Fuzzy-Smith-LADRC coagulation dosing control method based on improved dung beetle algorithm

Abstract

The invention provides a Fuzzy-Smith-LADRC coagulation dosing control method based on an improved dung beetle algorithm, and the method comprises the steps: firstly obtaining a transfer function of a coagulation dosing system through an alum consumption rising experiment data result, then applying the LADRC to the system, estimating and compensating the disturbance in the coagulation control system through an expansion observer, and finally obtaining a final control result. Meanwhile, a self-adaptive Smith controller combining a Smith predictor (Smith) and a fuzzy controller (Fuzzy) is designed to eliminate the influence of a large time lag link in the coagulation dosing system on the control effect, a Fuzzy-Smith-LADRC control method is proposed, and an improved dung beetle algorithm is introduced for parameter setting for the difficulty in controller parameter adjustment. Effective adjustment of the coagulation dosing system can be achieved under the conditions that the model is uncertain and external disturbance is unknown, and the safety of drinking water is ensured.

Description

Technical Field

[0001] The invention relates to the field of coagulation and dosing in water plants, and in particular to a coagulation and dosing control method based on an improved dung beetle algorithm Fuzzy-Smith-LADRC. Background Art

[0002] The coagulation and dosing system in a water plant is a crucial component of the water treatment process. It primarily involves adding an appropriate amount of coagulant to aggregate and precipitate suspended solids and colloids in the water. However, due to the significant time lag inherent in the coagulation process, conventional PID control methods struggle to achieve optimal results in control systems with frequently fluctuating raw water quality. Consequently, most water plants currently rely on beaker tests to evaluate coagulant dosing effectiveness and perform manual adjustments. This approach typically employs saturation dosing, but it also presents issues such as regulation lag. Summary of the Invention

[0003] In order to solve the above problems, that is, to solve the problems raised by the above background technology, the present invention proposes a Fuzzy-Smith-LADRC coagulation dosing control method based on the improved dung beetle algorithm. First, the transfer function of the coagulation dosing system can be obtained from the experimental data of alum consumption femtometer. Then, LADRC is applied to the system, and the disturbance occurring in the coagulation control system is estimated and compensated by the extended observer. At the same time, an adaptive Smith controller combining a Smith predictor (Smith) and a fuzzy controller (Fuzzy) is designed to eliminate the influence of the large time delay link in the coagulation system on the control effect. A Fuzzy-Smith-LADRC control method is proposed. In view of the difficulty in adjusting the controller parameters, the improved dung beetle algorithm is introduced for parameter tuning, which includes the following steps:

[0004] S1. Constructing the transfer function model of the coagulation dosing system based on the experimental data of alum consumption in femtometers;

[0005] S2. Design a second-order linear active disturbance rejection controller (LADRC), wherein the second-order linear active disturbance rejection controller (LADRC) includes a linear state observer (LESO) and a linear error feedback law (LSEF);

[0006] S3. Designing a Smith Predictor, wherein the Smith Predictor is used to eliminate the influence of the time lag link on the coagulation dosing system;

[0007] S4, designing a fuzzy controller (Fuzzy), inputting variables into the fuzzy controller (Fuzzy) to generate results, and converting them into an interpolation table;

[0008] S5. Combining the fuzzy controller with the Smith predictor, adjusting the time constant of the Smith predictor online, and realizing dynamic adjustment of the time delay link;

[0009] S6. In order to solve the difficulty of adjusting controller parameters, an improved dung beetle algorithm is introduced to perform parameter tuning;

[0010] The patent of the present invention is further configured as follows: the linear extended state observer (LESO) estimates the system state and total disturbance in real time based on the control quantity and object output, and is the core of the active disturbance rejection controller.

[0011] The patent of the present invention is further configured as follows: the Smith predictor can utilize its own characteristics to compensate for the influence of large time lag characteristics on the coagulation dosing control system.

[0012] The invention is further configured as follows: the E of the fuzzy control algorithm y , △E y The domain of △T is [-3,3], and the fuzzy domain is set to 5 variable levels: {negative large (NB), negative small (NS), zero (Z), positive small (PS), positive large (PB)}, Gaussian function is the membership function, and Mamdami method is applied to T f Perform fuzzy processing and finally use center of gravity clarification for reasoning.

[0013] The patent of the present invention is further set as follows: the specific improvement steps of the improved dung beetle algorithm are to introduce chaotic map initialization to replace the random variable initialization of the original algorithm, so that the initialization space search range for the solution is wider, and embed the improved sine algorithm to regularly guide the rolling ball dung beetles, promote the interaction of position updates between dung beetles, and at the same time deal with the sudden drop in population diversity in the late iteration, propose a Gaussian operator and Cauchy operator combined with a mutation perturbation strategy to improve the diversity of the population, and finally use the greedy rule to compare the current optimal solution of the operation, so as to further find a better solution.

[0014] The beneficial technical effects of the patent of this invention are:

[0015] (1) The Fuzzy-Smith-LADRC control method effectively improves the control accuracy and speed of the coagulation dosing system.

[0016] (2) Through fuzzy control, the time constant of the Smith predictor is adjusted online to reduce the impact of the time delay on the system.

[0017] (3) The interpolation table generated by fuzzy control simplifies the model structure and improves the running speed.

[0018] (4) By introducing the improved dung beetle algorithm for parameter tuning, the parameter adjustment time is simplified and the control accuracy of the system is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is the structural diagram of the coagulant dosing control principle.

[0020] Figure 2 This is the flow chart of the improved dung beetle algorithm.

[0021] Figure 3 The step response comparison response diagram is performed for the present invention.

[0022] Figure 4 This is a comparison response diagram of the anti-interference ability of the present invention. DETAILED DESCRIPTION

[0023] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the new embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0024] In the description of the present invention, it is to be understood that the terms “include” and “comprise” indicate the presence of the described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0025] The accompanying drawings illustrate various schematic diagrams of structures according to embodiments disclosed herein. These figures are not drawn to scale; for the purpose of clarity, some details are exaggerated and some details may be omitted. The shapes of the various regions and layers shown in the figures, as well as their relative sizes and positional relationships, are merely exemplary and may deviate in practice due to manufacturing tolerances or technical limitations. Those skilled in the art may design regions / layers with different shapes, sizes, and relative positions as needed.

[0026] The present invention proposes a coagulation dosing control method based on the improved dung beetle algorithm Fuzzy-Smith-LADRC. First, the transfer function of the coagulation dosing system is obtained from the experimental data of alum consumption femtoliters. Then, LADRC is applied to the system. The disturbances occurring in the coagulation control system are estimated and compensated using an extended observer. At the same time, an adaptive Smith controller combining a Smith predictor (Smith) and a fuzzy controller (Fuzzy) is designed to eliminate the influence of the large time delay link in the coagulation system on the control effect. The Fuzzy-Smith-LADRC control method is proposed. The improved dung beetle algorithm is introduced for parameter tuning to address the difficulty in adjusting the controller parameters. The method includes the following steps:

[0027] S1. Constructing the transfer function model of the coagulation dosing system based on the experimental data of alum consumption in femtometers;

[0028] S2. Design a second-order linear active disturbance rejection controller (LADRC), wherein the second-order linear active disturbance rejection controller (LADRC) includes a linear state observer (LESO) and a linear error feedback law (LSEF);

[0029] S3. Designing a Smith Predictor, wherein the Smith Predictor is used to eliminate the influence of the time lag link on the coagulation dosing system;

[0030] S4, designing a fuzzy controller (Fuzzy), inputting variables into the fuzzy controller (Fuzzy) to generate results, and converting them into an interpolation table;

[0031] S5. Combining the fuzzy controller with the Smith predictor, online adjusting the time constant in the Smith predictor to achieve dynamic adjustment of the time delay link;

[0032] S6. In order to solve the difficulty of adjusting controller parameters, an improved dung beetle algorithm is introduced to perform parameter tuning;

[0033] In this implementation scheme, the improved dung beetle algorithm is combined with the ADRC parameter setting to optimize the ADRC related parameters w0, w c , b0, combined with the actual operation requirements of the coagulation dosing system, the population size is set to 60 and the maximum number of iterations is 50.

[0034] Figure 3 A step response comparison response diagram is provided for the present invention. When the given turbidity is 1 NTU, the PID control reaches steady state at 372 seconds and has an overshoot of about 8%. The dynamic matrix DMC controller reaches steady state at 133 seconds with no overshoot, and the Fuzzy-Smith-LADRCC controller reaches steady state at 93 seconds with no overshoot.

[0035] Figure 4 The anti-interference capability comparison response diagram for the present invention shows that when a step signal with an amplitude of 0.1 is suddenly added at 600 seconds of simulation time, PID control reaches steady state again in 950 seconds; dynamic matrix DMC control reaches steady state again in 820 seconds; and Fuzzy-Smith-LADRC reaches steady state again in 745 seconds. Therefore, the Fuzzy-Smith-LADRC controller has better anti-interference and robustness in coagulation control.

[0036] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. The improved dung beetle algorithm Fuzzy-Smith-LADRC coagulation dosing control method is characterized by: First, the transfer function of the coagulation dosing system is obtained from the experimental data of alum consumption femtometer. Then, LADRC is applied to the system. The disturbances in the coagulation control system are estimated and compensated using an extended observer. At the same time, an adaptive Smith controller combining a Smith predictor and a fuzzy controller is designed to eliminate the influence of large time delay links in the coagulation system on the control effect. A fuzzy-Smith-LADRC control method is proposed. To address the difficulty in adjusting the controller parameters, an improved dung beetle algorithm is introduced for parameter tuning. The method includes the following steps: S1. Constructing the transfer function model of the coagulation dosing system based on the experimental data of alum consumption in femtometers; S2. Design a second-order linear active disturbance rejection controller (LADRC), wherein the second-order linear active disturbance rejection controller (LADRC) includes a linear state observer (LESO) and a linear error feedback law (LSEF); S3. Designing a Smith Predictor, wherein the Smith Predictor is used to eliminate the influence of the time lag link on the coagulation dosing system; S4, designing a fuzzy controller (Fuzzy), inputting variables into the fuzzy controller (Fuzzy) to generate results, and converting them into an interpolation table; S5. Combining the fuzzy controller with the Smith predictor, online adjusting the time constant in the Smith predictor to achieve dynamic adjustment of the time delay link; S6. Aiming at the difficulty in adjusting controller parameters, an improved dung beetle algorithm is introduced for parameter tuning.

2. The coagulation dosing control method based on the improved dung beetle algorithm Fuzzy-Smith-LADRC according to claim 1 is characterized in that: The alum consumption femtometer experimental data results show that the transfer function of the coagulation dosing system can be expressed as:

3. The coagulation dosing control method based on the improved dung beetle algorithm Fuzzy-Smith-LADRC according to claim 1 is characterized in that: The second-order linear active disturbance rejection controller (LADRC) is analyzed by the dynamic process of coagulation and dosing, and the turbidity change can be described by a second-order differential equation, which can be expressed by the following second-order system: Where z is turbidity, w(t) is external disturbance, The core idea of LADRC is to estimate the total disturbance in real time and compensate it in real time through the estimated value to eliminate the disturbance, so as to transform it into a simple integral series type. From the controlled object, we can know that the second-order LADRC controller can be introduced to realize the coagulation dosing control, and the state variables are selected: It can be transformed into a continuous expansion state equation, because unknown, it can be used when designing the state observer Omit; According to the Longgerberg state observer theory, the corresponding continuous linear state observer (LESO) equation is as follows: in is the state variable of the observer, l1, l2, l3 are the observer gains. When the observer gains obtain appropriate values, LESO can accurately track the state variables of the controlled object. The Laplace transform of the LESO equation can be obtained: The characteristic equation corresponding to LESO is: L′(s)=s 3 +l1s 2 +l2s+l3 To make the control system stable, the characteristic roots must all be in the left half plane of s, and the three poles of the observer must all be located at the left half real axis of the S plane -w0 (w0 is the observer bandwidth, w0>0), that is: L′(s)=(s+w3) 3 Therefore, the observer gain can be obtained as: l1=3w 0, l2=3w 2 0, ,l3=w 3 0 It can be seen that the observer gain is only related to the bandwidth of the observer. After the LESO estimates the system disturbance, it can feed the relevant information back to the controller for control. The result is shown in the following formula: Where v1 and v2 are the outputs of the tracking differentiator of the desired turbidity v in the dosing control, b0 and b1 are the control gains, and u0 is the error feedback control quantity. The transfer function of u0 and r0 can be obtained. By placing both poles of the controller at the left half real axis -w0 of the S plane, the controller gain can be obtained as: Among them, w c is the controller bandwidth (w c >0), the controller parameters are only related to the control bandwidth, and the compensation value of the total disturbance estimate can be added on the basis of u0; 4. The coagulation dosing control method based on the improved dung beetle algorithm Fuzzy-Smith-LADRC according to claim 1 is characterized in that: The LADRC controller is further integrated with the Smith predictor and the fuzzy controller to eliminate the influence of large time delay on the control effect. The specific operation is to add the correction amount △T of the controller to the initial value T0 to obtain the setting control parameter T f , then T f =△T+T0 to realize adaptive adjustment of time constant T f The numerical value of .

5. The coagulation dosing control method based on the improved dung beetle algorithm Fuzzy-Smith-LADRC according to claim 1 is characterized in that: The fuzzy control algorithm E y , △E y The domain of △T is [-3,3], and the fuzzy domain is set to 5 variable levels: {negative large (NB), negative small (NS), zero (Z), positive small (PS), positive large (PB)}. The Gaussian function is the membership function. The Mamdami method is used to calculate T. f Perform fuzzy processing and finally use center of gravity clarification for reasoning.

6. The coagulation dosing control method based on the improved dung beetle algorithm Fuzzy-Smith-LADRC according to claim 1 is characterized in that: The specific improvement steps of the improved dung beetle algorithm are to introduce chaotic map initialization to replace the random variable initialization of the original algorithm, so that the initialization space search range of the solution is wider, and embed an improved sine algorithm to regularly guide the rolling ball dung beetles, promote the interaction of position updates between dung beetles, and at the same time, to cope with the sudden drop in population diversity in the late iteration, propose a Gaussian operator and Cauchy operator combined with a mutation perturbation strategy to improve the diversity of the population, and finally use the greedy rule to compare the current optimal solution of the operation, so as to further find a better solution.

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