Self-adaptive path tracking control method and device for unmanned ship

Abstract

The invention discloses a self-adaptive path tracking control method and device for an unmanned ship, and relates to the technical field of unmanned ships. The method comprises the following steps of S1, dynamically generating real-time position, speed and course information of a virtual pilot ship based on input expected path geometric information and expected speed, and enabling the virtual pilot ship to move along an expected path at the expected speed; s2, acquiring navigation information of the unmanned ship in real time, and calculating a relative distance error and a relative speed error between the unmanned ship and the virtual pilot ship; and S3, constructing a sliding mode surface on the basis of the relative distance error and the relative speed error. According to the method, the concept of dynamic virtual pilot ships is introduced, a complex path tracking problem is converted into a visual queue tracking problem, and the defect that a traditional method depends on an accurate dynamic model is overcome. And sliding mode control based on relative errors is combined with a single adaptive law, so that the structure of the controller is remarkably simplified, and the parameter setting difficulty is reduced.

Description

Technical Field

[0001] This invention relates to the field of unmanned surface vessel (USV) technology, and specifically to an adaptive path tracking control method and device for USVs. Background Technology

[0002] With the rapid development of marine resource development, environmental monitoring, and maritime security, unmanned surface vessels (USVs), as intelligent operational platforms, have seen their core autonomous path tracking control technology become a research hotspot. The purpose of path tracking control is to drive USVs to navigate precisely and smoothly along a predetermined trajectory in complex marine environments; its performance directly determines the success and efficiency of USV missions. Traditional control methods, such as PID control, often rely on accurate ship dynamics models. However, in actual navigation, USVs face perturbations of model parameters, unmodeled dynamics, and interference from complex external environments such as wind, waves, and currents, leading to performance degradation or even instability of controllers designed based on accurate models.

[0003] Existing adaptive path tracking control methods still have some limitations. First, many methods [such as CN119045504B] focus on online identification and compensation of the complex dynamic model of the unmanned vessel itself, for example, using RBF neural networks to fit the unmodeled dynamics. This method is essentially a "passive response" compensation, with a complex controller structure and difficult parameter tuning. Its performance heavily depends on the accuracy of its own model identification, while ignoring the fact that the essence of the path tracking problem is "kinematic guidance." Second, in terms of path tracking strategies, existing technologies mostly adopt pure tracking methods based on the geometric calculation of the pre-aiming point. This method is prone to tracking lag and motion unsmoothness when dealing with high curvature paths or scenarios requiring large-angle turns. Furthermore, its control performance is closely related to the selection of parameters such as the pre-aiming distance, and its adaptive capability is insufficient.

[0004] To address this, an adaptive path tracking control method and device for unmanned vessels are proposed. Summary of the Invention

[0005] The purpose of this invention is to provide an adaptive path tracking control method and apparatus for unmanned vessels in order to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention specifically adopts the following technical solution:

[0007] An adaptive path tracking control method for unmanned surface vessels includes the following steps:

[0008] S1: Based on the input desired path geometry information and desired speed, dynamically generate the real-time position, speed and heading information of a virtual pilot ship, which moves along the desired path at the desired speed;

[0009] S2: Acquire the navigation information of the unmanned vessel in real time, and calculate the relative distance error and relative speed error between the unmanned vessel and the virtual pilot vessel;

[0010] S3: Construct a sliding surface based on the relative distance error and relative velocity error, and calculate a generalized control force command to eliminate tracking error based on the sliding surface. The generalized control force command includes a sway force command and a yaw moment command.

[0011] S4: Design an adaptive law to estimate the magnitude of the lumped disturbance experienced by the unmanned vessel in real time, and feed the estimated value forward to compensate the sliding mode control step. The lumped disturbance is the sum of the model uncertainty dynamics and the external environment disturbance.

[0012] S5: Based on the generalized control force command and the thruster layout of the unmanned vessel, calculate the actual thrust or rudder angle command of each thruster through the thrust distribution algorithm and send it to the actuator.

[0013] Furthermore, the motion state of the virtual pilot ship is generated by a first-order integral model, and its motion direction is always consistent with the tangent direction of the desired path at the current position.

[0014] Furthermore, the relative distance error is the Euclidean distance between the real-time position of the unmanned vessel and the real-time position of the virtual pilot vessel; the relative speed error is the difference between the real-time resultant speed of the unmanned vessel and the desired speed.

[0015] Furthermore, the sliding surface is a linear combination of the relative distance error and the relative velocity error; the calculation of the generalized control force command includes an equivalent control term and a switching control term, wherein the switching control term is used to suppress the lumped disturbance.

[0016] Furthermore, the adaptive law adjusts the estimate of the upper bound of the lumped disturbance online according to the rate of change of the sliding surface, and uses this estimate to calculate the gain of the switching control term.

[0017] Furthermore, the thrust distribution algorithm allocates the yaw torque command as the thrust difference between the left and right thrusters, and allocates the sway force command as the sum of the thrusts of the left and right thrusters.

[0018] An adaptive path tracking control device for unmanned surface vessels, used to implement the above-mentioned method, characterized in that the device comprises:

[0019] The navigation information sensing module is used to acquire the real-time position, speed, and heading information of the unmanned vessel;

[0020] The path processing and pilot ship generation module is used to perform step S1;

[0021] The error calculation module is used to perform step S2.

[0022] The core controller is used to execute steps S3 and S4;

[0023] A thrust distributor is used to perform step S5.

[0024] Furthermore, the core controller is a microprocessor or digital signal processor embedded with an adaptive sliding mode control algorithm; the thrust distributor is a programmable logic device pre-programmed with a thrust distribution algorithm or is simultaneously implemented by the microprocessor.

[0025] The beneficial effects of this invention are as follows:

[0026] 1. This invention abandons the passive response approach of "precisely identifying and compensating for its own complex dynamics online" in existing technologies. Instead, it creatively introduces the concept of a "virtual pilot ship," transforming the control objective into tracking a simple, dynamically advanced virtual target. This allows the controller design to be based entirely on intuitive relative kinematic relationships, rather than relying on its own dynamic parameters, thus greatly simplifying the control law structure (requiring only one adaptive law to compensate for lumped disturbances) and reducing the requirements for model accuracy. Therefore, this invention does not require redesigning complex identification and compensation algorithms for different ship types; only slight adjustments are needed at the thrust distribution layer to make it applicable to unmanned ships with various propeller configurations, such as twin-propeller and rudder propeller, fundamentally improving its versatility.

[0027] 2. Because the virtual pilot vessel can provide continuous and smooth future motion state information along the desired path, this invention realizes the transformation from "correcting deviations based on current geometric errors" to "tracking future desired states," endowing the control system with inherent foresight. This method can effectively avoid the "angle cut" and lag phenomena of traditional pure tracking methods when tracking high curvature paths, making the tracking trajectory of the unmanned vessel smoother and more accurate. At the same time, the robust design based on sliding mode control and adaptive laws can quickly estimate and compensate for unknown environmental disturbances such as wind, waves, and currents, ensuring the stability and tracking accuracy of the system under complex sea conditions. Attached Figure Description

[0028] Figure 1 This is a schematic diagram of the control method of the present invention; Detailed Implementation

[0029] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0030] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0031] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, the terms "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0032] All electrical components mentioned in this article are connected to an external main controller and 220V AC mains power, and the main controller can be a conventional known device such as a computer that can control it.

[0033] In the description of the embodiments of the present invention, it should be noted that the terms "inner", "outer", "upper", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship in which the product of the invention is usually placed when in use. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limiting the present invention.

[0034] As shown in Figure 1, an adaptive path tracking control method for unmanned surface vessels includes the following steps: S1: Based on the input desired path geometry information and desired speed, dynamically generate the real-time position, speed and heading information of a virtual pilot ship, which moves along the desired path at the desired speed; S2: Real-time acquisition of the unmanned vessel's own navigation information, and calculation of the relative distance error and relative speed error between the unmanned vessel and the virtual pilot vessel; S3: Construct a sliding surface based on relative distance error and relative velocity error, and calculate a generalized control force command to eliminate tracking error based on the sliding surface. The generalized control force command includes sway force command and pitching moment command. S4: Design an adaptive law to estimate the magnitude of the lumped disturbances experienced by the unmanned vessel in real time, and feed the estimated value forward to compensate the sliding mode control step. The lumped disturbance is the sum of the model uncertainty dynamics and the external environment disturbance. S5: Based on the generalized control force command and the propeller layout of the unmanned vessel, the actual thrust or rudder angle command of each propeller is calculated through the thrust distribution algorithm and sent to the actuator.

[0040] By creating a dynamic virtual pilot vessel as the tracking target, the control objective becomes very simple and explicit: minimizing the relative motion error between the unmanned vessel and the pilot vessel (steps S1, S2). The controller (step S3) is designed based on this relative error, rather than its own complex dynamic model, thus simplifying the control law structure. The adaptive law in step S4 treats all difficult-to-model dynamics and external disturbances as a whole (lumped disturbance) for estimation and compensation, greatly enhancing the robustness of the system, and requiring only one adaptive law, avoiding the complexity of multiple adaptations. Finally, step S5 intelligently assigns the calculated abstract force command to the physical actuator, making the method applicable to various propeller layouts.

[0041] The motion state of the virtual pilot ship is generated by a first-order integral model, and its motion direction is always consistent with the tangent direction of the desired path at the current position.

[0042] Its position derivative (velocity) can be calculated using [dx_l / dt, dy_l / dt] = [v_d*cos(θ_l), v_d*sin(θ_l)], where the heading angle θ_l is determined by the tangent direction of the desired path at the current position of the pilot vessel. This model is simpler and does not involve any dynamic parameters of the unmanned vessel itself, thus ensuring the universality and reliability of this step. The pilot vessel provides a smooth, continuous, and physically feasible desired trajectory for the unmanned vessel to follow.

[0043] The relative distance error is the Euclidean distance between the real-time position of the unmanned vessel and the real-time position of the virtual pilot vessel; the relative speed error is the difference between the real-time resultant speed and the desired speed of the unmanned vessel.

[0044] The relative distance error e_p = sqrt((x - x_l)^2 + (y - y_l)^2) intuitively represents the spatial difference between the two ships. The relative speed error e_v = v - v_d reflects whether the unmanned ship has kept up with the pilot ship in terms of speed. These two error quantities together constitute the input of the subsequent sliding mode controller. Their physical meaning is clear, easy to measure and calculate, and provide a clear target for stable control.

[0045] The sliding surface is a linear combination of relative distance error and relative velocity error; the calculation of the generalized control force command includes equivalent control terms and switching control terms, where the switching control terms are used to suppress lumped disturbances.

[0046] The sliding surface can be designed as s = c1 * e_p + e_v (where c1 > 0). This design ensures that when the system state is on the sliding surface (s = 0), the error converges exponentially to zero. The control force command τ consists of two parts: the equivalent control τ_eq (used to maintain sliding motion in the absence of disturbances) and the switching control τ_sw (e.g., -ρ_hat * sat(s / Φ), used to counteract lumped disturbances). The saturation function sat() is used to mitigate chattering. This separate design makes the controller both efficient and robust.

[0047] The adaptive law adjusts the estimate of the upper bound of the lumped disturbance online based on the rate of change of the sliding surface, and uses this estimate to calculate the gain of the switching control term.

[0048] The adaptive law can be designed as d(ρ_hat) / dt = γ*|s| (where γ>0 is the adaptive gain). When the system is disturbed and deviates from the sliding surface (|s| increases), this law automatically increases the estimate of the upper bound of the disturbance, ρ_hat. Subsequently, this increased ρ_hat is used to enhance the gain of the switching control term τ_sw, thereby generating a larger control force to pull the system state back to the sliding surface. This is a dynamic negative feedback process that enables the system to adapt to disturbances of different intensities.

[0049] The thrust distribution algorithm allocates the yaw torque command to the thrust difference between the left and right thrusters, and the sway force command to the sum of the thrusts of the left and right thrusters.

[0050] For the most common twin-propeller ship type, the allocation algorithm can be expressed as:

[0051] T_port=(X+N / d) / 2

[0052] T_stbd=(XN / d) / 2

[0053] Where T_port and T_stbd are the thrust commands for the left and right thrusters, respectively, X is the sway command, N is the pitching moment command, and d is the distance from the thrust application point to the ship's centerline (or equivalent moment coefficient). This algorithm efficiently and intuitively decodes the generalized force output by the controller into commands that the actuators can directly execute, demonstrating the practicality of the method.

[0054] On the other hand, the present invention also provides an unmanned vessel adaptive path tracking control device for implementing the above-mentioned method, the device comprising:

[0055] The navigation information sensing module is used to acquire the real-time position, speed, and heading information of the unmanned vessel;

[0056] The path processing and pilot ship generation module is used to execute step S1;

[0057] An error calculation module is used to execute step S2;

[0058] The core controller is used to execute steps S3 and S4;

[0059] The thrust distributor is used to perform step S5.

[0060] The navigation information sensing module (such as GPS or IMU) provides system input. The path processing and pilot ship generation module can be a processor running specific algorithms. The error calculation module performs simple mathematical operations. The core controller is the "brain" of the device, integrating sliding mode control and adaptive algorithms, and is responsible for making core decisions. The thrust distributor acts as a "translator," converting decision commands into actuator language. All modules work together to achieve automated and intelligent path tracking control.

[0061] The core controller is a microprocessor or digital signal processor with an embedded adaptive sliding mode control algorithm; the thrust distributor is a programmable logic device with a pre-programmed thrust distribution algorithm or is implemented by the microprocessor.

[0062] The core controller is preferably a microprocessor (such as an MCU) or a digital signal processor (DSP), which can efficiently perform the numerical calculations required for sliding mode control and adaptive updates. The thrust distributor's function can be implemented at high speed in hardware by an additional dedicated programmable logic device (such as an FPGA), or it can be implemented as a piece of program code and executed synchronously by the main microprocessor after completing the control calculations. This hardware configuration provides a flexible and reliable implementation solution.

[0063] In summary: 1. This invention abandons the passive response approach of "precisely identifying and compensating for its own complex dynamics online" in existing technologies, and creatively introduces the concept of a "virtual pilot ship," transforming the control objective into tracking a simple, dynamically advanced virtual target. This allows the controller design to be based entirely on intuitive relative kinematic relationships, rather than relying on its own dynamic parameters, thereby greatly simplifying the control law structure (requiring only one adaptive law to compensate for lumped disturbances) and reducing the requirements for model accuracy. Therefore, this invention does not require redesigning complex identification and compensation algorithms for different ship types; only slight adjustments are needed at the thrust distribution layer to make it applicable to unmanned ships with various propeller configurations such as twin propellers and rudder propellers, fundamentally improving its versatility.

[0064] 2. Because the virtual pilot vessel can provide continuous and smooth future motion state information along the desired path, this invention realizes the transformation from "correcting deviations based on current geometric errors" to "tracking future desired states," endowing the control system with inherent foresight. This method can effectively avoid the "angle cut" and lag phenomena of traditional pure tracking methods when tracking high curvature paths, making the tracking trajectory of the unmanned vessel smoother and more accurate. At the same time, the robust design based on sliding mode control and adaptive laws can quickly estimate and compensate for unknown environmental disturbances such as wind, waves, and currents, ensuring the stability and tracking accuracy of the system under complex sea conditions.

[0065] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention. The scope of protection claimed by the appended claims and their equivalents is defined.

Claims

1. An adaptive path tracking control method for unmanned surface vessels, characterized in that, Includes the following steps: S1: Based on the input desired path geometry information and desired speed, dynamically generate the real-time position, speed and heading information of a virtual pilot ship, which moves along the desired path at the desired speed; S2: Acquire the navigation information of the unmanned vessel in real time, and calculate the relative distance error and relative speed error between the unmanned vessel and the virtual pilot vessel; S3: Construct a sliding surface based on the relative distance error and relative velocity error, and calculate a generalized control force command to eliminate tracking error based on the sliding surface. The generalized control force command includes a sway force command and a yaw moment command. S4: Design an adaptive law to estimate the magnitude of the lumped disturbance experienced by the unmanned vessel in real time, and feed the estimated value forward to compensate the sliding mode control step. The lumped disturbance is the sum of the model uncertainty dynamics and the external environment disturbance. S5: Based on the generalized control force command and the thruster layout of the unmanned vessel, calculate the actual thrust or rudder angle command of each thruster through the thrust distribution algorithm and send it to the actuator.

2. The unmanned surface vessel adaptive path tracking control method according to claim 1, characterized in that, The motion state of the virtual pilot ship is generated by a first-order integral model, and its motion direction is always consistent with the tangent direction of the desired path at the current position.

3. The unmanned surface vessel adaptive path tracking control method according to claim 2, characterized in that, The relative distance error is the Euclidean distance between the real-time position of the unmanned vessel and the real-time position of the virtual pilot vessel; the relative speed error is the difference between the real-time resultant speed of the unmanned vessel and the desired speed.

4. The unmanned surface vessel adaptive path tracking control method according to claim 1, characterized in that, The sliding surface is a linear combination of the relative distance error and the relative velocity error; the calculation of the generalized control force command includes an equivalent control term and a switching control term, wherein the switching control term is used to suppress the lumped disturbance.

5. The unmanned surface vessel adaptive path tracking control method according to claim 1, characterized in that, The adaptive law adjusts the estimate of the upper bound of the lumped disturbance online according to the rate of change of the sliding surface, and uses this estimate to calculate the gain of the switching control term.

6. The unmanned surface vessel adaptive path tracking control method according to claim 5, characterized in that, The thrust allocation algorithm allocates the yaw torque command as the thrust difference between the left and right thrusters, and allocates the sway force command as the sum of the thrusts of the left and right thrusters.

7. An unmanned surface vessel adaptive path tracking control device, used to implement the method as described in any one of claims 1 to 6, characterized in that, The device includes: The navigation information sensing module is used to acquire the real-time position, speed, and heading information of the unmanned vessel; The path processing and pilot ship generation module is used to perform step S1; The error calculation module is used to perform step S2. The core controller is used to execute steps S3 and S4; A thrust distributor is used to perform step S5.

8. The unmanned vessel adaptive path tracking control device according to claim 7, characterized in that, The core controller is a microprocessor or digital signal processor with an embedded adaptive sliding mode control algorithm; the thrust distributor is a programmable logic device with a pre-programmed thrust distribution algorithm or is implemented by the microprocessor.

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