CN116627028B - Control method for crosslinked cable production line - Google Patents
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- G05B11/01—Automatic controllers electric
- G05B11/36—Automatic controllers electric with provision for obtaining particular characteristics, e.g. proportional, integral, differential
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Abstract
The application belongs to the technical field of automatic control of crosslinked cable production, and particularly relates to a crosslinked cable production line control method. Firstly, the system self-checks and obtains a plurality of parameter indexes and ranges of parameter indexes of the temperature control of an extruder and a catenary pipeline of a target crosslinked cable production line, then improves a PID controller, adds a filter in a differential link to filter out introduced high-frequency interference, and optimizes by using a BP neural network to obtain optimal PID parameters, wherein the BP neural network takes lag time as an input layer except for target temperature, actual temperature, temperature error and external bias constant, weight update and error items of an output layer and a hidden layer are obtained by a gradient descent method, and then the output of the PID controller and system sampling are given to a Kalman filter to perform state prediction to form negative feedback, so that the control performance of the system is optimized, and the temperature control of the extruder and the catenary pipeline of the crosslinked cable production line with quick response, high control precision, stable control effect and strong anti-interference capability is realized.
Description
Technical Field
The application belongs to the technical field of automatic control of crosslinked cable production, and particularly relates to a crosslinked cable production line control method.
Background
Cables play a vital role in the manufacturing industry in our country. Cables are also an internal component of many large devices as an intermediary for the requisite products and information transmission systems of the national electrical network. In the crosslinked cable production line, the function of the temperature control system is critical. It ensures that the extruder is able to sufficiently melt and extrude the material and cross-link it in the catenary. Once temperature control is problematic, insufficient melting of the material may result, uneven distribution of the material on the surface of the wire core, and exposed wire core, thereby resulting in product quality problems. Therefore, temperature control of the crosslinked cable production line is very important.
Currently, existing crosslinked cable lines use conventional PID algorithm modules provided by Siemens authorities for temperature control. Although this method can achieve a control effect, the accuracy is limited. In addition, the external environment of the production line has various interference factors, so that the control effect is not stable enough. In addition, the installation process requires on-site debugging parameters, is time-consuming and tedious, and increases the cost of manpower and material resources required for installation.
Disclosure of Invention
Aiming at the technical problems of the control of the crosslinked cable production line, the application provides the control method of the crosslinked cable production line, which has the advantages of reasonable design, simple method, strong theories, quick response, high control precision, stable control effect and strong anti-interference capability.
In order to achieve the above purpose, the application adopts the following technical scheme: a crosslinked cable production line control method comprising the steps of:
s1, performing system self-inspection and obtaining a plurality of parameter indexes and ranges of parameter indexes of an extruder and a catenary pipeline of a target crosslinked cable production line;
s2, at K p 、K p /T I s、Kp/T D Differential link addition of PID controller with s being proportional, integral and differential coefficientThe low-pass filter, the sampling time is set to be T, and the output of the differential link is expressed as:
wherein u is D (k) For the output of the differential element with a sampling instant k, u D (k-1) is the output of the differential element at sampling time k-1,and->In order to add the coefficient of the low-pass filter discretization post-differential link, e (k) is the input temperature deviation value of sampling time k, e (k-1) is the input temperature deviation value of (k-1) th sampling time, and the output of the improved PID controller is expressed as:
wherein K is P e (k) is the output of the proportional element,for the output of the integration section,the output of the differential link;
s3, adding lag time as an input layer of the BP neural network structure and a variation value of parameters of the PID controller as an output layer except for target temperature, actual temperature, temperature error and external bias constant, and optimizing by using the neural network to obtain optimal PID parameters;
s4, adding a Kalman filter to the PID controller to predict the state, wherein the equation is as follows:
wherein A is a state matrix; b is a control matrix;a prediction matrix for the state at the moment k; />A state estimation matrix for the k-1 moment; u (u) k The control output matrix is the k moment;
s5, obtaining a plurality of optimized index parameters, and controlling the temperatures of the extruder and the catenary pipeline of the crosslinked cable production line.
Preferably, in the step S3, the number of neurons in the hidden layer of the BP neural network structure is one more than the number of neurons in the input layer, and the number of neurons in the input layer is 5.
Preferably, in the step S3, the lag time is determined by a difference between a given time of the input signal and a time when the system has an obvious response.
Preferably, in the step S3, the BP neural network includes two stages of error forward propagation and error backward propagation, where the error backward propagation stage introduces learning efficiency and inertia coefficient, and obtains weight update and error terms of the output layer and the hidden layer by a gradient descent method.
Preferably, in the step S4, the output of the PID controller and the system sample are applied to a kalman filter, the system state is accurately estimated through the filter, and the difference between the system state and the set value is used as the input of the PID controller to form negative feedback.
Compared with the prior art, the application has the advantages and positive effects that:
1. and a low-pass filter is added in the differential link of the PID algorithm, so that the introduced high-frequency interference is further filtered, the error is reduced, and the robustness of the system is improved.
2. The lag time is used as 1 input layer of the BP neural network, so that the influence caused by error accumulation is reduced.
3. And a Kalman filter is introduced to perform real-time feedback control, so that the control precision, response speed and anti-interference capability of the system are improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort to a person skilled in the art.
Fig. 1 is a schematic structural diagram of a control method of a crosslinked cable production line according to an embodiment.
Detailed Description
In order that the above objects, features and advantages of the application will be more clearly understood, a further description of the application will be rendered by reference to the appended drawings and examples. It should be noted that, without conflict, the embodiments of the present application and features in the embodiments may be combined with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application, however, the present application may be practiced otherwise than as described herein, and therefore the present application is not limited to the specific embodiments of the disclosure that follow.
Embodiment, as shown in FIG. 1, the present application provides a method for controlling a crosslinked cable production line
Firstly, performing system self-checking and obtaining a plurality of parameter indexes and ranges of parameter indexes of an extruder and a catenary pipeline of a target crosslinked cable production line.
Secondly, proportional proportion links of PID control are considered to reflect deviation signals of a control system in proportion; the integration link is mainly used for eliminating static difference and improving the no-difference degree of the system; the differential link mainly reflects the variation trend of the deviation signal, so that the action speed of the system is increased, and the adjustment time is shortened. The introduced differential regulation can play a role when the temperature of the system changes, and when the temperature changes at a higher speed, the output signal is stronger, so that the regulation speed can be increased, the occurrence of overshoot is reduced, and the dynamic performance of the system is improved. But also brings high-frequency interference, and especially unstable system caused by abrupt change of error disturbance. Consider that a low pass filter can filter out the introduced high frequency interference.
Thus, at K p 、K p /T I s、Kp/T D Differential link addition of PID controller with s being proportional, integral and differential coefficientThe low-pass filter, the sampling time is set to be T, and the output of the differential link is expressed as:
wherein u is D (k) For the output of the differential element with a sampling instant k, u D (k-1) is the output of the differential element at sampling time k-1,and->In order to add the coefficient of the low-pass filter discretization post-differential link, e (k) is the input temperature deviation value of sampling time k, e (k-1) is the input temperature deviation value of (k-1) th sampling time, and the output of the improved PID controller is expressed as:
wherein K is P e (k) is the output of the proportional element,for the output of the integration section,is the output of the differential link.
Then, considering that in practical engineering application, the conventional PID common tuning method is a critical scaling method and a trial-and-error method adjusted according to experience, strictly speaking, the critical scaling method refers to a Z-N tuning method (Ziegler-Nichols tuning method), and the general tuning process is as follows: 1, closing the integral and differential functions of a controller, and only adjusting the proportional parameters to enable the system to achieve constant amplitude oscillation; 2, making the proportional parameter of the system achieving constant amplitude oscillation be recorded as a critical coefficient, and the oscillation period is called as a critical oscillation period; and 3, after the numerical values are determined, obtaining accurate three PID parameters by table look-up calculation. In practical application, parameters calculated by the Z-N tuning method are further fine-tuned according to the field conditions. The trial and error method sets PID parameters through human experience so that the system response meets the predetermined requirements, which makes it possible to perform tuning in a very fast time, and also makes it possible to obtain proper parameters without spending a lot of time and effort. In general, the PID parameters are regulated without a solidifying method, and an intermediate method which combines experience and calculation is often adopted in practice, so that the method has heavy experience and heavy practicability, and the method adopts BP neural network optimization to obtain the optimal PID parameters in order to simplify the process, avoid time-consuming and complicated on-site parameter debugging links and reduce the required cost of manpower and material resources. In addition, in actual production, the temperature control algorithm is required to heat the extruder to a constant temperature, and then the temperature is maintained until the formulation is changed or production is stopped, and if the lag time is not considered, error accumulation is generated, the response speed is reduced, and the control accuracy of the system is reduced.
Therefore, in addition to the target temperature, the actual temperature, the temperature error and the external bias constant, the hysteresis time is added as an input layer of the BP neural network structure, the variation value of the PID controller parameter is an output layer, and the BP neural network is utilized to optimize to obtain the optimal PID parameter.
Specifically, the lag time is determined by the difference between the given moment of the input signal and the moment when the system has obvious response, the number of neurons of the hidden layer of the BP neural network structure is one more than that of neurons of the input layer, and the number of neurons of the input layer is 5. The BP neural network comprises two stages of error forward propagation and error reverse propagation, learning efficiency and inertia coefficient are introduced in the error reverse propagation stage, and weight updating and error items of the output layer and the hidden layer are obtained through a gradient descent method.
Next, it is considered that in a complex industrial control environment, random disturbances have an increasing impact on the control system. Due to the limitation of the PID controller, the control performance of the PID controller on the aspects of control precision, response speed, interference resistance and the like of the system is poor. The Kalman filter is mainly used for obtaining an estimated value of a detected signal through an iterative algorithm according to a measured value and a predicted value of the extracted signal. Because the measurement noise and the process noise of the system are reduced in the iteration process, the Kalman filter can accurately estimate the measured signal, and is suitable for solving the problems of multidimensional, non-stable, time-varying, unstable power spectrum and the like of the random signal and the noise. When the process noise sequence and the observation noise sequence of the system are uncorrelated and are zero-mean Gaussian white noise random process sequences, a Kalman filtering equation can be adopted to conduct state prediction, and the system state at the current moment can be predicted according to the state estimation at the last moment and the current output through reasonable model construction. On the premise of accurately estimating the current system state, the covariance matrix of the prediction system is calculated, and the Kalman gain which is the weight of the measured value can be obtained under the condition of calculating the prediction state value and the prediction error covariance matrix. The current system state can be estimated based on the measured and predicted weights. In the production line temperature control of the crosslinked cable, certain process noise and measurement noise can be generated, in the long-term system operation, the noise can be amplified continuously, the control precision is reduced continuously, a Kalman filter is combined with PID control, the system state is recursively deduced based on a proper system model, the measurement noise and the process noise in the system are filtered effectively, the control robustness is improved, and the interference caused by time delay to the control is reduced.
Therefore, the application adds a Kalman filter to predict the state after the PID controller, and the equation is as follows:
wherein A is a state matrix; b is a control matrix;a prediction matrix for the state at the moment k; />A state estimation matrix for the k-1 moment; u (u) k The control output matrix is the k moment; the output of the PID controller and the system sample are given to a Kalman filter, the state of the system is estimated accurately through the filter, and the difference between the state and the set value is used as the input of the PID controller to form negative feedback, so that the control performance of the system is optimized.
Finally, a plurality of optimized index parameters are obtained, and the temperature of the extruder and the catenary pipeline of the crosslinked cable production line is controlled.
The present application is not limited to the above-mentioned embodiments, and any equivalent embodiments which can be changed or modified by the technical content disclosed above can be applied to other fields, but any simple modification, equivalent changes and modification made to the above-mentioned embodiments according to the technical substance of the present application without departing from the technical content of the present application still belong to the protection scope of the technical solution of the present application.
Claims (5)
1. The control method of the crosslinked cable production line is characterized by comprising the following steps of:
s1, performing system self-inspection and obtaining a plurality of parameter indexes and ranges of parameter indexes of an extruder and a catenary pipeline of a target crosslinked cable production line;
s2, at K p 、K p /T I s、Kp/T D Differential link addition of PID controller with s being proportional, integral and differential coefficientThe low-pass filter, the sampling time is set to be T, and the output of the differential link is expressed as:
wherein u is D (k) For the output of the differential element with a sampling instant k, u D (k-1) is the output of the differential element at sampling time k-1,and->In order to add the coefficient of the low-pass filter discretization post-differential link, e (k) is the input temperature deviation value of sampling time k, e (k-1) is the input temperature deviation value of (k-1) th sampling time, and the output of the improved PID controller is expressed as:
wherein K is P e (k) is the output of the proportional element,for the output of the integration section,the output of the differential link;
s3, adding lag time as an input layer of the BP neural network structure and a variation value of parameters of the PID controller as an output layer except for target temperature, actual temperature, temperature error and external bias constant, and optimizing by using the neural network to obtain optimal PID parameters;
s4, adding a Kalman filter to the PID controller to predict the state, wherein the equation is as follows:
wherein A is a state matrix; b is a control matrix;a prediction matrix for the state at the moment k; />A state estimation matrix for the k-1 moment; u (u) k The control output matrix is the k moment;
s5, obtaining a plurality of optimized index parameters, and controlling the temperatures of the extruder and the catenary pipeline of the crosslinked cable production line.
2. The control method of a crosslinked cable production line according to claim 1, wherein in the step S3, the number of neurons in the hidden layer of the BP neural network structure is one more than the number of neurons in the input layer, and the number of neurons in the input layer is 5.
3. A method of controlling a crosslinked cable line according to claim 1, wherein in step S3, the lag time is determined by the difference between a given time of the input signal and a time of apparent response of the system.
4. The control method of a crosslinked cable production line according to claim 1, wherein in the step S3, the BP neural network includes two stages of error forward propagation and error backward propagation, the error backward propagation stage introduces learning efficiency and inertia coefficient, and the weight update and error term of the output layer and the hidden layer are obtained by a gradient descent method.
5. The control method of a crosslinked cable line according to claim 1, wherein in the step S4, the output of the PID controller and the system sample are given to a kalman filter, the system state is estimated accurately via the filter, and the difference between the system state and the set value is used as the input of the PID controller to form negative feedback.
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