A control method of a small AUV roll control unit
Patent Information
- Application Number
- CN202311493121.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2023-11-10
- Publication Date
- 2026-10-27
- Estimated Expiration
- 2043-11-10
AI Technical Summary
[0005]本发明提供了一种小型AUV横滚控制单元的控制方法，其目的在于解决因小型AUV内部空间限制，造成舵机旋转角度受限，因此只能提供有限的控制力矩，导致影响控制系统稳定性与控制性能，甚至使得整个控制系统不稳定的问题
（1）利用RBF神经网络对横滚控制模型中不确定项进行逼近，削弱滑模控制造成的抖振。
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Figure CN117724515B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of autonomous underwater vehicle control, and more specifically to a control method for a small AUV roll control unit. Background Technology
[0002] As an unmanned, cable-free underwater vehicle, the AUV has an independent power source and control system, enabling it to autonomously perform navigation missions in complex environments. It has unique advantages and broad application prospects in many aspects.
[0003] Currently, the main control methods used by AUVs include PID control, sliding mode variable structure control, and fuzzy control. PID control is a linear control method that is simple and easy to implement, and is currently the most widely used and mature control method in the industrial control field. However, AUVs have characteristics such as strong coupling, nonlinearity, and time-varying nature, as well as complex and variable ocean current environments, which means that the parameters of PID control need to be constantly adjusted according to changes in the AUV's operating environment. Fuzzy control does not rely on a precise mathematical model of the controlled object and has good adaptability and robustness to strongly nonlinear and uncertain systems with changing model parameters. However, establishing fuzzy control rules and the fuzzification and defuzzification processes can sometimes be difficult, requiring operators to have certain control experience. Sliding mode variable structure control has the characteristics of fast response, easy implementation of control algorithms, and insensitivity to changes in parameters and disturbances. However, due to factors such as saturation limitations of control quantities and measurement errors, it is prone to chattering.
[0004] Small AUVs are characterized by their small size, light weight, small size, and strong concealment. However, the limited space structure of small AUVs restricts the rotation of the servo motors, which can only provide limited control torque. Therefore, the control methods mentioned above sometimes cannot achieve good control results. Summary of the Invention
[0005] This invention provides a control method for a small AUV roll control unit, which aims to solve the problem that the limited internal space of a small AUV restricts the rotation angle of the servo motor, thus limiting the control torque and affecting the stability and performance of the control system, and even making the entire control system unstable.
[0006] Furthermore, the purpose of this invention is to solve the problem that underwater vehicles are easily affected by various unknown external interferences due to the complex underwater environment, which affects their control performance.
[0007] Furthermore, the purpose of this invention is to address the problem that, due to the nonlinear, strongly coupled, and time-varying characteristics of the autonomous underwater vehicle's motion system, its control model cannot be accurately obtained. When using a traditional sliding mode controller, the large modeling uncertainty requires a large switching gain, which causes chattering.
[0008] To achieve the above objectives, the technical solution of the present invention is as follows: This invention provides a control method for a small AUV roll control unit, wherein the control method is applied to the control unit; characterized in that: The control unit includes: The adaptive observer module takes the roll angle output by the sliding mode controller as its input. The output of the model estimation module based on the RBF network and Actual roll angle and roll rate and The module outputs estimated values for the actual roll angle and roll rate. and ; The model estimation module is based on the RBF network. The input to this module is the roll angle output by the sliding mode controller. The state estimate output by the adaptive observer and Actual roll angle and roll rate and The module outputs: the uncertainty terms in the roll control model. and The approach; A saturation compensation module based on an RBF network is introduced, with the roll angle output from the sliding mode controller as the input. And the sliding mode function s, the output of the model estimation module based on the RBF network. The module outputs an estimate of the difference between the control input and the threshold before and after the threshold is set. ; The sliding mode controller module takes the desired roll angle as its input from an external source. The output of the model estimation module based on the RBF network and The state estimate output by the adaptive observer and The output of the saturation compensation module based on the RBF network Actual roll angle and roll rate and The module outputs: roll angle. and sliding mode function s; The control method includes the following steps: Step 1: Establish an AUV roll attitude control model;
[0009] in, Here, is the roll angle of the AUV, and p is the roll velocity. Let be the moment of inertia along the x-axis in the carrier coordinate system. Let ρ be the added mass along the x-axis in the carrier coordinate system, ρ be the density of the fluid surrounding the AUV, V be the velocity of the AUV, S be the maximum cross-sectional area of the AUV along the x-axis in the carrier coordinate system, and L be the length of the AUV. Let d(t) be the roll rudder angle, and d(t) be a bounded, unknown external disturbance. This is the AUV roll moment coefficient. This refers to the roll control torque coefficient of the AUV; Rewrite the model as
[0010]
[0011] in, ; and These are the uncertain parameters of the model; Step 2: Design sliding mode control based on RBF adaptive observer; (1) Design the observer as
[0012]
[0013] in, for The estimated value; The gain of the observer; for The estimate; For robust terms;
[0014] (2) Using RBF neural networks to To make an estimate, that is
[0015]
[0016] in, and To estimate the weights; is the Gaussian function, and m is the number of hidden layer nodes in the RBF neural network; Design the adaptive law of the neural network as follows:
[0017]
[0018] in, , ; This represents the roll angle estimation error; (3) Design of sliding mode controller
[0019] Define the roll angle error and its derivative as follows: and ,in The desired roll angle is typically zero degrees. Design the sliding mode control law as follows:
[0020]
[0021] Wherein, the sliding mode function is , For exponential convergence, The roll angle is obtained based on the sliding mode control law, thereby controlling the roll angle of the AUV. Step 3: Add saturation compensation based on RBF network to the sliding mode control law; When input is limited, the maximum control input limit is taken as... , To control the difference before and after input restrictions; The designed compensated sliding mode control law is as follows:
[0022]
[0023] in, To The estimate; Using RBF networks To make an estimate, that is , To estimate the weights; Design the adaptive law of the neural network as follows: .
[0024] in, s is the sliding mode function. This is the roll angle gain.
[0025] The beneficial effects achieved by this invention are as follows: (1) Use RBF neural network to approximate the uncertainty in the roll control model to reduce chattering caused by sliding mode control.
[0026] (2) The state (roll angle and roll velocity) is observed by an adaptive observer, and the observed value is used to replace the actual output value to obtain the sliding mode control law, which is used to overcome unknown bounded disturbances from the outside world.
[0027] (3) Use the RBF network to approximate the difference before and after the control input is restricted, and compensate the approximate value into the sliding mode control law to avoid the control performance from deterioration or even instability due to the restriction of the control input. Attached Figure Description
[0028] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.
[0029] Figure 1 This is a structural diagram of the small AUV roll control unit of the present invention.
[0030] Figure 2 These are simulation results of the roll angle of sliding mode control based on the RBF adaptive observer under the condition of no amplitude limit.
[0031] Figure 3 The results are simulations of the roll angle of sliding mode control based on an RBF adaptive observer without saturation compensation, under a limit of 1 rad.
[0032] Figure 4 The results are simulations of the roll angle of sliding mode control based on an RBF adaptive observer with saturation compensation, under the condition of a limit of 1 (rad).
[0033] Figure 5 The results are simulations of the roll angle of sliding mode control based on an RBF adaptive observer without saturation compensation, under a limit of 0.4 rad.
[0034] Figure 6 The results are simulations of the roll angle of sliding mode control based on an RBF adaptive observer with saturation compensation, under a limit of 0.4 rad.
[0035] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation
[0036] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.
[0037] It should be noted that if the embodiments of the present invention involve directional indicators (such as up, down, left, right, front, back, etc.), the directional indicators are only used to explain the relative positional relationship and movement of the components in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indicators will also change accordingly.
[0038] Furthermore, if the embodiments of this invention involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, if the word "and / or" appears throughout the text, it means including three parallel solutions; for example, "A and / or B" includes solution A, solution B, or a solution that simultaneously satisfies A and B. Furthermore, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by this invention.
[0039] This invention provides a control method for a small AUV roll control unit, wherein the control method is applied to the control unit; like Figure 1 As shown, the control unit includes: The adaptive observer module takes the roll angle output by the sliding mode controller as its input. The output of the model estimation module based on the RBF network and Actual roll angle and roll rate and The module outputs estimated values for the actual roll angle and roll rate. and For specific control methods, see step (1) of section 2 in Part 2.
[0040] The model estimation module is based on the RBF network. The input to this module is the roll angle output by the sliding mode controller. The state estimate output by the adaptive observer and Actual roll angle and roll rate and The module outputs: the uncertainty terms in the roll control model. and Approximation; for specific control methods, see step (2) of Part 2.
[0041] A saturation compensation module based on an RBF network is introduced, with the roll angle output from the sliding mode controller as the input. And the sliding mode function s, the output of the model estimation module based on the RBF network. The module outputs an estimate of the difference between the control input and the threshold before and after the threshold is set. For specific control methods, please refer to step three in Part 2.
[0042] The sliding mode controller module takes the desired roll angle as its input from an external source. The output of the model estimation module based on the RBF network and The state estimate output by the adaptive observer and The output of the saturation compensation module based on the RBF network Actual roll angle and roll rate and The module outputs: roll angle. And the sliding mode function s. For specific control methods, see step (3) of step two and step three in Part 2.
[0043] The control method includes the following steps: Step 1: Establish an AUV roll attitude control model;
[0044] in, Here, is the roll angle of the AUV, and p is the roll velocity. Let be the moment of inertia along the x-axis in the carrier coordinate system. Let ρ be the added mass along the x-axis in the carrier coordinate system, ρ be the density of the fluid surrounding the AUV, V be the velocity of the AUV, S be the maximum cross-sectional area of the AUV along the x-axis in the carrier coordinate system, and L be the length of the AUV. Let d(t) be the roll rudder angle, and d(t) be a bounded, unknown external disturbance. This is the AUV roll moment coefficient. This is the roll control torque coefficient for the AUV.
[0045] Rewrite the model as
[0046]
[0047] in, ; and These are the uncertain parameters of the model.
[0048] Step 2: Design a sliding mode controller based on an RBF adaptive observer; (1) Design the observer as
[0049]
[0050] in, for The estimated value; The gain of the observer; for The estimate; It is a robust term.
[0051] (2) Using RBF neural networks to Make an estimate
[0052] Select network input as The network output is
[0053]
[0054] in, and To estimate the weights. denoted as Gaussian function, and m is the number of hidden layer nodes in the RBF neural network.
[0055] (3) Design the adaptive law of the neural network as follows
[0056]
[0057] in, ; ; This represents the roll angle estimation error.
[0058] (4) Design of sliding mode controller
[0059] Define the roll angle error and its derivative as follows: and ,in The desired roll angle is zero degrees.
[0060] Design the sliding mode function as follows: Where c must satisfy the Hurwitz condition, i.e., c>0.
[0061] Choose Lyapunov functions as Differentiating both sides yields
[0062]
[0063] Selecting the exponential convergence rate ,in, .
[0064] In order to make The sliding mode control law is designed as follows:
[0065]
[0066] but The exponentially convergent form is obtained as follows: .when At that time, thus ,Right now And the exponent converges.
[0067] Step 3: Add saturation compensation based on RBF network to the sliding mode control law; When input is limited, the maximum control input limit is taken as... , To control the difference before and after input restrictions.
[0068] Using RBF neural networks Approximation
[0069] Select network input as The network output is .in, To The estimate, To estimate the weights, under the condition of limited input:
[0070]
[0071] The designed compensated sliding mode control law is as follows:
[0072]
[0073] but ,in , For ideal weights, This is for weight estimation.
[0074] Define Lyapunov functions ,in, .
[0075] but Where s is the sliding mode function and b is the roll angle gain.
[0076] (3) Design the adaptive law of the neural network as follows .
[0077] but The exponentially convergent form is obtained as follows: .when At that time, thus ,Right now And the exponent converges.
[0078] This embodiment was performed in the MATLAB environment. The roll angle was set to zero degrees, and an unknown external disturbance d(t) = 0.5 + 0.1*sin(t) was introduced. Sliding mode control based on an RBF adaptive observer was used for simulation, and saturation compensation was then added to the control. The simulation results... Figure 2 It can be seen that sliding mode control based on RBF adaptive observer can effectively overcome external disturbances, reduce sliding mode chattering, and achieve tracking of the desired roll angle. Comparison of simulation results... Figure 3 and Figure 4 It can be seen that, under a limit of 1 (rad), the performance of sliding mode control using only an RBF-based adaptive observer is significantly lower than the control performance after adding saturation compensation. From Figure 5 It can be seen that, under the limiting condition of 0.4 (rad), sliding mode control based solely on the RBF adaptive observer cannot converge; therefore... Figure 6 It can be seen that after adding saturation compensation, the system output gradually converges to the desired roll angle.
[0079] The above description is merely an optional embodiment of the present invention and does not limit the patent scope of the present invention. All equivalent structural transformations made using the contents of the present invention's specification and drawings under the inventive concept of the present invention, or direct / indirect applications in other related technical fields, are included within the patent protection scope of the present invention.
Claims
1. A control method for a small AUV roll control unit, characterized in that: The control unit includes: The adaptive observer takes the roll angle output by the sliding mode controller as its input. The output of the model estimation module based on the RBF network and Actual roll angle and roll rate and The module outputs estimated values for the actual roll angle and roll rate. and ; The model estimation module is based on the RBF network. The input to this module is the roll angle output by the sliding mode controller. The state estimate output by the adaptive observer and Actual roll angle and roll rate and The module outputs: the uncertainty terms in the roll control model. and The approach; A saturation compensation module based on an RBF network is introduced, with the roll angle output from the sliding mode controller as the input. And the sliding mode function s, the output of the model estimation module based on the RBF network. The module outputs an estimate of the difference between the control input and the threshold before and after the threshold is set. ; The sliding mode controller module takes the desired roll angle as its input from an external source. The output of the model estimation module based on the RBF network and The state estimate output by the adaptive observer and The output of the saturation compensation module based on the RBF network Actual roll angle and roll rate and The module outputs: roll angle. and sliding mode function s; The control method includes the following steps: Step 1: Establish an AUV roll attitude control model; in, Here, is the roll angle of the AUV, and p is the roll velocity. Let be the moment of inertia along the x-axis in the carrier coordinate system. Let ρ be the added mass along the x-axis in the carrier coordinate system, ρ be the density of the fluid surrounding the AUV, V be the velocity of the AUV, S be the maximum cross-sectional area of the AUV along the x-axis in the carrier coordinate system, and L be the length of the AUV. Let be the roll rudder angle, and d(t) be a bounded, unknown external disturbance. This is the AUV roll moment coefficient. This refers to the roll control torque coefficient of the AUV; Rewrite the model as in, ; and These are the uncertain parameters of the model; Step 2: Design sliding mode control based on RBF adaptive observer; (1) Design the observer as in, for The estimated value; The gain of the observer; for The estimate; For robust terms; (2) Using RBF neural networks to To make an estimate, that is in, and To estimate the weights; is the Gaussian function, and m is the number of hidden layer nodes in the RBF neural network; Design the adaptive law of the neural network as follows: in, , ; This represents the roll angle estimation error; (3) Design of sliding mode controller Define the roll angle error and its derivative as follows: and ,in The desired roll angle is zero degrees. Design the sliding mode control law as follows: Wherein, the sliding mode function is , For exponential convergence, The roll angle is obtained based on the sliding mode control law, thereby controlling the roll angle of the AUV. Step 3: Add saturation compensation based on RBF network to the sliding mode control law; When input is limited, the maximum control input limit is taken as... , To control the difference before and after input restrictions; The designed compensated sliding mode control law is as follows: in, To The estimate; Using RBF networks To make an estimate, that is , To estimate the weights; Design the adaptive law of the neural network as follows: ; in, s is the sliding mode function. This is the roll angle gain.