Anti-spoofing attack self-adaptive sliding mode control method and system for unmanned ship and medium

Abstract

The invention relates to the technical field of unmanned ship safety control, and discloses an anti-spoofing-attack self-adaptive sliding mode control method and system for an unmanned ship and a medium, and the method comprises the steps: S1, constructing an unmanned ship T-S fuzzy model under the spoofing attack and external disturbance; s2, designing a dual-adaptive sliding mode observer based on the unmanned ship T-S fuzzy model constructed in S1; s3, designing a sliding mode controller based on S1 and S2; and S4, forming a closed-loop control system based on S1-S3 to perform sliding mode control: integrating the unmanned ship T-S fuzzy model constructed in S1, the dual-adaptive sliding mode observer designed in S2 and the sliding mode controller designed in S3 to form the closed-loop control system, and realizing sliding mode control. According to the method, the dynamic suppression capability of the composite threat can be remarkably improved, precise compensation of a spoofing attack signal and external disturbance of the ocean is realized, high-precision zero-flutter control is realized under the coexistence of the spoofing attack and the external disturbance, and an autonomous control solution with safety, robustness and real-time performance is provided for the unmanned ship.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of unmanned ship safety control, and particularly relates to an anti-deception attack adaptive sliding mode control method and system for an unmanned ship and a medium. BACKGROUND

[0002] Unmanned ships play a key role in ocean exploration, environmental monitoring and offshore engineering due to their environmental adaptability and high efficiency. However, their operation faces two serious challenges. First, physical layer disturbance: dynamic disturbances such as wind, waves and currents in the ocean environment cause the system to exhibit strong nonlinearity, making it difficult for traditional linear controllers to achieve stable trajectory tracking. Second, network layer attack: remote control relies on wireless communication networks, which are vulnerable to deception attacks. Attackers can tamper with control commands by injecting false signals, causing system performance degradation or even instability.

[0003] Traditional sliding mode control (SMC) is robust to disturbances, but requires accurate knowledge of the disturbance boundary. However, the upper bound of actual ocean disturbances is unknown and time-varying. The fixed switching gain of SMC can induce high-frequency chattering, exacerbating actuator wear. Existing network security control research assumes fixed attack patterns and does not consider real-time adjustment of attack strategies. Existing observers cannot simultaneously estimate attack signals and disturbance boundaries, resulting in insufficient control accuracy under combined threats. T-S fuzzy modeling can linearize nonlinear systems, but lacks a coordinated control framework for network attacks, making it difficult to handle the coupling effects of deception attacks and disturbances.

[0004] Existing methods cannot achieve high-precision stable control of unmanned ships in the presence of unknown deception attacks and time-varying disturbances. The root cause is that the dynamic coupling of attack signals and disturbances has not been decoupled, there is a lack of online joint estimation mechanism for attack weights and disturbance boundaries, and chattering suppression and stability guarantee are not unified in a strict mathematical framework (input-state stability theory). SUMMARY

[0005] The technical problem to be solved by the present application is to overcome the shortcomings of the prior art. The present application provides an anti-deception attack adaptive sliding mode control method, system and medium for an unmanned ship. By online joint estimation of deception attack signals ε∇(x(t)) and disturbance boundaries α, β, real-time compensation of coupled disturbances, dynamic decoupling of combined threats, design of adaptive switching law for dynamic adjustment of sliding mode gain, elimination of actuator wear caused by fixed gain, suppression of chattering, and proof of input-state stability (ISS) of the closed-loop control system based on Lyapunov theory and linear matrix inequality (LMI), strict stability is guaranteed.

[0006] To achieve the above purpose, the first aspect of the present application provides an anti-deception attack adaptive sliding mode control method for an unmanned ship, comprising the following steps: S1, constructing a T-S fuzzy model of an unmanned ship under deception attack and external disturbance: The unmanned vessel TS fuzzy model is coupled with a deception attack. and external disturbances ; S2. Based on the unmanned vessel TS fuzzy model constructed in S1, a dual adaptive sliding mode observer is designed: The dual adaptive sliding mode observer utilizes measurable output Reconstructed state estimate And estimate deception attacks online. and external disturbances The upper bound; At least includes designing a dual adaptive law, which is used to counter deception attacks. and external disturbances Joint estimation of the boundary, through dynamic adjustment of the adaptive rate using the dual adaptive law. and adaptive rate , wherein the adaptive rate The adaptive rate is used to estimate the upper bound of external disturbances. Used to estimate the upper bound of deception attacks; and, At least include design robustness items The robustness term Using the adaptive rate and the adaptive rate Through symbolic functions Production compensation signals are used to proactively counteract deception attacks. and external disturbances The impact; S3. Design a sliding mode controller based on S1 and S2: The unmanned vessel TS fuzzy model constructed based on S1 utilizes the state estimates provided by the dual adaptive sliding mode observer in S2. Adaptive rate and adaptive rate Design a sliding mode controller; S4. Sliding mode control is performed based on a closed-loop control system formed by S1-S3: The unmanned vessel TS fuzzy model constructed in S1, the dual adaptive sliding mode observer designed in S2, and the sliding mode controller designed in S3 are integrated to form a closed-loop control system, realizing the sliding mode control of the unmanned vessel TS fuzzy model described in S1.

[0007] Optionally, the unmanned vessel TS fuzzy model constructed in S1 is represented as: , , (1); wherein, represents the system full state of the unmanned ship model, constant i = 1, 2, 3, 4, represents the performance output, represents the actual output, t is a time variable, is a linear velocity or angular velocity or position vector matrix, is a control rate, is a deception attack weighting matrix, is a deception attack mathematical model, is an external disturbance, is a premise variable, is a fuzzy set, is a damping ratio inertia matrix, is a mooring ratio inertia matrix, is a measurement output constant matrix, is a control output constant matrix.

[0008] Optionally, the S2 includes designing a double adaptive sliding mode observer, which adopts a T-S fuzzy model and injects an output estimation error term to reconstruct the unmeasurable system state , the dynamic equation of the double adaptive sliding mode observer is represented as: , , (2) ; wherein, represents the system full state of the sliding mode observer, is an estimated value of , is an estimated value of , is an estimated value of , is an estimated value of , is an observer gain, is a robust term.

[0009] Optionally, the S2 includes at least designing a double adaptive law, which is represented as: (4) ; (5) ; wherein, represents a disturbance boundary estimation update law, represents an attack boundary estimation update law, is an anti-network attack adaptive rate constant, is an anti-disturbance adaptive rate constant, is the upper bound of cyber attack, denotes the transpose of observer error, denotes the transpose of measurement output constant matrix, denotes the actual output norm.

[0010] Optionally, S2 further comprises designing a robust term , the robust term uses the adaptive rate and the adaptive rate to produce a compensation signal through a sign function to actively offset the effects of spoofing attacks and external disturbances ; the robust term is expressed as: (3) ; wherein, are the estimated values of , respectively, is the anti-disturbance adaptive rate, is the anti-cyber attack adaptive rate, is the robust term constant, is the observer error weight matrix, is the observer error.

[0011] Optionally, S2 further comprises designing an error dynamic analysis, specifically: define ; In combination with formula (1) and formula (2), the error dynamic equation is expressed as: (6) ; (7) ; wherein, denotes the observer error update rate, is the observer error, is the observer error residual term.

[0012] Optionally, S3 designs a sliding mode controller, including designing a sliding mode surface, expressed as: (8) ; wherein, is the sliding mode surface function, is the sliding mode matrix, is the controller gain, is the controller integral variable; In combination with formula (2) and formula (8), the sliding mode surface function change rate is expressed as: (9) ; Definition of time-varying weighting matrix Let , the equivalent control rate is obtained is expressed as: (10) ; Substitute formula (10) into formula (2) to obtain the sliding mode dynamics equation, which is expressed as: (11).

[0013] Optionally, the sliding mode controller designed in S3 further comprises: Construct Lyapunov function, which is expressed as: (38) ; Wherein, Indicates the transpose of ; The control law for realizing the sliding mode region reachability construction under the deception attack is expressed as: (39) ; Wherein, The controller combination gain is , which is expressed as the inverse of the time-varying weighting matrix; Definition ; Wherein, The anti-disturbance adaptive rate is The anti-network attack adaptive rate is , which is a robust term constant, The reaching condition constant is Derive the Lyapunov function , to obtain: (40) ; Wherein, Indicates the derivative of the Lyapunov function, and indicates the change rate of the sliding mode surface function, which indicates that under the condition of deception attack, the closed-loop system is driven to the sliding surface by the sliding mode control rate formula (39) .

[0014] To achieve the above purpose, the second aspect of the present application provides an anti-deception attack adaptive sliding mode control system of unmanned ship, the control system comprises: A first design unit constructs a T-S fuzzy model of unmanned ship under deception attack and external disturbance: the T-S fuzzy model of unmanned ship is coupled with deception attack And external disturbance ; The second design unit designs a double adaptive sliding mode observer based on the constructed T-S fuzzy model of the unmanned ship, wherein the double adaptive sliding mode observer uses the measurable output to reconstruct the state estimation value and online estimate the upper bound of the spoofing attack and the external disturbance ; The second design unit at least includes designing a double adaptive law for completing the joint estimation of the spoofing attack and the external disturbance boundary, and dynamically adjusting the adaptive rate and the adaptive rate by the double adaptive law, wherein the adaptive rate is used to estimate the upper bound of the external disturbance, and the adaptive rate is used to estimate the upper bound of the spoofing attack; and The second design unit at least includes designing a robust term , wherein the robust term uses the adaptive rate and the adaptive rate to generate a compensation signal by a sign function for actively offsetting the influence of the spoofing attack and the external disturbance ; The third design unit designs a sliding mode controller based on the first design unit and the second design unit, wherein the sliding mode controller is designed based on the constructed T-S fuzzy model of the unmanned ship, the state estimation value , the adaptive rate and the adaptive rate provided by the double adaptive sliding mode observer in S2. The closed-loop control unit forms a closed-loop control system for sliding mode control based on the first design unit, the second design unit and the third design unit, wherein the closed-loop control system is formed by integrating the T-S fuzzy model of the unmanned ship constructed by the first design unit, the double adaptive sliding mode observer designed by the second design unit and the sliding mode controller designed by the third design unit, and the sliding mode control of the T-S fuzzy model of the unmanned ship is realized.

[0015] To achieve the above object, the third aspect of the present application provides a computer readable storage medium storing a computer program, wherein the computer program is executed by a processor to implement the anti-spoofing attack adaptive sliding mode control method of the unmanned ship as described above.

[0016] After adopting the above technical solution, the present application has the following beneficial effects compared with the prior art: In the present application, by designing two independent online adaptive laws, the amplitude upper bound of the deception attack signal and the energy upper bound of the unknown ocean external disturbance are estimated in real time, respectively, the system output error is continuously monitored, and the compensation gain parameter in the controller is dynamically adjusted according to the above, so as to accurately match the threat intensity currently faced, overcome the drawbacks caused by the large gain of the traditional sliding mode control in response to the worst case, and effectively realize high-precision control.

[0017] In the present application, in response to the composite threat (simultaneous existence of deception attack signal injection and unknown ocean external disturbance) faced by the unmanned ship in the complex ocean environment, the complex nonlinear unmanned ship system is converted into a weighted sum of a series of linear subsystems through the T-S fuzzy modeling framework, and the anti-deception attack adaptive sliding mode controller is designed, which has the approximation ability of fuzzy logic, the strong robustness of sliding mode control and the online learning ability of adaptive algorithm, so that the closed-loop control system can dynamically identify, separate and suppress different types of threats, and significantly improve the overall suppression ability and survivability of the system to the composite threat.

[0018] In the present application, high-precision trajectory tracking or point control can be achieved under the condition that both the deception attack and the disturbance are unknown and coexist, high-precision zero-vibration control is realized, unknown ocean disturbance and model uncertainty can be overcome, control performance can be maintained without degradation, and through the online updating ability of the adaptive algorithm, offline precalculation is not required, time-varying threat environment can be responded to, and the unmanned ship can be ensured to safely, reliably and efficiently operate autonomously in real and harsh ocean missions.

[0019] The specific embodiments of the present application will be described in further detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF DRAWINGS

[0020] The accompanying drawings, which are part of the present application, serve to provide a further understanding of the present application, and the illustrative embodiments of the present application and their descriptions serve to explain the present application but do not constitute an improper limitation on the present application. Obviously, the drawings described below are only some embodiments, and other drawings can be obtained from these drawings by those of ordinary skill in the art without creating creative labor.

[0021] In the drawings: Figure 1 Flowchart of the anti-deception attack adaptive sliding mode control method of the unmanned ship in the present specific embodiment; Figure 2 Logic diagram of the anti-deception attack adaptive sliding mode control method of the unmanned ship in the present specific embodiment; Figure 3Fig. 1 is a schematic diagram of a position response curve and a speed response curve of an unmanned ship without any control input in the embodiment of the present application, wherein a is a schematic diagram of a position response curve of an unmanned ship without any control input, and b is a schematic diagram of a speed response curve of an unmanned ship without any control input; Figure 4 Fig. 2 is a schematic diagram of a position response curve and a speed response curve of an unmanned ship after adopting an anti-deception attack adaptive sliding mode control method in the embodiment of the present application, wherein a is a schematic diagram of a position response curve of an unmanned ship after adopting an anti-deception attack adaptive sliding mode control method, and b is a schematic diagram of a speed response curve of an unmanned ship after adopting an anti-deception attack adaptive sliding mode control method; Figure 5 Fig. 3 is a schematic diagram of a position observer error curve and a speed observer error curve of a double adaptive sliding mode observer in the embodiment of the present application, wherein a is a schematic diagram of a position observer error curve of a double adaptive sliding mode observer, and b is a schematic diagram of a speed observer error curve of a double adaptive sliding mode observer; Figure 6 Fig. 4 is a schematic diagram of a time response curve of a sliding mode surface function and a control rate in the embodiment of the present application, wherein a is a schematic diagram of a time response curve of a sliding mode surface function , and b is a schematic diagram of a time response curve of a control rate . Figure 7 Fig. 5 is a schematic diagram of an anti-deception attack adaptive sliding mode control system of an unmanned ship in the embodiment of the present application. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments will be described clearly and completely below with reference to the drawings in the embodiments of the present application. The following embodiments are used to illustrate the present application, but not to limit the scope of the present application.

[0023] As described in the background, an unmanned ship faces a compound threat of physical layer time-varying disturbance and network layer deception attack in a marine environment. The prior art has the following problems: a traditional sliding mode control (SMC) is robust to disturbance but relies on the known upper bound of disturbance, and a fixed switching gain causes chattering; an existing observer cannot simultaneously decouple and jointly estimate the boundary of time-varying disturbance and dynamic attack; there is a lack of a unified control framework, making it difficult to guarantee stability while suppressing chattering and countering the compound threat.

[0024] In the face of this problem, those skilled in the art may try to improve the traditional SMC, for example, adopt a single adaptive law to estimate the upper bound of a "total disturbance" instead of a fixed switching gain, but cannot distinguish the strength of the disturbance and the attack, resulting in blind compensation of the controller and inability to accurately regulate different types of threats; or respectively design a disturbance observer and an attack detector, and then simply superimpose the outputs of the two to control compensation, but the two independently designed modules lack cooperation, the dynamic response may not match, or even conflict with each other.

[0025] Based on this, see Figure 1 and Figure 2 , the application provides an anti-deception attack adaptive sliding mode control method for an unmanned ship, comprising the following steps: S1, constructing an unmanned ship T-S fuzzy model under deception attack and external disturbance: The unmanned ship T-S fuzzy model is coupled with deception attack and external disturbance ; S2, designing a double adaptive sliding mode observer based on the unmanned ship T-S fuzzy model constructed in S1: The double adaptive sliding mode observer reconstructs the state estimation value using the measurable output , and estimates the upper bound of the deception attack and external disturbance online; at least comprising designing a double adaptive law, the double adaptive law is used to complete the joint estimation of the upper bound of the deception attack and external disturbance , and dynamically adjusts the adaptive rate and adaptive rate by the double adaptive law, wherein the adaptive rate is used to estimate the upper bound of the external disturbance, and the adaptive rate is used to estimate the upper bound of the deception attack; and, at least comprising designing a robust term , the robust term uses the adaptive rate and the adaptive rate to produce a compensation signal through a sign function , which is used to actively offset the influence of the deception attack and external disturbance ; S3, designing a sliding mode controller based on S1 and S2: Based on the unmanned ship T-S fuzzy model constructed in S1, the state estimation value , the adaptive rate and adaptive law , a sliding mode controller is designed; S4, a closed-loop control system is formed based on S1-S3 to perform sliding mode control: The T-S fuzzy model of the unmanned ship constructed in S1, the double adaptive sliding mode observer designed in S2 and the sliding mode controller designed in S3 are integrated to form a closed-loop control system, so as to realize sliding mode control on the T-S fuzzy model of the unmanned ship.

[0026] It can be understood that the application realizes accurate distinction and independent estimation of disturbances and attacks by designing a double adaptive law, solves the problem of blind compensation, realizes more delicate and smoother control, realizes deep integration of perception and control in a unified sliding mode theory framework, and guarantees global stability of the closed-loop system.

[0027] It should be noted that the execution subject of the anti-deception attack adaptive sliding mode control method of the unmanned ship in the embodiment is an anti-deception attack adaptive sliding mode control device of the unmanned ship, which can be an electronic device, a component in an electronic device, an integrated circuit, or a chip. The electronic device can be a mobile electronic device or a non-mobile electronic device. Illustratively, the mobile electronic device can be a mobile phone, a tablet computer, a notebook computer, a palm computer, a vehicle-mounted electronic device, a wearable device, etc., and the non-mobile electronic device can be a server and a personal computer, etc., which are not limited in the application. The anti-deception attack adaptive sliding mode control method of the unmanned ship in the embodiment is described below with the execution subject being a server as an example.

[0028] In addition, the terms "first" and "second" are only for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Therefore, the features defined as "first" and "second" can explicitly or implicitly include one or more of the features. In the description of the embodiments of the application, the meaning of "multiple" is two or more, unless otherwise specifically limited.

[0029] As a specific implementation, the T-S fuzzy model of the unmanned ship constructed in S1 is represented as: , , (1); wherein, represents the system state of the unmanned ship model, the constant i = 1, 2, 3, 4, represents the performance output, represents the actual output, t is a time variable, is a linear velocity or angular velocity or position vector matrix, is a control rate, for the spoofing attack weighting matrix, for the spoofing attack mathematical model, for the external disturbance, for the premise variable, for the fuzzy set, for the damping ratio inertia matrix, for the mooring ratio inertia matrix, for the measurement output constant matrix, for the control output constant matrix.

[0030] As a specific embodiment, the S2 is designed as a double adaptive sliding mode observer, including: the double adaptive sliding mode observer uses the measurable system output , adopts the T-S fuzzy model and injects the output estimation error term to reconstruct the unmeasurable system state , the dynamic equation of the double adaptive sliding mode observer is expressed as: , , (2) ; wherein, represents the output system full state of the unmanned ship T-S fuzzy model of the double adaptive sliding mode observer, is the estimated value of , is the estimated value of , is the estimated value of , is the estimated value of , is the observer gain, is a robust term.

[0031] It should be noted that the estimation of the system state by the double adaptive sliding mode observer is completed through formula (2), and the robust term is used to ensure that the estimation error can still converge in the face of attacks and external disturbances.

[0032] In the presence of deception attacks and complex ocean disturbances, the total upper bound of the system uncertainty is unknown and time-varying. The traditional SMC has to choose a very large and extremely conservative fixed switching gain to ensure robustness, which inevitably leads to severe control input chattering, harmful to actuators and difficult to apply in practice. The dual adaptive law of the present application aims to fundamentally solve this problem, and the beneficial effects brought about are: the system does not need to know the worst working condition in advance, the adaptive law can learn by itself, completely eliminating the dependence on fixed upper bound; the gain varies as needed, rather than always being the maximum value, the generated control signal is smoother, significantly suppressing chattering; even if the attack mode or disturbance intensity changes over time, the sliding mode controller can continuously maintain excellent performance through adaptive adjustment, enhancing the system resilience.

[0033] The uniqueness of dual adaptation lies in: dual estimation targets, it is not to estimate a single "total disturbance", but to estimate two different physical meanings of unknown boundaries in parallel and independently, namely: the gain upper bound of external disturbance relative to output And the weighted matrix norm upper bound of attack signal Dual design channels: although the structures of the two adaptive laws are similar, the driving signals and normalization factors are designed for their respective targets, namely: the external disturbance adaptive rate is coupled with the output energy The attack adaptive law is coupled with the system information known by the attacker .

[0034] As a specific embodiment, S2 at least includes designing a dual adaptive law, the dual adaptive law is represented as: (4); (5); Wherein, represents the disturbance boundary estimation update law, represents the attack boundary estimation update law, is the anti-network attack adaptive rate constant, is the anti-disturbance adaptive rate constant, is the upper limit of network attack, represents the transpose of the observer error, represents the transpose of the measured output constant matrix, represents the actual output norm.

[0035] As a specific embodiment, S2 at least further includes designing a robust term The robust term uses the adaptive rate And the adaptive rate , through the sign function Generate a compensation signal to actively counteract deception attacks and external disturbances . robust term , is expressed as: (3). wherein, are the estimated values of , is the anti-disturbance adaptive rate, is the anti-network attack adaptive rate, is the robust term constant, is the observer error weight matrix, is the observer error.

[0036] It can be understood that the robust term in formula (3) and the switching control term in the following formula (39) jointly act to realize real-time compensation for deception attacks and external disturbances, the robust term uses the adaptive rates and to produce a compensation signal through the sign function and inject it into the double adaptive sliding mode observer and the sliding mode controller to actively counteract the effects of deception attacks and external disturbances . The switching term in formula (39) further ensures that the system trajectory can approach and remain on the sliding surface, enhancing the robustness of the compensation.

[0037] It should be noted that the joint estimation of the deception attack and external disturbance boundary is completed through formula (4) and formula (5), which dynamically adjusts the adaptive rates and the adaptive rate online through information such as the double adaptive sliding mode observer error and the actual output , wherein, is directed against the anti-disturbance adaptive rate , is directed against the anti-network attack adaptive rate .

[0038] As a specific embodiment, S2 further includes at least a design error dynamic analysis, specifically: defined as: ; In combination with formula (1) and formula (2), the error dynamic equation is expressed as: (6). (7). wherein, denotes the dual adaptive sliding mode observer error update rate, is the dual adaptive sliding mode observer error, is the dual adaptive sliding mode observer error residual.

[0039] Notably, the dual adaptive sliding mode observer error quantifies the difference between the real state of the system and the estimated state of the system , the goal of the sliding mode controller is to drive the dual adaptive sliding mode observer error to zero, which in turn guarantees the accuracy of and ultimately the effectiveness of in the control law (39). The update rates of equations (4) and (5) are directly dependent on , it is clear that the dual adaptive sliding mode observer error is the source of the adaptation process. When the attack or disturbance increases, causing the dual adaptive sliding mode observer error to increase, the adaptation law accelerates the adjustment of the adaptive rate and the adaptive rate ; when the attack or disturbance decreases, the state estimation becomes more and more accurate, causing the dual adaptive sliding mode observer error to decrease, the adaptation law adjustment will also slow down, while avoiding excessive drift of the adaptive rate and the adaptive rate .

[0040] Ideally, the fuzzy model weights used by the dual adaptive sliding mode observer should be exactly the same as the weights of the real system, due to modeling errors and other reasons, this consistency is almost impossible. The dual adaptive sliding mode observer error residual can exactly capture and encapsulate this modeling uncertainty caused by fuzzy weight mismatch, the dual adaptive sliding mode observer error residual is composed of two terms, the weight error and the system dynamics , indicating that the dual adaptive sliding mode observer error residual is not an arbitrary disturbance, but a term related to the system state with clear physical meaning, assuming the dual adaptive sliding mode observer error residual , the complexity of stability analysis and proof can be simplified.

[0041] The adaptive law and compensation mechanism form a closed loop of perception, learning, compensation and convergence, in which perception: the dual adaptive sliding mode observer error increase, indicating that there is a mismatched disturbance / attack; learning: the adaptive law formula (4) and formula (5) adjust the adaptive rate according to the double adaptive sliding mode observer error and the system state, increase the adaptive rate and the estimate value of the adaptive rate ; compensation: the increase of the adaptive rate and the adaptive rate , so that the amplitude of the robust term increases, thereby generating a stronger control action to suppress disturbances and attacks; convergence: after the disturbance and attack are suppressed, the double adaptive sliding mode observer error decreases, the adjustment effect of the adaptive law weakens, and the system reaches a new balance.

[0042] As a specific embodiment, the sliding mode controller in S3 is designed, including designing a sliding mode surface, denoted as: (8); wherein, is a sliding mode surface function, is a sliding mode matrix, is a sliding mode controller gain, is a sliding mode controller integral variable; Combining formula (2) and formula (8), the rate of change of the sliding mode surface function is obtained, denoted as: (9); Define the time-varying weighting matrix , let , obtain the equivalent control rate , denoted as: (10); Substitute formula (10) into formula (2) to obtain the sliding mode dynamics equation, denoted as: (11).

[0043] As a specific embodiment, the sliding mode controller in S3 is also designed, including: Construct a Lyapunov function, denoted as: (38); wherein, denotes the transpose of ; and Implement the control law for constructing the reachability of the sliding mode region under deception attacks, denoted as: (39); wherein, is a combined gain of the controller, denoted as time-varying weighted matrix inverse; Definition where, is the anti-disturbance adaptive rate, is the anti-network attack adaptive rate, is a robust term constant, is a reaching condition constant; deriving the Lyapunov function , we have: (40) ; where, denotes the derivative of the Lyapunov function, denotes the change rate of the sliding mode surface function, which indicates that under the condition of a deception attack, the closed-loop control system is driven onto the sliding surface by the sliding mode control law formula (39) .

[0044] In an implementable embodiment, it further includes a state stability analysis, which is specifically analyzed as follows: Construct a Lyapunov function function: (12) ; Take , where, is the anti-disturbance adaptive rate error, is the anti-network attack adaptive rate error, denotes a disturbance upper bound coefficient, denotes an attack weight upper bound coefficient, is a state estimation weight matrix, is an estimation error weight matrix; deriving the constructed Lyapunov function function, we have: (14) ; where, denotes the inverse of the anti-network attack adaptive rate constant, denotes the inverse of the anti-disturbance adaptive rate constant; substituting formula (6) and formula (11) into formula (14), we have: (15) ; Take into formula (15), we have: (16) ; Take , then into formula (3), we have: (17) ; where, denotes the norm of the fuzzy weighted observation error; Substituting formula (4) and formula (5) into and calculating can obtain: (18); Adding formula (17) and formula (18) can obtain: (19); Using Young's inequality, can obtain: (20); wherein, is the disturbance weighted matrix, denotes the maximum eigenvalue of the inverse of the disturbance weighted matrix , and denotes the upper bound of the fuzzy modeling residual error energy.

[0045] Substituting formula (19) and formula (20) into formula (16) can obtain: (21); wherein , is the augmented state vector; (22); wherein, is the condition matrix. The state intermediate matrix is constructed, wherein, denotes the sliding mode dynamic relaxation matrix, denotes the observation error dynamic relaxation matrix, and (23); The conversion matrix is constructed, satisfying wherein, denotes the unit matrix; Multiplying formula (23) by from left and right respectively can obtain: (24); wherein, the controller gain intermediate variable ; since: (25); (26); In order to facilitate the MATLAB LMI toolbox to solve, formula (35) and formula (36) are converted into the following inequalities: (27); (28); Among them, intermediate state variables Perturbation of intermediate variables ; From formulas (21) and (24), we can obtain: (29); in, This is the intermediate state matrix. intermediate state matrix The smallest eigenvalue, For augmented state vectors; Define state transition parameters Substituting the perturbation transformation parameter into formula (29), we can obtain: (30); Solving the differential inequalities of formulas (12) and (29), we get: (31); Continuing the solution process, we can obtain: (32); in, Indicates the integral variable in the stability analysis, and the comprehensive transformation parameters. ; Therefore, function and function : (33); (34); when When fixed, for In other words, Linear growth is Function; when When fixed, for In other words, The exponential decays to 0, satisfying the condition. Properties of functions; Continuous and strictly increasing, and ,satisfy Properties of functions; Therefore, formula (30) is a sufficient condition for the input-state stability of the closed-loop system.

[0046] In summary, if it exists satisfy: (35); (36); (37); For , the sliding mode dynamic equation formula (11) is input to the state stability.

[0047] In an implementable embodiment, further comprising the step of simulating by giving the parameters of the closed-loop control system composed of the controller and the observer, specifically comprising: , , , , , , , .

[0048] wherein A is the damping characteristic matrix, B is the restoring force characteristic matrix, D is the control and disturbance input matrix, is the control output constant matrix 1, is the control output constant matrix 2, is the control output constant matrix 3, is the control output constant matrix 4.

[0049] , , , , , ; wherein, are rotation matrix 1, rotation matrix 2, rotation matrix 3 and rotation matrix 4 respectively. The parameters in the sliding mode control rate are set to respectively. The parameters in the deception attack model are set to respectively. The ocean disturbance is set to ocean disturbance 1, , ocean disturbance 2, , ocean disturbance 3, . The sliding mode parameters are set to , wherein represents the generalized inverse. Obviously, is nonsingular.

[0050] According to the simulation conditions given above, the system is simulated to verify the input-state stability of the system. The present application provides a comparative simulation diagram to highlight the necessity of the control strategy of the present application.

[0051] Please refer to Figure 3 , Figure 3It is shown that the open-loop response of the USV system under no control input is only subject to deception attacks and external disturbances . The position response curve represents the position and as well as the yaw angle of the USV over time. The position response curve exhibits divergence or sustained large oscillations, indicating that the USV cannot maintain the predetermined heading or position and is out of control. The velocity response curve represents the longitudinal velocity , lateral velocity and yaw angular velocity of the USV over time, and the velocity response curve also exhibits the characteristics of divergence or severe jitter, indicating that the kinetic energy is not effectively controlled.

[0052] Please refer to Figure 3 , Figure 3 as a control chart, which shows that the USV system cannot maintain stability without effective control strategy under the security threat of deception attacks and external disturbances, which demonstrates the necessity and importance of the control strategy proposed in the present application.

[0053] Please refer to Figure 4 , Figure 4 is the position and velocity response curve of the USV after using the adaptive sliding mode control law proposed in the present application, which is used to demonstrate the control effect of the present application. The position response curve represents the position and as well as the yaw angle of the USV, which can quickly converge to the expected value or remain bounded small fluctuations in its neighborhood, which indicates that the controller of the present application can effectively overcome the influence of attacks and disturbances, and achieve accurate trajectory tracking or point keeping. The velocity response curve represents the longitudinal velocity , lateral velocity and yaw angular velocity of the USV over time, which also exhibits good convergence and smoothness, and finally tends to be stable or zero, indicating that the system dynamic process is smooth.

[0054] Please refer to Figure 3 and Figure 4 , Figure 4 and Figure 3 form a sharp contrast, which directly verifies the effectiveness and robustness of the control method proposed in the present application. It is proved that the present application can ensure that the USV still maintains the stability of the closed-loop system and satisfactory performance indicators when it is simultaneously subjected to network deception attacks and complex ocean environmental disturbances.

[0055] Please refer to Figure 5 , Figure 5is the state estimation error curve of the double adaptive sliding mode observer, which is used to show the estimation accuracy of the double adaptive sliding mode observer. Figure 5 The double adaptive sliding mode observer error is shown over time, which contains 6 lines corresponding to the estimation errors of different state components of the system, and all the estimation error curves show the characteristics of fast convergence from the initial error to a very small neighborhood around zero, and then always remain in the bounded range.

[0056] Please continue to see Figure 5 , Figure 5 It is proved that the double adaptive sliding mode observer designed in the application has good performance, which can accurately and quickly reconstruct all the states of the system only by relying on the actual output , and provides reliable input for the control law based on state feedback. The bounded convergence of the double adaptive sliding mode observer error also provides key support for the stability proof of the entire closed-loop system.

[0057] Please see Figure 6 , Figure 6 is the sliding surface function and the time response curve of the control input is used to show the achievement of sliding mode motion and the physical realizability of the control input. The sliding surface function curve shows the value of the sliding surface function defined according to formula (8) over time. The curve quickly converges to a boundary layer around zero in a limited time, and is maintained in this area thereafter. This strictly proves that the system trajectory is driven and maintained near the sliding surface, meeting the basic requirements of sliding mode control, thereby ensuring the robustness of the system. The control input curve shows the control signal of the actual output of the controller. Although the signal is continuous, it contains a high-frequency switching component. It is worth noting that its amplitude is bounded, and the strength of the switching chattering is effectively suppressed within an acceptable range.

[0058] Please continue to see Figure 6 , Figure 6 It is verified that the control objective is achieved by the sliding surface convergence, and the inherent "chattering" problem of traditional sliding mode control is effectively weakened by the adaptive mechanism formula (4) and formula (5), generating a more smooth and more easily implemented physical control signal by the actuator, proving the engineering practicability of the application.

[0059] The above simulation results show that the adaptive sliding mode control method based on T-S fuzzy modeling of the application can effectively combine the strong robustness of sliding mode control, the approximation ability of fuzzy logic to nonlinear systems, and the online learning advantages of adaptive technology, to realize dynamic compensation for deception attacks and external disturbances.

[0060] Please see Figure 3 , Figure 4 and Figure 6 ,like Figure 3 and Figure 4 As shown in the comparison, this application enables the system to recover quickly and remain stable when subjected to complex threats, with rapid response and strong anti-interference capabilities. Figure 4 This demonstrates that the dual adaptive sliding mode observer designed in this application can provide accurate state estimation, high tracking accuracy, and small estimation error, thus providing a reliable basis for the control law. Figure 6 This application further demonstrates that it effectively suppresses chattering of the control input through an adaptive mechanism, ensuring robustness while also possessing good control flexibility and engineering feasibility. Figure 6 middle It is a longitudinal sliding surface. It is a transverse sliding surface. It is a steering sliding surface; It is a longitudinal control input. It is a lateral control input. It is the steering control input.

[0061] Based on the same inventive concept, please see Figure 7 This application also provides an adaptive sliding mode control system for unmanned surface vessels to resist spoofing attacks. The sliding mode control system includes: The first design unit constructs a fuzzy TS model of the unmanned vessel under deception attacks and external disturbances: the fuzzy TS model of the unmanned vessel is coupled with deception attack... and external disturbances ; The second design unit, based on the constructed unmanned vessel TS fuzzy model, designs a dual adaptive sliding mode observer: the dual adaptive sliding mode observer utilizes measurable output Reconstructed state estimate And estimate deception attacks online. and external disturbances The upper bound; At least includes designing a dual adaptive law, which is used to counter deception attacks. and external disturbances The joint estimation of the boundary is achieved by dynamically adjusting the adaptive rate through the dual adaptive law. and adaptive rate , wherein the adaptive rate The adaptive rate is used to estimate the upper bound of external disturbances. Used to estimate the upper bound of deception attacks; and, At least include design robustness items The robustness term Using the adaptive rate and the adaptive rate , by a sign function a production compensation signal for actively canceling the spoofing attack and the influence of external disturbances . a third design unit for designing a sliding mode controller based on the first design unit and the second design unit: based on the constructed T-S fuzzy model of the unmanned ship, the state estimation value provided by the double adaptive sliding mode observer in S2 , the adaptive rate and the adaptive rate , the sliding mode controller is designed a closed-loop control unit for forming a closed-loop control system for sliding mode control based on the first design unit, the second design unit and the third design unit: integrating the T-S fuzzy model of the unmanned ship constructed by the first design unit, the double adaptive sliding mode observer designed by the second design unit and the sliding mode controller designed by the third design unit, forming a closed-loop control system to realize sliding mode control.

[0062] Based on the same inventive concept, the present application also provides a computer readable storage medium storing a computer program, which, when executed by a processor, implements the anti-spoofing attack adaptive sliding mode method of the unmanned ship as described above.

[0063] The program product of the present application for implementing the above method can adopt a portable compact disc read-only memory and include program code, and can be run on a terminal device, such as a personal computer. However, the program product of the present application is not limited thereto, and in the present application, the readable storage medium can be any tangible medium containing or storing a program, which can be used or combined with an instruction execution system, device or apparatus.

[0064] It should be noted that the computer readable storage medium can include a data signal propagating in a baseband or as a part of a carrier wave, in which readable program codes are borne. Such a propagating data signal can adopt various forms, including but not limited to electromagnetic signals, optical signals or any suitable combination of the above. The readable storage medium can also be any readable medium other than the readable storage medium, which can send, propagate or transmit programs for use or in combination with an instruction execution system, device or apparatus. The program code contained on the readable storage medium can be transmitted by any suitable medium, including but not limited to wireless, wired, optical cable, RF, etc. or any suitable combination of the above.

[0065] The above merely preferred embodiments of the present application, and not any form of the present application, although the present application has been disclosed as above, however, not in order to limit the present application, any skilled in the art of the technical personnel in the present application within the scope of the present application, when can use the above-mentioned technical content of the prompt to make some more changes or modification of equivalent embodiments of equivalent changes, the implementation of the above-mentioned embodiments can be further combined or replaced, but as long as it does not deviate from the technical scheme of the present application, according to the technical essence of the present application, the above-mentioned embodiments of any simple modification, equivalent change and modification, still belongs to the scope of the present application.

Claims

1. An anti-deception attack adaptive sliding mode control method for an unmanned ship, characterized in that, The method comprises the following steps: S1, constructing a T-S fuzzy model of the unmanned ship under a deception attack and external disturbance: The unmanned ship T-S fuzzy model is coupled with deception attack and external disturbance ; S2, designing a double adaptive sliding mode observer based on the T-S fuzzy model of the unmanned ship constructed in S1: The dual adaptive sliding mode observer utilizes measurable outputs to reconstruct state estimates and online estimate upper bounds of spoofing attacks and external disturbances ​ at least comprising a double adaptive law for accomplishing a joint estimation of the upper bound of the spoofing attack and the external disturbance and the adaptive rate and the adaptive rate , wherein the adaptive rate is used for estimating the upper bound of the external disturbance is used for estimating the upper bound of the spoofing attack; and, At least include design robustness items The robustness term Using the adaptive rate and the adaptive rate Through symbolic functions Production compensation signals are used to proactively counteract deception attacks. and external disturbances The impact; S3, designing a sliding mode controller based on S1 and S2: Based on the S1 constructed unmanned ship T-S fuzzy model, using S2 in the state estimation value provided by double adaptive sliding mode observer , adaptive rate and adaptive rate , design sliding mode controller; S4, forming a closed-loop control system based on S1-S3 for sliding mode control: The T-S fuzzy model of the unmanned ship constructed in S1, the double adaptive sliding mode observer designed in S2, and the sliding mode controller designed in S3 are integrated to form a closed-loop control system, thereby realizing the sliding mode control of the T-S fuzzy model of the unmanned ship in S1.

2. The method of claim 1, wherein, The T-S fuzzy model of the unmanned ship constructed in S1 is expressed as: ， ， (1)； wherein, x represents the system full state of the unmanned ship model, constant i = 1, 2, 3, 4, y represents the performance output, y represents the actual output, t is a time variable, is a linear velocity or angular velocity or position vector matrix, is a control rate, is a spoofing attack weighting matrix, is a spoofing attack mathematical model, is an external disturbance, is a premise variable, is a fuzzy set, is a damping ratio inertia matrix, is a mooring ratio inertia matrix, is a measurement output constant matrix, is a control output constant matrix.

3. The method of claim 2, wherein, In S2, a double adaptive sliding mode observer is designed, including: the double adaptive sliding mode observer utilizes measurable system outputs , adopts a T-S fuzzy model and injects an output estimation error term to reconstruct unmeasurable system states , and a dynamic equation of the double adaptive sliding mode observer is expressed as: ， ， (2)； wherein, represents the system full state of the dual adaptive sliding mode observer, is an estimate of is an estimate of is an estimate of is an estimate of is an estimate of is an estimate of is an estimate of is an estimate of is the dual adaptive sliding mode observer gain, is the robust term.

4. The method of claim 3, wherein, In S2, at least a double adaptive law is designed, which is expressed as: (4)； (5)； wherein, represents a disturbance boundary estimation update law, represents an attack boundary estimation update law, represents a double adaptive sliding mode observer error weight matrix, is an anti-network attack adaptive rate constant, is an anti-disturbance adaptive rate constant, is a network attack upper bound, represents a transpose of a double adaptive sliding mode observer error, represents a transpose of a measurement output constant matrix, represents an actual output norm.

5. The method of claim 4, wherein, S2 comprises at least a design robustness term , said robustness term utilizes said adaptive rate and said adaptive rate , a compensation signal is produced by a sign function for actively counteracting the influence of spoofing attacks and external disturbances ; The robust term is represented as: (3)； wherein are respectively an estimate of is an anti-disturbance adaptive rate, is an anti-network attack adaptive rate, is a robust term constant, is a dual adaptive sliding mode observer error weight matrix, is a dual adaptive sliding mode observer error.

6. The method of claim 4, wherein, In S2, at least an error dynamic analysis is also designed, which is specifically: Definitions ; In combination with formula (1) and formula (2), the error dynamic equation is expressed as: (6)； (7)； wherein, represents a double adaptive sliding mode observer error update rate, is a double adaptive sliding mode observer error, is a double adaptive sliding mode observer error residual term.

7. The method of claim 4, wherein, In S3, the sliding mode controller is designed, including a sliding mode surface, which is expressed as: (8)； wherein, is a sliding mode surface function, is a sliding mode matrix, is a sliding mode controller gain, is a sliding mode controller integral variable; Combining equation (2) and equation (8), the rate of change of the sliding mode surface function is obtained , which is expressed as: (9)； Defining the time-varying weighting matrix , let , the equivalent control rate is expressed as: (10)； Formula (10) is substituted into formula (2) to obtain a sliding mode dynamics equation, which is expressed as: (11)； wherein, is denoted as a time-varying weighting matrix inverse.

8. The method of claim 7, wherein, In S3, the sliding mode controller also includes: A Lyapunov function is constructed, which is expressed as: (38)； wherein denotes the transpose of A control law for realizing the reachability of the sliding mode region under a deception attack is constructed, which is expressed as: (39)； wherein, Gc is the controller combination gain; Definitions ; wherein, is an anti-jamming adaptive rate, is an anti-network attack adaptive rate, is a robust term constant, is a reach condition constant; lyapunov function taking the derivative, we obtain: (40)； wherein, denotes the derivative of the Lyapunov function, denotes the rate of change of the sliding surface function, which indicates that under the condition of a deception attack, the closed-loop system is driven onto the sliding surface by the sliding mode control law (39) .

9. An anti-deception attack adaptive sliding mode control system for an unmanned ship, characterized by, The sliding mode control system comprises: A first design unit, a T-S fuzzy model of the unmanned ship under the deception attack and external disturbance is constructed: the T-S fuzzy model of the unmanned ship is coupled with the deception attack and the external disturbance and the external disturbance ​ The second design unit, based on the constructed unmanned vessel TS fuzzy model, designs a dual adaptive sliding mode observer: the dual adaptive sliding mode observer utilizes measurable output Reconstructed state estimate And estimate deception attacks online. and external disturbances The upper bound; at least comprising a double adaptive law for accomplishing a joint estimation of the upper bound of the spoofing attack and the external disturbance and the adaptive rate and the adaptive rate , wherein the adaptive rate is used for estimating the upper bound of the external disturbance is used for estimating the upper bound of the spoofing attack; and, at least comprising a design robust term , the robust term utilizing the adaptive rate and the adaptive rate , a compensation signal is produced by a sign function for actively counteracting the influence of spoofing attacks and external disturbances ; A third design unit designs the sliding mode controller based on the first design unit and the second design unit: based on the constructed T-S fuzzy model of the unmanned ship, the state estimation value provided by the double adaptive sliding mode observer in S2 is used to design the sliding mode controller , the adaptive rate and the adaptive rate ​ A closed-loop control unit forms a closed-loop control system based on the first design unit, the second design unit, and the third design unit for sliding mode control: the T-S fuzzy model of the unmanned ship constructed by the first design unit, the double adaptive sliding mode observer designed by the second design unit, and the sliding mode controller designed by the third design unit are integrated to form a closed-loop control system, thereby realizing the sliding mode control of the T-S fuzzy model of the unmanned ship.

10. A computer readable storage medium storing a computer program, characterized in that, The computer program, when executed by a processor, is used to realize the adaptive sliding mode control method of the unmanned ship against deception attacks according to any one of claims 1-8.

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