CN115390476A - Simulink-based servo mechanism performance and reliability joint simulation method - Google Patents
Simulink-based servo mechanism performance and reliability joint simulation method Download PDFInfo
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Abstract
The invention provides a Simulink-based servo mechanism performance and reliability joint simulation method which comprises the steps of firstly establishing a performance model of a servo mechanism in a fault interference free state, secondly, carrying out reliability simulation on key parts of the servo mechanism based on ANSYS and Flotherm, and carrying out reliability prediction to obtain the service life distribution of the key parts. And injecting the service life distribution into the performance model, and truly and accurately depicting the fault logic of the key parts to obtain the service life condition of the whole model. The method realizes the joint simulation of the performance and the reliability of the servo mechanism and provides a basic model for optimizing the performance and the reliability of the electromechanical product. The simulation result analysis result of the performance and the reliability of the servo mechanism is obtained based on the solution of each software platform, the result is more practical and scientific, and the method provides a new research idea for the development of the performance and reliability combined analysis work of other scholars.
Description
Technical Field
In the existing research aiming at the simulation of the working performance of the servo mechanism, the problem that the reliability analysis and the performance design analysis are disjointed generally exists, the reliability principle and the performance analysis are fused by utilizing the existing performance model and based on the technical expansibility of matlab, and the performance and reliability joint simulation is carried out on the servo mechanism and is evaluated. Firstly, establishing a motor driving module, a motor-screw module and a control algorithm module based on simulink and forming a servo mechanism performance model; then, reliability modeling is carried out on the servo mechanism functional circuit part, and service life distribution parameters of the functional circuit are obtained based on Monte Carlo sampling; and injecting the service life distribution of the functional circuit into a performance simulation model based on the logic relation among the servo mechanism structures, establishing a performance and reliability joint simulation model of the servo mechanism, and solving based on a reliability principle to obtain a reliability evaluation result.
Background
The servo mechanism is a control system that controls the motion state of the controlled object so that the motion state can be changed in accordance with a system command, and in a normal state, the servo mechanism controls the position, speed, and the like of the mechanical system by a closed loop. The servo mechanism can be divided into various types according to different driving motors, relates to knowledge systems of a plurality of subjects such as electric, electronic and automatic control, needs to research the content of design and control, information measurement, structural design and the like of a servo loop, and mainly comprises a servo control driver, an electromechanical actuator, a position sensor and the like.
In the design stage of the servo mechanism, whether the product has better performance in the working process is generally researched in an analog simulation mode, however, in the simulation operation process of the servo mechanism, the phenomenon of disjointed reliability and performance analysis generally exists, so that although a large amount of resources are input in the research and development process of the servo mechanism, the reliability of the servo mechanism still cannot be effectively guaranteed in the use process of the product, and therefore the problems that the performance level of the servo mechanism is reduced due to design defects, disturbance and the like, even serious faults and the like occur, serious economic loss can be caused, and even serious safety accidents are brought. In the research of simulation modeling, xu et al propose a jet pipe servo valve long-period simulation modeling method, wang propose a performance simulation method based on radar mechanism wear degradation, however, the modeling is only carried out aiming at a single hydraulic mechanism or a mechanical structure from a performance level, and the consideration of a comprehensive reliability simulation level is lacked in the aspect of modeling. Therefore, the mechanical and electrical structures of the servo system are comprehensively considered, and the combined simulation modeling method research of the expansion performance and the reliability is carried out.
Disclosure of Invention
1. The purpose is as follows: the invention provides a performance and reliability joint simulation method for a research object by using a typical servo mechanism, which is a modeling method based on Simulink simulation. Firstly, a performance model of the servo mechanism in a fault interference free state is established, secondly, reliability simulation is carried out on key parts of the servo mechanism based on ANSYS and Flotherm, and reliability prediction is carried out to obtain service life distribution of the key parts. And injecting the service life distribution into the performance model, and truly and accurately depicting the fault logic of the key parts to obtain the service life condition of the whole model. The method realizes the joint simulation of the performance and the reliability of the servo mechanism and provides a basic model for optimizing the performance and the reliability of the electromechanical product.
2. The technical scheme is as follows: the invention is realized by the following technical scheme.
First, several definitions are introduced.
Definition 1: a servo mechanism: the servo mechanism mainly comprises a servo control driver and an electromechanical actuator, and is a typical electromechanical system, and the working principle of the servo mechanism is as shown in fig. 1: the servo control driver comprises a control panel combination and a power amplifier panel combination, the two parts are responsible for controlling an algorithm and driving a motor, a Pulse Width Modulation (PWM) signal is generated according to a set control rule after a control command sent by a control system and a feedback signal of an electromechanical actuator are received, and the motor is driven to rotate after power amplification is carried out on the PWM signal through a power circuit. The electromechanical actuator is a kinematic pair consisting of a motor and a screw rod, the motor drives the screw rod pair to move after being decelerated by a gear pair, a screw rod nut is pushed to output a linear position, and then the angle and the torque of a controlled object are output.
Definition 2: PWM signal: the Pulse Width Modulation (PWM) technique controls the on-off of the switching device of the inverter circuit, so that a series of pulses with equal amplitude but different widths are obtained at the output end, and the pulses are used to replace sine waves or required waveforms. That is, a plurality of pulses are generated in a half cycle of an output waveform, so that the equivalent voltage of each pulse is a sine waveform, and the obtained output is smooth and has few low-order harmonics. The width of each pulse is modulated according to a certain rule, so that the magnitude of the output voltage of the inverter circuit can be changed, and the output frequency can also be changed.
Definition 3: weibull distribution: is the theoretical basis of reliability analysis and life test. The Weibull distribution is widely applied to reliability engineering, is particularly suitable for the distribution form of wear cumulative failure of electromechanical products, and can describe the failure process with gradually increased failure rate and the failure process with gradually decreased failure rate. It can easily deduce distribution parameters by using probability values, and thus is widely applied to data processing of various life tests, and the unreliable function is:
wherein g is 1 ，g 2 ，g 3 Respectively a shape parameter, a scale parameter and a position parameter.
The invention relates to a Simulink-based servo mechanism performance and reliability simulation method, which specifically comprises the following steps:
the method comprises the following steps: servo mechanism performance simulation method research
In Simulink modeling simulation, a servo mechanism is divided into a motor driving module, a motor-screw module and a control algorithm module, and a model is established. The motor driving module converts the PWM signal into a sine wave signal; the motor-screw module enables the three-phase signals to drive a load to move after passing through the motor model and outputs information such as stator current, rotor speed, rotor angle, electromagnetic torque and the like; and the control algorithm module reduces the displacement of motion output, the electromagnetic torque and the speed error of the screw pair through negative feedback. The modeling step is detailed in the detailed description of the embodiments.
Step two: servo control driver reliability simulation method research
Failure of any of the components of the servomechanism may cause the system to fail. Compared with the frequent fault condition of the functional circuit, the fault occurrence probability of the mechanical components of the system is low, and the fault of any key circuit can cause the performance level of the whole system to drop suddenly and break down, even has serious potential safety hazard. Reliability simulations are therefore performed for the functional circuits in the servomechanism. Historical data shows that more than 75% of causes of electronic equipment faults are caused by temperature and vibration, reliability research for electronic products at home and abroad focuses on influences caused by the temperature and the vibration, and therefore reliability simulation methods of typical servo mechanism functional circuits under the influences of the temperature and the vibration are used for research.
Establishing a CFD digital prototype of the product based on thermal simulation software Flotherm, correcting the CFD digital prototype and simulating thermal stress; establishing a simplified model based on ANSYS software, setting parameters, dividing grids, analyzing modes, testing the modes, correcting the models and adding random loads to carry out random vibration analysis. And establishing a PCB model based on CalcePWA software, and carrying out failure prediction analysis based on failure physics on each PCB by taking the Flotherm thermal simulation result, the ANSYS vibration simulation result and the environmental profile as input conditions. And (3) obtaining a fault matrix of the servo control driver through fault prediction, carrying out fitting-sampling-fitting solving process on fault data, obtaining the service life of each functional circuit according with Weibull distribution, and calculating the distribution parameters of the functional circuits.
Step three: servo mechanism performance and reliability joint simulation research
And (3) carrying out research on a performance and reliability joint simulation method of the servo mechanism. Firstly, a reliability mathematical model is established in simulink: and establishing a Monte Carlo sampling module based on the service life distribution of the functional circuits in the servo control driver in the step two. And establishing a fault trigger module based on the first fault sending time obtained in the step two. Because a plurality of functional circuits such as an A/D circuit, a secondary power supply circuit and the like exist in the servo control driver, fault trigger modules are respectively established, and connection is established according to the functional logic relationship of each circuit in the servo mechanism, so that a reliability mathematical simulation model is obtained.
The final implementation of injecting the reliability simulation model into the performance model is completed by a multi-point breakpoint module, which is shown in fig. 2: and (3) setting a breakpoint at the output (stator current, rotor speed, rotor angle and electromagnetic torque) of the motor module, and adding a fault trigger output signal for judgment. Therefore, when the functional circuit breaks down, the output signal of the fault trigger module can cause the output of the motor module to be interrupted, otherwise, the output signal of the motor module which is not influenced continues to enter the next feedback.
After the joint simulation is realized, a simulation termination module is established to carry out joint evaluation on performance and reliability, an ideal signal is compared with a feedback signal, if the ideal signal is lower than an error threshold value, the performance level accords with a system instruction, otherwise, the ideal signal exceeds the system instruction, and the simulation is terminated at the moment. On the basis of a performance and reliability combined simulation model, N times of simulation is carried out, the times of fault-free operation of the servo mechanism and the times of the performance level of the servo mechanism according with the system instruction are recorded, and the reliability of the servo mechanism in time T and the probability of the performance level of the position, the rotating speed and the torque according with the system instruction are calculated by a reliability basic principle.
3. Compared with the prior art, the invention has the following advantages:
with the complication of the servo mechanism structure, the traditional design scheme which focuses on the performance design and neglects the reliability can not effectively ensure the reliability of the servo mechanism and has huge potential safety hazard. The invention provides an improvement method aiming at the defects of interface limitation and the like in the performance and reliability joint analysis process at the present stage, and provides a Simulink-based servo mechanism performance and reliability joint simulation method. A simulation method for the performance and reliability of the Simulink-based servo mechanism is provided according to the structural composition, the working principle and the specific functions of each component of the servo mechanism by using a servo mechanism research object. The method realizes the joint simulation of the performance and the reliability of the servo mechanism, solves the simulation result analysis result of the performance and the reliability of the servo mechanism based on each software platform, and the result is more practical and scientific.
Drawings
Fig. 1 is a schematic diagram of the operation of the servo mechanism.
Fig. 2 is a multi-point breakpoint block diagram.
Fig. 3 is a typical servo configuration.
Fig. 4 is a motor drive module.
Fig. 5 is a motor-lead screw module.
FIG. 6 is a control algorithm module.
FIG. 7 is a performance simulation model.
Fig. 8a is a current performance simulation result.
Fig. 8b is a position performance simulation result.
Fig. 9 is a reliability simulation method flow.
Fig. 10a is a CFD digital prototype simplified front model.
Fig. 10b is a simplified model of a CFD digital prototype.
FIG. 11 is a fault trigger module.
Fig. 12a is a logical combination of control board functional circuit trigger modules.
Fig. 12b is a logic combination of the functional circuit triggering module of the pendulum power amplifier board.
Fig. 12c is a logical combination of the trigger modules of the air rudder power amplifier board functional circuit.
Fig. 12d is a logical combination of the current detection circuit trigger block.
FIG. 13 is a schematic diagram of a fault implantation.
FIG. 14 is a system emulation termination logic diagram.
Detailed Description
The method takes a typical servo mechanism as an example, and based on a performance model and a reliability simulation analysis result, a performance and reliability joint simulation method is used for carrying out simulation analysis on the servo mechanism to obtain a reliability evaluation result of the servo mechanism. The above technical solution will now be described in further detail.
The method comprises the following steps: and establishing a servo mechanism performance model. A performance model is built based on Simulink, and a typical servo mechanism is shown in fig. 3 as an example: the servo mechanism consists of 1 servo control driver and six electromechanical actuators, wherein the servo control driver consists of a control board and six power amplification boards, and the power amplification boards are divided into two swing-jet power amplification boards and four air rudder power amplification boards. The air vane power amplifier board and the pendulum spraying power amplifier board have the same composition structure, and the six electromechanical actuators have the same composition structure.
A simulation model of the individual servo control actuators-electromechanical actuators is first built. The system is divided into a motor driving module, a motor-screw module and a control algorithm module. The motor drive module is shown in fig. 4: the signals are converted in the motor driving module, and the PWM signals g are converted into three-phase voltage signals A, B and C. The motor-screw module is shown in fig. 5: the permanent magnet synchronous motor receives the three-phase voltage and provides the three-phase voltage to the stator. The permanent magnet synchronous motor can drive the load screw pair to do linear motion, and the permanent magnet synchronous motor submodel outputs information such as stator current, rotor speed, rotor angle, electromagnetic torque and the like of the electromechanical actuator. Wherein the electromagnetic torque:
ψ d 、ψ q are respectively d-and q-axis flux linkage, i q 、i d D and q axis currents. If can control i d =0, the torque equation can be simplified to:
now only control i q The torque can be controlled, and the d-axis voltage is only equal to i q In relation to a typical separately excited DC motor, the stator has only a quadrature component, and the space vector of the stator magnetomotive force is just orthogonal to the space vector of the permanent magnet field. So to reduce the loss, i can be completely combined d =0, reduce losses.
In addition, the rotor angle theta is converted into screw displacement s through the screw module by controlling the gear ratio k of the speed reducer to the screw, wherein c is the initial position of the screw.
s＝k*θ+c (3)
The control algorithm module consists of three closed loops of position and speed current, as shown in fig. 6: inputting a simulation screw displacement parameter s and an ideal position parameter s1 as models, and reducing errors of the position parameters through negative feedback; because the position control does not allow overshoot, a proportional regulator is arranged, the difference value of the ideal position signal and the simulation position detection signal is set as the speed of a control module, the speed is compared with the speed of a rotor, and the error is reduced through negative feedback; the q-axis stator current (i.e., electromagnetic torque) is controlled by proportional-integral control and negative feedback to reduce the error, and the maximum electromagnetic torque can be generated on the d-axis by controlling id = 0. The three-closed-loop negative feedback system outputs a voltage Ud, a voltage Uq and a rotor angle theta. The output signal generates a PWM signal through an SVPWM (Space Vector Pulse Width Modulation) algorithm, and the PWM signal returns to the motor driving module to generate three-phase voltage signals A, B and C again for next correction and feedback. Compared with the traditional pulse width modulation algorithm, the SVPWM has the advantages that the harmonic component of the winding current waveform is small, the motor torque pulsation is reduced, the rotating magnetic field is closer to a circle, and the utilization rate of the direct-current bus voltage is greatly improved.
An overall servo mechanism model is established, because a servo mechanism consists of 1 servo control driver and six electromechanical actuators, wherein the servo control driver consists of one control board and six power amplification boards, six motor driving modules, a control algorithm module and a motor lead screw module are established in the performance model simulation process, and the model is as shown in fig. 7.
Different time length simulations are carried out based on the servo mechanism performance model, the simulation time length of the performance model is set to be 0.08s, the ideal position is 1mm, and whether the performance design meets the expected standard or not is judged through the current and position output variation waveforms. Taking the combination of swing-jet power amplifier boards as an example, fig. 8a shows the current output result: when the speed of the early-stage rotating speed ring is lower than a given value, the maximum current borne by the output motor is 40A, so that the maximum torque of the motor is increased, after the given rotating speed is reached, the motor rapidly outputs reverse torque, and the speed is reduced to the given value. Fig. 8b shows a motor position waveform: because of the hysteresis of sine wave tracking, the step signal reaches a stable output state after 0.05s of regulation time, the output response accords with a given position, and the performance design of the servo mechanism accords with an expected standard.
Step two: and researching a reliability simulation method of the servo control driver. A typical servo control driver is used for researching a reliability simulation method of a functional circuit under the influence of two environments, namely temperature and vibration. Fig. 9 shows a reliability simulation method flow: thermal stress simulation is carried out by using Flotherm and vibration stress simulation is carried out by using ANSYS aiming at typical electronic products. And injecting the simulation results of the thermal stress and the vibration stress into a CalcePWA platform to carry out fault prediction, and finally carrying out data fitting according to the fault prediction results to obtain data distribution.
(1) Thermal stress simulation: based on numerical heat transfer and computational fluid dynamics methods, the thermal stress simulation servo control driver developed by the Flotherm has five PCB boards, and thermal simulation is carried out on the servo control driver at the temperature of-45 ℃, 20 ℃, 35 ℃ and 70 ℃ according to the environmental profile requirements.
Establishing a CFD digital prototype: as shown in fig. 10a and 10 b. A CAD model or a manual mode is introduced to establish a model as shown in figure 10a, and holes, bosses, round corners, thermal analysis-independent connecting pieces and components with low power consumption in the product are deleted as shown in figure 10b, so that the calculation efficiency of solution is improved. And then carrying out mesh division and boundary environment setting to determine the finite element analysis precision. And (3) carrying out grid refinement on key parts by referring to the solid model, automatically dividing grids at other positions, and setting the total number of the grids to be 2567604.
Unfolding CFD digital prototype verification: the method comprises the steps of carrying out a thermal measurement test on a product, carrying out temperature measurement on components and the like by utilizing equipment such as a temperature sensor, a thermostat and the like under a product working state, setting 28 temperature test points in the process of carrying out the thermal measurement test, wherein 24 points are key components, 4 points are case shells, and measuring temperature values of the measurement points at the temperature of 25 ℃ and 45 ℃ under the power-on and stable state of a servo control driver. Due to the reasons of simplification and the like in the process of establishing the CFD digital prototype model, the contents of specific parameters, grids and the like in the simulation model are adjusted, so that the error between the result of the thermal measurement test and the result of the thermal simulation is finally lower, and the thermal stress simulation result is ensured to be more accurate.
And (4) carrying out thermal stress simulation analysis, and simulating the CFD digital prototype model at-45 ℃, 20 ℃, 35 ℃ and 70 ℃ according to the environmental profile requirement of the servo control driver. And obtaining a high-temperature device and a temperature distribution cloud chart of the servo control driver, and providing input conditions for fault prediction.
(2) And (5) simulating the vibration stress. And carrying out vibration stress simulation analysis on the servo control driver based on a finite element analysis method, and carrying out vibration stress simulation analysis on the servo control driver by applying ANSYS.
Establishing an FEA digital prototype: and performing parameter injection on material attribute parameters such as elastic modulus, poisson's ratio and the like, and meshing the digital sample machine to increase the model solving precision.
Carrying out modal analysis: and forming a discrete mathematical model of the digital prototype, setting the solved modal number as the first three orders, setting the frequency range to be 10-2000 Hz, solving the characteristic value to obtain the results of the natural frequency, the vibration mode and the like of the product, and providing input conditions for random vibration analysis.
Correcting the FEA digital prototype: and carrying out modal test according to the physical entity of the servo mechanism, and solving to obtain the natural frequency and the vibration mode of the product. Because three PCBs cannot be disassembled and equipment conditions are limited, the modal test of the whole machine cannot be carried out, so that the modal test is carried out only on the swinging power amplifier board combination 2 and the air rudder power amplifier board combination 1 in a knocking mode, and the parameters and grids are adjusted according to the modal test result, so that the errors of the modal test and the modal analysis result are within 10%.
And (3) vibration stress analysis: according to the actual working environment profile of the product, the setting of the random vibration spectrum, the acceleration frequency spectrum and the random vibration parameters of the product in the X direction, the Y direction and the Z direction is completed, and when the output response of the product is measured, the position and the speed of the product in the X direction, the Y direction and the Z direction are selected. And (3) carrying out random vibration analysis on the servo control driver, taking a control panel combined plate as an example, analyzing a response result to obtain a displacement cloud chart and an acceleration cloud chart, and providing an input condition for fault prediction.
(3) Failure prediction: and carrying out fault prediction on the servo control driver based on the CalcePWA platform. Establishing a fault prediction model: different from the establishment of a digital prototype in thermal and vibration stress simulation, all components are modeled under the condition of permission, and the model comprises the size and the position distribution of all the components in the control board combination.
Thermal simulation: setting a heat dissipation mode and a temperature profile, selecting natural cooling according to the characteristic heat dissipation mode of the driver, setting the distance between an upper plate and a lower plate to be 12mm and 10mm respectively, and setting the environmental temperature to be minus 45 ℃, minus 20 ℃, 35 ℃ and 70 ℃ according to the temperature profile. And filling the boundary temperature of the control panel composite board in CalcePWA according to the thermal simulation result in Flotherm, then performing thermal simulation, and adjusting the thermal simulation result in CalcePWA according to the thermal simulation result in Flotherm to obtain the thermal stress simulation result, wherein the form of the result is presented by a thermal stress cloud chart.
Vibration simulation: adding a fixed support to the control panel combination according to the fixed support condition of the control panel combination, selecting a random vibration mode to input a response PSD frequency spectrum of an acceleration maximum point derived after ANSYS vibration simulation, adjusting a grid and a fixed support mode of the control panel combination in CalcePWA according to a simulation result in ANSYS, performing simulation analysis to obtain a vibration simulation result of the control panel combination, and presenting a result form by a vibration stress cloud chart.
Failure prediction: thermal and vibration stress simulation in CalcePWA is used as input, and Monte Carlo sampling is carried out for 1000 times to obtain the first failure time and various failure modes of the components. And carrying out data preprocessing on the first failure time of the component according to the failure mode of the component to obtain the failure data of a single component, and carrying out data fitting on the failure data to obtain single-point failure distribution. Selecting 10 components with the lowest initial fault time of each functional circuit, and carrying out a fitting processing process on fault data of the components so as to obtain the service life distribution of each functional circuit:
(1) For each functional circuit, 10 periods are selected, failure data of each device caused by two failure mechanisms are coupled, and a set of failure data of the device is obtained based on the device life and obeying an exponential distribution assumption:
wherein λ is 1 、λ 2 MTTF for failure of two failure modes of components 1 、MTTF 2 And obtaining the failure rate of the component for the first failure time corresponding to the two failure modes through the preprocessing formula. And fitting the data after preprocessing of each component to obtain the service life distribution of each component.
(2) According to the obtained 10 single-point distributions, the Monte Carlo sampling is used to obtain the failure time of 10 devices, the sampling is carried out for 1000 times, and therefore, a failure time matrix of 10 x 1000 can be obtained
(3) And (3) regarding the failure time of the whole functional circuit in one sampling as the earliest failure time of 10 devices, thereby obtaining the failure time of 1000 functional circuits, and then performing distribution fitting by using MATLAB to obtain the fault distribution of the whole module.
Finally, the service life distribution of each functional circuit obtained by fitting and solving obeys three-parameter Weibull distribution, and the table 1 is a service life distribution table of each functional circuit.
TABLE 1 Life distribution Table for each functional circuit
As can be seen from table 1, the probability of failure of the functional circuit on the air rudder power amplifier board is high, and the probability of failure of other circuits except the secondary power supply in the control board assembly is low. Aiming at the analysis result, the device on the air vane power amplification board can be focused, and the design of the servo control driver is also focused.
Step three: simulation method research of servo mechanism performance and reliability
The invention establishes a reliability model of the circuit based on the service life distribution of the servo control driver functional circuit, injects the reliability model into a performance model of a servo mechanism, carries out the combined simulation of the performance and the reliability of the servo mechanism, and evaluates the performance and the reliability according to a performance and reliability evaluation rule.
(1) Servo mechanism performance and reliability joint simulation model construction
A fault trigger module for a single functional circuit is built in Simulink based on monte carlo sample data, as depicted in fig. 11: and (4) taking the service life distribution parameters g1, g2 and g3 of each functional circuit obtained in the step two as the input of the Monte Carlo sampling module, obtaining the fault time according to the output of the formula 3.1, and comparing the fault time with the set initial fault time 9000h to judge whether the functional circuit can normally operate. When the output value of the sampling module is smaller than the set value, the functional circuit triggers the fault, otherwise, the functional circuit operates normally, and therefore the fault starting module outputs the Boolean logic value finally.
T＝g 3 +g 2 τ(1+1/g 1 ) (5)
The servo mechanism has a plurality of functional circuits, a Monte Carlo sampling model and a fault triggering model are respectively established, and the logical relation between the fault triggering modules is described according to the influence of the functional circuit faults on the performance of the servo mechanism, as shown in Table 2. According to the description connection failure triggering module, as described in fig. 12: as any one circuit fault of the CAN bus, the DSP circuit, the A/D circuit, the signal acquisition circuit and the secondary power supply circuit in the control panel combination CAN cause the performance model of the servo mechanism system to have a fault, the five single-circuit fault trigger modules are connected with logic, as shown in fig. 12 a; any circuit fault of a grid drive circuit, a logic processing circuit, a secondary power supply circuit (power amplifier board combination) and a power drive circuit in the pendulum spray power amplifier board combination and the air rudder power amplifier board combination can cause the fault of an electromechanical actuator controlled by the corresponding power amplifier board and influence the ideal position parameters of the lead screw, so that four single circuit fault trigger modules of the power amplifier board are connected with logic, and the output of a combined logic module influences the ideal position signals of the lead screw, as shown in fig. 12b and 12 c. The current monitoring circuit fault has no effect on the ideal position of the lead screw but has an effect on the electromechanical actuator feedback signal, thus maintaining a single circuit fault trigger module whose output will affect the feedback signal, as shown in figure 12 d.
TABLE 2 influence table of functional circuit fault on servo mechanism performance model
| Control panel circuit | Impact on servomechanism performance model | Power amplifier board circuit | Influencing the servomechanism performance model |
| CAN bus | No signal input, failure | Logic processing circuit | No signal input to corresponding electromechanical actuator |
| DSP circuit | No signal output, failure | Current monitoring circuit | Without feedback signal of corresponding electromechanical actuator |
| A/D circuit | No feedback signal, failure | Secondary power supply circuit | No signal input to corresponding motor |
| Signal acquisition circuit | No feedback signal, failure | Power driving circuit | No signal input of corresponding motor |
| Secondary power supply circuit | No signal, failure | Gate drive circuit | No signal input to corresponding electromechanical actuator |
And establishing a breakpoint module, and finally realizing the combination of the reliability simulation model and the performance model through multi-point breakpoints.
The multi-point breakpoint module of a single servo is shown in fig. 2: a breakpoint is set at the output (stator current, rotor speed, rotor angle, electromagnetic torque) of the motor module, and a fault trigger output signal is added for determination: when the functional circuit breaks down, the output signal of the fault trigger module can cause the output of the motor module to be interrupted, otherwise, the output signal of the motor module which is not influenced by the fault trigger module continues to enter the next feedback.
And establishing a multi-point breakpoint module of the whole servo mechanism. The known overall servo mechanism consists of 1 servo control driver and six electromechanical actuators, wherein the servo control driver consists of a control board and six power amplifier boards. And (3) performing breakpoint model injection in a servo control driver:
the method comprises the steps that firstly, fault judgment is carried out on a lead screw position and a control plate fault trigger module through a single-point breakpoint, output position quantity and a power amplification plate fault trigger module are subjected to fault judgment, the lead screw position quantity obtained through double judgment of the control plate and the power amplification plate is input into a performance model, if any component of the control plate or the power amplification plate breaks down, the fault trigger module outputs a signal to cause interruption, and the lead screw position quantity is 0.
Meanwhile, the output of the motor module (stator current, rotor speed, rotor angle, electromagnetic torque) and the current detection triggering module perform fault determination through a multi-point breakpoint, and the final result is input into the performance model as a motor parameter m, as shown in fig. 13.
And finally, establishing a simulation termination module for reliability evaluation, respectively comparing three ideal signals of the position, the current and the speed of the six motors with the feedback signal, and if the ideal signals are lower than an error threshold, determining that the performance level accords with the system instruction simulation and continues, otherwise, terminating the simulation, as shown in fig. 14. The simulated values of position, speed and torque and the system command error rate are defined to be 1%, 0.5% and 0.5%, respectively.
(2) Joint performance and reliability assessment
And (3) carrying out N times of simulation based on a performance and reliability combined simulation model, and recording the times of fault-free operation of the servo mechanism and the times of the performance level of the servo mechanism according with the system instruction. Setting the running time as T, developing joint simulation analysis, and solving the reliability of the servo mechanism in the time T and the probability that the performance levels of the position, the rotating speed and the torque meet the system instruction according to the formulas (3.2) to (3.10).
In the formula, N is the total simulation times; n is a radical of hydrogen i =1 represents that the simulation system of the ith time runs without fault, otherwise, the system is in fault; n is a radical of hydrogen k =1 indicates that the simulation system at the k time fails because the position error is lower than the threshold value, whereas the simulation system fails because the position error exceeds the threshold value; n is a radical of hydrogen d =1 indicates that the simulation system at the d-th time fails because the speed error is lower than the threshold value, whereas the simulation system fails because the speed error exceeds the threshold value; n is a radical of hydrogen s =1 indicates that the simulation system of the s-th time fails because the torque error is below the threshold, whereas the system fails because the torque error exceeds the threshold. P is the error rate of system command and feedback value, P k 、P d 、P s Error rates of position, velocity and torque feedback values with system commands, respectively,
error rate thresholds, R, for position, speed and torque feedback values and system commands,
The reliability of the system at time T and the probability of the three performance levels of position, rotating speed and torque according with the system command are respectively indicated, and the reliability of the servo mechanism and the probability of the performance level according with the system command can be obtained through statistics of N times of simulation results.
Here, the first failure time of each functional circuit is 9000h. Under different simulation durations, the performance and reliability joint simulation model of the servo mechanism is subjected to joint simulation analysis, and the simulation results have no obvious difference under the condition that no fault occurs in a functional circuit, so that the simulation process is simplified, the simulation duration is set to be 0.08s, the cycle simulation times are set to be 1000 times, the simulation results can find that the position, the speed and the torque under a control circuit of each functional circuit generate large errors due to the fault of each functional circuit, the probability that the three performance levels of the position, the rotating speed and the torque accord with system instructions is the same under the condition, and the probability that the reliability and the three performance levels of the position, the rotating speed and the torque accord with the system instructions is 0.765 within 9000h according to joint simulation data statistics and expressions (3.2) - (3.10).
Claims (10)
1. A Simulink-based servo mechanism performance and reliability joint simulation method specifically comprises the following steps:
the method comprises the following steps: servo mechanism performance simulation method
In Simulink modeling simulation, a servo mechanism is divided into a motor driving module, a motor-screw module and a control algorithm module, and a model is established; the motor driving module converts the PWM signal into a sine wave signal; the motor-screw module enables the three-phase signals to drive the load to move after passing through the motor model and outputs stator current, rotor speed, rotor angle and electromagnetic torque; the control algorithm module reduces the displacement of motion output, the electromagnetic torque and the speed error of the screw pair through negative feedback;
step two: reliability simulation method for servo control driver
Establishing a CFD digital prototype of the product, correcting the CFD digital prototype and simulating the thermal stress based on thermal simulation software Flotherm; establishing a simplified model based on ANSYS software, setting parameters, dividing grids, carrying out modal analysis, carrying out modal test and model correction, and adding random loads to carry out random vibration analysis; establishing a PCB model based on CalcePWA software, and carrying out failure prediction analysis based on failure physics on each PCB by taking Flotherm thermal simulation results, ANSYS vibration simulation results and environmental profiles as input conditions; the method comprises the steps of obtaining a fault matrix of a servo control driver through fault prediction, carrying out fitting-sampling-fitting solving processes on fault data, obtaining service life of each functional circuit according with Weibull distribution, and calculating distribution parameters of the functional circuits;
step three: servo mechanism performance and reliability joint simulation
A reliability mathematical model is established in simulink: establishing a Monte Carlo sampling module based on the service life distribution of the functional circuits in the servo control driver in the second step; establishing a fault trigger module based on the first fault sending time obtained in the step two; because the servo control driver is provided with the A/D circuit and the secondary power supply circuit, the fault trigger modules are respectively established, and the connection is established according to the functional logic relationship of each circuit in the servo mechanism, so that the reliability mathematical simulation model is obtained.
2. The Simulink-based servo performance and reliability joint simulation method of claim 1, wherein: injecting a reliability simulation model into the performance model to realize the completion of the simulation by the multi-point breakpoint module, setting a breakpoint at the output of the motor module, and adding a fault trigger output signal for judgment; therefore, when the functional circuit breaks down, the output signal of the fault trigger module can cause the output of the motor module to be interrupted, otherwise, the output signal of the motor module without influence continues to enter the next feedback; after the joint simulation is realized, a simulation termination module is established to carry out joint evaluation on performance and reliability, an ideal signal is compared with a feedback signal, if the ideal signal is lower than an error threshold value, the performance level accords with a system instruction, otherwise, the ideal signal exceeds the system instruction, and the simulation is terminated; on the basis of a performance and reliability combined simulation model, N times of simulation is carried out, the times of fault-free operation of the servo mechanism and the times of the performance level of the servo mechanism according with the system instruction are recorded, and the reliability of the servo mechanism in time T and the probability of the performance level of the position, the rotating speed and the torque according with the system instruction are calculated by a reliability basic principle.
3. Simulink-based servo performance and reliability joint simulation method according to claim 1 or 2, characterized in that: in the first step, a performance model is established based on Simulink, a servo mechanism consists of 1 servo control driver and six electromechanical actuators, wherein the servo control driver consists of one control board and six power amplification boards, and the power amplification boards are divided into two swinging power amplification boards and four air steering power amplification boards; the air rudder power amplification plate and the swing jet power amplification plate have the same structure, and the six electromechanical actuators have the same structure;
establishing a simulation model of a single servo control driver-electromechanical actuator; the system is divided into a motor driving module, a motor-screw module and a control algorithm module; the signals are converted in the motor driving module, and the PWM signals g are converted into three-phase voltage signals A, B and C; the permanent magnet synchronous motor receives the three-phase voltage and provides the three-phase voltage for the stator; a rotating magnetic field is generated under the action of three-phase current of the stator, the rotor rotates under the action of an electromagnetic field, the permanent magnet synchronous motor drives the load screw pair to do linear motion at the moment, and the permanent magnet synchronous motor submodel outputs the stator current, the rotor speed, the rotor angle and the electromagnetic torque of the electromechanical actuator;
wherein the electromagnetic torque:
ψ d 、ψ q are respectively d-and q-axis flux linkage, i q 、i d D and q axis currents; if can control i d =0, the torque equation is simplified to:
now only control i q The magnitude of the torque, the d-axis voltage and the i can be controlled q The stator is equivalent to a separately excited direct-current motor, only has an alternating-axis component, and the space vector of the magnetomotive force of the stator is just orthogonal to the space vector of the magnetic field of the permanent magnet; to reduce the loss, i is finished d =0, reduction of losses;
the rotor angle theta is converted into screw displacement s through the control of a gear ratio k of a speed reducer and a screw by a screw module, wherein c is the initial position of the screw;
s＝k*θ+c (3)
the control algorithm module consists of three closed loops of position and speed current, a simulation screw displacement parameter s and an ideal position parameter s1 are used as model input, and errors of the position parameters are reduced through negative feedback; because the position control does not allow overshoot, a proportional regulator is arranged, the difference value of the ideal position signal and the simulation position detection signal is set as the speed of a control module, the speed is compared with the speed of a rotor, and the error is reduced through negative feedback; the q-axis stator current, i.e. the electromagnetic torque, is controlled by the proportional-integral control and the negative feedback to reduce the error and control i d =0, maximum electromagnetic torque is generated on the d-axis; the three-closed-loop negative feedback system outputs voltage Ud, voltage Uq and a rotor angle theta; the output signal generates a PWM signal through a Space Vector Pulse Width Modulation (SVPWM) algorithm, and the PWM signal returns to the motor driving module to generate three-phase voltage signals A, B and C again for next correction and feedback;
the method comprises the steps of establishing an integral servo mechanism model, wherein a servo mechanism consists of 1 servo control driver and six electromechanical actuators, wherein the servo control driver consists of one control board and six power amplification boards, and therefore six motor driving modules, a control algorithm module and a motor lead screw module are established in the performance model simulation process.
4. The Simulink-based servo performance and reliability joint simulation method of claim 1, wherein: in the second step, thermal stress simulation: based on numerical heat transfer and computational fluid dynamics methods, the thermal stress simulation servo control driver developed by the Flotherm has five PCB boards, and is subjected to thermal simulation under the temperature environment of-45 ℃, 20 ℃, 35 ℃ and 70 ℃ according to the environmental profile requirement;
establishing a CFD digital prototype: a CAD model is introduced or a model is established in a manual mode, and holes, bosses, fillets, thermal analysis irrelevant connecting pieces and low-power-consumption components in the product are deleted, so that the calculation efficiency of solving is improved; then, carrying out mesh division and boundary environment setting to determine finite element analysis precision;
and (3) unfolding CFD digital prototype verification: carrying out a thermal measurement test on a product, carrying out temperature measurement on components by using a temperature sensor and a thermostat under the working state of the product, setting 28 temperature test points in the process of carrying out the thermal measurement test, wherein 24 points are key components, 4 are case shells, and measuring the temperature values of the measurement points at the temperature of 25 ℃ and 45 ℃ under the power-on and stable state of a servo control driver;
carrying out thermal stress simulation analysis, and simulating the CFD digital prototype model at-45 deg.C, -20 deg.C, 35 deg.C and 70 deg.C according to the environmental profile requirement of the servo control driver; and obtaining a high-temperature device and a temperature distribution cloud chart of the servo control driver, and providing input conditions for fault prediction.
5. The Simulink-based servo performance and reliability joint simulation method of claim 1, wherein: in the second step, simulating the vibration stress; carrying out vibration stress simulation analysis on the servo control driver based on a finite element analysis method, and carrying out vibration stress simulation analysis on the servo control driver by applying ANSYS;
establishing an FEA digital prototype: performing parameter injection on material attribute parameters, and performing grid division on a digital sample machine to increase the model solving precision;
carrying out modal analysis: forming a discrete mathematical model of a digital prototype, setting solved modal numbers as the first three orders, setting the frequency range to be 10-2000 Hz, solving the characteristic values to obtain the natural frequency and the vibration mode result of the product, and providing input conditions for random vibration analysis;
correcting the FEA digital prototype: carrying out modal test according to a physical entity of the servo mechanism, and solving to obtain the natural frequency and the vibration mode of the product; because three PCBs cannot be disassembled and equipment conditions are limited, the modal test cannot be carried out on the whole machine, so that the modal test is carried out only on the swinging power amplifier board combination 2 board and the air rudder power amplifier board combination 1 board in a knocking mode, and the parameters and grids are adjusted according to the modal test result, so that the errors of the modal test and the modal analysis result are within 10%;
and (3) vibration stress analysis: according to the actual working environment profile of the product, the setting of the random vibration spectrum, the acceleration frequency spectrum and the random vibration parameters of the product in the X direction, the Y direction and the Z direction is completed, and when the measurement of the output response of the product is carried out, the position and the speed of the product in the X direction, the Y direction and the Z direction are selected; and carrying out random vibration analysis on the servo control driver, and analyzing a response result to obtain a displacement cloud picture and an acceleration cloud picture, thereby providing input conditions for fault prediction.
6. The Simulink-based servo performance and reliability joint simulation method of claim 1, wherein: in step two, failure prediction: carrying out fault prediction on the servo control driver based on the CalcePWA platform; establishing a fault prediction model: different from the establishment of a digital prototype in thermal and vibration stress simulation, all components are modeled under the condition that the conditions allow, and the model comprises the size and the position distribution of the models of all the components in the control panel combination;
thermal simulation: setting a heat dissipation mode and a temperature profile, selecting natural cooling according to the characteristic heat dissipation mode of the driver, setting the distance between an upper plate and a lower plate to be 12mm and 10mm respectively, and setting the environmental temperature to be-45 ℃, 20 ℃, 35 ℃ and 70 ℃ respectively according to the temperature profile; filling the boundary temperature of the control panel composite board in CalcePWA according to the thermal simulation result in Flotherm, then performing thermal simulation, and adjusting the thermal simulation result in CalcePWA according to the thermal simulation result in Flotherm to obtain the thermal stress simulation result, wherein the result form is presented by a thermal stress cloud chart;
vibration simulation: adding a fixed support to the control panel combination according to the fixed support condition of the control panel combination, selecting a random vibration mode to input a response PSD frequency spectrum of an acceleration maximum point derived after ANSYS vibration simulation, adjusting a grid and a fixed support mode of the control panel combination in CalcePWA according to a simulation result in ANSYS, performing simulation analysis to obtain a vibration simulation result of the control panel combination, and presenting a result form in a vibration stress cloud chart;
failure prediction: taking thermal and vibration stress simulation in CalcePWA as input, and carrying out Monte Carlo sampling for 1000 times to obtain the first failure time and various failure modes of the components; performing data preprocessing on the first failure time of the component according to the failure mode of the component to obtain the failure data of a single component, and performing data fitting on the failure data to obtain single-point failure distribution; and selecting 10 components with the lowest initial fault time of each functional circuit, and carrying out a fitting processing process on fault data of the components so as to obtain the service life distribution of each functional circuit.
7. The Simulink-based servo performance and reliability joint simulation method of claim 6, wherein: in step two, (1), for each functional circuit, selecting 10 periods, coupling failure data of each device caused by two failure mechanisms, and obtaining a set of failure data of the device based on the device life obeying an exponential distribution assumption:
wherein λ is 1 、λ 2 MTTF for failure of two failure modes of components 1 、MTTF 2 Obtaining failure rates of the components for the first failure time corresponding to the two failure modes through the preprocessing formula; fitting the data preprocessed by each component to obtain the service life distribution of each component;
(2) Obtaining the failure time of 10 devices by Monte Carlo sampling according to the obtained 10 single-point distributions, wherein the sampling is carried out for 1000 times, thereby obtaining a failure time matrix of 10 x 1000
(3) And identifying the failure time of the whole functional circuit in one sampling as the earliest failure time of 10 devices, thereby obtaining the failure time of 1000 functional circuits, and then carrying out distribution fitting by MATLAB to obtain the fault distribution of the whole module.
8. The Simulink-based servo performance and reliability joint simulation method of claim 1, wherein: in the third step, a combined simulation model of the performance and the reliability of the servo mechanism is constructed:
establishing a fault trigger module of a single functional circuit in Simulink based on Monte Carlo sampling data, taking the service life distribution parameters g1, g2 and g3 of each functional circuit obtained in the step two as the input of the Monte Carlo sampling module, obtaining fault time according to the output of the formula (1), comparing the fault time with the set first fault time 9000h, and judging whether the functional circuit can normally operate; when the output value of the sampling module is smaller than a set value, the functional circuit triggers a fault, otherwise, the functional circuit operates normally, and therefore the fault starting module outputs a Boolean logic value finally;
T＝g 3 +g 2 τ(1+1/g 1 ) (5)
the servo mechanism is provided with a plurality of functional circuits, a Monte Carlo sampling model and a fault triggering model are respectively established, and the logical relation between the fault triggering modules is described according to the influence of the faults of the functional circuits on the performance of the servo mechanism; according to the description, the fault trigger module is connected, and because any one circuit fault of a CAN bus, a DSP circuit, an A/D circuit, a signal acquisition circuit and a secondary power supply circuit in the control panel combination CAN cause the performance model of the servo mechanism system to have a fault, the five single-circuit fault trigger modules are connected with logic by using; any circuit fault of a grid drive circuit, a logic processing circuit, a secondary power supply circuit and a power drive circuit in the pendulum spraying power amplification board combination and the air rudder power amplification board combination can cause the fault of an electromechanical actuator controlled by the corresponding power amplification board and influence the ideal position parameters of the lead screw, so that four single circuit fault trigger modules of the power amplification board are connected with logic, and the output of a combined logic module influences the ideal position signals of the lead screw; the current monitoring circuit fault has no influence on the ideal position of the screw rod, but has influence on the feedback signal of the electromechanical actuator, so that the single circuit fault trigger module is kept, and the output of the single circuit fault trigger module influences the feedback signal; and establishing a breakpoint module, and finally realizing the combination of the reliability simulation model and the performance model through multi-point breakpoints.
9. The Simulink-based servo performance and reliability joint simulation method of claim 8, wherein: in the third step, the multipoint breakpoint module of a single servo mechanism sets breakpoints at the output of the motor module, and adds the fault trigger output signal for judgment: when the functional circuit is in fault, the output signal of the fault trigger module causes the output of the motor module to be interrupted, otherwise, the output signal of the motor module without influence continues to enter the next feedback;
establishing a multi-point breakpoint module of the whole servo mechanism; the known integral servo mechanism consists of 1 servo control driver and six electromechanical actuators, wherein the servo control driver consists of a control plate and six power amplification plates; and (3) performing breakpoint model injection in a servo control driver:
the method comprises the following steps that firstly, a screw position and a control plate fault trigger module carry out fault judgment through a single-point breakpoint, output position quantity and a power amplification plate fault trigger module carry out fault judgment, therefore, the screw position quantity obtained through double judgment of the control plate and the power amplification plate is input into a performance model, if any component of the control plate or the power amplification plate breaks down, the fault trigger module outputs a signal to cause interruption, and the screw position quantity is 0; meanwhile, the output of the motor module and the current detection trigger module carry out fault judgment through multi-point breakpoints, and the final result is input into the performance model as a motor parameter m;
and finally establishing a simulation termination module for reliability evaluation, respectively comparing the position, current and speed ideal signals of the six motors with the feedback signal, and if the ideal signals are lower than an error threshold, determining that the performance level accords with the system instruction simulation continuation, otherwise, terminating the simulation.
10. Simulink-based servo performance and reliability joint simulation method according to claim 1 or 9, characterized in that: in step three, the performance and reliability are jointly evaluated:
carrying out N times of simulation based on the performance and reliability combined simulation model, and recording the times of fault-free operation of the servo mechanism and the times of the performance level of the servo mechanism according with the system instruction; setting the running time as T, developing joint simulation analysis, and solving the reliability of the servo mechanism in the time T and the probability that the performance levels of the position, the rotating speed and the torque meet the system instruction according to the formulas (2) to (10);
in the formula, N is the total simulation times; n is a radical of i =1 represents that the simulation system of the ith time runs without fault, otherwise, the system is in fault; n is a radical of hydrogen k =1 indicates that the simulation system at the k time fails because the position error is lower than the threshold value, whereas the simulation system fails because the position error exceeds the threshold value; n is a radical of d =1 indicates that the simulation system at the d-th time fails because the speed error is lower than the threshold value, whereas the simulation system fails because the speed error exceeds the threshold value; n is a radical of s =1 indicates that the simulation system at the s-th time fails because the torque error is lower than the threshold value, whereas the system fails because the torque error exceeds the threshold value; p is the error rate of system command and feedback value, P k 、P d 、P s Error rates of position, velocity and torque feedback values with system commands, respectively,
error rate thresholds, R, for position, speed and torque feedback values and system commands,
The reliability of the system at the time T and the probability of the performance levels of the position, the rotating speed and the torque according with the system instruction are respectively indicated, and the reliability of the servo mechanism and the probability of the performance level according with the system instruction can be obtained through statistics of N times of simulation results.
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| CN117666446A (en) * | 2024-01-30 | 2024-03-08 | 湖南高至科技有限公司 | Servo control driver test system |
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| CN117666446A (en) * | 2024-01-30 | 2024-03-08 | 湖南高至科技有限公司 | Servo control driver test system |
| CN117666446B (en) * | 2024-01-30 | 2024-04-19 | 湖南高至科技有限公司 | Servo control driver test system |
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