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Stochastic model predictive control technology-based autonomous underwater vehicle path tracking method

An underwater robot, predictive control technology, applied in the direction of height or depth control, can solve the problems of AUV path tracking error, parameter perturbation and other problems

Pending Publication Date: 2019-06-18
HARBIN ENG UNIV
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  • Abstract
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  • Application Information

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Problems solved by technology

[0005] The purpose of the present invention is to solve the problem of large AUV path tracking error due to parameter perturbation in existing existing AUV models, and propose an autonomous underwater robot path tracking method based on stochastic model predictive control technology

Method used

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  • Stochastic model predictive control technology-based autonomous underwater vehicle path tracking method
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  • Stochastic model predictive control technology-based autonomous underwater vehicle path tracking method

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specific Embodiment approach 1

[0018] Specific implementation mode one: the specific process of the autonomous underwater robot path tracking method based on stochastic model predictive control technology in this implementation mode is as follows:

[0019] Step 1. Measure the state measurement value of the AUV at the initial moment, set the expected path p(σ) of the AUV; set the probability distribution function f of the uncertain parameters in the AUV model θ , the basis function of the polynomial expansion and the initial value of the control input sequence (moment and force of the control AUV);

[0020] Step 2, measuring the state measurement value of the current AUV, and obtaining the path tracking error of the AUV according to the state measurement value of the current AUV and the expected path p(σ) of the AUV;

[0021] Step 3. Make the path tracking error e obtained in step 2 p (t) Convergence, the control input of the AUV is obtained, and the control input of the AUV includes the torque and force of...

specific Embodiment approach 2

[0023] Specific embodiment two: the difference between this embodiment and specific embodiment one is that the state measurement value of the current AUV is measured in the second step, and the path of the AUV is obtained according to the current state measurement value of the AUV and the expected path p(σ) of the AUV Tracking error; the specific process is:

[0024] Set the mathematical model of AUV horizontal plane motion:

[0025]

[0026] where η=[x′ y ψ] T are the coordinates and attitude angle in the geodetic coordinate system, x′, y are the position of the AUV in the geodetic coordinate system, ψ is the heading of the AUV in the geodetic coordinate system, and the superscript T means transpose, is the relationship between the earth coordinate system and the satellite coordinate system, R(ψ) is the horizontal plane coordinate transformation matrix; M=M RB +M A is the inertia matrix; M RB is the rigid body inertial matrix, M A is the additional mass matrix, v=[...

specific Embodiment approach 3

[0049] Embodiment 3: The difference between this embodiment and Embodiment 1 or 2 is that the path tracking error e obtained in Step 2 in Step 3 is p (t) Convergence, the control input of the AUV is obtained, and the control input of the AUV includes the torque and force of the AUV; the specific process is:

[0050] Suppose the AUV state quantity (coordinates and attitude angle η=[x′ y ψ] in the earth coordinate system of AUV T And the velocity vector v=[u′ v′ r] in the satellite coordinate system T ) is measurable, the tracking problem under the random MPC framework is described as follows:

[0051]

[0052] where J is the performance function, u N =[u 0 , u 1 ,...,u k ,...,u N-1 ] is the control input sequence in the prediction time domain, u k is the control input, x k is the state quantity calculated according to formula (1), x(k) is the measured value of the AUV state quantity at time k, θ is a random parameter, h( ) is the state constraint, U is the input quan...

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Abstract

The invention relates to an autonomous underwater vehicle path tracking method, in particular, a stochastic model predictive control technology-based autonomous underwater vehicle path tracking method. The invention aims to solve the problem of large AUV (autonomous underwater vehicle) path tracking error caused by the parameter perturbation of an existing established AUV model. The implementationprocess of the method includes the following steps that: 1, the state measurement value of an AUV at an initial time point is measured, and the expected path of the AUV, the probability distributionfunction of uncertain parameters in the AUV model, the basis function of polynomial expansion, and the initial value of a control input sequence are set; 2, the path tracking error of the AUV is obtained; 3, the path tracking error obtained in the step 2 is made to converge, so that the control input of the AUV can be obtained; and 4, whether the AUV completes a tracking path is judged, if the AUVcompletes the tracking path, the control input of the AUV is obtained, if the AUV does not complete the tracking path, the step 2 to step 4 are executed again until the AUV completes the tracking path. The method of the invention is applied to the autonomous underwater vehicle path tracking field.

Description

technical field [0001] The invention relates to a path tracking method for an autonomous underwater robot. Background technique [0002] Autonomous underwater vehicle (AUV) is a new generation of underwater robot, which has the advantages of large range of motion, good mobility, safety, and intelligence, and has become an important tool for completing various underwater tasks. The path tracking problem is a basic problem of AUV motion control, that is, by controlling the forward speed to converge to a desired value (usually a constant in the path tracking problem), and acting on the direction of motion of the robot to keep it on the desired path superior. Due to the complexity of task requirements, the requirements for AUV control accuracy will be further increased, so precise control methods are required to meet complex control requirements. [0003] Model predictive control (Model Predictive Control, MPC) is a model-based control method, which can also be regarded as an ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05D1/06
Inventor 秦洪德孙延超万磊张靖宇李骋鹏陈辉李晓佳
Owner HARBIN ENG UNIV
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