Atmosphere push plate furnace driving system based on servo motor frequency conversion control

Abstract

The invention provides an atmosphere push plate furnace driving system based on servo motor frequency conversion control. The atmosphere push plate furnace driving system comprises a real-time detection module, a multi-mode control module, a strategy generation module and a system optimization and evaluation module. The method comprises the following steps: analyzing collected real-time data of different targets, identifying a current working condition stage of a system, switching to a corresponding control mode according to the identified working condition stage so as to generate a corresponding control strategy, dynamically adjusting the rotating speed and the torque of a motor according to the control strategy, and carrying out optimization test and evaluation on the system; a real-time load monitoring and feedback mechanism is adopted, a control algorithm and a rotating speed and torque adjusting strategy are applied, effective vibration suppression measures are combined, the dynamic response characteristics of the servo motor can be fully utilized, the rotating speed and torque of the servo motor can be adjusted in real time according to load changes, and start-stop impact and mechanical vibration of the atmosphere pusher furnace are remarkably reduced.

Description

Technical Field

[0001] The invention relates to the technical field of atmosphere push plate furnaces, and in particular to an atmosphere push plate furnace driving system based on servo motor frequency conversion control. Background Art

[0002] Atmosphere pusher furnaces are widely used in industrial production, and the drive performance of their pusher plates directly impacts production efficiency and product quality. When servo motors are used as the drive mechanism for atmosphere pusher furnaces, the pusher plates must be frequently started and stopped to achieve material transport. This causes the servo motor to be subjected to significant inertial shock and mechanical vibration. Long-term operation can cause motor shaft wear, encoder failure, or loose transmission components. Frequent starts and stops also increase the motor's thermal load, further exacerbating heat dissipation pressure. Furthermore, in actual applications, the weight of the material varies, and existing technologies do not account for the vibration caused by this weight change.

[0003] Therefore, when using a servo motor as the push plate drive mechanism, how to adjust the servo motor speed and torque in real time according to the dynamic response characteristics of the servo motor and combined with load changes to reduce start-stop impact and mechanical vibration is a technical problem that needs to be solved urgently. Summary of the Invention

[0004] To solve the above problems, the present invention provides an atmosphere push plate furnace drive system based on servo motor frequency conversion control. By analyzing the real-time data of different targets collected, the current working condition stage of the system is identified, and the corresponding control mode is switched according to the identified working condition stage, thereby generating a corresponding control strategy, dynamically adjusting the motor speed and torque according to the control strategy, and optimizing the system for testing and evaluation.

[0005] To achieve the above object, the technical solution adopted by the present invention is: The present invention provides an atmosphere push plate furnace drive system based on servo motor variable frequency control, comprising a real-time monitoring module, a multi-modal control module, a strategy generation module and a system optimization and evaluation module which are sequentially communicatively connected; The real-time monitoring module is used to obtain real-time data of different targets of the atmosphere push plate furnace; The multimodal control module is used to divide the push plate furnace into different stages according to its workflow, identify the stage the current system is in by analyzing the real-time data collected by the real-time detection module, and switch to the corresponding control mode for the identified stage; The strategy generation module is used to generate a corresponding control strategy according to the control mode switched by the multi-modal control module, and dynamically adjust the motor speed and torque according to the control strategy; The system optimization and evaluation module, based on an adaptive control algorithm, optimizes control parameters according to real-time monitoring of system status and load changes, and conducts comprehensive performance testing on the optimized system. The test data is compared and analyzed with the system's design requirements and performance indicators to evaluate whether the system's performance meets the requirements. Based on the performance test and evaluation results, further optimization suggestions are made for problems existing in the system.

[0006] Furthermore, real-time monitoring of different targets of the atmosphere push plate furnace system is achieved through the hardware composition and integration of the real-time monitoring module. The real-time monitoring module has a built-in controller and a variety of types of sensors deployed in a distributed manner. The sensors include weighing sensors, encoders, vibration sensors, and electrical sensors. Different types of data are collected through the sensors and transmitted to the controller in the form of electrical signals to calculate the required parameters.

[0007] Furthermore, the load weight data is collected in real time by the weighing sensor and transmitted to the controller in the form of an electrical signal. The controller calculates the load weight value and the load change rate, sets the normal range and change rate threshold of the load weight. When the monitored load weight value exceeds the normal range or the load change rate exceeds the threshold, the controller determines that the load is abnormal, and the system sends an alarm signal to prompt the operator to check the equipment and load conditions.

[0008] Furthermore, in the multi-modal control module, the operating stages of the push plate furnace include a startup stage, a uniform speed operation stage, a deceleration and stop stage, and a stage with large load changes; When the startup phase is identified, it switches to torque control mode; When the uniform speed operation stage is identified, it switches to the speed control mode; When the deceleration stop stage is identified, it switches to position control mode; When a stage with large load changes is identified, the system switches to model predictive control mode.

[0009] The multimodal control module has a built-in prediction model construction module, which is used to establish an overall prediction model of the push plate furnace system based on the dynamic model of the push plate furnace, the mathematical model of the servo motor and the load model, and switch to the model prediction control mode according to the identified current working condition stage of the system.

[0010] Furthermore, the prediction model building module builds an overall prediction model of the push plate furnace system, including the following steps: Conduct force analysis on the push plate during movement and establish a dynamic model; Establish a motor dynamic model based on the electrical and mechanical characteristics of the servo motor; Build a load model based on the dynamic characteristics of the load; Couple the push plate dynamics model, motor dynamics model and load model, and establish the state space equation; Based on the state space equation, in each control cycle, the state of the next N steps is predicted by the current state and the future control input sequence; With the goal of reducing start-stop shock and mechanical vibration, an objective function is constructed; Simulate the model, input control signals of typical working conditions, compare the model output with the measured values, and correct the model parameters.

[0011] Preferably, when constructing the objective function, the start-stop impact and mechanical vibration are quantified, a motor torque penalty term is introduced, and physical constraints are introduced. The physical constraints include motor torque limit and speed limit. The constructed objective function formula is: , Where ω1, ω2, and ω3 are non-negative weight coefficients, which are adjusted according to process requirements. a(k+i|k) is the predicted acceleration of the push plate in the kth step, and v ref is the reference speed, v(k+i|k) is the predicted speed, T m (k+i|k) is the motor torque predicted in the kth step, δ1 and δ2 are large weight coefficients, max(0, T m -T max ) 2 is the motor torque limit, max(0, vv max ) 2 For speed limit.

[0012] Furthermore, it also includes a human-machine interface module for providing an interface for operators to interact with the system, set the working parameters of the push plate furnace, and display the operating status of the system in real time.

[0013] The beneficial effects of the present invention are: The present invention provides an atmosphere push plate furnace drive system based on servo motor frequency conversion control, comprising a real-time detection module, a multimodal control module, a strategy generation module, and a system optimization and evaluation module; by analyzing the real-time data of different targets collected, the current operating stage of the system is identified, and the corresponding control mode is switched according to the identified operating stage, thereby generating a corresponding control strategy, dynamically adjusting the motor speed and torque according to the control strategy, and performing optimization testing and evaluation on the system; adopting a real-time load monitoring and feedback mechanism, applying a control algorithm and a speed and torque adjustment strategy, and combining effective vibration suppression measures, it is possible to fully utilize the dynamic response characteristics of the servo motor, adjust the speed and torque of the servo motor in real time according to load changes, and significantly reduce the start-stop impact and mechanical vibration of the atmosphere push plate furnace. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1This is a block diagram of an atmosphere push plate furnace drive system based on servo motor frequency conversion control of the present invention.

[0015] Figure 2 The present invention is a flowchart of the execution process of an atmosphere push plate furnace driving system based on servo motor frequency conversion control. DETAILED DESCRIPTION

[0016] See also Figure 1 and Figure 2 As shown, the present invention relates to an atmosphere push plate furnace drive system based on servo motor variable frequency control, comprising a real-time monitoring module, a multi-modal control module, a strategy generation module and a system optimization and evaluation module that are sequentially communicatively connected; The real-time monitoring module is used to obtain real-time data of different targets of the atmosphere push plate furnace; Real-time monitoring of different targets of the atmosphere push plate furnace system is achieved through the hardware composition and integration of the real-time monitoring module. The real-time monitoring module has a built-in controller and a variety of types of sensors deployed in a distributed manner. The sensors include weighing sensors, encoders, vibration sensors, and electrical sensors. Different types of data are collected through the sensors and transmitted to the controller in the form of electrical signals to calculate the required parameters.

[0017] Specifically, the electrical sensors are voltage sensors and current sensors.

[0018] Weighing sensors are installed on the push plate or material-carrying area. These sensors collect load weight data in real time and transmit it to the controller as electrical signals. The controller then filters the received signals to remove noise and obtain accurate material weight values. Simultaneously, the controller calculates the load change rate based on load data collected at adjacent moments. The load change rate reflects the speed of load changes and is an important reference for timely adjustment of control strategies. A normal range for load weight and a load change rate threshold are set. If the monitored load weight value exceeds the normal range or the load change rate exceeds the threshold, the controller determines that the load is abnormal and the system issues an alarm, prompting the operator to check the equipment and load conditions.

[0019] The servo motor has its own encoder, which is used to feedback the motor's position and speed information. It can also be combined with a linear encoder installed on the push plate motion track to further improve the accuracy of the push plate position detection.

[0020] Vibration sensors are installed at key locations of the push plate, servo motor or furnace body to monitor mechanical vibrations and feed vibration signals back to the controller. The controller adjusts the control strategy of the servo motor based on the vibration feedback signals.

[0021] Feedback signals such as load, position, speed and vibration collected by the sensor are transmitted to the controller through cables or field buses. The controller performs a series of processing on the received feedback signals, including signal amplification, analog-to-digital conversion, filtering, and resolution.

[0022] In some embodiments, the servo motor can be a permanent magnet synchronous servo motor with high dynamic response capabilities. Its high power density and fast torque response characteristics can adapt to the frequent starts and stops and load fluctuations of the pusher furnace. A driver matched with the permanent magnet synchronous servo motor is selected, utilizing its advanced control algorithms and powerful computing power to receive commands from the controller and drive the servo motor.

[0023] The controller can be a programmable logic controller (PLC) or a motion controller. The PLC has the advantages of high reliability and simple programming, and can realize the logical control of the entire process of the push plate furnace. The motion controller performs well in processing complex motion trajectories and high-precision control, and can accurately plan the motion curve of the servo motor.

[0024] The system stores various data collected in real time, and also stores various data during the system operation process, such as motor operating parameters, load change curves, vibration data, etc., for subsequent data analysis and system optimization.

[0025] The multimodal control module is used to divide the push plate furnace into different working stages according to its workflow, identify the working stage of the current system by analyzing the real-time data collected by the real-time detection module, and switch to the corresponding control mode for the identified working stage; The working stages of the push plate furnace include the starting stage, the uniform speed operation stage, the deceleration and stop stage and the stage with large load changes; When the startup phase is identified, it switches to torque control mode; When the uniform speed operation stage is identified, it switches to the speed control mode; When the deceleration stop stage is identified, it switches to position control mode; When a stage with large load changes is identified, the system switches to model predictive control mode.

[0026] Specifically: Monitor the push plate speed v and acceleration a in real time. When the speed increases significantly from 0, such as v> 0.1mm / s, and the acceleration shows a positive mutation, such as a> 0.5mm / s², it is determined to be in the startup phase. At this time, switch to torque control mode, so that the motor can quickly output sufficient torque to overcome the inertia of the push plate and material, achieve a smooth startup, and reduce impact. Motor output torque T m Increment according to the preset ramp function, such as It rises linearly within 2 seconds and compensates the torque in real time in combination with the load model.

[0027] Real-time monitoring of the push plate speed v is stable near the set value, such as the deviation is less than ±5%, and the acceleration a ≈ 0, while the load weight m load When the rate of change is less than the threshold, such as 0.5kg / s, it is determined to be in the uniform speed operation stage. At this time, it switches to the speed control mode to maintain the push plate speed stable, adapt to load fluctuations, and ensure uniform material transportation. PID closed-loop control is used to compare the measured speed v with the reference speed v in real time. ref , adjust the motor voltage, and when the load changes, dynamically adjust the speed loop gain through the adaptive algorithm, such as increasing the proportional gain K when the load increases p .

[0028] Real-time monitoring of the push plate speed v starts to decrease from the uniform speed value, such as v<0.8v ref If acceleration a shows a negative change, such as a < -0.3mm / s², and position x approaches the target position, and the remaining distance is less than 10mm, the system is considered to be in the deceleration phase. At this point, the system switches to position control mode to precisely control the push plate's stopping position and reduce overshoot and mechanical vibration. An S-shaped deceleration curve is used (acceleration first increases linearly and then decreases linearly). For example, deceleration is initiated at 50mm from the target position. In combination with encoder position feedback, feedforward control is used to compensate for inertia errors. By preemptively applying reverse torque, the system achieves position accuracy within ±0.1mm at stop, with vibration amplitude less than 0.05mm.

[0029] When the load cell detects the load weight m load If the change in acceleration exceeds a preset threshold, such as a single change exceeding 10% or a rate of change exceeding 1kg / s, and if motor current or torque fluctuates significantly, the system is considered to be in a stage of significant load change. At this point, the system switches to model predictive control mode to quickly respond to sudden load changes, optimize torque and speed output, and suppress vibration. Based on a coupled model of the push plate dynamics model, the motor dynamics model, and the load model, the system predicts the state for the next N steps. By constructing an objective function to minimize acceleration fluctuations and speed deviations, the system solves the optimal control sequence and adjusts torque and speed in real time.

[0030] The multimodal control module comprehensively analyzes multi-dimensional data such as speed, acceleration, position, load weight and motor current, determines the current operating stage through threshold comparison and pattern recognition algorithm (such as finite state machine), switches to the corresponding control mode according to the identified operating stage, and adopts transition function (such as first-order inertia link) when switching control mode to avoid sudden changes in control quantity and improve the system's operating performance under various operating conditions.

[0031] The multimodal control module has a built-in prediction model construction module, which is used to establish an overall prediction model of the push plate furnace system based on the dynamic model of the push plate furnace, the mathematical model of the servo motor and the load model, and switch to the model prediction control mode according to the identified current working condition stage of the system.

[0032] The prediction model building module builds an overall prediction model of the push plate furnace system, including the following steps: Conduct force analysis on the push plate during movement and establish a dynamic model; First, define the model boundaries and objects, take the push plate and the load as the core objects, consider the movement of the push plate on the track inside the furnace, focus on the dynamic relationship between the drive system and the push plate load, and ignore the influence of other irrelevant structures of the furnace. m (from servo motor torque conversion), friction force F f (related to load and friction coefficient), inertia force F a (determined by the push plate and material quality) and resistance F d (such as the influence of atmosphere resistance in the furnace).

[0033] The driving force is the thrust output by the servo motor through the transmission mechanism (such as ball screw, synchronous belt); the friction force reflects the resistance between the push plate and the track that needs to be overcome during operation; the inertial force reflects the resistance caused by inertia when the push plate starts, accelerates, and decelerates, and is the main source of start-stop shock; in addition, it will also be affected by the gas flow resistance in the furnace, the air friction between the push plate and the furnace wall, etc., which usually accounts for a small proportion and can be simplified to viscous resistance that is proportional to the speed.

[0034] The dynamic model is established based on the force of the push plate, and the formula is: F m -F f - F d =ma, where m represents the total mass of the push plate and the material, and a represents the overall acceleration of the push plate and the material, which can be calculated by the differential of the velocity signal or the second-order difference of the position, or obtained by the acceleration feedback of the motor encoder.

[0035] An analytical model is established for friction resistance, and the formula is: F f =μ(mg+F load ), where μ represents the friction coefficient, g represents the acceleration due to gravity, and F load Indicates the material load force, which is calculated in real time by the load model.

[0036] Inertial force F m = ma.

[0037] Resistance F d It can be simplified as a viscous resistance proportional to the speed, , where k represents the resistance coefficient, which is deduced by measuring the stable driving force at different speeds, and v represents the push plate speed, which is calculated by the encoder signal differential or position difference.

[0038] Establish a motor dynamic model based on the electrical and mechanical characteristics of the servo motor; The servo motor converts torque into push plate driving force through a transmission mechanism (such as a screw or gear). First, it is necessary to determine the transmission ratio i (such as i=1 in a screw transmission), the transmission efficiency η, and the equivalent radius r (such as the screw lead / 2π). The conversion equation between motor torque and driving force is established as follows: , where T m Indicates the motor output torque, , K t is the torque constant and V is the voltage.

[0039] The motor dynamic model is established based on the relationship between the motor electromagnetic torque and mechanical motion. The formula is: , Where, T L Indicates the load torque (converted from the push plate resistance), J m Indicates the motor's moment of inertia, B m represents the viscous damping coefficient, and ω represents the motor angular velocity.

[0040] Wherein, the load torque T L Converted from the push plate resistance, the moment of inertia J is derived from the push plate dynamics model formula. m = J 电机 +J load , J 电机 is the rotor moment of inertia, J load is the equivalent load inertia, and the calculation formula is: . Viscous damping coefficient B m It can be estimated by the motor no-load test. When the motor has no load, a small torque T0 is applied and the stable angular velocity ω0 is measured. . Through the inertial force F m and load torque T L The motor is coupled to the push plate motion to form a closed loop system.

[0041] Build a load model based on the dynamic characteristics of the load; Material load force F load Mainly depends on the push plate position x and material weight m load To solve the problem of load change, the weighing sensor can be used to collect material weight data in real time, establish a mapping relationship between load and position, and use the first-order inertia link to describe the dynamic process of load change. Considering the delay characteristics when adding / removing materials, the formula is: , Where τ represents the load change time constant, reflecting the load change hysteresis, which is determined by the step response test of the material loading process, k load It represents the proportional coefficient, which reflects the conversion efficiency of material weight to load force. It can be calculated through static calibration experiment. The material load force F load It is calculated through the discretization model of the first-order inertia link.

[0042] The load model is constructed through the method of "dynamic characteristic modeling + parameter experimental calibration + online adaptive correction" to ensure the real-time response accuracy of the model to material changes and provide accurate load feedback for the overall predictive control of the system.

[0043] Couple the push plate dynamics model, motor dynamics model and load model, and establish the state space equation; Select the push plate position x, speed v, motor angular velocity ω, load force F load is the state variable, expressed as: x=[x, v, ω, F load ] T .

[0044] The input u is determined to be the motor control voltage V, which is converted into a torque command through the driver. The output y is the push plate position x, push plate velocity v and vibration signal. The vibration signal is essentially a reflection of the high-frequency dynamic response of the system, and the high-frequency fluctuation of acceleration can directly reflect the vibration characteristics. Therefore, the instantaneous rate of change of acceleration a in the push plate motion or the root mean square value (RMS) of acceleration is used to characterize the vibration intensity.

[0045] Expressed as: or , where y vib represents the vibration signal, and T represents the sampling period. The accelerometer directly acquires a(t). After filtering (extracting high-frequency components) and signal processing (such as FFT spectrum analysis), the vibration amplitude or frequency characteristics are obtained as one of the outputs of the state-space equation.

[0046] Combining the plate dynamics model, motor dynamics model and load model, the state space equation is constructed, and the formula is: , where matrices A, B, C, and D are composed of model parameters (such as m, J m , k, etc.), and the specific expression needs to be derived through mechanics and motor control theory. Matrix A reflects the coupling relationship between state variables (such as the differential of speed with respect to position, and the response of motor angular velocity to load torque), matrix B represents the influence of input on state (such as how control voltage is converted into driving force and motor torque), and matrix C defines the mapping from state to output (such as direct output of position and speed, and indirect calculation of vibration signal through acceleration).

[0047] Based on the state space equation, in each control cycle, the state of the next N steps is predicted by the current state x(k) and the future control input sequence; First, set the prediction time domain N according to the system response speed. The value of N needs to balance the prediction accuracy and the amount of calculation. For example, if the push plate furnace start and stop cycle is 10s and the sampling period T=0.01s, N=500 can be taken, that is, predicting the state of the next 5s. Get the current state vector x(k|k), input the current control quantity u(k), and the motor control voltage u(k) is determined by the optimization result of the previous cycle, such as the optimal input calculated by the model predictive control. Based on the recursion of the discretized state equation, the N-step state is predicted. For example, the first step predicts x(k+1|k)=Ax(k|k)+Bu(k). When predicting the second step, assume that the control quantity at the k+1th moment is u(k+1), which comes from the future input sequence, then x(k+2|k)=Ax(k+1|k)+Bu(k+1), recursively to the Nth step, x(k+i|k)=Ax(k+i-1|k)+Bu(k+i-1), i=1, 2,...,N.

[0048] At the same time, constraints are introduced to limit the motor control voltage range, such as V min ≤u(k+i)≤V max To avoid exceeding the motor's rated parameters. For prediction error correction, when the actual state deviates from the predicted state, correction is performed using a Kalman filter or state observer. After each prediction cycle, the state equation parameters are updated using the latest measured data, such as by recursive least squares correction of matrices A and B.

[0049] With the goal of reducing start-stop shock and mechanical vibration, an objective function is constructed; When constructing the objective function, the start-stop impact and mechanical vibration are quantified, a motor torque penalty term is introduced, and physical constraints are introduced, including motor torque limit and speed limit.

[0050] Quantify the start-stop impact. The impact is mainly caused by acceleration fluctuations, so it is expressed by the mean square value of acceleration. , where a(k+i|k) is the predicted acceleration of the push plate at the kth step. To quantify the mechanical vibration, the vibration is related to the speed / position deviation and is expressed as, , where v ref is the reference speed, v(k+i|k) is the k-th step prediction speed Measured speed.

[0051] In order to avoid the sudden change of motor torque and aggravate the vibration, the motor torque penalty term is introduced, which is expressed as: , where T m(k+i|k) is the motor torque predicted at step k.

[0052] At the same time, physical constraints are introduced, and the motor torque limit T min ≤T m (k+i|k)≤T max , and the speed limit v min ≤v(k+i|k)≤v max For the introduced constraints, a penalty term is added to the objective function. , where δ1 and δ2 are large weight coefficients, such as 10 4 .

[0053] The final objective function is: Where ω1, ω2, and ω3 are non-negative weight coefficients, which are adjusted according to process requirements. For example, in the precision ceramic sintering scenario, impact is given priority, and ω1>ω2>ω3 can be set.

[0054] Simulate the model, input control signals of typical working conditions, compare the model output with the measured values, and correct the model parameters.

[0055] The model is simulated using tools such as MATLAB / Simulink. Control signals for typical operating conditions (such as starting, constant speed, and deceleration) are input. The simulated results for pusher position, speed, and torque are compared with actual test data to modify model parameters (such as friction coefficient and transmission efficiency). During actual operation, algorithms such as recursive least squares are used to identify model parameters online using real-time sensor data (such as encoder position, load cell load, and vibration signals) to improve prediction accuracy.

[0056] The overall predictive model, through multi-physics coupling between dynamics, motor, and load, transforms mechanical motion and electrical control into computable state-space equations, providing the mathematical foundation for model predictive control. In practical applications, parameter identification and real-time correction are combined to ensure the model's adaptability to load variations and system nonlinearities, thereby achieving precise speed / torque regulation and shock suppression.

[0057] The strategy generation module is used to generate a corresponding control strategy according to the control mode switched by the multi-modal control module, and dynamically adjust the motor speed and torque according to the control strategy; During the pusher's startup phase, the system switches to torque control mode. Considering the significant static friction the pusher may face during startup, the system estimates the additional torque compensation required for startup using a load model and real-time monitoring data. At the instant of startup, the motor's torque output is appropriately increased to overcome static friction and ensure a smooth start. As the pusher's speed increases, the torque compensation value is gradually reduced, transitioning to normal operation.

[0058] During the push plate's uniform speed operation phase, the system switches to speed control mode. The uniform speed of the push plate is set according to the process requirements of the push plate furnace. The servo motor maintains a stable speed at the set value through servo closed-loop control. The speed controller adjusts the motor's output voltage and frequency based on the deviation between the actual speed and the set speed as fed back by the encoder, keeping the speed error within a small range. Load changes are monitored in real time. When the load changes, the motor's torque output is automatically adjusted to maintain the push plate's uniform speed through a model predictive control algorithm. For example, when the material weight increases, causing the load to increase, the motor automatically increases torque output to ensure the push plate's speed is not affected. When the load decreases, the motor reduces torque output accordingly to avoid energy waste and motor overheating.

[0059] During the push plate's deceleration phase, the system switches to model predictive control mode. When the push plate reaches a certain distance from the target position, the target position, speed, and acceleration are predicted in advance using the prediction model. A deceleration curve is then planned in advance, allowing the push plate to gradually decelerate and avoid the shock and vibration caused by sudden stops. When the push plate's speed decreases to a certain level, the servo motor is controlled to output reverse torque, achieving braking. The magnitude of the reverse torque is adjusted in real time based on the push plate's remaining kinetic energy and load, ensuring a smooth stop at the target position while reducing wear on the mechanical brake device.

[0060] System optimization and evaluation module: Based on the adaptive control algorithm, the control parameters are optimized according to the real-time monitoring of system status and load changes, and a comprehensive performance test is conducted on the optimized system. The test data is compared and analyzed with the system design requirements and performance indicators to evaluate whether the system performance meets the requirements. Based on the performance test and evaluation results, further optimization suggestions are made for the problems existing in the system.

[0061] First, the system parameters were initialized. The system's operating status was observed by actually operating the pusher furnace, collecting data such as motor speed, torque, load, and vibration. Based on this test data, the control parameters were gradually optimized using parameter tuning methods such as trial and error and the Ziegler-Nichols method. During system operation, an adaptive control algorithm was used to automatically optimize the control parameters based on real-time monitoring of system status and load changes.

[0062] The optimized system undergoes comprehensive performance testing, including testing of indicators such as the push plate's positioning accuracy, speed stability, start-stop impact, and mechanical vibration. Multiple tests are conducted under different load conditions, such as no load, full load, and conditions with significant load fluctuations, collecting extensive data to ensure the accuracy and reliability of the test results. The test data is compared and analyzed with the system's design requirements and performance indicators to assess whether the system's performance meets the requirements. For example, the push plate's positioning accuracy is required to reach ±0.5mm. The actual positioning error is calculated from the test data to determine whether it is within the allowable range. For start-stop impact and mechanical vibration, parameters such as vibration amplitude and acceleration are measured and compared with industry standards or empirical values ​​to assess whether their magnitude is within the acceptable range.

[0063] Based on the performance test and evaluation results, further optimization suggestions are made for existing system issues. If positioning accuracy is insufficient, it may be necessary to check the encoder installation accuracy, adjust the control algorithm parameters, or optimize the mechanical structure. If the start-stop impact is large, further optimization of the control strategy during the start and stop phases can be carried out. If mechanical vibration exceeds the standard, mechanical structure optimization, control algorithm improvement, and enhanced vibration suppression measures can be taken to continuously improve system performance.

[0064] It also includes a human-machine interface (HMI) module, which is used to provide an interface for operators to interact with the system and set the working parameters of the push plate furnace, such as the start and stop time, movement speed, target position, etc. of the push plate. At the same time, it displays the operating status of the system in real time, including the motor speed, torque, load size and vibration conditions.

[0065] This technical solution builds a complete system architecture, adopts a real-time load monitoring and feedback mechanism, uses advanced control algorithms and reasonable speed and torque adjustment strategies, and combines effective vibration suppression measures. It can fully utilize the dynamic response characteristics of the servo motor, adjust the speed and torque of the servo motor in real time according to load changes, and significantly reduce the start-up and shutdown impact and mechanical vibration of the atmosphere push plate furnace.

[0066] Building on the above solution, the system can also be optimized from a mechanical perspective. High-strength, lightweight materials, such as silicon carbide ceramics or carbon fiber reinforced composites, can be used to manufacture the push plate. This improves its rigidity and wear resistance while reducing its weight and inertia. The push plate's structural shape can also be optimized to evenly distribute its center of gravity, minimizing sway and tilt during movement. For example, a symmetrical design and reinforcing ribs at the bottom of the push plate can improve its resistance to deformation.

[0067] Select high-precision, low-clearance transmission components, such as ball screws and synchronous belts, to reduce mechanical play and vibration transmission during the transmission process. Perform dynamic balancing tests and adjustments on the transmission system to ensure even mass distribution among rotating components and reduce vibration caused by imbalance. Additionally, install elastic couplings or vibration-damping pads within the transmission system to further isolate and absorb vibration energy.

[0068] The above embodiments are merely descriptions of the preferred embodiments of the present invention and do not limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary engineering technicians in this field should fall within the scope of protection determined by the claims of the present invention.

Claims

1. An atmosphere push plate furnace drive system based on servo motor frequency conversion control, characterized in that: It includes a real-time monitoring module, a multi-modal control module, a strategy generation module and a system optimization and evaluation module which are sequentially connected in communication; The real-time monitoring module is used to obtain real-time data of different targets of the atmosphere push plate furnace; The multimodal control module is used to divide the push plate furnace into different stages according to its workflow, identify the stage the current system is in by analyzing the real-time data collected by the real-time detection module, and switch to the corresponding control mode for the identified stage; The strategy generation module is used to generate a corresponding control strategy according to the control mode switched by the multi-modal control module, and dynamically adjust the motor speed and torque according to the control strategy; The system optimization and evaluation module, based on an adaptive control algorithm, optimizes control parameters according to real-time monitoring of system status and load changes, and conducts comprehensive performance testing on the optimized system. The test data is compared and analyzed with the system's design requirements and performance indicators to evaluate whether the system's performance meets the requirements. Based on the performance test and evaluation results, further optimization suggestions are made for problems existing in the system.

2. The atmosphere push plate furnace drive system based on servo motor frequency conversion control according to claim 1 is characterized in that: Real-time monitoring of different targets of the atmosphere push plate furnace system is achieved through the hardware composition and integration of the real-time monitoring module. The real-time monitoring module has a built-in controller and a variety of types of sensors deployed in a distributed manner. The sensors include weighing sensors, encoders, vibration sensors, and electrical sensors. Different types of data are collected through the sensors and transmitted to the controller in the form of electrical signals to calculate the required parameters.

3. The atmosphere push plate furnace driving system based on servo motor frequency conversion control according to claim 2 is characterized in that: The load weight data is collected in real time by the weighing sensor and transmitted to the controller in the form of an electrical signal. The controller calculates the load weight value and load change rate, sets the normal range of load weight and the change rate threshold. When the monitored load weight value exceeds the normal range or the load change rate exceeds the threshold, the controller determines that the load is abnormal and the system sends an alarm signal to prompt the operator to check the equipment and load conditions.

4. The atmosphere push plate furnace drive system based on servo motor frequency conversion control according to claim 1 is characterized in that: In the multi-modal control module, the operating stages of the push plate furnace include the startup stage, the uniform speed operation stage, the deceleration and stop stage, and the stage with large load changes; When the startup phase is identified, it switches to torque control mode; When the uniform speed operation stage is identified, it switches to the speed control mode; When the deceleration stop stage is identified, it switches to position control mode; When a stage with large load changes is identified, the system switches to model predictive control mode.

5. The atmosphere push plate furnace driving system based on servo motor frequency conversion control according to claim 1 is characterized in that: The multimodal control module has a built-in prediction model construction module, which is used to establish an overall prediction model of the push plate furnace system based on the dynamic model of the push plate furnace, the mathematical model of the servo motor and the load model, and switch to the model prediction control mode according to the identified current working condition stage of the system.

6. The atmosphere push plate furnace drive system based on servo motor frequency conversion control according to claim 5 is characterized in that: The prediction model building module builds an overall prediction model of the push plate furnace system, including the following steps: Conduct force analysis on the push plate during movement and establish a dynamic model; Establish a motor dynamic model based on the electrical and mechanical characteristics of the servo motor; Build a load model based on the dynamic characteristics of the load; Couple the push plate dynamics model, motor dynamics model and load model, and establish the state space equation; Based on the state space equation, in each control cycle, the state of the next N steps is predicted by the current state and the future control input sequence; With the goal of reducing start-stop shock and mechanical vibration, an objective function is constructed; Simulate the model, input control signals of typical working conditions, compare the model output with the measured values, and correct the model parameters.

7. The atmosphere push plate furnace driving system based on servo motor frequency conversion control according to claim 6 is characterized in that: When constructing the objective function, the start-stop impact and mechanical vibration are quantified, a motor torque penalty term is introduced, and physical constraints are introduced. The physical constraints include motor torque limit and speed limit. The constructed objective function formula is: ， Where ω1, ω2, and ω3 are non-negative weight coefficients, which are adjusted according to process requirements. a(k+i|k) is the predicted acceleration of the push plate in the kth step, and v ref is the reference speed, v(k+i|k) is the predicted speed, T m (k+i|k) is the motor torque predicted in the kth step, δ1 and δ2 are large weight coefficients, max(0, T m -T max ) 2 is the motor torque limit, max(0, vv max ) 2 For speed limit.

8. The atmosphere push plate furnace driving system based on servo motor frequency conversion control according to claim 1 is characterized in that: It also includes a human-machine interface module, which is used to provide an interface for operators to interact with the system, set the working parameters of the push plate furnace, and display the operating status of the system in real time.

Images