Double-helix synchronous conveying freezing control algorithm based on model prediction and application of double-helix synchronous conveying freezing control algorithm

Abstract

The invention belongs to the technical field of food processing, and particularly relates to a double-helix synchronous conveying and freezing control algorithm based on model prediction and application of the double-helix synchronous conveying and freezing control algorithm. According to the algorithm, precise synchronous control over the double servo motors in the spiral refrigerator is achieved through model predictive control. The system is composed of a servo motor, a motor driver, a sensor and a controller. Establishing a mathematical model of the servo motor, and selecting current and rotating speed state variables to construct a prediction model; a target function is designed, speed tracking errors and control quantity change constraints are included, and the operation precision and stability are improved; setting constraint conditions of current, voltage and rotating speed of the motor, solving optimal control input through an optimization algorithm based on the objective function and the constraint conditions, and performing feedback correction by using discrete incremental PID (Proportion Integration Differentiation). A frequency conversion energy-saving control technology is introduced, and a sampling period and control parameters are dynamically adjusted according to load changes. According to the algorithm, the dual-motor synchronization precision is remarkably improved, the freezing efficiency is optimized, and the quality uniformity and stability of food in the freezing process are guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of food processing, and particularly to a model prediction-based dual-helix synchronous conveying freezing control algorithm and its application. Background Art

[0002] Existing spiral freezers usually include two spiral conveying devices. The two spiral conveying devices drive a metal mesh belt that is connected end to end to run, and spirally convey frozen products for freezing. The tangential transition of the metal mesh belt between the first spiral conveying device and the second spiral conveying device is in a straight line state. The frozen products enter the freezer from the low-position feeding port, are conveyed upward to the top of the first spiral conveying device, are conveyed through the straight-section mesh belt to the top of the second spiral conveying device, and are conveyed downward to the bottom of the second spiral conveying device. The frozen products leave the freezer from the low-position discharging port, realizing low feeding and low discharging of the frozen products and high freezing efficiency. However, existing control methods often cannot accurately synchronize the driving motors of the two spiral conveying devices. The traditional dual-servo-motor control method mainly adopts a master-slave control strategy, that is, a main motor is set, and its speed set value is used as the reference speed of the slave motor, and the output torque of the slave motor is adjusted to achieve speed matching with the main motor. However, this method has some limitations. For example, when there are external disturbances, motor parameter changes, and load dynamic changes in the system, it is difficult to quickly and accurately achieve the co-speed operation of the two motors, and it is easy to generate speed deviation and torque fluctuation, thereby affecting the operation accuracy and stability of the system and resulting in the uniformity and efficiency of food during the freezing process being affected. Summary of the Invention

[0003] Based on the above object, the present invention provides a model prediction-based dual-helix synchronous conveying freezing control algorithm and its application.

[0004] The model prediction-based dual-servo-motor co-speed operation system of the present invention includes a dual-servo motor, a motor driver, a sensor, and a controller. Among them, the dual-servo motor is respectively connected to the corresponding motor driver, and the motor driver is used to convert the control signal output by the controller into an electrical energy signal for driving the motor; the sensor includes an encoder installed on the motor shaft, which is used to collect the rotational speed and position information of the motor in real time and feedback it to the controller; the controller is the core control unit of the entire system, and it uses a model prediction control algorithm to coordinately control the dual-servo motor.

[0005] The purpose of the present invention is to provide a model prediction-based dual-servo-motor co-speed operation system to solve the problems of large synchronous error and poor anti-interference ability existing in the existing dual-servo-motor control method during co-speed operation.

[0006] The model prediction-based dual-servo-motor co-speed operation system of the present invention includes the following main parts:

[0007] 1. Motor model establishment module:

[0008] Establish the mathematical models of two servo motors respectively. For a DC servo motor, its voltage balance equation is:

[0009]

[0010] Where, (U) is the motor terminal voltage, (R) is the motor armature resistance, (i) is the armature current, (L) is the armature inductance, and (e) is the back electromotive force. The electromagnetic torque equation of the motor is:

[0011] [T e = K t i]

[0012] Where, (T e ) is the electromagnetic torque, and (K t ) is the torque constant. The motion equation of the motor is:

[0013]

[0014] Where, (T L ) is the load torque, (J) is the motor moment of inertia, (ω) is the motor angular velocity, and (B) is the viscous friction coefficient. For an AC servo motor, its mathematical model in the synchronous rotating coordinate system can be established through coordinate transformation, and its voltage equation is:

[0015]

[0016] Where, (u d ), (u q ) are the d-axis and q-axis voltages, (i d ), (i q ) are the d-axis and q-axis currents, (R s ) is the stator resistance, (L s ) is the stator inductance, (ω e ) is the synchronous electrical angular velocity, and (λ m ) is the permanent magnet flux linkage. The electromagnetic torque equation is:

[0017]

[0018] Where, (p n ) is the number of pole pairs of the motor. The motion equation is similar to that of a DC motor. Through these mathematical models, the dynamic characteristics of the motor can be accurately described, providing a basis for subsequent model predictive control.

[0019] 2. State variable selection and prediction model construction module:

[0020] Select the current, speed, etc. of the motor as state variables, and construct a state - space prediction model based on model prediction. Taking a DC servo motor as an example, assume the state variables are (x = [i, ω] T ), the input variable is (u = U), and the output variable is (y = ω). Then the state - space equation can be expressed as:

[0021]

[0022] Among them, C = [0 1]

[0023] For an AC servo motor, the state variables can be selected as (x = [i d , i q , ω] T ), the input variable is (u = [u d , u q T ). After a series of derivations and simplifications, the corresponding state - space prediction model can be obtained. By discretizing the state - space model, the discrete - time prediction model is obtained:

[0024] [x(k + 1)=A d x(k)+B d u(k)]

[0025] Among them, (A d ), (B d ) are the discretized system matrices, and (k) is the sampling time.

[0026] 3. Objective - function design module:

[0027] Design the objective function of model predictive control to achieve the co - speed operation and good dynamic performance of the dual - servo motors. The objective function includes terms such as speed - tracking error and control - variable change. For example:

[0028]

[0029] Among them, N p is the prediction horizon, (N c ) is the control horizon, (ω ref ) is the reference speed, (ω 1 ), (ω 2 ) are the actual speeds of the two motors respectively, and (Δu) is the change in the control variable. The first and second terms respectively represent the sum of the squares of the tracking errors between the speeds of the two motors and the reference speed, which is used to ensure the speed - tracking accuracy of the motors; the third term represents the constraint on the change in the control variable, which is used to prevent the control variable from changing violently and improve the stability of the system.

[0030] 4. Constraint - condition setting module: ​

[0031] Set the constraint conditions for physical quantities such as the current and voltage of the motor to ensure that the motor operates within a safe and reliable range. For example, for the motor current, there are: (i min ≤i 1 (k + j)≤i max ), (i min ≤i 2 (k + j)≤i max ), where (i min ) and (i max ) are the minimum and maximum values of the motor current respectively. Similar constraint conditions also exist for the motor voltage. These constraint conditions play a restrictive role in the optimization solution process of model predictive control, ensuring the feasibility of the control strategy.

[0032] 5. Optimization solution module:

[0033] At each sampling moment, according to the current measured values of the state variables and the prediction model, solve the optimal control sequence of the objective function under the constraint conditions. Adopt a suitable optimization algorithm, such as the quadratic programming (QP) algorithm, to solve the objective function. Through the solution, obtain the optimal control input (u * (k)) at the current moment, and apply it to the two servo motors.

[0034] 6. Feedback correction module:

[0035] In current spiral freezers, the synchronous output states of the two spiral device motors mostly change according to the pressure range, which will lead to problems such as low compressor utilization rate, slow response speed, and large response overshoot. The method of shutting down and starting the freezer or continuously controlling the output of the freezer through sensors is also very unfavorable for energy conservation. A low-power distributed constant temperature control algorithm for a reconfigurable refrigeration unit proposed by the present invention adds low-power technology on the basis of the discrete PID algorithm, ensuring a low-power dynamic preset constant temperature control effect while the system responds quickly; introducing variable-frequency energy-saving technology, and intelligently selecting the corresponding parameter settings of the discrete PID sampling period T according to the output of the machine to avoid unnecessary waste of electric energy.

[0036] This module reads the rotational speed data of the motor in real time through an encoder and compares it with the optimal expected voltage u * (k) calculated by the prediction model. In order to calibrate the motor rotational speed, this module adopts a discrete incremental PID (proportional-integral-derivative) control algorithm. The specific expression of this algorithm is as follows:

[0037] Δu(k) = u(k) - u(k - 1)

[0038] =K p (e k -ek-1 ) + K i e(k) + K d (e k -2e k-1 +e k-2 )

[0039] = K p ((J k -J k-1 )-(J k-1 -u k-2 )) + K i (J k -J k-1 ) + K d ((J k -J k-1 )-2(J k-1 -u k-2 )+(J k-2 -J k-3 ))

[0040] where: (Δu(k)) represents the voltage error adjustment value of the output at the k-th moment and the (k - 1)-th moment, (u(k)) is the output voltage at the k-th time, (K p ) is the proportional coefficient, (K i ) is the integral coefficient, (K d ) is the differential coefficient, (e k ) represents the control deviation at the k-th time, that is, the difference between the actual voltage and the desired voltage

[0041] Through this incremental PID control strategy, the system can dynamically adjust the control input to minimize the deviation of the motor speed, ensure that the motor operates in the optimal state, and thus improve the accuracy and stability of the entire double - helix synchronous refrigeration control algorithm.

[0042] Furthermore, by introducing variable - frequency energy - saving technology, the sampling period T of the discrete PID algorithm is variably adjusted according to the requirements of the application scenario, and the corresponding parameter settings of the discrete PID sampling period T are intelligently selected according to the output of the machine. Specifically, when the machine is in a high - load environment, a high sampling period and corresponding parameters are adopted to meet the requirements of high precision and high demand. When the machine is in a low - load environment, a low sampling rate and corresponding parameters are adopted to achieve power reduction and energy - saving effects. The high and low sampling rates are relatively expressed, and specific settings can implement multiple different discrete cases corresponding to the load environment to set multiple sampling rates and corresponding parameters for adjustment and selection.

[0043] The control steps of the variable - frequency control of the discrete PID algorithm are as follows

[0044] S1. Preset a parameter table containing different sampling periods and corresponding parameters

[0045] S2. Select the parameters corresponding to the appropriate sampling period according to the current load environment

[0046] S3. Start the discrete PID algorithm according to the selected discrete PID parameters for update control

[0047] S4. Detect whether the current load environment has changed through the data of the end sensor. If it has changed, perform step S2; otherwise, perform step S3

[0048] The discrete form of this PID algorithm is further expressed as:

[0049]

[0050] where T I is the integral time, and T D is the differential time

[0051] Advantages of the present invention:

[0052] The double - helix synchronous freezing control algorithm of the present invention can achieve precise synchronization of the motors of two helix devices, improve the freezing efficiency, reduce the nutritional loss of food during the freezing process, and maintain the original flavor of the food Description of the Drawings

[0053] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only those of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings

[0054] Figure 1 It is a schematic diagram of the variable - frequency control process of the discrete PID algorithm according to the embodiment of the present invention Specific Embodiments

[0055] The present invention will be described in detail below in conjunction with the drawings and specific embodiments. At the same time, it should be noted here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments. For some well - known technologies, those skilled in the art can also adopt other alternative methods for implementation; and the drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention

[0056] It should be noted that in the specification, the mention of "an embodiment", "embodiment", "exemplary embodiment", "some embodiments", etc. indicates that the described embodiments may include specific features, structures or characteristics, but not necessarily every embodiment includes such specific features, structures or characteristics. Additionally, when combining an embodiment to describe a specific feature, structure or characteristic, implementing such feature, structure or characteristic in combination with other embodiments (whether explicitly described or not) should be within the knowledge scope of those skilled in the relevant art.

[0057] Generally, terms can be understood at least in part from their use in the context. For example, at least in part depending on the context, the term "one or more" used herein can be used to describe any feature, structure or characteristic in a singular sense, or can be used to describe a combination of features, structures or characteristics in a plural sense. Additionally, the term "based on" can be understood as not necessarily aiming to convey a set of exclusive factors, but instead, at least in part depending on the context, allowing for the existence of other factors that are not necessarily explicitly described.

[0058] The model prediction-based double servo motor co-speed operation system of the present invention includes the following main parts:

[0059] 1. Motor model establishment module:

[0060] Mathematical models of two servo motors are established respectively. For a DC servo motor, its voltage balance equation is:

[0061]

[0062] Where, (U) is the motor terminal voltage, (R) is the motor armature resistance, (i) is the armature current, (L) is the armature inductance, and (e) is the back electromotive force. The electromagnetic torque equation of the motor is:

[0063] [T e =K t i]

[0064] Where, (T e ) is the electromagnetic torque, and (K t ) is the torque constant. The motion equation of the motor is:

[0065]

[0066] Where, (T L ) is the load torque, (J) is the motor moment of inertia, (ω) is the motor angular velocity, and (B) is the viscous friction coefficient. For an AC servo motor, its mathematical model in the synchronous rotating coordinate system can be established through coordinate transformation, and its voltage equation is:

[0067]

[0068] Among them, (u d ), (u q ) are the (d), (q)-axis voltages, (i d ), (i q ) are the (d), (q)-axis currents, (R s ) is the stator resistance, (L s ) is the stator inductance, (ω e ) is the synchronous electrical angular velocity, (λ m ) is the permanent magnet flux linkage. The electromagnetic torque equation is:

[0069]

[0070] Among them, (p n ) is the number of pole pairs of the motor. The motion equation is similar to that of a DC motor. Through these mathematical models, the dynamic characteristics of the motor can be accurately described, providing a basis for subsequent model predictive control.

[0071] 2. State variable selection and prediction model construction module:

[0072] Select the current, speed, etc. of the motor as state variables to construct a state space prediction model based on model prediction. Taking a DC servo motor as an example, let the state variable be (x = [i, ω] T ), the input variable be (u = U), and the output variable be (y = ω), then the state space equation can be expressed as:

[0073]

[0074] Among them, C = [0 1]

[0075] For an AC servo motor, the state variables can be selected as (x = [i d , i q , ω] T ), the input variable be (u = [u d , u q T ), and through a series of derivations and simplifications, the corresponding state space prediction model can be obtained. By discretizing the state space model, the discrete-time prediction model is obtained:

[0076] [x(k + 1) = A d x(k) + B d u(k)]

[0077] Among them, (A d ), (B d ) are the discretized system matrices, and (k) is the sampling time.

[0078] 3. Objective function design module:​

[0079] Design the objective function of model predictive control to achieve the co-speed operation and good dynamic performance of the dual servo motors. The objective function includes terms such as speed tracking error and control variable change. For example:

[0080]

[0081] where N p is the prediction horizon, (N c ) is the control horizon, (ω ref ) is the reference speed, (ω 1 ), (ω 2 ) are the actual speeds of the two motors respectively, and (Δu) is the change in the control variable. The first and second terms respectively represent the sum of the squares of the tracking errors between the speeds of the two motors and the reference speed, which is used to ensure the speed tracking accuracy of the motors; the third term represents the constraint on the change in the control variable, which is used to prevent the control variable from changing violently and improve the stability of the system.

[0082] 4. Constraint setting module:

[0083] Set the constraint conditions for physical quantities such as the current and voltage of the motor to ensure that the motor operates within a safe and reliable range. For example, for the motor current, there are: (i min ≤i 1 (k + j)≤i max ), (i min ≤i 2 (k + j)≤i max ), where (i min ) and (i max ) are the minimum and maximum values of the motor current respectively. There are similar constraint conditions for the motor voltage. These constraint conditions play a restrictive role in the optimization solution process of model predictive control and ensure the feasibility of the control strategy.

[0084] 5. Optimization solution module:

[0085] At each sampling time, according to the measured values of the current state variables and the prediction model, solve the optimal control sequence of the objective function under the constraint conditions. Use a suitable optimization algorithm, such as the quadratic programming (QP) algorithm, to solve the objective function. Obtain the optimal control input (u * (k)) at the current time and apply it to the two servo motors.

[0086] 6. Feedback correction module:

[0087] In most current spiral refrigerators, the output state of the two spiral device motors being synchronized mostly changes according to the pressure range. This approach will lead to problems such as low utilization rate of the compressor, slow response speed, and large response overshoot. The method of shutting down and starting the refrigerator or continuously controlling the output of the refrigerator through sensors is also very unfavorable for energy conservation. A low-power distributed constant-temperature control algorithm for a reconfigurable refrigeration unit proposed in the present invention adds low-power technology on the basis of the discrete PID algorithm, ensuring that while the system responds quickly, it achieves a low-power dynamic preset constant-temperature control effect; introducing variable-frequency energy-saving technology, intelligently selecting the corresponding parameter settings of the discrete PID sampling period T according to the output of the machine, and avoiding unnecessary waste of electric energy.

[0088] This module reads the rotational speed data of the motor in real time through an encoder and compares it with the optimal expected voltage u * (k) calculated by the prediction model. In order to calibrate the rotational speed of the motor, this module adopts a discrete incremental PID (Proportional-Integral-Derivative) control algorithm. The specific expression of this algorithm is as follows:

[0089] Δu(k) = u(k) - u(k - 1)

[0090] = K p (e k - e k-1 ) + K i e(k) + K d (e k - 2e k-1 + e k-2 )

[0091] = K p ((J k - J k-1 ) - (J k-1 - u k-2 )) + K i (J k - J k-1 ) + K d ((J k - J k-1 ) - 2(J k-1 - u k-2 ) + (J k-2 - J k-3 ))

[0092] Where: (Δu(k)) represents the voltage error adjustment value output at the k-th moment and the (k - 1)-th moment, (u(k)) is the voltage output at the k-th time, (K p ) is the proportionality coefficient, (K i ) is the integral coefficient, (K d ) is the differential coefficient, (e k) represents the control deviation at the k-th time, that is, the difference between the actual voltage and the desired voltage

[0093] Through this incremental PID control strategy, the system can dynamically adjust the control input to minimize the deviation of the motor speed, ensure the motor operates in an optimal state, and thus improve the accuracy and stability of the entire double helix synchronous refrigeration control algorithm.

[0094] Furthermore, the variable frequency energy-saving technology is introduced. The sampling period T of the discrete PID algorithm is realized with variable frequency according to the requirements of the application scenario, and the corresponding parameter settings of the discrete PID sampling period T are intelligently selected according to the output situation of the machine. Specifically, when the machine is in a high-load environment, a high sampling period and corresponding parameters are adopted to meet the requirements of high precision and high demand. When the machine is in a low-load environment, a low sampling rate and corresponding parameters are adopted to reduce power consumption and achieve energy-saving effects. The high and low sampling rates are relatively expressed, and specific settings can be made to set multiple sampling rates and corresponding parameters for different discrete situations corresponding to the load environment for adjustment and selection.

[0095] The control steps for the variable frequency control of the discrete PID algorithm are as follows

[0096] S1. Preset a parameter table containing different sampling periods and corresponding parameters

[0097] S2. Select the parameters corresponding to the appropriate sampling period according to the current load environment

[0098] S3. Start the discrete PID algorithm according to the selected discrete PID parameters for update control

[0099] S4. Detect whether the current load environment has changed through the data of the end sensor. If it has changed, go to step S2; otherwise, go to step S3

[0100] The discrete form of this PID algorithm is further expressed as:

[0101]

[0102] where T I is the integral time, and T D is the differential time.

[0103] Embodiment 1

[0104] Such as Figure 1As shown, the variable-frequency energy-saving technology specifically includes: presetting different sampling times and corresponding parameters in the system, and detecting the current load environment through the data of the end sensors; determining the most matching parameters according to the current load environment, starting the discrete PID algorithm with the selected discrete PID parameters for update control; detecting whether the current load environment changes through the data of the end sensors. If it changes, perform step S2, otherwise perform step S3.

[0105] The present invention covers any alternatives, modifications, equivalent methods, and solutions made to the essence and scope of the present invention. To enable the public to have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention. However, those skilled in the art can fully understand the present invention without the description of these details. Additionally, well-known methods, processes, procedures, components, and circuits, etc. are not described in detail to avoid unnecessary confusion to the essence of the present invention.

[0106] The above are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A double-helix synchronous transport freezing control algorithm based on model prediction, characterized in that: ** Includes the following modules: Motor model building module: The mathematical models of the two spiral transmission device servo motors are established respectively. The voltage balance equation, torque equation and motion equation of the motor are: [T e =K t i] Among them, (U) is the motor terminal voltage, (R) is the motor armature resistance, (i) is the armature current, (L) is the armature inductance, (e) is the back electromotive force, (T e ) is the electromagnetic torque, (K t ) is the torque constant, (T L ) is the load torque, (J) is the motor moment of inertia, (ω) is the motor angular velocity, and (B) is the viscous friction coefficient; State variable selection and prediction model construction module: Based on the motor mathematical model, the motor current and speed are selected as state variables to build a prediction model; taking the DC servo motor as an example, the state space prediction model is: in, C = [0 1]; For AC servo motors, the state variable can be selected as (x = [i d ,i q ,ω] T ), input variable (u=[u d ,u q ] T ), after a series of derivations and simplifications, the corresponding state space prediction model can be obtained; by discretizing the state space model, the discrete time prediction model is obtained: [x(k+1)=A d x(k)+B d u(k)] Among them, (A d )、(B d ) is the discretized system matrix, (k) is the sampling time; Objective function design module: Design the objective function of model predictive control. The objective function is: Among them, N p is the prediction time domain, (N c ) is the control time domain, (ω ref ) is the reference speed, (ω1) and (ω2) are the actual speeds of the two motors, and (Δu) is the change in the control quantity; Constraint setting module: Set the physical constraints of motor current, voltage and speed, including: (i min ≤i1(k+j)≤i max ), (i min ≤i2(k+j)≤i max ), where (i min )、(i max ) are the minimum and maximum values ​​of the motor current respectively; Optimization solution module: Based on the prediction model and constraints, the quadratic programming algorithm is used to optimize the objective function and obtain the optimal control input; Feedback correction module: discrete incremental PID control is used to correct the motor speed. The algorithm formula is: Δu(k)=u(k)-u(k-1) =K p (e k -e k-1 )+K i e(k)+K d (e k -2e k-1 +e k-2 ) =K p ((J k -J k-1 )-(J k-1 -u k-2 ))+K i (J k -J k-1 )+K d ((J k -J k-1 )-2(J k-1 -u k-2 )+(J k-2 -J k-3 )) Where: (Δu(k)) represents the voltage error adjustment value output at the kth moment and the k-1th moment, (u(k)) is the kth output voltage, (K p ) is the proportionality coefficient, (K i ) is the integral coefficient, (K d ) is the differential coefficient, (e k ) represents the kth control deviation, that is, the difference between the actual voltage and the expected voltage.

2. The double-helix synchronous transport freezing control algorithm according to claim 1 is characterized in that: It further includes a variable frequency energy-saving control module, which dynamically selects the sampling period and corresponding parameters according to the load conditions by setting a variety of discrete PID sampling periods. The specific control steps are as follows: S1, preset sampling period and corresponding parameter table; S2. Select the sampling period and corresponding parameters according to the current load environment fed back by the sensor; S3, enable discrete PID control and adjust in real time; S4. If the load environment changes, reselect the parameters and adjust the sampling period.

3. A double spiral freezer based on the algorithm of claim 1 or 2, characterized in that: include: Double helix transmission device, each driven by a servo motor; A motor driver, used for receiving a control signal and driving a servo motor; Encoder, used to collect motor speed and position information; A controller for implementing synchronization control based on model prediction; Freezer housings for enclosing spiral conveyors and frozen food.

4. The double spiral freezer according to claim 3, characterized in that: The freezer uses dual servo motors to operate synchronously. Two spiral transmission devices drive the metal mesh belts connected end to end to operate, realizing the freezing process of low-level feeding and low-level discharging of food.

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