CN111427269A - Dynamic positioning model test control method based on fuzzy PID control - Google Patents

Dynamic positioning model test control method based on fuzzy PID control Download PDF

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CN111427269A
CN111427269A CN202010354802.8A CN202010354802A CN111427269A CN 111427269 A CN111427269 A CN 111427269A CN 202010354802 A CN202010354802 A CN 202010354802A CN 111427269 A CN111427269 A CN 111427269A
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CN111427269B (en
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王磊
贺华成
蒋旭
于特
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Shanghai Jiaotong University
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Abstract

The invention relates to a dynamic positioning model test control method based on fuzzy PID control, which comprises the steps of arranging a wave height instrument at the gravity center position of a ship, monitoring the wave height η in real time, obtaining the position of the ship through measurement of a position measurement system, sending the position information to a filter, estimating the low-frequency position x and the low-frequency speed v of the ship through the filter, further sending the information to a central PID controller, calculating the required control force tau, and finally distributing the thrust to each propeller of a dynamic positioning barge through a thrust distribution module, wherein tau is kp*η+kd*v+kiThe method comprises the steps of multiplying integral multiple factor η dt, enabling kp, kd and ki to be proportional coefficients, differential coefficients and integral coefficients respectively, inputting filtered low-frequency position x and low-frequency speed v, and outputting current optimal PID control parameters through fuzzy reasoning.

Description

Dynamic positioning model test control method based on fuzzy PID control
Technical Field
The invention relates to a dynamic positioning model test control method based on fuzzy PID control, and belongs to the technical field of ocean engineering.
Background
In the field of ocean engineering, in order to determine the stress and the movement of an ocean structure and the movement response of a mooring system and a riser system and provide an important basis for future design, a physical model test is an important means. The dynamic positioning model test can simulate important information such as positioning accuracy, power consumption and the like of an ocean structure under a specified sea condition in a wind, wave and current pool, and provides important reference for the design of a dynamic positioning system in practical application, so that the dynamic positioning model test is very important. Once the positioning accuracy of the structure does not meet the requirement and the deviation is too large, various parameters of the system under the real positioning condition are difficult to obtain, and accurate positioning must be ensured. In order to ensure the positioning accuracy, an operator is usually required to adjust the PID control parameters in real time according to the positioning conditions, so that high requirements are provided for the experience and level of the operator, and a method capable of automatically adjusting the control parameters according to the positioning accuracy is urgently needed.
Disclosure of Invention
The invention aims to realize centimeter-level positioning accuracy in a dynamic positioning model test without manually adjusting PID control parameters. The invention provides a PID parameter adjusting method based on fuzzy control, which automatically selects the optimal PID control parameter under the current condition according to the fuzzy reasoning result through the ship position measured in real time without human intervention, thereby ensuring high-precision positioning.
The invention adopts the following technical scheme:
a dynamic positioning model test control method based on fuzzy PID control sets a wave height instrument at the gravity center position of a ship, monitors the wave height η in real time, obtains the position of the ship by the measurement of a position measurement system, sends the position information to a filter, estimates the low-frequency position x and the low-frequency speed v of the ship by the filter, further sends the information to a central PID controller, calculates the required control force tau, and finally distributes the thrust to each propeller of a dynamic positioning barge through a thrust distribution module;
τ＝kp*η+kd*v+kiintegral multiple η dt, kp, kd and ki are respectively proportional coefficient, differential coefficient and integral coefficient, the current optimal PID control parameter is output by inputting the filtered low-frequency position x and low-frequency speed v and through fuzzy inference, the process of the fuzzy inference is that the low-frequency position and the low-frequency speed of the ship are divided into the following 7 fuzzy subsets, NB, NM, NS, ZO, PS, PM and PB, membership functions of the subsets, N for negative, P for positive, ZO for 0, S for small, M for medium and B for large, fuzzifying the low-frequency position η and the low-frequency velocity v by fuzzification factors, performing inference by using a well-defined fuzzy rule, and integrating the coefficient kiAlways take a fixed small value, and kpAnd kdControlling the output by a fuzzy system; coefficient of proportionality kpAnd a differential coefficient kdDivided into fuzzy subsets S and B, i.e. small and large, scale factor kpAnd a differential coefficient kdThe corresponding membership functions are:
Figure BDA0002473084700000021
and
Figure BDA0002473084700000022
said x represents a proportionality coefficient kpOr differential coefficient kd(ii) a The musmalRepresents S, the mubigRepresents B; determining 7 from said 7 fuzzy subsets determined2A fuzzy rule is set, and the reasoning process of fuzzy control is carried out according to the fuzzy rule; after fuzzy reasoning, carrying out ambiguity resolution to obtain a finally adopted proportionality coefficient kpAnd a differential coefficient kd。
A system for implementing a dynamic positioning model test control method based on fuzzy PID control comprises a central PID controller, a filter, a fuzzy PID controller, a dynamic positioning ship and a position measurement system; the central PID controller, the dynamic positioning ship, the position measuring system, the filter and the central PID controller are connected in sequence; the filter, the fuzzy PID controller and the central PID controller are connected in sequence.
The invention has the beneficial effects that:
1) the method for automatically adjusting the PID control parameters according to the current positioning condition is provided, and high positioning accuracy is guaranteed.
2) A whole set of dynamic positioning floating support installation model test control system is constructed;
3) the positioning error of the barge is reduced;
4) according to the wind speed, the flow velocity and the wave height, a multiple feedforward mode is provided, the positioning accuracy of the dynamic positioning model test is obviously improved, and the measured data can provide important reference for the design of an actual dynamic positioning system.
5) The method solves the problems that in a dynamic positioning ship, the control of wave pure feedback needs to be fed back forcefully only by prior deviation, so that the actual control is easily disturbed by the outside to generate larger deviation, and the system can not reach the required positioning precision due to oscillation back and forth near a positioning point;
drawings
Fig. 1 is a graph of membership functions for low frequency location η and low frequency velocity v.
FIG. 2 shows the scaling factor kpAnd a differential coefficient kdA graph of membership functions of (a).
Fig. 3 is a general control flow diagram of the system.
FIG. 4 is a fuzzy PID control flow chart.
Detailed Description
The invention is further described with reference to the following figures and specific examples.
The system mainly comprises a central PID controller, a position measuring system, a filter, a fuzzy PID controller and a dynamic positioning ship.
Assuming that the vessel is positioned at the origin, for the conventional control method, the position of the vessel at that time is measured by the position measurement system, the position information is sent to the filter, the low frequency position η and low frequency velocity v of the vessel at the location estimated by the filter, this information is further sent to the central PID controller, the required control force τ is calculated, and finally the thrust is distributed to the various thrusters of the dynamically positioned barge via the thrust distribution module
τ＝kp*η+kd*v+ki*∫ηdt
kp, kd, and ki are a proportional coefficient, a differential coefficient, and an integral coefficient, respectively. In order to ensure high-precision positioning, PID control parameters must be adjusted in real time according to a positioning effect, and generally speaking, the positioning precision is better, and when the external environmental force is smaller, small control parameters are adopted; and when the positioning precision is poor and the external disturbance is large, large control parameters are adopted. During model test, an operator is required to adjust parameters in real time, and high requirements are provided for experience and level of the operator.
Aiming at the control method of the invention, a fuzzy controller for adjusting PID parameters in real time according to the positioning effect is provided, and the current optimal PID control parameters are output through inputting the low-frequency motion deviation and speed after filtering and through fuzzy reasoning, and the specific flow is shown in the attached drawing.
The low-frequency position and the low-frequency speed of a ship are divided into the following 7 fuzzy subsets, NB, NM, NS, ZO, PS, PM and PB, wherein the membership functions of the subsets are respectively triangular distribution with vertexes of [ -1-0.667-0.33300.3330.6671 ] and a span of 0.667, N represents negative, P represents positive, ZO represents 0, S represents small, M represents medium and B represents large.
Figure BDA0002473084700000041
Figure BDA0002473084700000042
Fuzzy rules are essentially a series of if-then type linguistic control rules, determined by human operator experience and expert knowledge. From the fuzzy subsets determined in fig. 1, 49 fuzzy rules can be determined, as shown in tables 1 and 2. The fuzzy control reasoning process is carried out according to a fuzzy rule table, and fuzzy reasoning is carried out by adopting a single-point fuzzification and product reasoning mechanism. After fuzzy reasoning, the final adopted proportionality coefficient kp and differential coefficient kd can be obtained by resolving fuzzy.
Table 1, fuzzy rule table for the proportionality coefficient kp:
Figure BDA0002473084700000051
table 2, fuzzy rule table of differential coefficient kd:
Figure BDA0002473084700000052

Claims (2)

1. a dynamic positioning model test control method based on fuzzy PID control is characterized in that:
arranging a wave height instrument at the gravity center position of the ship, and monitoring the wave height η in real time;
the position of the ship is measured by a position measuring system, the position information is sent to a filter, the low-frequency position x and the low-frequency speed v of the ship are estimated by the filter, the information is further sent to a central PID controller, the required control force tau is calculated, and finally the thrust is distributed to each propeller of the dynamic positioning barge through a thrust distribution module;
τ＝kp*η+kd*v+ki*∫ηdt；
kp，kdand kiProportional coefficient, differential coefficient and integral coefficient;
outputting the current optimal PID control parameter through inputting the filtered low-frequency position x and low-frequency speed v and through fuzzy reasoning;
the fuzzy reasoning process is as follows:
the low frequency position and low frequency velocity of the vessel are divided into the following 7 fuzzy subsets: NB, NM, NS, ZO, PS, PM, PB, membership functions of the subsets, N representing negative, P representing positive, ZO representing 0, S representing small, M representing medium, B representing large;
blurring the low frequency position η and the low frequency velocity v by a blurring factor;
reasoning by using a well-defined fuzzy rule; integral coefficient kiAlways take a fixed small value, and kpAnd kdControlling the output by a fuzzy system; coefficient of proportionality kpAnd a differential coefficient kdDivided into fuzzy subsets S and B, i.e. small and large, scale factor kpAnd a differential coefficient kdThe corresponding membership functions are:
Figure FDA0002473084690000011
and
Figure FDA0002473084690000012
said x represents a proportionality coefficient kpOr differential coefficient kd(ii) a The musmalRepresents S, the mubigRepresents B;
determining 7 from said 7 fuzzy subsets determined2A fuzzy rule is set, and the reasoning process of fuzzy control is carried out according to the fuzzy rule; after fuzzy reasoning, carrying out ambiguity resolution to obtain a finally adopted proportionality coefficient kpAnd a differential coefficient kd。
2. A system for implementing a dynamic positioning model test control method based on fuzzy PID control is characterized in that:
the system comprises a central PID controller, a filter, a fuzzy PID controller, a dynamic positioning ship and a position measuring system;
the central PID controller, the dynamic positioning ship, the position measuring system, the filter and the central PID controller are connected in sequence;
the filter, the fuzzy PID controller and the central PID controller are connected in sequence.
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