identification calculation, the Laplace transformation of equation (1) is performed to obtain equation (2):

[0090]

[0091] In the formula, J1 is the moment of inertia of the rotor on the 150-degree side of the motor, θ1 is the electromagnetic rotation angle, s is the Laplace operator, and T... e The electromagnetic torque of the motor is 150, T w J1 is the torque of transmission mechanism 160, J2 is the equivalent moment of inertia of load mechanism 130, θ2 is the load rotation angle, and T is the torque of transmission mechanism 160. l The torque on the load mechanism 130 side is given by K, the torsional stiffness coefficient is given by ω. m For the motor's 150 side speed, ω l The load mechanism operates at a speed of 130 on one side.

[0092] Please see Figure 3 This is a flowchart illustrating a load inertia identification method according to an embodiment of this application. The method can be derived from... Figure 1 The electronic device 1 shown is used to perform this function and can be applied to applications such as... Figures 2A-2B In the load inertia identification scenario shown, the method aims to achieve accurate identification of load inertia. The method includes the following steps:

[0093] Step 301: Obtain the input control command, motor speed and load speed of the mechanical transmission system under test in the previous state.

[0094] In this step, the input control command of the mechanical transmission system under test in the previous state can be various parameters characterizing the system in the previous state, such as the electromagnetic torque T calculated based on the three-phase current and the motor model. e The motor speed in the previous state can be obtained by converting the electromagnetic angle θ1 measured by the position sensor on the motor side 150 into the feedback speed ω on the motor side 150. m It can also be obtained directly or indirectly by other sensors; the load rotation speed in the previous state can be obtained by converting the load rotation angle θ2 measured by the position sensor on the load mechanism 130 side into the feedback speed ω on the load mechanism 130 side. l It can also be obtained directly or indirectly by other sensors.

[0095] In an embodiment, the input control instruction of the mechanical transmission system to be measured at the previous state can also be the q-axis current calculated from the three-phase current; the electromagnetic torque T calculated from the three-phase current of the motor measured by the sensor and the basic parameters of the motor itself e The input control instruction of the mechanical transmission system to be measured at the previous state in the present application can be selected by the inertia identification system according to actual needs, and is not limited thereto.

[0096] Step 302: predicting the current system predicted state of the mechanical transmission system according to the input control instruction, the motor speed, the load speed and the state prediction equation of the mechanical transmission system.

[0097] In the present step, the current system predicted state of the mechanical transmission system can be predicted according to the input control instruction, the motor speed, the load speed and the state prediction equation of the mechanical transmission system obtained in step 301.

[0098] In an embodiment, the current system predicted state is calculated by the following formula:

[0099]

[0100]

[0101]

[0102] In the formula, k represents the period in which the previous state of the mechanical transmission system is located, k+1 represents the period in which the current state of the mechanical transmission system is located, is the system state of the mechanical transmission system in the kth period, is the prediction function of the system state of the mechanical transmission system in the kth period, is the system predicted state of the mechanical transmission system in the (k+1)th period, T s is the sampling period of the mechanical transmission system, is the predicted output state of the mechanical transmission system in the (k+1)th period, u(k) is the input control instruction of the mechanical transmission system in the kth period, is the noise covariance of the mechanical transmission system in the kth period, is the prediction noise covariance of the mechanical transmission system in the (k+1)th period, Q(k) is the covariance of the system error of the mechanical transmission system in the kth period, J1 is the rotational inertia of the motor rotor of the mechanical transmission system, M l is the inverse of the load inertia J2 of the mechanical transmission system.

[0103] The Jacobian matrix F(x) is:

[0104]

[0105] wherein k represents a period in which a previous state of the mechanical transmission system is located, T s is a sampling period of the mechanical transmission system, J1 is a moment of inertia of a rotor of a motor of the mechanical transmission system, M l is an inverse of a load inertia J2 of the mechanical transmission system.

[0106] Step 303: obtaining a current motor speed measurement value and a current load speed measurement value of the mechanical transmission system.

[0107] In this step, the current motor speed measurement value can be obtained by a position sensor arranged at the motor 150 side, or can be obtained by other sensors arranged at the motor 150 side; the current load speed measurement value can be obtained by a position sensor arranged at the load mechanism 130 side, or can be obtained by other sensors arranged at the load mechanism 130 side.

[0108] Step 304: determining a current optimal estimation state of the mechanical transmission system according to the current system prediction state, the current motor speed measurement value and the current load speed measurement value.

[0109] In this step, the Kalman gain of the mechanical transmission system can be calculated by the following formula:

[0110]

[0111] The current optimal estimation state is calculated by the following formula:

[0112]

[0113] The error mean square of the current optimal estimation is calculated by the following formula:

[0114]

[0115] wherein

[0116]

[0117] wherein k represents a period in which a previous state of the mechanical transmission system is located, k+1 represents a period in which a current state of the mechanical transmission system is located, K(k+1) represents the Kalman gain of the mechanical transmission system in the (k+1)th period; is a prediction noise covariance of the mechanical transmission system in the (k+1)th period; R(k) is a covariance of a measurement error of the mechanical transmission system in the kth period; is an optimal estimation state of the mechanical transmission system in the (k+1)th period; a predicted state of the mechanical transmission system in the (k+1)th period; a predicted output state of the mechanical transmission system in the (k+1)th period; y(k+1) is an output state measurement value of the mechanical transmission system in the (k+1)th period determined based on a current motor speed measurement value and a current load speed measurement value; an error mean square of the optimal estimation of the mechanical transmission system in the (k+1)th period.

[0118] Step 305: determining load inertia information of the mechanical transmission system in the current state according to the current optimal estimation state.

[0119] In this step, the optimal estimation state of the mechanical transmission system in the (k+1)th period obtained in step 304 can be used as a basis to determine the load inertia information of the mechanical transmission system in the current state. Thus, the load inertia information of the mechanical transmission system in the current state is determined.

[0120] In the above load inertia identification method, the connection between the motor and the load end in the mechanical transmission system is identified as a flexible connection, and the torsional stiffness coefficient K, the motor side speed ω m , the load side speed ω l , the transmission shaft torque T w , and the load torque T l are introduced, so as to establish a double inertia system state model and an observation matrix; then the system state prediction matrix and the covariance matrix are determined; and then the extended Kalman filtering algorithm is used for iterative identification of the load inertia, thereby improving the accuracy of the load side inertia identification.

[0121] Please refer to Figure 4 , which is a flowchart of a load inertia identification method according to an embodiment of the present application. The method can be executed by the electronic device 1 shown in Figure 1 , and can be applied to a load inertia identification scenario as shown in Figures 2A-2B , so as to achieve accurate identification of the load inertia. The method comprises the following steps:

[0122] Step 401: determining a state equation and an output equation of the mechanical transmission system according to double inertia parameters of the mechanical transmission system and a mechanical motion equation.

[0123] In this step, taking the double inertia mechanical transmission device 3 shown in Figure 2B as an example, the parameters in the double inertia mechanical transmission device 3 include: the rotational inertia J1 of the rotor of the motor 150, the damping coefficient C1 of the motor 150, the electromagnetic torque T e of the motor 150, the electromagnetic rotation angle θ1, the torque T w of the transmission mechanism 160, and the damping coefficient C w, the torsional stiffness coefficient K of the transmission mechanism 160, the load mechanism 130 side rotation angle θ2, the equivalent rotational inertia J2 of the load mechanism 130, the damping coefficient C2 of the load mechanism 130, the torque T on the load mechanism 130 side l ;

[0124] Based on the double inertia mechanical transmission device 3 and the above parameters, formula (1) is established, and formula (2) can be obtained based on formula (1). For the explanation of formula (1) and formula (2), refer to the description of the formula (1) and formula (2), which will not be repeated here. Figure 2B

[0125] Therefore, based on formula (2), the state equation and the output equation of the mechanical transmission system are determined by using the following formula:

[0126]

[0127] y=Cx (12)

[0128] In formula (12), the matrix C is as shown in formula (10),

[0129] x=[ω m ω l T w T l M l ] T (13)

[0130] y=[ω m ω l ] T (14)

[0131] u=T e =K e ·I q (15)

[0132]

[0133]

[0134] In the formula, k represents the period in which the previous state of the mechanical transmission system is located; x is the state variable of the mechanical transmission system; y is the output variable of the mechanical transmission system; ω m is the motor speed of the mechanical transmission system; ω l is the load side speed of the mechanical transmission system; T w is the transmission shaft torque; T l is the load torque; J1 is the rotational inertia of the motor rotor; M l is the inverse of the load inertia J2; the electromagnetic torque T e can be obtained by collecting the q-axis current I​q obtained; approximate, q-axis current of the motor I q proportional to electromagnetic torque T e K e is its proportional factor; T s is the sampling period of the mechanical transmission system. Since T l and M l vary slowly, their derivatives can be considered as zero.

[0135] Step 402: determining the state prediction equation of the mechanical transmission system according to the state equation and the output equation of the mechanical transmission system.

[0136] In this step, the characteristics that the extended Kalman filter can directly solve the nonlinear equation by iteration are used, that is, the system state variable of the last state can be predicted according to the system state equation to calculate the system state of the current state. Based on formulas (11)-(17), the state prediction equation of the mechanical transmission system is obtained:

[0137] x(k+1) = x(k) + T s ·f(x(k)) + T s ·Bu(k) (18)

[0138] y(k) = Cx(k) (19)

[0139] In the formula, k represents the period in which the last state of the mechanical transmission system is located, k+1 represents the period in which the current state of the mechanical transmission system is located, x(k) is the prediction value of the kth period of the mechanical transmission system, x(k+1) is the prediction value of the prediction state of the (k+1)th period of the mechanical transmission system, T s is the sampling period of the mechanical transmission system, u(k) is the input control instruction of the kth period of the mechanical transmission system, and y(k) is the prediction value of the output variable of the kth period of the mechanical transmission system.

[0140] In an embodiment, there are uncertainties and variabilities in the system, such as measurement noise, so formulas (18)-(19) can be expressed as:

[0141] x(k+1) = x(k) + T s ·f(x(k)) + T s ·Bu(k) + w(k) (20)

[0142] y(k) = Cx(k) + v(k) (21)

[0143] where w represents the influence of system parameter error, and v represents the noise and disturbance in the measurement process, including the quantization error of the code disc measurement position signal. The noise can be stationary Gaussian white noise with a mean of zero. Therefore, the covariance matrix of the noise can be defined as:

[0144] Q = cov(w) = E{ww T}

[0145] R = cov(v) = E{vv T}

[0146] Step 403: Obtain the input control instruction of the mechanical transmission system to be measured at the previous state, the motor speed and the load speed. For details, refer to the description of step 301 in the above embodiment.

[0147] Step 404: According to the input control instruction, the motor speed, the load speed and the state prediction equation of the mechanical transmission system, the current system prediction state of the mechanical transmission system is predicted. For details, refer to the description of step 302 in the above embodiment.

[0148] Step 405: Obtain the current motor speed measurement value and the current load speed measurement value of the mechanical transmission system. For details, refer to the description of step 303 in the above embodiment.

[0149] Step 406: According to the current system prediction state, the current motor speed measurement value and the current load speed measurement value, the current optimal estimation state of the mechanical transmission system is determined. For details, refer to the description of step 304 in the above embodiment.

[0150] Step 407: According to the current optimal estimation state, the load inertia information of the mechanical transmission system at the current state is determined. For details, refer to the description of step 305 in the above embodiment. Step 408: Output the load inertia information of the mechanical transmission system.

[0151] The load inertia information identified in this step can be output, and the output load inertia information can be used for the mechanical transmission system to adjust.

[0152] Please refer to Figure 5 , which is a mechanical arm servo motion data waveform diagram of an embodiment of the present application. The load inertia identification method in the above embodiment is used to observe the mechanical arm servo motion.

[0153] In this embodiment, the servo driver 110 is the base joint servo driver of the six-axis mechanical arm, and the EKF extended Kalman observer is used as the inertia identifier 140 to observe the load inertia at the rear end of the joint in real time during the operation of the mechanical arm. The longitudinal coordinate is the load side inertia unit (Kg·m 2), the abscissa is the sampling time unit ms. Among them, the solid line is the change curve of the rear end inertia of the base joint of the mechanical arm calculated according to the theoretical dynamics model of the mechanical arm, and the dotted line around the solid line is the rear end load inertia observed in real time by the Kalman observer of the case. From the data graph, it can be seen that the double-inertia system Kalman online inertia observer can well follow the load inertia change, and the real-time performance is good.

[0154] Please refer to Figure 6 , which is a load inertia identification device 600 of an embodiment of the application. The method can be executed by the electronic device 1 shown in Figure 1 , and can be applied to the load inertia identification scene as shown in Figures 2A-2B , so as to more accurately identify the load inertia. The device comprises a first acquisition module 601, a prediction module 602, a second acquisition module 603, a calculation module 604 and a determination module 605, and the principle relationship of each module is as follows:

[0155] The first acquisition module 601 is configured to acquire the input control instruction, the motor speed and the load speed of the mechanical transmission system to be measured at the previous state.

[0156] The prediction module 602 is configured to predict the current system prediction state of the mechanical transmission system according to the input control instruction, the motor speed, the load speed and the state prediction equation of the mechanical transmission system.

[0157] The second acquisition module 603 is configured to acquire the current motor speed measurement value and the current load speed measurement value of the mechanical transmission system.

[0158] The calculation module 604 is configured to determine the current optimal estimation state of the mechanical transmission system according to the current system prediction state, the current motor speed measurement value and the current load speed measurement value.

[0159] The determination module 605 is configured to determine the load inertia information of the mechanical transmission system at the current state according to the current optimal estimation state.

[0160] In an embodiment, the load inertia identification device 600 further comprises an establishment module, which is configured to: determine the state equation and the output equation of the mechanical transmission system according to the double-inertia parameters and the mechanical motion equation of the mechanical transmission system; and determine the state prediction equation of the mechanical transmission system according to the state equation and the output equation of the mechanical transmission system.

[0161] In an embodiment, the establishment module determines the state equation and the output equation of the mechanical transmission system by using the following formula:

[0162]

[0163] y=Cx

[0164] wherein,

[0165] x = [ω m ω l T w T l M l ] T

[0166] y = [ω m ω l ] T

[0167] u = T e = K e · I q

[0168]

[0169]

[0170]

[0171] wherein, k represents a period in which a previous state of the mechanical transmission system is located, x is a state variable of the mechanical transmission system, y is an output of the mechanical transmission system, ω m is a motor speed of the mechanical transmission system, ω l is a load speed of the mechanical transmission system, T w is a transmission shaft torque of the mechanical transmission system, T l is a load torque of the mechanical transmission system, M l is an inverse of a load inertia J2 of the mechanical transmission system, K e is a proportional factor, I q is a q-axis current of the motor, and f(x) represents a prediction function of the kth period of the mechanical transmission system, and k is an integer.

[0172] In an embodiment, the prediction module 602 calculates a current system prediction state by using the following formula:

[0173]

[0174]

[0175]

[0176] wherein,

[0177]

[0178] wherein, k represents a period in which a previous state of the mechanical transmission system is located, and k+1 represents a period in which a current state of the mechanical transmission system is located. is a system state of the mechanical transmission system at the kth cycle, is a prediction function of the system state of the mechanical transmission system at the kth cycle, is a system predicted state of the mechanical transmission system at the (k+1)th cycle, T s is a sampling cycle of the mechanical transmission system, is a predicted output state of the mechanical transmission system at the (k+1)th cycle, u(k) is an input control instruction of the mechanical transmission system at the kth cycle, is a noise covariance of the mechanical transmission system at the kth cycle, is a predicted noise covariance of the mechanical transmission system at the (k+1)th cycle, Q(k) is a covariance of a system error of the mechanical transmission system at the kth cycle, J1 is a moment of inertia of a motor rotor of the mechanical transmission system, M l is an inverse of a load inertia J2 of the mechanical transmission system.

[0179] In an embodiment, the calculation module 604 calculates the Kalman gain of the mechanical transmission system by using the following formula:

[0180]

[0181] calculates the current optimal estimation by using the following formula:

[0182]

[0183] calculates the error mean square of the current optimal estimation by using the following formula:

[0184]

[0185] wherein k represents a cycle in which a previous state of the mechanical transmission system is located, k+1 represents a cycle in which a current state of the mechanical transmission system is located, and K(k+1) represents the Kalman gain of the mechanical transmission system at the (k+1)th cycle; is a predicted noise covariance of the mechanical transmission system at the (k+1)th cycle; and R(k) is a covariance of a measurement error of the mechanical transmission system at the kth cycle; is an optimal estimation state of the mechanical transmission system at the (k+1)th cycle; is a system predicted state of the mechanical transmission system at the (k+1)th cycle; is a predicted output state of the mechanical transmission system at the (k+1)th cycle; and y(k+1) is an output state measurement value of the mechanical transmission system at the (k+1)th cycle, which is determined based on a current motor speed measurement value and a current load speed measurement value; is an error mean square of the optimal estimation of the mechanical transmission system at the (k+1)th cycle.

[0186] In one embodiment, the load inertia identification device 600 further comprises an output module configured to output the load inertia information of the mechanical transmission system.

[0187] The detailed description of the load inertia identification device 600 is described in the above-mentioned method steps.

[0188] Although the embodiments of the present application have been described in conjunction with the drawings, various modifications and changes can be made by those skilled in the art without departing from the spirit and scope of the present application, and such modifications and changes fall within the scope defined by the appended claims.

Claims

1. A load inertia identification method characterized by, The method includes: Acquire the input control commands, motor speed, and load speed of the mechanical transmission system under test in the previous state; Based on the input control command, the motor speed, the load speed, and the state prediction equation of the mechanical transmission system, the current predicted state of the mechanical transmission system is predicted. Obtain the current motor speed measurement value and the current load speed measurement value of the mechanical transmission system; Based on the current system prediction state, the current motor speed measurement value, and the current load speed measurement value, determine the current optimal estimated state of the mechanical transmission system; Based on the current optimal estimated state, determine the load inertia information of the mechanical transmission system in the current state; The method further includes the step of establishing the state prediction equation of the mechanical transmission system. Based on the dual inertia parameters and mechanical motion equations of the mechanical transmission system, determine the state equations and output equations of the mechanical transmission system. Based on the state equation and the output equation of the mechanical transmission system, determine the state prediction equation of the mechanical transmission system; The input control command is a motor electromagnetic torque command; the method further includes: determining the state equation and the output equation of the mechanical transmission system using the following formulas: in, · x wherein, denotes a period in which the previous state of the mechanical transmission system is located, is a state variable of the mechanical transmission system, is an output variable of the mechanical transmission system, is a motor speed of the mechanical transmission system, is a load speed of the mechanical transmission system, is a transmission shaft train torque of the mechanical transmission system, is a load torque of the mechanical transmission system, is a load inertia of the mechanical transmission system J is an inverse of 2, is an input control variable carried by the input control instruction, is a motor electromagnetic torque, is a proportional factor, is a q-axis current of the motor of the mechanical transmission system, denotes a prediction function of the first k period of the mechanical transmission system, k is an integer.

2. The method of claim 1, wherein, The step of predicting the current predicted state of the mechanical transmission system based on the input control command, the motor speed, the load speed, and the state prediction equation of the mechanical transmission system includes: The current system prediction state is calculated using the following formula: in, wherein, denotes a period in which a previous state of the mechanical transmission system is located, denotes a period in which a current state of the mechanical transmission system is located, is a system state of the mechanical transmission system at the th period, is a system state of the mechanical transmission system at the th period, is a predicted state of the mechanical transmission system at the th period, is a sampling period of the mechanical transmission system, is a predicted output state of the mechanical transmission system at the k th period, is an input control command of the mechanical transmission system at the th period, is a noise covariance of the mechanical transmission system at the th period, is a predicted noise covariance of the mechanical transmission system at the k th period, is a covariance of a system error of the mechanical transmission system at the th period, J 1 is a moment of inertia of a motor rotor of the mechanical transmission system, M l is an inverse of a load inertia J 2 of the mechanical transmission system.

3. The method of claim 2, wherein, The step of determining the current optimal estimate of the mechanical transmission system based on the current system prediction state, the current motor speed measurement value, and the current load speed measurement value includes: The Kalman gain of the mechanical transmission system is calculated using the following formula: The current optimal estimated state is calculated using the following formula: The mean square error of the current best estimate is calculated using the following formula: in, This indicates the period in which the previous state of the mechanical transmission system occurred; This indicates the current cycle of the mechanical transmission system. Indicates that the mechanical transmission system is in the ( ) k +1) cycles of the Kalman gain; For the mechanical transmission system in the () k +1) periods of prediction noise covariance; For the mechanical transmission system in the first k The covariance of measurement error over one period; For the mechanical transmission system in the () k The optimal estimated state for +1) cycles; For the mechanical transmission system in the () k +1) cycles of system predicted state; For the mechanical transmission system in the () k +1) cycles of predicted output state; The mechanical transmission system, determined based on the current motor speed measurement and the current load speed measurement, in the ( ) k +1) output status measurement values ​​for each cycle; For the mechanical transmission system in the () k The mean square error of the optimal estimate for +1) periods.

4. The method of claim 1, wherein, Also includes: Output the load inertia information of the mechanical transmission system.

5. A load inertia identification device characterized by comprising: The apparatus for implementing the method according to any one of claims 1-4, the apparatus comprising: The first acquisition module is used to acquire the input control commands, motor speed and load speed of the mechanical transmission system under test in the previous state; The prediction module is used to predict the current system prediction state of the mechanical transmission system based on the input control command, the motor speed, the load speed, and the state prediction equation of the mechanical transmission system. The second acquisition module is used to acquire the current motor speed measurement value and the current load speed measurement value of the mechanical transmission system; The calculation module is used to determine the current optimal estimated state of the mechanical transmission system based on the current system predicted state, the current motor speed measurement value, and the current load speed measurement value. The determination module is used to determine the load inertia information of the mechanical transmission system in the current state based on the current optimal estimated state.

6. The apparatus of claim 5, wherein, The apparatus further includes a setup module, the setup module being used for: determining a state equation and an output equation of the mechanical transmission system according to a double-inertia parameter and a mechanical motion equation of the mechanical transmission system; determining the state prediction equation of the mechanical transmission system according to the state equation and the output equation of the mechanical transmission system.

7. An electronic device, comprising: comprising: a memory for storing a computer program; a processor for executing the computer program to implement the method of any one of claims 1 to 4.

8. A load inertia identification system characterized by, The system for implementing the method of any one of claims 1 to 4 comprises: a mechanical transmission system; a servo driver connected to the mechanical transmission system for driving the mechanical transmission system to operate; an inertia identifier connected to the mechanical transmission system and the servo driver for identifying load inertia information of the mechanical transmission system.

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