Under-actuated unmanned ship finite time trajectory tracking sliding mode control method based on filter

Abstract

The invention discloses an under-actuated unmanned surface vehicle finite time trajectory tracking sliding mode control method based on a filter, and relates to the technical field of unmanned surface vehicle control. In order to solve the technical problems in the prior art that the control design under an under-actuated structure is limited and full-state trajectory tracking is difficult to realize, the technical scheme provided by the invention is as follows: constructing kinematics and dynamics models of the under-actuated unmanned ship; performing coordinate transformation on the model, and introducing a transformation coefficient to transform an original under-actuated model into a control model with a regular form; based on the conversion model, a finite time disturbance observer is constructed, the input is system state deviation, and the output is a disturbance estimation value; the system error is used as input, a sliding mode surface with terminal sliding mode characteristics is constructed, and a smooth sliding mode variable is output; the disturbance estimation value and the sliding mode variable serve as input, a finite time sliding mode controller is designed, and a control instruction of the unmanned ship is output; the unmanned ship can rapidly and stably track the preset trajectory in a complex disturbance environment.

Description

Technical Field

[0001] The invention relates to the technical field of surface unmanned boat control, and is a filter-based finite-time trajectory tracking sliding mode control method for an under-actuated unmanned boat. Background Art

[0002] Unmanned surface vehicles (USVs), quintessential intelligent marine equipment, have demonstrated broad application prospects in recent years across a wide range of fields, including environmental monitoring, ocean mapping, water patrol, military reconnaissance, and emergency rescue. To accomplish their missions, they often require autonomous path planning and precise trajectory tracking control in complex water environments. Trajectory tracking control, a critical component of USV navigation and control, directly impacts the platform's path accuracy, response speed, and mission performance, and is a crucial indicator of control system performance.

[0003] However, surface unmanned vehicles (USVs) often exhibit underactuation in their structural design, meaning the number of control inputs to the system is fewer than the degrees of freedom of motion. For example, a common dual-thruster configuration only controls propulsion and steering, but lacks direct control of lateral motion. This structure limits the effectiveness of traditional control methods, particularly when faced with the challenge of precise multi-dimensional trajectory tracking. Underactuation leads to insufficient control redundancy, significantly increasing the difficulty of controller design.

[0004] To address these issues, various nonlinear control strategies have been widely applied in existing research, such as sliding mode control (SMC), model predictive control (MPC), fuzzy control, and backstepping. Sliding mode control has attracted considerable attention due to its robustness and adaptability to system uncertainties and external disturbances. Terminal sliding mode control, in particular, introduces nonlinear terms into the sliding surface design, enabling finite-time convergence of system states. This results in faster response times and higher tracking accuracy, making it suitable for unmanned aerial vehicle (UAV) missions requiring high timeliness.

[0005] However, in practical applications, terminal sliding mode control also has significant shortcomings. The introduction of strong nonlinear functions into the control law can easily lead to high-frequency chattering near the sliding surface, making the control signal unstable. In severe cases, this can affect the lifespan and control accuracy of actuators such as propulsion systems or servos. Furthermore, many existing control methods assume that external disturbances are negligible or change slowly, failing to fully account for the existence of complex dynamic disturbances. In actual maritime environments, factors such as wind, waves, tidal currents, and changes in target loads can all cause severe disturbances, posing a challenge to system stability.

[0006] Some current methods attempt to estimate and offset disturbances through high-gain observers or nonlinear disturbance compensation techniques, but these methods often converge slowly and fail to meet the responsiveness requirements of unmanned vehicles in dynamically changing environments. Furthermore, some control strategies ignore the controllable structure of underactuated systems during modeling and controller design, resulting in significant discrepancies between actual control performance and expected results.

[0007] In summary, existing technologies for trajectory tracking control of unmanned underwater vehicles (UAVs) still face the following core challenges: First, the control design of underactuated structures is limited, making full-state trajectory tracking difficult; second, the system's control accuracy decreases in the presence of uncertain disturbances, lacking an effective compensation mechanism; and third, terminal sliding mode control suffers from sliding mode chattering, which affects system stability and controller life. Therefore, it is urgent to propose a trajectory tracking sliding mode control method suitable for underactuated structures, capable of observing and compensating for finite-time disturbances, and mitigating sliding mode chattering. This approach can improve the control performance and practical application value of UAVs in complex environments. Summary of the Invention

[0008] In order to solve the technical problems existing in the prior art, such as the limited control design under the underactuated structure and the difficulty in achieving full-state trajectory tracking, the technical solution provided by the present invention is as follows:

[0009] A filter-based finite-time trajectory tracking sliding mode control method for an underactuated unmanned vehicle comprises:

[0010] Step 1: Construct the kinematic and dynamic models of the underactuated unmanned vehicle;

[0011] Step 2: Perform coordinate transformation on the model obtained in step 1 and introduce transformation coefficients to transform the original underactuated model into a regular control model.

[0012] Step 3: Based on the transformation model obtained in step 2, a finite-time disturbance observer is constructed, with the input being the system state deviation and the output being the disturbance estimate.

[0013] Step 4: Take the system error in step 2 as input, construct a sliding surface with terminal sliding characteristics, and output the smooth sliding variable;

[0014] Step 5: Take the disturbance estimate output from step 3 and the sliding mode variable generated from step 4 as input, design a finite-time sliding mode controller, and output the control command for the unmanned boat;

[0015] Step 6: Apply the control instructions generated in step 5 to the underactuated unmanned vehicle to achieve finite-time tracking control of the preset trajectory.

[0016] Furthermore, a preferred embodiment is provided, wherein the kinematic and dynamic models of the underactuated unmanned vehicle include the position information of the unmanned vehicle in the earth coordinate system, the velocity information in the body coordinate system, and the limited control input and external disturbance information.

[0017] Furthermore, a preferred embodiment is provided, in step 4, a sliding mode surface having terminal sliding mode characteristics is constructed through low-pass filter processing.

[0018] Furthermore, a preferred embodiment is provided, wherein the low-pass filter is a first-order filter, the initial state of the filter is set to zero, its output responds to the rate of change of the system error, and the filter parameter is greater than or equal to 1.

[0019] Furthermore, a preferred embodiment is provided, in step three, the disturbance observer processes the state deviation by introducing a nonlinear sliding mode variable, wherein the sliding mode variable is composed of a linear term of the state deviation and a nonlinear term in the form of a hyperbolic tangent function.

[0020] Furthermore, a preferred embodiment is provided, in step five, the sliding mode controller constructs a control law based on a disturbance compensation mechanism, and the control input includes a disturbance estimation term, a sliding surface term, and a nonlinear approach term.

[0021] Based on the same inventive concept, the present invention also provides a filter-based finite-time trajectory tracking sliding mode control device for an underactuated unmanned vehicle, comprising:

[0022] Module 1: Constructing the kinematic and dynamic model of underactuated unmanned vehicle;

[0023] Module 2: Perform coordinate transformation on the model obtained in Module 1 and introduce transformation coefficients to transform the original underactuated model into a regular control model.

[0024] Module 3: Based on the transformation model obtained in Module 2, a finite-time disturbance observer is constructed, with the input being the system state deviation and the output being the disturbance estimate.

[0025] Module 4: Take the system error in module 2 as input, construct a sliding surface with terminal sliding characteristics, and output a smooth sliding variable;

[0026] Module 5: Take the disturbance estimate output by module 3 and the sliding mode variable generated by module 4 as input, design a finite-time sliding mode controller, and output the control instructions for the unmanned boat;

[0027] Module 6: Apply the control instructions generated in Module 5 to the under-actuated unmanned vehicle to achieve finite-time tracking control of the preset trajectory.

[0028] Based on the same inventive concept, the present invention also provides a computer storage medium for storing a computer program. When the computer program is read by a computer, the computer executes the method described above.

[0029] Based on the same inventive concept, the present invention also provides a computer, comprising a processor and a storage medium. When the processor reads the computer program stored in the storage medium, the computer executes the method described above.

[0030] Based on the same inventive concept, the present invention also provides a computer program product, which is a computer program. When the computer program is executed, the method described above is implemented.

[0031] Compared with the prior art, the technical solution provided by the present invention is beneficial in that:

[0032] This approach introduces a finite-time disturbance observer to dynamically estimate unknown external disturbances with bounded first-order derivatives, effectively compensating for the impact of external disturbances on the trajectory tracking performance of the unmanned vehicle. Compared to traditional sliding mode control, which ignores disturbance estimation or uses a conventional observer, this observer has finite-time convergence capability, enabling faster and more accurate disturbance estimation. This significantly improves the system's robustness and interference tolerance, avoiding steady-state errors caused by disturbance uncertainty.

[0033] This approach designs a terminal sliding surface constructed in conjunction with a low-pass filter to mitigate the sliding mode chattering problem caused by the introduction of nonlinear terms in conventional terminal sliding mode control. The low-pass filter smoothes the sliding surface variables, effectively suppressing high-frequency chattering. This improves the controller's practical feasibility while ensuring rapid system convergence. Compared to existing studies that directly utilize nonlinear sliding surfaces, which can lead to significant fluctuations in the control signal, this approach achieves a better balance between stability and controllability.

[0034] This approach transforms the underactuated model by introducing a conversion coefficient through coordinate transformation, rewriting the original dynamic model of the unmanned vehicle into a form more suitable for controller design. This allows for effective control of two-dimensional displacement and heading even with limited input degrees of freedom. Compared to traditional methods that directly design control laws based on the original underactuated model, this approach significantly reduces system coupling and design complexity, while improving control accuracy and system response speed. It is particularly suitable for typical underactuated platforms such as dual thrusters.

[0035] The finite-time sliding mode control law employed in this solution ensures that the tracking error and sliding mode variable converge to the vicinity of the origin within a finite time. Compared to traditional asymptotically stable or exponentially stable control methods, the finite-time control strategy offers greater time responsiveness and precision, enabling faster state adjustments and maintaining stability. This improves the adaptability of the unmanned vehicle in rapid-response missions and is particularly suitable for water environments with complex dynamics and frequent mission changes.

[0036] This approach systematically verifies the finite-time stability of the overall closed-loop system by constructing a two-level Lyapunov function to sequentially analyze the stability of the disturbance estimator and sliding mode controller. Compared to existing methods that rely solely on simulation verification or fuzzy processing of the overall system, this theoretical analysis approach is more rigorous and provides engineering guidance, providing a theoretical basis for controller parameter adjustment and facilitating the application of this control strategy in engineering applications.

[0037] It can enable the unmanned boat to quickly and stably track the preset trajectory in a complex disturbance environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 This is a flow chart of the filter-based finite-time trajectory tracking control method;

[0039] Figure 2 This is the trajectory tracking effect diagram of the under-actuated unmanned boat. DETAILED DESCRIPTION

[0040] In order to make the advantages and benefits of the technical solution provided by the present invention more clearly reflected, the technical solution provided by the present invention is now further described in detail with reference to the accompanying drawings, specifically:

[0041] Implementation 1: This implementation provides a filter-based finite-time trajectory tracking sliding mode control method for an underactuated unmanned vehicle, including:

[0042] Step 1: Construct the kinematic and dynamic models of the underactuated unmanned vehicle;

[0043] Step 2: Perform coordinate transformation on the model obtained in step 1 and introduce transformation coefficients to transform the original underactuated model into a regular control model.

[0044] Step 3: Based on the transformation model obtained in step 2, a finite-time disturbance observer is constructed, with the input being the system state deviation and the output being the disturbance estimate.

[0045] Step 4: Take the system error in step 2 as input, construct a sliding surface with terminal sliding characteristics, and output the smooth sliding variable;

[0046] Step 5: Take the disturbance estimate output from step 3 and the sliding mode variable generated from step 4 as input, design a finite-time sliding mode controller, and output the control command for the unmanned boat;

[0047] Step 6: Apply the control instructions generated in step 5 to the underactuated unmanned vehicle to achieve finite-time tracking control of the preset trajectory.

[0048] The kinematic and dynamic models of the underactuated unmanned vehicle include the position information of the unmanned vehicle in the earth coordinate system, the velocity information in the body coordinate system, and the limited control input and external disturbance information.

[0049] In step 4, a sliding surface with terminal sliding mode characteristics is constructed after low-pass filter processing.

[0050] The low-pass filter is a first-order filter, the initial state of the filter is set to zero, the output thereof responds to the rate of change of the system error, and the filter parameter is greater than or equal to 1.

[0051] In step three, the disturbance observer processes the state deviation by introducing a nonlinear sliding mode variable, where the sliding mode variable is composed of a linear term of the state deviation and a nonlinear term in the form of a hyperbolic tangent function.

[0052] In step five, the sliding mode controller constructs a control law based on the disturbance compensation mechanism, and the control input includes a disturbance estimation term, a sliding surface term, and a nonlinear approach term.

[0053] Implementation Method 2: This implementation method further describes the technical solution provided in Implementation Method 1 in detail. Specifically:

[0054] Step 1: Construct the kinematic and dynamic model of the underactuated unmanned vehicle

[0055] The present invention first establishes a mathematical model of an unmanned surface vehicle based on the geodetic coordinate system and the body-mounted coordinate system. In practical applications, unmanned vehicles typically possess three degrees of freedom: longitudinal, lateral, and in-plane rotational motion (heading angle). However, due to the limited number of propulsion devices, they can typically only directly control longitudinal thrust and heading angular velocity, leaving them with relatively weak lateral control capabilities, making them typical underactuated systems.

[0056] In this step, define:

[0057] State variables: including the position information (position and heading angle) of the unmanned boat in the earth coordinate system;

[0058] Control variables: including longitudinal propulsion force and bow control torque;

[0059] External disturbances: including uncertain external interference caused by waves, wind currents, etc.

[0060] The model considers key hydrodynamic factors such as mass, added mass, Coriolis force, and damping force, establishing a nonlinear state-space representation. The modeling results serve as the basis for subsequent coordinate transformation and controller design.

[0061] Step 2: Perform coordinate transformation to account for underactuated structural characteristics

[0062] Because underactuated structures limit direct control of all degrees of freedom, this paper introduces auxiliary variables and performs nonlinear coordinate transformation to simplify controller design and enhance the system's control capabilities. By introducing a transformation coefficient, the original state variables (particularly the lateral variables) are combined with the controllable variables to form a new control target variable.

[0063] Specifically, a new position reference variable is constructed to transform the coupling structure of the original underactuated system into a new separable state equation. This transformation process retains the original system's dynamic characteristics but expresses the system in a controllable standard form, facilitating the subsequent design of sliding surfaces and control laws.

[0064] The output of this step is the control model after coordinate transformation, which has better structural properties and can be directly used as the object of the sliding mode controller and disturbance observer.

[0065] Step 3: Design a finite-time disturbance observer to estimate the external disturbance

[0066] Based on the transformation model, this paper designs a disturbance observer with finite-time convergence to improve the system's robustness in complex environments. The disturbance observer employs a sliding mode concept to construct a sliding surface and its auxiliary variables, approximating the effects of disturbances through a nonlinear gain function.

[0067] The observer has the following features:

[0068] Construct a perturbation model with bounded first-order derivatives;

[0069] The dual nonlinear term is introduced to enhance the ability to respond quickly to disturbances;

[0070] Through Lyapunov stability analysis, it is proved that the observation error can converge to zero in a finite time.

[0071] The disturbance estimate output by the disturbance observer will be used as a compensation term in the control law to effectively suppress the impact of uncertain disturbances on trajectory tracking performance.

[0072] Step 4: Construct a terminal sliding surface based on a low-pass filter to improve control stability

[0073] To avoid the high-frequency chattering problem common in terminal sliding mode control, the present invention introduces a first-order low-pass filter to smooth the tracking error and construct the sliding mode variable. The sliding mode surface is constructed nonlinearly and exhibits terminal convergence, meaning it can drive the system state to a position close to the desired trajectory within a finite time.

[0074] The filter setting parameters must meet the following conditions:

[0075] The initial value of the filter is set to zero to ensure that the error response evolves smoothly from zero;

[0076] The filter gain is not less than 1 to ensure the system response speed;

[0077] The sliding mode variable is a nonlinear combination of the error value and its filtered output, which enhances the sensitivity of the sliding surface.

[0078] The output result of the sliding mode variable has anti-noise performance and will not produce excessively high-frequency control input in the actual platform, thus meeting the actual engineering constraints.

[0079] Step 5: Design a finite-time trajectory tracking sliding mode controller

[0080] Based on the above sliding mode variables and disturbance estimation results, the present invention designs a trajectory tracking control law with finite time convergence characteristics. The controller forms the control input by combining the system state error, sliding mode variables, disturbance compensation terms, and system nonlinear terms.

[0081] Design ideas include:

[0082] Utilize the driving force of the system dynamic state provided by sliding mode variables;

[0083] Introducing disturbance estimation term into the control law for active compensation;

[0084] A nonlinear reaching law is introduced to ensure that the sliding surface converges to zero in a finite time.

[0085] The control parameters are adjustable through presets to adapt to different platform characteristics and disturbance intensities.

[0086] The controller outputs control signals that drive the propulsion and steering systems of the unmanned boat in real time. The control effect has been verified to be feasible both in theory and simulation, and it has high-precision trajectory tracking performance.

[0087] Step 6: Verify the stability and robustness of the closed-loop system

[0088] To ensure the stable operation of the overall control system under complex disturbances, this paper conducts a theoretical stability analysis. Lyapunov functions are constructed for the disturbance observer subsystem and the sliding mode control subsystem, and their derivatives are derived and proven to be negatively definite, thereby deriving the convergence properties of the system in finite time.

[0089] The analysis process takes into account the effects of observation error, filter delay, and system nonlinear coupling, and comprehensively verifies:

[0090] The sliding surface will converge stably to the preset area;

[0091] The control error can be compressed to the neighborhood of the origin within a finite time;

[0092] The overall closed-loop system remains stable in the presence of disturbances.

[0093] This analysis provides theoretical support for engineering implementation and parameter tuning, ensuring that the control strategy is highly robust and reliable.

[0094] Implementation Method 3: Combination Figure 1-2 This embodiment further describes the above technical solution in detail through specific examples, specifically:

[0095] This is achieved through the following scheme:

[0096] 1) Obtain the status of the unmanned boat in real time, consider external complex disturbances and under-actuated characteristics, and build a mathematical model of the unmanned boat;

[0097] 2) The underactuated model is transformed by introducing the model transformation coefficient l;

[0098] 3) Design a finite-time disturbance observer based on sliding mode variables for external disturbances;

[0099] 4) Combine the low-pass filter with the terminal sliding surface and design a finite-time trajectory tracking controller based on the disturbance observer in step 3;

[0100] 5) Based on the designed unmanned boat trajectory tracking controller, the stability and robustness of the under-actuated unmanned boat closed-loop system are verified.

[0101] Since the navigation state of the underactuated unmanned vehicle is defined on the horizontal plane, the earth coordinate system E and the body coordinate system B are used to describe the platform motion, and the kinematic and dynamic models are established as follows

[0102]

[0103] in, is the position and heading vector of the unmanned boat in the earth coordinate system; ν=[u,v,r] TRepresents the velocity vector in coordinate system B, which is composed of the longitudinal velocity u, the transverse velocity v and the yaw velocity r. Rotation matrix satisfy Its expression is

[0104]

[0105] In addition, M = diag{m 11 ,m 22 ,m 33} is the inertia matrix, m 11 、m 22 With m 33 Indicates the additional mass. C(ν)=[0,0,c 13 ;0,0,c 23 ;c 31 ,c 32 ,0] and D(ν)=diag{d 11 ,d 22 ,d 33} are the Coriolis-centripetal force matrix and the damping matrix respectively, where c 13 =-m 22 v,c 23 =m 11 u,c 31 =m 22 v and c 32 =-m 11 u is the scientific term, d 11 , d 22 with d 33 is the hydrodynamic coefficient of the unmanned boat. u ,0,τ r ] T With τ w =[τ w1 ,τ w2 ,τ w3 ] T are the control signal and the external disturbance respectively, where the external position disturbance satisfies

[0106] By introducing coordinate transformation To deal with the underactuated problem of the above unmanned boat model, where l is the model conversion coefficient, the derivative of p is

[0107]

[0108] The unmanned boat system can be transformed into

[0109]

[0110] Among them, f u =(m 22 vr-d11 u) / m 11 , f v =(-m 11 ur-d 22 v) and f r =((m 11 -m 22 )uv-d 33 r) / m 33 is the nonlinear term in the transformed model, τ s =[τ u ,τ r ] T is the input of the above system. In addition, G is the input transformation matrix, and its inverse is expressed as follows

[0111]

[0112] Consider τ w And the expression of d, then it satisfies In order to compensate for the influence of disturbance on the trajectory tracking control of the unmanned boat, the following disturbance observation system is designed. First, the auxiliary variable ξ=zq is defined, and its derivative is

[0113]

[0114] Define auxiliary variable z to satisfy the system

[0115]

[0116] Among them, the variable θ satisfies Design sliding mode variables β i (i=1,2) are design positive parameters. Based on the above auxiliary variables, the unknown disturbance estimation algorithm is defined as follows:

[0117]

[0118] For the above disturbance estimation system, firstly, the sliding mode variable s ξ The derivation is as follows

[0119]

[0120] According to the definition of variables ξ and z, we have Furthermore, to prove the stability of the disturbance estimator, the following Lyapunov function is selected:

[0121]

[0122] Taking its derivative and considering the above perturbation estimation system, the Lyapunov function satisfies

[0123]

[0124] Where κ=0.2785, then the sliding surface s ξ In a limited time T ob Inner convergence, that is but In a finite time T ξ The inner part is also restrained. Then the estimation error satisfy

[0125]

[0126] Therefore, the finite time convergence characteristic of the unknown disturbance estimation algorithm proposed above is verified.

[0127] Furthermore, the finite-time unmanned boat trajectory tracking controller is implemented by the following steps: For a given unmanned boat desired reference trajectory p d , define the following error variable

[0128]

[0129] First, based on the low-pass filter, for the above trajectory tracking error, the following sliding surface is defined as:

[0130]

[0131] Among them, α1 and α2 are positive constants to be designed, μ≥1 is the filter parameter to be designed, and the initial value of the low-pass filter is defined to satisfy ζ(0) = 0. Considering the disturbance estimation algorithm, the following compensation sliding mode variables are defined:

[0132] s2=s1+ξ

[0133] The derivative of the sliding surface variable is:

[0134]

[0135] Then the following finite-time trajectory tracking controller is designed:

[0136]

[0137] Among them, α3 and α4 are positive control coefficients. In order to verify the robustness and stability of the underactuated unmanned vehicle trajectory tracking system, the following Lyapunov function is selected:

[0138]

[0139] Considering the above unmanned boat trajectory tracking controller and disturbance estimator stability, the derivative of the above function is

[0140]

[0141] Then the sliding surface s2 converges to the vicinity of the origin within a finite time T2. At the same time, considering the finite time convergence of ξ, s1 will converge to the vicinity of the origin within a finite time T1.

[0142] Considering the above sliding mode variable s1 and low-pass filter definition, we have

[0143]

[0144] in, is the tracking error vector The j=1,2th component of , considering the convergence of sliding mode variable s1, defines is a positive number Δ pj , then

[0145]

[0146] like Will continue to converge until as well as but It will converge to an acceptable region containing the origin. In this way, it is verified that under the action of the proposed controller, the unmanned boat will successfully track the preset desired trajectory within a limited time under the influence of external unknown disturbances.

[0147] The above further describes the technical solution provided by the present invention in detail through several specific embodiments in order to highlight the advantages and benefits of the technical solution provided by the present invention. However, the several specific embodiments described above are not intended to limit the present invention. Any reasonable modification and improvement of the present invention, combination of embodiments and equivalent replacement based on the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A filter-based finite-time trajectory tracking sliding mode control method for an underactuated unmanned vehicle, characterized in that: include: Step 1: Construct the kinematic and dynamic models of the underactuated unmanned vehicle; Step 2: Perform coordinate transformation on the model obtained in step 1 and introduce transformation coefficients to transform the original underactuated model into a regular control model. Step 3: Based on the transformation model obtained in step 2, a finite-time disturbance observer is constructed, with the input being the system state deviation and the output being the disturbance estimate. Step 4: Take the system error in step 2 as input, construct a sliding surface with terminal sliding characteristics, and output the smooth sliding variable; Step 5: Take the disturbance estimate output from step 3 and the sliding mode variable generated from step 4 as input, design a finite-time sliding mode controller, and output the control command for the unmanned boat; Step 6: Apply the control instructions generated in step 5 to the underactuated unmanned vehicle to achieve finite-time tracking control of the preset trajectory.

2. The filter-based finite-time trajectory tracking sliding mode control method for an underactuated unmanned vehicle according to claim 1, characterized in that: The kinematic and dynamic models of the underactuated unmanned vehicle include the position information of the unmanned vehicle in the earth coordinate system, the velocity information in the body coordinate system, as well as the restricted control input and the external disturbance.

3. The filter-based finite-time trajectory tracking sliding mode control method for an underactuated unmanned vehicle according to claim 1, characterized in that: In step 4, a sliding surface with terminal sliding mode characteristics is constructed after low-pass filter processing.

4. The filter-based finite-time trajectory tracking sliding mode control method for an underactuated unmanned vehicle according to claim 3, characterized in that: The low-pass filter is a first-order filter, the initial state of the filter is set to zero, the output thereof responds to the rate of change of the system error, and the filter parameter is greater than or equal to 1.

5. The filter-based finite-time trajectory tracking sliding mode control method for an underactuated unmanned vehicle according to claim 1, characterized in that: In step three, the disturbance observer processes the state deviation by introducing a nonlinear sliding mode variable, where the sliding mode variable is composed of a linear term of the state deviation and a nonlinear term in the form of a hyperbolic tangent function.

6. The filter-based finite-time trajectory tracking sliding mode control method for an underactuated unmanned vehicle according to claim 1, characterized in that: In step five, the sliding mode controller constructs a control law based on the disturbance compensation mechanism, and the control input includes a disturbance estimation term, a sliding surface term, and a nonlinear approach term.

7. A filter-based finite-time trajectory tracking sliding mode control device for an underactuated unmanned vehicle, characterized in that: include: Module 1: Constructing the kinematic and dynamic model of underactuated unmanned vehicle; Module 2: Perform coordinate transformation on the model obtained in Module 1 and introduce transformation coefficients to transform the original underactuated model into a regular control model. Module 3: Based on the transformation model obtained in Module 2, a finite-time disturbance observer is constructed, with the input being the system state deviation and the output being the disturbance estimate. Module 4: Take the system error in module 2 as input, construct a sliding surface with terminal sliding characteristics, and output a smooth sliding variable; Module 5: Take the disturbance estimate output by module 3 and the sliding mode variable generated by module 4 as input, design a finite-time sliding mode controller, and output the control instructions for the unmanned boat; Module 6: Apply the control instructions generated in Module 5 to the under-actuated unmanned vehicle to achieve finite-time tracking control of the preset trajectory.

8. A computer storage medium for storing a computer program, characterized in that When the computer program is read by a computer, the computer executes the method according to claim 1 .

9. A computer comprising a processor and a storage medium, characterized in that When the processor reads the computer program stored in the storage medium, the computer executes the method according to claim 1 .

10. A computer program product, being a computer program, characterized in that When the computer program is executed, the method according to claim 1 is implemented.

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