An unmanned ship track self-adaptive fuzzy tracking control method with state quantization and input quantization

Abstract

This invention provides an adaptive fuzzy tracking control method for unmanned surface vessels (USVs) with state quantization and input quantization. The method includes: acquiring information about the surrounding environment and sea state of other vessels; establishing a kinematic and dynamic model of the USV; designing a system control law based on an adaptive backstepping method; approximating uncertainties in the control system using a fuzzy logic system and designing an adaptive law to ensure the stability of the control system; quantizing the state variables and input variables in the control system using a uniform quantizer, and using the quantized state feedback information for the design of the USV trajectory tracking controller; proving the boundedness of the error between quantized and unquantized variables in the closed-loop control system using a recursive method; and proving the stability of the designed fuzzy adaptive feedback tracking control system with state quantization and input quantization, considering both state quantization and input quantization, based on Lyapunov stability theory.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of artificial intelligence, in particular, especially relates to a kind of unmanned ship track adaptive fuzzy tracking control method with state quantization and input quantization. BACKGROUND

[0002] In recent years, with the rapid development of intelligent shipping, unmanned ships with the characteristics of miniaturization, intelligence and autonomous navigation have attracted widespread attention. Unmanned ships are widely used in water search and rescue, near-shore monitoring and other fields. As one of the key technologies of unmanned ships, track tracking control has become an important direction of research and discussion in the industry.

[0003] Track tracking control refers to the ship reaching the originally set position within a specified time under the action of the control system. Compared with dynamic positioning control and path tracking control, its control task has typical time constraints, and is therefore more challenging. In maritime practice, control signals need to be transmitted through a communication channel. Considering the limited bandwidth of maritime communication, to ensure the normal operation of the system within the given bandwidth, considering the state and input quantization of the ship track tracking control has more practical significance in navigation.

[0004] Quantization is the process of converting continuous signals into a set of discrete symbols or integer values. In ship control systems, subjective quantization of control inputs not only reduces the burden of signal transmission and reduces the frequency of actuator execution, but also reduces the control amplitude and improves the efficiency of the control system, which is more in line with the control rules of the bottom-level actuators in maritime practice. SUMMARY

[0005] According to the above-mentioned problem of track tracking control of unmanned ships under the condition of limited maritime communication bandwidth, a kind of unmanned ship track adaptive fuzzy tracking control method and system with state quantization and input quantization is provided. Based on the adaptive backstepping method, the system control law is designed, and the dynamic surface technology is combined to effectively reduce the calculation amount expansion problem of the virtual control law. For the uncertain terms in the control system, fuzzy logic system is used for approximation. At the same time, a uniform quantizer is used to quantize the state variables and input variables in the control system, and the quantized state feedback information is used for the design of the unmanned ship track tracking controller. The boundedness of the error between the quantized variables and the non-quantized variables in the closed-loop control system is proved by recursive method, and based on Lyapunov stability theory, it is proved that the stability of the designed fuzzy adaptive feedback tracking control system with state quantization and input quantization is considered.

[0006] The technical means adopted by the present application are as follows:

[0007] A kind of unmanned ship track adaptive fuzzy tracking control method with state quantization and input quantization, comprising:

[0008] S1, acquire the sea state information of the surrounding environment and other ships, establish the kinematics and dynamics model of the unmanned ship, and design a system control law based on an adaptive backstepping method;

[0009] S2, for the uncertain terms in the control system, a fuzzy logic system is used for approximation, and an adaptive rate is designed to ensure the stability of the control system;

[0010] S3, a uniform quantizer is used to quantize the state variables and input variables in the control system respectively, and the quantized state feedback information is used for the design of the unmanned ship track tracking controller;

[0011] S4, the boundedness of the error between the quantized variables and the non-quantized variables in the closed-loop control system is proved by a recursive method; based on Lyapunov stability theory, the stability of the designed fuzzy adaptive feedback tracking control system with state quantization and input quantization is proved while considering state quantization and input quantization.

[0012] Further, the step S1 specifically comprises:

[0013] S11, in the case of considering only the longitudinal, lateral and yaw three degrees of freedom motion in the horizontal plane, the kinematics and dynamics model of the unmanned ship is expressed as:

[0014]

[0015] wherein J(φ) represents a rotation matrix, v=[u,v,r] T represents the velocity vector of the ship, u and v represent the linear velocity of the ship in the x-axis and y-axis directions, and r represents the angular velocity of the ship; M, C(ν) and D(ν) represent the ship inertia mass matrix, Coriolis centripetal force matrix and damping matrix respectively; q(τ)=[τ1,τ2,τ3] T represents the quantized control input vector;

[0016] S12, define the position error as follows:

[0017] z1=η-η d

[0018] wherein η d represents the ideal track of the ship, and

[0019] S13, define the Lyapunov function as follows:

[0020]

[0021] then

[0022] S14, define z2 = v - a1, then Since in the inverse design, take Will cause the solution When the differential explosion occurs, a first-order low-pass filter is introduced;

[0023] S15, take a1 as The output of the low-pass filter , define Where c1>0 and satisfies

[0024] S16, take The filtering error generated is:

[0025]

[0026] S17, define Lyapunov function, Then:

[0027]

[0028] S18, design the unquantized system control input, as follows:

[0029]

[0030] Where, F(v) = M -1 (-C(v)v-D(v)v), c2>0.

[0031] Further, the step S2 specifically comprises:

[0032] S21, for the uncertain term F(v) = M -1 (-C(v)v-D(v)v) in the control input, according to the universal approximation theorem, for any small constant ∈i, there exists a fuzzy logic system θ *T ξ(i) such that F(v) = θ *T ξ(i)+∈; Where θ * is the ideal weight of the fuzzy system, ξ(i) is the fuzzy basis vector, i is the input variable x, y, φ, u, v, r of the fuzzy system, ∈ is the approximation error of the fuzzy system, satisfies |∈|≤∈ i ;

[0033] S22, let Be the estimated value of F(v), and Where, Is the estimated value of the ideal weight θ * , and Then:

[0034]

[0035] wherein,

[0036] S23, define Lyapunov function Take derivative of V3, get:

[0037]

[0038] S24, let get:

[0039]

[0040] Thus, design adaptive law as:

[0041]

[0042] Further, the step S3 specifically comprises:

[0043] S31, for state variables x, y, φ, u, y, r in the control system and control inputs τ1, τ2, τ3, use uniform quantizer for quantization, and the specific quantization process is as follows:

[0044]

[0045] wherein, s=x, y, φ, u, v, r, τ1, τ2, τ3, i∈z + χ(>0) represents quantization step and satisfies L i =χ, L i+1 =L i +χ, quantization error s-q(s) satisfies |s-q(s)|≤χ;

[0046] S32, for the designed control law and adaptive law, quantize the state variables, error surface and intermediate signals in the system to get:

[0047]

[0048]

[0049]

[0050]

[0051]

[0052] wherein, q(η)=[q(x), q(y), q(φ)] T, q(v)=[q(u), q(v), q(r)] T The intermediate signal for quantization is

[0053] S33. Based on the quantized state variables, error surface, and intermediate signals, the quantized feedback adaptive control input is obtained as follows:

[0054] Further, step S4 specifically includes:

[0055] S41. Without considering quantization, prove that the designed closed-loop feedback control system has stability;

[0056] S42. Prove the boundedness of the error between quantized and non-quantized variables in a closed-loop control system using a recursive method.

[0057] S43. While considering both state quantization and input quantization, prove the stability of the designed fuzzy adaptive feedback tracking control system with state quantization and input quantization.

[0058] Further, step S41 specifically includes:

[0059] S411. Define the Lyapunov function as follows:

[0060]

[0061] set up Then we have:

[0062]

[0063] Where B1 represents z1, z2, and γ1. If B1 is a function, and B1 has an upper bound, then ||B1|| 2 There is an upper boundary, denoted as ||B1|| 2 If the upper bound is N1, then

[0064] S412. When quantization is not considered, we get:

[0065]

[0066] Combining steps S18 and S24, we get:

[0067]

[0068] S413. Scaling the above equation, we get:

[0069]

[0070] Since ||J(φ)||≤A, we have:

[0071]

[0072] S414、Since the approximation error ∈ is small, when c1 is small enough and c2 is large enough and ensures , the error of the adaptive feedback closed-loop control system is uniformly ultimately bounded.

[0073] Further, the step S42 specifically comprises:

[0074] S421、Define the error surface, filtered error, intermediate signal and quantization error of control input as follows:

[0075]

[0076]

[0077]

[0078]

[0079]

[0080]

[0081] S422、From step S31, we have:

[0082] |x-q(x)|≤χ

[0083] |y-q(y)|≤χ

[0084] |φ-q(φ)|≤χ

[0085] Thus we have i.e. is bounded;

[0086] S423、From step S15, we have:

[0087]

[0088] Since J -1 (φ) is in the interval [-1, 1], there exists a constant R such that the inequality holds, so i.e. is bounded;

[0089] S424、From step S41, we know that the system error without introducing the quantization process is uniformly ultimately bounded, so the filtered error γ1 is bounded, i.e. there exists a constant such that Thus, there is

[0090]

[0091] That is Bounded;

[0092] S425, since Thus, there is

[0093]

[0094] That is Bounded;

[0095] S426, from step S31, we have:

[0096] |u-q(u)|≤χ

[0097] |v-q(v)|≤χ

[0098] |r-q(r)|≤χ

[0099] Therefore, there is a constant such that Thus, we have:

[0100]

[0101] That is Bounded;

[0102] S427, from step S18, we have:

[0103]

[0104] From step S31, we have |s-q(s)|≤χ, so there is a real number E such that Consider M as a real number matrix, and Therefore, there is such that That is, ||β τ || is bounded;

[0105] S428, according to steps S422-S427, there is a constant such that That is, the quantization error existing in the quantized feedback adaptive tracking closed-loop control system is bounded.

[0106] Further, the step S43 specifically includes:

[0107] S431, define the Lyapunov function as follows:

[0108]

[0109] Based on steps S23-S24, we obtain:

[0110]

[0111] S432, Let Then we have:

[0112]

[0113] Where B2 represents z1, z2, γ1, The function, denoted as ||B2|| 2 If the upper bound is N2, then

[0114] S433. Based on steps S413 and S414, we obtain:

[0115]

[0116] Based on steps S31 and S427, we obtain:

[0117]

[0118] Further results were obtained:

[0119]

[0120] S434. Since the approximation error ∈ is very small, when c1 is sufficiently small and c2 is sufficiently large, and it is guaranteed that... At that time, the error of the unmanned vessel fuzzy adaptive closed-loop feedback tracking control system with state and input quantization is consistent and eventually bounded.

[0121] Compared with the prior art, the present invention has the following advantages:

[0122] 1. The adaptive fuzzy tracking control method for unmanned vessels with state quantization and input quantization provided by this invention quantizes the state variables and input variables in the control system, which solves the problem of unmanned vessel tracking control under the condition of limited communication bandwidth at sea. While ensuring effective tracking, it reduces the burden of maritime communication signal transmission, reduces the frequency of actuator execution, and reduces the control amplitude.

[0123] 2. The unmanned vessel trajectory adaptive fuzzy tracking control method with state quantization and input quantization provided by the present invention constructs the system control input based on the adaptive backstepping method, and uses the universal approximation characteristic of fuzzy systems to perform fuzzy approximation on the uncertain terms in the control input. In a quantization-based control environment, trajectory tracking control of the unmanned vessel is realized.

[0124] 3. This invention proposes a method for quantization error consideration and closed-loop system stability determination that combines systematicity and universality. After quantizing the state variables, input variables and intermediate signals in the control system, the boundedness of the quantization error is proved by recursion, and the stability of the designed quantization feedback adaptive tracking control system is proved by Lyapunov stability theory.

[0125] 4. This invention conducted comparative experiments on the Matlab platform, comparing the ship control input before and after quantization. Simultaneously, a controller was designed using the quantized control input to track the unmanned vessel's trajectory. After considering input quantization, the system control input curve better aligns with maritime engineering practices, reducing the actuator execution frequency and control amplitude. This further verifies the effectiveness and rationality of the proposed adaptive fuzzy tracking control method with state and input quantization.

[0126] Based on the above reasons, this invention can be widely applied in fields such as artificial intelligence. Attached Figure Description

[0127] 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.

[0128] Figure 1 This is a flowchart of the method of the present invention.

[0129] Figure 2 This is a diagram showing the tracking results of an unmanned vessel provided in an embodiment of the present invention.

[0130] Figure 3 The displacement error and heading error diagrams for unmanned vessel trajectory tracking provided in this embodiment of the invention.

[0131] Figure 4 The control input curve before quantization is provided for an embodiment of the present invention.

[0132] Figure 5 The quantized control input curve is provided for an embodiment of the present invention.

[0133] Figure 6 The diagram shows the approximation result of the fuzzy logic system provided in the embodiment of the present invention. Detailed Implementation

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] In the description of this invention, it should be understood that the orientation or positional relationship indicated by directional terms such as "front, back, up, down, left, right", "horizontal, vertical, horizontal" and "top, bottom" is generally based on the orientation or positional relationship shown in the accompanying drawings, and is only for the convenience of describing this invention and simplifying the description. Unless otherwise stated, these directional terms 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, and therefore should not be construed as a limitation on the scope of protection of this invention. The directional terms "inner" and "outer" refer to the inner and outer contours relative to the outline of each component itself.

[0139] For ease of description, spatial relative terms such as "above," "over," "on the upper surface of," "above," etc., are used herein to describe the spatial positional relationship of a device or feature as shown in the figures to other devices or features. It should be understood that spatial relative terms are intended to encompass different orientations in use or operation besides the orientation of the device as described in the figures. For example, if the device in the figures is inverted, a device described as "above" or "above" other devices or structures would subsequently be positioned as "below" or "under" other devices or structures. Thus, the exemplary term "above" can include both "above" and "below." The device may also be positioned in other different ways (rotated 90 degrees or in other orientations), and the spatial relative descriptions used herein will be interpreted accordingly.

[0140] Furthermore, it should be noted that the use of terms such as "first" and "second" to define components is merely for the purpose of distinguishing the corresponding components. Unless otherwise stated, the above terms have no special meaning and therefore should not be construed as limiting the scope of protection of this invention.

[0141] like Figure 1 As shown, this invention provides an adaptive fuzzy tracking control method for unmanned surface vessels with state quantization and input quantization, comprising:

[0142] S1. Acquire information on the surrounding environment and sea state of other vessels, establish the kinematic and dynamic models of the unmanned vessel, and design the system control law based on the adaptive backstepping method; combine dynamic surface technology to solve the problem of computational expansion of the virtual control law;

[0143] S2. To address the uncertainties in the control system, a fuzzy logic system is used for approximation, and an adaptive rate is designed to ensure the stability of the control system.

[0144] S3. Use a uniform quantizer to quantize the state variables and input variables in the control system respectively, and use the quantized state feedback information for the design of the unmanned vessel trajectory tracking controller.

[0145] S4. Prove the boundedness of the error between quantized and unquantized variables in a closed-loop control system using a recursive method; based on Lyapunov stability theory, prove the stability of the designed fuzzy adaptive feedback tracking control system with state quantization and input quantization, considering both state quantization and input quantization.

[0146] In a specific implementation, as a preferred embodiment of the present invention, step S1 specifically includes:

[0147] S11. Considering only the three degrees of freedom of motion in the horizontal plane—swell, roll, and yaw—the kinematic and dynamic model of the unmanned vessel is expressed as follows:

[0148]

[0149] Where J(φ) denotes the rotation matrix, and v = [u, v, r] T Let represent the ship's velocity vector, where u and v represent the ship's linear velocities along the x and y axes, respectively, and r represent the ship's angular velocity; M, C(ν), and D(ν) represent the ship's inertial mass matrix, Coriolis centripetal force matrix, and damping matrix, respectively; q(τ) = [τ1, τ2, τ3]. T This represents the quantized control input vector;

[0150] S12. Define the position error as follows:

[0151] z1=η-η d

[0152] Where, η d To represent the ideal trajectory of a ship, then

[0153] S13. Define the Lyapunov function as follows:

[0154]

[0155] Then there is

[0156] S14. Define z2 = ν - α1, then Because in the inversion design, taking This will lead to the demand Differential explosion occurs at this time, so a first-order low-pass filter is introduced;

[0157] S15, take α1 as low-pass filter The output is defined. Where c1 > 0 and satisfies

[0158] S16, Take The resulting filtering error is:

[0159]

[0160] S17. Define Lyapunov functions. Then we have:

[0161]

[0162] S18. Design unquantified system control inputs, as shown below:

[0163]

[0164] Where F(v)=M -1 (-C(v)vD(v)v), c2>0.

[0165] In a specific implementation, as a preferred embodiment of the present invention, step S2 specifically includes:

[0166] S21. Regarding the uncertainty term F(v) = M in the control input. -1 (-C(v)vD(v)ν), according to the universal approximation theorem, for any small constant ∈ i There exists a fuzzy logic system θ *T ξ(ι) such that F(v)=θ *T ξ(ι)+∈; where θ * Let ξ(ι) be the ideal weights of the fuzzy system, ι be the fuzzy basis vectors, ι be the input variables of the fuzzy system (x, y, φ, u, v, r), and ∈ be the approximation error of the fuzzy system, satisfying |∈|≤∈ i ;

[0167] S22, Order Let F(ν) be an estimate, and in, For ideal weights θ * The estimated value, and Then we have:

[0168]

[0169] in,

[0170] S23. Define Lyapunov functions. Taking the derivative of V3, we get:

[0171]

[0172] S24, Order get:

[0173]

[0174] Therefore, the adaptive law is designed as follows:

[0175]

[0176] In a specific implementation, as a preferred embodiment of the present invention, step S3 specifically includes:

[0177] S31. For the state variables x, y, φ, u, y, r and the control inputs τ1, τ2, τ3 in the control system, a uniform quantizer is used for quantization. The specific quantization process is as follows:

[0178]

[0179] Among them, s=x, y, φ, u, v, r, τ1, τ2, τ3, i∈z + χ(>0) represents the quantization step size and satisfies L i =χ,L i+1 =L i +χ, the quantization error sq(s) satisfies |sq(s)|≤χ;

[0180] S32. Based on the designed control law and adaptive law, the state variables, error surface, and intermediate signals in the system are quantized to obtain:

[0181]

[0182]

[0183]

[0184]

[0185]

[0186] in, q(η) = [q(x), q(y), q(φ)] T , q(ν)=[q(u), q(v), q(r)] T The intermediate signal for quantization is

[0187] S33. Based on the quantized state variables, error surface, and intermediate signals, the quantized feedback adaptive control input is obtained as follows:

[0188] In a specific implementation, as a preferred embodiment of the present invention, step S4 specifically includes:

[0189] S41. Without considering quantization, prove that the designed closed-loop feedback control system has stability;

[0190] S42. Prove the boundedness of the error between quantized and non-quantized variables in a closed-loop control system using a recursive method.

[0191] S43. While considering both state quantization and input quantization, prove the stability of the designed fuzzy adaptive feedback tracking control system with state quantization and input quantization.

[0192] In a specific implementation, as a preferred embodiment of the present invention, step S41 specifically includes:

[0193] S411. Define the Lyapunov function as follows:

[0194]

[0195] set up Then we have:

[0196]

[0197] Where B1 represents z1, z2, and γ1. If B1 is a function, and B1 has an upper bound, then ||B1|| 2 There is an upper boundary, denoted as ||B1|| 2 If the upper bound is N1, then

[0198] S412. When quantization is not considered, we get:

[0199]

[0200] Combining steps S18 and S24, we get:

[0201]

[0202] S413. Scaling the above equation, we get:

[0203]

[0204] Since ||J(φ)||≤A, then:

[0205]

[0206] S414. Since the approximation error ∈ is very small, when c1 is sufficiently small and c2 is sufficiently large, and it is guaranteed that... At that time, the error of the adaptive feedback closed-loop control system becomes consistent and eventually bounded.

[0207] In a specific implementation, as a preferred embodiment of the present invention, step S42 specifically includes:

[0208] S421. Define the error surface, filtering error, intermediate signal, and quantization error of the control input as follows:

[0209]

[0210]

[0211]

[0212]

[0213]

[0214]

[0215] S422. From step S31, we can obtain:

[0216] |xq(x)|≤χ

[0217] |yq(y)|≤χ

[0218] |φ-q(φ)|≤χ

[0219] Therefore, there is Right now Bounded;

[0220] S423. From step S15, we can obtain:

[0221]

[0222] Because J -1 Since all elements in (φ) belong to the interval [-1, 1], there exists a constant R such that the inequality... Established, and thus Right now Bounded;

[0223] S424. From step S41, it can be seen that the systematic error without introducing a quantization process is consistently bounded. Therefore, the filtering error γ1 is bounded, that is, there exists a constant. make Therefore:

[0224]

[0225] Right now Bounded;

[0226] S425, due to Therefore:

[0227]

[0228] Right now Bounded;

[0229] S426. From step S31, we can obtain:

[0230] |uq(u)|≤χ

[0231] |vq(v)|≤χ

[0232] |rq(r)|≤χ

[0233] Therefore, there exists a constant. make Therefore, we can conclude that:

[0234]

[0235] Right now Bounded;

[0236] S427. From step S18, we get:

[0237]

[0238] From step S31, we know that |sq(s)|≤χ, so there exists a real number E such that Consider M as a real matrix, and Therefore, it exists. make That is, ||β τ ||Bounded;

[0239] S428. According to steps S422-S427, there exists a constant. Make That is, the quantization error in the quantization feedback adaptive tracking closed-loop control system is bounded.

[0240] In a specific implementation, as a preferred embodiment of the present invention, step S43 specifically includes:

[0241] S431. Define the Lyapunov function as follows:

[0242]

[0243] Based on steps S23-S24, we obtain:

[0244]

[0245] S432, Let Then we have:

[0246]

[0247] Where B2 represents z1, z2, γ1, The function, denoted as ||B2|| 2 If the upper bound is N2, then

[0248] S433. Based on steps S413 and S414, we obtain:

[0249]

[0250] Based on steps S31 and S427, we obtain:

[0251]

[0252] Further results were obtained:

[0253]

[0254] S434. Since the approximation error ∈ is very small, when c1 is sufficiently small and c2 is sufficiently large, and it is guaranteed that... At that time, the error of the unmanned vessel fuzzy adaptive closed-loop feedback tracking control system with state and input quantization is consistent and eventually bounded.

[0255] Example

[0256] To verify the effectiveness of the adaptive fuzzy tracking control method and system for unmanned surface vessels with input and state quantization proposed in this invention, this embodiment uses MATLAB / Simulink for computer simulation. The parameter settings are as follows:

[0257] Let the ideal trajectory of the unmanned vessel be [sin 2t, t, t]. T The actual initial position is (0, 1), and the quantization level χ = 0.001. Based on the quantization feedback adaptive tracking scheme designed in steps S2 and S3, the design parameters are taken as c1 = 25, c2 = 20, and κ = 0.01.

[0258] Based on the Matlab platform, a comparative experiment was conducted on the ship control input before and after quantization. At the same time, a controller designed using the quantized control input was used to track and control the ship's trajectory.

[0259] Experimental results are as follows Figures 2-6 As shown, Figure 2 The ship tracking results show that the designed tracking scheme achieves ideal tracking performance. Figure 3 The displacement and heading errors for ship trajectory tracking show that the errors almost converge to zero, further verifying the effectiveness of the controller. Figure 4 The control input curve before quantization. Figure 5 The figure shows the quantized control input curve. As can be seen from the figure, after considering the input quantization, the system control input curve is more in line with marine engineering practice, reducing the execution frequency of the actuator and reducing the control amplitude. Figure 6 The diagram shows the approximation results of the fuzzy logic system, demonstrating that the fuzzy system can approximate the unknown function very well.

[0260] Simulation results show that the tracking control strategy designed in this invention yields ideal simulation results, with small displacement tracking errors and heading errors. After considering input quantization, the system control input curve better matches nautical engineering practice, reducing the actuator execution frequency and control amplitude. This further verifies the effectiveness and rationality of the adaptive fuzzy tracking control method with state and input quantization proposed in this invention.

[0261] 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. An adaptive fuzzy tracking control method for unmanned surface vessel (USV) trajectory with state quantization and input quantization, characterized in that, include: S1. Obtain information on the surrounding environment and sea conditions of other vessels, establish the kinematics and dynamics model of the unmanned vessel, and design the system control law based on the adaptive backstepping method; S2. To address the uncertainties in the control system, a fuzzy logic system is used for approximation, and an adaptive rate is designed to ensure the stability of the control system. S3. A uniform quantizer is used to quantize the state variables and input variables in the control system, and the quantized state feedback information is used in the design of the unmanned vessel trajectory tracking controller, including: S31. For the state variables x, y, φ, u, y, r and the control inputs τ1, τ2, τ3 in the control system, a uniform quantizer is used for quantization. The specific quantization process is as follows: Among them, s=x, y, φ, u, v, r, τ1, τ2, τ3, i∈z + χ(>0) represents the quantization step size and satisfies L i =χ,L i+1 =L i +χ, the quantization error sq(s) satisfies |sq(s)|≤χ; S32. Based on the designed control law and adaptive law, the state variables, error surface, and intermediate signals in the system are quantized to obtain: in, q(η)=[q(x),q(y),q(φ)] T , q(v)=[q(u), q(v), q(r)] T The intermediate signal for quantization is S33. Based on the quantized state variables, error surface, and intermediate signals, the quantized feedback adaptive control input is obtained as follows: S4. Prove the boundedness of the error between quantized and unquantized variables in a closed-loop control system using a recursive method; based on Lyapunov stability theory, prove the stability of the designed fuzzy adaptive feedback tracking control system with state quantization and input quantization, considering both state quantization and input quantization.

2. The adaptive fuzzy tracking control method for unmanned surface vessel trajectory with state quantization and input quantization as described in claim 1, characterized in that, Step S1 specifically includes: S11. Considering only the three degrees of freedom of motion in the horizontal plane—swell, roll, and yaw—the kinematic and dynamic model of the unmanned vessel is expressed as follows: Where J(φ) denotes the rotation matrix, and v = [u, v, r] T Let represent the ship's velocity vector, where u and v represent the ship's linear velocities along the x and y axes, respectively, and r represent the ship's angular velocity; M, C(ν), and D(ν) represent the ship's inertial mass matrix, Coriolis centripetal force matrix, and damping matrix, respectively; q(τ) = [τ1, τ2, τ3]. T This represents the quantized control input vector; S12. Define the position error as follows: z1=n-n d Where, η d To represent the ideal trajectory of a ship, S13. Define the Lyapunov function as follows: Then there is S14. Define z2 = v - α1, then Because in the inversion design, taking This will lead to the demand Differential explosion occurs at this time, so a first-order low-pass filter is introduced; S15, Take α1 as low-pass filter The output is defined. Where c1 > 0 and satisfies S16, Take The resulting filtering error is: S17. Define the Lyapunov function. Then we have: S18. Design unquantified system control inputs, as shown below: Where F(v)=M -1 (-C(v)vD(v)v), c2>0.

3. The adaptive fuzzy tracking control method for unmanned surface vessel trajectory with state quantization and input quantization as described in claim 1, characterized in that, Step S2 specifically includes: S21. Regarding the uncertainty term F(v) = M in the control input. -1 (-C(v)vD(v)v), according to the universal approximation theorem, for any small constant ∈ i There exists a fuzzy logic system θ *T ξ(ι) such that F(v)=θ *T ξ(ι)+∈; where θ * Let ζ(ι) be the ideal weights of the fuzzy system, ι be the fuzzy basis vectors, ι be the input variables x, y, φ, u, v, r of the fuzzy system, and ε be the approximation error of the fuzzy system, satisfying | ∈ | ≤ ∈ i ; S22, Order Let F(v) be an estimate, and in, For ideal weights θ * The estimated value, and Then we have: in, S23. Define Lyapunov functions. Taking the derivative of V3, we get: S24, Order get: Therefore, the adaptive law is designed as follows:

4. The adaptive fuzzy tracking control method for unmanned surface vessel trajectory with state quantization and input quantization as described in claim 1, characterized in that, Step S4 specifically includes: S41. Without considering quantization, prove that the designed closed-loop feedback control system has stability; S42. Prove the boundedness of the error between quantized and non-quantized variables in a closed-loop control system by using a recursive method. S43. While considering both state quantization and input quantization, prove the stability of the designed fuzzy adaptive feedback tracking control system with state quantization and input quantization.

5. The adaptive fuzzy tracking control method for unmanned surface vessel trajectory with state quantization and input quantization according to claim 4, characterized in that, Step S41 specifically includes: S411. Define the Lyapunov function as follows: set up Then we have: Where B1 represents z1, z2, and γ1. If B1 is a function, and B1 has an upper bound, then ||B1|| 2 There is an upper boundary, denoted as ||B1|| 2 If the upper bound is N1, then S412. When quantization is not considered, we get: Combining steps S18 and S24, we get: S413. Scaling the above equation, we get: Since ||J(φ)||≤A, then: S414. Since the approximation error ∈ is very small, when c1 is sufficiently small, c2 is sufficiently large, and c1 < -1 is guaranteed. At that time, the error of the adaptive feedback closed-loop control system becomes consistent and eventually bounded.

6. The adaptive fuzzy tracking control method for unmanned surface vessel trajectory with state quantization and input quantization according to claim 4, characterized in that, Step S42 specifically includes: S421. Define the error surface, filtering error, intermediate signal, and quantization error of the control input as follows: S422. From step S31, we can obtain: |xq(x)|≤χ |yq(y)|≤χ |φ-q(φ)|≤χ Therefore, there is Right now Bounded; S423. From step S15, we can obtain: Because J -1 Since all elements in (φ) belong to the interval [-1, 1], there exists a constant R such that the inequality... Established, and thus Right now Bounded; S424. From step S41, it can be seen that the systematic error without introducing a quantization process is consistently bounded. Therefore, the filtering error γ1 is bounded, that is, there exists a constant. make Therefore: Right now Bounded; S425, due to Therefore: Right now Bounded; S426. From step S31, we can obtain: |uq(u)|≤χ |vq(v)|≤χ rq(r)|≤χ Therefore, there exists a constant. make Therefore, we can conclude that: Right now Bounded; S427. From step S18, we get: From step S31, we know that |sq(s)|≤χ, so there exists a real number E such that Consider M as a real matrix, and Therefore, it exists. make That is, ||β τ ||Bounded; S428. According to steps S422-S427, there exists a constant. Make That is, the quantization error in the quantization feedback adaptive tracking closed-loop control system is bounded.

7. The adaptive fuzzy tracking control method for unmanned surface vessel trajectory with state quantization and input quantization according to claim 4, characterized in that, Step S43 specifically includes: S431. Define the Lyapunov function as follows: Based on steps S23-S24, we obtain: S432, Let Then we have: Where B2 represents z1, z2, γ1 The function, denoted as ||B2|| 2 If the upper bound is N2, then S433. Based on steps S413 and S414, we obtain: Based on steps S31 and S427, we obtain: Further results were obtained: S434. Since the approximation error ∈ is very small, when c1 is sufficiently small and c2 is sufficiently large, and it is guaranteed that c1 < -1, At that time, the error of the unmanned vessel fuzzy adaptive closed-loop feedback tracking control system with state and input quantization is consistent and eventually bounded.

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