CN108427414A - A kind of horizontal surface self-adaption Trajectory Tracking Control method of Autonomous Underwater Vehicle - Google Patents
A kind of horizontal surface self-adaption Trajectory Tracking Control method of Autonomous Underwater Vehicle Download PDFInfo
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Abstract
The present invention proposes a kind of horizontal surface self-adaption Trajectory Tracking Control method of Autonomous Underwater Vehicle, the speed and angular speed of AUV are estimated using the method for high gain state observer, use radial basis function (Radial Basis Function, RBF) the highly precise approach function compensation model parameter indeterminate and external disturbance item of neural network, tracking problem under polar coordinate system is converted to by AUV track followings problem through coordinate transform.The expectation input of design kinematics model first when specific solution, then the expectation input of design motivation model, it finally uses RBF neural estimation it is expected the uncertain item in input, designs neural network weight more new law, AUV is finally made to track desired track.
Description
Technical Field
The invention designs a horizontal plane self-adaptive trajectory tracking control method for an autonomous underwater vehicle, and belongs to the field of trajectory tracking control.
Background
An Autonomous Underwater Vehicle (AUV) is used as an intelligent Underwater carrying platform, sails in an Autonomous mode, can complete tasks such as Underwater measurement, sea area surveying and mapping, Underwater landform surveying and mapping and the like, and has important application in the fields of military investigation, port safety maintenance, marine environment monitoring and the like.
The tracking control of the AUV is the basis for realizing a plurality of tasks, and the tasks all require the AUV to navigate to a specified position according to a specified track and then carry out a specific operation task. The trajectory tracking of the AUV requires that the AUV track a desired trajectory depending on time, and needs to move to a specific place according to a specific trajectory within a specific time.
If the space dimension formed by the control input of a certain system is less than the space dimension of the state variable of the system, the system is called as an under-actuated system, and the under-actuated system can obviously increase the flexibility of the system, reduce the manufacturing cost and the like. In practical application of the AUV, the driving mode of the AUV is a mode of adding a steering engine to a propeller or propelling the AUV by two independent propellers, the control dimension is 2, and the space dimension of the horizontal plane motion state variable of the autonomous underwater vehicle is 3, so that the horizontal plane motion of the autonomous underwater vehicle is motion under a typical under-actuated condition, and the AUV tracking control problem under the under-actuated condition has extremely strong practical significance in research.
In the course of AUV navigation, it is easy to be interfered by wave, flow and other external factors, at the same time, the model parameter of AUV can not be accurately obtained, and because of the control of cost and other factors, it is difficult to adopt high-precision sensor to obtain the speed and angular velocity of AUV, so that the control effect is poor under the condition of adopting the control method depending on model parameter, and how to realize accurate track tracking under the conditions of unknown speed and angular velocity, uncertain model parameter and external interference, it has strong theoretical significance and practical value.
Disclosure of Invention
The invention provides a horizontal plane self-adaptive trajectory tracking control method of an autonomous underwater vehicle, aiming at the situation that the AUV speed cannot be obtained in practical application due to various reasons in the AUV navigation process. Specifically, expected input of a kinematic model is designed firstly, then expected input of a dynamic model is designed, finally an RBF neural network is used for estimating an uncertainty item in the expected input, a neural network weight updating law is designed, and finally the AUV is enabled to track an expected track.
The technical scheme of the invention is as follows:
the horizontal plane self-adaptive trajectory tracking control method of the autonomous underwater vehicle is characterized by comprising the following steps: the method comprises the following steps:
step 1, establishing a model, namely defining a generalized position vector of the AUV current horizontal plane as η ═ x y ψ]TThe generalized velocity vector v ═ u v r]T(ii) a Wherein x represents the position of AUV in the north-south direction, y represents the position of AUV in the east-west direction, psi represents the current course angle of AUV, u represents the forward movement speed of AUV, v represents the lateral movement speed of AUV, r represents the course angular speed of AUV, and the horizontal plane movement equation of AUV is
WhereinThe kinetic equation for AUV is shown below:
wherein m is11,m22,m33As inertial model parameters of the AUV, d11,d22,d33Is a resistance model parameter of the AUV, τ1,τ2Is a control input of AUV, w1,w2,w3Is an external disturbance;
the initial point position of the AUV under the inertial coordinate system is X0＝[x0y0]TAt an initial heading angle psi0Start tracking desired trajectory Xd＝[xdyd]TThe track of the target point satisfies
Wherein U denotes the velocity of movement, psi, of the tracked target pointwRepresenting a direction of motion of the tracked target point;
step 2: determining a control input as
In the formula k3,k4Are control parameters and are all positive real numbers;
in the formulaThe estimated speed of the AUV is obtained by estimation of a high-gain observer; the high-gain observer is
Wherein, pi1∈R3And pi2∈R3Is the state vector of the high gain observer, epsilon is a positive constant, lambda is more than 0, and then the velocity estimation value of AUV under the body coordinate system is
In the formulaAndeach represents rvirAnd uvirDerivative of rvirAnd uvirThe expected input, representing the kinematic model, is calculated by:
uvir＝k2(ρ-δ)+Ucosχ
wherein k is1,k2Are control parameters and are all positive real numbers; deltaFor the set tracking error value, ρ is the distance between the current position of the AUV and the position of the target point, α is the angle between the longitudinal axis of the AUV and the vector from the AUV to the target point, and χ is the angle between the velocity vector of the target point and the vector from the AUV to the target point;
in the formula z1And z2For the velocity error, it is calculated by:
in the formulaAndthe relationship between the actually obtained value and the true value is obtained as the values of the inertial model parameter and the resistance model parameter of the AUV which can be obtained in the actual situation according to the following formula:
wherein (·) represents true value, (·)*Represents the actual value obtained, and delta (·) represents the uncertain value of the parameter;
in the formulaAnd (Z) is the output of hidden layer nodes of the RBF neural network, and Z is the input of the RBF neural network:
the RBF neural network has the weight value updating law of
Wherein, gamma is1,Γ2And σ1,σ2Updating parameters for the weight of the neural network, wherein the parameters are positive and real;
wherein, CiCoordinate vector representing the central point of the gaussian base function of the ith neuron of the hidden layer in the RBF neural network, biRepresenting the width of the i-th neuron gaussian basis function of the hidden layer.
Advantageous effects
The method has the advantages that the method adopts the AUV adaptive trajectory tracking control based on the RBF neural network, adopts the method of a high-gain state observer to estimate the speed and the angular velocity of the AUV under the condition that the AUV velocity information and the angular velocity cannot be acquired, adopts coordinate conversion to convert the trajectory tracking problem of the under-actuated AUV into the control problem of the distance rho between the AUV and a target point and the angle α between the AUV longitudinal axis and the AUV and the vector from the AUV to the target point, designs control input on the kinematics and dynamics level of the under-actuated AUV respectively, and adopts the RBF neural network to compensate model parameter uncertainty items and external interference items so as to finally enable the tracking error to gradually converge to a set value.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
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The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a schematic diagram of AUV trajectory tracking;
FIG. 2AUV real trajectory compared to expected trajectory;
FIG. 3 is a comparison between the actual value and the estimated value of the AUV forward speed;
FIG. 4 comparing the real value of AUV lateral velocity with the estimated value;
FIG. 5 comparing the true value of AUV angular velocity with the estimated value;
FIG. 6 tracking error variation;
fig. 7 is a graph of two-norm change of RBF neural network weights.
Detailed Description
The following detailed description of embodiments of the invention is intended to be illustrative, and not to be construed as limiting the invention.
The method is based on a self-adaptive trajectory tracking control method under the condition of no speed of a neural network, estimates the speed and the angular speed of an AUV (autonomous underwater vehicle) by using a high-gain state observer, compensates a model parameter uncertainty item and an external interference item by using a high-precision approximation Function (RBF) of a Radial Basis Function (RBF) neural network, and converts the AUV trajectory tracking problem into the tracking problem under a polar coordinate system through coordinate transformation. Specifically, expected input of a kinematic model is designed firstly, then expected input of a dynamic model is designed, finally an RBF neural network is used for estimating an uncertainty item in the expected input, a neural network weight updating law is designed, and finally the AUV is enabled to track an expected track.
The specific derivation process of the method is as follows:
step 1, defining the current generalized position vector of the AUV as η ═ x y ψ]TThe velocity vector is v ═ u v r]T. The horizontal plane kinematic equation of the underactuated AUV is
Wherein,the kinetic equation for AUV is shown below:
wherein m is11,m22,m33As inertial model parameters of the AUV, d11,d22,d33Is a resistance model parameter of the AUV, τ1,τ2Is a control input of AUV, w1,w2,w3Is an external disturbance.
The initial point position of the AUV under the inertial coordinate system is X0＝[x0y0]TAt an initial heading angle psi0Start tracking desired trajectory Xd＝[xdyd]TThe track of the target point satisfies
Wherein U denotes the velocity of movement of the target point, #wRepresenting the direction of movement of the target point.
Step 2: designing high gain observer
Wherein, pi1∈R3And pi2∈R3Is a state vector of the high-gain observer, epsilon is a positive constant, lambda is more than 0, and s is satisfied2+ λ s +1 is a Hurwitz polynomial.
And step 3, calculating the distance rho between the current position of the AUV and the position of the target point, the angle α between the longitudinal axis of the AUV and the vector from the AUV to the target point, and the angle χ between the velocity vector of the target point and the vector from the AUV to the target point by considering coordinate transformation, wherein the angles are shown in figure 1.
Step 4, taking the position η of the AUV at the current moment as the input of the high-gain state observer, and acquiring an AUV speed estimated value of the AUV under a hull coordinate system
And 5: desired input r for designing a kinematic modelvirAnd uvir：
uvir＝k2(ρ-δ)+Ucosχ
Wherein k is1,k2All are control parameters and are positive real numbers; δ is a set tracking error value, and can be any small positive real number.
Step 6: calculating speed error
And 7: the input of the RBF neural network is
The RBF neural network has the weight value updating law of
Wherein S isi(Z) is the output of hidden layer nodes of the RBF neural network, and is defined as the following formula,is the weight of the RBF neural network, gamma1,Γ2And σ1,σ2Parameters are updated for the neural network weights, and are all positive and real.
Wherein, CiCoordinate vector representing the central point of the gaussian base function of the ith neuron of the hidden layer, biRepresenting the width of the i-th neuron gaussian basis function of the hidden layer.
And 8: taking into account the parameter m11，m22，d11，d33Equivalence cannot be fully obtained and parameters are satisfied
Wherein (·) represents true value, (·)*Representing the estimated value, Δ (-) represents the uncertainty of the parameter, and the input of the adaptive trajectory tracking control using the RBF neural network output to approximate the model parameter uncertainty and the external disturbance is:
wherein,k3,k4are control parameters and are all positive and real.
Based on the above derivation, specific examples are given below:
step 1: the AUV performs trajectory tracking starting from an initial point (-2, -2) in the inertial frame at an initial heading angle of 0 deg.. The initial forward and lateral velocities are both 0m/s and 0m/s, the angular velocity is 0m/s, and the desired reference trajectory is:
external interference of AUV is
Step 2: select control input as
Wherein k is3＝300，k4The initial value of the high gain observer is 0, the parameters of the high gain observer are selected to be e 0.01 and λ 1, and the estimated value of the speed of the AUV in the body coordinate system is 150
Selecting a parameter k1＝0.1，k20.1, the tracking error value δ is selected to be 0.2, and the desired input r of the kinematic model is calculatedvirAnd uvir：
uvir＝k2(ρ-δ)+Ucosχ
Calculating the distance rho between the current position of the AUV and the position of the target point, the angle α between the longitudinal axis of the AUV and the vector from the AUV to the target point, and the angle χ between the velocity vector of the target point and the vector from the AUV to the target point:
calculating speed error
Without loss of generality, consider the parameter m11，m22，d11，d33The equivalence can not be completely obtained, so the equivalence is set in simulation
Wherein (·) represents true value, (·)*Representing the actual value obtained.
The input of the RBF neural network is
The RBF neural network node number is selected to be 9, and the central vector CjIs uniformly distributed in
[0,2.0]×[0,0.8]×[-2.0,2.0]×[-1.0,1.0]×[-0.4,0.4]×[-0.2,0.2]×[-0.1,0.1]
Middle and wideSelecting a parameter Γ1＝5，Γ2＝5，σ1＝0.001，σ2The RBF neural network weight update law is 0.001
The simulation shows that the self-adaptive trajectory tracking method based on the RBF neural network has good trajectory tracking performance under the conditions of model parameter uncertainty and external interference, the AUV speed and the angular speed can be effectively estimated through a high-gain observer, and the effectiveness of the algorithm is verified.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made in the above embodiments by those of ordinary skill in the art without departing from the principle and spirit of the present invention.
Claims (1)
1. A horizontal self-adaptive trajectory tracking control method for an autonomous underwater vehicle is characterized by comprising the following steps: the method comprises the following steps:
step 1, establishing a model, namely defining a generalized position vector of the AUV current horizontal plane as η ═ x y ψ]TThe generalized velocity vector v ═ u v r]T(ii) a Wherein x represents the position of AUV in the north-south direction, y represents the position of AUV in the east-west direction, psi represents the current course angle of AUV, u represents the forward movement speed of AUV, v represents the lateral movement speed of AUV, r represents the course angular speed of AUV, and the horizontal plane movement equation of AUV is
WhereinThe kinetic equation for AUV is shown below:
wherein m is11,m22,m33As inertial model parameters of the AUV, d11,d22,d33Is a resistance model parameter of the AUV, τ1,τ2Is a control input of AUV, w1,w2,w3Is an external disturbance;
the initial point position of the AUV under the inertial coordinate system is X0＝[x0y0]TAt an initial heading angle psi0Start tracking desired trajectory Xd＝[xdyd]TThe track of the target point satisfies
Wherein U denotes the velocity of movement, psi, of the tracked target pointwRepresenting a direction of motion of the tracked target point;
step 2: determining a control input as
In the formula k3,k4Are control parameters and are all positive real numbers;
in the formulaThe estimated speed of the AUV is obtained by estimation of a high-gain observer; the high-gain observer is
Wherein, pi1∈R3And pi2∈R3Is the state vector of the high gain observer, epsilon is a positive constant, lambda is more than 0, and then the velocity estimation value of AUV under the body coordinate system is
In the formulaAndeach represents rvirAnd uvirDerivative of rvirAnd uvirThe expected input, representing the kinematic model, is calculated by:
uvir＝k2(ρ-δ)+U cosχ
wherein k is1,k2Delta is a set tracking error value, rho is the distance between the current position of the AUV and the position of a target point, α is the angle between the longitudinal axis of the AUV and the vector from the AUV to the target point, and chi is the angle between the velocity vector of the target point and the vector from the AUV to the target point;
in the formula z1And z2For the velocity error, it is calculated by:
in the formulaAndthe relationship between the actually obtained value and the true value is obtained as the values of the inertial model parameter and the resistance model parameter of the AUV which can be obtained in the actual situation according to the following formula:
wherein (·) represents true value, (·)*Represents the actual value obtained, and delta (·) represents the uncertain value of the parameter;
in the formulaAnd (Z) is the output of hidden layer nodes of the RBF neural network, and Z is the input of the RBF neural network:
the RBF neural network has the weight value updating law of
Wherein, gamma is1,Γ2And σ1,σ2Updating parameters for the weight of the neural network, wherein the parameters are positive and real;
wherein, CiCoordinate vector representing the central point of the gaussian base function of the ith neuron of the hidden layer in the RBF neural network, biRepresenting the width of the i-th neuron gaussian basis function of the hidden layer.
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| CN112363538A (en) * | 2020-11-09 | 2021-02-12 | 哈尔滨工程大学 | AUV (autonomous underwater vehicle) area tracking control method under incomplete speed information |
| CN112859889A (en) * | 2021-01-18 | 2021-05-28 | 湖南大学 | Autonomous underwater robot control method and system based on self-adaptive dynamic planning |
| CN112965370A (en) * | 2021-01-29 | 2021-06-15 | 西北工业大学 | Underwater vehicle track tracking control method based on improved firefly algorithm optimization |
| CN112947505A (en) * | 2021-03-22 | 2021-06-11 | 哈尔滨工程大学 | Multi-AUV formation distributed control method based on reinforcement learning algorithm and unknown disturbance observer |
| CN113190030A (en) * | 2021-04-13 | 2021-07-30 | 杭州电子科技大学 | Underwater robot and attitude control method thereof |
| CN113359448A (en) * | 2021-06-03 | 2021-09-07 | 清华大学 | Autonomous underwater vehicle track tracking control method aiming at time-varying dynamics |
| CN114323552A (en) * | 2021-11-18 | 2022-04-12 | 厦门大学 | Method for judging stability of water entering and exiting from cross-medium navigation body |
| CN114460945A (en) * | 2022-02-14 | 2022-05-10 | 四川大学 | Mobile robot trajectory tracking method and device and electronic equipment |
| CN115167484A (en) * | 2022-05-13 | 2022-10-11 | 西北工业大学 | Autonomous underwater vehicle model prediction path tracking method based on neural network |
| CN115167484B (en) * | 2022-05-13 | 2024-04-19 | 西北工业大学 | Autonomous underwater vehicle model prediction path tracking method based on neural network |
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