Design method of unmanned watercraft controller based on switching dynamic event triggering strategy

Abstract

The present invention discloses a design method for an unmanned boat controller based on a switching dynamic event triggering strategy. By designing a switching dynamic event triggering strategy, introducing dynamic parameters and switching strategies, and dynamically adjusting and switching the event triggering strategy, the network bandwidth occupied by the unmanned boat control system is effectively reduced, and the life of the actuator is extended. At the same time, the Hadamard product operation is introduced, and the switching dynamic event triggering strategy is applied to each actuator respectively, avoiding the shortcomings of the centralized event triggering strategy, and further reducing the update frequency of the actuator. Finally, the actuator deviation caused by the switching dynamic event triggering strategy is compensated by utilizing adaptive control technology and sliding mode algorithm. The advantage is that the unmanned boat controller obtained by the design method of the present invention can significantly reduce the actuator action frequency during the control process, thereby greatly reducing the waste of communication resources and improving the life of the actuator, and both the communication resource saving and the actuator life extension are relatively sufficient.

Description

Technical Field

[0001] The present invention relates to an unmanned boat controller design method, in particular to an unmanned boat controller design method based on a switching dynamic event triggering strategy. Background Art

[0002] With the rapid development of the marine economy, unmanned boats (UAVs), as a type of automated and intelligent vessel, have broad application prospects in various fields. As the core module of an UAV, the UAV control system controls its operation. The UAV control system primarily consists of a controller, actuators, and sensors. The sensors detect the UAV's status in real time and transmit it to the controller. Based on this sensor-generated status data, the controller generates corresponding control instructions to control the actuators to implement specific operations.

[0003] Traditional UAV controller design methods are mostly based on periodic update strategies, which result in frequent actuator activation, leading to high communication resource usage and reduced actuator lifespan. Currently, event-triggered strategies have been widely researched and are gradually being applied to UAV controller design. These UAV controller design methods primarily intelligently determine the controller update timing based on the UAV's state, thereby reducing communication resource usage and extending actuator lifespan while ensuring control performance.

[0004] The event trigger parameters involved in the existing unmanned boat controller design method based on event triggering strategy are unchanged, and the event triggering strategy is centralized. The unchanged event triggering parameters will lead to a greater conservatism in the event triggering strategy, and the centralized event triggering strategy will lead to passive updates of some actuators in the multi-actuator system. For example, the unmanned boat cluster distributed safety control method disclosed in the Chinese patent application with application number CN202210338174.3 and the unmanned boat model-free control method based on dynamic event triggering disclosed in the Chinese patent application with application number CN202210410554.3. Therefore, the existing unmanned boat controller design method based on event triggering strategy is not sufficient in terms of saving communication resources and extending the life of actuators through event triggering strategies, and there is a large room for improvement. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a design method for an unmanned boat controller based on a switching dynamic event triggering strategy, which can fully save communication resources and extend the life of actuators.

[0006] The technical solution adopted by the present invention to solve the above technical problems is: a design method for an unmanned boat controller based on a switching dynamic event triggering strategy, comprising the following steps:

[0007] Step (1): According to the Newton-Euler method, the three-degree-of-freedom motion equation of the unmanned boat is established as shown in the following equation (1):

[0008]

[0009] Where M = diag{m 11 ,m 22 ,m 33} is the inertia parameter matrix, m 11 is the inertia parameter in the forward direction, m 22 is the inertia parameter in the roll direction, m 33 is the inertia parameter in the direction of rotation, diag{m 11 ,m 22 ,m 33} is based on m 11 ,m 22 ,m 33 is a diagonal matrix with diagonal elements, D=diag{d 11 ,d 22 ,d 33} is the hydrodynamic damping parameter matrix, d 11 is the hydrodynamic damping parameter in the forward direction, d 22 is the hydrodynamic damping parameter in the roll direction, d 33 is the hydrodynamic damping parameter in the rotation direction, diag{d 11 ,d 22 ,d 33} is based on d 11 ,d 22 ,d 33 is a diagonal matrix with diagonal elements, η = [χ y ψ] T is the position vector of the unmanned boat in the ground coordinate system, χ is the coordinate of the unmanned boat's forward direction, y is the coordinate of the unmanned boat's roll direction, and ψ is the coordinate of the unmanned boat's rotation direction. The derivative of η is represented by τ = [τ1 0 τ3] T , f(t) represents the external disturbance force, t represents the current control moment, θ=[μv r] T is the velocity component of the unmanned boat in its own coordinate system, μ is the forward speed of the unmanned boat, v is the rolling speed of the unmanned boat, r is the angular velocity of the unmanned boat, represents the derivative of θ, τ=[τ1 0 τ3] T is the control vector, τ1 is the forward force, τ3 is the roll force, C(θ) is the Coriolis force and centripetal force matrix, J(ψ) is the coordinate system transformation rotation matrix, and the superscript T indicates the transpose of the matrix. C(θ) and J(ψ) are expressed as follows using Equations (2) and (3), respectively:

[0010]

[0011] Step 2: Expand the three-degree-of-freedom motion equation of the unmanned boat to obtain equation (4):

[0012]

[0013] in, represents the derivative of μ, represents the derivative of v, represents the derivative of r, represents the derivative of χ, represents the derivative of y, represents the derivative of ψ;

[0014] Step 3: Define the coordinate mapping as shown in formula (5):

[0015]

[0016] Among them, s1, s2, and s3 are three intermediate variables. Arranging equations (4) and (5) yields equation (6):

[0017]

[0018] in, is the derivative of s1, is the derivative of s2, is the derivative of s3;

[0019] Step 4: Define the intermediate vector x(t) = [x1 x2 x3 x4 x5 x6] T =[μv r s1 s2 s3] T , the control parameter output by the controller is recorded as u(t), u(t)=[u1 u2] T =[τ1τ3] T , by substituting and sorting out formula (6), we can get the state space expression model of the unmanned boat at the current control time t as shown in formula (7):

[0020]

[0021] Where C is the output matrix, is the derivative of x(t), ξ(t) is the output of the state space expression model, and the coefficients A0 and B0 are expressed using Equation (8):

[0022]

[0023] Step 5: For navigation safety, the rolling velocity and angular velocity of the unmanned boat have upper limits. The upper limit of the rolling velocity of the unmanned boat is expressed as The upper bound of angular velocity is expressed as Set the intermediate parameters x2(t) and x3(t), when When , the following fuzzy rules are designed:

[0024]

[0025] Rule 1: When When , the state space expression model of the unmanned boat shown in formula (7) is simplified to

[0026]

[0027] Rule 2: When When , the state space expression model of the unmanned boat shown in formula (7) is simplified to:

[0028]

[0029] Rule 3: When When , the state space expression model of the unmanned boat shown in formula (7) is simplified to:

[0030]

[0031] Rule 4: When When , the state space expression model of the unmanned boat shown in formula (7) is simplified to:

[0032]

[0033] Among them, M1(x2(t)), M2(x2(t)), N1(x3(t)), N2(x3(t)) are the four intermediate variables of the fuzzy rule, and the coefficients A1 and B1, A2 and B2, A3 and B3, A4 and B4 are expressed using equations (14) to (17):

[0034]

[0035] Step 6: Calculate the membership function of the fuzzy rule:

[0036]

[0037] Where h1(z(t)) is the membership function of rule 1, h2(z(t)) is the membership function of rule 2, h3(z(t)) is the membership function of rule 3, h4(z(t)) is the membership function of rule 4, z1(t) = x2(t), z2(t) = x3(t);

[0038] Step 7: Based on the fuzzy rules and its membership function, the model of the unmanned boat is obtained as follows:

[0039]

[0040] in, B=B1=B2=B3=B4, ε(t) is the fuzzy approximation error, expressed as ε(t)=MF(t)Nx(t), where M and N are known constraint matrices, determined according to fuzzy rules, and F(t) is an unknown time-varying parameter that satisfies the condition F T (t)F(t)≤1;

[0041] Step 8. Solve the controller parameters and set the parameter G to satisfy GB>0; set λ1=[0 0] T ,λ1=[0 1] T ,λ1=

[10] T and λ1=[1 1] T Substituting the four cases into the inequality of formula (20) respectively, we get the symmetric matrix inequalities corresponding to the four cases:

[0042]

[0043] Among them, 11 =AX+BY+(AX+BY) T +0.1X,Ψ 14 =[I2-B(GB) -1 G],Ψ 29 =QBλ1⊙(GB) -1 G,Ψ 22 =QA-HC+(QA-HC) T +0.1Q, ⊙ represents Hadamard product operation, Y,H, is the parameter to be solved, Q, X are the symmetric positive definite parameters to be solved, Q>0, X>0, * indicates the omitted symmetric elements in the symmetric matrix, I2 is the 2D identity matrix;

[0044] Step 9: Solve the inequality system obtained in step 8 to obtain the parameters Y,H, And symmetrical positive parameters Q, X, according to the parameters solved above, calculate the controller feedback gain parameter K = YX -1 and the observer feedback gain parameter L = Q -1 H;

[0045] Step 10: Based on the observer feedback gain parameters solved in step 9, design the observer as shown in equation (21):

[0046]

[0047] in, is the estimated value of x(t), yes The derivative of uc (t) is the compensator to be designed, is an estimate of ξ(t), is the estimation error;

[0048] Step 11: Design the sliding surface as shown in formula (22):

[0049]

[0050] Step 12: Use the inequality f(t)≤ε1+ε2||y(t)|| to describe the upper bound of the external disturbance force f(t), and design the adaptive law as shown in Equation (23):

[0051]

[0052] Among them, ε1 and ε2 are both set intermediate parameters. is an estimate of ε1, is an estimate of ε2, yes The derivative of yes The derivative of , || || represents the two-norm calculation, and proj() represents the projection operation;

[0053] Step 13: Design the compensator as shown in equation (24):

[0054]

[0055] in, δ =0.9, max{} is the maximum value function, tanh v () is the hyperbolic tangent function of the vector, η1 is a given constant between [0.1, 10], used to adjust the controller smoothness, 1 m represents a 6-dimensional column vector whose elements are all 1, a 2l (t) is expressed as:

[0056]

[0057] Among them, t k Indicates the time when the actuator is updated for the kth time, u l (t k ) indicates that at t k The control parameter u(t k ), l∈{1,2}, || represents the absolute value symbol, a 2l (t) Yes 's points;

[0058] Step 14: Based on the solved controller feedback gain parameters and observer feedback gain parameters, the designed sliding surface and the designed compensator, design a controller as shown in the following equation (26):

[0059]

[0060] Step 15: Design the switching dynamic event triggering strategy of the controller-actuator channel as shown in formula (27):

[0061]

[0062] Where inf{} represents the minimum function.

[0063] Compared with the existing technology, the advantages of the present invention are that by designing the switching dynamic event triggering strategy of the controller-actuator channel, introducing dynamic parameters and switching strategies, and dynamically adjusting and switching event triggering strategies, the network bandwidth occupation of the unmanned boat control system is effectively reduced, and the life of the actuator is extended. At the same time, the present invention introduces Hadamard product operation, and applies the switching dynamic event triggering strategy of the controller-actuator channel to each actuator respectively, avoiding the shortcomings of the centralized event triggering strategy, and further reducing the update frequency of the actuator. Finally, for the actuator deviation caused by the switching dynamic event triggering strategy, the adaptive law and sliding surface are introduced, and the adaptive control technology and sliding mode algorithm are used to compensate for the actuator deviation. Therefore, the unmanned boat controller obtained by the design method of the present invention can greatly reduce the actuator action frequency during the control process, thereby greatly reducing the waste of communication resources and improving the life of the actuator. The communication resource saving and the extension of the actuator life are relatively sufficient. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 Schematic diagram of the flow of the unmanned boat controller design method based on the switching dynamic event triggering strategy of the present invention;

[0065] Figure 2 This is a simulation diagram of the event triggering moment under the traditional event triggering strategy adopted in the existing unmanned boat controller design method based on event triggering strategy;

[0066] Figure 3 This is a simulation diagram of event triggering moments under the dynamic event triggering strategy adopted in the unmanned boat controller design method based on switching dynamic event triggering strategy of the present invention. DETAILED DESCRIPTION

[0067] The present invention will be described in further detail below with reference to the accompanying drawings and embodiments.

[0068] Example: Figure 1As shown, a design method for an unmanned boat controller based on a switching dynamic event triggering strategy includes the following steps:

[0069] Step (1): According to the Newton-Euler method, the three-degree-of-freedom motion equation of the unmanned boat is established as shown in the following equation (1):

[0070]

[0071] Where M = diag{m 11 ,m 22 ,m 33} is the inertia parameter matrix, m 11 is the inertia parameter in the forward direction, m 22 is the inertia parameter in the roll direction, m 33 is the inertia parameter in the direction of rotation, diag{m 11 ,m 22 ,m 33} is based on m 11 ,m 22 ,m 33 is a diagonal matrix with diagonal elements, D=diag{d 11 ,d 22 ,d 33} is the hydrodynamic damping parameter matrix, d 11 is the hydrodynamic damping parameter in the forward direction, d 22 is the hydrodynamic damping parameter in the roll direction, d 33 is the hydrodynamic damping parameter in the rotation direction, diag{d 11 ,d 22 ,d 33} is based on d 11 ,d 22 ,d 33 is a diagonal matrix with diagonal elements, η=[χyψ] T is the position vector of the unmanned boat in the ground coordinate system, χ is the coordinate of the unmanned boat's forward direction, y is the coordinate of the unmanned boat's roll direction, and ψ is the coordinate of the unmanned boat's rotation direction. The derivative of η is represented by τ = [τ10τ3] T , f(t) represents the external disturbance force, t represents the current control moment, θ=[μ vr] T is the velocity component of the unmanned boat in its own coordinate system, μ is the forward speed of the unmanned boat, v is the rolling speed of the unmanned boat, r is the angular velocity of the unmanned boat, represents the derivative of θ, τ=[τ1 0 τ3] Tis the control vector, τ1 is the forward force, τ3 is the roll force, C(θ) is the Coriolis force and centripetal force matrix, J(ψ) is the coordinate system transformation rotation matrix, and the superscript T indicates the transpose of the matrix. C(θ) and J(ψ) are expressed as follows using Equations (2) and (3), respectively:

[0072]

[0073] Step 2: Expand the three-degree-of-freedom motion equation of the unmanned boat to obtain equation (4):

[0074]

[0075] in, represents the derivative of μ, represents the derivative of v, represents the derivative of r, represents the derivative of χ, represents the derivative of y, represents the derivative of ψ;

[0076] Step 3: Define the coordinate mapping as shown in formula (5):

[0077]

[0078] Among them, s1, s2, and s3 are three intermediate variables. Arranging equations (4) and (5) yields equation (6):

[0079]

[0080] in, is the derivative of s1, is the derivative of s2, is the derivative of s3;

[0081] Step 4: Define the intermediate vector x(t) = [x1 x2 x3 x4 x5 x6] T =[μv r s1 s2 s3] T , the control parameter output by the controller is recorded as u(t), u(t)=[u1 u2] T =[τ1τ3] T , by substituting and sorting out formula (6), we can get the state space expression model of the unmanned boat at the current control time t as shown in formula (7):

[0082]

[0083] Where C is the output matrix, is the derivative of x(t), ξ(t) is the output of the state space expression model, and the coefficients A0 and B0 are expressed using Equation (8):

[0084]

[0085] Step 5: For navigation safety, the rolling velocity and angular velocity of the unmanned boat have upper limits. The upper limit of the rolling velocity of the unmanned boat is expressed as The upper bound of angular velocity is expressed as Set the intermediate parameters x2(t) and x3(t), when When , the following fuzzy rules are designed:

[0086]

[0087] Rule 1: When When , the state space expression model of the unmanned boat shown in formula (7) is simplified to

[0088]

[0089] Rule 2: When When , the state space expression model of the unmanned boat shown in formula (7) is simplified to:

[0090]

[0091] Rule 3: When When , the state space expression model of the unmanned boat shown in formula (7) is simplified to:

[0092]

[0093] Rule 4: When When , the state space expression model of the unmanned boat shown in formula (7) is simplified to:

[0094]

[0095] Among them, M1(x2(t)), M2(x2(t)), N1(x3(t)), N2(x3(t)) are the four intermediate variables of the fuzzy rule, and the coefficients A1 and B1, A2 and B2, A3 and B3, A4 and B4 are expressed using equations (14) to (17):

[0096]

[0097]

[0098] Step 6: Calculate the membership function of the fuzzy rule:

[0099]

[0100] Where h1(z(t)) is the membership function of rule 1, h2(z(t)) is the membership function of rule 2, h3(z(t)) is the membership function of rule 3, h4(z(t)) is the membership function of rule 4, z1(t) = x2(t), z2(t) = x3(t);

[0101] Step 7: Based on the fuzzy rules and its membership function, the model of the unmanned boat is obtained as follows:

[0102]

[0103] in, B=B1=B2=B3=B4, ε(t) is the fuzzy approximation error, expressed as ε(t)=MF(t)Nx(t), where M and N are known constraint matrices, determined according to fuzzy rules, and F(t) is an unknown time-varying parameter that satisfies the condition F T (t)F(t)≤1;

[0104] Step 8. Solve the controller parameters and set the parameter G to satisfy GB>0; set λ1=[0 0] T ,λ1=[0 1] T ,λ1=

[10] T and λ1=[1 1] T Substituting the four cases into the inequality of formula (20) respectively, we get the symmetric matrix inequalities corresponding to the four cases:

[0105]

[0106] Among them, 11 =AX+BY+(AX+BY) T +0.1X,Ψ 14 =[I2-B(GB) -1 G],Ψ 29 =QBλ1⊙(GB) -1 G,Ψ 22 =QA-HC+(QA-HC) T +0.1Q, ⊙ represents Hadamard product operation, Y,H, is the parameter to be solved, Q, X are the symmetric positive definite parameters to be solved, Q>0, X>0, * indicates the omitted symmetric elements in the symmetric matrix, I2 is the 2D identity matrix;

[0107] Step 9: Solve the inequality system obtained in step 8 to obtain the parameters Y,H, And symmetrical positive parameters Q, X, according to the parameters solved above, calculate the controller feedback gain parameter K = YX -1 and the observer feedback gain parameter L = Q-1 H;

[0108] Step 10: Based on the observer feedback gain parameters solved in step 9, design the observer as shown in equation (21):

[0109]

[0110] in, is the estimated value of x(t), yes The derivative of u c (t) is the compensator to be designed, is an estimate of ξ(t), is the estimation error;

[0111] Step 11: Design the sliding surface as shown in formula (22):

[0112]

[0113] Step 12: Use the inequality f(t)≤ε1+ε2||y(t)|| to describe the upper bound of the external disturbance force f(t), and design the adaptive law as shown in Equation (23):

[0114]

[0115] Among them, ε1 and ε2 are both set intermediate parameters. is an estimate of ε1, is an estimate of ε2, yes The derivative of yes The derivative of , || || represents the two-norm calculation, and proj() represents the projection operation;

[0116] Step 13: Design the compensator as shown in equation (24):

[0117]

[0118] in, δ =0.9, max{} is the maximum value function, tanh v () is the hyperbolic tangent function of the vector, η1 is a given constant between [0.1, 10], used to adjust the controller smoothness, 1 m represents a 6-dimensional column vector whose elements are all 1, a 2l (t) is expressed as:

[0119]

[0120] Among them, t k Indicates the time when the actuator is updated for the kth time, u l (t k ) indicates that at t k The control parameter u(t k ), l∈{1,2}, | | represents the absolute value symbol, a 2l (t) Yes 's points;

[0121] Step 14: Based on the solved controller feedback gain parameters and observer feedback gain parameters, the designed sliding surface and the designed compensator, design a controller as shown in the following equation (26):

[0122]

[0123] Step 15: Design the switching dynamic event triggering strategy of the controller-actuator channel as shown in formula (27):

[0124]

[0125] Where inf{} represents the minimum function.

[0126] In order to verify the effect of the present invention, the unmanned boat controller design method based on the switching dynamic event trigger strategy of the present invention is compared with the existing unmanned boat controller design method based on the event trigger strategy. The simulation diagram of the event triggering moment under the traditional event triggering strategy adopted in the existing unmanned boat controller design method based on the event trigger strategy is shown in FIG. Figure 2 As shown; the event triggering moment simulation diagram under the dynamic event triggering strategy adopted in the unmanned boat controller design method based on the switching dynamic event triggering strategy of the present invention is as shown Figure 3 Analysis Figure 2 It can be seen that the traditional event triggering strategy can make some communication intervals greater than the sampling interval of 0.001 seconds, reducing the communication frequency. However, most communication intervals are still less than 0.01 seconds, and the effect is limited. Figure 3 It can be seen that based on the switching dynamic event triggering strategy proposed by the present invention, the communication interval is longer than the communication interval under the traditional event triggering strategy, and some communication intervals are greater than 0.1 seconds, greatly reducing the communication frequency. Therefore, compared with the existing technology, the unmanned watercraft controller design method based on the switching dynamic event triggering strategy of the present invention can significantly reduce the actuator operation frequency during the control process, greatly reducing the waste of communication resources and improving the actuator lifespan. Both communication resource savings and actuator life extension are relatively sufficient.

Claims

1. A design method for an unmanned boat controller based on a switching dynamic event triggering strategy, characterized in that The following steps are involved: Step (1): According to the Newton-Euler method, the three-degree-of-freedom motion equation of the unmanned boat is established as shown in the following equation (1): Where M = diag{m 11 ,m 22 ,m 33 } is the inertia parameter matrix, m 11 is the inertia parameter in the forward direction, m 22 is the inertia parameter in the roll direction, m 33 is the inertia parameter in the direction of rotation, diag{m 11 ,m 22 ,m 33 } is based on m 11 ,m 22 ,m 33 is a diagonal matrix with diagonal elements, D=diag{d 11 ,d 22 ,d 33 } is the hydrodynamic damping parameter matrix, d 11 is the hydrodynamic damping parameter in the forward direction, d 22 is the hydrodynamic damping parameter in the roll direction, d 33 is the hydrodynamic damping parameter in the rotation direction, diag{d 11 ,d 22 ,d 33 } is based on d 11 ,d 22 ,d 33 is a diagonal matrix with diagonal elements, η = [χ y ψ] T is the position vector of the unmanned boat in the ground coordinate system, χ is the coordinate of the unmanned boat's forward direction, y is the coordinate of the unmanned boat's roll direction, and ψ is the coordinate of the unmanned boat's rotation direction. The derivative of η is represented by τ = [τ1 0 τ3] T , f(t) represents the external disturbance force, t represents the current control moment, θ=[μ vr] T is the velocity component of the unmanned boat in its own coordinate system, μ is the forward speed of the unmanned boat, v is the rolling speed of the unmanned boat, r is the angular velocity of the unmanned boat, represents the derivative of θ, τ=[τ1 0 τ3] T is the control vector, τ1 is the forward force, τ3 is the roll force, C(θ) is the Coriolis force and centripetal force matrix, J(ψ) is the coordinate system transformation rotation matrix, and the superscript T indicates the transpose of the matrix. C(θ) and J(ψ) are expressed as follows using Equations (2) and (3), respectively: Step 2: Expand the three-degree-of-freedom motion equation of the unmanned boat to obtain equation (4): in, represents the derivative of μ, represents the derivative of v, represents the derivative of r, represents the derivative of χ, represents the derivative of y, represents the derivative of ψ; Step 3: Define the coordinate mapping as shown in formula (5): Among them, s1, s2, and s3 are three intermediate variables. Arranging equations (4) and (5) yields equation (6): in, is the derivative of s1, is the derivative of s2, is the derivative of s3; Step 4: Define the intermediate vector x(t) = [x1 x2 x3 x4 x5 x6] T =[μ vr s1 s2 s3] T , the control parameter output by the controller is recorded as u(t), u(t)=[u1 u2] T =[τ1 τ3] T , by substituting and sorting out formula (6), we can get the state space expression model of the unmanned boat at the current control time t as shown in formula (7): Where C is the output matrix, is the derivative of x(t), ξ(t) is the output of the state space expression model, and the coefficients A0 and B0 are expressed using Equation (8): Step 5: For navigation safety, the rolling velocity and angular velocity of the unmanned boat have upper limits. The upper limit of the rolling velocity of the unmanned boat is expressed as The upper bound of angular velocity is expressed as Set the intermediate parameters x2(t) and x3(t), when When , the following fuzzy rules are designed: Rule 1: When When , the state space expression model of the unmanned boat shown in formula (7) is simplified to Rule 2: When When , the state space expression model of the unmanned boat shown in formula (7) is simplified to: Rule 3: When When , the state space expression model of the unmanned boat shown in formula (7) is simplified to: Rule 4: When When , the state space expression model of the unmanned boat shown in formula (7) is simplified to: Among them, M1(x2(t)), M2(x2(t)), N1(x3(t)), N2(x3(t)) are the four intermediate variables of the fuzzy rule, and the coefficients A1 and B1, A2 and B2, A3 and B3, A4 and B4 are expressed using equations (14) to (17): Step 6: Calculate the membership function of the fuzzy rule: Where h1(z(t)) is the membership function of rule 1, h2(z(t)) is the membership function of rule 2, h3(z(t)) is the membership function of rule 3, h4(z(t)) is the membership function of rule 4, z1(t) = x2(t), z2(t) = x3(t); Step 7: Based on the fuzzy rules and its membership function, the model of the unmanned boat is obtained as follows: in, B=B1=B2=B3=B4, ε(t) is the fuzzy approximation error, expressed as ε(t)=MF(t)Nx(t), where M and N are known constraint matrices, determined according to fuzzy rules, and F(t) is an unknown time-varying parameter that satisfies the condition F T (t)F(t)≤1; Step 8. Solve the controller parameters and set the parameter G to satisfy GB>0; set λ1=[0 0] T ,λ1=[0 1] T ,λ1=[1 0] T and λ1=[1 1] T Substituting the four cases into the inequality of formula (20) respectively, we get the symmetric matrix inequalities corresponding to the four cases: Among them, 11 =AX+BY+(AX+BY) T +0.1X,Ψ 14 =[I2-B(GB) -1 G],Ψ 29 =QBλ1⊙(GB) -1 G,Ψ 22 =QA-HC+(QA-HC) T +0.1Q, ⊙ represents Hadamard product operation, Y,H, is the parameter to be solved, Q, X are the symmetric positive definite parameters to be solved, Q>0, X>0, * indicates the omitted symmetric elements in the symmetric matrix, I2 is the 2D identity matrix; Step 9: Solve the inequality system obtained in step 8 to obtain the parameters Y,H, And symmetrical positive parameters Q, X, according to the parameters solved above, calculate the controller feedback gain parameter K = YX -1 and the observer feedback gain parameter L = Q -1 H; Step 10: Based on the observer feedback gain parameters solved in step 9, design the observer as shown in equation (21): in, is the estimated value of x(t), yes The derivative of u c (t) is the compensator to be designed, is an estimate of ξ(t), is the estimation error; Step 11: Design the sliding surface as shown in formula (22): Step 12: Use the inequality f(t)≤ε1+ε2||y(t)|| to describe the upper bound of the external disturbance force f(t), and design the adaptive law as shown in Equation (23): Among them, ε1 and ε2 are both set intermediate parameters. is an estimate of ε1, is an estimate of ε2, yes The derivative of yes The derivative of , || || represents the two-norm calculation, and proj() represents the projection operation; Step 13: Design the compensator as shown in equation (24): in, is the maximum value function, tanh v () is the hyperbolic tangent function of the vector, η1 is a given constant between [0.1, 10], used to adjust the controller smoothness, 1 m represents a 6-dimensional column vector whose elements are all 1, a 2l (t) is expressed as: Among them, t k Indicates the time when the actuator is updated for the kth time, u l (t k ) indicates that at t k The control parameter u(t k ), l∈{1,2}, | | represents the absolute value symbol, a 2l (t) Yes 's points; Step 14: Based on the solved controller feedback gain parameters and observer feedback gain parameters, the designed sliding surface and the designed compensator, design a controller as shown in the following equation (26): Step 15: Design the switching dynamic event triggering strategy of the controller-actuator channel as shown in formula (27): Where inf{} represents the minimum function.

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