CN111158383A - Unmanned ship track tracking control method based on interference observer and RBFNN - Google Patents

Unmanned ship track tracking control method based on interference observer and RBFNN Download PDF

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CN111158383A
CN111158383A CN202010060945.8A CN202010060945A CN111158383A CN 111158383 A CN111158383 A CN 111158383A CN 202010060945 A CN202010060945 A CN 202010060945A CN 111158383 A CN111158383 A CN 111158383A
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unmanned ship
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陈正
张有功
唐建中
聂勇
朱世强
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Zhejiang University ZJU
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Abstract

The invention discloses an unmanned ship track tracking control method based on a disturbance observer and RBFNN. On the basis of a traditional sliding mode control method, the unmanned ship track tracking control method has the advantages that the unmanned ship nonlinear dynamics model is established, the interference observer is designed to observe and compensate strong external interference such as storm flow, the RBFNN is designed to estimate and compensate model uncertain parts (including parameter uncertainty, modeling error and the like) of the unmanned ship, the stability of the system is guaranteed, meanwhile, the unmanned ship track tracking error is reduced, and the track tracking control effect is greatly improved. The unmanned ship track tracking control method ensures the stability of the control system, reduces the track tracking error of the unmanned ship, and improves the track tracking control performance.

Description

Unmanned ship track tracking control method based on interference observer and RBFNN
Technical Field
The invention belongs to the field of track tracking control of unmanned boats, and particularly relates to a track tracking control method of an unmanned boat under a complex marine environment (sea wind, sea waves and ocean currents).
Background
In recent years, ocean security investigation and resource exploration gradually become hot spots, and along with the development of electromechanical control technology, the automation and intelligence level of unmanned boats is also continuously improved. Because unmanned ship has advantages such as modularization and intellectuality, through carrying on intelligent sensor, can be long-time, on a large scale, low-cost execution complicated ocean operation task, have extensive demand in military affairs and civilian field. In the military field, unmanned boats can be used for various combat missions such as information collection, anti-submarine combat, monitoring reconnaissance and the like. In the civil field, the unmanned ship can be used for marine environment monitoring, marine geological exploration, maritime search and rescue and the like. The application of the unmanned ship not only ensures the safety of personnel and equipment, but also can greatly improve the operation efficiency. Good track following performance is critical for the control of unmanned boats. However, most of the existing unmanned ship track tracking controllers are based on a linearized unmanned ship dynamic model, system nonlinearity and model uncertainty are not fully considered, and in addition, the existing unmanned ship track tracking controllers are often influenced by external interference such as sea wind, sea waves and ocean currents in the sailing process, so that the existing track tracking controllers are difficult to ensure a good track tracking control effect.
Disclosure of Invention
The invention provides an unmanned ship track tracking control method based on an interference observer and a Radial Basis Function Neural Network (RBFNN) aiming at the defects of the existing unmanned ship track tracking control technology, which is used for solving the influence of model uncertainty (modeling error and parameter uncertainty) and external interference (sea wind, sea waves, ocean currents and the like) on track tracking control in the unmanned ship navigation process, reducing track tracking error and improving the track tracking control performance of the unmanned ship while ensuring the stability of a control system.
In order to achieve the purpose, the specific technical scheme of the invention is as follows:
step 1: comprehensively considering model uncertainty and external interference, establishing a non-linear dynamic model of the unmanned ship:
Figure BDA0002374445470000021
Figure BDA0002374445470000022
where τ represents the control force/moment of the drone. M0Representing the inertial matrix of the unmanned ship, C0Representing the Coriolis and centripetal force matrices of unmanned boats, D0Representing the damping matrix of the unmanned boat. dsRepresenting external disturbances (sea wind, sea waves, currents, etc.), dmModel uncertainty (modeling error and parameter uncertainty) representing unmanned boat η ═ x y ψ]TThe pose of the unmanned ship in an inertial coordinate system is shown, x and y represent the position of the unmanned ship in the inertial coordinate system, psi represents the heading angle of the unmanned ship,
Figure BDA0002374445470000023
and
Figure BDA0002374445470000024
r represents a rotation matrix of the unmanned ship from an inertial coordinate system { b } to a ship body coordinate system { i }, and R is satisfiedTR ═ I and | | | | R | | ═ 1.
Step 2: an unmanned ship track tracking controller based on a disturbance observer and a Radial Basis Function Neural Network (RBFNN) is designed, and model uncertainty parts (including parameter uncertainty, modeling errors and the like) of the unmanned ship are estimated and compensated by introducing the RBFNN.
Designing a slip form surface:
Figure BDA0002374445470000031
wherein e is η - ηdIndicating the track following error of the unmanned boat, ηdThe target track tracked by the unmanned ship is shown, η shows the actual track of the unmanned ship, and k shows the adjustable parameter of the sliding mode surface.
The design of the control force/moment based on the disturbance observer and the RBFNN is as follows:
Figure BDA0002374445470000032
wherein the content of the first and second substances,
Figure BDA0002374445470000033
Figure BDA0002374445470000039
represents the estimation value of RBFNN (radial basis function neural network) to the uncertain part of the unmanned boat model,
Figure BDA0002374445470000034
is an estimated value of the external interference of the interference observer to the unmanned ship in the sailing process, KvAnd ξ are adjustable controller parameters, sgn (·) represents a step function.
h(p)＝[h1(p),...,hj(p),...,hn(p)]TThe activation function of the RBFNN radial basis function neural network is shown, n represents the number of network nodes of the RBFNN hidden layer,
Figure BDA0002374445470000035
indicates RBFNN input amount, hjThe specific expression (p) is as follows:
Figure BDA0002374445470000036
the adaptive law of RBFNN is designed as follows:
Figure BDA0002374445470000037
wherein b isj，cjAnd delta denotes an adjustable parameter of RBFNN (radial basis function neural network).
And step 3: and designing an interference observer, observing and compensating external interference such as wind, wave and flow and the like, and reducing the influence of the external interference on the track tracking control effect of the unmanned ship.
Figure BDA0002374445470000038
Here, the first and second liquid crystal display panels are,
Figure BDA0002374445470000041
h denotes the adjustable constant matrix parameters,
Figure BDA0002374445470000042
and the observation value of the disturbance observer to external disturbance such as wind wave flow is represented.
By the step 1, external interferences such as system nonlinearity, wind wave flow and the like and model uncertainty (modeling error and parameter uncertainty) are comprehensively considered, and the unmanned ship nonlinear dynamics model is established. The unmanned ship track tracking controller is designed based on the nonlinear dynamics model, model uncertainty of the unmanned ship is estimated and compensated through the RBFNN (radial basis function neural network) designed in the step 2, and external interference such as wind, wave and flow is observed and compensated through the interference observer designed in the step 3, so that the proposed track tracking controller can effectively reduce the influence of the external interference and the model uncertainty on the unmanned ship track tracking control, and good track tracking control performance is ensured.
Compared with the prior art, the invention has the following beneficial effects:
1. the unmanned ship track tracking control method based on the interference observer and the RBFNN is provided by establishing the unmanned ship nonlinear dynamics model, so that the stability of a control system is ensured, the unmanned ship track tracking error is reduced, and the track tracking control performance is improved.
2. The interference observer designed by the invention can effectively observe and compensate the influence of external interference (sea wind, sea wave, ocean current and the like) on track tracking control.
3. The RBFNN radial basis function neural network designed by the invention can effectively estimate and compensate model uncertainty (modeling error and parameter uncertainty) in the unmanned ship navigation process.
Drawings
Fig. 1 is an unmanned boat motion model of the present invention.
Fig. 2 is a block diagram of the unmanned ship track following control system based on the disturbance observer and RBFNN, which specifically describes the signal transmission of each part of the track following controller.
Fig. 3 is a target track tracked by the unmanned boat of the present invention.
Fig. 4a, 4b and 4c are graphs comparing the control effect of the flight path tracking controller based on the disturbance observer and RBFNN designed by the present invention with the conventional PID controller and linear controller.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
The implementation technical scheme of the invention is as follows:
step 1: comprehensively considering model uncertainty and external interference, establishing a non-linear dynamic model of the unmanned ship, as shown in fig. 1, wherein the established non-linear dynamic model of the unmanned ship is as follows:
Figure BDA0002374445470000051
Figure BDA0002374445470000052
where τ represents the control force/moment of the drone. M0Representing the inertial matrix of the unmanned ship, C0Representing the Coriolis and centripetal force matrices of unmanned boats, D0Representing the damping matrix of the unmanned boat. dsRepresenting external disturbances (sea wind, sea waves, currents, etc.), dmModel uncertainty (modeling error and parameter uncertainty) representing unmanned boat η ═ x y ψ]TRepresenting the pose of the unmanned ship in an inertial coordinate system, x and y representing the position of the unmanned ship in the inertial coordinate system, psi representing the course angle of the unmanned ship, R representing a rotation matrix of the unmanned ship from the inertial coordinate system { b } to a ship body coordinate system { i }, and satisfying RTR ═ I and | | | | R | | ═ 1.
Step 2: an unmanned ship track tracking controller based on a disturbance observer and an RBFNN (radial basis function neural network) is designed, and the RBFNN (radial basis function neural network) is introduced to estimate and compensate model uncertainty (including parameter uncertainty, modeling error and the like) of the unmanned ship, and the figure 2 shows.
Designing a slip form surface:
Figure BDA0002374445470000061
wherein e is η - ηdIndicating the track following error of the unmanned boat, ηdThe target track tracked by the unmanned ship is shown, η shows the actual track of the unmanned ship, and k shows the adjustable parameter of the sliding mode surface.
The design of the control force/moment based on the disturbance observer and the RBFNN is as follows:
Figure BDA0002374445470000062
wherein the content of the first and second substances,
Figure BDA0002374445470000063
Figure BDA0002374445470000067
represents the estimation value of RBFNN (radial basis function neural network) to the uncertain part of the unmanned boat model,
Figure BDA0002374445470000064
is an estimated value of the external interference of the interference observer to the unmanned ship in the sailing process, KvAnd ξ are adjustable controller parameters, sgn (·) represents a step function.
h(p)＝[h1(p),...,hj(p),...,hn(p)]TThe activation function of the RBFNN radial basis function neural network is shown, n represents the number of network nodes of the RBFNN hidden layer,
Figure BDA0002374445470000065
indicates RBFNN input amount, hjThe specific expression (p) is as follows:
Figure BDA0002374445470000066
the adaptive law of RBFNN is designed as follows:
Figure BDA0002374445470000071
wherein b isj，cjAnd delta denotes an adjustable parameter of RBFNN (radial basis function neural network).
And step 3: and designing an interference observer, observing and compensating external interference such as wind, wave and flow and the like, and reducing the influence of the external interference on the track tracking control effect of the unmanned ship.
Figure BDA0002374445470000072
Here, the first and second liquid crystal display panels are,
Figure BDA0002374445470000073
h denotes the adjustable constant matrix parameters,
Figure BDA0002374445470000074
and the observation value of the disturbance observer to external disturbance such as wind wave flow is represented.
By the step 1, external interferences such as system nonlinearity, wind wave flow and the like and model uncertainty (modeling error and parameter uncertainty) are comprehensively considered, and the unmanned ship nonlinear dynamics model is established. The unmanned ship track tracking controller is designed based on the nonlinear dynamics model, model uncertainty of the unmanned ship is estimated and compensated through the RBFNN (radial basis function neural network) designed in the step 2, and external interference such as wind, wave and flow is observed and compensated through the interference observer designed in the step 3, so that the proposed track tracking controller can effectively reduce the influence of the external interference and the model uncertainty on the unmanned ship track tracking control, and good track tracking control performance is ensured.
Finally, MATLAB/Simulink simulation is carried out on the control method, and compared with a PID controller and a linear controller, and then the comparison result is testedThe control effect of the track tracking control method provided by the invention is proved. During verification, the flight path tracking control parameter is K ═ diag {10,10,10}, Kv＝diag{100,100,100}，ξ＝diag{100,100,100}，H＝diag{0.1309,0.1309,0.1309}。
The external interference and model uncertainty assumptions are:
Figure BDA0002374445470000081
Figure BDA0002374445470000082
the unmanned ship simulation model parameters are shown in table 1.
TABLE 1 simulation model parameters of unmanned surface vehicle
Figure BDA0002374445470000083
The unmanned boat target track is designed as follows (as shown in figure 3):
Figure BDA0002374445470000084
Figure BDA0002374445470000091
Figure BDA0002374445470000092
in the above formula, the first and second carbon atoms are,
Figure BDA0002374445470000093
Figure BDA0002374445470000094
the simulation results of the track following control are shown in fig. 4a, 4b and 4c, the solid line represents a target track curve tracked by the unmanned ship, the double-dashed line represents a control curve of the linear controller, the dotted line represents a control curve of the PID controller, and the dot-dash line represents a control curve of the unmanned ship track following controller based on the disturbance observer and RBFNN (radial basis function neural network). As can be seen from fig. 4a and 4c, the unmanned surface vehicle can accurately and smoothly track a target trajectory curve under the conditions of external disturbance (sea wind, sea wave, sea current and the like) and model uncertainty (modeling error and parameter uncertainty). Meanwhile, a control tracking error curve is shown in fig. 4b, can be quickly converged into a small range, and embodies the stability and effectiveness of control. When the target track is tracked, compared with a traditional linear controller and a PID (proportion integration differentiation) controller, the track tracking controller based on the interference observer and the RBFNN is smaller in track tracking error, so that the interference observer and the RBFNN have superior transient response performance and better robustness, the influence of external interference and model uncertainty on unmanned ship track tracking control can be effectively compensated, the stability of a system is guaranteed, meanwhile, the track tracking control error of the unmanned ship is reduced, and the track tracking control performance is improved.
The above-mentioned contents are only technical ideas of the present invention, and the protection scope of the present invention is not limited thereby, and any modifications made on the basis of the technical ideas proposed by the present invention fall within the protection scope of the claims of the present invention.

Claims (2)

1. The unmanned ship track tracking control method based on the disturbance observer and the RBFNN is characterized by comprising the following steps of:
the first step is as follows: comprehensively considering model uncertainty and external interference, and establishing a non-linear dynamic model of the unmanned ship;
the second step is that: designing an unmanned ship track tracking controller based on an interference observer and a radial basis function neural network, and introducing a model uncertain part of the unmanned ship estimated and compensated by the radial basis function neural network, wherein the model uncertain part comprises parameter uncertainty and modeling error;
the third step: designing an interference observer, observing and compensating external interference, and reducing the influence of the external interference on the track tracking control effect of the unmanned ship;
in the first step, the non-linear dynamic model of the unmanned ship is established as follows:
Figure FDA0002374445460000011
wherein τ represents the control force/moment of the unmanned vehicle; m0Representing the inertia matrix of the unmanned ship, C0Showing the Coriolis and centripetal force matrices of the unmanned vessel, D0Representing a damping matrix of the unmanned vehicle; dsIndicating external interference, dmRepresenting model uncertainty of the unmanned boat, η ═ x y ψ]TThe pose of the unmanned ship in an inertial coordinate system is shown, x and y represent the position of the unmanned ship in the inertial coordinate system, psi represents the heading angle of the unmanned ship,
Figure FDA0002374445460000012
and
Figure FDA0002374445460000013
respectively representing η first derivative and second derivative, R represents a rotation matrix from an inertial coordinate system b to a boat body coordinate system i of the unmanned boat, and R is satisfiedTR ═ I and | | | | R | ═ 1;
in a second step, the slip-form face is designed:
Figure FDA0002374445460000014
wherein e is η - ηdIndicating the track following error of the unmanned boat, ηdRepresenting a target track tracked by the unmanned ship, η representing an actual track of the unmanned ship, and k representing an adjustable parameter of a sliding mode surface;
the control force/moment based on the disturbance observer and the radial basis function neural network is designed as follows:
Figure FDA0002374445460000021
wherein the content of the first and second substances,
Figure FDA0002374445460000022
Figure FDA0002374445460000023
represents the estimated value of the radial basis function neural network to the uncertain part of the unmanned ship model,
Figure FDA0002374445460000024
an estimated value, K, representing the external disturbance of the disturbance observer during the navigation of the unmanned shipvAnd ξ for adjustable controller parameters, sgn (·) for a step function, h (p) for an activation function of the radial basis function;
in a third step, the disturbance observer is designed as follows:
Figure FDA0002374445460000025
wherein the content of the first and second substances,
Figure FDA0002374445460000026
h denotes the adjustable constant matrix parameters,
Figure FDA0002374445460000027
representing the observed value of the disturbance observer for the external disturbance.
2. The unmanned ship track-following control method based on disturbance observer and RBFNN of claim 1, wherein h (p) ═ h in the second step1(p),...,hj(p),...,hn(p)]TN represents the number of network nodes of the hidden layer of the radial basis function neural network,
Figure FDA0002374445460000028
representing the input of the radial basis function neural network, hjThe specific expression (p) is as follows:
Figure FDA0002374445460000029
the adaptive law of the radial basis function neural network is designed as follows:
Figure FDA00023744454600000210
wherein b isj，cjAnd δ represents an adjustable parameter of the radial basis function neural network.
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CN112198798A (en) * 2020-10-26 2021-01-08 江南大学 Disturbance processing method, apparatus and storage medium for manned submersible system
CN112327926A (en) * 2020-11-24 2021-02-05 南京工业大学 Self-adaptive sliding mode control method for unmanned aerial vehicle formation
CN112558477A (en) * 2020-12-03 2021-03-26 大连海事大学 Unmanned ship state and disturbance observer based on acceleration information
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CN115291522A (en) * 2022-08-30 2022-11-04 浙江大学 Self-adaptive fuzzy AUV stable tracking control method and device and electronic equipment

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