CN111443611B - Multi-model switching-based high-speed unmanned ship speed controller determination method and system - Google Patents
Multi-model switching-based high-speed unmanned ship speed controller determination method and system Download PDFInfo
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Abstract
The invention discloses a method and a system for determining a high-speed unmanned ship navigational speed controller based on multi-model switching, which relate to the field of unmanned ship motion control and specifically comprise the following steps: firstly, clustering acquired experimental kinetic data of the high-speed unmanned ship in an acceleration stage and a deceleration stage, determining a theoretical kinetic model corresponding to each type of data in a clustering result, and further determining a controller corresponding to each theoretical kinetic model; and then switching the navigational speed controller of the high-speed unmanned ship to a controller corresponding to a theoretical dynamic model enabling the performance switching index to be optimal according to the performance switching index updated in real time during the navigation of the high-speed unmanned ship. The invention can achieve the aim of effectively improving the speed control accuracy of the high-speed unmanned ship.
Description
Technical Field
The invention relates to the field of unmanned ship motion control, in particular to a method and a system for determining a high-speed unmanned ship navigational speed controller based on multi-model switching.
Background
China has a wide ocean field and a very long coastline. With national exploration and exploitation, activities of utilizing ocean resources are increasingly frequent, and the development of high-speed unmanned ship technology is also increasingly important. The high-speed unmanned ship has good control performance, and is the basis for putting the high-speed unmanned ship into practical application, wherein accurate control of the speed is a very important part. The speed control of the high-speed unmanned ship is influenced by factors such as wind waves, current and the like in the marine environment, and the traditional PID control method is usually poor in effect. With the development and improvement of an intelligent control theory, more control methods with excellent performance are introduced into the design of a high-speed unmanned ship speed controller, such as fuzzy control, neural network control, model reference adaptive control and the like, so that the control performance is improved in various aspects.
However, the prior art still has the following problems:
after the speed of the high-speed unmanned ship is increased to a certain degree, the posture of the body of the whole high-speed unmanned ship begins to change, the bow of the ship gradually tilts, high-speed water flow gives a new lifting force to the high-speed unmanned ship, hydrodynamic factors present stronger nonlinear interference, and the control effect of the speed controllers is poor when the high-speed unmanned ship runs at a high speed.
Disclosure of Invention
The invention aims to provide a method and a system for determining a high-speed unmanned ship speed controller based on multi-model switching, so as to effectively improve the speed control accuracy of the high-speed unmanned ship.
In order to achieve the purpose, the invention provides the following scheme:
a method for determining a high-speed unmanned ship navigational speed controller based on multi-model switching comprises the following steps:
acquiring experimental dynamics data of the high-speed unmanned ship in an acceleration stage and a deceleration stage; the experimental dynamics data comprises speed data and throttle data;
clustering the experimental dynamics data to obtain a clustering result;
constructing a theoretical dynamic model corresponding to each type of data in the clustering result, and then determining a controller corresponding to each theoretical dynamic model; the input of the theoretical dynamic model is a throttle value, and the output of the theoretical dynamic model is a speed value;
during the navigation of the high-speed unmanned ship, switching a navigation speed controller of the high-speed unmanned ship to a controller corresponding to a theoretical dynamic model enabling the performance switching index to be optimal according to the performance switching index updated in real time; the performance switching index is the weighted sum of the current error and the historical error; the current error represents the error between the actual speed value of the high-speed unmanned ship at the current sailing moment and the theoretical speed value of the q-th theoretical dynamic model; the historical error is an error obtained after a set forgetting effect is given to the error in a past set time period; and the theoretical dynamic model with the optimal performance switching index is the corresponding theoretical dynamic model when the weighted sum is minimum.
Optionally, the acquiring of the experimental dynamics data of the high-speed unmanned ship at the acceleration stage and the deceleration stage specifically includes:
determining a parameter identification experiment;
and acquiring throttle data and speed data of the high-speed unmanned ship from rest to maximum speed in an acceleration stage and throttle data and speed data of the high-speed unmanned ship from maximum speed to rest in a deceleration stage according to the parameter identification experiment.
Optionally, before performing clustering processing on the experimental dynamics data to obtain a clustering result, the method further includes:
and carrying out abnormal value elimination and smooth filtering processing on the experimental dynamics data to obtain the processed experimental dynamics data.
Optionally, the clustering the experimental dynamics data to obtain a clustering result specifically includes:
determining different adjustable parameters; the adjustable parameters are adjustable parameters in a subtractive clustering algorithm;
clustering the processed experimental dynamics data according to the subtractive clustering algorithm and different adjustable parameters to obtain different clustering results; different clustering results comprise different clustering data;
and screening out the optimal clustering result from all the clustering results according to a DB clustering index minimization principle, and then determining the optimal clustering result as a final clustering result.
Optionally, the constructing a theoretical dynamic model corresponding to each type of data in the clustering result, and then determining a controller corresponding to each theoretical dynamic model specifically includes:
constructing an initial theoretical dynamic model by adopting a single-input single-output autoregressive model with a controlled variable;
determining a performance indicator function; the performance index function is determined according to the error between the theoretical dynamic model output and the actual dynamic model output;
determining an identification estimation parameter of an initial theoretical dynamic model corresponding to each type of the clustering data according to the performance index function and each type of data in the clustering result;
and sequentially substituting different identification estimation parameters into the initial theoretical dynamic model to obtain a theoretical dynamic model corresponding to each type of the clustering data, and then determining a controller corresponding to each theoretical dynamic model.
Optionally, during the sailing of the high-speed unmanned ship, according to the performance switching index updated in real time, the switching of the sailing speed controller of the high-speed unmanned ship to the controller corresponding to the theoretical dynamic model with the optimal performance switching index specifically includes:
acquiring an actual speed value and an actual throttle value of the high-speed unmanned ship at the current sailing moment;
calculating a theoretical speed value of each theoretical dynamic model according to the actual throttle value;
calculating the performance switching index of each theoretical dynamic model at the current navigation moment according to the actual speed value and each theoretical speed value;
and screening out the optimal performance switching indexes from all the performance switching indexes, and then switching the navigational speed controller of the high-speed unmanned ship to the controller corresponding to the theoretical dynamic model corresponding to the optimal performance switching indexes.
A high-speed unmanned boat cruise controller determination system based on multi-model switching, comprising:
the experimental dynamics data acquisition module is used for acquiring experimental dynamics data of the high-speed unmanned ship in an acceleration stage and a deceleration stage; the experimental dynamics data comprises speed data and throttle data;
the clustering processing module is used for clustering the experimental dynamics data to obtain a clustering result;
the controller building module is used for building a theoretical dynamic model corresponding to each type of data in the clustering result and then determining a controller corresponding to each theoretical dynamic model; the input of the theoretical dynamic model is a throttle value, and the output of the theoretical dynamic model is a speed value;
the real-time determining module of the navigational speed controller is used for switching the navigational speed controller of the high-speed unmanned ship to a controller corresponding to a theoretical dynamic model which enables the performance switching index to be optimal according to the performance switching index updated in real time during the navigation of the high-speed unmanned ship; the performance switching index is the weighted sum of the current error and the historical error; the current error represents the error between the actual speed value of the high-speed unmanned ship at the current sailing moment and the theoretical speed value of the q-th theoretical dynamic model; the historical error is an error obtained after a set forgetting effect is given to the error in a past set time period; and the theoretical dynamic model with the optimal performance switching index is the corresponding theoretical dynamic model when the weighted sum is minimum.
Optionally, the experimental kinetic data acquiring module specifically includes:
a parameter identification experiment determining unit for determining a parameter identification experiment;
and the experimental dynamics data acquisition unit is used for acquiring accelerator data and speed data of the high-speed unmanned ship from rest to the maximum speed in an acceleration stage and accelerator data and speed data of the high-speed unmanned ship from the maximum speed to rest in a deceleration stage according to the parameter identification experiment.
Optionally, the method further includes:
and the data processing module is used for carrying out abnormal value elimination and smooth filtering processing on the experimental dynamics data to obtain the processed experimental dynamics data.
Optionally, the clustering processing module specifically includes:
the adjustable parameter determining unit is used for determining different adjustable parameters; the adjustable parameters are adjustable parameters in a subtractive clustering algorithm;
a clustering result initial determining unit, configured to perform clustering processing on the processed experimental dynamics data according to the subtractive clustering algorithm and the different adjustable parameters, so as to obtain different clustering results; different clustering results comprise different clustering data;
and the clustering result final determining unit is used for screening out the optimal clustering result from all the clustering results according to a DB clustering index minimization principle and then determining the optimal clustering result as the final clustering result.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention provides a method and a system for determining a high-speed unmanned ship navigational speed controller based on multi-model switching. Firstly, clustering acquired experimental kinetic data of the high-speed unmanned ship in an acceleration stage and a deceleration stage, determining a theoretical kinetic model corresponding to each type of data in a clustering result, and further determining a controller corresponding to each theoretical kinetic model; then switching a navigational speed controller of the high-speed unmanned ship to a controller corresponding to a theoretical dynamic model enabling the performance switching index to be optimal according to the performance switching index updated in real time during the navigation of the high-speed unmanned ship; according to the invention, a plurality of theoretical dynamic models are constructed, the performance switching indexes are utilized to carry out online evaluation on the theoretical dynamic models simultaneously, and the navigational speed controller is accurately switched to the controller corresponding to the optimal theoretical dynamic model, so that the response speed of the navigational speed controller is higher, and the aim of effectively improving the navigational speed control accuracy of the high-speed unmanned ship is fulfilled.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a schematic flow chart of a high-speed unmanned ship cruise controller determination method based on multi-model switching according to the present invention;
FIG. 2 is a schematic structural diagram of a high-speed unmanned ship cruise controller determination system based on multi-model switching according to the present invention;
FIG. 3 is a flow chart of the design of the high-speed unmanned ship cruise controller based on multi-model switching according to the present invention;
FIG. 4 is a schematic diagram of experimental data of the high-speed unmanned ship in an acceleration stage and a deceleration stage;
FIG. 5 is a block diagram of the high-speed unmanned ship speed control based on multi-model switching according to the present invention;
FIG. 6 is a comparison of the actual output speed of the high speed unmanned surface vehicle of the present invention versus the target tracking speed;
FIG. 7 is a graph of throttle value output curves for the high-speed unmanned surface vehicle of the present invention;
fig. 8 is a schematic diagram of model selection corresponding to the high-speed unmanned ship cruise controller based on multi-model switching.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention aims to provide a method and a system for determining a high-speed unmanned ship speed controller based on multi-model switching, so as to effectively improve the speed control accuracy of the high-speed unmanned ship.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
Example one
As shown in fig. 1, the present embodiment provides a method for determining a cruise controller of a high-speed unmanned ship based on multi-model switching, which includes the following steps.
Step 101: acquiring experimental dynamics data of the high-speed unmanned ship in an acceleration stage and a deceleration stage, wherein the experimental dynamics data comprise speed data and accelerator data; the method specifically comprises the following steps:
a parameter identification experiment is determined. The method specifically comprises the following steps: selecting a weather with good sea conditions (the sea conditions are less than or equal to 2 grades), erecting and testing a communication antenna, and then lowering the high-speed unmanned ship into the sea water by using a crane at a wharf of a certain sea area. The communication mode of the high-speed unmanned ship is composed of a shore-based antenna and a high-speed unmanned ship antenna, and a straight flight path and an accelerator control method are planned in a shore-based navigation control interface program.
And acquiring throttle data and speed data of the high-speed unmanned ship from rest to maximum speed in an acceleration stage and throttle data and speed data of the high-speed unmanned ship from maximum speed to rest in a deceleration stage according to the parameter identification experiment.
Step 102: and carrying out abnormal value elimination and smooth filtering processing on the experimental dynamics data to obtain the processed experimental dynamics data.
Step 103: clustering the experimental dynamics data to obtain a clustering result; the method specifically comprises the following steps:
determining different adjustable parameters; the adjustable parameters are adjustable parameters in a subtractive clustering algorithm.
Clustering the processed experimental dynamics data according to the subtractive clustering algorithm and different adjustable parameters to obtain different clustering results; different ones of the clustering results include different clustering data.
And screening out the optimal clustering result from all the clustering results according to a DB clustering index minimization principle, and then determining the optimal clustering result as a final clustering result.
Step 104: constructing a theoretical dynamic model corresponding to each type of data in the clustering result, and then determining a controller corresponding to each theoretical dynamic model; the input of the theoretical kinetic model is a throttle value, and the output of the theoretical kinetic model is a speed value; the method specifically comprises the following steps:
and constructing an initial theoretical dynamic model by adopting a single-input single-output autoregressive model with a controlled variable.
Determining a performance indicator function; the performance indicator function is determined based on an error between a theoretical kinetic model output and an actual kinetic model output.
And determining the identification estimation parameters of the initial theoretical dynamic model corresponding to each type of the clustering data according to the performance index function and each type of data in the clustering result.
And sequentially substituting different identification estimation parameters into the initial theoretical dynamic model to obtain a theoretical dynamic model corresponding to each type of the clustering data, and then determining a controller corresponding to each theoretical dynamic model.
Step 105: during the navigation of the high-speed unmanned ship, switching a navigation speed controller of the high-speed unmanned ship to a controller corresponding to a theoretical dynamic model enabling the performance switching index to be optimal according to the performance switching index updated in real time; the performance switching index is the weighted sum of the current error and the historical error; the current error represents the error between the actual speed value of the high-speed unmanned ship at the current sailing moment and the theoretical speed value of the q-th theoretical dynamic model; the historical error is an error obtained after a set forgetting effect is given to the error in a past set time period; the theoretical dynamic model with the optimal performance switching index is the corresponding theoretical dynamic model when the weighted sum is minimum; the error in the past set time period is the error between the actual speed value of the high-speed unmanned ship in the past set time period and the theoretical speed value of the q-th theoretical dynamic model; the method specifically comprises the following steps:
and acquiring an actual speed value and an actual throttle value of the high-speed unmanned ship at the current sailing moment.
And calculating a theoretical speed value of each theoretical dynamic model according to the actual throttle value.
And calculating the performance switching index of each theoretical dynamic model at the current sailing moment according to the actual speed value and each theoretical speed value.
And screening out the optimal performance switching indexes from all the performance switching indexes, and then switching the navigational speed controller of the high-speed unmanned ship to the controller corresponding to the theoretical dynamic model corresponding to the optimal performance switching indexes.
Example two
To achieve the above object, the present embodiment provides a high-speed unmanned ship cruise controller determination system based on multi-model switching, as shown in fig. 2, including:
the experimental dynamics data acquisition module 201 is used for acquiring experimental dynamics data of the high-speed unmanned ship in an acceleration stage and a deceleration stage; the experimental dynamics data includes speed data and throttle data.
And the data processing module 202 is configured to perform outlier rejection and smooth filtering on the experimental dynamics data to obtain processed experimental dynamics data.
And the clustering processing module 203 is used for clustering the experimental dynamics data to obtain a clustering result.
The controller building module 204 is configured to build a theoretical dynamic model corresponding to each type of data in the clustering result, and then determine a controller corresponding to each theoretical dynamic model; the input of the theoretical dynamic model is a throttle value, and the output of the theoretical dynamic model is a speed value.
The real-time determining module 205 of the navigational speed controller is used for switching the navigational speed controller of the high-speed unmanned ship to a controller corresponding to a theoretical dynamic model which enables the performance switching index to be optimal according to the performance switching index updated in real time during the navigation of the high-speed unmanned ship; the performance switching index is the weighted sum of the current error and the historical error; the current error represents the error between the actual speed value of the high-speed unmanned ship at the current sailing moment and the theoretical speed value of the q-th theoretical dynamic model; the historical error is an error obtained after a set forgetting effect is given to the error in a past set time period; and the theoretical dynamic model with the optimal performance switching index is the corresponding theoretical dynamic model when the weighted sum is minimum.
The experimental dynamics data acquisition module 201 specifically includes:
and the parameter identification experiment determining unit is used for determining the parameter identification experiment.
And the experimental dynamics data acquisition unit is used for acquiring accelerator data and speed data of the high-speed unmanned ship from rest to the maximum speed in an acceleration stage and accelerator data and speed data of the high-speed unmanned ship from the maximum speed to rest in a deceleration stage according to the parameter identification experiment.
The clustering module 203 specifically includes:
the adjustable parameter determining unit is used for determining different adjustable parameters; the adjustable parameters are adjustable parameters in a subtractive clustering algorithm.
A clustering result initial determining unit, configured to perform clustering processing on the processed experimental dynamics data according to the subtractive clustering algorithm and the different adjustable parameters, so as to obtain different clustering results; different ones of the clustering results include different clustering data.
And the clustering result final determining unit is used for screening out the optimal clustering result from all the clustering results according to a DB clustering index minimization principle and then determining the optimal clustering result as the final clustering result.
The controller building module 204 specifically includes:
and the initial theoretical dynamic model building unit is used for building the initial theoretical dynamic model by adopting the single-input single-output autoregressive model with the controlled variable.
A performance indicator function determination unit for determining a performance indicator function; the performance indicator function is determined based on an error between a theoretical kinetic model output and an actual kinetic model output.
And the identification estimation parameter calculation unit is used for determining the identification estimation parameters of the initial theoretical dynamic model corresponding to each type of the clustering data according to the performance index function and each type of data in the clustering result.
And the controller construction unit is used for sequentially substituting different identification estimation parameters into the initial theoretical dynamic model to obtain a theoretical dynamic model corresponding to each type of the clustering data, and then determining a controller corresponding to each theoretical dynamic model.
The real-time determination module 205 of the cruise controller specifically includes:
and the actual speed value and actual throttle value acquisition unit is used for acquiring the actual speed value and the actual throttle value of the high-speed unmanned ship at the current sailing moment.
And the theoretical speed value calculating unit is used for calculating the theoretical speed value of each theoretical dynamic model according to the actual throttle value.
And the performance switching index calculation unit is used for calculating the performance switching index of each theoretical dynamic model at the current sailing moment according to the actual speed value and each theoretical speed value.
And the navigation speed controller determining unit is used for screening out the optimal performance switching indexes from all the performance switching indexes, and then switching the navigation speed controller of the high-speed unmanned ship to the controller corresponding to the theoretical dynamic model corresponding to the optimal performance switching indexes.
EXAMPLE III
The embodiment provides a method for determining a high-speed unmanned ship navigational speed controller based on multi-model switching, which mainly comprises the following steps: (1) obtaining dynamic data of the high-speed unmanned ship under different operation domains in the acceleration and deceleration process by adopting a parameter identification experiment; (2) carrying out abnormal value elimination and smooth filtering processing on the dynamic data; (3) clustering the processed dynamic data and establishing a corresponding theoretical dynamic model for each clustered data; (4) and setting a performance switching index, and switching the navigational speed controller to a controller corresponding to a theoretical dynamic model with the optimal performance switching index according to the performance switching index in the running process of the high-speed unmanned ship, so that the design of the navigational speed controller of the high-speed unmanned ship based on multi-model switching is realized, and the navigational speed control accuracy of the high-speed unmanned ship is effectively improved finally.
As shown in fig. 3, the method for determining the cruise controller of the high-speed unmanned ship based on multi-model switching provided by this embodiment includes the following specific steps:
s1: acquiring experimental data of the high-speed unmanned ship in an acceleration stage and a deceleration stage, namely a design parameter identification experiment, and acquiring accelerator data and speed data of the high-speed unmanned ship from rest to maximum speed in the acceleration stage and from maximum speed to rest in the deceleration stage.
S2: and (4) removing abnormal values of the data and performing smooth filtering, namely performing abnormal value removal and smooth filtering on the experimental data obtained in the step S1 to obtain an accelerator-speed diagram as shown in FIG. 4.
S3: and clustering the data, namely clustering the processed experimental data.
S4: and performing parameter identification on each cluster to obtain corresponding sub-models under different working conditions, namely establishing a corresponding theoretical dynamic model of the high-speed unmanned ship for each cluster data to further obtain a multi-model set.
S5: and designing a corresponding controller for each submodel, namely designing a corresponding controller for each theoretical dynamic model in the multi-model set.
S6: designing a performance switching index and switching the controller to the optimal sub-model, namely defining a performance switching index related to multi-model output errors, and then designing a speed controller switching strategy based on performance switching index optimization, and switching to the optimal speed controller in real time when the high-speed unmanned ship runs, wherein the control overall architecture is shown in fig. 5.
Step S1 specifically includes:
the parameter identification protocol was as follows: selecting a weather with good sea conditions (the sea conditions are less than or equal to 2 grades), erecting and testing a communication antenna, and then lowering the high-speed unmanned ship into the sea water by using a crane at a wharf of a certain sea area of the Qingdao. The communication mode of the high-speed unmanned ship is formed by a shore-based antenna and a high-speed unmanned ship antenna together, and a straight air route is planned in a shore-based navigation control interface program; the high-speed unmanned ship is set to increase from the throttle value of 0% to 100%, specifically, after the speed corresponding to each throttle value is stabilized and maintained for 5 seconds, the throttle values are sequentially increased by 5%, and after the speed corresponding to each throttle value is increased to the maximum speed and stabilized for 5 seconds, the throttle values are further decreased from 100% to 0%, and similarly, after the speed corresponding to each throttle value is stabilized and maintained for 5 seconds, the throttle values are sequentially decreased by 5%.
After the air route and the accelerator control method are set, a shore-based navigation control interface program calculates to obtain an instruction signal, and the instruction signal is sent to a high-speed unmanned ship industrial personal computer through a shore-based antenna so as to execute a task issued by the shore-based navigation control interface program. In the process of executing tasks, an accelerator measurement feedback device and a GPS device on the high-speed unmanned ship can record and store accelerator value data and GPS coordinate point data at the current moment in a navigation log report in real time. After the task is executed, the shore-based navigation control interface program receives a navigation log report transmitted by the high-speed unmanned ship, analyzes the navigation log report according to a corresponding interface protocol to obtain throttle value data, converts GPS coordinate point data into coordinate values in a plane coordinate system, calculates the distance between two adjacent coordinate values and divides the distance by a sampling time interval to obtain a speed value, so that the throttle data and the speed data of the high-speed unmanned ship in the navigation process are obtained, and the data collection work is completed.
Step S2 specifically includes:
firstly, removing abnormal values of throttle data and speed data; the abnormal value is a numerical value which is recorded by the sensor equipment under the high-frequency interference signal and is seriously deviated from the true value, and the abnormal value is directly removed to ensure the accuracy of the data.
And then carrying out smooth filtering processing on the accelerator data and the speed data after the abnormal value removing processing, aiming at reducing the influence of low-frequency interference signals such as wind wave and surge current in the marine environment, and carrying out smooth filtering processing on the accelerator data and the speed data after the abnormal value removing processing by using a moving average method so as to ensure the effectiveness of the data.
Step S3 specifically includes:
m data points (processed accelerator data and speed data) X ═ X recorded in the sailing process by using the high-speed unmanned ship1,x2,…,xmIs the subject, where xm＝[ym,um]The data point representing the m-th sampling instant (alternatively referred to as the sampled data vector), ymIs the output speed value of the high-speed unmanned ship, umThe input throttle value of the high-speed unmanned ship.
Clustering the processed throttle data and the speed data by adopting a subtractive clustering algorithm, which specifically comprises the following steps:
the method comprises the following steps: calculate each data point xi(i ═ 1,2, …, m), the density index, the formula for which is calculated as follows:
wherein i, j is 1,2, …, m, | xi-xj||2For any two non-adjacent data points xi,xjOf between, the Euclidean distance raIs the effective neighborhood radius of the set cluster center.
Step two: selecting the data point x with the maximum density indexmax1Is the first cluster center point. Assume data point xmax nIs the cluster center point selected for the nth time according to the Density index of the data point as Densitymax nThe density index is updated for all data points according to the following formula. After the updating is finished, the data point x with the maximum density index is selected againmax n+1As a new cluster center point. The update formula is as follows:
wherein epsilon is the attenuation factor of the density index, and epsilon is more than 1.
Step three: judgment of
And if the judgment result is positive, continuing to step two for updating, and if the judgment result is negative, indicating that the clustering is finished. And determining the number omega of the finally generated clustering center points, wherein the smaller the delta is, the more the number of the generated clustering center points is, namely the larger the omega value is.
Step four: setting different delta values, and sequentially executing the three steps to obtain clustering results corresponding to different clustering central point numbers omega (different clustering results comprise different clustering central point numbers, namely different types of clustering data). And finally, finding the optimal clustering result from different clustering results according to the DB clustering index minimization principle.
The DB clustering index is an index for evaluating the quality of a cluster by adopting intra-class compactness (data are similar as much as possible between the same classes) and inter-class separability (data similarity between different classes) and is defined as follows:
where ω is the number of cluster center points, S (U)c) Is the distance within the c-th cluster data, d (U)c,Uγ) The distance between the c-th clustering center and the gamma-th clustering center is defined as c is more than or equal to 1, omega is more than or equal to gamma, and the calculation formulas of the distance between data in the same clustering and the distance between different clustering centers are respectively as follows:
d(Uc,Uγ)＝||xc,xγ||2；
in the formula, numcIndicates the number of data points in the c-th clustering data, xc,xγRespectively representing the cluster center points of the c-th cluster data and the gamma-th cluster data.
To sum up the aboveDifferent clustering results can be obtained by setting different delta values, and the number omega of the clustering central points obtained by adopting the clustering algorithm enables S (U)c) Minimum sum d (U)c,Uγ) And when the clustering performance is the maximum, namely the intra-class compactness is the maximum and the inter-class separability is the maximum, the DB clustering index just reaches the minimum, the clustering result can be considered to have the best clustering performance, and the clustering result corresponding to the number omega of the clustering central points is the optimal clustering result.
Step S4 is to establish a corresponding theoretical dynamic model for each type of data in the optimal clustering result, and further form a multi-model set, that is, the multi-model set includes a plurality of theoretical dynamic models, and the specific steps are as follows:
selecting a single-input single-output control quantity autoregressive model for discussion, wherein the difference equation form of each submodel (namely a theoretical dynamic model) is as follows:
A(z-1)y(k)＝B(z-1)u(k-d)+ξ(k)；
where ξ (k) is white noise interference, both the system order and the system delay d are known, and
the difference equation form of the submodel is rewritten into a least squares form, which is:
in the formula, na,nbStructural parameters of system output and system input, u (k), y (k) are respectively system input and system output and respectively correspond to the throttle value and the speed value of the high-speed unmanned ship, ai,biThe coefficients are input quantity and output quantity, namely estimated parameters to be identified;
is a data vector composed of input and output, theta is a parameter vector to be identified and estimated for the sub-model, and
assuming that the parameter vector to be identified and estimated is
The prediction output calculated according to the current parameter vector at the time k is:
the error between the actual system model output (i.e., the actual kinetic model output) and the model prediction output under the current parameter conditions (i.e., the theoretical kinetic model output) is:
for the L times of data, take the following performance indicator function:
the performance index function is rewritten into a matrix form, and the matrix form is as follows:
wherein E ═ E [ [ epsilon (1), …, epsilon (L)]，
Y＝[y(1),y(2),…,y(k)]。
According to the performance index function J, calculating
And is given as the first derivative of0, namely:
solving the above equation to obtain a solution:
to facilitate the calculation, let na＝n b2, a linear local model M of the optimal clustering result is obtained1,M2,…,McAnd a multi-model set is constructed as shown in table 1.
TABLE 1 multiple model set Table
Step S5 specifically includes:
when the theoretical kinetic model parameters are obtained by calculation
Then, the following navigational speed controller is designed:
wherein q is 1,2, …, C represents each theoretical kinetic model in the multiple model set, ω is [ y (t), …, y (t-n)a+1),u(t-1),…,u(t-nb+1)]TRepresenting a set of speed outputs and throttle inputs at a plurality of sample times,
is the coefficient, u, corresponding to the speed output and throttle inputq(t) is the throttle input to the qth cruise control, y is the speed.
Step S6 specifically includes:
at any one of the operating times k, eq(t)＝y(t)-yqAnd (t) represents the current error of the actual speed of the high-speed unmanned ship and the theoretical speed of the q-th theoretical dynamic model.
The performance switching index is a weighted sum of a current error and a historical error which endows a certain forgetting effect to the error in a certain past time period, a corresponding theoretical dynamic model when the weighted sum is minimum is selected as a switching model, and a mathematical expression of the switching model is as follows:
in the formula, q is 1,2, …, C represents each theoretical dynamic model in the multi-model set, a is greater than 0, b is greater than 0 and represents weighting factors of errors of current time and past time, respectively, 0 ≦ ρ ≦ 1 represents the degree of memory of each past time error, the smaller ρ is, the more forgotten is, and l represents the length of sampling the past time.
And selecting the theoretical dynamic model which enables J (k) to be the minimum at each sampling moment, and switching the cruise controller to the controller corresponding to the theoretical dynamic model, so that the design of the multi-model cruise controller is completed.
The finally obtained control effect is shown in fig. 6-8, fig. 6 is a comparison graph between the actual output speed and the target tracking speed of the high-speed unmanned ship, fig. 7 is a throttle value output curve of the controller output, namely the high-speed unmanned ship, and fig. 8 is a submodel sequence selected by the cruise controller based on multi-model switching in the whole operation process.
Compared with the prior art, the invention has the following beneficial effects:
compared with the existing speed controller of the unmanned surface vehicle, the speed controller of the unmanned surface vehicle emphasizes the consideration of hydrodynamic factors during high-speed operation, establishes a dynamic model for the high-speed unmanned surface vehicle running at high speed, and realizes accurate speed control in a high-speed state.
Compared with the existing speed controller of the unmanned surface vehicle, the invention adopts an efficient clustering method for identification data, establishes corresponding dynamic models under a plurality of working conditions, and describes the dynamic characteristics of a complex nonlinear system more accurately by a 'divide-and-conquer' method.
Compared with the existing unmanned surface vehicle speed controller, the invention establishes a multi-model set, simultaneously carries out online evaluation on a plurality of theoretical dynamic models by using performance switching indexes, and accurately switches to the controller corresponding to the optimal theoretical dynamic model, so that the response speed of the controller is higher, and the switching is more stable.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.
Claims (10)
1. A method for determining a high-speed unmanned ship navigational speed controller based on multi-model switching is characterized by comprising the following steps:
acquiring experimental dynamics data of the high-speed unmanned ship in an acceleration stage and a deceleration stage; the experimental dynamics data comprises speed data and throttle data;
clustering the experimental dynamics data to obtain a clustering result;
constructing a theoretical dynamic model corresponding to each type of data in the clustering result, and then determining a controller corresponding to each theoretical dynamic model; the input of the theoretical dynamic model is a throttle value, and the output of the theoretical dynamic model is a speed value;
during the navigation of the high-speed unmanned ship, switching a navigation speed controller of the high-speed unmanned ship to a controller corresponding to a theoretical dynamic model enabling the performance switching index to be optimal according to the performance switching index updated in real time; the performance switching index is the weighted sum of the current error and the historical error; the current error represents the error between the actual speed value of the high-speed unmanned ship at the current sailing moment and the theoretical speed value of the q-th theoretical dynamic model; the historical error is an error obtained after a set forgetting effect is given to the error in a past set time period; the theoretical dynamic model with the optimal performance switching index is the corresponding theoretical dynamic model when the weighted sum is minimum; where q ═ 1,2, …, C represent the total number of theoretical kinetic models.
2. The method for determining the cruise controller of the high-speed unmanned ship based on multi-model switching according to claim 1, wherein the acquiring of the experimental dynamics data of the high-speed unmanned ship in an acceleration stage and a deceleration stage specifically comprises:
determining a parameter identification experiment;
and acquiring throttle data and speed data of the high-speed unmanned ship from rest to maximum speed in an acceleration stage and throttle data and speed data of the high-speed unmanned ship from maximum speed to rest in a deceleration stage according to the parameter identification experiment.
3. The method for determining the cruise controller of the high-speed unmanned ship based on multi-model switching according to claim 1, wherein before performing clustering processing on the experimental dynamics data to obtain a clustering result, the method further comprises:
and carrying out abnormal value elimination and smooth filtering processing on the experimental dynamics data to obtain the processed experimental dynamics data.
4. The method for determining the cruise controller of the high-speed unmanned ship based on multi-model switching according to claim 3, wherein the clustering processing is performed on the experimental dynamics data to obtain a clustering result, and specifically comprises:
determining different adjustable parameters; the adjustable parameters are adjustable parameters in a subtractive clustering algorithm;
clustering the processed experimental dynamics data according to the subtractive clustering algorithm and different adjustable parameters to obtain different clustering results; different clustering results comprise different clustering data;
and screening out the optimal clustering result from all the clustering results according to a DB clustering index minimization principle, and then determining the optimal clustering result as a final clustering result.
5. The method for determining the cruise controller of the high-speed unmanned ship based on multi-model switching according to claim 1, wherein the step of constructing a theoretical dynamic model corresponding to each type of cluster data in the clustering result and then determining the controller corresponding to each theoretical dynamic model specifically comprises the steps of:
constructing an initial theoretical dynamic model by adopting a single-input single-output autoregressive model with a controlled variable;
determining a performance indicator function; the performance index function is determined according to the error between the theoretical dynamic model output and the actual dynamic model output;
determining an identification estimation parameter of an initial theoretical dynamic model corresponding to each type of the clustering data according to the performance index function and each type of data in the clustering result;
and sequentially substituting different identification estimation parameters into the initial theoretical dynamic model to obtain a theoretical dynamic model corresponding to each type of the clustering data, and then determining a controller corresponding to each theoretical dynamic model.
6. The method for determining the cruise controller of the high-speed unmanned ship based on multi-model switching according to claim 1, wherein during the navigation of the high-speed unmanned ship, the cruise controller of the high-speed unmanned ship is switched to a controller corresponding to a theoretical dynamic model for optimizing the performance switching index according to the performance switching index updated in real time, and specifically comprises:
acquiring an actual speed value and an actual throttle value of the high-speed unmanned ship at the current sailing moment;
calculating a theoretical speed value of each theoretical dynamic model according to the actual throttle value;
calculating the performance switching index of each theoretical dynamic model at the current navigation moment according to the actual speed value and each theoretical speed value;
and screening out the optimal performance switching indexes from all the performance switching indexes, and then switching the navigational speed controller of the high-speed unmanned ship to the controller corresponding to the theoretical dynamic model corresponding to the optimal performance switching indexes.
7. A high-speed unmanned ship navigational speed controller determination system based on multi-model switching, comprising:
the experimental dynamics data acquisition module is used for acquiring experimental dynamics data of the high-speed unmanned ship in an acceleration stage and a deceleration stage; the experimental dynamics data comprises speed data and throttle data;
the clustering processing module is used for clustering the experimental dynamics data to obtain a clustering result;
the controller building module is used for building a theoretical dynamic model corresponding to each type of data in the clustering result and then determining a controller corresponding to each theoretical dynamic model; the input of the theoretical dynamic model is a throttle value, and the output of the theoretical dynamic model is a speed value;
the real-time determining module of the navigational speed controller is used for switching the navigational speed controller of the high-speed unmanned ship to a controller corresponding to a theoretical dynamic model which enables the performance switching index to be optimal according to the performance switching index updated in real time during the navigation of the high-speed unmanned ship; the performance switching index is the weighted sum of the current error and the historical error; the current error represents the error between the actual speed value of the high-speed unmanned ship at the current sailing moment and the theoretical speed value of the q-th theoretical dynamic model; the historical error is an error obtained after a set forgetting effect is given to the error in a past set time period; the theoretical dynamic model with the optimal performance switching index is the corresponding theoretical dynamic model when the weighted sum is minimum; where q ═ 1,2, …, C represent the total number of theoretical kinetic models.
8. The system for determining the cruise control of the high-speed unmanned ship based on multi-model switching according to claim 7, wherein the experimental dynamics data acquisition module specifically comprises:
a parameter identification experiment determining unit for determining a parameter identification experiment;
and the experimental dynamics data acquisition unit is used for acquiring accelerator data and speed data of the high-speed unmanned ship from rest to the maximum speed in an acceleration stage and accelerator data and speed data of the high-speed unmanned ship from the maximum speed to rest in a deceleration stage according to the parameter identification experiment.
9. The system for determining the cruise control of the unmanned ship based on multi-model switching according to claim 7, further comprising:
and the data processing module is used for carrying out abnormal value elimination and smooth filtering processing on the experimental dynamics data to obtain the processed experimental dynamics data.
10. The system for determining the cruise control of the high-speed unmanned ship based on multi-model switching according to claim 9, wherein the clustering module specifically comprises:
the adjustable parameter determining unit is used for determining different adjustable parameters; the adjustable parameters are adjustable parameters in a subtractive clustering algorithm;
a clustering result initial determining unit, configured to perform clustering processing on the processed experimental dynamics data according to the subtractive clustering algorithm and the different adjustable parameters, so as to obtain different clustering results; different clustering results comprise different clustering data;
and the clustering result final determining unit is used for screening out the optimal clustering result from all the clustering results according to a DB clustering index minimization principle and then determining the optimal clustering result as the final clustering result.
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