An autonomous surface vehicle system tracking control method with unknown measurement sensitivity

Abstract

The present application relates to the technical field of autonomous surface vehicle control, and is a kind of autonomous surface vehicle system tracking control method with unknown measurement sensitivity, comprising: coordinate transformation is carried out on expected trajectory and filter output;First Lyapunov function in backstepping framework is constructed, and virtual controller and first adaptive law are designed;Second Lyapunov function in backstepping framework is constructed, and actual controller and second adaptive law are designed;Based on the results of the control scheme, a three-degree-of-freedom autonomous surface vehicle system mathematical model considering the problem of unknown measurement sensitivity is constructed;In view of the problem that the system state is unobservable due to unknown sensor sensitivity, a new fuzzy adaptive control mechanism is designed by combining the properties of fuzzy basis function and adaptive technology to process.

Description

Technical Field

[0001] This invention relates to the field of autonomous surface vehicle control technology, and in particular to a tracking control method for an autonomous surface vehicle system with unknown measurement sensitivity. Background Technology

[0002] In the trajectory tracking control scheme of a three-degree-of-freedom autonomous surface vehicle (ASW) system, the following problems are prone to occur: the system trajectory tracking accuracy is significantly affected by time-varying disturbances (such as water flow disturbances and wind and wave disturbances), and traditional control methods are difficult to achieve real-time compensation for dynamic disturbances, and the steady-state tracking error is prone to exceed the allowable range; when there are unknown dynamic models in the system (such as perturbations of hull hydrodynamic parameters and changes in propulsion system characteristics), it is easy to cause a mismatch between the control law design and the actual system characteristics, directly causing control output deviations, and the stability of the closed-loop system is difficult to guarantee; traditional backstepping control methods generally do not have optimization mechanisms designed for the problem of repeated differentiation, and as the control order increases, the computational load of the algorithm increases exponentially, which seriously restricts the real-time control response speed of the three-degree-of-freedom ASW vehicle.

[0003] Furthermore, some adaptive solutions are not deeply integrated with fuzzy control technology, resulting in insufficient ability to collaboratively handle time-varying disturbances and unknown dynamics, making them unsuitable for tracking and control requirements in complex aquatic environments. Traditional solutions give less consideration to uncertainties in the measurement process; when sensors have unknown sensitivity deviations, tracking errors can be further amplified, affecting control accuracy.

[0004] The aforementioned shortcomings limit the practical application of three-degree-of-freedom autonomous surface vehicle systems in high-dynamic and high-precision scenarios. Therefore, it is necessary to provide a novel fuzzy adaptive tracking control scheme that takes into account both time-varying interference suppression and unknown measurement sensitivity. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a tracking control method for an autonomous surface vehicle (ASW) system with unknown measurement sensitivity. This invention employs a fuzzy logic system to approximate the unknown nonlinear terms in a three-degree-of-freedom ASW system. To address the unmeasurable state problem caused by the unknown measurement sensitivity, the properties of fuzzy basis functions combined with adaptive techniques are utilized. To address the repeated differentiation problem that may arise from the backstepping design method, filtering techniques are introduced during the controller design process, effectively reducing computational complexity. This invention effectively mitigates the impact of the unknown dynamic model on system performance, achieving the expected tracking performance. Based on Lyapunov stability theory, it is rigorously proven that all signals in the closed-loop system are bounded under this control strategy. This invention effectively solves the trajectory tracking control problem of a three-degree-of-freedom ASW system affected by unknown measurement sensitivity.

[0006] The technical means employed in this invention are as follows: A tracking control method for an autonomous surface vehicle (ASW) system with unknown measurement sensitivity includes: performing coordinate transformation on the desired trajectory and filter output; constructing a first Lyapunov function in the backstepping framework and designing a virtual controller and a first adaptive law; constructing a second Lyapunov function in the backstepping framework and designing an actual controller and a second adaptive law; deriving the results based on the control scheme and constructing a mathematical model of the three-degree-of-freedom ASW system considering the unknown measurement sensitivity problem; and addressing the problem of unmeasurable system state caused by unknown sensor sensitivity by designing a novel fuzzy adaptive control mechanism using the properties of fuzzy basis functions and combining adaptive techniques.

[0007] Furthermore, the filter can be represented as:

[0008] in, For design parameters, For virtual controllers, For filter output, express The first derivative with respect to time, and satisfy ,in, This refers to boundary layer error; Perform coordinate transformation on the desired trajectory and filter output:

[0009] in, For the desired trajectory, and This represents the two-level state vector of a three-degree-of-freedom autonomous surface vehicle system considering the problem of unknown measurement sensitivity. and These represent the corresponding tracking errors.

[0010] Furthermore, the first Lyapunov function Represented as:

[0011] in, and For design parameters, and Adaptive parameters and With the estimated value of the adaptive parameters and The estimation error satisfies and ; The virtual controller and the first adaptive law are represented as follows:

[0012]

[0013]

[0014] in, , and For design parameters, This represents the coordinate transformation matrix. Indicates a positive design parameter.

[0015] Furthermore, the second Lyapunov function can be expressed as:

[0016] in, , and For design parameters, , and For adaptive parameters , and The estimation error, and satisfying , , ,in, , and For adaptive parameters , and The estimated value; The actual controller and the second adaptive law are expressed as follows:

[0017]

[0018]

[0019] in, , , , and For design parameters; Represents the basis functions of a fuzzy vector. .

[0020] Furthermore, the results derived from the control scheme are... Represented as:

[0021]

[0022]

[0023] The mathematical model for constructing a three-degree-of-freedom autonomous surface vehicle system that considers the problem of unknown measurement sensitivity is expressed as follows:

[0024]

[0025]

[0026]

[0027] in, , , and These represent lateral displacement and longitudinal displacement, respectively. Indicates the heading angle. and These represent the lateral velocity and the longitudinal velocity, respectively. Indicates the angular velocity of the heading. This indicates the state of a three-degree-of-freedom autonomous surface vehicle system considering the unknown measurement sensitivity issue. It represents the first derivative of the system state with respect to time. and Indicates unknown time-varying interference. and Indicates system input and output; For an unknown but bounded constant, , , and These represent the coordinate transformation matrix, Coriolis centripetal force matrix, damping matrix, and inertia matrix, respectively. Let be an unknown smooth function matrix.

[0028] Furthermore, the novel fuzzy adaptive control mechanism can be expressed as follows:

[0029] in, For fuzzy logic weight vectors, , , It is the approximation error, and satisfies , It is an unknown constant that is greater than zero; Utilizing the properties of the fuzzy basis functions Perform the following processing:

[0030] in, , .

[0031] Compared with the prior art, the present invention has the following advantages: This invention provides a tracking control method for an autonomous surface vehicle (ASW) system with unknown measurement sensitivity. The core of this method includes adaptive technology, a fuzzy logic system, filter technology, and a backstepping control framework. For a three-degree-of-freedom ASW system, a mathematical model is established based on its dynamic equations, taking into account the unknown measurement sensitivity, to construct a system tracking control model, providing a model foundation for subsequent controller design. To address the problem of unmeasurable state due to unknown sensor sensitivity, the properties of fuzzy logic systems combined with adaptive technology are used to design a fuzzy adaptive tracking controller. The backstepping control structure is designed based on filter technology: within the backstepping control framework, a first-order filter is introduced to avoid potential repeated differentiation problems and alleviate the computational burden on the controller. System stability and boundedness verification of all signals: based on the designed Lyapunov function, it is proven that all signals within the closed-loop system are bounded.

[0032] The method of this invention ensures that the system can still achieve trajectory tracking control that balances steady-state and dynamic performance even when the sensor sensitivity is unknown, and ensures that all signals in the closed-loop system are bounded. Attached Figure Description

[0033] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0034] Figure 1 This is a framework diagram of the tracking and control method for an autonomous surface vehicle system with unknown measurement sensitivity in this invention.

[0035] Figure 2 As described in the embodiments of the present invention A schematic diagram of tracking performance in the direction.

[0036] Figure 3 As described in the embodiments of the present invention A schematic diagram of tracking performance in the direction.

[0037] Figure 4 The system heading angle in this embodiment of the invention A schematic diagram of the tracking performance.

[0038] Figure 5These are the two adaptive laws of the first part of the system in this embodiment of the invention. and A schematic diagram of the curve.

[0039] Figure 6 The three adaptive laws of the second part of the system in this embodiment of the invention , and A schematic diagram of the curve.

[0040] Figure 7 This is the system control input in the embodiments of the present invention. A schematic diagram of the curve. Detailed Implementation

[0041] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0042] 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 following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the present invention or its application or use. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0043] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0044] Unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps described in these embodiments do not limit the scope of the invention. It should also be understood that, for ease of description, the dimensions of the various parts shown in the drawings are not drawn to actual scale. Techniques, methods, and devices known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and devices should be considered part of the specification. In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values. It should be noted that similar reference numerals and letters in the following figures denote similar items; therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0045] like Figure 1 As shown, this invention provides a tracking control method for an autonomous surface vehicle system with unknown measurement sensitivity, comprising: performing coordinate transformation on the desired trajectory and the filter output; in a preferred embodiment of this invention, the filter is represented as:

[0046] in, For design parameters, For virtual controllers, For filter output, express The first derivative with respect to time, and satisfy ,in, This refers to boundary layer error; Perform coordinate transformation on the desired trajectory and filter output:

[0047] in, For the desired trajectory, and This represents the two-level state vector of a three-degree-of-freedom autonomous surface vehicle system considering the problem of unknown measurement sensitivity. and These represent the corresponding tracking errors.

[0048] Based on the backstepping control framework, a first-order filter is introduced to avoid the problem of repeated differentiation and reduce the computational burden on the controller. The first step is to design an adaptive law to adapt to unknown parameters, construct a virtual controller, and combine Lyapunov theory to suppress the errors caused by measurement sensitivity and disturbances, laying the foundation for the design of the actual controller.

[0049] The first Lyapunov function in the backstepping framework is constructed, and a virtual controller and a first adaptive law are designed; in a preferred embodiment of the invention, the first Lyapunov function is... Represented as:

[0050] in, and For design parameters, and Adaptive parameters and With the estimated value of the adaptive parameters and The estimation error satisfies and ; The virtual controller and the first adaptive law are expressed as:

[0051]

[0052]

[0053] in, , and For design parameters, This represents the coordinate transformation matrix. Indicates a positive design parameter.

[0054] The second Lyapunov function in the backstepping framework is constructed, and the actual controller and the second adaptive law are designed. In a preferred embodiment of the present invention, the second Lyapunov function is expressed as:

[0055] in, , and For design parameters, , and For adaptive parameters , and The estimation error, and satisfying , , ,in, , and For adaptive parameters , and The estimated value.

[0056] The actual controller and the second adaptive law are expressed as follows:

[0057]

[0058]

[0059] in, , , and For design parameters; Represents the basis functions of a fuzzy vector. .

[0060] Based on the results derived from the control scheme, a mathematical model of a three-degree-of-freedom autonomous surface vehicle system considering the unknown measurement sensitivity problem is constructed. In a preferred embodiment of this invention, the results derived from the control scheme are... Represented as:

[0061]

[0062]

[0063] The mathematical model for constructing a three-degree-of-freedom autonomous surface vehicle system that considers the problem of unknown measurement sensitivity is expressed as follows:

[0064]

[0065]

[0066]

[0067] in, , , and These represent lateral displacement and longitudinal displacement, respectively. Indicates the heading angle. and These represent the lateral velocity and the longitudinal velocity, respectively. Indicates the angular velocity of the heading. This indicates the state of a three-degree-of-freedom autonomous surface vehicle system considering the unknown measurement sensitivity issue. It represents the first derivative of the system state with respect to time. and Indicates unknown time-varying interference. and Indicates system input and output; For an unknown but bounded constant, This is the coordinate transformation matrix. The Coriolis centripetal force matrix, Here is the damping matrix. The inertia matrix, Let be an unknown smooth function matrix.

[0068] Due to the existence of problems such as unknown model parameters, external time-varying disturbances, and unknown measurement sensitivity, this invention, when designing a fuzzy logic system to approximate an unknown dynamic model, addresses the problem of unmeasurable system state caused by unknown sensor sensitivity by designing a novel fuzzy adaptive control mechanism that utilizes the properties of fuzzy basis functions and combines them with adaptive techniques. Specifically, as a preferred embodiment of this invention, the novel fuzzy adaptive control mechanism is expressed as follows:

[0069] in, For fuzzy logic weight vectors, , , It is the approximation error, and satisfies , It is an unknown constant that is greater than zero; Limited by measurement sensitivity, It cannot be directly obtained for controller construction; the properties of fuzzy basis functions are utilized. Perform the following processing:

[0070] in, , .

[0071] Example 1 This embodiment presents an adaptive fuzzy trajectory tracking control method for a three-degree-of-freedom autonomous surface vehicle, which includes adaptive technology, fuzzy logic system, filter technology, and backstepping control framework. (1) Establish a system model: For a three-degree-of-freedom autonomous surface vehicle system, a mathematical model is established based on its dynamic equations, taking into account the unknown measurement sensitivity, and a system tracking control model is constructed to provide a model basis for the subsequent controller design. (2) To address the problem of unmeasurable state caused by unknown sensor sensitivity, a fuzzy adaptive tracking controller is designed by combining the properties of fuzzy logic systems with adaptive techniques. (3) Backstepping control structure based on filter technology: Under the backstepping control framework, a first-order filter is introduced to avoid the possible problem of "repeated differentiation" and alleviate the computational pressure on the controller; (4) Verification of system stability and boundedness of all signals: Based on the designed Lyapunov function, it is proved that all signals in the closed-loop system are bounded.

[0072] The technical solution is as follows: Model building:

[0073] in, and These represent the position-heading and linear angular velocity status information, respectively. and Indicates unknown time-varying interference. and This indicates the system input and output. ,in It is an unknown but bounded constant. , , and These represent the coordinate transformation matrix, the Coriolis centripetal force matrix, the damping matrix, and the inertia matrix, respectively. Let be an unknown smooth function matrix.

[0074] Assumption 1: Desired signal and its first derivative and second derivative All are continuous and bounded.

[0075] Assumption 2: Time-varying disturbances in a three-degree-of-freedom autonomous surface vehicle system and satisfy and ,in, and It is an unknown bounded positive parameter.

[0076] Assumption 3: For and There are unknown parameters. and satisfy: Therefore, we can know , and .

[0077] The following first-order filter is used to solve the problem of repeated differentiation that may occur during the backstepping design process, which significantly reduces the computational burden:

[0078] in, These are positive design parameters. and The outputs are for the virtual controller and the filter, respectively. satisfy ,in, It is boundary layer error.

[0079] Define the coordinate transformation as:

[0080] in, The desired trajectory.

[0081] To address issues such as unknown model parameters, external disturbances, and unknown measurement sensitivity, this invention proposes a control strategy based on an adaptive control mechanism. The specific form of the designed controller is as follows:

[0082]

[0083] in, , , , , and These are positive design parameters. Explanations of other parameters will be provided in the subsequent design section.

[0084] The backstepping design in this embodiment is carried out in two steps.

[0085] S1. Construct the corresponding adaptive parameter update law, design a virtual controller, and combine Lyapunov theory to effectively suppress the influence of measurement sensitivity on the system, laying a solid foundation for the subsequent design of the actual controller.

[0086] The Lyapunov function constructed in this step has the following specific form:

[0087] in, and Positive design parameters and Adaptive parameters and Its estimated value and The estimation error satisfies and .

[0088] Regarding time Differentiation yields:

[0089] After simplification, we get:

[0090] Combining assumptions 2 and 3, we can use Young's inequality to perform the following scaling on the terms of the above equation:

[0091] in, .

[0092] Substituting the scaling terms into the derivative terms, we get:

[0093] Substituting the designed virtual controller into the above equation, we get:

[0094] in, .

[0095] In addition, the following scaling holds true in the above formula:

[0096] Easy to obtain:

[0097] In this step, the following adaptive law is designed:

[0098] in, and The design parameters are positive.

[0099] Furthermore, we can obtain:

[0100] in, .

[0101] S2. Based on the virtual controller in S1, construct the Lyapunov function in S2, use the fuzzy logic system to approximate the unknown nonlinear terms, design the adaptive law and the actual controller, and ensure the boundedness of all signals in the closed-loop system and the system tracking performance.

[0102] Construct the following Lyapunov function:

[0103] in, , and Positive design parameters , and Adaptive parameters , and Its estimated value , and The estimation error satisfies , and .

[0104] Regarding time Differentiation yields:

[0105] in, satisfy .

[0106] Similar to the processing method in the first step, the following scaling methods exist:

[0107] in, , .

[0108] Substituting the scaling terms into the derivative terms, we get:

[0109] in, .

[0110] Construct the following fuzzy logic system pair Approximation:

[0111] in, , It is the approximation error, and satisfies , It is an unknown and greater than zero constant. Limited by measurement sensitivity, It cannot be directly obtained for controller construction; therefore, the properties of fuzzy basis functions are utilized. Perform the following processing:

[0112] in, , .

[0113] Therefore, we can obtain:

[0114] in, .

[0115] Substitute the actual controller designed in this invention We can obtain:

[0116] in, .

[0117] Easy to obtain:

[0118] In this step, the following adaptive law is designed:

[0119] in, , and The design parameters are positive.

[0120] Therefore, we can conclude that:

[0121] Next, we can obtain the following system of inequalities:

[0122] Substituting the above system of inequalities back, we get:

[0123] in, , .

[0124] Based on the above derivation results and combined with Lyapunov stability theory, the constructed control algorithm can ensure that the tracking error of the three-degree-of-freedom autonomous surface vehicle system considering unknown measurement sensitivity proposed in this invention is consistent and eventually bounded, and that all signals in the closed-loop system are bounded.

[0125] Example 2 This example provides a three-degree-of-freedom autonomous surface vehicle system as a specific application, and details the implementation process of the present invention.

[0126] To verify the effectiveness of the proposed control scheme, a three-degree-of-freedom autonomous surface vehicle (ASW) system was selected. Based on the dynamic equations of the three-degree-of-freedom ASW system, the following system model was constructed:

[0127] The specific parameters of the three-degree-of-freedom autonomous surface vehicle system are as follows:

[0128]

[0129]

[0130]

[0131]

[0132]

[0133] The unknown measurement sensitivity matrix is: and

[0134] The controller and adaptive law related parameters are designed as follows: .

[0135] The filter parameters and other design parameters are as follows: , , .

[0136] The initial values ​​are designed as follows: .

[0137] Based on the above simulation parameters and initial value settings, the technical solution proposed in this invention was verified by numerical simulation. The simulation results are as follows: Figures 2-7 As shown. By Figures 2-7 As can be seen, the system output under the control method proposed in this invention... , , Both can track the corresponding reference trajectory relatively well. , , ,in, , , Traditional adaptive fuzzy control methods fail to consider the impact of unknown measurement sensitivity in the control design, resulting in a significant tracking error between the system output and the reference trajectory. This result demonstrates that the proposed method can reduce the impact of unknown measurement sensitivity on the system output and improve the tracking accuracy of each channel's output state relative to the reference trajectory. Figure 5 and Figure 6 The adaptive parameters are given respectively. , , , , The estimated value curves are readily apparent. It is easy to see that each adaptive parameter can be adjusted in a short time and eventually remain bounded and stable. Figure 7 Three control input channels , , The process of change.

[0138] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A tracking and control method for an autonomous surface vehicle system with unknown measurement sensitivity, characterized in that, include: Perform coordinate transformation on the desired trajectory and the filter output; Construct the first Lyapunov function in the backstepping framework, and design the virtual controller and the first adaptive law; Construct the second Lyapunov function in the backstepping framework and design the practical controller and the second adaptive law; Based on the results derived from the control scheme, a mathematical model of a three-degree-of-freedom autonomous surface vehicle system considering the unknown measurement sensitivity problem is constructed. To address the problem of unpredictable system state caused by unknown sensor sensitivity, a novel fuzzy adaptive control mechanism is designed by utilizing the properties of fuzzy basis functions and combining them with adaptive techniques.

2. The tracking and control method for an autonomous surface vehicle system with unknown measurement sensitivity according to claim 1, characterized in that, The filter is represented as: in, For design parameters, For virtual controllers, For filter output, express The first derivative with respect to time, and satisfy ,in, This refers to boundary layer error; Perform coordinate transformation on the desired trajectory and filter output: in, For the desired trajectory, and This represents the two-level state vector of a three-degree-of-freedom autonomous surface vehicle system considering the problem of unknown measurement sensitivity. and These represent the corresponding tracking errors.

3. The tracking and control method for an autonomous surface vehicle system with unknown measurement sensitivity according to claim 2, characterized in that, The first Lyapunov function Represented as: in, and For design parameters, and Adaptive parameters and With the estimated value of the adaptive parameters and The estimation error satisfies and ; The virtual controller and the first adaptive law are represented as follows: in, , and For design parameters, This represents the coordinate transformation matrix. Indicates a positive design parameter.

4. The tracking and control method for an autonomous surface vehicle system with unknown measurement sensitivity according to claim 3, characterized in that, The second Lyapunov function can be expressed as: in, , and For design parameters, , and For adaptive parameters , and The estimation error, and satisfying , , ,in, , and For adaptive parameters , and The estimated value; The actual controller and the second adaptive law are expressed as follows: in, , , and For design parameters, Represents the basis functions of a fuzzy vector. .

5. The tracking and control method for an autonomous surface vehicle system with unknown measurement sensitivity according to claim 4, characterized in that, The result derived from the control scheme Represented as: The mathematical model for constructing a three-degree-of-freedom autonomous surface vehicle system that considers the problem of unknown measurement sensitivity is expressed as follows: in, , , and These represent lateral displacement and longitudinal displacement, respectively. Indicates the heading angle. and These represent the lateral velocity and the longitudinal velocity, respectively. Indicates the angular velocity of the heading. This indicates the state of a three-degree-of-freedom autonomous surface vehicle system considering the unknown measurement sensitivity issue. This represents the first derivative of the system state with respect to time. and Indicates unknown time-varying interference. and Indicates system input and output; For an unknown but bounded constant, , , and These represent the coordinate transformation matrix, Coriolis centripetal force matrix, damping matrix, and inertia matrix, respectively. Let be an unknown smooth function matrix.

6. The tracking and control method for an autonomous surface vehicle system with unknown measurement sensitivity according to claim 5, characterized in that, The novel fuzzy adaptive control mechanism is expressed as follows: in, , For fuzzy logic weight vectors, , It is the approximation error, and satisfies , It is an unknown constant that is greater than zero; Utilizing the properties of the fuzzy basis functions Perform the following processing: in, , .

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