CN106054616B - The titanium strip coil continuous acid-washing looper height control method of fuzzy logic PID controller parameter - Google Patents
The titanium strip coil continuous acid-washing looper height control method of fuzzy logic PID controller parameter Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 31
- 239000010936 titanium Substances 0.000 title claims abstract description 25
- RTAQQCXQSZGOHL-UHFFFAOYSA-N Titanium Chemical compound [Ti] RTAQQCXQSZGOHL-UHFFFAOYSA-N 0.000 title claims abstract description 22
- 229910052719 titanium Inorganic materials 0.000 title claims abstract description 22
- 238000005406 washing Methods 0.000 title abstract 4
- 230000004044 response Effects 0.000 claims abstract description 13
- 238000005070 sampling Methods 0.000 claims description 24
- 230000008859 change Effects 0.000 claims description 20
- 238000005457 optimization Methods 0.000 claims description 20
- 238000005554 pickling Methods 0.000 claims description 16
- 230000003044 adaptive effect Effects 0.000 claims description 13
- 230000001133 acceleration Effects 0.000 claims description 6
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- 239000002253 acid Substances 0.000 description 2
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- NAWXUBYGYWOOIX-SFHVURJKSA-N (2s)-2-[[4-[2-(2,4-diaminoquinazolin-6-yl)ethyl]benzoyl]amino]-4-methylidenepentanedioic acid Chemical compound C1=CC2=NC(N)=NC(N)=C2C=C1CCC1=CC=C(C(=O)N[C@@H](CC(=C)C(O)=O)C(O)=O)C=C1 NAWXUBYGYWOOIX-SFHVURJKSA-N 0.000 description 1
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Abstract
The present invention relates to the titanium strip coil continuous acid-washing looper height control methods of fuzzy logic PID controller parameter, belong to metallurgical engineering control field.The present invention first designs PID controller of the titanium plate with continuous acid-washing looper height；When Object transition Process response curve is stablized in control accuracy rating, the parameter value of fuzzy controller is obtained according to P, I, D parameter value at this time, to design fuzzy controller.Within the scope of error precision of the looper height in permission, keep the value of previous moment fuzzy controller parameter constant, looper height recalculates the value of Fuzzy Controller Parameters except accuracy rating.The present invention can quickly design the parameter of fuzzy logic PID controller, make full use of conventional PID controllers that there are highly developed parameter regulation means, simplify fuzzy logic PID controller design complexity, it can be achieved that titanium plate band looper height during continuous acid-washing accurate control.
Description
Technical Field
The invention relates to a method for controlling the height of a titanium strip coil continuous pickling loop by fuzzy logic optimization PID controller parameters, belonging to the technical field of metallurgical engineering control.
Background
The traditional PID controller has the advantages of simple structure, robustness to process model control errors, contribution to realization and the like, and is widely applied to the field of industrial process control of metallurgy, petrifaction, building materials, electric power and the like. Along with the complexity of a production device and the improvement of product quality requirements, the complexity of a controlled process is continuously deepened, and particularly for a complex system which is continuous, time-varying and has unstable noise, the traditional PID control cannot meet the requirement of control target accuracy. The self-adaptive fuzzy algorithm combining self-adaptive control and fuzzy control based on the universal approximation characteristic theory has arbitrary approximation capability and self-learning capability to a nonlinear function, can acquire the structure, parameters, uncertainty and nonlinearity of a system through the self-learning process, and obtains the control rule required by the system, thereby being widely applied in the control field. Due to the inherent nonlinearity and the independence of a model of the fuzzy controller, the PID control based on fuzzy adaptation shows strong adaptability and robustness to uncertain systems and controlled systems with time lag and controlled object parameter change. Conventional fuzzy PID controllers have long been designed by means of expert experience, which causes many inconveniences to the application of the fuzzy PID controller. An intensive study on the analytical structure of the fuzzy controller shows that the controller is a variable gain nonlinear controller and has many similarities with a PID controller. Although extensive research has been conducted on fuzzy PIDs in conjunction with both, none of these studies have given a method of adjusting fuzzy controller parameters. The method for adjusting the parameters of the fuzzy controller based on the PID is provided, directly influences the design efficiency and the design complexity of the fuzzy control, and is one of the most key factors for determining the industrial control problem. The invention provides a method for adaptively adjusting parameters of a fuzzy PID controller based on a traditional PID control method.
Disclosure of Invention
The invention provides a titanium strip coil continuous pickling loop height control method for fuzzy logic optimization of PID controller parameters, aiming at the problem that the traditional PID controller cannot meet the requirement of parameter control accuracy of a complex object, reducing the complexity of fuzzy PID controller design, avoiding the expert dependence of the fuzzy PID controller parameter design, and realizing the simplicity and increment of industrial control upgrading.
The technical scheme of the invention is as follows: a titanium strip coil continuous pickling loop height control method for fuzzy logic optimization of PID controller parameters is characterized in that a PID controller for titanium strip coil continuous pickling loop height is designed by adopting a traditional method, and P, I, D control parameters are set according to a conventional method; secondly, when the response curve of the object transition process is stabilized within the control precision range, the parameter value of the fuzzy controller is obtained according to the P, I, D parameter value at the moment, and therefore the fuzzy controller is designed. Then, when the loop height is within the allowable error precision range, the value of the fuzzy PID controller parameter at the previous moment is kept unchanged, otherwise, the loop height is out of the precision range, and the value of the fuzzy PID controller parameter is recalculated.
According to the PI controller and the PD controller, the steady-state error of the system can be eliminated, the response speed is increased, and the complexity of calculation of the fuzzy controller is avoided. The control function of the fuzzy PID controller is determined by overlapping a PI fuzzy controller with two input quantities (output error and error change) and two fuzzy sub-controllers of the PD fuzzy controller. The self-adaptive adjustment of the fuzzy PID controller is realized by combining the mature parameter adjustment method of the traditional PID controller;
the method for controlling the height of the continuous pickling loop of the titanium strip coil by optimizing PID controller parameters through fuzzy logic comprises the following specific steps:
step1, firstly designing a titanium coil continuous pickling loop height PID controller, and adjusting P, I, D parameters of the PID controller by a Ziegler-Nichols method to respectively obtain proportional gain K of the PID controllerpIntegral gain KiAnd a differential gain KdThen calculating each coefficient of the incremental discrete PID controller
Wherein,respectively is the gain coefficient of the current sampling moment, the gain coefficient of the previous 1 st sampling moment and the gain coefficient of the previous 2 nd sampling moment of the incremental discrete PID controller;
step2, establishing the fuzzy adaptive PID controller, and acquiring four parameters K of the fuzzy adaptive PID controllere、Kde、KPI、KPDA value of (d); the controller consists of two parts: firstly, theThe traditional PID controller is used for directly carrying out negative feedback control on a controlled object and realizing the online setting of P, I, D three parameters; the fuzzy logic optimization PID controller is composed of a PI type fuzzy controller and a PD type fuzzy controller, the PI type fuzzy controller and the PD type fuzzy controller both adopt two inputs and single output, each input variable has a positive fuzzy value and a negative fuzzy value, and the output variable has a positive fuzzy value, a negative fuzzy value and a zero fuzzy value;
wherein, Ke、Kde、KPI、KPDRespectively representing the deviation change rate and the deviation change acceleration of the output height of the loop and an expected value, the output gain of a PI type fuzzy controller and the output gain of a PD type fuzzy controller;
step3, self-adaptive and real-time control; after passing through Step1 and Step2, four parameter values of the fuzzy self-adaptive PID controller under the steady state condition are obtained, the performance of the fuzzy self-adaptive PID controller is ensured to be the same as that of the PID controller under the steady state condition, and then the four parameters are finely adjusted according to a response curve of a closed-loop system to achieve the expected performance;
firstly, observing the closed loop response curve of the fuzzy self-adaptive PID controller, estimating the overshoot of the fuzzy self-adaptive PID controller, and repeatedly pairing the fuzzy self-adaptive PID controller according to the overshootAdjusting, and finely adjusting the parameters of the controller in such a way until the response curve of the closed-loop system reaches the expected performance; if the difference is out of the error range of the loop height, the loop goes through steps 1 to 2, and if the difference is within the error range, the value of the fuzzy adaptive PID controller parameter at the previous moment is kept unchanged.
Wherein,three parameters representing the incremental fuzzy adaptive PID controller, corresponding to K of the traditional PID controllerp、KiAnd KdThree coefficients representing proportional gain and integral gain of the incremental fuzzy adaptive PID controllerThe gain and the differential gain.
In Step1, the PID controller is an incremental discrete PID controller, and the incremental expression is as follows:
in the formula
In the formulaRespectively is the gain coefficient of the current sampling moment, the gain coefficient of the previous 1 st sampling moment and the gain coefficient of the previous 2 nd sampling moment of the incremental discrete PID controller; e (k) is the control deviation of the PID controller; kp、Ki＝Kp.Ts/TiAnd Kd＝Kp.Td/TsProportional gain, integral gain and differential gain of the PID controller respectively; t isi、TdAnd TsIntegration time, differentiation time, and sampling time, respectively;
in Step2, the increment expression of the fuzzy controller in PI mode is:
wherein,
wherein y (k) is a measurement value of the height of the loop at the time k, and x (k) is a value representing the intensity of the dynamic change of the system, and the magnitude of the intensity is defined as the maximum value between the product of the absolute value of the loop output deviation and the deviation change rate at the time k and the product of the absolute value of the loop output deviation change and the deviation change acceleration;
similarly, the incremental expression of the PD type fuzzy controller is as follows:
in summary, the incremental expression of the fuzzy logic optimized PID controller is:
in the formula,
the corresponding item coefficients of the fuzzy logic optimization PID controller and the PID controller are equal, namely the fuzzy controller parameter and the PID parameter are correspondingly equal when in a steady state,
meanwhile, when the membership function parameter L is 1, the input variable is considered to fall in the region [ -1,1 ] as much as possible]×[-1,1]Interior, therefore, KeShould be selected to satisfy
(ysp-y0)·Ke＝1 (11)
The parameter K of the fuzzy logic optimization PID controller can be obtained by the formulas (10) and (11)e、Kde、KPI、KPD；
Wherein, Delta UF-PI(k) Is the incremental output, Delta U, of a fuzzy PI-type controllerF-PD(k) Is the incremental output, Delta U, of a fuzzy PD type controllerF-PID(k) Is the incremental output, y, of a fuzzy logic optimized PID controllerspIs the desired value, y, of the loop output height0The initial output height value of the loop is the same as the previous values of other parameters.
The invention has the beneficial effects that: the method can quickly design the parameters of the fuzzy logic optimization PID controller, fully utilizes the mature parameter adjustment method of the traditional PID controller, simplifies the complexity of the design of the fuzzy logic optimization PID controller, and can realize the accurate control of the loop height of the titanium plate strip in the continuous pickling process.
Drawings
FIG. 1 is a block diagram of a fuzzy adaptive PID control in accordance with the present invention;
FIG. 2 is a graph of membership functions for input and output variables according to the present invention;
FIG. 3 is a graph of the control results of the present invention for a conventional PID controller for a noisy unstable system with acid wash loop height for titanium plate;
FIG. 4 is a graph of the control results of the fuzzy logic optimized PID controller for a noisy system with acid wash loop height for titanium plate of the present invention;
FIG. 5 is a graph of the control results of the method of the present invention for a fuzzy logic optimized PID controller for a noiseless system.
Detailed Description
Example 1: as shown in fig. 1-4, a method for controlling the height of a titanium strip coil continuous pickling loop by fuzzy logic optimization of PID controller parameters comprises the following specific steps:
step1, firstly designing a titanium coil continuous pickling loop height PID controller, and adjusting P, I, D parameters of the PID controller by a Ziegler-Nichols method to respectively obtain proportional gain K of the PID controllerpIntegral gain KiAnd a differential gain KdThen calculating each coefficient of the incremental discrete PID controller
Wherein,respectively is the gain coefficient of the current sampling moment, the gain coefficient of the previous 1 st sampling moment and the gain coefficient of the previous 2 nd sampling moment of the incremental discrete PID controller;
step2, establishing the fuzzy adaptive PID controller, and acquiring four parameters K of the fuzzy adaptive PID controllere、Kde、KPI、KPDA value of (d); the controller consists of two parts: the PID controller is used for directly performing negative feedback control on a controlled object and realizing on-line setting of P, I, D three parameters; the fuzzy logic optimization PID controller is composed of a PI type fuzzy controller and a PD type fuzzy controller, the PI type fuzzy controller and the PD type fuzzy controller both adopt two inputs and single output, each input variable has a positive fuzzy value and a negative fuzzy value, and the output variable has a positive fuzzy value, a negative fuzzy value and a zero fuzzy value;
wherein, Ke、Kde、KPI、KPDRespectively representing the deviation change rate and the deviation change acceleration of the output height of the loop and the expected value, the output gain of the PI type fuzzy controller and the output of the PD type fuzzy controllerGain;
step3, self-adaptive and real-time control; after passing through Step1 and Step2, four parameter values of the fuzzy self-adaptive PID controller under the steady state condition are obtained, the performance of the fuzzy self-adaptive PID controller is ensured to be the same as that of the PID controller under the steady state condition, and then the four parameters are finely adjusted according to a response curve of a closed-loop system to achieve the expected performance;
firstly, observing the closed loop response curve of the fuzzy self-adaptive PID controller, estimating the overshoot of the fuzzy self-adaptive PID controller, and repeatedly pairing the fuzzy self-adaptive PID controller according to the overshootAdjusting, and finely adjusting the parameters of the controller in such a way until the response curve of the closed-loop system reaches the expected performance; if the difference is out of the error range of the loop height, the loop goes through steps 1 to 2, and if the difference is within the error range, the value of the fuzzy adaptive PID controller parameter at the previous moment is kept unchanged.
Wherein,three parameters representing the incremental fuzzy adaptive PID controller, corresponding to K of the traditional PID controllerp、KiAnd KdThree coefficients;
in Step1, the PID controller is an incremental discrete PID controller, and the incremental expression is as follows:
in the formula
In the formulaRespectively is the gain coefficient of the current sampling moment, the gain coefficient of the previous 1 st sampling moment and the gain coefficient of the previous 2 nd sampling moment of the incremental discrete PID controller; e (k) is the control deviation of the PID controller; kp、Ki＝Kp.Ts/TiAnd Kd＝Kp.Td/TsProportional gain, integral gain and differential gain of the PID controller respectively; t isi、TdAnd TsIntegration time, differentiation time, and sampling time, respectively;
in Step2, the increment expression of the fuzzy controller in PI mode is:
wherein,
wherein y (k) is a measurement value of the height of the loop at the time k, and x (k) is a value representing the intensity of the dynamic change of the system, and the magnitude of the intensity is defined as the maximum value between the product of the absolute value of the loop output deviation and the deviation change rate at the time k and the product of the absolute value of the loop output deviation change and the deviation change acceleration;
similarly, the incremental expression of the PD type fuzzy controller is as follows:
in summary, the incremental expression of the fuzzy logic optimized PID controller is:
in the formula,
the corresponding item coefficients of the fuzzy logic optimization PID controller and the PID controller are equal, namely the fuzzy controller parameter and the PID parameter are correspondingly equal when in a steady state,
meanwhile, when the membership function parameter L is 1, the input variable is considered to fall in the region [ -1,1 ] as much as possible]×[-1,1]Interior, therefore, KeShould be selected to satisfy
(ysp-y0)·Ke＝1 (11)
The parameter K of the fuzzy logic optimization PID controller can be obtained by the formulas (10) and (11)e、Kde、KPI、KPD；
Wherein, Delta UF-PI(k) Is the incremental output, Delta U, of a fuzzy PI-type controllerF-PD(k) Is the incremental output, Delta U, of a fuzzy PD type controllerF-PID(k) Is the incremental output, y, of a fuzzy logic optimized PID controllerspIs the desired value, y, of the loop output height0The initial output height value of the loop is the same as the previous values of other parameters.
In the fuzzy logic optimization PID controller, two sub-controllers adopt two inputs and single output, each input variable of the controller is assumed to have two fuzzy values of positive and negative, each output variable of the controller has three fuzzy values of positive, negative and zero, and membership functions of the input and output variables are shown in FIG. 2.
The invention utilizes MATLAB environment, and uses the actual parameters of the titanium plate strip continuous pickling loop height control system of the Yunnan titanium industry company to deduce that the transfer function of the controlled object is G(s) 0.58/(0.04s multiplied by 0.036s + 1). In fact, the system is an unstable system, and various disturbances are often accompanied in the pickling process. A Fuzzy controller is designed by utilizing a Fuzzy Logic Toolbox, and simulation experiment comparison of conventional PID control and Fuzzy self-adaptive setting PID control is carried out on the loop height control system by adopting a step signal. Initial value y0Set value y for loop height equal to 0sp1. The traditional PID design method is used for controlling the parameter values to be as follows: kp05.46, integration time Ti086 and a differential time Td04. Further, the parameter of the formula (10) can be calculatedMake it equal to each parameter of (6) type fuzzy controller in steady state(the sampling time K in the expression (6) is infinite, namely, the steady state is reached), and then the parameter values of the fuzzy control can be respectively obtained as K by combining the expression (11)e＝1，Kde＝0.5975，KPI＝1.3509，KPD24.015. The sampling time is 1ms, random noise interference with the amplitude delta within +/-3.0 is added into the system at each sampling time point, and the closed loop response results of PID control and fuzzy control simulation are shown in figures 3-5.
As can be seen from fig. 3, the conventional PID controller is uncontrollable for noisy, unstable systems. As can be seen from FIG. 4, the fuzzy logic optimization PID controller continuously adjusts the system error to optimize the control performance index, and for an unstable system with random noise, such as the continuous pickling loop height of a titanium plate strip, the fuzzy logic optimization PID controller can control the loop height error to be below 5%, so that a better control effect can be achieved. As can be seen from FIG. 5, if the system is a noiseless system, the control performance index of the fuzzy logic optimization PID controller is better, and the control effect is better.
While the present invention has been described in detail with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, and various changes can be made without departing from the spirit of the present invention within the knowledge of those skilled in the art.
Claims (2)
1. The method for controlling the height of the continuous pickling loop of the titanium strip coil by optimizing PID controller parameters through fuzzy logic is characterized by comprising the following steps: the method comprises the following specific steps:
step1, firstly designing a titanium coil continuous pickling loop height PID controller, and adjusting P, I, D parameters of the PID controller by a Ziegler-Nichols method to respectively obtain proportional gain K of the PID controllerpIntegral gain KiAnd a differential gain KdThen calculating each coefficient of the incremental discrete PID controller
Wherein,respectively is the gain coefficient of the current sampling moment, the gain coefficient of the previous 1 st sampling moment and the gain coefficient of the previous 2 nd sampling moment of the incremental discrete PID controller;
step2, establishing the fuzzy adaptive PID controller, and acquiring four parameters K of the fuzzy adaptive PID controllere、Kde、KPI、KPDA value of (d); the controller consists of two parts: the PID controller is used for directly performing negative feedback control on a controlled object and realizing on-line setting of P, I, D three parameters; the fuzzy logic optimization PID controller is composed of a PI type fuzzy controller and a PD type fuzzy controller, the PI type fuzzy controller and the PD type fuzzy controller both adopt two inputs and single output, each input variable has a positive fuzzy value and a negative fuzzy value, and the output variable has a positive fuzzy value, a negative fuzzy value and a zero fuzzy value;
wherein, Ke、Kde、KPI、KPDRespectively representing the deviation change rate and the deviation change acceleration of the output height of the loop and an expected value, the output gain of a PI type fuzzy controller and the output gain of a PD type fuzzy controller;
step3, self-adaptive and real-time control; after passing through Step1 and Step2, four parameter values of the fuzzy self-adaptive PID controller under the steady state condition are obtained, the performance of the fuzzy self-adaptive PID controller is ensured to be the same as that of the PID controller under the steady state condition, and then the four parameters are finely adjusted according to a response curve of a closed-loop system to achieve the expected performance;
firstly, observing the closed loop response curve of the fuzzy self-adaptive PID controller, estimating the overshoot of the fuzzy self-adaptive PID controller, and repeatedly pairing the fuzzy self-adaptive PID controller according to the overshootAdjusting, and finely adjusting the parameters of the controller in such a way until the response curve of the closed-loop system reaches the expected performance; if it is notOutside the error range of the height of the loop, circularly performing steps 1-2, and if the error range is within the error range, keeping the value of the fuzzy self-adaptive PID controller parameter at the previous moment unchanged;
wherein,respectively representing the proportional gain, integral gain and differential gain of the incremental fuzzy adaptive PID controller.
2. The method for controlling the height of the titanium strip coil continuous pickling loop with fuzzy logic optimized PID controller parameters according to claim 1, characterized in that: in Step1, the PID controller is an incremental discrete PID controller, and the incremental expression is as follows:
in the formula
In the formulaRespectively is the gain coefficient of the current sampling moment, the gain coefficient of the previous 1 st sampling moment and the gain coefficient of the previous 2 nd sampling moment of the incremental discrete PID controller; e (k) is the control deviation of the PID controller; kp、Ki＝Kp.Ts/TiAnd Kd＝Kp.Td/TsProportional gain, integral gain and differential gain of the PID controller respectively; t isi、TdAnd TsIntegration time, differentiation time, and sampling time, respectively;
in Step2, the increment expression of the fuzzy controller in PI mode is:
wherein,
wherein y (k) is a measurement value of the height of the loop at the time k, and x (k) is a value representing the intensity of the dynamic change of the system, and the magnitude of the intensity is defined as the maximum value between the product of the absolute value of the loop output deviation and the deviation change rate at the time k and the product of the absolute value of the loop output deviation change and the deviation change acceleration;
similarly, the incremental expression of the PD type fuzzy controller is as follows:
in summary, the incremental expression of the fuzzy logic optimized PID controller is:
in the formula,
the corresponding item coefficients of the fuzzy logic optimization PID controller and the PID controller are equal, namely the fuzzy controller parameter and the PID parameter are correspondingly equal when in a steady state,
meanwhile, when the membership function parameter L is 1, the input variable is considered to fall in the region [ -1,1 ] as much as possible]×[-1,1]Interior, therefore, KeShould be selected to satisfy
(ysp-y0)·Ke＝1 (11)
The parameter K of the fuzzy logic optimization PID controller can be obtained by the formulas (10) and (11)e、Kde、KPI、KPD；
Wherein, Delta UF-PI(k) Is the incremental output, Delta U, of a fuzzy PI-type controllerF-PD(k) Is the incremental output, Delta U, of a fuzzy PD type controllerF-PID(k) Is the incremental output, y, of a fuzzy logic optimized PID controllerspIs the desired value, y, of the loop output height0The initial output height value of the loop is the same as the previous values of other parameters.
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| CN1858666A (en) * | 2005-05-20 | 2006-11-08 | 鞍钢新轧钢股份有限公司 | Control method for acid washing and edge cutting of super low carbon soft steel |
| CN103823370A (en) * | 2014-01-24 | 2014-05-28 | 广州市精源电子设备有限公司 | Self-adaptive control method for micro-arc oxidation process and system |
| EP2937747A1 (en) * | 2014-04-24 | 2015-10-28 | Siemens Aktiengesellschaft | Optimisation of a sequence of strips to be pickled, by modelling a pickling line |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN1858666A (en) * | 2005-05-20 | 2006-11-08 | 鞍钢新轧钢股份有限公司 | Control method for acid washing and edge cutting of super low carbon soft steel |
| CN103823370A (en) * | 2014-01-24 | 2014-05-28 | 广州市精源电子设备有限公司 | Self-adaptive control method for micro-arc oxidation process and system |
| EP2937747A1 (en) * | 2014-04-24 | 2015-10-28 | Siemens Aktiengesellschaft | Optimisation of a sequence of strips to be pickled, by modelling a pickling line |
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