A method, device, equipment and storage medium for position-based compact format model-free adaptive heading control for marine robots

Abstract

The present application discloses a method and device for position-type compact format model-free adaptive heading control for a marine robot, an electronic device, and a storage medium. The method includes: constructing a position-type compact format dynamic input / output data model for the marine robot heading control system; determining an adaptive control input solution criterion function, and determining a position-type compact format model-free adaptive control law according to the adaptive control input solution criterion function; estimating data model parameters at the current moment and determining pseudo-partial derivative estimates according to historical input and output information of the marine robot heading control system; calculating input information of the marine robot heading control system at the current moment according to the pseudo-partial derivative estimates and the position-type compact format model-free adaptive control law; and executing the input information to perform heading control on the marine robot. The present application solves the problem that the model-free adaptive control method cannot be directly applied to the marine robot heading control system.

Description

Technical Field

[0001] The embodiments of the present application relate to the technical field of motion control of marine robots, and in particular to a position-based compact-format model-free adaptive heading control method and device, electronic equipment, and storage medium for a marine robot. Background Art

[0002] Marine robots include unmanned surface vessels, unmanned submersibles, and other marine unmanned systems. During the operation of marine robots, the mathematical model of the bow control system changes with the draft and speed of the marine robot, and is also disturbed by wind, waves and currents. Therefore, the control method designed based on the mathematical model often cannot achieve good results. On the other hand, the proportional-integral-differential (PID) controller based on data-driven is difficult to adapt to the changing system characteristics, and the reinforcement learning control method requires a training process and a large amount of calculation.

[0003] The model-free adaptive control (MFAC) method can adaptively adjust the control parameters online without the need for an accurate system model, thereby achieving effective control of the system output.

[0004] However, the incremental input / output data model of the traditional ordinary compact format MFAC cannot be applied to the heading control system. The main reasons are: (1) non-quasi-linear characteristics: there is an integral link between the heading angle (i.e., system output) and the rudder angle (i.e., system input). When the rudder angle increases, the heading angle may not increase; (2) non-self-balancing characteristics: the system integrates each input, which manifests as a rapid expansion process, which conflicts with the slow-changing process of the MFAC system input / output data model identification; (3) the heading control system requires the input to be zero when the output converges. Ordinary MFAC based on incremental design is difficult to avoid the problem of system output oscillation divergence.

[0005] The existing MFAC heading control method for marine robots attempts to solve the problem of system output oscillation by reconstructing the system output using the angular velocity gain. However, when the gain is too small, the controller is close to the ordinary MFAC algorithm, which will cause the system output oscillation problem; when the gain is too large, the response speed of the system will be greatly reduced, so it is difficult to apply to some large marine robots with slow turning speeds. The existing technology also attempts to suppress the oscillation problem by limiting the change in the heading angle. Although the system output oscillation problem can be suppressed, it uses an incremental input / output data model that does not match the heading system, which still affects the response speed and anti-interference ability, especially under angular velocity noise, the system output is prone to jitter. At present, the model-free adaptive control method is still not compatible with the marine robot heading control system. Summary of the invention

[0006] In view of this, the embodiments of the present application provide a position-based compact format model-free adaptive heading control method and device, electronic device, and storage medium for a marine robot, which solves the problem of system output oscillation divergence caused by directly applying the model-free adaptive control method to the heading control system of a marine robot.

[0007] According to a first aspect of an embodiment of the present application, a method for controlling the heading of a marine robot is provided, wherein the method comprises:

[0008] Construct a position-based compact format dynamic input / output data model for the marine robot bow control system;

[0009] Determine an adaptive control input solution criterion function, and determine a position-type compact format model-free adaptive control law according to the adaptive control input solution criterion function;

[0010] According to the historical input and output information of the marine robot bow control system, the data model parameters at the current moment are estimated to determine the pseudo partial derivative estimation value;

[0011] Calculate the input information of the marine robot bow control system at the current moment according to the pseudo partial derivative estimation value and the position-type compact format model-free adaptive control law;

[0012] The input information is executed to control the heading of the marine robot.

[0013] In some exemplary embodiments, the method further includes: determining whether the marine robot's bow deviation is less than a preset threshold; when the marine robot's bow deviation is not less than the preset threshold, re-estimating the data model parameters at the current moment and determining the pseudo partial derivative estimate.

[0014] In some exemplary embodiments, constructing a position-based compact format dynamic input / output data model for a marine robot heading control system includes:

[0015] Build The heading control system outputs information at all times. The bow control system input information at all times, The correspondence between the model parameters at each moment is called a pseudo-partial derivative;

[0016] The positional compact format dynamic input / output data model is:

[0017] ;

[0018] in and They are Moment and The heading control system outputs information at all times. for The bow direction control system inputs information at all times. yes The moments are called pseudo-partial derivatives of the model parameters, The sign of remains unchanged.

[0019] In some exemplary embodiments, determining an adaptive control input solution criterion function includes:

[0020] according to Input information of the marine robot bow control system at all times, Output information of the marine robot bow control system at all times, The desired heading angle at time, the input limit coefficient, The model parameters at the moment called pseudo partial derivatives determine the adaptive control input solution criterion function;

[0021] The adaptive control input solution criterion function is:

[0022] ;

[0023] in, for The input information of the marine robot bow control system at all times, for Output information of the marine robot bow control system at each moment. for The desired heading angle at the moment, is the input restriction coefficient, yes The moments are called pseudo-partial derivatives of the model parameters.

[0024] In some exemplary embodiments, determining the position-type compact format model-free adaptive control law according to the adaptive control input solution criterion function includes:

[0025] Dynamically import / export positional compact formats into / from data models Substitute the adaptive control input solution criterion function In, and order , determine the position-type compact format model-free adaptive control law:

[0026] ;

[0027] Among them, the pseudo partial derivative estimate is the model parameter The estimated value of is the input restriction coefficient, for The desired heading angle at the moment, is the introduced step size factor.

[0028] In some exemplary embodiments, the historical input and output information of the marine robot bow control system includes:

[0029] Input information at the time , Output information at any time , Output information at any time as well as The pseudo partial derivative estimate at time .

[0030] In some exemplary embodiments, according to the historical input and output information of the marine robot bow control system, the data model parameters at the current moment are estimated based on the following model:

[0031] ;

[0032] in for The pseudo-partial derivative estimate at time , is the introduced step size factor, To limit the weight coefficient of the pseudo partial derivative estimate change, .

[0033] In some exemplary embodiments, determining the pseudo partial derivative estimate includes:

[0034] Introducing the pseudo partial derivative estimation reset mechanism, when , When ;in, is a positive number and its value is very small. is the initial pseudo partial derivative estimate.

[0035] In some exemplary embodiments, determining whether the heading deviation of the marine robot is less than a preset threshold comprises:

[0036] Calculate the expected heading angle of the marine robot at the current moment Actual heading angle The difference between ;

[0037] judge Whether the absolute value of is less than the preset deviation threshold.

[0038] According to a second aspect of an embodiment of the present application, a marine robot heading control device is provided, the device comprising:

[0039] A model building unit, used for building a position-type compact format dynamic input / output data model for the marine robot bow control system;

[0040] A function design unit, used to determine an adaptive control input solution criterion function, and determine a position-type compact format model-free adaptive control law according to the adaptive control input solution criterion function;

[0041] The partial derivative estimation unit is used to estimate the data model parameters at the current moment and determine the pseudo partial derivative estimation value according to the historical input and output information of the bow control system of the marine robot;

[0042] An information calculation unit, used to calculate the input information of the marine robot bow control system at the current moment according to the pseudo partial derivative estimation value and the position-type compact format model-free adaptive control law;

[0043] The control execution unit is used to execute the input information and perform heading control on the marine robot.

[0044] The deviation judgment unit is used to judge whether the heading deviation of the marine robot is less than a preset threshold. When the heading deviation of the marine robot is not less than the preset threshold, the data model parameters at the current moment are re-estimated to determine the pseudo partial derivative estimation value.

[0045] According to a third aspect of an embodiment of the present application, an electronic device is provided, comprising a processor and a memory for storing instructions executable by the processor, wherein the processor is configured to execute the steps of the above-mentioned position-based compact-format model-free adaptive heading control method for a marine robot when calling the executable instructions in the memory.

[0046] According to a fourth aspect of an embodiment of the present application, a computer-readable storage medium is provided, characterized in that a computer program is stored in the computer-readable storage medium, and when the computer program is executed by a processor, the steps of the above-mentioned position-based compact format model-free adaptive heading control method for a marine robot are implemented.

[0047] The position-based compact format model-free adaptive heading control method for a marine robot in the embodiment of the present application converts the incremental input / output data model in the original compact format model-free adaptive control into a Improved positional input / output data model , and then improved the control input solution criterion function and pseudo partial derivative estimation algorithm, and proposed a position-type compact format model-free adaptive control method for marine robots. Without adding other parameters, the problem that the model-free adaptive control method cannot be directly applied to the bow control system of marine robots is solved. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0049] Figure 1 A schematic flow chart of a position-based compact format model-free adaptive heading control method for a marine robot according to an embodiment of the present application is shown;

[0050] Figure 2 A schematic diagram of a position-based compact format model-free adaptive heading control method for a marine robot according to an embodiment of the present application is shown;

[0051] Figure 3 A pseudo-partial derivative geometric interpretation diagram of a position-based compact format model-free adaptive heading control method for a marine robot according to an embodiment of the present application is shown;

[0052] Figure 4 A comparison diagram of the control effect of the position-based input / output data model of an embodiment of the present application is shown;

[0053] Figure 5 A schematic diagram showing the composition structure of a position-based compact format model-free adaptive heading control device for a marine robot according to an embodiment of the present application is shown;

[0054] Figure 6 A schematic diagram of the structure of an electronic device according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0055] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0056] The marine robot mentioned in the present invention covers a wide range, and unmanned boats are a typical marine robot. represents the system input, In the marine robot heading angle system, the system input is the rudder angle, the system output The control algorithm realizes that, given a desired heading angle, the control algorithm automatically adjusts the rudder angle according to the input and output information, and finally makes the actual heading angle reach the desired heading angle. Assuming it is always positive, perhaps other systems can assume is always a negative number, but the heading control system of the present invention assumes Always a positive number. The true value of cannot be obtained, so an estimated value is used . Always positive. If the estimation algorithm estimates a negative number, the reset algorithm will Reset to .

[0057] For a system that meets the assumptions, it can be represented as the following positional compact form dynamic linearization (CFDL) input / output data model:

[0058]

[0059] in, is a model parameter called a pseudo-partial derivative (PPD), where the model parameter The sign remains unchanged.

[0060] First, the following adaptive control input criterion function is designed:

[0061]

[0062] in, For the system The expected output at the moment; is the input item restriction coefficient; this item Used to constrain how fast the input changes.

[0063] Will Bring in , and Find the partial derivative and take the extreme value, and simplify to get the following control input expression:

[0064]

[0065] in, The step size factor introduced makes the algorithm more general.

[0066] To obtain the control input Numerical, design the following PPD estimation criterion function:

[0067]

[0068] in, is the weight factor.

[0069] beg Taking the partial derivative and finding the extreme value, we can get The estimated value expression of is as follows:

[0070]

[0071] in, The step size factor introduced makes the algorithm more general and flexible; for The estimated value of .

[0072] In summary, the control scheme of the position-based CFDL-MFAC algorithm can be obtained as follows:

[0073]

[0074]

[0075]

[0076]

[0077] in, , , , ; is a small enough positive number, the initial pseudo-partial derivative estimate yes The initial value of the PPD reset mechanism can enhance the algorithm's ability to track the time-varying PDD.

[0078] The essence of the technical solution of the embodiment of the present application is explained in detail below with reference to the accompanying drawings.

[0079] In order to solve the problem of system output oscillation divergence when the model-free adaptive control method is directly applied to the heading control system of a marine robot, an embodiment of the present application provides a position-based compact model-free adaptive heading control method for a marine robot. Figure 1 A schematic flow chart of a position-based compact format model-free adaptive heading control method for a marine robot according to an embodiment of the present application is shown; Figure 2 A schematic diagram of a position-based compact format model-free adaptive heading control method for a marine robot according to an embodiment of the present application is shown; Figure 3 The pseudo-partial derivative geometric interpretation diagram of the position-based compact format model-free adaptive heading control method for the marine robot in the embodiment of the present application is shown. The control system in the embodiment of the present application is a single-input single-output (SISO) control system, such as Figures 1 to 3As shown, the position-based compact format model-free adaptive heading control method for a marine robot according to an embodiment of the present application comprises the following steps:

[0080] Step 101: constructing a position-based compact format dynamic input / output data model for a marine robot bow control system;

[0081] Establish a linear relationship between the system input and the change in the system output;

[0082] The method of constructing a position-based compact format dynamic input / output data model for the marine robot heading control system includes: constructing The heading control system outputs information at all times. The bow control system input information at all times, The correspondence between the model parameters at each moment is called a pseudo-partial derivative;

[0083] In the embodiment of the present application, the positional compact format dynamic input / output data model is:

[0084]

[0085] in and They are Moment and The heading control system outputs information at each moment, that is, the marine robot is Moment and Heading angle at all times; for The bow direction control system inputs information at the moment, that is, the marine robot Rudder angle at all times; yes The moments are called the model parameters of the pseudo-partial derivatives, and The positive and negative signs of remain unchanged. The position input / output data model of the embodiment of the present application is more consistent with the heading control system. The change in the heading angle (i.e., output) depends on the rudder angle (i.e., input) itself, not the change in the rudder angle. The geometric explanation is as follows Figure 3 shown.

[0086] Step 102: determining an adaptive control input solution criterion function, and determining a position-type compact format model-free adaptive control law according to the adaptive control input solution criterion function;

[0087] In the embodiment of the present application, the determination of the adaptive control input solution criterion function includes: Input information of the marine robot bow control system at all times, Output information of the marine robot bow control system at all times, The desired heading angle at time, the input limit coefficient, The pseudo partial derivative at time instant determines the adaptive control input solution criterion function.

[0088] Specifically, in the control input solution criterion function of the model-free adaptive control method, The control input is restricted by the term to obtain the adaptive control input solution criterion function, which is:

[0089] ,

[0090] in, for The input information of the marine robot bow control system at all times, for Output information of the marine robot bow control system at each moment. for The desired heading angle at the moment, is the input restriction coefficient, yes The moments are called pseudo-partial derivatives of the model parameters.

[0091] Determining the position-type compact format model-free adaptive control law according to the adaptive control input solution criterion function comprises:

[0092] The positional compact format dynamic input / output data model constructed in step 101 Substitute the adaptive control input solution criterion function In, and order , thus determining the position-type compact format model-free adaptive control law:

[0093] ,

[0094] Among them, the pseudo partial derivative estimate is the model parameter The estimated value of is the input restriction coefficient, for The desired heading angle at the moment, is the introduced step size factor.

[0095] Step 103: according to the historical input and output information of the marine robot bow control system, the data model parameters at the current moment are estimated to determine the pseudo partial derivative estimation value;

[0096] The historical input and output information of the marine robot bow control system includes:

[0097] Input information at the previous moment (k-1 moment) , the output information at the current time (time k) , the output information of the previous moment (k-1 moment) And the pseudo partial derivative estimate of the previous moment (k-1 moment) .

[0098] According to the historical input and output information of the marine robot bow control system, the current time is estimated based on the following model ( Time) data model parameters Estimated value of :

[0099] ;

[0100] in, for The pseudo-partial derivative estimate at time , for The input information of the marine robot bow control system at all times, for The output information of the heading control system at the moment, is the introduced step size factor, To limit the weight coefficient of the pseudo partial derivative estimate change, .

[0101] Determining the pseudo partial derivative estimate comprises:

[0102] Introducing the pseudo partial derivative estimation reset mechanism, when , When ,in, is a very small positive number, such as The value is . is the initial pseudo partial derivative estimate.

[0103] Step 104: Calculate the input information of the marine robot bow control system at the current moment according to the pseudo partial derivative estimation value and the position-type compact format model-free adaptive control law;

[0104] According to the estimated value obtained in step 103 and the position-type model-free adaptive control law obtained in step 102, calculate Input information of the marine robot bow control system at all times .

[0105] Step 105: Execute the input information , to control the heading of the marine robot.

[0106] Step 106: Make , determine whether the heading deviation of the marine robot is less than a preset threshold. When the heading deviation of the marine robot is less than the preset threshold, execute step 107 to end the control of the marine robot; when the heading deviation of the marine robot is not less than the preset threshold, return to step 103 to re-estimate the data model parameters at the current moment and determine the pseudo partial derivative estimate.

[0107] Step 107: End.

[0108] In the embodiment of the present application, determining whether the heading deviation of the marine robot is less than a preset threshold comprises: calculating the marine robot at the current moment ( The expected heading angle at time Actual heading angle The difference between ;judge Whether the absolute value of is less than a preset deviation threshold, if this condition is met, the control of the marine robot is terminated, otherwise, it returns to step 103.

[0109] This implementation method converts the incremental data model in the original compact format model-free adaptive control into Positional data model , and then improved the control input solution criterion function and pseudo partial derivative estimation algorithm, and proposed a position-type compact format model-free adaptive control method for marine robots. Without adding other parameters, the problem that the model-free adaptive control method cannot be directly applied to the bow control system of marine robots is solved.

[0110] like Figure 2 and Figure 3 As shown in the figure, the pseudo partial derivative estimation includes two algorithms, one is the pseudo partial derivative estimation algorithm, and the other is the pseudo partial derivative estimation reset algorithm. First, the pseudo partial derivative estimation algorithm is used for estimation, and then if the estimation result does not meet the requirements or other conditions do not meet the requirements, it is reset to the initial value of the pseudo partial derivative estimation.

[0111] In the embodiment of the present application, k Indicates a time node. The controller executes once every fixed time, for example, 0.1 seconds. , and continuously circulates to complete the control function. is the expected system output at the next moment, which corresponds to the expected heading angle in the heading angle system; Represents the system output, which corresponds to the actual heading angle in the heading angle system. Represents the system input, which corresponds to the rudder angle in the heading angle system.

[0112] The final control scheme of the position-based compact model-free adaptive heading control method for a marine robot described in the embodiment of the present application is as follows:

[0113]

[0114] in k Indicates a time node; is the weight factor; , is the introduced step size factor; y is the output of the controlled system; It is the system control input; Output for the system; is the expected value; is the initial pseudo partial derivative estimate; is a sufficiently small positive number, such as 0.001; The reset condition in the present invention It is a combination of , , The controller adjustable parameters are: , , , , , .

[0115] Figure 3 middle, Represents the system output, which corresponds to the heading angle in the heading angle system. Represents the system input, which corresponds to the rudder angle in the heading angle system. Figure 3 It mainly represents the model parameters called pseudo partial derivatives in the positional input / output data model proposed by the present invention. Note that in the MFAC algorithm The true value of cannot be solved, and the pseudo partial derivative estimation algorithm and pseudo partial derivative estimation reset algorithm are needed to calculate the estimated value .

[0116] Compared with the common CFDL-MFAC, the position-based compact model-free adaptive heading control method for a marine robot described in the embodiment of the present application has the following improvements:

[0117] First, the incremental input / output data model of the common MFAC is , The sign of remains unchanged. In the heading control system, the incremental input / output data model The sign-invariance assumption often fails to hold. For example, in a heading control system Assuming it is always positive, the rudder angle Heading angle reduced from 30° to 20° Still tends to increase, which is inconsistent with the incremental input / output data model. What needs to be explained here is that in this incremental input / output data model, a decrease in the rudder angle will reduce the heading angle, but in this case the heading angle tends to increase. This situation is common in the operation of marine robots. In the operation of marine robots, the change in the heading angle Mainly depends on the rudder angle Itself, not the change in rudder angle Therefore, the incremental input / output data model of ordinary MFAC does not match the heading control system.

[0118] The present application embodiment adopts a positional input / output data model , The sign of remains unchanged. In the above example, the rudder angle Heading angle reduced from 30° to 20° It still tends to increase, only the increase amplitude decreases, which is consistent with the position input / output data model. What needs to be explained here is that when manipulating the marine robot, reducing the rudder angle will not necessarily reduce the heading angle. It depends on the rudder angle itself. Reducing the rudder angle from 30° to 20° will only reduce the increase amplitude of the heading angle, that is, Match.

[0119] Second, due to the improved input / output data model, the control input solution criterion function The solution process of the ordinary MFAC

[0120]

[0121] Improved to:

[0122]

[0123] The above formula is only a change made to fit the position input / output data model. In order to further improve the control anti-disturbance effect, it is further improved to:

[0124]

[0125] In the above formula, Adaptive control input solution criterion function. In the data model of the present invention Down, The larger the rudder angle, the greater the change in heading. So that the obtained The smaller the value, the less likely it is that the rudder angle will be excessively deflected, which will cause the heading angle to oscillate. Replaced , because this parameter can only be estimated in real time and the true value cannot be obtained. It is a parameter introduced artificially later to enhance the flexibility of the algorithm.

[0126] Third, due to the improved input / output data model, the pseudo partial derivative estimation algorithm is improved from the ordinary MFAC

[0127]

[0128] Improved to:

[0129]

[0130] Fourth, due to the improved input / output data model, the pseudo partial derivative estimation reset algorithm is improved from the ordinary MFAC

[0131]

[0132] Improved to:

[0133]

[0134] Due to the heading control system , the above formula is equivalent to:

[0135]

[0136] The position-based compact format model-free adaptive heading control method for a marine robot described in the embodiment of the present application has the following advantages:

[0137] First, the position-based input / output data model is more suitable for the heading angle system. , Hengcheng is a common compact format MFAC data model widely used at present. In the present invention, it is called an incremental input / output data model. The existing improved compact format MFAC is based on this model, but this model does not match the heading control system. For example, unmanned surface vessels are a type of marine robot. Represents the change of heading, the common sense of control is the rudder angle The larger the rudder angle, the larger the , the faster the heading changes. A good example is: the rudder angle is changed from 30° to 20°. Although the rudder angle is decreasing, the direction of the turn does not and will not change. Only the speed of the turn decreases. This situation corresponds exactly to the position-based input / output data model of the present invention. , The turning speed decreases as the rudder angle decreases, but the turning direction remains consistent with the rudder angle.

[0138] Second, the present invention solves the problem that the model-free adaptive control method cannot be directly applied to the marine robot heading control system without adding other parameters. The control law is more concise, the parameter adjustment process is simplified, and it is easy to deploy on the marine robot. Other improvement methods all need to add new improved parameters to suppress the marine robot heading angle oscillation problem caused by MFAC, and the control effect depends on the value of the improved parameters.

[0139] Third, the adaptive ability is stronger. The adaptive control criterion function proposed in the present invention has It has strong adaptive ability. The control algorithm is , so as to obtain Reaching a minimum .therefore, The larger the In the data model of the present invention Down, The larger the rudder angle, the greater the change in heading caused by the same rudder angle. Compared with the common control input solution criterion function, Become smaller to prevent the rudder angle from being excessively deflected and causing the heading angle to oscillate back and forth.

[0140] Figure 4 This is a comparison diagram of the control effect of the position input / output data model of the embodiment of the present application, such as Figure 4 As shown, the solid line is the desired heading (Desired-Heading) of the marine machine, the dashed lines of equal length are the control effect of the ordinary CFDL-MFAC, and the long and short dashed lines are the control effect of the position-increased CFDL-MFAC of the embodiment of the present application.

[0141] Since the common compact format dynamic linearization MFAC adopts an incremental input / output data model:

[0142] The embodiment of the present application adopts a positional input / output data model:

[0143]

[0144] from Figure 4 It can be clearly seen that the position-based compact model-free adaptive heading control method for the marine robot described in the embodiment of the present application can effectively solve the oscillation problem.

[0145] The embodiment of the present application provides a position-type compact format model-free adaptive heading control method for marine robots, which improves the incremental input / output data model of the original MFAC into a position-type input / output data model. Based on the position-type compact format data model, the control input criterion function and the pseudo-partial derivative estimation algorithm are redesigned, and a position-type compact format MFAC control law is proposed. The improved algorithm is applicable to the marine robot heading control system and has good anti-interference ability. At the same time, compared with other improvement schemes, no other parameters are added, which overcomes the application difficulties of MFAC in the marine robot heading control system and reduces the workload of parameter adjustment in actual use.

[0146] The present application also discloses a marine robot heading control device. Figure 5 FIG. 1 is a schematic diagram of the structure of a position-based compact model-free adaptive heading control device for a marine robot according to an embodiment of the present application. Figure 5 As shown, the device comprises:

[0147] The model building unit 51 is used to build a position-type compact format dynamic input / output data model for the marine robot heading control system;

[0148] A function design unit 52, used to determine an adaptive control input solution criterion function, and determine a position-type compact format model-free adaptive control law according to the adaptive control input solution criterion function;

[0149] The partial derivative estimation unit 53 is used to estimate the data model parameters at the current moment and determine the pseudo partial derivative estimation value according to the historical input and output information of the marine robot bow control system;

[0150] An information calculation unit 54 is used to calculate the input information of the marine robot bow control system at the current moment according to the pseudo partial derivative estimation value and the position-type compact format model-free adaptive control law;

[0151] The control execution unit 55 is used to execute the input information and perform heading control on the marine robot.

[0152] The deviation judgment unit 56 is used to judge whether the heading deviation of the marine robot is less than a preset threshold. When the heading deviation of the marine robot is not less than the preset threshold, the data model parameters at the current moment are re-estimated to determine the pseudo partial derivative estimation value.

[0153] In an exemplary embodiment, the model building unit 51, the function design unit 52, the partial derivative estimation unit 53, the information calculation unit 54, the control execution unit 55, the deviation judgment unit 56, etc. can be implemented by one or more central processing units (CPU, Central Processing Unit), graphics processing units (GPU, Graphics Processing Unit), application specific integrated circuits (ASIC, Application Specific Integrated Circuit), DSP, programmable logic devices (PLD, Programmable Logic Device), complex programmable logic devices (CPLD, Complex Programmable Logic Device), field programmable gate arrays (FPGA, Field-Programmable Gate Array), general-purpose processors, controllers, microcontrollers (MCU, Micro Controller Unit), microprocessors (Microprocessors), or other electronic components.

[0154] In the embodiments of the present application, Figure 5 The specific manner in which each unit in the marine robot bow control device performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0155] An embodiment of the present application also provides an electronic device, including a processor and a memory for storing processor executable instructions, wherein the processor is configured to execute the steps of the above-mentioned position-based compact format model-free adaptive heading control method for a marine robot when calling the executable instructions in the memory.

[0156] Figure 6 Schematic diagram of the structure of the electronic device according to the embodiment of the present application. Figure 6 As shown, the electronic device 800 supports multi-screen output, and the electronic device 800 may include one or more of the following components: a processing component 802, a memory 804, a power component 806, a multimedia component 808, an audio component 810, an input / output (I / O) interface 812, a sensor component 814, and a communication component 816.

[0157] The processing component 802 generally controls the overall operation of the electronic device 800, such as operations associated with display, phone calls, data communications, camera operations, and recording operations. The processing component 802 may include one or more processors 820 to execute instructions to complete all or part of the steps of the above-mentioned method. In addition, the processing component 802 may include one or more modules to facilitate the interaction between the processing component 802 and other components. For example, the processing component 802 may include a multimedia module to facilitate the interaction between the multimedia component 808 and the processing component 802.

[0158] The memory 804 is configured to store various types of data to support operations on the device 800. Examples of such data include instructions for any application or method operating on the electronic device 800, contact data, phone book data, messages, pictures, videos, etc. The memory 804 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0159] The power supply component 806 provides power to the various components of the electronic device 800. The power supply component 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the electronic device 800.

[0160] The multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundaries of the touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 808 includes a front camera and / or a rear camera. When the device 800 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera may receive external multimedia data. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.

[0161] The audio component 810 is configured to output and / or input audio signals. For example, the audio component 810 includes a microphone (MIC), and when the electronic device 800 is in an operation mode, such as a call mode, a recording mode, and a voice recognition mode, the microphone is configured to receive an external audio signal. The received audio signal can be further stored in the memory 804 or sent via the communication component 816. In some embodiments, the audio component 810 also includes a speaker for outputting audio signals.

[0162] I / O interface 812 provides an interface between processing component 802 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include but are not limited to: home button, volume button, start button, and lock button.

[0163] The sensor assembly 814 includes one or more sensors for providing various aspects of status assessment for the electronic device 800. For example, the sensor assembly 814 can detect the open / closed state of the device 800, the relative positioning of components, such as the display and keypad of the electronic device 800, and the sensor assembly 814 can also detect the position change of the electronic device 800 or a component of the electronic device 800, the presence or absence of contact between the user and the electronic device 800, the orientation or acceleration / deceleration of the electronic device 800, and the temperature change of the electronic device 800. The sensor assembly 814 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly 814 may also include an optical sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 814 may also include an accelerometer, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0164] The communication component 816 is configured to facilitate wired or wireless communication between the electronic device 800 and other devices. The electronic device 800 can access a wireless network based on a communication standard, such as Wi-Fi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 816 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 816 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

[0165] In an exemplary embodiment, the electronic device 800 can be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components to execute the steps of the marine robot heading control method of the above-mentioned embodiment.

[0166] The embodiment of the present application also records a computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium, and when the computer program is executed by a processor, the steps of the position-based compact format model-free adaptive heading control method for a marine robot of the embodiment are implemented. For example, the non-temporary computer-readable storage medium may be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0167] It should be understood that "one embodiment" or "an embodiment" mentioned throughout the specification means that specific features, structures or characteristics related to the embodiment are included in at least one embodiment of the present invention. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. In addition, these specific features, structures or characteristics can be combined in one or more embodiments in any suitable manner. It should be understood that in various embodiments of the present invention, the size of the sequence number of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present application. The above-mentioned sequence numbers of the embodiments of the present application are for description only and do not represent the advantages and disadvantages of the embodiments.

[0168] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0169] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or do not exist.

[0170] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0171] The above is only an embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A position-based compact model-free adaptive heading control method for a marine robot, characterized in that: The method comprises: Construct a position-based compact format dynamic input / output data model for the marine robot bow control system; Determine an adaptive control input solution criterion function, and determine a position-type compact format model-free adaptive control law according to the adaptive control input solution criterion function; According to the historical input and output information of the marine robot bow control system, the data model parameters at the current moment are estimated to determine the pseudo partial derivative estimation value; Calculate the input information of the marine robot bow control system at the current moment according to the pseudo partial derivative estimation value and the position-type compact format model-free adaptive control law; Executing the input information to control the heading of the marine robot; The method of constructing a position-based compact format dynamic input / output data model for the marine robot heading control system includes: Build The heading control system outputs information at all times. The bow control system input information at all times, The correspondence between the model parameters at each moment is called a pseudo-partial derivative; The positional compact format dynamic input / output data model is: ； in and They are Moment and The heading control system outputs information at all times. for The bow input information to the control system at the moment, yes The moments are called pseudo-partial derivatives of the model parameters, The sign of remains unchanged; The adaptive control input solution criteria function includes: according to Input information of the marine robot bow control system at all times, Output information of the marine robot bow control system at all times, The desired heading angle at time, the input limit coefficient, The pseudo partial derivative at the time instant determines the adaptive control input solution criterion function; The adaptive control input solution criterion function is: ； in, for The input information of the marine robot bow control system at all times, for Output information of the marine robot bow control system at each moment. for The desired heading angle at the moment, is the input restriction coefficient, yes The moments are called the model parameters of the pseudo-partial derivatives; The historical input and output information of the marine robot bow control system includes: Input information at the time , Output information at any time , Output information at any time as well as The pseudo partial derivative estimate at time ; The estimating of the data model parameters at the current moment based on the historical input and output information of the marine robot bow control system includes: According to the historical input and output information of the marine robot bow control system, the data model parameters at the current moment are estimated based on the following model: ， in for The pseudo-partial derivative estimate at time , is the introduced step size factor, To limit the weight coefficient of the pseudo partial derivative estimate change, ; Determining the pseudo partial derivative estimate comprises: Introducing the pseudo partial derivative estimation reset mechanism, when , When ;in, is a positive number and its value is very small. is the initial pseudo partial derivative estimate.

2. The position-based compact model-free adaptive heading control method for a marine robot according to claim 1, characterized in that: The method also includes: judging whether the heading deviation of the marine robot is less than a preset threshold value; when the heading deviation of the marine robot is not less than the preset threshold value, re-estimating the data model parameters at the current moment and determining the pseudo partial derivative estimation value.

3. The position-based compact model-free adaptive heading control method for a marine robot according to claim 1, characterized in that: Determining the position-type compact model-free adaptive control law according to the adaptive control input solution criterion function comprises: Dynamically import / export positional compact formats into / from data models Substitute the adaptive control input solution criterion function In, and order , determine the position-type compact format model-free adaptive control law: ； Among them, the pseudo partial derivative estimate is the model parameter The estimated value of is the input restriction coefficient, for The desired heading angle at the moment, is the introduced step size factor.

4. The position-based compact model-free adaptive heading control method for a marine robot according to claim 2, characterized in that: The determining whether the heading deviation of the marine robot is less than a preset threshold comprises: Calculate the expected heading angle of the marine robot at the current moment Actual heading angle The difference between ; judge Whether the absolute value of is less than the preset deviation threshold.

5. A position-based compact model-free adaptive heading control device for a marine robot, characterized in that: The device comprises: A model building unit, used for building a position-type compact format dynamic input / output data model for the marine robot bow control system; A function design unit, used to determine an adaptive control input solution criterion function, and determine a position-type compact format model-free adaptive control law according to the adaptive control input solution criterion function; The partial derivative estimation unit is used to estimate the data model parameters at the current moment and determine the pseudo partial derivative estimation value according to the historical input and output information of the bow control system of the marine robot; An information calculation unit, used to calculate the input information of the marine robot bow control system at the current moment according to the pseudo partial derivative estimation value and the position-type compact format model-free adaptive control law; A control execution unit, used to execute the input information and perform heading control on the marine robot; A deviation judgment unit is used to judge whether the heading deviation of the marine robot is less than a preset threshold value. When the heading deviation of the marine robot is not less than the preset threshold value, the data model parameters at the current moment are re-estimated to determine the pseudo partial derivative estimation value; The method of constructing a position-based compact format dynamic input / output data model for the marine robot heading control system includes: Build The heading control system outputs information at all times. The bow control system input information at all times, The correspondence between the model parameters at each moment is called a pseudo-partial derivative; The positional compact format dynamic input / output data model is: ； in and They are Moment and The heading control system outputs information at all times. for The bow input information to the control system at the moment, yes The moments are called pseudo-partial derivatives of the model parameters, The sign of remains unchanged; The adaptive control input solution criteria function includes: according to Input information of the marine robot bow control system at all times, Output information of the marine robot bow control system at all times, The desired heading angle at time, the input limit coefficient, The pseudo partial derivative at the time instant determines the adaptive control input solution criterion function; The adaptive control input solution criterion function is: ； in, for The input information of the marine robot bow control system at all times, for Output information of the marine robot bow control system at each moment. for The desired heading angle at the moment, is the input restriction coefficient, yes The moments are called the model parameters of the pseudo-partial derivatives; The historical input and output information of the marine robot bow control system includes: Input information at the time , Output information at any time , Output information at any time as well as The pseudo partial derivative estimate at time ; The estimating of the data model parameters at the current moment based on the historical input and output information of the marine robot bow control system includes: According to the historical input and output information of the marine robot bow control system, the data model parameters at the current moment are estimated based on the following model: ， in for The pseudo-partial derivative estimate at time , is the introduced step size factor, To limit the weight coefficient of the pseudo partial derivative estimate change, ; Determining the pseudo partial derivative estimate comprises: Introducing the pseudo partial derivative estimation reset mechanism, when , When ;in, is a positive number and its value is very small. is the initial pseudo partial derivative estimate.

6. An electronic device comprising a processor and a memory for storing instructions executable by the processor, characterized in that: The processor is configured to execute the steps of the position-based compact model-free adaptive heading control method for a marine robot as described in any one of claims 1 to 4 when calling the executable instructions in the memory.

7. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the position-based compact-format model-free adaptive heading control method for a marine robot according to any one of claims 1 to 4 are implemented.

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