Constant-tension self-adaptive cable wind control method based on fuzzy algorithm
By using a fuzzy algorithm for cable wind control, the tension of the cable wind winch is adjusted in real time, solving the problems of untimely response of cable wind equipment and insufficient stability of PID control algorithm. This achieves high-precision cable wind control, reducing construction risks and equipment costs.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-03
- Publication Date
- 2026-03-10
AI Technical Summary
Existing cable wind equipment cannot respond to external conditions in real time, resulting in uncontrolled movement of the hoisted object during the hoisting process, which increases construction risks. Traditional PID control algorithms have insufficient stability, large steady-state errors, and rely on manual adjustment.
A constant tension adaptive cable winch control method based on fuzzy algorithm is adopted. By fuzzifying real-time monitoring data such as wind force and hoisting height, fuzzy control rules are formulated to adjust the constant tension control of the cable winch in real time.
It achieves high-precision and robust cable wind control, reduces the amplitude of suspended object movement, quickly responds to wind force changes, reduces steady-state error, reduces construction risks, and saves equipment costs.
Smart Images
Figure CN121634832A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The application belongs to a constant tension adaptive cable wind control method based on a fuzzy algorithm. BACKGROUND
[0002] With the investment and construction of large-scale engineering construction projects in China, the hoisting of components is gradually becoming large-scale, and the requirements for hoisting equipment are gradually increasing due to the characteristics of being heavier, larger and higher. The high-altitude hoisting operation of the project faces the problems of strong wind and complex environment, and the movement of the hoisted object is not controlled during hoisting, which significantly increases the construction risk. In the process of hoisting large components, the cable wind system is an important link to ensure the posture of the hoisted object, but the current domestic and foreign cable wind equipment mainly adjusts the tension on the cable wind rope by manually changing the output of the cable wind winch. The response to external conditions is not timely, and the cable wind force cannot be adjusted in real time according to the hoisting wind environment and lifting height, which not only cannot guarantee reliable cable wind effect, but also increases the construction risk of the operating personnel.
[0003] The traditional PID control algorithm has the following shortcomings when applied to adaptive cable wind control: insufficient stability (easy to produce overshoot when the movement state of the hoisted object changes suddenly), large steady-state error (the cable wind force deviation is accumulated after a long time of operation, affecting the control precision), dependence on manual experience to adjust control parameters, feedback lag and lack of real-time adaptive ability.
[0004] Therefore, a constant tension adaptive cable wind control system based on a fuzzy algorithm is provided. SUMMARY
[0005] To solve the above problems existing in the prior art, the application provides a constant tension adaptive cable wind control method based on a fuzzy algorithm, which realizes high-precision and high-robustness adaptive regulation of the cable wind force.
[0006] The technical scheme to achieve the above-mentioned purpose is: A constant tension adaptive cable wind control method based on a fuzzy algorithm, comprising: Step S1, modeling and parameter initialization: analyzing the dynamic characteristics of the cable wind system, constructing the mathematical relationship between the tension and the influencing factors, initializing the input / output variable range, membership function and fuzzy control rule of the fuzzy controller; Step S2, fuzzy controller design: fuzzy processing, fuzzy control rule library construction and defuzzification; Step S3, real-time monitoring of the wind environment of the current hoisting scene and the hoisting height in the process by a wind speed and direction instrument and a crane hoisting encoder, and setting the fuzzy output as the input of the fuzzy controller; Step S4, decision making according to the fuzzy control rule, and adding the output after defuzzification to the constant tension control value of the cable wind winch frequency converter to complete the control.
[0007] Preferably, in the step S1, according to the actual engineering requirements and the use of boundary conditions of large component hoisting, the movement of the hoisted object is affected by wind force and hoisting height during the hoisting of the large component, and then the two are taken as input variables of the fuzzy control.
[0008] Preferably, in the step S1, the fuzzy language adopts five words of negative big (NB), negative small (NS), zero (ZO), positive small (PS) and positive big (PB) to describe two input variables, and adopts seven words of negative big (NB), negative medium (NM), negative small (NS), zero (ZO), positive small (PS), positive medium (PM) and positive big (PB) to represent the output variable.
[0009] Preferably, in the step S1, for the input variable 1, i.e. the wind force level of the hoisting site, the fuzzy set {NB, NS, ZO, PS, PB} is divided, and the deviation value range is [0, 8], representing the wind force 0-8 level; for the input variable 2, i.e. the hoisting height of the hoisted object, the fuzzy set {NB, NS, ZO, PS, PB} is divided, and the deviation value range is [0, 800], representing the hoisted object 0-800 m from the ground; for the output variable, i.e. the constant tension on the cable winch, the fuzzy set {NB, NM, NS, ZO, PS, PM, PB} is divided, and the constant tension deviation value range is related to the weight and shape of the hoisted object. The heavier the hoisted object, the larger the wind area, and the larger the deviation value range. The maximum value should not exceed the rated tension of the cable winch.
[0010] Preferably, in the step S1, a triangular membership function is adopted, and the general structure of the membership function is preliminarily determined by the expert experience method, and then the previously selected membership function is further improved and optimized during simulation debugging.
[0011] Preferably, in the step S1, according to the field experience, the fuzzy rules are formulated as: The greater the wind force, the greater the constant tension, the greater the hoisting height, and the greater the constant tension. The language description rule is converted into the sentence "IF 'wind force' is 'A' and 'hoisting height' is 'B' Then 'constant tension' is 'C'".
[0012] Preferably, in the step S2, the precise input quantity is converted into a fuzzy language variable, and the belonging degree is quantified by a triangular membership function; After being fuzzed, the control quantity is solved by inference according to the fuzzy control rules based on expert experience or experimental data; The fuzzy control quantity output is converted into a precise control quantity by using the barycentric method or the maximum membership degree method.
[0013] Preferably, in the step S4, when the tension of the steel wire rope is greater than the set tension value, the winch is used to release the cable to reduce the tension, and when the tension of the steel wire rope is less than the set value, the winch is used to tighten the cable to increase the tension, so that the tension of the steel wire rope is always maintained near the set value.
[0014] Preferably, in the step S4, the fuzzy decision is made by using the fuzzy control rule, the output matrix is obtained by synthesizing the deviation matrix and the fuzzy relationship matrix, and the area center method is used to defuzzify to obtain the constant tension control value of the winch.
[0015] Compared with the prior art, the beneficial effects of the present application are as follows: the present application describes the hoisting wind environment, hoisting height and constant tension by using fuzzy language, formulates a fuzzy control rule, and obtains the control value after defuzzification, so that the movement amplitude of the hoisted object can be effectively reduced in an environment below 7-level wind; the anti-disturbance adaptability design in combination with the fuzzy rule library can quickly recover in the case of sudden change of the hoisted object movement state, effectively reduce the steady-state error, and significantly improve the anti-interference ability under complex working conditions; no additional motion reference unit or other sensors need to be installed, thereby saving construction steps and equipment costs; the constant tension adjustment reduces the overshoot of the winch frequency, and effectively avoids frequent shaking of the hoisted object. BRIEF DESCRIPTION OF DRAWINGS
[0016] The accompanying drawings are included to provide a further understanding of the present application, and constitute a part of the specification, which together with the embodiments of the present application, serve to explain the present application, and do not constitute a limitation on the present application. In the drawings: Figure 1 is a flow chart of a constant tension self-adaptive cable wind control method based on a fuzzy algorithm of the present application; Figure 2 is a fuzzy control flow chart in the present application; Figure 3 is a schematic diagram of an input variable wind force membership function in the present application; Figure 4 is a schematic diagram of an input variable hoisting height membership function in the present application; Figure 5 is a schematic diagram of an output variable constant tension membership function in the present application; Figure 6 is a schematic diagram of a fuzzy control simulation result in the present application. DETAILED DESCRIPTION
[0017] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.
[0018] Fuzzy control is a kind of intelligent control method which imitates the fuzzy reasoning and decision-making process of human being based on fuzzy set theory, fuzzy language variable and fuzzy logic reasoning. The method firstly compiles the experience of operators or experts into fuzzy rules, then fuzzifies the real-time signals from sensors, takes the fuzzified signals as the input of fuzzy rules, completes fuzzy reasoning, and adds the output of reasoning to the actuator. Therefore, the application provides a constant tension adaptive cable wind control method based on fuzzy algorithm, as shown below.
[0019] As shown in Figure 1 , 2 The constant tension adaptive cable wind control method based on fuzzy algorithm comprises the following steps. Step S1, modeling and parameter initialization: analyzing the dynamic characteristics of the cable wind system, constructing the mathematical relationship between tension and influencing factors, initializing the input / output variable range, membership function and fuzzy control rule of the fuzzy controller.
[0020] In the embodiment, according to the actual engineering requirements and boundary conditions of large component hoisting, it is known that the movement of the hoisted object is affected by wind force and hoisting height during large component hoisting, and then the two are taken as input variables of fuzzy control.
[0021] In the embodiment, fuzzy language is the main feature of the fuzzy controller. In order to use fuzzy control to solve the problem of precise variable, the first work is to convert the input and output signals into fuzzy language that can be recognized and processed by the fuzzy controller. Describing the input and output with fuzzy language, determining the domain corresponding to the input and output variables, and selecting appropriate membership functions are the three main works of fuzzy processing. In the application, the two input variables are described by five words of negative big (NB), negative small (NS), zero (ZO), positive small (PS) and positive big (PB), and the output variable is described by seven words of negative big (NB), negative medium (NM), negative small (NS), zero (ZO), positive small (PS), positive medium (PM) and positive big (PB).
[0022] In the embodiment, for the input variable 1, i.e. the wind force level of the hoisting site, the fuzzy set {NB, NS, ZO, PS, PB} is divided, and the deviation value range is [0, 8], representing wind force 0-8 levels; For the input variable 2, i.e. the hoisting height of the hoisted object, the fuzzy set {NB, NS, ZO, PS, PB} is divided, and the deviation value range is [0, 800], representing the hoisted object 0-800m from the ground; For the output variable, i.e. the constant tension on the cable winch, is divided into fuzzy set {NB, NM, NS, ZO, PS, PM, PB}, the constant tension deviation value range is related to the weight and shape of the hoisted object, the heavier the hoisted object, the larger the wind area, the larger the deviation value range, and the maximum value should not exceed the rated tension of the cable winch, i.e. 5t, and the deviation value range in the application is temporarily set as [0, 3], representing the constant on the cable winch is 0 to 3t.
[0023] In the embodiment, the membership function is used to fuzz the value of the fuzzy subset, and the essence is a mathematical logic operation mode, which has the characteristics of evaluating the result of things only by fuzzy language without making positive or negative judgment, and in the setting process of the fuzzy controller, there is no fixed method for selecting the membership function, and in the application process, the fuzzy statistical method, expert experience method and other methods are needed to determine the membership function according to the actual situation, and the most common membership functions include Gaussian type, trapezoidal type, triangular type and S type and other forms; in the design process of the fuzzy controller, the triangular membership function is adopted, the general structure of the membership function is preliminarily determined by the expert experience method, and then the previously selected membership function is further perfected and optimized in the simulation debugging, and the membership functions of the input variable and the output variable are as shown in the following table. Figures 3-5
[0024] In the embodiment, when the hoisted object amplitude increases during hoisting of the large component, the cable wind rope needs to be tightened, at this time the tension on the cable wind rope increases, and vice versa, therefore, the tension on the steel wire rope of the cable winch is selected as the control variable, i.e. the output variable, which can be more intuitive and effective to write fuzzy rules compared with the output frequency of the cable winch frequency converter; The fuzzy control rule is mainly used to describe the relationship between various fuzzy quantities, and its common expression form is "if…and…then…", and the selection of the fuzzy control rule is a key content in the design process of the fuzzy controller, and whether the fuzzy rule is appropriate or not will have a decisive influence on the actual effect of the controller; According to the field experience, the fuzzy rule is formulated as follows: The greater the wind force, the greater the constant tension, the greater the hoisting height, the greater the constant tension, the language description rule is converted into the sentence "IF 'wind force' is 'A' and 'hoisting height' is 'B' Then 'constant tension' is 'C'", wherein A, B and C are shown in Table 1. Table 1: Fuzzy control rule Step S2, fuzzy controller design: fuzzing, fuzzy control rule library construction and defuzzing.
[0025] In this embodiment, precise input values are converted into fuzzy linguistic variables, and their degree of belonging is quantified using a triangular membership function; After fuzzification, the control quantity is solved by reasoning based on fuzzy control rules formulated based on expert experience or experimental data. The fuzzy control output is converted into a precise control output by using the centroid method or the maximum membership method.
[0026] Step S3: Monitor the wind environment and midpoint lifting height of the current lifting scenario in real time using an anemometer and crane height encoder, and set the fuzzified data as the input to the fuzzy controller.
[0027] Step S4: Make a decision based on the fuzzy control rules, and after defuzzification, add the output to the constant tension control value of the cable winch frequency converter to complete the control.
[0028] In this embodiment, when the wire rope is subjected to a force greater than the set tension value, the winch releases the cable to reduce the tension; when the wire rope is subjected to a force less than the set value, the winch tightens the cable to increase the tension, so that the tension on the wire rope is always kept near the set value.
[0029] In this embodiment, the input variables are substituted into the fuzzy controller, and fuzzy rules are applied to complete the fuzzy decision-making. The control matrix can be synthesized from the deviation matrix and the fuzzy relation matrix. The area center method is used for defuzzification to obtain the constant tension control value of the winch. An application program is developed based on the algorithm for simulation, and the results are as follows. Figure 6 As shown, its input / output relationship conforms to the control logic.
[0030] In this invention, fuzzy language is used to describe the hoisting wind environment, hoisting height, and constant tension, and fuzzy control rules are formulated. The fuzzified signal is used as the input of the fuzzy rules to complete fuzzy inference. The output obtained after inference is added to the actuator to control the tension on the cable winch wire rope so that it is always kept near the set value, so as to achieve an adaptive cable winch effect.
[0031] Finally, it should be noted that the above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.
Claims
1. A constant tension adaptive cable wind control method based on fuzzy algorithm, characterized in that, Comprise: Step S1, modeling and parameter initialization: analyze the dynamic characteristics of the cable wind system, construct the mathematical relationship between tension and influencing factors, initialize the input / output variable range, membership function and fuzzy control rule of the fuzzy controller; Step S2, fuzzy controller design: fuzzy processing, fuzzy control rule base construction and defuzzification; Step S3, through the wind speed and direction instrument and the crane hoisting height encoder, the wind environment and the current hoisting height of the current hoisting scene are monitored in real time, and after being fuzzed, they are set as the input of the fuzzy controller; Step S4, according to the fuzzy control rule, the decision is made, and after being de-fuzzed, the output is added to the constant tension control value of the cable wind winch frequency converter to complete the control.
2. The constant tension adaptive cable wind control method based on fuzzy algorithm according to claim 1, characterized in that, In the step S1, according to the actual engineering requirements and the use boundary conditions of large component hoisting, it is known that the movement of the hoisted object is affected by wind force and lifting height during large component hoisting, and then the two are taken as the input variables of fuzzy control.
3. The constant tension adaptive cable wind control method based on fuzzy algorithm according to claim 2, characterized in that, In the step S1, the fuzzy language adopts five words of negative big (NB), negative small (NS), zero (ZO), positive small (PS) and positive big (PB) to describe the two input variables, and seven words of negative big (NB), negative medium (NM), negative small (NS), zero (ZO), positive small (PS), positive medium (PM) and positive big (PB) to represent the output variable.
4. The constant tension adaptive cable wind control method based on fuzzy algorithm according to claim 3, characterized in that, In the step S1, for the input variable 1, i.e. the wind grade of the hoisting site, it is divided into a fuzzy set {NB, NS, ZO, PS, PB}, and the deviation value range is [0, 8], representing wind force 0-8 levels; For the input variable 2, i.e. the lifting height of the hoisted object, it is divided into a fuzzy set {NB, NS, ZO, PS, PB}, and the deviation value range is [0, 800], representing that the hoisted object is 0-800 m away from the ground; For the output variable, i.e. the constant tension on the cable wind winch, it is divided into a fuzzy set {NB, NM, NS, ZO, PS, PM, PB}, and the constant tension deviation value range is related to the weight and shape of the hoisted object. The heavier the hoisted object, the larger the wind area, and the larger the deviation value range. The maximum value should not exceed the rated tension of the cable wind winch.
5. The constant tension adaptive cable wind control method based on fuzzy algorithm according to claim 1, characterized in that, In the step S1, triangular membership functions are used, and the general structure of the membership function is preliminarily determined by expert experience method, and then the previously selected membership function is further improved and optimized during simulation debugging.
6. The constant tension adaptive cable wind control method based on fuzzy algorithm according to claim 1, characterized in that, In the step S1, according to the field experience, the fuzzy rules are formulated as follows: The greater the wind force, the greater the constant tension, the greater the lifting height, the greater the constant tension, the language description rule is converted into the sentence "IF 'wind force' is 'A' and 'lifting height' is 'B' Then 'constant tension' is 'C'".
7. The constant tension adaptive cable wind control method based on fuzzy algorithm according to claim 5, characterized in that, In the step S2, the precise input is converted into a fuzzy language variable, and the belonging degree is quantified by a triangular membership function; After being fuzzed, the control quantity is solved by reasoning according to the fuzzy control rule based on expert experience or experimental data; The fuzzy control quantity output is converted into a precise control quantity by using the barycentric method or the maximum membership degree method.
8. The constant tension adaptive cable wind control method based on fuzzy algorithm according to claim 1, characterized in that, In the step S4, when the tension of the steel wire rope is greater than the set tension value, the winch releases the cable to reduce the tension, and when the tension of the steel wire rope is less than the set value, the winch tightens the cable to increase the tension, so that the tension of the steel wire rope is always kept near the set value.
9. The constant tension adaptive cable wind control method based on fuzzy algorithm according to claim 1, characterized in that, In the step S4, the fuzzy control rule is applied to complete the fuzzy decision, the output quantity matrix can be obtained by synthesizing the deviation matrix and the fuzzy relation matrix, the area center method is used to defuzzify, and the winch constant tension control value is obtained.