CN111221244A - Ship rudder rolling reduction control method - Google Patents
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Abstract
The invention discloses a ship rudder rolling reduction control method, which comprises the following steps: establishing a ship linearization mathematical model, establishing a random wave spectrum modeling and simulation module, and establishing a steering engine model; defining input signals e and input signal change rate ec of a controller and a domain and fuzzy set of output u of the controller, dividing a fuzzy control strategy into three layers according to domains of | e | and | ec |, and adopting an outer layer control strategy, a middle layer control strategy and an inner layer control strategy by a system according to different values of | e | and | ec |; defining membership functions of e, ec and u, and designing control strategies of all layers by a fuzzy reasoning method; and selecting quantization and scale factors of each layer according to indexes such as robustness, rapidity and disturbance suppression performance requirements of the system. The invention integrates the respective advantages of PID and fuzzy control, applies a self-adaptive hierarchical control strategy and ensures that the system has good control performance and stabilization effect under different sea conditions and different encountering frequencies.
Description
Technical Field
The invention belongs to the technical field of ship rolling reduction control, in particular to a ship rudder rolling reduction control method, and particularly relates to a ship rudder rolling reduction control method based on adaptive hierarchical fuzzy PID control.
Background
When a ship sails on the sea, various interferences such as sea wind, sea waves, ocean currents and the like greatly limit the sailing speed, reduce the transportation efficiency, influence the operability of the ship and influence the safety of goods and personnel on the ship. Therefore, rudder stabilization technology for ships is one of important issues in motion control of ships and oceanographic engineering. The rudder roll reduction system uses a rudder as an actuator to reduce the roll angle while maintaining the heading.
Rudder stabilization is an active method for reducing rolling, and the rolling is reduced while the course is kept by using a steering engine of a ship. The ship can generate two motions of yaw and roll simultaneously in the steering process, the two motions are mutually complementary and mutually coupled, so that the steering roll-reduction control system uses an actuating mechanism (a control quantity) to simultaneously control two outputs (roll and yaw), research shows that the roll frequency response and the yaw frequency response of the ship are completely different, the roll frequency response is basically in a high-frequency section, the yaw frequency response is basically in a low-frequency section, and the roll self-rolling period and the yaw period of the ship are obviously different from the steering frequency response, so that the steering roll-reduction controller can be independently designed according to the idea of frequency band separation on the basis of the original heading control (autopilot). Moreover, as long as the frequency, the direction, the size and the phase of the rudder are correctly and reasonably controlled, a good rudder rolling reduction effect can be realized on the premise of not influencing the course (heading).
At present, the technology of rudder stabilization is mature at home and abroad, a better control effect is obtained in large and medium ship control, and compared with other stabilization devices, the rudder stabilization device has the advantages of low price, convenience in use, small occupied space and the like, so that the rudder stabilization device is widely applied. A great deal of research is carried out on rudder stabilization control methods at home and abroad, but no document and patent for rudder stabilization by applying a method based on adaptive hierarchical fuzzy PID control is found.
Foreign research on rudder rolling begins in the 70 th 19 th century, progresses slowly from beginning to 80 th century, and Baitis publishes relevant experimental reports indicating that a certain naval vessel system in the United states achieves 50% of rolling reduction rate after rudder rolling control is added. In 1987, Vandert Klugt and Van Amerongen applied LQG controllers to rudder stabilization systems, and verified the stabilization effect and advantage of rudder stabilization through ship model experiments, real ship sea trials and simulations. By 90's in the 20 th century, Yang Chengen, Stoustrop et al designed rudder anti-yaw H∞And the controller obtains a satisfactory experimental effect. In 1998, Blanke et al proposed a theoretical system of LQ control algorithm and quantitative feedback, and designed a corresponding rudder stabilizing controller. Oda et al improved the rudder stabilization system using a multivariate autoregressive model. Some intelligent control algorithms are also applied to the field of rudder stabilization and achieve good effects. In the 20 s, Perez and Tzcng implemented rudder roll reduction control using a model predictive control method. In 2008, Jong-Koo Park and Seong-Sik Yoon proposed a dynamic anti-saturation algorithm for rudder stabilization.
The research on the rudder stabilization technology in China starts late, the experiment is mainly carried out on warships, and most of related documents are theoretical researches aiming at simple models. In 1982, muo national level indicated the feasibility of rudders as roll-reducing devices. In the 90 s, the rudder stabilization system was designed by the traditional PID control method, such as Juwen, Huiyan and the like, and the stabilization rate was pointed out to be 50%. And designing an optimal rudder stabilization controller with an adaptive criterion by settling and the like, and performing simulation. Zhengming and the like apply the traditional fuzzy control theory to design a rudder stabilizing control system. In the 21 st century, the popikun applies a closed-loop gain shaping algorithm to a rudder stabilization system, and simulation results show that the algorithm has robust performance and the stabilization rate reaches over 25%. The self-adaptive neural fuzzy inference system is applied to rudder stabilization in the rural area, and 25% stabilization rate is obtained. Xunetian and the like apply the hybrid genetic algorithm to the rudder stabilization system, and a high stabilization rate effect is obtained.
Disclosure of Invention
Aiming at the prior art, the invention aims to provide a ship rudder roll reducing control method based on self-adaptive hierarchical fuzzy PID control, which integrates the advantages of fuzzy control and PID control and applies a self-adaptive hierarchical control strategy.
In order to solve the technical problem, the invention provides a control method for reducing rolling of a ship rudder, which comprises the following steps:
the method comprises the following steps: establishing mathematical models of all parts of a ship rudder stabilizing system: setting a rated navigational speed, substituting the rated navigational speed into a hydrodynamic derivative of the ship, and establishing a ship linearization model and a simulation module which take rudder angle and sea wave interference as input and a roll angle and a bow angle as output; establishing a model and a simulation module of the steering engine, wherein the model comprises a nonlinear restriction link on a rudder angle and a rudder speed; carrying out wave interference modeling by utilizing a segmented superposition method according to a P-M spectrum of the waves;
step two: selecting a three-layer self-adaptive fuzzy control strategy as a roll controller, and collecting ship roll angle signals
The desired roll angle r is set to 0 and the input e to the roll controller is defined as the roll angle
Input rate of change ec is roll angular rate
The output u of the controller is a rudder angle delta;
the domains of discourse of e, ec and u are all { -5, -4, -3, -2, -1,0,1,2,3,4,5}, the domains of discourse are divided into 3 layers according to the deviation size, the size of the domains of discourse follows the principle of gradually reducing from the outer layer to the middle layer and then to the inner layer, and then the fuzzy control strategy is divided into three layers according to the domains of | e | and | ec |: when | e | >2.5 or | ec | >2.5, the system adopts an outer layer control strategy; when 1.25< | e | < 2.5 or 1.25< | ec | < 2.5, the system adopts a middle-layer control strategy; when | e | < 1.25 or | ec | < 1.25, the system adopts an inner layer control strategy;
step three: defining membership functions of e, ec and u, and designing control strategies of all layers by a fuzzy reasoning method: for the outer layer, the universe set of e and ec is the union of [ -5, -2.5] and [2.5, 5], the fuzzy subsets of e, ec and u are set to obey the triangular membership function, the rolling fuzzy control rule base of the outer layer is established, and the curved surface graphs of e, ec and u are drawn; for the middle layer, the universe of discourse of e and ec is the union of [ -2.5, -1.25] and [1.25, 2.5], fuzzy subsets of e, ec and u are set to obey the triangular membership function, a rolling fuzzy control rule base of the middle layer is established, and curved surface graphs of e, ec and u are drawn; and for the inner layer, the domain sets of e and ec are [ -1.25, 1.25], fuzzy subsets of e, ec and u are set to obey triangular and trapezoidal membership functions, a rolling fuzzy control rule base of the inner layer is established, and curved surface graphs of e, ec and u are drawn.
Step four: introducing sea wave interference, simulating a closed loop system for ship stabilization, observing a time domain step response characteristic of the system, drawing a closed loop sensitivity characteristic and an open loop Nyquist diagram, and selecting an input scale factor K of each layer according to the robustness, rapidity, overshoot and disturbance suppression requirements of the system and the parameter optimization design and selection rules of a PID controllere(i)、Kec(i) And outputting the quantization factor Ku(i) And i is 1,2,3, which respectively represent an outer layer, a middle layer and an inner layer.
The invention also comprises
1. And in the third step, the universe of discourse and fuzzy subsets of the input error e, the error change rate ec and the output control quantity u of the fuzzy controller meet the following conditions:
e. the domains of ec and u are { -5, -4, -3, -2, -1,0,1,2,3,4,5}, each layer of control input has nine fuzzy sets, each layer has at most 81 fuzzy rules, and fuzzy decision adopts a Mamdani decision method;
the hierarchical fuzzy controller consists of a plurality of multi-input single-output fuzzy systems and a group of If-Then control rules, and the specific form is as follows:
If x1is A1j，x2Is A2j，…，xnIs Anj，Then yj＝bj
Wherein x1，x2，…，xnIs a system variable, yjIs the output variable of the output of the converter,
Aij∈A(i＝1,2,..,n；j＝1,2,...,m)bje, B (j is 1,2,.., m), wherein A and B are fuzzy sets;
membership function value
Describes the variable xiAnd fuzzy subset AijThe degree of correspondence of;
control output from jth rule:
then y isjSatisfy the requirement of
Wherein
The total output of the fuzzy control is obtained by:
wherein: k is the number of fuzzy rules and n is the number of inputs.
2. Selecting input scale factor K of each layer in step foure(i)、Kec(i) And outputting the quantization factor Ku(i) I is 1,2,3 is specifically:
Ke(i+1)＝0.5Ke(i)
Kec(i+1)＝2Kec(i)
Ku(i+1)＝0.5Ku(i)
the invention has the beneficial effects that: the invention combines fuzzy control and PID control aiming at the characteristics of a rudder stabilization system, integrates the respective advantages of PID and fuzzy control, and applies a self-adaptive hierarchical control strategy. By drawing the closed-loop sensitivity characteristic, the open-loop Nyquist characteristic, the observation system time domain response characteristic and the like of the system, according to the indexes of the robustness, the rapidity, the overshoot, the disturbance suppression requirement and the like of the system, the quantization and the scale factor of the controller are reasonably designed and selected, and the system is ensured to have good control performance and stabilization effect under different sea conditions and different encountering frequencies. The invention has simple structure, is easy to realize and can well meet the requirement of practical engineering application.
Drawings
FIG. 1 is a flow chart of the present invention.
Fig. 2 is a model of a rudder actuator.
Fig. 3 is a schematic block diagram of hierarchical fuzzy control.
FIG. 4 is a hierarchy of discourse domains.
Detailed Description
The following further describes the embodiments of the present invention with reference to the drawings.
The invention comprises the following steps: establishing a ship hydrodynamic model and a linearized mathematical model, establishing a random wave spectrum modeling and simulation module, and establishing a steering engine model; organically combining a self-adaptive layered fuzzy control strategy and PID control, defining input signals e and input signal change rate ec of a controller and discourse domain and fuzzy set of controller output u according to rudder stabilization control characteristics, dividing the fuzzy control strategy into three layers according to the discourse domain of | e | and | ec |, and dividing the fuzzy control strategy into three layers when | e | is normal>2.5 or | ec>At 2.5, the system adopts an outer control strategy, and the outer control strategy is 1.25<Less than or equal to 2.5 or 1.25 of | e |<When | ec | < 2.5, the system adopts a middle-layer control strategy, and when | e | < 1.25 or | ec | < 1.25, the system adopts an inner-layer control strategy; defining membership functions of e, ec and u, and designing control strategies of all layers by a fuzzy reasoning method; according to the indexes of robustness, rapidity, disturbance suppression performance requirement and the like of the system, the quantization and scale factor K of each layer is selected by combining the parameter optimization design and the selection rule of the PID controllere(i)、Kec(i)、Ku(i) And i is 1,2 and 3. The invention combines fuzzy control and PID control, integrates respective advantages of PID and fuzzy control, applies self-adaptive hierarchical control strategy and ensures that the system is differentThe sea state has good control performance and stabilization effect under different encountering frequencies.
With reference to the attached drawings 1-4, the invention comprises the following steps:
the method comprises the following steps: and establishing mathematical models of all parts of the ship rudder stabilizing system.
For a ship object, setting a rated navigational speed U as 11m/s, substituting into each hydrodynamic derivative of the ship, and establishing a ship linearization model and a simulation model which take a rudder angle and sea wave interference as input and a roll angle and a bow angle as output;
the actuator, the steering engine, functions to make the actual steering angle equal to the desired steering angle command from the autopilot or steering operator. The mathematical model adopts a simplified model as shown in figure 2, and the model comprises two nonlinear limiting links, one is a rudder angle limiting link, and the other is a rudder speed limiting link. The rudder angle delta and the rudder speed
The following ranges are limited: speed of rudder
The rudder angle is more than or equal to minus 35 degrees and less than or equal to delta 35 degrees.
For the modeling of the wave interference module, the wave interference modeling is carried out by utilizing a segmented superposition method according to a P-M spectrum of waves, and the P-M spectrum is described as follows:
wherein: a is 0.0081g2，
g represents the acceleration of gravity, H1/3Indicating the sense wave height.
Step two: according to the characteristics of rudder stabilization control, a three-layer adaptive fuzzy control strategy is selected as a rolling controller, as shown in the attached figure 3. Collecting ship roll angle signals
And roll angle rate
As input signals e and ec for the fuzzy controller. Defining the universe of discourse and fuzzy sets of the controller input signals e, ec and the controller output u, wherein the universe of discourse of e, ec and u is { -5, -4, -3, -2, -1,0,1,2,3,4,5}, and then dividing the fuzzy control strategy into three layers according to the universe of | e | and | ec |.
As shown in FIG. 4, the size of each domain follows the principle of large outer layer, medium middle layer and small inner layer. The inner-layer domain has more steps and finer steps, and the outer-layer control strategy and the middle-layer control strategy are equivalent to coarse adjustment to slow down the transient process; the inner-layer control strategy is equivalent to a steady-state process of performing fine adjustment and is used for improving the steady-state precision of the whole controller. Finally, when | e | >2.5 or | ec | >2.5 is set, the system adopts an outer layer control strategy; when 1.25< | e | < 2.5 or 1.25< | ec | < 2.5, the system adopts a middle-layer control strategy; when | e | ≦ 1.25 or | ec | ≦ 1.25, the system adopts an inner-layer control strategy.
Step three: and (5) defining membership functions of e, ec and u, and designing control strategies of all layers by a fuzzy reasoning method. And obtaining the domain set of the input signals of the controllers of each layer according to the domains { -5, -4, -3, -2, -1,0,1,2,3,4,5} of e, ec and u defined in the previous step. Nine fuzzy sets are input into each layer of control, so that each layer has 81 fuzzy rules at most, and the fuzzy decision adopts a Mamdani decision method.
For the outer layer, the set of discourse fields of e and ec is the union of [ -5, -2.5] and [2.5, 5], firstly, the input precise quantity is converted into a fuzzy quantity through a fuzzification interface; then determining a membership function of the language value, setting fuzzy subsets of e, ec and u to obey a triangular membership function, establishing an outer-layer rolling fuzzy control rule base, carrying out fuzzy reasoning by the system according to the database and the rule base, calculating a fuzzy output quantity u, and drawing a curved surface graph of e, ec and u; finally, defuzzification processing is carried out and is realized by a defuzzification interface, a fuzzy controller converts the fuzzy quantity into the clear quantity which is actually applied in engineering through the interface, the defuzzification is also called as defuzzification, and a weighted average method (a gravity center method) is adopted to realize the defuzzification.
For the middle layer, the universe of discourse of e and ec is the union of [ -2.5, -1.25] and [1.25, 2.5], firstly, the input precise quantity is converted into fuzzy quantity through a fuzzification interface; then determining a membership function of the language value, setting fuzzy subsets of e, ec and u to obey a triangular membership function, establishing a middle-layer rolling fuzzy control rule base, carrying out fuzzy reasoning by the system according to the database and the rule base, calculating a fuzzy output quantity u, and drawing a curved surface graph of e, ec and u; finally, defuzzification processing is carried out and is realized by a defuzzification interface, a fuzzy controller converts the fuzzy quantity into the clear quantity which is actually applied in engineering through the interface, the defuzzification is also called as defuzzification, and a weighted average method (a gravity center method) is adopted to realize the defuzzification.
For the inner layer, the universe sets of e and ec are [ -1.25, 1.25], and firstly, the input accurate quantity is converted into a fuzzy quantity through a fuzzification interface; then determining a membership function of the language value, setting fuzzy subsets of e, ec and u to obey triangular and trapezoidal membership functions, establishing a rolling fuzzy control rule base of an inner layer, carrying out fuzzy reasoning by the system according to the database and the rule base, calculating a fuzzy output u, and drawing a curved surface graph of e, ec and u; finally, defuzzification processing is carried out and is realized by a defuzzification interface, a fuzzy controller converts the fuzzy quantity into the clear quantity which is actually applied in engineering through the interface, the defuzzification is also called as defuzzification, and a weighted average method (a gravity center method) is adopted to realize the defuzzification.
Step four: firstly, designing the scale factor K of the outer layere(1)、Kec(1) And a quantization factor Ku(1) As shown in FIG. 3, Ke(1) Is a factor (corresponding to a proportional coefficient of PID), K, corresponding to the input signal eec(1) Is a factor (corresponding to a differential coefficient of PID) corresponding to the input signal ec, Ku(1) Is a quantization factor output to the outer layer controller. For the outer control strategy of hierarchical fuzzy control, because of large error, in order to obtain fast response, a large K is selectedeAnd KuSelecting a smaller KecTo quickly reduce errors. Optimization parameters in conjunction with classical PID controllersCounting, initially selecting Ke(1)、Kec(1) And Ku(1) The value of (c).
The quantization and scaling factors K for the middle and inner layers in FIG. 3 are next designede(i)、Kec(i)、Ku(i) And i is 2 and 3. For middle layer and inner layer control, the more the inner layer, the higher the control accuracy requirement, the finer the grading, and in order to make the system converge and reduce overshoot as soon as possible, the output of the controller is reduced appropriately, i.e. a smaller K is selectedeAnd KuSelecting a larger Kec. According to Ke(1)、Kec(1) And Ku(1) The other middle layer and inner layer control parameters are selected according to the following formula:
Kε(2)＝0.5Kε(1),Kε(3)＝0.5Kε(2) (2)
Kec(2)＝2Kec(1),Kec(3)＝2Kec(2) (3)
Ku(2)＝0.5Ku(1),Ku(3)＝0.5Ku(2) (4)
and then carrying out comprehensive simulation and analysis of the system. Sea wave interference is introduced to simulate a closed loop system for ship stabilization. And drawing a closed-loop sensitivity characteristic and an open-loop Nyquist diagram, and simulating a time-domain step response characteristic of the system. Judging whether the following index requirements are met: 1) the robustness design requirement is as follows: the distance between the distance-1 point of the open-loop Nyquist diagram and the main guide frequency omega of the sea wave is more than 0.5eIn the vicinity, the open-loop characteristic is distributed in the right half plane of the s-plane. 2) Disturbance rejection performance requirements: at the dominant frequency omega of sea waveseNear, minimum | S of amplitude-frequency characteristic of closed-loop sensitivitymin|<7dB, open-loop Nyquist plot at ωeThere is a maximum open loop gain nearby. 3) Rapidity and overshoot requirements: on the premise of ensuring robustness and anti-rolling performance, the method has the advantages of as fast response capability as possible and minimum overshoot.
Finally, according to the indexes of robustness, rapidity, overshoot, disturbance suppression requirement and the like of the system, further optimization design and adjustment are carried out on each control parameter, and finally a group of optimized quantization sums is determinedScale factor Ke(i)、Kec(i)、Ku(i)，i＝1,2,3。
The specific implementation mode of the invention can also comprise:
step 1: establishing a ship mathematical model, setting a rated navigational speed U to be 11m/s, bringing each hydrodynamic derivative into a ship, and establishing a ship linearization model and a simulation module which take rudder angle and sea wave interference as input and take a roll angle and a bow angle as output; establishing a model and a simulation module of the steering engine, and adding a nonlinear limit link for protecting a rudder angle and a rudder speed into the module; and carrying out wave interference modeling by utilizing a segmented superposition method according to the P-M spectrum of the waves.
Step 2: collecting ship roll angle signals
The desired roll angle r is set to 0 and the input e to the roll controller is defined as the roll angle
Input rate of change ec is roll angular rate
The controller output u is the rudder angle δ.
The domains of discourse defining e, ec and u are all { -5, -4, -3, -2, -1,0,1,2,3,4,5 }. According to the characteristics of rudder anti-rolling control, the discourse domain is divided into 3 layers according to the deviation, and the size of the discourse domain follows the principle that the outer layer is large, the middle layer is medium and the inner layer is small. Then, the fuzzy control strategy is divided into three layers according to the universe of speaking | e | and | ec |, when | e | is greater than 2.5 or | ec | is greater than 2.5, the system adopts an outer layer control strategy, when 1.25< | e | is less than or equal to 2.5 or 1.25< | ec | is less than or equal to 2.5, the system adopts a middle layer control strategy, and when | e | is less than or equal to 1.25 or | ec | is less than or equal to 1.25, the system adopts an inner layer control strategy.
And step 3: and (5) defining membership functions of e, ec and u, and designing control strategies of all layers by a fuzzy reasoning method. For the outer layer, the universe set of e and ec is the union of [ -5, -2.5] and [2.5, 5], the fuzzy subsets of e, ec and u are set to obey the triangular membership function, the rolling fuzzy control rule base of the outer layer is established, and the curved surface graphs of e, ec and u are drawn; for the middle layer, the universe of discourse of e and ec is the union of [ -2.5, -1.25] and [1.25, 2.5], fuzzy subsets of e, ec and u are set to obey the triangular membership function, a rolling fuzzy control rule base of the middle layer is established, and curved surface graphs of e, ec and u are drawn; and for the inner layer, the domain sets of e and ec are [ -1.25, 1.25], fuzzy subsets of e, ec and u are set to obey triangular and trapezoidal membership functions, a rolling fuzzy control rule base of the inner layer is established, and curved surface graphs of e, ec and u are drawn.
And 4, step 4: sea wave interference is introduced to simulate a closed loop system for ship stabilization. Observing the time domain step response characteristic of the system, drawing the closed-loop sensitivity characteristic and the open-loop Nyquist diagram, and selecting the quantization and the scale factor K of each layer according to the robustness, the rapidity, the overshoot, the disturbance suppression requirement and other indexes of the system by combining the parameter optimization design and the selection rule of the PID controllere(i)、Kec(i)、Ku(i)，i＝1,2,3。
The present invention may further comprise:
the input error e, the error change rate ec and the domain and fuzzy set of the output control quantity u of the fuzzy controller in the step 3 are specified as follows:
e. ec and u are all { -5, -4, -3, -2, -1,0,1,2,3,4,5}, and each layer of control input has nine fuzzy sets, so that each layer has 81 fuzzy rules at most, and the fuzzy decision adopts a Mamdani decision method.
The hierarchical fuzzy controller consists of a plurality of multi-input single-output fuzzy systems, gives consideration to the characteristics of dynamic and static performances of the system, and also consists of a group of If-Then control rules in the following specific form:
If x1is A1j，x2Is A2j，…，xnIs Anj，Then yj＝bj
Wherein x1，x2，…，xnIs a system variable, yjIs the output variable of the output of the converter,
Aij∈A(i＝1,2,..,n；j＝1,2,...,m)bje.g. B (j ═ 1, 2.., m), a, B are fuzzy sets.
In selecting fuzzy subsetsAnd when the membership function is used, selecting according to the actual condition of the system. Membership function value
Describes the variable xiAnd fuzzy subset AijTo a degree of correspondence.
Control output from jth rule:
then y isjSatisfy the requirement of
Wherein
The overall output of the fuzzy control can be obtained by:
wherein: k is the number of fuzzy rules and n is the number of inputs.
Output quantization factor K of each layer in step 4uAnd inputting the scale factor KeAnd KecThe following options are selected:
for the outer control strategy of hierarchical fuzzy control, because of large error, in order to obtain fast response, a large K is selectedeAnd KuSelecting a smaller KecThe error is reduced rapidly; for middle layer and inner layer control, the more the inner layer, the higher the control accuracy requirement, the finer the grading, and in order to make the system converge and reduce overshoot as soon as possible, the output of the controller is reduced appropriately, i.e. a smaller K is selectedeAnd KuSelecting a larger KecFor convenience of adjusting quantization and scale factors of each layer, i is 1,2, and 3 respectively denote an outer layer, a middle layer, and an inner layer, each of which is expressed asThe layer proportions and quantization factors are selected according to the following formula:
Ke(i+1)＝0.5Ke(i) (8)
Kec(i+1)＝2Kec(i) (9)
Ku(i+1)＝0.5Ku(i) (10)
the robustness requirement in step 4 is: the distance between the distance-1 point of the open-loop Nyquist diagram and the main guide frequency omega of the sea wave is more than 0.5eIn the vicinity, the open-loop characteristic is distributed in the right half plane of the s-plane.
The disturbance rejection performance requirements in step 4 are: at the dominant frequency omega of sea waveseNear, minimum | S of amplitude-frequency characteristic of closed-loop sensitivitymin|<7dB, open-loop Nyquist plot at ωeThere is a maximum open loop gain nearby.
The requirements of rapidity and overshoot in the step 4 are as follows: on the premise of ensuring robustness and anti-rolling performance, the method has the advantages of as fast response capability as possible and minimum overshoot.
The invention aims to provide a ship rudder roll reducing control method based on self-adaptive hierarchical fuzzy PID control, which comprises the following steps: building a ship object, an actuating mechanism and a mathematical modeling and simulation module of sea wave interference of the rudder stabilizing control system; by ship roll angle signals
And roll angle rate
Taking a rudder angle u as an input signal e and an rudder angle ec of a fuzzy controller for output, organically combining an adaptive layered fuzzy control strategy and PID control, defining discourse domains and fuzzy sets of the e, the ec and the u, setting a three-layer fuzzy layered control strategy, and determining input signal discourse domain ranges of all layers according to the principle that an outer layer is large, a middle layer is medium and an inner layer is small; defining membership functions of e, ec and u in each layer, designing control strategies of each layer through a fuzzy reasoning method, calculating control output, and drawing a curved surface graph of each layer of e, ec and u; introducing wave interference, according to closure of the systemThe loop and open loop frequency characteristics and the time domain response characteristics are combined with the indexes of robustness, rapidity, overshoot, disturbance suppression requirement and the like of the system to optimally design and adjust various control parameters and provide a group of optimized quantization and scale factors Ke(i)、Kec(i)、Ku(i) And i is 1,2 and 3. The hierarchical fuzzy control method organically combines the fuzzy controller and the self-adaptive PID controller, has the characteristic that the fuzzy controller can overcome model nonlinearity and uncertainty, and ensures that the designed rudder stabilizing system has good self-adaptability and robustness on the premise of meeting the optimal performance.
Claims (3)
1. A ship rudder rolling reduction control method is characterized by comprising the following steps:
the method comprises the following steps: establishing mathematical models of all parts of a ship rudder stabilizing system: setting a rated navigational speed, substituting the rated navigational speed into a hydrodynamic derivative of the ship, and establishing a ship linearization model and a simulation module which take rudder angle and sea wave interference as input and a roll angle and a bow angle as output; establishing a model and a simulation module of the steering engine, wherein the model comprises a nonlinear restriction link on a rudder angle and a rudder speed; carrying out wave interference modeling by utilizing a segmented superposition method according to a P-M spectrum of the waves;
step two: selecting a three-layer self-adaptive fuzzy control strategy as a roll controller, and collecting ship roll angle signals
The desired roll angle r is set to 0 and the input e to the roll controller is defined as the roll angle
Input rate of change ec is roll angular rate
The output u of the controller is a rudder angle delta;
the domains of discourse of e, ec and u are all { -5, -4, -3, -2, -1,0,1,2,3,4,5}, the domains of discourse are divided into 3 layers according to the deviation size, the size of the domains of discourse follows the principle of gradually reducing from the outer layer to the middle layer and then to the inner layer, and then the fuzzy control strategy is divided into three layers according to the domains of | e | and | ec |: when | e | >2.5 or | ec | >2.5, the system adopts an outer layer control strategy; when 1.25< | e | < 2.5 or 1.25< | ec | < 2.5, the system adopts a middle-layer control strategy; when | e | < 1.25 or | ec | < 1.25, the system adopts an inner layer control strategy;
step three: defining membership functions of e, ec and u, and designing control strategies of all layers by a fuzzy reasoning method: for the outer layer, the universe set of e and ec is the union of [ -5, -2.5] and [2.5, 5], the fuzzy subsets of e, ec and u are set to obey the triangular membership function, the rolling fuzzy control rule base of the outer layer is established, and the curved surface graphs of e, ec and u are drawn; for the middle layer, the universe of discourse of e and ec is the union of [ -2.5, -1.25] and [1.25, 2.5], fuzzy subsets of e, ec and u are set to obey the triangular membership function, a rolling fuzzy control rule base of the middle layer is established, and curved surface graphs of e, ec and u are drawn; and for the inner layer, the domain sets of e and ec are [ -1.25, 1.25], fuzzy subsets of e, ec and u are set to obey triangular and trapezoidal membership functions, a rolling fuzzy control rule base of the inner layer is established, and curved surface graphs of e, ec and u are drawn.
Step four: introducing sea wave interference, simulating a closed loop system for ship stabilization, observing a time domain step response characteristic of the system, drawing a closed loop sensitivity characteristic and an open loop Nyquist diagram, and selecting an input scale factor K of each layer according to the robustness, rapidity, overshoot and disturbance suppression requirements of the system and the parameter optimization design and selection rules of a PID controllere(i)、Kec(i) And outputting the quantization factor Ku(i) And i is 1,2,3, which respectively represent an outer layer, a middle layer and an inner layer.
2. The rudder roll reducing control method according to claim 1, wherein: thirdly, the universe of discourse and fuzzy subsets of the input error e, the error change rate ec and the output control quantity u of the fuzzy controller meet the following conditions:
e. the domains of ec and u are { -5, -4, -3, -2, -1,0,1,2,3,4,5}, each layer of control input has nine fuzzy sets, each layer has at most 81 fuzzy rules, and fuzzy decision adopts a Mamdani decision method;
the hierarchical fuzzy controller consists of a plurality of multi-input single-output fuzzy systems and a group of If-Then control rules, and the specific form is as follows:
If x1is A1j，x2Is A2j，…，xnIs Anj，Then yj＝bj
Wherein x1，x2，…，xnIs a system variable, yjIs the output variable of the output of the converter,
Aij∈A(i＝1,2,..,n；j＝1,2,...,m)bje, B (j is 1,2,.., m), wherein A and B are fuzzy sets;
membership function value
Describes the variable xiAnd fuzzy subset AijThe degree of correspondence of;
control output from jth rule:
then y isjSatisfy the requirement of
Wherein
X＝(x1,x2,...,xn)T；
The total output of the fuzzy control is obtained by:
wherein: k is the number of fuzzy rules and n is the number of inputs.
3. A rudder roll reducing control method according to claim 1 or 2, characterized in that: step four, selecting input scale factor K of each layere(i)、Kec(i) And outputting the quantization factor Ku(i) I is 1,2,3 is specifically:
Ke(i+1)＝0.5Ke(i)
Kec(i+1)＝2Kec(i)
Ku(i+1)＝0.5Ku(i) 。
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