CN111427269B - 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 PDFInfo
- Publication number
- CN111427269B CN111427269B CN202010354802.8A CN202010354802A CN111427269B CN 111427269 B CN111427269 B CN 111427269B CN 202010354802 A CN202010354802 A CN 202010354802A CN 111427269 B CN111427269 B CN 111427269B
- Authority
- CN
- China
- Prior art keywords
- fuzzy
- coefficient
- low
- ship
- frequency
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
- G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
Abstract
The invention relates to a dynamic positioning model test control method based on fuzzy PID control, wherein a wave height instrument is arranged at the gravity center position of a ship, and the wave height eta is monitored in real time; the position of the ship is measured by a position measuring system, the position information is sent to a filter, and the ship is estimated by the filterThe 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×. η dt; kp, kd and ki are respectively a proportional coefficient, a differential coefficient and an integral coefficient; and outputting the current optimal PID control parameter through inputting the filtered low-frequency position x and low-frequency speed v and fuzzy reasoning. According to the invention, the PID control parameters are automatically adjusted according to the current positioning condition, so that high positioning precision is ensured.
Description
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 is characterized in that a wave height instrument is arranged at the gravity center position of a ship, and the wave height eta is monitored 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 eta 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×. η dt; kp, kd and ki are respectively a proportional coefficient, a differential coefficient and an integral coefficient; outputting the current optimal PID control parameter through inputting the filtered low-frequency position eta and the filtered 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; fuzzifying the low-frequency position eta and the low-frequency speed v by a fuzzification 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:
and
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 fuzzy solution to obtainFinally 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 position 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 ship is positioned at the origin, aiming at the traditional control method, the position of the ship at the moment is measured by a position measuring system, the position information is sent to a filter, the low-frequency position eta 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. The control force can be expressed as
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 the ship are divided into 7 fuzzy subsets, NB, NM, NS, ZO, PS, PM and PB, wherein the membership functions of the subsets are respectively distributed in a triangular mode, the vertex of each fuzzy subset is between-1 and 0.667 and 0.33300.3330.6671, and the span of each fuzzy subset is 0.667. Where N is negative, P is positive, ZO is 0, S is small, M is medium, and B is large. Thus, PM represents medium positive, NB represents negative large, and so on. Firstly, the low-frequency position eta and the low-frequency velocity v are fuzzified by fuzzification factors, and then reasoning can be carried out by using a well-defined fuzzy rule. In order to ensure the stability of the system, the integral coefficient ki always takes a small value, and kp and kd are output by the fuzzy system control. The proportionality coefficient kp and the differentiation coefficient kd are then divided into two fuzzy subsets, S and B, i.e. small and large, whose corresponding membership functions are shown below.
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:
table 2, fuzzy rule table of differential coefficient kd:
Claims (1)
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 eta 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 eta 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 eta and the filtered 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;
fuzzifying the low-frequency position eta and the low-frequency speed v by a fuzzification 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:
x represents a proportionality coefficient kpOr differential coefficient kd；μsmallDenotes S, μbigRepresents 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。
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010354802.8A CN111427269B (en) | 2020-04-29 | 2020-04-29 | Dynamic positioning model test control method based on fuzzy PID control |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010354802.8A CN111427269B (en) | 2020-04-29 | 2020-04-29 | Dynamic positioning model test control method based on fuzzy PID control |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111427269A CN111427269A (en) | 2020-07-17 |
| CN111427269B true CN111427269B (en) | 2021-05-18 |
Family
ID=71552162
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010354802.8A Active CN111427269B (en) | 2020-04-29 | 2020-04-29 | Dynamic positioning model test control method based on fuzzy PID control |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111427269B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112925316A (en) * | 2021-01-25 | 2021-06-08 | 中国船舶工业集团公司第七0八研究所 | Vector control thrust distribution optimization method for double-pump water jet propulsion ship |
Family Cites Families (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN100489702C (en) * | 2005-07-07 | 2009-05-20 | 上海交通大学 | Ship power-positioning control system based on fuzzy self-adaption algorithm |
| CN1776555A (en) * | 2005-12-08 | 2006-05-24 | 上海交通大学 | Marine power positioning control method based on fuzzy adaptive algorithm |
| CN102331717A (en) * | 2011-10-10 | 2012-01-25 | 哈尔滨工程大学 | Intelligent control method of navigational speed of ship |
| CN105955269A (en) * | 2016-05-12 | 2016-09-21 | 武汉理工大学 | Fuzzy PID algorithm based ship course controller |
| CN106773741A (en) * | 2017-03-02 | 2017-05-31 | 华南理工大学 | A kind of unmanned boat dynamic positioning system and method |
| CN107247413A (en) * | 2017-06-27 | 2017-10-13 | 哈尔滨工程大学 | A kind of high-speed trimaran pitching stabilization control method based on fuzzy control |
| CN108052009B (en) * | 2018-01-23 | 2021-05-04 | 哈尔滨工程大学 | Water surface target rescue tracking observation controller design method based on filtering backstepping method |
| CN108490946A (en) * | 2018-04-13 | 2018-09-04 | 武汉理工大学 | A kind of marine power positioning control method |
| CN109164701A (en) * | 2018-07-17 | 2019-01-08 | 袁乐铮 | A kind of course heading control method and system based on fuzzy control |
| CN110032075B (en) * | 2019-05-28 | 2022-04-05 | 哈尔滨工程大学 | Design method of saturation compensation control system of dynamic positioning ship |
-
2020
- 2020-04-29 CN CN202010354802.8A patent/CN111427269B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN111427269A (en) | 2020-07-17 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Wilkie et al. | An Introductory Course | |
| CN108549208B (en) | Four-rotor aircraft attitude control method based on factor self-adaptive fuzzy PID | |
| CN108845576B (en) | Thrust distribution method based on combination of particle swarm optimization and sequence quadratic programming | |
| Fossen et al. | Nonlinear output feedback control of underwater vehicle propellers using feedback form estimated axial flow velocity | |
| US6185470B1 (en) | Neural network predictive control method and system | |
| Kraus | Wave glider dynamic modeling, parameter identification and simulation | |
| CN111427269B (en) | Dynamic positioning model test control method based on fuzzy PID control | |
| CN101763033A (en) | Device for self-correcting control for multi-model RBF neural network of deep submersible rescue vehicle and method thereof | |
| CN108267955A (en) | Towards the motion control method of the autonomous berthing of unmanned boat | |
| CN1718378A (en) | S face control method of flotation under water robot motion | |
| CN102331717A (en) | Intelligent control method of navigational speed of ship | |
| CN111897347B (en) | Course maintaining method of double-motor propulsion unmanned ship based on neural network PID control | |
| CN113341693B (en) | Course control method based on asymmetric phase difference and amplitude of flapping wings | |
| CN109189075B (en) | Model-free adaptive course control method for ship by using fuzzy forgetting factor | |
| CN116819950B (en) | Ship and floating ocean platform dynamic positioning control method and system | |
| CN113342012A (en) | Course control method of sliding and flapping integrated aircraft | |
| CN111538232A (en) | Unmanned driving anti-shaking positioning method and system based on adaptive neural fuzzy control | |
| CN113900372A (en) | Unmanned ship course keeping method based on neural network active disturbance rejection control | |
| CN109828462A (en) | Wave glider becomes under the speed of a ship or plane adaptive bow to controller and control method | |
| Katebi et al. | LQG adaptive ship autopilot | |
| Li et al. | Research on heading control of USV with the lateral thruster | |
| Liu et al. | Research on key methods of autonomous sailboat’s position keeping control | |
| CN110837254B (en) | Longitudinal motion control method for hydrofoil craft | |
| CN104216398A (en) | Control system for wave piercing catamaran and control parameter online-tuning method thereof | |
| CN111338370B (en) | Control method for attitude stabilization of sea-air amphibious rotor robot |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |