Track tracking control method of underactuated unmanned boat based on rbf identification and ica-cmac neural network

A neural network and track tracking technology, applied in the field of track tracking control of underactuated unmanned boats, can solve the problems of poor robustness and adaptability of traditional control algorithms, difficulty in establishing USV models, and complex system dynamic characteristics. Improve the learning speed and track tracking accuracy, enhance the adaptive adjustment ability and anti-interference ability, and reduce the effect of dependence

Active Publication Date: 2020-04-03
HARBIN ENG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In addition, there are uncertain model parameters and unknown external disturbances in the actual USV track tracking control process, so it is difficult to establish an accurate USV model. At the same time, the dynamic characteristics of the control system are relatively complex, and the robustness and adaptability of traditional control algorithms are poor.

Method used

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  • Track tracking control method of underactuated unmanned boat based on rbf identification and ica-cmac neural network
  • Track tracking control method of underactuated unmanned boat based on rbf identification and ica-cmac neural network
  • Track tracking control method of underactuated unmanned boat based on rbf identification and ica-cmac neural network

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Experimental program
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specific Embodiment approach 1

[0034] Step 1. Obtain the position and attitude parameters of the USV

[0035] The position information of the USV is measured by the position reference system, and the heading attitude information of the USV is measured by the attitude reference system; the acquired USV attitude and position signals are filtered and space-time aligned to obtain the current precise position and attitude of the USV.

[0036] Step 2. Adaptive integral separation PID control using RBF identification

[0037] The RBF neural network is used to approach the controlled object, obtain the Jacobian identification information of the USV output to the input, and perform online adjustment of the PID control parameters. The output of the controller after parameter adjustment becomes the input of the controlled object. The performance index function of the RBF adjustment is The square of the difference between the identified output and the actual output.

[0038] Step 3. Parallel control of ICA-CMAC neural...

specific Embodiment approach 2

[0045] Embodiment 2. This embodiment is a further description of the underactuated USV track tracking control method based on the ICA-CMAC neural network of RBF identification described in Embodiment 1.

[0046] The position reference system and attitude reference system described in step 1 collect data information from the integrated position and attitude sensor, and use Kalman filtering or unscented information filtering to filter out the outliers and high-frequency noise in the signal, and the obtained USV attitude and position signals are aligned in time by curve fitting, and spatially aligned with data in different coordinate systems to obtain the precise position and attitude of the current USV.

specific Embodiment approach 3

[0047] Specific Embodiment 3. This embodiment is a further description of an underactuated USV track tracking control method based on ICA-CMAC neural network for RBF identification described in Embodiments 1 to 2.

[0048] The establishment of the error equation in the USV hull coordinate system includes:

[0049]

[0050]

[0051]

[0052]

[0053]

[0054]

[0055] where, η = [x, y, ψ] T , υ=[u, ν, r] T , τ=[τ u ,0,τ r ] T Respectively represent the position, velocity and propulsion force in the northeast coordinate system, x, y, ψ represent the north position, east position and heading angle, respectively, u, ν, r represent the longitudinal velocity, lateral velocity and yaw angular velocity, respectively, τ u , τ r represent the longitudinal force and bow turning moment, respectively, represents its first derivative, d 11 =-X u -X u|u| |u|, d 22 =-Y ν -Y ν|ν| |ν|, d 33 =-N r -N r|r| | r |; Y ν , X u , N r , X u|u| , Y ν|ν| |, ...

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Abstract

The invention provides an under-actuation unmanned light board track tracking control method of ICA-CMAC neural network based on RBF identification. The under-actuation unmanned light boat track tracking control method of the ICA-CMAC neural network based on RBF identification uses a position reference system and a posture reference system to measure USV position information and heading posture information, performing filtering and space-time alignment on obtained USV posture and position signals to obtain a current USV accurate position and a posture, and adopting parallel control of an ICA-CMAC neural network and an integration divided type PID. The ICA-CMAC neural network realize feedforward control; credibility distribution is performed through introducing a balance learning constant; an USV inverse model is identified according to an adjustment index and a sigma learning rule; a generated output is used part of an USV input; and then a controller master control output including a PID controller and the ICA-CMAC neural network. The under-actuation unmanned light board track tracking control method of the ICA-CMAC neural network based on RBF identification solves a problem of USV track tracking under a condition that external interference is not determined, reduces dependence on an accurate mathematic model, enhances an adaptive adjustment capability and an interference-resistance capability of a system and improves on-line learning speed of an algorithm and track tracking accuracy.

Description

technical field [0001] The invention relates to an underactuated unmanned boat track tracking control method, in particular to an underactuated unmanned boat track tracking control method based on an RBF-identified ICA-CMAC neural network. Background technique [0002] Unmanned vehicle (USV) is a kind of sea intelligent motion platform that can safely and autonomously navigate in the actual ocean environment and complete various tasks. It can be equipped with sensors, communication devices, etc., and it has the characteristics of flexible manipulation and automatic driving. In various harsh environments, different tasks such as water area survey, marine resource detection, coastal protection, formation control, etc. are performed, so it is of great significance to study the track tracking control of unmanned vehicles. [0003] Underactuated USV lacks lateral driving force and is not constrained by the conditions of Brockett's theorem. Many methods developed for nonholonomic ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G05B13/04
CPCG05B13/042
Inventor 付明玉王莎莎王元慧庹玉龙王泰琪张放周利孙嘉霖尚文飞
Owner HARBIN ENG UNIV
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