Underwater robot adaptive control method and device, electronic equipment and medium

Abstract

The invention provides an underwater robot adaptive control method and device, electronic equipment and a medium, and the method comprises the steps: determining a first posture prediction model of an underwater robot according to a dynamic model of the underwater robot; identifying a to-be-identified parameter of the first attitude prediction model according to the motion data; determining a first attitude of the underwater robot according to the first attitude prediction model, the identification parameters and the motion data; predicting a second attitude of the underwater robot at a second moment according to the first attitude and a control algorithm to obtain a prediction result; determining a control algorithm of the underwater robot according to the first attitude and nonlinear parameters of the underwater robot; according to the control algorithm, a control instruction for the underwater robot is determined, and self-adaptive control over the underwater robot is completed according to the control instruction. The method has the beneficial effect that the accurate adaptive control of the underwater unmanned aerial vehicle is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of robot motion control, and in particular to an underwater robot adaptive control method, device, electronic equipment and medium. Background Art

[0002] The underwater robot has a complex structure, severe model state coupling and nonlinear characteristics, and the traditional PID-based control algorithm cannot meet the high-precision control requirements of the underwater robot. Summary of the invention

[0003] The main purpose of the embodiments of the present invention is to provide an underwater robot adaptive control method, device, electronic equipment and medium, so as to realize accurate adaptive posture control of the underwater robot.

[0004] One aspect of the present invention provides an underwater robot adaptive control method, comprising:

[0005] Determine a first posture prediction model of the underwater robot according to a power model of the underwater robot, wherein the first posture prediction model is a model to be identified, and the first posture prediction model includes parameters to be identified and nonlinear parameters;

[0006] Acquire motion data of the underwater robot at a first moment, and identify the parameters to be identified of the first posture prediction model according to the motion data to obtain identification parameters;

[0007] Determine a first posture of the underwater robot according to the first posture prediction model, the identification parameters and the motion data;

[0008] Determining a control algorithm of the underwater robot according to the first posture and the nonlinear parameters of the underwater robot;

[0009] Predicting a second posture of the underwater robot at a second moment according to the first posture and the control algorithm to obtain a prediction result;

[0010] According to the prediction result, a control instruction for the underwater robot is determined, and adaptive control of the underwater robot is performed according to the control instruction.

[0011] According to the underwater robot adaptive control method, the first posture prediction model includes:

[0012]

[0013] Where X is the state of the motion model, X = [pqr] T , p, q and r represent the rolling angular velocity, pitching angular velocity and yaw angular velocity respectively; Z = [p 2 q2 r 2 ] T represents the nonlinear term related to the water damping moment characteristic; U = [u p u q r q ] T represents the variables to be input into the first attitude prediction model, including roll command, pitch command and yaw command; V = [sinθ cosθsinφ cosθcosφ] T represents the nonlinear restoring torque term related to the robot's posture angle, θ represents the pitch angle, φ represents the roll angle; k represents the current moment, k+1 represents the next moment; n a and n b Represent the order of model state and input respectively; T is transpose; where A i , B j , C, D and E are the model matrices of the parameters to be identified.

[0014] According to the underwater robot adaptive control method, the motion data of the underwater robot at a first moment is obtained, and the parameters to be identified of the first posture prediction model are identified according to the motion data to obtain the identification parameters, including:

[0015] Collect sensor data of the underwater robot at a first moment, update the motion model of the underwater robot according to the sensor data, and update the obtained

[0016]

[0017] Among them, k is the first moment;

[0018] Determining state data for updating the first posture prediction model according to the sensor data and historical sensor data of the underwater robot, including

[0019]

[0020] According to the state data, the first posture prediction model is updated using a recursive least squares identification algorithm based on a forgetting factor

[0021]

[0022] Among them, P i express The covariance matrix of the parameter identification algorithm, K i express The gain matrix of the parameter identification algorithm, x i represents the i-th element of the model motion state X, μ i express The forgetting factor of the parameter identification algorithm; θ i (k) is the model parameter matrix obtained by identification, express The i-th row element of , i = 1, 2, 3, indicates that the recursive least squares identification algorithm includes three inputs and a single output, where

[0023]

[0024] Represents the parameter matrix A l The identification value of the internal elements of c ij d ij 、e i 、z i and v i Respectively represent B l , C, D, E, Z and U internal elements’ identification values.

[0025] According to the underwater robot adaptive control method, the first posture of the underwater robot is determined according to the first posture prediction model, the identification parameters and the motion data, including:

[0026] According to the model parameter matrix θ i (k) Calculate the first posture to obtain the first posture, where the model matrix of the first posture is

[0027]

[0028]

[0029]

[0030]

[0031]

[0032] According to the underwater robot adaptive control method, the control algorithm of the underwater robot is determined according to the first posture and the nonlinear parameters of the underwater robot, including:

[0033] According to the model matrix C, D, E and the nonlinear states Z and V, the model matrix Ξ is obtained, which is used to characterize the nonlinear terms and interference terms.

[0034] Ξ=CZ+DV+E;

[0035] According to the model matrices A, B and Ξ, the matrix of the control algorithm is obtained as follows:

[0036]

[0037]

[0038]

[0039] in

[0040]

[0041]

[0042]

[0043]

[0044] H( 1 )=Ξ

[0045] Where r is the state dimension of the controlled object, s represents the control input dimension of the passive object, and l is the length of the predictive control time.

[0046] According to the underwater robot adaptive control method, the second posture of the underwater robot at the second moment is predicted according to the first posture and the control algorithm to obtain a prediction result, including:

[0047] According to the matrix of the control algorithm and the predicted control time length, the predicted control matrix is ​​obtained as follows:

[0048]

[0049] in,

[0050]

[0051]

[0052]

[0053]

[0054] Ω=[H( 1 ) T H( 2 ) T …H( l ) T ] T

[0055]

[0056]

[0057]

[0058] and

[0059] in, represents the desired state of the underwater robot, is the average state value, and Respectively represent the historical control output and the current control output, and They represent the state required for prediction and the number of input historical data, respectively, and Greater than and Respectively The minimum and maximum values ​​of

[0060] The posture of the robot at a second moment is predicted according to the prediction control matrix, wherein the second moment is the sum of the prediction control time length and the first moment, and the second moment is greater than the first moment.

[0061] According to the underwater robot adaptive control method, the method further comprises:

[0062] Generate a quadratic programming matrix based on the predictive control matrix, including

[0063]

[0064]

[0065] in, is a 3×3 diagonal matrix;

[0066] According to the quadratic programming matrix, the operator partitioning quadratic programming method is used for online calculation to determine the optimal value in the prediction result, including

[0067]

[0068] in, in is the optimal value, where Used to minimize the control index, where the control index is

[0069]

[0070] Another aspect of an embodiment of the present invention provides an underwater robot adaptive posture device, comprising:

[0071] A first module is used to determine a first posture prediction model of the underwater robot according to a power model of the underwater robot, wherein the first posture prediction model is a model to be identified, and the first posture prediction model includes parameters to be identified and nonlinear parameters;

[0072] The second module is used to obtain motion data of the underwater robot at a first moment, identify the parameters to be identified of the first posture prediction model according to the motion data, and obtain identification parameters;

[0073] A third module is used to determine a first posture of the underwater robot according to the first posture prediction model, the identification parameters and the motion data;

[0074] A fourth module, configured to determine a control algorithm of the underwater robot according to the first posture and the nonlinear parameters of the underwater robot;

[0075] A fifth module is used to predict a second posture of the underwater robot at a second moment according to the first posture and the control algorithm to obtain a prediction result;

[0076] The sixth module is used to determine the control instructions for the underwater robot according to the prediction results, and complete the adaptive control of the underwater robot according to the control instructions.

[0077] Another aspect of an embodiment of the present invention provides an electronic device, including a processor and a memory;

[0078] The memory is used to store programs;

[0079] The processor executes the program to implement the method described above.

[0080] The embodiment of the present invention also discloses a computer program product or a computer program, which includes a computer instruction stored in a computer-readable storage medium. A processor of a computer device can read the computer instruction from the computer-readable storage medium, and the processor executes the computer instruction, so that the computer device executes the method described above.

[0081] The beneficial effects of the present invention are as follows: the motion state of the underwater robot is identified online by using the ARX multidimensional model of the typical nonlinear characteristics of the underwater robot, so that the prediction model can more accurately describe the motion characteristics of the underwater robot; the model-based predictive motion control is adopted, and the influence of the maximum and minimum input values ​​on the control algorithm is considered in the predictive control, and at the same time, the control effect of the predictive control algorithm is improved by improving the accuracy of the predictive model; the optimal quadratic programming problem is adopted to improve the calculation efficiency of the control algorithm; the online parameter identification is adopted to correct the predictive model parameters in real time, thereby adjusting the output of the predictive control algorithm to achieve the model adaptive control effect.

[0082] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0084] Figure 1 Schematic diagram of an underwater robot adaptive control system according to an embodiment of the present invention.

[0085] Figure 2 It is a flow chart of the adaptive control method of an underwater robot according to an embodiment of the present invention.

[0086] Figure 3 1 is a schematic diagram of a parameter update process of an underwater robot according to an embodiment of the present invention.

[0087] Figure 4 Schematic diagram of adaptive control of an underwater robot according to an embodiment of the present invention.

[0088] Figure 5 2 is a diagram of an underwater robot adaptive control analysis device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0089] The embodiments of the present invention are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements with the same or similar functions. In the subsequent description, the use of suffixes such as "module", "component" or "unit" used to represent elements is only for the purpose of facilitating the description of the present invention, and has no specific meaning in itself. Therefore, "module", "component" or "unit" can be used in a mixed manner. "First", "second" and the like are only used for the purpose of distinguishing technical features, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features or implicitly indicating the order of the indicated technical features. In this subsequent description, the continuous numbering of the method steps is for the convenience of review and understanding. In combination with the overall technical solution of the present invention and the logical relationship between the various steps, adjusting the implementation order between the steps will not affect the technical effect achieved by the technical solution of the present invention. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0090] like Figure 1 As shown, an embodiment of the present invention provides a schematic diagram of an underwater robot adaptive control system, which includes an underwater robot 100, wherein the underwater robot 100 includes a control center 200, a sensor device 300 and a posture control device 400, wherein the sensor device 300 is connected to the posture control device 400, and collects motion data of the underwater drone 100 when the posture changes, wherein the control center 200 is used to determine a first posture prediction model of the underwater robot 100 according to a power model of the underwater robot 100, the first posture prediction model is a model to be identified, and the first posture prediction model includes parameters to be identified and nonlinear parameters; obtain the underwater robot 1 00 is a motion data at a first moment, and the parameters to be identified of the first posture prediction model are identified according to the motion data to obtain the identification parameters; the first posture of the underwater robot 100 is determined according to the first posture prediction model, the identification parameters and the motion data; the second posture of the underwater robot 100 at a second moment is predicted according to the first posture and the control algorithm to obtain the prediction result; the control algorithm of the underwater robot 100 is determined according to the first posture and the nonlinear parameters of the underwater robot 100; the control instructions for the underwater robot 100 are determined according to the control algorithm, and the posture control device 400 completes the adaptive control of the underwater robot 100 according to the control instructions.

[0091] In some embodiments, the underwater robot 100 may be Figure 2 The underwater robot ROV is shown.

[0092] In some embodiments, the motion data collected by the sensor device 300 includes a roll angular velocity, a pitch angular velocity, and a yaw angular velocity.

[0093] In some embodiments, the instructions sent by the control center 200 to the attitude control device include roll instructions, pitch instructions, and yaw instructions.

[0094] like Figure 2 As shown, the embodiment of the present invention provides a process of an underwater robot adaptive control method, which specifically includes but is not limited to steps S100-S600:

[0095] S100, determining a first posture prediction model of the underwater robot according to a power model of the underwater robot, the first posture prediction model is a model to be identified, and the first posture prediction model includes parameters to be identified and nonlinear parameters.

[0096] In some embodiments, the first posture prediction model includes an AutoRegressive eXogenous (ARX) discrete prediction model with nonlinear terms, and the first posture prediction model is an unidentified model. The underwater robot system model to be identified including nonlinear elements obtained according to the power model of the underwater robot is:

[0097]

[0098] Where X is the state of the motion model, X = [pqr] T , p, q and r represent the rolling angular velocity, pitching angular velocity and yaw angular velocity respectively; Z = [p 2 q 2 r 2 ] T represents the nonlinear term related to the water damping moment characteristic; U = [u p u q r q ] T represents the variables to be input into the first attitude prediction model, including roll command, pitch command and yaw command; V = [sinθ cosθsinφ cosθcosφ] T represents the nonlinear restoring torque term related to the robot's posture angle, θ represents the pitch angle, φ represents the roll angle; k represents the current moment, k+1 represents the next moment; n a and n b Represent the order of model state and input respectively; T is transpose; where A i , B j , C, D and E are the model matrices of the parameters to be identified.

[0099] S200, obtaining motion data of the underwater robot at a first moment, identifying parameters to be identified of a first posture prediction model according to the motion data, and obtaining identification parameters.

[0100] In some embodiments, reference Figure 3 The parameter updating process diagram shown in the figure includes but is not limited to steps S210 to S230:

[0101] S210, collecting sensor data of the underwater robot at a first moment, and updating a motion model of the underwater robot according to the sensor data.

[0102] In some embodiments, the motion model represents the motion state of the underwater robot.

[0103] Exemplarily, the updated model state is

[0104]

[0105] Here, k is the current moment, i.e., the first moment, which indicates the moment when the sensor data is collected, and k+1, k+2, etc. indicate future moments, i.e., the second moment.

[0106] S220, determining state data for updating a first posture prediction model according to the sensor data and historical sensor data of the underwater robot;

[0107] In some embodiments, the state data required for updating the model parameter identification according to the newly sampled data and the historical data is:

[0108]

[0109] Among them, P i express The covariance matrix of the parameter identification algorithm, K i express The gain matrix of the parameter identification algorithm, x i represents the i-th element of the model motion state X, μ i express The forgetting factor of the parameter identification algorithm; θ i (k) is the model parameter matrix obtained by identification, express The i-th row element of , i = 1, 2, 3, indicates that the recursive least squares identification algorithm includes three inputs and a single output, where

[0110] S230, updating the first posture prediction model using a recursive least squares identification algorithm based on a forgetting factor according to the state data.

[0111] In some embodiments, the updated model is

[0112]

[0113] Among them, θ i (k) is the identification parameter,

[0114]

[0115] Represents the parameter matrix A l The identification value of the internal elements of c ij ,d ij 、e i 、z i and v i Respectively represent B l , C, D, E, Z and U internal elements’ identification values.

[0116] S300, determining a first posture of the underwater robot according to a first posture prediction model, identification parameters and motion data.

[0117] In some embodiments, according to the model parameter matrix θ i (k) Calculate the first posture and obtain the first posture, where the model matrix of the first posture is

[0118]

[0119] S400, predicting a second posture of the underwater robot at a second moment according to the first posture and the control algorithm to obtain a prediction result.

[0120] In some embodiments, reference Figure 4 The schematic diagram of the adaptive control process shown includes but is not limited to steps S410 to S430:

[0121] S410, obtaining a predictive control matrix according to the matrix of the control algorithm and the predictive control time length;

[0122] S420, predicting the posture of the robot at the second moment according to the prediction control matrix;

[0123] S430, performing online calculations using an operator-segmented quadratic programming method based on a quadratic programming matrix to determine an optimal value in the prediction result, and determining a control instruction for the control indicator based on the optimal value.

[0124] S500, determining a control algorithm of the underwater robot according to the first posture and nonlinear parameters of the underwater robot.

[0125] In some embodiments, the control algorithm is used to calculate based on the model matrices C, D, E and the nonlinear states Z and V to obtain a model matrix Ξ, where Ξ is used to characterize the nonlinear terms and the interference terms,

[0126] Ξ=CZ+DV+E (6)

[0127] According to the model matrices A, B and Ξ, the matrix of the control algorithm is obtained as follows:

[0128]

[0129] in

[0130]

[0131]

[0132]

[0133]

[0134] H (1) =Ξ

[0135] Where r is the state dimension of the controlled object, s represents the control input dimension of the passive object, and l is the length of the predictive control time.

[0136] In some embodiments, the control algorithm matrix and the set predictive control time length l value calculate the predictive control matrix as shown below:

[0137]

[0138]

[0139]

[0140]

[0141] Ω=[H (1 )TH( 2 )T…H( l ) T ] T

[0142]

[0143]

[0144]

[0145]

[0146]

[0147]

[0148] in, represents the desired state of the underwater robot, is the average state value, and Respectively represent the historical control output and the current control output, and They represent the state required for prediction and the number of input historical data, respectively, and Greater than and Respectively The minimum and maximum values ​​of ;.

[0149] In order to solve the predictive control equation (9), the predictive control problem is transformed into a quadratic programming problem and the following quadratic programming matrix is ​​calculated:

[0150]

[0151]

[0152] The operator partitioning quadratic programming method is used to solve the following optimal quadratic programming problem online:

[0153]

[0154] subject to:

[0155]

[0156] in, is a 3×3 diagonal matrix, that is, looking for The optimal value of Make the control index Minimum.

[0157] S600, determining a control instruction for the underwater robot according to the control algorithm, and completing adaptive control of the underwater robot according to the control instruction.

[0158] In some embodiments, after completing the adaptive control, the underwater robot continues to use the online real-time detection method to achieve continuous adaptive control of the underwater drone.

[0159] Figure 5 6 is a diagram of an underwater robot adaptive control analysis device according to an embodiment of the present invention. The device includes a first module 610 , a second module 620 , a third module 630 , a fourth module 640 , a fifth module 650 and a sixth module 660 .

[0160] Among them, the first module is used to determine the first posture prediction model of the underwater robot according to the power model of the underwater robot. The first posture prediction model is a model to be identified, and the first posture prediction model includes parameters to be identified and nonlinear parameters; the second module is used to obtain the motion data of the underwater robot at the first moment, identify the parameters to be identified of the first posture prediction model according to the motion data, and obtain the identification parameters. The first moment is used to characterize the current moment; the third module is used to determine the first posture of the underwater robot according to the first posture prediction model, the identification parameters and the motion data; the fourth module is used to predict the second posture of the underwater robot at the second moment according to the first posture and the motion data at the third moment, and obtain the prediction result. The second moment is used to characterize the future moment, and the third moment is used to characterize the historical moment; the fifth module is used to determine the control algorithm of the underwater robot according to the first posture and the nonlinear parameters of the underwater robot of the prediction result; the sixth module is used to determine the control instructions for the underwater robot according to the control algorithm, and complete the adaptive control of the underwater robot according to the control instructions.

[0161] Exemplarily, under the cooperation of the first module, the second module, the third module, the fourth module, the fifth module and the sixth module in the device, the embodiment device can implement any of the aforementioned underwater robot adaptive control methods, that is,

[0162] According to the power model of the underwater robot, a first posture prediction model of the underwater robot is determined, the first posture prediction model is a model to be identified, and the first posture prediction model includes parameters to be identified and nonlinear parameters; the motion data of the underwater robot at the first moment is obtained, and the parameters to be identified of the first posture prediction model are identified according to the motion data to obtain identification parameters, and the first moment is used to characterize the current moment; the first posture of the underwater robot is determined according to the first posture prediction model, the identification parameters and the motion data; the second posture of the underwater robot at the second moment is predicted according to the first posture and the motion data at the third moment to obtain the prediction result, the second moment is used to characterize the future moment, and the third moment is used to characterize the historical moment; the control algorithm of the underwater robot is determined according to the first posture and the nonlinear parameters of the underwater robot of the prediction result; according to the control algorithm, the control instructions for the underwater robot are determined, and the adaptive control of the underwater robot is completed according to the control instructions. The beneficial effects of the present invention are as follows: the motion state of the underwater robot is identified online by using the ARX multidimensional model of the typical nonlinear characteristics of the underwater robot, so that the prediction model can more accurately describe the motion characteristics of the underwater robot; the model-based predictive motion control is adopted, and the influence of the maximum and minimum input values ​​on the control algorithm is considered in the predictive control, and at the same time, the control effect of the predictive control algorithm is improved by improving the accuracy of the predictive model; the optimal quadratic programming problem is adopted to improve the calculation efficiency of the control algorithm; the online parameter identification is adopted to correct the prediction model parameters in real time, thereby adjusting the output of the predictive control algorithm and realizing the adaptive control of the underwater unmanned aerial vehicle.

[0163] An embodiment of the present invention further provides an electronic device, the electronic device comprising a processor and a memory;

[0164] The memory stores a program;

[0165] The processor executes the program to execute the aforementioned underwater robot adaptive control method; the electronic device has the function of carrying and running the underwater robot adaptive control software system provided by the embodiment of the present invention, for example, a computer, a speed regulator and a speed regulating device.

[0166] An embodiment of the present invention further provides a computer-readable storage medium, wherein the storage medium stores a program, and the program is executed by a processor to implement the underwater robot adaptive control method as described above.

[0167] In some selectable embodiments, the function / operation mentioned in the block diagram may not occur in the order mentioned in the operation diagram. For example, depending on the function / operation involved, the two boxes shown in succession can actually be executed substantially simultaneously or the boxes can sometimes be executed in reverse order. In addition, the embodiment presented and described in the flow chart of the present invention is provided by way of example, for the purpose of providing a more comprehensive understanding of technology. The disclosed method is not limited to the operation and logic flow presented herein. Selectable embodiments are expected, wherein the order of various operations is changed and the sub-operation of a part for which is described as a larger operation is performed independently.

[0168] The embodiment of the present invention also discloses a computer program product or a computer program, which includes a computer instruction stored in a computer-readable storage medium. A processor of a computer device can read the computer instruction from the computer-readable storage medium, and the processor executes the computer instruction, so that the computer device executes the aforementioned underwater robot adaptive control method.

[0169] In addition, although the present invention is described in the context of functional modules, it should be understood that, unless otherwise specified, one or more of the functions and / or features described may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in separate physical devices or software modules. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding the present invention. More specifically, in view of the properties, functions, and internal relationships of the various functional modules in the device disclosed herein, the actual implementation of the module will be understood within the conventional skills of the engineer. Therefore, those skilled in the art can implement the present invention set forth in the claims without excessive experimentation using ordinary techniques. It is also understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the present invention, which is determined by the full scope of the appended claims and their equivalents.

[0170] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0171] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0172] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0173] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0174] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0175] Although the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the claims and their equivalents.

[0176] The above is a specific description of the preferred implementation of the present invention, but the present invention is not limited to the described embodiments. Those skilled in the art may make various equivalent modifications or substitutions without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of this application.

Claims

1. An adaptive control method for an underwater robot, characterized in that: include: Determine a first posture prediction model of the underwater robot according to a power model of the underwater robot, wherein the first posture prediction model is a model to be identified, and the first posture prediction model includes parameters to be identified and nonlinear parameters; Acquire motion data of the underwater robot at a first moment, identify the parameters to be identified of the first posture prediction model according to the motion data, and obtain identification parameters, wherein the first moment is used to characterize the current moment; Determine a first posture of the underwater robot according to the first posture prediction model, the identification parameters and the motion data; Predicting a second posture of the underwater robot at a second moment according to the first posture and the motion data at a third moment to obtain a prediction result, wherein the second moment is used to represent a future moment, and the third moment is used to represent a historical moment; Determine a control algorithm of the underwater robot according to the first posture and the nonlinear parameters of the underwater robot according to the prediction result; According to the control algorithm, a control instruction for the underwater robot is determined, and adaptive control of the underwater robot is performed according to the control instruction.

2. The underwater robot adaptive control method according to claim 1, characterized in that: The first posture prediction model includes: Where X is the state of the motion model, X = [pqr] T , p, q and r represent the rolling angular velocity, pitching angular velocity and yaw angular velocity respectively; Z = [p 2 q 2 r 2 ] T represents the nonlinear term related to the water damping moment characteristic; U = [u p u q r q ] T represents the variables to be input into the first attitude prediction model, including roll command, pitch command and yaw command; V = [sinθ cosθsinφcosθcosφ] T represents the nonlinear restoring torque term related to the robot's posture angle, θ represents the pitch angle, φ represents the roll angle; k represents the current moment, k+1 represents the next moment; n a and n b Represent the order of model state and input respectively; T is transpose; where A i , B j , C, D and E are the model matrices of the parameters to be identified.

3. The underwater robot adaptive control method according to claim 2, characterized in that: According to the motion data of the underwater robot at the first moment, the parameters to be identified of the first posture prediction model are identified according to the motion data to obtain the identification parameters, including: Collect sensor data of the underwater robot at a first moment, update the motion model of the underwater robot according to the sensor data, and update the obtained Among them, k is the first moment; Determining state data for updating the first posture prediction model according to the sensor data and historical sensor data of the underwater robot, including h(k)=[p(k),q(k),r(k),…,p(k-n a +1),q(k-n a +1),r(k-n a +1),u p (k),u q (k),u r (k),…,u p (k-n b +1),u q (k-n b +1),u r (k-n b +1),;z1(k),z2(k),z3(k),v1(k),v2(k),v3(k),1] T According to the state data, the first posture prediction model is updated using a recursive least squares identification algorithm based on a forgetting factor Among them, P i express The covariance matrix of the parameter identification algorithm, K i express The gain matrix of the parameter identification algorithm, x i represents the i-th element of the model motion state X, μ i express The forgetting factor of the parameter identification algorithm; θ i (k) is the model parameter matrix obtained by identification, express The i-th row element of , i = 1, 2, 3, indicates that the recursive least squares identification algorithm includes three inputs and a single output, where Represents the parameter matrix A l The identification value of the internal elements of c ij d ij 、e i 、z i and v i Respectively represent B l , C, D, E, Z and U internal elements’ identification values.

4. The underwater robot adaptive control method according to claim 3, characterized in that: The determining the first posture of the underwater robot according to the first posture prediction model, the identification parameter and the motion data includes: According to the model parameter matrix θ i (k) Calculate the first posture to obtain the first posture, where the model matrix of the first posture is 5. The underwater robot adaptive control method according to claim 4, characterized in that: The method predicts the second posture of the underwater robot at the second moment according to the motion data of the first posture and the third moment to obtain a prediction result, wherein the second moment is used to represent a future moment, and the third moment is used to represent a historical moment, including: According to the model matrix C, D, E and the nonlinear states Z and V, the model matrix Ξ is obtained, which is used to characterize the nonlinear terms and interference terms. Ξ=CZ+DV+E; According to the model matrices A, B and Ξ, the matrix of the control algorithm is obtained as follows: in H (1) =Ξ Where r is the state dimension of the controlled object, s represents the control input dimension of the passive object, and l is the length of the predictive control time.

6. The underwater robot adaptive control method according to claim 5, characterized in that: The step of determining a control algorithm of the underwater robot according to the first posture and the nonlinear parameters of the underwater robot according to the prediction result includes: According to the matrix of the control algorithm and the predicted control time length, the predicted control matrix is ​​obtained as follows: in, Ω=[H( 1 ) T H( 2 ) T …H( l ) T ] T and in, represents the desired state of the underwater robot, is the average state value, and Respectively represent the historical control output and the current control output, and They represent the state required for prediction and the number of input historical data, respectively, and Greater than and Respectively The minimum and maximum values ​​of The posture of the robot at a second moment is predicted according to the prediction control matrix, wherein the second moment is the sum of the prediction control time length and the first moment, and the second moment is greater than the first moment.

7. The underwater robot adaptive control method according to claim 6, characterized in that: The method further comprises: Generate a quadratic programming matrix based on the predictive control matrix, including in, is a 3×3 diagonal matrix; According to the quadratic programming matrix, the operator partitioning quadratic programming method is used for online calculation to determine the optimal value in the prediction result, including in, in is the optimal value, where Used to minimize the control index, where the control index is 8. An adaptive control device for an underwater robot, characterized in that: include: A first module is used to determine a first posture prediction model of the underwater robot according to a power model of the underwater robot, wherein the first posture prediction model is a model to be identified, and the first posture prediction model includes parameters to be identified and nonlinear parameters; The second module is used to obtain motion data of the underwater robot at a first moment, identify the parameters to be identified of the first posture prediction model according to the motion data, and obtain identification parameters, wherein the first moment is used to characterize the current moment; A third module is used to determine a first posture of the underwater robot according to the first posture prediction model, the identification parameters and the motion data; A fourth module is used to predict a second posture of the underwater robot at a second moment according to the first posture and the motion data at a third moment to obtain a prediction result, wherein the second moment is used to represent a future moment, and the third moment is used to represent a historical moment; A fifth module, configured to determine a control algorithm of the underwater robot according to the first posture and the nonlinear parameters of the underwater robot according to the prediction result; The sixth module is used to determine the control instructions for the underwater robot according to the control algorithm, and complete the adaptive control of the underwater robot according to the control instructions.

9. An electronic device, characterized in that: including a processor and a memory; The memory is used to store programs; The processor executes the program to implement the underwater robot adaptive control method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The storage medium stores a program, and the program is executed by a processor to implement the underwater robot adaptive control method according to any one of claims 1 to 7.

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