Unmanned ship dynamic positioning control method based on fuzzy adaptive event triggering mechanism

Abstract

The invention provides an unmanned ship dynamic positioning control method based on a fuzzy adaptive event triggering mechanism, and the method is based on a fuzzy subsystem differentiation event triggering mechanism: independently designing event triggering conditions for each fuzzy subsystem of a T-S fuzzy model, the trigger threshold is dynamically adjusted according to the state change before and after the system and a fuzzy membership function Es (theta (t)); by setting a bounded aperiodic sampling interval and based on aperiodic sampling input, all fuzzy subsystems share the same set of sampling data, a trigger moment sequence is a subset of aperiodic sampling moments, and an event trigger mechanism is independently designed for subsystem characteristics; a Lyapunov-Krasovskii function containing a time-varying delay characteristic is constructed, the stability condition of a closed-loop system is ensured, and a controller gain matrix Ks and an event trigger weight matrix psi s are jointly solved through matrix contract transformation, so that unmanned ship dynamic positioning control is achieved.

Description

Technical Field

[0001] The present invention belongs to the technical field of dynamic positioning control of unmanned vessels, and particularly relates to a dynamic positioning control method for unmanned vessels based on a fuzzy adaptive event-triggering mechanism. Background Art

[0002] With the growth of the demand for ocean development and utilization, unmanned vessels are widely used in many key fields such as geomorphological exploration, water quality monitoring, and data collection, effectively supporting numerous scientific research works. The dynamic positioning system of unmanned vessels is one of the key technologies to achieve precise operation in the marine environment. It should be noted that surface unmanned vessels face many challenges during the actual dynamic positioning process: the complex and changeable hydrodynamic environment and the influence of external disturbances lead to a highly nonlinear dynamic model of unmanned vessels. At the same time, the uncertainty of the unmanned vessel's own model further causes difficulties in system modeling. The emergence of the T-S fuzzy control theory provides a new idea for solving such problems. It decomposes the nonlinear system into a series of fuzzy rules and corresponding fuzzy subsystems, thereby equivalently modeling the complex nonlinear system as a weighted combination form of a group of linear systems. On the other hand, unmanned vessels are small in size and cannot carry a large amount of energy, and their offshore working environment cannot support frequent round trips for energy replenishment operations. In order to save system resources and improve control effects, sampled-data control is widely used due to its low cost, high reliability, and easy implementation. However, during the process of updating the control output through sampled data, a large number of redundant signals will inevitably be transmitted. At the same time, high-frequency communication will greatly waste the communication and computing resources of the vessel, increase the loss of components, and shorten the working time of the unmanned vessel. Therefore, an event-triggering control mechanism is introduced. Compared with the traditional sampling control mode, event-triggering control only activates communication when specific conditions are met, thereby reducing unnecessary signal transmission and update.

[0003] When designing an event-triggering mechanism for the dynamic positioning model of an unmanned vessel based on the T-S fuzzy model, its triggering method is often designed as a static event-triggering mechanism with a fixed threshold, which cannot adjust the triggering threshold in real time according to the system state and thus cannot adjust the data transmission frequency. Inevitably, unnecessary information transmission will be caused. Therefore, some scholars further proposed to dynamically adjust the triggering threshold according to the change of the system state, reducing the update of control signals when the system is relatively stable and saving the communication resources of the system. However, the existing theory ignores the internal structural characteristics of the fuzzy system and the information contained in the fuzzy membership function. The same set of event-triggering mechanisms and triggering thresholds are used for all fuzzy subsystems, which will inevitably lead to the conservativeness of the control results and cause unnecessary waste of communication resources. Summary of the Invention

[0004] Aiming at the defects and deficiencies existing in the prior art, the present invention provides a dynamic positioning control method for unmanned vessels based on a fuzzy adaptive event-triggering mechanism. Its main innovative design points include: Differentiated event-triggering mechanism for fuzzy subsystems: Design the triggering conditions independently for each fuzzy subsystem in the T-S fuzzy model, and achieve resource conservation and optimized triggering frequency through a dynamic threshold function (adjusting the triggering threshold in real time based on the membership function and the rate of change of the state). Co-design of aperiodic sampling: Set a bounded aperiodic sampling interval. Based on the aperiodic sampling input, all subsystems share the sampling data, and the triggering time sequence is a subset of the sampling times, avoiding Zeno behavior. Construction of the Lyapunov-Krasovskii function: Reduce the conservatism of stability analysis through time-varying delay integral terms and asymmetric weighted integral terms, and obtain a set of nonlinear matrix inequalities by combining integral inequality scaling and the Schur complement lemma. Co-solution of the controller and the triggering weight: Obtain a set of linear matrix inequalities through matrix congruence transformation and jointly solve the controller gain matrix and the event-triggering weight matrix to ensure the stability of the closed-loop system and the H ∞ disturbance rejection performance. Design of the nonlinear membership function: Approximate the strongly nonlinear dynamic characteristics of the unmanned ship through the fuzzy membership function to improve the model adaptability.

[0005] Through the above-mentioned technology integration, this scheme significantly reduces the communication resource consumption and the conservatism of controller design, and realizes the high-precision dynamic positioning control of the unmanned ship in complex marine environments.

[0006] The present invention specifically adopts the following technical solutions: A dynamic positioning control method for an unmanned ship based on a fuzzy adaptive event-triggering mechanism: Based on the differentiated event-triggering mechanism for fuzzy subsystems: For each fuzzy subsystem of the T-S fuzzy model, design the event-triggering conditions independently, and the triggering threshold is dynamically adjusted according to the changes in the system's front and rear states and the fuzzy membership function E s (θ(t)) dynamically. Adopt co-design of aperiodic sampling and triggering: By setting a bounded aperiodic sampling interval, based on the aperiodic sampling input, all fuzzy subsystems share the same set of sampling data, and the triggering time sequence is a subset of the aperiodic sampling times, while the event-triggering mechanism is designed independently according to the subsystem characteristics; And perform co-solution of the stability condition and the controller: Construct a Lyapunov-Krasovskii function with time-varying delay characteristics to ensure the stability condition of the closed-loop system, and jointly solve the controller gain matrix K s and the event-triggering weight matrix Ψ s , to achieve the dynamic positioning control of the unmanned ship.

[0007] Furthermore, the trigger condition is satisfied as follows:

[0008] where represents the system state at the current sampling time corresponding to the s -th fuzzy rule related to the event trigger mechanism , and the difference between the system state at the current sampling time and the system state at the last time when the transmission system was triggered . The current sampling time is composed of the last trigger time and ( +1) non-triggered sampling intervals, and is defined as μ ; ; is the aperiodic sampling interval and satisfies ; is the positive definite weight matrix, is the event trigger threshold greater than 0 and satisfies the boundedness condition , λ m and λ M represent the predefined minimum and maximum event trigger thresholds respectively;

[0009] where and represent the new event trigger threshold and the previous event trigger threshold corresponding to the s -th fuzzy subsystem. The trigger threshold update function δ s is jointly calculated by the fuzzy membership function and the state change rate .

[0010] Furthermore, the specific form of the trigger threshold update function δs is as follows:

[0011] where and are defined as follows: where sign is the sign function, is the adjustment parameter, represents 's Euclidean norm, arctan is the arctangent function, exp represents the exponential function with the natural constant e as the base, is the fuzzy membership function of the s -th fuzzy subsystem.

[0012] Furthermore, the co - design of aperiodic sampling and triggering is specifically as follows: An aperiodic sampling interval with bounds is adopted , where τ m is the lower bound of the sampling interval, τ M is the upper bound of the sampling interval. All fuzzy subsystems share the same set of sampling data. The trigger - time sequence { t S j} is a subset of the aperiodic sampling - time sequence { Λ μ}.

[0013] Furthermore, the Lyapunov - Krasovskii function is specifically as follows:

[0014] where G(t) is the state - energy term, used to directly evaluate the energy of the current state x(t) , W is the weight matrix; for , the subscript l = 1, 2, 3, 4 ， where V 1 （t） is the time - varying delay integral term, constructed by the product of the time - interval coefficient and the extended state vector, used to enhance the flexibility of time - varying delay analysis; V 2 （t） is the historical - state energy integral term, integrating the state energy over the past time period to adjust the influence weight of the historical state on the current stability; V 3 （t） is the asymmetric weighted integral term, combining the weighted integral of the current time window and the negative integral of the future time window to reduce the conservatism of the stability condition; V 4 （t） is the double - time - delay rate integral term, covering the dynamic influence of the time - delay rate on the system derivative through double integration to enhance the robustness to disturbances and noises.

[0015] Furthermore, the controller gain matrix K s and the event - trigger weight matrix Ψ s are solved by matrix congruence transformation:

[0016]

[0017] where is a non-singular transformation matrix for converting non-linear matrix inequalities into linear matrix inequalities, and is a feasible solution of the linear matrix inequality.

[0018] Furthermore, the control method satisfies the H ∞ performance index:

[0019] where γ is a predefined disturbance rejection level for quantifying the upper limit of the influence of external disturbance w(t) on the system control output z(t) .

[0020] Furthermore, the fuzzy membership function E s (θ(t)) contains non-linear terms, thereby using a set of linear systems to approximate the strong non-linear characteristics of the unmanned ship dynamics model by weighting.

[0021] Furthermore, the triggering threshold increases adaptively with the system stability; the stability condition is achieved by constructing a Lyapunov-Krasovskii function and deriving a sufficient condition for its time derivative to be less than zero; the sufficient condition is converted into a linear matrix inequality through integral inequality scaling and Schur complement lemma for solution.

[0022] In addition, an electronic device includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the steps of the above method are implemented.

[0023] A non-transitory computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0024] Compared with the prior art, the present invention and its preferred solutions at least include the following beneficial effects: Significantly reduce communication resource consumption: Through the collaborative design of the fuzzy subsystem differential triggering mechanism and aperiodic sampling, the triggering threshold is dynamically adjusted and the minimum sampling interval is limited, effectively reducing the redundant signal transmission frequency and solving the resource waste problem caused by the traditional fixed threshold triggering or unified sampling mechanism; Improve control stability and robustness: Based on the construction of the Lyapunov-Krasovskii function with time-varying delay characteristics and the collaborative solution strategy of the controller, while ensuring the asymptotic stability of the closed-loop system, the H ∞ disturbance rejection performance is achieved, enhancing the anti-interference ability in complex marine environments; Breaking through the limitations of non - linear modeling: Approximating the strong non - linear dynamic characteristics of an unmanned ship through a fuzzy membership function with non - linear terms, and combining with the T - S fuzzy model decomposition to reduce the conservatism of controller design; Avoiding the risk of hardware overload: Based on the bounded aperiodic sampling constraint and the trigger threshold adaptive growth mechanism, fundamentally avoiding signal blocking or component loss problems caused by Zeno behavior and improving system reliability; Expanding the design freedom: Allowing different fuzzy subsystems to independently design event - triggering rules, providing a more flexible control parameter optimization space compared with traditional single - trigger mechanisms. Brief Description of the Drawings

[0025] The present invention will be further described in detail below in conjunction with the drawings and specific embodiments: Figure 1 This is the flow chart of the dynamic positioning control method for an unmanned ship based on a fuzzy adaptive event - triggering mechanism in an embodiment of the present invention.

[0026] Figure 2 This is a schematic diagram of the inertial coordinate system and the appended - body coordinate system of an unmanned ship in an embodiment of the present invention.

[0027] Figure 3 This is the structural diagram of the closed - loop system for the dynamic positioning control of an unmanned ship based on a fuzzy adaptive event - triggering mechanism in an embodiment of the present invention.

[0028] Figure 4 This is a schematic diagram of the aperiodic sampling time and the triggering time of the fuzzy subsystem in an embodiment of the present invention.

[0029] Figure 5 This is the state - space trajectory diagram of the position and yaw angle of an unmanned ship in an embodiment of the present invention.

[0030] Figure 6 This is the state - space trajectory diagram of the linear velocity and angular velocity of an unmanned ship in an embodiment of the present invention.

[0031] Figure 7 This is the control input trajectory diagram of the closed - loop system of an unmanned ship in an embodiment of the present invention.

[0032] Figure 8 This is the state trajectory diagram of the external disturbance of an unmanned ship in an embodiment of the present invention.

[0033] Figure 9 This is the triggering moment and triggering interval diagram of the fuzzy subsystem in an embodiment of the present invention.

[0034] Figure 10 This is the trajectory diagram of the fuzzy adaptive event - triggering threshold in an embodiment of the present invention. Specific Embodiments

[0035] To make the features and advantages of the present invention more obvious and understandable, specific embodiments are given below for detailed description as follows: It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used in this specification have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present application belongs.

[0036] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular forms are also intended to include the plural forms. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0037] Regarding how to comprehensively consider system state information on the basis of existing research and design a targeted event-triggering mechanism has important practical significance for the dynamic positioning control performance of unmanned ships.

[0038] Therefore, the purpose of the present invention is to provide a dynamic positioning control method for unmanned ships based on fuzzy adaptive event triggering to overcome the deficiencies of the prior art and provide a design with better information utilization rate for the dynamic positioning control of unmanned ships. Based on the T-S fuzzy theory, the dynamic and kinematic models of the unmanned ship are equivalently established, and the relevant system parameters and fuzzy membership functions are given. Further, an improved fuzzy correlation adaptive event-triggering mechanism is proposed, which comprehensively considers the fuzzy subsystem structure and fuzzy membership function, and designs a targeted event-triggering mechanism for each fuzzy subsystem, with higher design freedom compared to traditional control schemes. At the same time, a new Lyapunov-Krasovskii function is constructed to obtain the co-design of the event-triggering mechanism and the sampling controller, which can fully save system resources while ensuring the stability of the system.

[0039] Thus, as Figure 1 shown, the design process of a dynamic positioning control method for unmanned ships based on a fuzzy adaptive event-triggering mechanism proposed in an embodiment of the present invention includes the following steps: S001: Step S1: Establish the dynamic and kinematic models of the dynamic positioning system of the unmanned ship under unknown external disturbances, obtain a generalized system model, and perform T-S equivalent fuzzy modeling.

[0040] Step S1-1: Establish the three-degree-of-freedom full-drive dynamic and kinematic equations of the unmanned ship under unknown external disturbances, as shown in the following equations: (1) Wherein, M, N, Grepresent the additional inertia matrix of the ship and hydrodynamic forces, the hydrodynamic damping parameter matrix, and the Coriolis matrix, respectively, represents the position of the unmanned ship in the Earth inertial coordinate system ( x p (t) y p (t)) and the yaw angle θ(t) to form a vector, is the surge linear velocity of the unmanned ship in the body-fixed coordinate system v x (t), the sway linear velocity v y (t) and the yaw angular velocity r(t) to form a vector. A schematic diagram of the relationship between the inertial coordinate system and the body-fixed coordinate system of the unmanned ship is shown in Figure 2 as shown, where X B 、Y B 、Z B represent the body-fixed coordinate system, and X E , Y E , Z E represent the inertial coordinate system fixed on the Earth's surface. represents the longitudinal thrust generated by the ship's propeller u 1 、the lateral thrust u 2 and the steering moment u 3 to form a three-dimensional control input vector, represents the lateral disturbance force w 1 、the longitudinal disturbance force w 2 and the yaw disturbance moment w 3 to form the system external disturbance vector. T represents the transpose of the matrix.

[0041] Through coordinate transformation, there is the following relationship: (2) where J ( θ ) is the rotation matrix for coordinate transformation, θ is the yaw angle at the current moment, represents η(t) the first derivative of.

[0042] Further transformation of formula (2) gives: (3) where, , .

[0043] Define Further define the following generalized unmanned ship dynamic positioning system model: (4) Wherein, , .

[0044] Step S1-2: By means of the T-S fuzzy theory, the above system is equivalently T-S fuzzified, and the dynamic behavior of the system is described by a series of fuzzy rules: , then: (5) Wherein, A i 、 B i 、 C 2i are parameter matrices related to the i th fuzzy rule, , C is the system output matrix, y ( t ) is the system measurement output, z ( t ) is the system control output; is the antecedent variable, is the i th fuzzy set corresponding to the z th fuzzy rule.

[0045] The fuzzified equivalent unmanned ship dynamic positioning system can be described by the following formula: (6) Fuzzy membership function , fuzzy subset membership is defined as The fuzzy membership function should satisfy .

[0046] S002: Step S2: Based on the fuzzy membership function and the system state change, an adaptive event-triggering mechanism and an improved event-triggering threshold change function are introduced.

[0047] Step S2-1: Fuzzify the event-triggering mechanism and design a corresponding event-triggering mechanism for each fuzzy subsystem: If , then: (7) Wherein, Indicates the current sampling moment corresponding to the s th fuzzy rule related to the event triggering mechanism of the system state and the difference between the current sampling moment and the system state at the last trigger transmission is composed of the previous trigger moment and ( μ +1) untriggered sampling intervals, defined as . is the aperiodic sampling interval and satisfies . is a positive definite weight matrix, is the event triggering threshold greater than 0 and satisfies the boundedness condition , λ m and λ M represent the predefined minimum and maximum event triggering thresholds respectively.

[0048] Step S2-2: Construct a fuzzy adaptive event triggering change function based on the current system state and fuzzy membership function: (8) and represent the new event triggering threshold and the previous event triggering threshold corresponding to the s th fuzzy subsystem, is the trigger threshold update function designed by comprehensively considering the system state and fuzzy membership function: (9) and are defined as follows: (10) where sign is the sign function, , is the adjustment parameter, represents the Euclidean norm of, arctan is the arctangent function, exp represents the exponential function with the natural constant e as the base, is the s th fuzzy membership function of the

[0049] For the s th fuzzy subsystem, its fuzzy related adaptive event triggering moment is: (11) In the formula, the j+1) trigger moment from the previous trigger moment and multiple untriggered sampling intervals until the trigger condition is met consist of denotes the set of positive integers.

[0050] S003: Step S3: Based on the sampling control principle, design a fuzzy adaptive event-triggered unmanned surface vehicle dynamic positioning control system under aperiodic sampling, saving system communication resources while avoiding Zeno behavior.

[0051] Step S3-1: First, based on the fuzzy adaptive event-triggering mechanism and the sampling control principle, determine the event-triggering time sequence. The system sampling mode is aperiodic sampling, and its sampling interval time satisfies the boundedness condition , where τ m is the lower bound of the sampling interval, τ M is the upper bound of the sampling interval. To save the resources of the unmanned surface vehicle system, all fuzzy subsystems share the same set of sampling data, and each fuzzy subsystem determines whether to trigger for state update and determines the next trigger threshold according to its own state. Therefore, the time sequence of triggering for each fuzzy subsystem is a subset of the aperiodic sampling time sequence, that is: (12) The μ th aperiodic sampling interval , the event trigger must occur at the aperiodic sampling moment. Therefore, any aperiodic sampling moment P is divided by , and , , the relationship between the aperiodic sampling time and the triggering time of the fuzzy subsystem is as shown in Figure 4 .

[0052] Step S3-2: The form of the fuzzy adaptive event-triggered controller under aperiodic sampling input is as follows: (13) In the formula K s represents the controller gain corresponding to the s-th fuzzy subsystem. Based on the interval sequence division result in Step S3-1, substitute into formula (13), and further obtain the closed-loop control system of the unmanned surface vehicle dynamic positioning based on the fuzzy adaptive event-triggering mechanism under sampling input as follows: (14) S004: Step S4: Construct a Lyapunov-Krasovskii function, derive the asymptotic stability conditions of the closed-loop system based on the Lyapunov stability theory, and construct inequality conditions.

[0053] First, to simplify the expression of the conclusion, the following symbols are defined:

[0054] , ,

[0055]

[0056]

[0057] In the above formula represents the symmetric block part of the symmetric matrix, I n is n the identity matrix of order represents a zero matrix of n rows ( l -1) columns, N 1 , N 2 and N 3 are matrices with appropriate dimensions, h 1 , h 2 are sampling interval division parameters, is the parameter obtained after sampling interval division, φ 1 ( t ) and φ 2 ( t ) are the results after sampling interval division, l 1 ( t ) and l 2 ( t ) are the sampling interval intervals.

[0058] Furthermore, by constructing a new Lyapunov-Krasovskii function, combining the free-weight matrix zero equality and the fuzzy adaptive event-triggering condition, the sufficient conditions for the asymptotic stability of the unmanned surface vehicle dynamic positioning system based on the fuzzy adaptive event-triggering mechanism are obtained through the Schur complement lemma as follows: For , , , given values that meet the conditions , , , and parameters . If there exist positive definite matrices W , O l , P l , Q l and , and matrices R l , S l , , N , K s and a non-singular matrix H such that the following matrix inequalities hold: (15) (16) The definitions of each symbol are as follows:

[0059]

[0060] The construction and proof process are as follows: First, construct a new Lyapunov-Krasovskii function: (17) The terms of the function in the formula are: (18) (19) (20) (21) (22) In the formula all satisfy the condition , thus relaxing the positive definiteness constraint condition of the function at the sampling points and increasing the design freedom. Further, by differentiating formula (17), the following expression can be obtained: (23) (24) (25) (26) (27) Furthermore, the following lemmas are given to complete the proof process of system stability: Lemma 1: Given matrices with appropriate dimensions M , a positive definite matrix N and a vector , if for , x (t) is continuous and differentiable, then the following inequality holds: (28) Lemma 2 (Schur complement): Given a matrix , and satisfying then the following two conditions are equivalent: 1.

[0061] 2.

[0062] Based on the above theory, the task of this method is to design a state feedback controller (13) based on the fuzzy adaptive event-triggering mechanism (7) such that the system satisfies: 1): When the external disturbance of the system is 0, the system is asymptotically stable.

[0063] 2): Under zero initial conditions and there exists a positive constant γ such that the following inequality holds: (29) Then the system is said to be asymptotically stable and satisfy the performance index with a disturbance rejection level of γ .

[0064] Using Lemma 1 to scale equations (25) and (26) gives: (30) (31) Furthermore, considering the fuzzy adaptive event-triggering constraint condition: (32) And introducing the following identities based on the free-weighting matrix method: (33) Combining the above equations, when the external disturbance is 0, we have: (34) where , and there is:

[0065] Combined with the constraint conditions of equations (32) and (33), when the external disturbance is 0, , the dynamic positioning control system of the unmanned ship based on the fuzzy adaptive event-triggering mechanism is asymptotically stable.

[0066] When the external disturbance is not 0, there is: (35) Integrating both sides of the above equation with respect to time t from 0 to ∞ and combining the zero initial conditions and the system stability conditions, we have: (36) That is, the H ∞ performance bound is γ .

[0067] Based on the integral inequality (35) and the Schur complement lemma, inequalities (15) and (16) are proven. And when the external disturbance of the system is 0, , , based on the Lyapunov stability theory, the system is asymptotically stable. When the external disturbance is not 0 and inequality (36) holds, it can be obtained that the system is asymptotically stable and satisfies the performance index with the disturbance rejection level of γ .

[0068] S005: Step S5: Through matrix congruence transformation, solve to obtain the control gain matrix K s and the event-triggering weight matrix , to achieve the dynamic positioning control of the unmanned ship based on the fuzzy adaptive event-triggering mechanism.

[0069] By performing matrix congruence transformation on the inequality constructed in step S4, calculate the control gain matrix K s and the weight matrix of the closed-loop dynamic positioning system of the unmanned ship based on the T-S fuzzy theory, as follows: For , , , given values , , , and parameters that meet the conditions. If there exist positive definite matrices , , , and and matrices , , , , , and a non - singular matrix such that the following matrix inequalities hold: (37) (38) Then the controller gain of the unmanned surface vehicle (USV) dynamic positioning control system , and the event - triggered weight matrix . Where:

[0070]

[0071] First, define , denotes H the inverse matrix of I is the identity matrix with appropriate dimensions, and diag represents the diagonal matrix. Left - multiply equations (15) and (16) by and right - multiply by to perform a congruence transformation to obtain equations (37) and (38). Further define: , , , , , , , , , , . The control gain matrix and the event - triggered weight matrix can be obtained by the inverse transformation of the above formula, and the above result is proved.

[0072] Figure 1 is the overall flowchart of the USV dynamic positioning control method based on the fuzzy - adaptive event - triggered mechanism in the embodiment of the present invention, Figure 3 and Figure 1 is the structure diagram of the closed - loop system of the USV dynamic positioning control based on the fuzzy - adaptive event - triggered mechanism. Through Figure 3 the method given, the design scheme of the USV dynamic positioning controller is obtained. The sampler performs aperiodic sampling on the system to obtain the discrete internal state of the system and determines whether to trigger. If triggered, the controller outputs a control signal and the zero - order hold converts the discrete signal into a continuous signal for output, realizing the closed - loop control of the system.

[0073] Furthermore, design the USV dynamic positioning control scheme through the above - mentioned method, and verify the effectiveness of the control scheme based on the fuzzy - adaptive event - triggered mechanism proposed by the present invention and its superiority in saving communication resources through simulation experiments.

[0074] First, the parameters of the unmanned ship dynamic positioning system model (3) are as follows:

[0075]

[0076] For the yaw angle When, the above system is equivalently T-S fuzzified through T-S fuzzy theory, and a T-S fuzzy unmanned ship dynamic positioning system can be obtained. The parameter matrices of system (5) are as follows: ,

[0077] Among them represents the third-order identity matrix, represents the third-order 0 matrix, , and further given:

[0078] Aperiodic sampling control input , and its fuzzy membership function is as follows:

[0079] Among them .

[0080] The parameter settings related to the adaptive event-triggering mechanism are as follows: the minimum value of the adaptive trigger threshold, the maximum value , the initial values of the four fuzzy subsystem event triggers are all set to 0.001, the sampling interval division parameter , the minimum sampling interval , the maximum sampling interval , the disturbance rejection level and the identity parameter .

[0081] The state space trajectory of the external disturbance received by the unmanned ship changing with time is as Figure 8 shown, and its mathematical expression is: (39) The system external disturbance vector w = w 1 w 2 w 3] T , and by solving equations (37) and (38), the state feedback control gain matrix of the unmanned ship dynamic positioning control system based on the T-S fuzzy model and the event-triggering weight matrix As follows:

[0082]

[0083]

[0084]

[0085]

[0086]

[0087]

[0088]

[0089] The initial state of the system is , and the system adjustment parameters are given . To eliminate the uncertainty influence brought by non-periodic sampling and compare with other triggering mechanisms, periodic sampling is adopted and the system sampling period is set to . The relevant experimental results are as Figure 5 — Figure 10 shown. Among them, Figure 5 is the state space trajectory diagram of the position and yaw angle of the unmanned ship, Figure 6 is the state space trajectory diagram of the surge linear velocity, sway linear velocity and yaw angular velocity of the unmanned ship, Figure 7 is the control input trajectory diagram of the unmanned ship closed-loop system, Figure 8 is the state space trajectory diagram of the external disturbance received by the unmanned ship, Figure 9 is the triggering time and triggering interval diagram corresponding to the four fuzzy subsystems, Figure 10 is the change trajectory diagram of the adaptive triggering threshold of each of the four fuzzy subsystems.

[0090] From Figure 5 and Figure 6 simulation results, it can be seen that the system can still maintain stability under the action of external disturbances, demonstrating the effectiveness of the proposed dynamic positioning control method for unmanned ships based on fuzzy adaptive event triggering mechanism. At the same time, this method has a certain robustness and has an H ∞ disturbance attenuation level with a disturbance rejection level of γ, which has practical application value in engineering applications.

[0091] In this simulation verification, the simulation time is set to 40s. In the traditional time trigger mechanism, all fuzzy subsystems are triggered at the same time, and each fuzzy subsystem is triggered 200 times. In the traditional event trigger mechanism, all fuzzy subsystems use the same trigger mechanism. In other words, all fuzzy subsystems have the same trigger frequency. In this method, the four fuzzy subsystems are triggered 50, 45, 42, and 78 times respectively, with an average trigger of 53.75 times, an average trigger time of 0.744 seconds, and an average data transmission rate of 26.875%. Compared with the traditional method, the number of data transmissions is greatly reduced, thereby reducing unnecessary information transmission, which is conducive to extending the working time of the unmanned ship, saving system communication resources and reducing control costs. At the same time, as the system tends to stabilize, the adaptive event trigger threshold continues to increase, the number of information transmissions continues to decrease, and the dynamic adjustment of the event triggering number and the communication frequency is realized, thereby effectively avoiding the transmission of redundant information and realizing the adaptive control of the unmanned ship's dynamic positioning.

[0092] In summary, it can be seen that the embodiments of the present invention demonstrate the following outstanding features of the present invention: 1. By constructing a new Lyapunov-Krasovskii function, the stability condition of the unmanned ship closed-loop system is derived, and a fuzzy adaptive event-triggered controller under non-periodic sampling input is designed to achieve the dynamic positioning of the unmanned ship. This method effectively solves the problem of difficult system modeling under the highly nonlinear dynamic model of the unmanned ship, greatly reduces the conservatism of the controller design process, and provides a more flexible design framework. On the other hand, this method ensures the H of the system while achieving the predetermined control effect. ∞ Performance indicators make the system robust and have practical application value and application scenarios.

[0093] 2. The fuzzy adaptive trigger mechanism proposed in the present invention is dynamically adjusted based on the current sampling state of the system, the trigger state of the previous moment and the internal information of the fuzzy membership function. It can trigger events and transmit signals in a targeted manner while ensuring the stability of the system, thereby more effectively saving the internal communication and computing resources of the unmanned ship. At the same time, the fuzzy adaptive event trigger mechanism of the present invention is based on non-periodic sampling input, which can effectively avoid the occurrence of Zeno behavior, that is, avoid the system out of control phenomenon caused by unlimited triggering within a limited time interval.

[0094] Based on the same inventive concept, the present invention further provides a computer device, which includes: one or more processors, and a memory for storing one or more computer programs; the program includes program instructions, and the processor is configured to execute the program instructions stored in the memory. The processor may be a Central Processing Unit (CPU), or may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, and is used to implement one or more instructions. Specifically, it is used to load and execute one or more instructions in the computer storage medium to implement the above method.

[0095] It should be further noted that, based on the same inventive concept, the present invention further provides a computer storage medium, on which a computer program is stored, and the computer program, when run by a processor, executes the above method. The storage medium may adopt any combination of one or more computer-readable media. The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium may, for example, but not be limited to, be an electrical, magnetic, optical, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (non-exhaustive list) of the computer-readable storage medium include: an electrical connection having one or more wires, a portable computer disk, a hard disk, a Random Access Memory (RAM), a Read-Only Memory (ROM), an Erasable Programmable Read-Only Memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, the computer-readable storage medium may be any tangible medium that contains or stores a program, and the program may be used by or combined with an instruction execution system, apparatus, or device.

[0096] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should have the ordinary meanings understood by those with ordinary skills in the field to which the present invention pertains. The "first", "second" and similar terms used in the present invention do not denote any order, quantity or importance, but are only used to distinguish different components. Words such as "comprising" or "including" mean that the elements or objects appearing before the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. Words such as "connected" or "coupled" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Upper", "lower", "left", "right", etc. are only used to represent relative positional relationships, and when the absolute position of the object being described changes, the relative positional relationship may also change accordingly.

[0097] As described above, it is only the preferred embodiment of the present invention, and it is not a limitation to the present invention in other forms. Any person skilled in the art may use the technical content disclosed above to make changes or modifications into equivalent embodiments with equivalent changes. However, any simple modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the technical solution content of the present invention still fall within the protection scope of the technical solution of the present invention.

[0098] The present invention is not limited to the above-mentioned best mode. Anyone can obtain other various forms of an unmanned ship dynamic positioning control method based on a fuzzy adaptive event-triggering mechanism under the inspiration of the present invention. All equal changes and modifications made according to the scope of the patent application of the present invention shall fall within the scope covered by the present invention.

Claims

1. A dynamic positioning control method for an unmanned ship based on a fuzzy adaptive event-triggering mechanism, characterized in that: Differentiated Event Triggering Mechanism Based on Fuzzy Subsystems: For each fuzzy subsystem of the T-S fuzzy model, an event triggering condition is designed independently, and the triggering threshold is dynamically adjusted according to the changes in the system's front and rear states and the fuzzy membership function E s (θ(t)) Dynamic adjustment; Adopt aperiodic sampling and trigger co-design: By setting a bounded aperiodic sampling interval, based on the aperiodic sampling input, all fuzzy subsystems share the same set of sampling data, and the trigger time sequence is a subset of the aperiodic sampling times; The event-triggering mechanism is independently designed according to the characteristics of the subsystems; And perform collaborative solution of stability conditions and the controller: construct a Lyapunov-Krasovskii function with time-varying delay characteristics to ensure the stability conditions of the closed-loop system, and jointly solve the controller gain matrix through matrix congruence transformation K s and the event-triggered weight matrix Ψ s to achieve the dynamic positioning control of the unmanned ship.

2. The dynamic positioning control method for an unmanned ship based on a fuzzy adaptive event-triggering mechanism according to claim 1, characterized in that: The trigger condition is satisfied: Among them, represents the current sampling time s corresponding to the th fuzzy rule related to the event triggering mechanism and the difference between the system state at the previous trigger transmission system state at the current sampling time, which is composed of the previous trigger time and ( μ +1) non-triggered sampling intervals, defined as ; is the aperiodic sampling interval and satisfies ; is the positive definite weight matrix, is the event triggering threshold greater than 0 and satisfies the boundedness condition , λ m and λ M represent the predefined minimum and maximum event triggering thresholds respectively; Among them, and represent the new event triggering threshold and the previous event triggering threshold corresponding to the s th fuzzy subsystem. The triggering threshold update function δ s is jointly calculated by the fuzzy membership function and the state change rate ​ 3. The dynamic positioning control method for an unmanned ship based on a fuzzy adaptive event-triggering mechanism according to claim 2, characterized in that: The trigger threshold update function δs has the following specific form: Among them, and are defined as follows: where sign is the sign function, is the adjustment parameter, denotes the Euclidean norm of, arctan is the arctangent function, and exp represents the exponential function with the natural constant e as the base, is the s fuzzy membership function of the 4. A dynamic positioning control method for an unmanned ship based on a fuzzy adaptive event-triggered mechanism according to claim 1, characterized in that: The non-periodic sampling and triggering co-design is specifically as follows: a bounded non-periodic sampling interval is adopted , where τ m is the lower bound of the sampling interval, τ M is the upper bound of the sampling interval, and all fuzzy subsystems share the same set of sampling data. The trigger time sequence { t S j} is a subset of the non-periodic sampling time sequence { Λ μ}.

5. The dynamic positioning control method for an unmanned ship based on a fuzzy adaptive event-triggering mechanism according to claim 1, characterized in that: The Lyapunov-Krasovskii function is specifically: Among them, G(t) is the state energy term, which is used to directly evaluate the energy of the current state x(t) . W is the weight matrix; for , the subscript l = 1, 2, 3, 4 ， Among them, V 1 （t） is the time-varying delay integral term, which is constructed by the product of the time interval coefficient and the extended state vector, and is used to enhance the flexibility of time-varying delay analysis; V 2 （t） is the historical state energy integral term, which integrates the state energy in the past time period to adjust the influence weight of the historical state on the current stability; V 3 （t） is the asymmetric weighted integral term, which combines the weighted integral of the current time window and the negative integral of the future time window to reduce the conservatism of the stability condition; V 4 （t） is the double time-delay rate integral term, which covers the dynamic influence of the time-delay rate on the system derivative through double integration to enhance the robustness to disturbances and noises.

6. The dynamic positioning control method for an unmanned ship based on a fuzzy adaptive event-triggering mechanism according to claim 1, characterized in that: The controller gain matrix K s and the event-triggering weight matrix Ψ s are solved by matrix congruence transformation: Among them, is a non-singular transformation matrix used to convert a non-linear matrix inequality into a linear matrix inequality, and are feasible solutions of the linear matrix inequality.

7. The dynamic positioning control method for an unmanned ship based on a fuzzy adaptive event-triggering mechanism according to claim 1, characterized in that: The control method meets H ∞ Performance indicators: Among them, γ is a predefined disturbance rejection level, which is used to quantify the upper limit of the influence of external disturbances w(t) on the system control output z(t) of the system.

8. A dynamic positioning control method for an unmanned ship based on a fuzzy adaptive event-triggering mechanism according to claim 1, characterized in that: The fuzzy membership function E s (θ(t)) contains a non - linear term, so as to approximate the strong non - linear characteristics of the unmanned ship dynamics model with a set of linear systems.

9. A dynamic positioning control method for an unmanned ship based on a fuzzy adaptive event-triggered mechanism according to claim 1, characterized in that: The trigger threshold increases adaptively with the system stability; The stability condition is realized by constructing a Lyapunov-Krasovskii function and deriving the sufficient condition that its time derivative is less than zero; The sufficient condition is converted into a linear matrix inequality through integral inequality scaling and the Schur complement lemma for solution.

10. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the method according to any one of claims 1-9.

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