An autonomous tugboat event-triggered quantized towing control method based on command filtering

Abstract

The application discloses an autonomous tugboat event-triggered quantization towing control method based on command filtering, comprising the following steps: constructing a nonlinear system model of an AT and a towed ship, and using a guide virtual ship to obtain a reference route of the AT and the towed ship; constructing a guide law according to the positions of the guide virtual ship and the actual ship to generate expected trajectories of the AT and the towed ship; designing a virtual control law of the AT and the towed ship in the forward freedom degree and the yaw freedom degree, introducing a filter to compensate for a dynamic error, and obtaining a compensated dynamic error; designing an intermediate control law of the AT and the towed ship in the forward freedom degree and the yaw freedom degree and an update law of an adaptive law according to the compensated dynamic error; designing an event-triggered quantization AT controller based on command filtering and a towed ship controller based on command filtering based on the intermediate control law of the AT and the towed ship in the forward freedom degree and the yaw freedom degree and the update law of the adaptive law; and using the two controllers to realize towing control of the AT on the towed ship; and the application can improve the response speed of the system, avoid frequent adjustment caused by slight fluctuations, significantly reduce unnecessary signal transmission and calculation amount, and thus save limited communication bandwidth and calculation resources.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of ship motion control research, and particularly relates to an autonomous tug event trigger quantization towing control method based on command filtering. BACKGROUND

[0002] In the existing ship path tracking control system, continuous time control algorithm is usually relied on to realize real-time control of dynamic system. However, this method has significant defects, especially in resource-limited network control systems (such as sensor networks, embedded systems, etc.), periodic update of control signals will cause unnecessary waste of resources, and it is difficult to cope with complex environmental dynamics. In addition, the filter error influence existing in the signal processing process of the existing system has not been effectively compensated. Specifically, the existing control technology faces the following main problems:

[0003] Based on the above analysis, the existing path tracking control algorithm has the following two defects:

[0004] In the continuous time algorithm, the update of the control signal is periodic, and the system must calculate and send the control signal regularly regardless of whether the state has changed significantly. Although the controller uses the MLP and DOB-based control algorithm, which can effectively estimate the disturbance and has robustness to model uncertainty, this mode may cause a large amount of invalid calculation and communication in the actual engineering background, wasting valuable bandwidth and computing resources. Especially in systems with limited network bandwidth and high communication cost, the waste of resources will significantly affect the system performance.

[0005] The existing MLP and DOB-based control algorithm lacks effective response to external disturbances and noise. Since the control signal is updated continuously without discrimination, the system may produce frequent signal adjustments when facing small amplitude noise or uncertainty, causing system jitter, further affecting the stability and dynamic response performance of the system. In addition, the control algorithm often uses a first-order filter to avoid the "complexity explosion phenomenon" caused by the derivation of the virtual controller, but the first-order filter will introduce errors in the signal processing process, thereby affecting the control accuracy. These errors have not been effectively compensated, resulting in delays and error accumulation when the system responds to rapidly changing signals, further reducing the performance and stability of the system. SUMMARY

[0006] The application provides an autonomous tugboat event-triggered quantized towing control method based on command filtering to overcome the technical problems that the existing path tracking control algorithm causes a large amount of resource waste when implementing control, lacks effective response to external interference and noise, causes system jitter, and introduces errors when introducing a filter to respond to interference and noise, and reduces the performance and stability of the control system.

[0007] To achieve the above-mentioned purpose, the technical scheme of the application is:

[0008] An autonomous tugboat event-triggered quantized towing control method based on command filtering, comprising:

[0009] S1: Constructing a nonlinear system model of the AT and the towed ship respectively as the control object of the controller designed in the subsequent steps, and using the guide virtual ship of the AT and the towed ship to obtain the reference course of the AT and the towed ship;

[0010] S2: Constructing a guidance law according to the positions of the guide virtual ship in the reference course and the actual AT and towed ship, and generating the expected trajectory of the AT and the towed ship according to the guidance law;

[0011] S3: Designing a virtual control law of the AT and the towed ship in the forward freedom degree and the yaw freedom degree respectively, introducing a filter to compensate the dynamic error between the actual trajectory and the expected trajectory of the AT and the towed ship in the virtual control law, and obtaining the compensated dynamic error of the AT and the towed ship;

[0012] S4: Designing an intermediate control law of the AT and the towed ship in the forward freedom degree and the yaw freedom degree according to the compensated dynamic error of the AT and the towed ship respectively, and designing an update law of the adaptive law of the AT and the towed ship according to the compensated dynamic error of the AT and the towed ship and the two intermediate control laws respectively;

[0013] S5: Designing an event-triggered quantized AT controller based on command filtering and a towed ship controller based on command filtering based on the intermediate control law of the AT and the towed ship in the forward freedom degree and the yaw freedom degree and the update law of the adaptive law of the AT and the towed ship;

[0014] S6: Realizing the towing control of the AT on the towed ship by using the event-triggered quantized AT controller based on command filtering and the towed ship controller based on command filtering.

[0015] Further, constructing a nonlinear system model of the AT and the towed ship respectively as the control object of the controller designed in the subsequent steps, and using the guide virtual ship of the AT and the towed ship to obtain the reference course of the AT and the towed ship, comprising:

[0016] S11, a nonlinear mathematical model of the AT and the towed ship is respectively constructed, as shown in formulas (1) and (2),

[0017]

[0018]

[0019] wherein T represents the autonomous tug, i.e., the AT, S represents the towed ship, x i ,y i , i = T, S represents the position coordinates of the AT and the towed ship, ψ i represents the heading angles of the AT and the towed ship, u i represents the forward velocities of the AT and the towed ship, v i represents the cross drift velocities of the AT and the towed ship, r i represents the yaw angular velocities of the AT and the towed ship, represents the hydrodynamic added masses of the AT and the towed ship in the forward freedom degree, represents the hydrodynamic added masses of the AT and the towed ship in the cross drift freedom degree, represents the hydrodynamic added masses of the AT and the towed ship in the yaw freedom degree, f vi (v), f ri (v) represent the nonlinear terms of the AT and the towed ship in the forward, cross drift and yaw freedom degrees, respectively, d wui , d wvi , d wri are the disturbance forces and moments of the external environment acting on the AT and the towed ship in the forward, cross drift and yaw freedom degrees, T u (·), F r (·) represent the control gains of the main engine speed and the rudder angle of the tug, n p represents the main engine speed of the tug, δ p represents the rudder angle of the tug, Q(n p |n p |), Q(δ r ) represent the quantized inputs of the hysteresis quantizer of the tug in the forward freedom degree and the yaw freedom degree, respectively, H u represents the towing force acting on the towed ship, L1 represents the length of the tug, L2 represents the length of the towed ship, θ2 represents the included angle between the tow rope and the tug, and θ1 represents the included angle between the tow rope and the towed ship.

[0020] The hysteresis quantizer is shown in formula (3),

[0021] Q(k) = G(k)k + D (3)

[0022] wherein Q(k) represents a hysteresis quantizer, k represents an actual hysteresis quantization input of the AT, G(k) represents a quantization control gain, as shown in equation (4), and D represents an additional disturbance term, as shown in equation (5);

[0023]

[0024]

[0025] The hysteresis quantizer is piecewise linearly represented, as shown in equation (6),

[0026]

[0027] wherein k j = ρ (1-j) k min , is a constant, is a derivative of k, sgn is a sign function, k j is a threshold value of the hysteresis quantizer, j is a quantization level, k min is a quantization dead zone, ρ is a quantization density, Q(k(t - )) represents a quantization value of an actual input k at a left limit time of time t;

[0028] S12, using a guide virtual ship to obtain a reference course of the AT and the towed ship, as shown in equation (7),

[0029]

[0030] wherein x d (t), y d (t) represent position coordinates of the guide virtual ship on the reference course, ψ d (t) represents a heading angle of the guide virtual ship, r d (t) represents an expected yaw angular velocity of the guide virtual ship, and t represents a time of continuous movement of the virtual guide ship.

[0031] Further, a guidance law is constructed according to positions of the guide virtual ship and the actual AT and the towed ship in the reference course, comprising:

[0032] The guidance law is shown in equation (8),

[0033]

[0034] wherein ψ ri represents a bearing angle of the AT and the towed ship relative to the guide virtual ship, with a transverse distance between the towed ship and the towing ship as an x axis and a longitudinal distance as a y axis, x ei represents a position error of the AT and the towed ship relative to the guide virtual ship on the x axis, and yei represents the position error of AT and the towed ship relative to the guide virtual ship in y-axis, z ei represents the distance between AT and the towed ship relative to the guide virtual ship.

[0035] Further, a filter is introduced to compensate the dynamics error between the actual trajectory and the desired trajectory of AT and the towed ship in the virtual control law, and the compensated dynamics error of AT and the towed ship is obtained, including:

[0036] S31, defining the position error and the heading error of AT and the towed ship, as shown in formula (9),

[0037]

[0038] In the formula, represents the derivative of the distance between AT and the towed ship relative to the guide virtual ship, represents the derivative of the heading error of AT and the towed ship, ψ ri represents the azimuth angle of AT and the towed ship relative to the guide virtual ship, r i represents the yaw angle velocity of AT and the towed ship; i = T, S, T represents AT, and S represents the towed ship.

[0039] S32, designing the virtual control law of AT and the towed ship in the forward freedom degree and the yaw freedom degree, as shown in formula (10),

[0040]

[0041] In the formula, α ui represents the virtual control law of AT and the towed ship in the forward freedom degree, α ri represents the virtual control law of AT and the towed ship in the yaw freedom degree, represents the design parameter of the virtual control law of AT and the towed ship in the forward freedom degree and the yaw freedom degree, represents the constant of the virtual control law of AT and the towed ship in the forward freedom degree;

[0042] S33, introducing a filter to compensate the dynamics error of AT and the towed ship, and the filter is shown in formula (11),

[0043]

[0044] In the formula, β ιi , i = u, r represents the filter variable of AT and the towed ship in the forward freedom degree and the yaw freedom degree; ∈ ιi is a time constant greater than zero, α ιi (0), β ιi (0) is α ιi , βιi The initial value of q ιi =α ιi -β ιi , represents the first-order filter error;

[0045] S34. Define the dynamic error of the AT and the towed vessel according to formula (11), as shown in formula (12),

[0046] ι ei =β ιi -ι i (12)

[0047] Where, ι ei represents the dynamic error of AT and towed vessel;

[0048] S35. Design a filter error compensation signal based on the filter variables of the AT and the towed vessel in the forward degree of freedom and the pitching degree of freedom, as shown in formula (13):

[0049]

[0050] Where, ζ ιi represents the filter error compensation signal of AT and towed vessel, variable ζ ιi The initial value of k is zero, ιi are the design parameters of the AT and towed vessel filter error compensation signals;

[0051] S36, compensating the dynamic error according to the filter error compensation signal to obtain a compensated dynamic error, as shown in formula (14),

[0052] χ ei =β ιi -ι ei -ζ ιi (14)

[0053] Where, χ ei Represents the dynamic error after compensation, χ=U,R.

[0054] Furthermore, according to the compensated dynamic errors of the AT and the towed vessel, intermediate control laws for the forward and yaw degrees of freedom of the AT and the towed vessel are designed, respectively, including:

[0055] S41. Combining formulas (1), (2), (11), (13) and (14), the dynamic errors of AT and the towed vessel are derived, as shown in formula (24).

[0056]

[0057] Where, denote the neural network weights of AT and the towed ship in the surge and yaw freedom, respectively, S uT (v), S rT (v) and S uS (v), S rS (v) denote the Gaussian functions of AT and the towed ship in the surge and yaw freedom, respectively, v denotes the input of the neural network of AT and the towed ship in the surge and yaw freedom, ε uT , ε uS , and ε uT , ε rS denote the approximation errors of AT and the towed ship in the surge and yaw freedom, G(N u ) denotes the quantized control gain of AT in the surge freedom, G(δ r ) denotes the quantized control gain of AT in the yaw freedom, N u = n p | n p | denotes the control input of AT in the surge freedom without hysteresis quantization;

[0058] S42, design the intermediate control law of AT in the surge and yaw freedom and the intermediate control law of the towed ship in the surge and yaw freedom, as shown in equations (16) and (17),

[0059]

[0060]

[0061] wherein, denote the design parameters of the intermediate control law of AT, k uT , k rT denote the design parameters of the filter compensation signal of AT, are the design parameters of the intermediate control law of the towed ship, k uS , k rS denote the design parameters of the filter compensation signal of the towed ship, u = T, S are the estimates of the boundary of the external disturbance of AT and the towed ship in the surge and yaw freedom, b ui , b ri , i = T, S are the design constants of the disturbance estimate of AT and the towed ship in the surge and yaw freedom, U ei denote the surge velocity error of AT and the towed ship in the surge freedom, R ei denote the yaw angular velocity error of AT and the towed ship in the yaw freedom, ∈ ui denote the time constant of the filter variable of AT and the towed ship in the surge freedom, ∈ ridenotes the time constant of the filter variable of AT and the towed ship in the surge degree of freedom, denotes the derivative of the filter variable of AT and the towed ship in the surge degree of freedom, denotes the derivative of the filter variable of AT and the towed ship in the yaw degree of freedom, ζ ui denotes the filter error compensation signal of AT and the towed ship in the surge degree of freedom, ζ ri denotes the filter error compensation signal of AT and the towed ship in the yaw degree of freedom, S ui denotes the basis function of the neural network of AT and the towed ship in the surge degree of freedom, S ri denotes the basis function of the neural network of AT and the towed ship in the yaw degree of freedom.

[0062] Further, the update law of the adaptive law of AT and the towed ship is designed according to the compensated dynamics error of AT and the towed ship and two intermediate control laws, including:

[0063] S43, the event-triggered measurement error and the event-triggered condition of AT in the surge degree of freedom and the yaw degree of freedom are defined according to the dynamics error and the position error, as shown in formulas (18) and (19),

[0064]

[0065]

[0066] In formula (18), e n denotes the event-triggered measurement error of AT in the surge degree of freedom, e δ denotes the event-triggered measurement error of AT in the yaw degree of freedom, denotes the sampling time at the event-triggered moment in the surge degree of freedom and the yaw degree of freedom, k = n, δ denotes the sampling time at the next event-triggered moment in the surge degree of freedom and the yaw degree of freedom;

[0067] In formula (19), e denotes the event-triggered condition of AT in the surge degree of freedom, denotes the event-triggered condition of AT in the yaw degree of freedom, W is the flag of event triggering, and inf denotes the lower limit, denotes the triggering threshold of the event-triggered condition of AT in the surge degree of freedom and the yaw degree of freedom and are the design parameters of the event-triggered condition of AT in the surge degree of freedom and the yaw degree of freedom ‖S r (v)‖ denotes the neural network basis function S​​r The norm of (v), ‖S u (v)‖ represents the neural network basis function S on the forward degree of freedom u (v) norm;

[0068] S44. During the time period between the sampling time of the event triggering moment and the sampling time of the next event triggering moment, the updating law of the adaptive law of the neural network weight estimator at the AT event triggering moment is designed according to the intermediate control law of the AT, as shown in formula (20).

[0069]

[0070] Where, α k represents the intermediate control law of AT in forward and yaw degrees of freedom, and represents the adaptive law of the neural network weight estimator at the time of AT event triggering; S uT (ν) and S rT (ν) represents the Gaussian function of AT in the forward and yaw degrees of freedom;

[0071] S45. Design an update law for the AT adaptive law based on the intermediate control law of the AT. The update law for the AT adaptive law is shown in formula (21):

[0072]

[0073] Where, represents the update law of the AT adaptive law, represents the gain adaptive design parameter of AT in forward and yaw degrees of freedom; for The initial value of

[0074] S46. Design the update law of the towed ship's adaptive law based on the intermediate control law of the towed ship. The update law of the towed ship's adaptive law is shown in formula (22):

[0075]

[0076] Where, represents the update law of the towed ship's adaptive law; represents the gain adaptive design parameters of the towed vessel in the forward and bow degrees of freedom; for The initial value of

[0077] S47. Design an update law for the adaptive law of the neural network weight estimator at the event triggering moment of the towed ship based on the intermediate control law of the towed ship, as shown in formula (23):

[0078]

[0079] wherein, denotes the update law of the neural network weight estimator adaptive law at the event-triggered moment of the tugboat, is a design constant of the weight adaptive law of the towed vessel in the forward and yaw freedom; S uS (v) and S rS (v) denotes the Gaussian function of the tugboat in the forward and yaw freedom;

[0080] S48, the update law of the adaptive law of the external disturbance of the AT and the towed vessel is designed as formula (24),

[0081]

[0082] wherein, denotes the design constant of the external disturbance adaptive law of the AT and the towed vessel in the forward and yaw freedom; is the estimation of the external disturbance boundary of the AT and the towed vessel; is the initial value of .

[0083] Further, based on the intermediate control law of the AT and the towed vessel in the forward and yaw freedom and the update law of the adaptive law of the AT and the towed vessel, an event-triggered quantized AT controller based on command filtering and a towed vessel controller based on command filtering are designed, comprising:

[0084] S51, based on the intermediate control law of the AT and the towed vessel in the forward and yaw freedom and the update law of the adaptive law of the AT and the towed vessel, an event-triggered quantized AT controller based on command filtering and a towed vessel controller based on command filtering are designed as formula (25) and (26),

[0085]

[0086]

[0087] wherein, n p denotes the rotation speed of the controller of the AT in the forward freedom, α U denotes the intermediate control law of the towed vessel in the forward freedom, denotes the control law of the controller of the towed vessel in the forward freedom, denotes the adaptive law of the intermediate control law of the AT in the forward freedom, α n denotes the intermediate control law of the AT in the forward freedom, δ r denotes the rudder angle of the controller of the AT in the yaw freedom, denotes the adaptive law of AT in the yaw degree of freedom, a δ denotes the intermediate control law of AT in the yaw degree of freedom, denotes the adaptive law of the tugboat in the forward degree of freedom, a R denotes the intermediate control law of the tugboat in the yaw degree of freedom, denotes the control law of the controller of the tugboat in the yaw degree of freedom, denotes the adaptive law of the tugboat in the yaw degree of freedom.

[0088] Beneficial effects: the application provides an autonomous tugboat event-triggered quantitative towing control method based on command filtering, by designing virtual control laws of AT and the towed ship, and introducing a filter to compensate for errors in the virtual control law, eliminating the delay and error caused by the first-order filter, by designing intermediate control laws and adaptive update laws of AT and the towed ship, and designing event-triggered controllers of AT and the towed ship according to the intermediate control laws and the adaptive update laws, the response speed of the system can be improved, frequent adjustment caused by slight fluctuations can be avoided, unnecessary signal transmission and calculation amount can be significantly reduced, thereby saving limited communication bandwidth and calculation resources. BRIEF DESCRIPTION OF DRAWINGS

[0089] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, below will be a brief introduction to the drawings needed to be used in the embodiment or prior art description, obviously, the drawings in the following description are some embodiments of the present application, and for those skilled in the art, without creative labor, other drawings can also be obtained according to these drawings.

[0090] Figure 1 The method flow chart of the autonomous tugboat event-triggered quantitative towing control method based on command filtering of the present application;

[0091] Figure 2 The coordinate system diagram of the tugboat and the towed ship in the embodiment of the present application;

[0092] Figure 3 The event-triggered quantitative control path tracking control structure diagram of the tugboat of the present application;

[0093] Figure 4 The trajectory comparison curve diagram of comparing the present application with the prior art in the embodiment of the present application;

[0094] Figure 5 The error change curve diagram of comparing the present application with the prior art in the embodiment of the present application;

[0095] Figure 6 The control input change curve diagram of comparing the present application with the prior art in the embodiment of the present application. DETAILED DESCRIPTION

[0096] In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0097] The embodiment provides an autonomous tugboat event-triggered quantized towing control method based on command filtering, as shown in Figure 1 , which comprises the following steps.

[0098] S1: Nonlinear system models of an AT and a towed ship are respectively constructed as control objects of controllers designed in subsequent steps, and reference courses of the AT and the towed ship are obtained by using guide virtual ships of the AT and the towed ship.

[0099] S2: A guidance law is constructed according to positions of the guide virtual ships in the reference courses and actual positions of the AT and the towed ship, and expected trajectories of the AT and the towed ship are generated according to the guidance law.

[0100] S3: Virtual control laws of the AT and the towed ship in a forward freedom degree and a yaw freedom degree are respectively designed, a filter is introduced to compensate for dynamic errors between actual trajectories and expected trajectories of the AT and the towed ship in the virtual control laws, and compensated dynamic errors of the AT and the towed ship are obtained.

[0101] S4: Intermediate control laws of the AT and the towed ship in the forward freedom degree and the yaw freedom degree are respectively designed according to the compensated dynamic errors of the AT and the towed ship, and update laws of adaptive laws of the AT and the towed ship are respectively designed according to the compensated dynamic errors of the AT and the towed ship and the two intermediate control laws.

[0102] S5: An event-triggered quantized AT controller based on command filtering and a towed ship controller based on command filtering are designed based on the intermediate control laws of the AT and the towed ship in the forward freedom degree and the yaw freedom degree and the update laws of the adaptive laws of the AT and the towed ship.

[0103] S6: Towing control of the AT on the towed ship is realized by using the event-triggered quantized AT controller based on command filtering and the towed ship controller based on command filtering.

[0104] Specifically, first, the nonlinear system models of the AT and the towed ship are constructed respectively as the control objects of the controllers designed in the subsequent steps, and the reference course of the AT and the towed ship is obtained using the guide virtual ship of the AT and the towed ship, the guide virtual ship is used to form a reference course of the AT and the towed ship, and the AT and the towed ship need to track the reference course, and the reference course is used as a reference for obtaining the expected trajectory; second, the guidance law is constructed according to the positions of the guide virtual ship in the reference course and the actual AT and the towed ship, and the expected trajectory of the AT and the towed ship is generated according to the guidance law, the guidance law is constructed and the expected trajectory is generated, which provides a path reference for the subsequent construction of the controller to control the AT and the towed ship to move along the expected trajectory, and eliminates the error between the actual trajectory and the expected trajectory; second, the virtual control law of the AT and the towed ship in the forward freedom degree and the yawing freedom degree is designed respectively, a filter is introduced to compensate the dynamic error between the actual trajectory and the expected trajectory of the AT and the towed ship in the virtual control law, and the compensated dynamic error of the AT and the towed ship is obtained, the filter is introduced, which can avoid the complexity explosion caused by repeated differentiation of the virtual control law, and the compensation signal is designed, which can eliminate the influence of the first-order filter error on the control system, and avoids the performance degradation caused by the filtering error; second, the intermediate control law of the AT and the towed ship in the forward freedom degree and the yawing freedom degree is designed according to the compensated dynamic error of the AT and the towed ship, and the update law of the adaptive law of the AT and the towed ship is designed according to the compensated dynamic error of the AT and the towed ship and the two intermediate control laws, which can effectively reduce the influence of the error on the control effect, improve the performance of the control system, improve the control precision of the AT and the towed ship, effectively cope with the time-varying or unknown dynamic characteristics of the AT and the towed ship system, and enhance the robustness of the AT and the towed ship; third, the event-triggered quantized AT controller based on command filtering and the towed ship controller based on command filtering are designed based on the intermediate control law of the AT and the towed ship in the forward freedom degree and the yawing freedom degree and the update law of the adaptive law of the AT and the towed ship, the event trigger mechanism is set for the position error, the heading error and the dynamic error, and the system is ensured to update the control signal only when the signal changes exceed a certain threshold, so as to realize the synchronous update of the controller and the neural network weight, thereby improving the response speed of the system; in addition, combined with the hysteresis quantization mechanism, the system can avoid frequent adjustment caused by small fluctuations, significantly reduce unnecessary signal transmission and calculation amount, thereby saving limited communication bandwidth and calculation resources; finally, the event-triggered quantized AT controller based on command filtering and the towed ship controller based on command filtering are used to realize the towing control of the AT on the towed ship.

[0105] In specific embodiments, the scheme of constructing the nonlinear system models of the AT and the towed ship respectively as the control objects of the controllers designed in the subsequent steps, and obtaining the reference course of the AT and the towed ship using the guide virtual ship of the AT and the towed ship is:

[0106] S11, construct the nonlinear mathematical model of AT and the towed ship respectively, as shown in formula (27) and formula (28),

[0107]

[0108]

[0109] In the formula, T represents the autonomous tug, that is, AT, S represents the towed ship, x i ,y i , i = T, S represents the position coordinates of AT and the towed ship, ψ i represents the heading angle of AT and the towed ship, u i represents the forward speed of AT and the towed ship, v i represents the transverse drift speed of AT and the towed ship, r i represents the yaw angle speed of AT and the towed ship, represents the hydrodynamic added mass of AT and the towed ship in the forward freedom degree, represents the hydrodynamic added mass of AT and the towed ship in the transverse drift freedom degree, represents the hydrodynamic added mass of AT and the towed ship in the yaw freedom degree, represents the nonlinear terms of AT and the towed ship in the forward, transverse drift and yaw freedom degrees respectively, d wui ,d wvi ,d wri is the disturbance force and torque of the external environment acting on the forward, transverse drift and yaw freedom degrees of AT and the towed ship, T u (·), F r (·) respectively represent the control gain of the main engine speed and the rudder angle of the tug, n p represents the main engine speed of the tug, δ p represents the rudder angle of the tug, Q(n p |n p |), Q(δ r ) respectively represent the quantized input of the hysteresis quantizer in the forward freedom degree and the yaw freedom degree of the tug, H u represents the towing force acting on the towed ship, L1 represents the length of the tug, L2 represents the length of the towed ship, θ2 represents the included angle between the tow rope and the tug, θ1 represents the included angle between the tow rope and the towed ship;

[0110] The hysteresis quantizer is shown in formula (29),

[0111] Q(k) = G(k)k + D (29)

[0112] where Q(k) represents the hysteresis quantizer, k represents the actual hysteresis quantized input of AT, G(k) represents the quantization control gain, as shown in equation (30), and D represents an additional disturbance term, as shown in equation (31);

[0113]

[0114]

[0115] The piecewise linearization of the hysteresis quantizer is represented as shown in equation (32),

[0116]

[0117] where k j = ρ (1-j) k min , is a design constant greater than 0, is the derivative of k, sgn is the sign function, k j is the threshold of the hysteresis quantizer of k, j is the quantization level, k min is the quantization dead zone, ρ is the quantization density, Q(k(t - )) represents the quantized value of the actual input k at the left limit time of time t;

[0118] S12, using the guide virtual ship to obtain the reference course of the AT and the towed ship, as shown in equation (33),

[0119]

[0120] where x d (t), y d (t) represent the position coordinates of the guide virtual ship on the reference course, ψ d (t) represents the heading angle of the guide virtual ship, r d (t) represents the desired yaw angular velocity of the guide virtual ship, and t represents the time of continuous movement of the virtual guide ship; the angular velocity r d can control the curvature of the virtual guide ship to generate a straight or curved trajectory, if r d = 0, the heading angle ψ d of the guide virtual ship remains unchanged, and the guide virtual ship moves along a straight line, and the reference course is a straight line, if r d ≠ 0, the heading angle ψ d of the guide virtual ship changes over time, and the guide virtual ship moves along a curve, and the reference course is a curve.

[0121] Specifically, the coordinate system diagram of the AT and the towed ship is as shown in Figure 2As shown in the figure, AT represents the tugboat, and l1 represents half of the length of the tugboat, l2 represents half of the length of the towed ship, x b ,y b are the horizontal and vertical coordinate axes of the coordinate system of the AT and the towed ship, respectively, the horizontal coordinate axis is based on the horizontal distance between the towed ship and the tugboat, and the vertical coordinate axis is based on the vertical distance between the towed ship and the tugboat. E ,Y E ,O E are the horizontal and vertical coordinate axes and the origin of the earth coordinate system, respectively.

[0122] The steps for obtaining the reference course of the AT and the towed ship using the virtual guide ship are as follows:

[0123] First, initialize the state of the virtual guide ship, set the initial position (x d0 ,y d0 ) and initial heading angle ψ d0 of the virtual guide ship, set the speed u d and angular velocity r d of the virtual guide ship, which are usually determined by the desired task or trajectory planning goal.

[0124] Second, update x d (t), y d (t), and ψ d (t) at each time t using a numerical integration method according to formula (33), as shown in formula (34),

[0125]

[0126] By iterating the above formulas, the continuous motion trajectory (x d (t), y d (t)) of the virtual guide ship is obtained.

[0127] Finally, set the reference course, and the position trajectory (x d (t), y d (t)) of the virtual guide ship is the desired reference course.

[0128] In this scheme, a nonlinear system model of the AT and the towed ship is designed as the control object of the controller in the subsequent steps, a hysteresis quantizer is introduced to ensure that the control signal is only updated when the system state changes significantly, avoiding frequent adjustments caused by small fluctuations.

[0129] In a specific embodiment, the scheme for constructing a guidance law based on the positions of the guide virtual ship and the actual AT and towed ship in the reference course and generating the desired trajectory of the AT and the towed ship according to the guidance law is:

[0130] The guidance law is constructed according to the positions of the guided virtual ship, the actual AT and the towed ship in the reference route. The guidance law is shown in formula (35):

[0131]

[0132] Where, ψ ri AT and the azimuth of the towed vessel relative to the guided virtual ship are represented by Figure 2 The horizontal distance between the towed vessel and the tugboat is the x-axis, and the longitudinal distance is the y-axis. ei represents the position error of the AT and the towed ship relative to the guided virtual ship on the x-axis, and y ei represents the position error of the AT and the towed ship relative to the guided virtual ship on the y axis, ei Indicates the distance between the AT and the towed vessel relative to the guided virtual ship.

[0133] The desired trajectories of the AT and the towed vessel are generated according to the guidance law. For details, see Zhang G, Liu S, Zhang X, et al. Event-triggered cooperative formation control for autonomous surface vehicles under the maritime search operation[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(11): 21392-21404.

[0134] In this embodiment, a guidance law is constructed and a desired trajectory is generated to provide a path reference for the subsequent construction of a controller to control the AT and the towed vessel to move along the desired trajectory, thereby eliminating the error between the actual trajectory and the desired trajectory.

[0135] In a specific embodiment, virtual control laws for the AT and the towed vessel in terms of forward and yaw degrees of freedom are designed respectively. A filter is introduced to compensate for the dynamic errors between the actual and expected trajectories of the AT and the towed vessel in the virtual control laws. The scheme for obtaining the compensated dynamic errors of the AT and the towed vessel is:

[0136] S31. Define the position error and heading error of AT and the towed vessel, as shown in formula (36):

[0137]

[0138] Where, represents the derivative of AT and the distance between the towed ship and the guided virtual ship, ψ = ψ - ψ ri r = r - r i ψ = ψ - ψ

[0139] S32, design the virtual control law of the AT and the towed ship in the forward freedom and the yaw freedom, as shown in equation (37),

[0140]

[0141] wherein, α ui r = r - r ri ψ = ψ - ψ design parameters of the virtual control law of the AT and the towed ship in the forward freedom and the yaw freedom, a constant of the virtual control law of the AT and the towed ship in the forward freedom; both the design parameters and the constant are greater than 0;

[0142] S33, introduce a filter to compensate for the dynamics error of the AT and the towed ship, the filter being shown in equation (38),

[0143]

[0144] wherein, β ιi , i = u, r represents the filter variable of the AT and the towed ship in the forward freedom and the yaw freedom; ∈ ιi is a time constant greater than 0, α ιi (0), β ιi (0) are initial values of α ιi , β ιi ; q ιi = α ιi - β ιi , representing a first-order filter error;

[0145] S34, define the dynamics error of the AT and the towed ship according to equation (38), as shown in equation (39),

[0146] i ei = β ιi - i i (39)

[0147] wherein, i ei represents the dynamics error of the AT and the towed ship;

[0148] S35, design filter error compensation signal according to filter variable of AT and towed ship in forward freedom degree and yaw freedom degree, as shown in formula (40),

[0149]

[0150] In the formula, ζ ιi represents filter error compensation signal of AT and towed ship, variable ζ ιi The initial value is zero, k ιi is the design parameter of AT and towed ship filter error compensation signal greater than 0;

[0151] S36, compensate the dynamic error according to the filter error compensation signal, get compensated dynamic error, as shown in formula (41),

[0152] χ ei = β ιi - ι ei - ζ ιi (41)

[0153] In the formula, χ ei represents compensated dynamic error, χ = U, R, the motion state at this time has eliminated the influence of filter error.

[0154] In this embodiment, the virtual control law of AT and towed ship in forward freedom degree and yaw freedom degree is designed respectively, which can stabilize the position error and heading error generated by AT and towed ship, and the filter is introduced to avoid the complexity explosion caused by repeated differentiation of virtual control law; The compensation signal can eliminate the influence of first-order filter error q ιi = α ιi - β ιi On control system, which not only makes the system can effectively smooth control signal, but also maintains high precision control performance, avoids the performance decline caused by filter error.

[0155] In specific embodiments, according to the compensated dynamic error of AT and towed ship, the intermediate control law of AT and towed ship in forward freedom degree and yaw freedom degree is designed respectively, and the scheme of designing the update law of AT and towed ship adaptive law according to the compensated dynamic error of AT and towed ship and two intermediate control laws is:

[0156] S41, derive the dynamic error of AT and towed ship according to formula (27), (28), (38), (40) and (41) as shown in formula (42),

[0157]

[0158] In the formula, are the neural network weights of AT and the towed vessel in the forward and bow degrees of freedom, respectively. uT (v),S rT (ν) and S uS (ν),S rS (ν) represents the Gaussian function of the AT and the towed vessel on the forward and yaw degrees of freedom, ν represents the input of the neural network of the AT and the towed vessel on the forward and yaw degrees of freedom, ε uT ,ε uS and ε rT ,ε rS are the approximation errors of AT and the towed vessel in the forward and bow degrees of freedom, respectively. G(N u ) represents the quantized control gain of AT on the forward degree of freedom, G(δ r ) represents the quantitative control gain N of AT on the yaw degree of freedom u =n p |n p |, represents the control input of AT in the forward degree of freedom without hysteresis quantization;

[0159] S42. Design the intermediate control law of the AT in the forward and yaw degrees of freedom and the intermediate control law of the towed vessel in the forward and yaw degrees of freedom, as shown in formulas (43) and (44).

[0160]

[0161]

[0162] Where, represents the design parameter greater than 0 of the intermediate control law of AT, k uT ,k rT Indicates the design parameter greater than 0 of the AT filter compensation signal, is the design parameter greater than 0 of the intermediate control law of the towed vessel, k uS ,k rS represents the design parameter greater than 0 of the towed vessel filter compensation signal, i=T, S is the estimate of the external disturbance boundary of AT and the towed vessel in the forward and bow degrees of freedom, respectively. ui ,b ri , i=T, S are the design constants greater than 0 for the interference estimation of AT and the towed vessel in the forward and bow degrees of freedom, respectively, U ei represents the forward speed error between AT and the towed vessel in the forward degree of freedom, R ei represents the angular velocity error of AT and the towed vessel in the yaw degree of freedom, ∈ ui represents the time constant of the filter variables of AT and the towed vessel in the forward degree of freedom, ∈ri denotes the time constant of the filter variable of AT and the towed ship in the yaw degree of freedom, denotes the derivative of the filter variable of AT and the towed ship in the forward degree of freedom, denotes the derivative of the filter variable of AT and the towed ship in the yaw degree of freedom, ui denotes the filter error compensation signal of AT and the towed ship in the forward degree of freedom, ri denotes the filter error compensation signal of AT and the towed ship in the yaw degree of freedom, ui denotes the basis function of the neural network of AT and the towed ship in the forward degree of freedom, ri denotes the basis function of the neural network of AT and the towed ship in the yaw degree of freedom;

[0163] S43, defines the event-triggered measurement error and the event-triggered condition of AT in the forward and yaw degrees of freedom according to the dynamic error and the position error, as shown in equations (45) and (46),

[0164]

[0165]

[0166] In equation (45), e n denotes the event-triggered measurement error of AT in the forward degree of freedom, δ denotes the event-triggered measurement error of AT in the yaw degree of freedom, denotes the sampling time at the event-triggered moment in the forward and yaw degrees of freedom, k = n, δ denotes the sampling time at the next event-triggered moment in the forward and yaw degrees of freedom;

[0167] In equation (46), e denotes the event-triggered condition of AT in the forward degree of freedom, denotes the event-triggered condition of AT in the yaw degree of freedom, W is the flag of event triggering, and inf denotes the lower limit, denotes the triggering threshold of the event-triggered condition of AT in the forward and yaw degrees of freedom, and is a design parameter greater than 0 of the event-triggered condition of AT in the forward and yaw degrees of freedom, ‖S r (v)‖ denotes the norm of the neural network basis function S r (v) in the yaw degree of freedom, u (v)‖ denotes the norm of the neural network basis function S unorm of (v) ;

[0168] S44, the update law of the neural network weight estimator adaptive law of the event-triggered time of the AT is designed according to the intermediate control law of the AT, and the time period of the sampling time of the event-triggered time and the sampling time of the next event-triggered time is as shown in formula (47),

[0169]

[0170] In the formula, α k The intermediate control law of the AT on the forward degree of freedom and the yaw degree of freedom is represented as And The neural network weight estimator adaptive law of the event-triggered time of the AT is represented as S uT (v) and S rT (v) represents the Gaussian function of the AT on the forward degree of freedom and the yaw degree of freedom;

[0171] S45, the update law of the AT adaptive law is designed according to the intermediate control law of the AT, and the update law of the AT adaptive law is as shown in formula (48),

[0172]

[0173] In the formula, The update law of the AT adaptive law is represented as The gain adaptive design parameter of the AT on the forward degree of freedom and the yaw degree of freedom is represented as The initial value of ;

[0174] S46, the update law of the towed ship adaptive law is designed according to the intermediate control law of the towed ship, and the update law of the towed ship adaptive law is as shown in formula (49),

[0175]

[0176] In the formula, The update law of the towed ship adaptive law is represented as The gain adaptive design parameter of the towed ship on the forward degree of freedom and the yaw degree of freedom is represented as The initial value of ;

[0177] S47, the update law of the neural network weight estimator adaptive law of the event-triggered time of the towed ship is designed according to the intermediate control law of the towed ship, and the update law of the neural network weight estimator adaptive law of the event-triggered time of the towed ship is as shown in formula (50),

[0178]

[0179] In the formula, an update law of the neural network weight estimator adaptive law representing the event-triggered moment of the towed ship, a design constant greater than 0 of the weight adaptive law of the towed ship in the forward freedom and the yaw freedom; S uS (v) and S rS (v) represents a Gaussian function of the towed ship in the forward freedom and the yaw freedom;

[0180] S48, an update law of the adaptive law of the external disturbance of the AT and the towed ship, as shown in formula (51),

[0181]

[0182] wherein, a design constant greater than 0 of the external disturbance adaptive law of the AT and the towed ship in the forward freedom and the yaw freedom; an estimate of the external disturbance boundary of the AT and the towed ship; an initial value of .

[0183] In this embodiment, the nonlinear terms in the autonomous tug system are approximated online by a neural network approximator, and the intermediate control law is designed by compensating for the dynamic error, which can effectively reduce the influence of the error on the control effect, improve the performance of the control system, and improve the control accuracy of the AT and the towed ship. And the adaptive technology designs the actuator and quantizer gain adaptive law and the update law of the adaptive law, which can effectively cope with the time-varying or unknown dynamic characteristics of the AT and the towed ship system, and enhance the robustness of the AT and the towed ship.

[0184] In specific embodiments, the scheme of designing the command-filter-based event-triggered quantized AT controller and the command-filter-based towed ship controller based on the intermediate control law of the AT and the towed ship in the forward freedom and the yaw freedom and the update law of the adaptive law of the AT and the towed ship is:

[0185] S51, designing a command-filter-based event-triggered quantized AT controller and a command-filter-based towed ship controller based on the intermediate control law of the AT and the towed ship in the forward freedom and the yaw freedom and the update law of the adaptive law of the AT and the towed ship, as shown in formulas (52) and (53),

[0186]

[0187]

[0188] wherein, n p represents the rotational speed of the controller of the AT in the forward freedom ((the input of the controller of the AT in the forward freedom is expressed in rotational speed), and a Udenotes the intermediate control law of the tugboat in the forward freedom degree, denotes the control law of the controller of the tugboat in the forward freedom degree, denotes the adaptive law of the intermediate control law of the AT in the forward freedom degree, a n denotes the intermediate control law of the AT in the forward freedom degree, d r denotes the rudder angle of the controller of the AT in the yaw freedom degree (the input of the controller of the AT in the yaw freedom degree is expressed in the rudder angle), denotes the adaptive law of the AT in the yaw freedom degree, a δ denotes the intermediate control law of the AT in the yaw freedom degree, denotes the adaptive law of the tugboat in the forward freedom degree, a R denotes the intermediate control law of the tugboat in the yaw freedom degree, denotes the control law of the controller of the tugboat in the yaw freedom degree, denotes the adaptive law of the tugboat in the yaw freedom degree.

[0189] The controller can adaptively adjust the weights of the neural network, improve the stability and control effect of the system, adjust the update law of the adaptive law according to the value of the system state v, and thus ensure that the system can obtain the best performance under different states.

[0190] In specific embodiments, the scheme for realizing the towing control of the AT on the tugboat by using the event-triggered quantized AT controller based on command filtering and the tugboat controller based on command filtering is as follows:

[0191] As shown in Figure 3 , the event-triggered quantized control path tracking control of the tugboat includes a guidance system, a control system and a navigation system;

[0192] The guidance system sets the initial state and the waypoints, and generates the related path, i.e., the expected trajectory.

[0193] The control system multiplies the expected trajectory with the data of the actual navigation system, obtains and generates the dynamic error, performs coordinate conversion on the dynamic error, generates the adaptive and tugboat controllers, adds a hysteresis quantizer and an event trigger in the tugboat controller, performs neural network weight estimation and radial basis neural network approximation on the system, eliminates the dynamic error, and realizes the path tracking control on the tugboat.

[0194] In this embodiment, by setting event trigger mechanisms for position error, heading error and dynamic error, the system is ensured to update the control signal only when the signal change exceeds a certain threshold, realizing the synchronous update of the controller and the neural network weight, thereby improving the response speed of the system; in addition, combined with the hysteresis quantization mechanism, the system can avoid frequent adjustment caused by slight fluctuations, significantly reducing unnecessary signal transmission and calculation, thereby saving limited communication bandwidth and computing resources.

[0195] The scheme of the present application and the path tracking technology based on MLP and DOB were compared in numerical simulation under simulated external marine environment, and the differences are shown in Table 1,

[0196] Table 1 Differences between the present application and the prior art

[0197] Indicator Disturbance observer Filter error compensation Event-triggered quantized control Method of the invention No Yes Yes Prior art Yes No No

[0198] The method of the present application and the prior art were compared in simulation on an industrial computer (Intel(R) Core(TM) i5-7300 HQ CPU@2.50GHz, RAM: 8.00GB), Figures 4-6 The main comparison results are shown:

[0199] Figure 4 The comparison results of ship path tracking under the action of the two methods are shown, although both methods achieve satisfactory control effect, but due to the action of filter error compensation signal, the trajectory of the method of the present application is more stable, while the prior art fails to solve the input filter error problem;

[0200] Figure 5 The error curves of the two methods are described, and it can be seen from the figure that the error of the method of the present application is relatively small and more stable, which means that the method of the present application has more advantages in the smoothness and accuracy of path tracking;

[0201] It can be seen from Figure 6 that the method of the present application will perform step control action at each trigger time, which effectively reduces the execution frequency of the actuator, in addition, thanks to the introduction of the hysteresis quantizer, the problem of frequent jitter of input is effectively solved, while the prior art cannot cope with such problems, such improvement improves the stability and responsiveness of the system.

[0202] Therefore, combined with the existing path tracking control technology based on MLP and DOB, the present application has achieved the following 3 beneficial effects in the field of AT motion control:

[0203] 1) Compared with existing control algorithms, the event-triggered quantized control method based on command filtering proposed in this invention exhibits significant advantages in multiple aspects. First, by introducing a filter compensation signal, the influence of filter error can be effectively predicted and compensated, ensuring that the control signal can accurately respond in the process of rapid change. This feature eliminates the delay and error accumulation problems caused by traditional first-order filters, allowing the control system to maintain high-precision control performance while smoothing the control signal and avoiding performance degradation caused by filtering errors.

[0204] 2) Second, by setting an event-triggering mechanism for position error, heading error, and dynamics error, this invention ensures that the system only updates the control signal when the signal changes exceed a certain threshold. This mechanism enables simultaneous updating of the controller and neural network weights, thereby improving the system's response speed. In addition, combined with the hysteresis quantization mechanism, the system can avoid frequent adjustments caused by minor fluctuations, significantly reducing unnecessary signal transmission and computational load, thereby saving limited communication bandwidth and computational resources.

[0205] 3) Through numerical simulation verification, the control method of this invention exhibits superior control performance in the towing control task of autonomous tugboats, not only overcoming the shortcomings of traditional control algorithms in dealing with filter errors and signal triggering, but also significantly improving the system's adaptability and reliability in complex environments, providing a more energy-efficient, efficient, safe, and stable solution for the motion control of autonomous tugboats. This innovation lays a solid foundation for the application of autonomous tugboats in marine engineering and other fields.

[0206] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the invention, and not to limit it; although the invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solution to deviate from the scope of the technical solutions of the embodiments of the invention.

Claims

1. A command filter based autonomous tug event triggered quantized tug control method, characterized in that, Comprise: S1: respectively construct the nonlinear system model of AT and the towed ship, as the control object of the controller designed in the subsequent step, and use the guidance virtual ship of the AT and the towed ship to obtain the reference course of the AT and the towed ship, the specific steps are as follows: S11, respectively construct the nonlinear mathematical model of AT and the towed ship, as shown in formula (1) and formula (2), （1） （2） wherein, represents the autonomous tug, i.e. AT, represents the towed ship, represents the position coordinates of the AT and the towed ship, represents the heading angles of the AT and the towed ship, represents the forward velocities of the AT and the towed ship, represents the cross drift velocities of the AT and the towed ship, represents the yaw angular velocities of the AT and the towed ship, represents the hydrodynamic added masses of the AT and the towed ship in the forward freedom, represents the hydrodynamic added masses of the AT and the towed ship in the cross drift freedom, represents the hydrodynamic added masses of the AT and the towed ship in the yaw freedom, represents the nonlinear terms of the AT and the towed ship in the forward, cross drift and yaw freedoms, respectively, represents the disturbance forces and moments of the external environment acting on the AT and the towed ship in the forward, cross drift and yaw freedoms, represents the control gains of the main engine speed and the rudder angle of the tug, represents the main engine speed of the tug, represents the rudder angle of the tug, represents the quantized inputs of the hysteresis quantizer of the tug in the forward freedom and the yaw freedom, respectively, represents the towing force acting on the towed ship, represents the length of the tug, represents the length of the towed ship, represents the angle between the towrope and the tug, represents the angle between the towrope and the towed ship.​​ The hysteresis quantizer is as shown in formula (3), （3） wherein denotes a hysteresis quantizer, denotes a hysteresis quantizer, denotes a quantization control gain as shown in equation (4), denotes an additional disturbance term as shown in equation (5); （4） （5） The hysteresis quantizer is as shown in formula (3), （6） wherein , is a constant, is a derivative of is a sign function, is a threshold of the hysteresis quantizer of is a quantization level, is a quantization dead zone, is a quantization density, denotes an actual input at the time of the left limit of the quantization value; S12, use the guidance virtual ship to obtain the reference course of the AT and the towed ship, as shown in formula (7), （7） wherein represents the position coordinates of the virtual ship on the reference course, represents the heading angle of the virtual ship, represents the desired yaw angular velocity of the virtual ship, and t represents the time of continuous movement of the virtual ship. S2: according to the position of the guidance virtual ship in the reference course and the actual AT and the towed ship, construct the guidance law, and generate the expected trajectory of the AT and the towed ship according to the guidance law; S3: respectively design the virtual control law of the AT and the towed ship in the forward freedom degree and the bow swing freedom degree, introduce the filter to compensate the dynamic error between the actual trajectory and the expected trajectory of the AT and the towed ship in the virtual control law, and obtain the compensated dynamic error of the AT and the towed ship; S4: according to the compensated dynamic error of the AT and the towed ship, respectively design the intermediate control law of the AT and the towed ship in the forward freedom degree and the bow swing freedom degree, and according to the compensated dynamic error of the AT and the towed ship and the two intermediate control laws, respectively design the update law of the adaptive law of the AT and the towed ship; S5: based on the intermediate control law of the AT and the towed ship in the forward freedom degree and the bow swing freedom degree and the update law of the adaptive law of the AT and the towed ship, design the event triggered quantization AT controller based on command filtering and the towed ship controller based on command filtering; S6: use the event triggered quantization AT controller based on command filtering and the towed ship controller based on command filtering to realize the towing control of the AT to the towed ship.

2. A command filter based autonomous tug event triggered quantized tug control method according to claim 1, characterized in that, According to the position of the guidance virtual ship in the reference course and the actual AT and the towed ship, construct the guidance law, comprising: The guidance law is as shown in formula (8), （8） wherein represents the azimuth angle of the AT and the towed vessel relative to the guiding virtual vessel, with the lateral distance between the towed vessel and the tug as the x-axis and the longitudinal distance as the y-axis, represents the position error of the AT and the towed vessel relative to the guiding virtual vessel in the x-axis, represents the position error of the AT and the towed vessel relative to the guiding virtual vessel in the y-axis, represents the distance between the AT and the towed vessel relative to the guiding virtual vessel.

3. A command filter based autonomous tug event triggered quantized tug control method according to claim 2, characterized in that, Respectively design the virtual control law of the AT and the towed ship in the forward freedom degree and the bow swing freedom degree, introduce the filter to compensate the dynamic error between the actual trajectory and the expected trajectory of the AT and the towed ship in the virtual control law, and obtain the compensated dynamic error of the AT and the towed ship, comprising: S31, define the position error and the heading error of the AT and the towed ship, as shown in formula (9), （9） wherein represents the derivative of the distance between the AT and the towed vessel relative to the guidance virtual vessel, represents the derivative of the heading error of the AT and the towed vessel, represents the azimuth angle of the AT and the towed vessel relative to the guidance virtual vessel, represents the yaw angular velocity of the AT and the towed vessel; , represents the AT, represents the towed vessel; S32, design the virtual control law of the AT and the towed ship in the forward freedom degree and the bow swing freedom degree, as shown in formula (10), （10） wherein denotes the virtual control law of the AT and the towed ship in the forward progress degree of freedom, denotes the virtual control law of the AT and the towed ship in the yawing degree of freedom, denotes the design parameters of the virtual control law of the AT and the towed ship in the forward progress and yawing degrees of freedom, denotes a constant of the virtual control law of the AT and the towed ship in the forward progress degree of freedom; S33, introduce the filter to compensate the dynamic error of the AT and the towed ship, the filter is as shown in formula (11), （11） wherein denote the filter variables of the AT and the tug in the forward and yaw freedom; is a time constant greater than zero, is the initial value of denotes the error of the first order filter.​ S34, define the dynamic error of the AT and the towed ship according to formula (11), as shown in formula (12), （12） wherein denotes the dynamics error of the AT and the towed vessel; S35, design the filter error compensation signal according to the filter variable of the AT and the towed ship in the forward freedom degree and the bow swing freedom degree, as shown in formula (13), （13） wherein denotes the filter error compensation signal for the AT and the towed vessel, the variable has an initial value of zero, is a design parameter for the filter error compensation signal for the AT and the towed vessel; S36, compensate the dynamic error according to the filter error compensation signal, and obtain the compensated dynamic error, as shown in formula (14), （14） In the formula, denotes the compensated dynamic error, .

4. The autonomous tug event triggered quantized tug control method based on command filtering according to claim 3, characterized in that, According to the compensated dynamics error of the AT and the towed ship, intermediate control laws of the AT and the towed ship in the forward freedom degree and the yaw freedom degree are designed, including: S41, the derivatives of the dynamics error of the AT and the towed ship are calculated according to the formulas (1), (2), (11), (13) and (14), as shown in the formula (15), （15） wherein and represent the neural network weights of the AT and the towed ship in the forward and yaw degrees of freedom, respectively, and and represent the Gaussian functions of the AT and the towed ship in the forward and yaw degrees of freedom, respectively, and represent the inputs of the neural network of the AT and the towed ship in the forward and yaw degrees of freedom, respectively, and and represent the approximation errors of the AT and the towed ship in the forward and yaw degrees of freedom, respectively, represents the quantized control gain of the AT in the forward degree of freedom, represents the quantized control gain of the AT in the yaw degree of freedom represents the control input of the AT in the forward degree of freedom without hysteresis quantization; S42, the intermediate control laws of the AT in the forward freedom degree and the yaw freedom degree and the intermediate control laws of the towed ship in the forward freedom degree and the yaw freedom degree are designed, as shown in the formulas (16) and (17), （16） （17） wherein denote design parameters of the intermediate control law of the AT, denote design parameters of the filter compensation signal of the AT, denote design parameters of the intermediate control law of the towed vessel, denote design parameters of the filter compensation signal of the towed vessel, denote estimates of the boundary of the external disturbance in the forward and yaw freedom of the AT and the towed vessel, respectively, denote design constants of the disturbance estimates in the forward and yaw freedom of the AT and the towed vessel, respectively, denote the forward velocity error of the AT and the towed vessel in the forward freedom, denote the yaw angular velocity error of the AT and the towed vessel in the yaw freedom, denote the time constant of the filter variable in the forward freedom of the AT and the towed vessel, denote the time constant of the filter variable in the yaw freedom of the AT and the towed vessel, denote the derivative of the filter variable in the forward freedom of the AT and the towed vessel, denote the derivative of the filter variable in the yaw freedom of the AT and the towed vessel, denote the filter error compensation signal in the forward freedom of the AT and the towed vessel, denote the filter error compensation signal in the yaw freedom of the AT and the towed vessel, denote the basis functions of the neural network in the forward freedom of the AT and the towed vessel, denote the basis functions of the neural network in the yaw freedom of the AT and the towed vessel.

5. A command filter based autonomous tug event triggered quantized tug control method according to claim 4, characterized in that, According to the compensated dynamics error of the AT and the towed ship and the two intermediate control laws, update laws of the adaptive laws of the AT and the towed ship are designed, including: S43, the event-triggered measurement error and the event-triggered condition of the AT in the forward freedom degree and the yaw freedom degree are defined according to the dynamics error and the position error, as shown in the formulas (18) and (19), （18） （19） In equation (18), denotes the event triggered measurement error of the AT in the surge degree of freedom, denotes the event triggered measurement error of the AT in the yaw degree of freedom, denotes the sampling time at the event triggered instant in the surge and yaw degrees of freedom, denotes the sampling time at the next event triggered instant in the surge and yaw degrees of freedom; in equation (19), denotes the event-triggering condition of the AT in the surge degree of freedom, denotes the event-triggering condition of the AT in the yaw degree of freedom, is a flag for event-triggering, denotes the lower bound, denotes the triggering threshold of the event-triggering condition in the surge and yaw degrees of freedom, ; and are design parameters of the event-triggering condition in the surge and yaw degrees of freedom, , ; denotes the norm of the neural network basis function in the yaw degree of freedom, denotes the norm of the neural network basis function in the surge degree of freedom; S44, during the time period from the sampling time of the event-triggered time to the sampling time of the next event-triggered time, the update law of the neural network weight estimator adaptive law of the event-triggered time of the AT is designed according to the intermediate control law of the AT, as shown in the formula (20), （20） wherein represents the intermediate control law of the AT in the surge and yaw degrees of freedom, and represents the neural network weight estimator adaptive law at the AT event-triggered time instant; and represents the Gaussian function of the AT in the surge and yaw degrees of freedom. S45, the update law of the adaptive law of the AT is designed according to the intermediate control law of the AT, as shown in the formula (21), （21） wherein represents the update law of the AT adaptive law, represents the gain adaptive design parameter of the AT in the forward freedom and the yaw freedom; is the initial value of is the initial value of S46, the update law of the adaptive law of the towed ship is designed according to the intermediate control law of the towed ship, as shown in the formula (22), （22） In the formula, represents the update law of the tugboat self-adaptive law; represents the gain self-adaptive design parameter of the tugboat in the forward freedom degree and the yaw freedom degree; is the initial value of is the initial value of S47, the update law of the neural network weight estimator adaptive law of the event-triggered time of the towed ship is designed according to the intermediate control law of the towed ship, as shown in the formula (23), （23） wherein represents the update law of the neural network weight estimator adaptive law at the event-triggered time instant of the tugboat, are design constants of the weight adaptive law of the towed ship in the surge and yaw degrees of freedom; and represent the Gaussian functions of the towed ship in the surge and yaw degrees of freedom. S48, the update law of the adaptive law of the external disturbance of the AT and the towed ship is designed, as shown in the formula (24), （24） wherein denote the design constants of the AT and towed ship external disturbance adaptive law in the forward and yaw freedom; is the estimate of the AT and towed ship external disturbance boundary; is the initial value of is the initial value of 6. A command filter based autonomous tug event triggered quantized tug control method according to claim 5, characterized in that, Based on the intermediate control laws of the AT and the towed ship in the forward freedom degree and the yaw freedom degree and the update laws of the adaptive laws of the AT and the towed ship, event-triggered quantized AT controllers based on command filtering and towed ship controllers based on command filtering are designed, including: S51, based on the intermediate control laws of the AT and the towed ship in the forward freedom degree and the yaw freedom degree and the update laws of the adaptive laws of the AT and the towed ship, event-triggered quantized AT controllers based on command filtering and towed ship controllers based on command filtering are designed, as shown in the formulas (25) and (26), （25） （26） wherein denotes the rudder angle of the controller of the AT in the yaw freedom, denotes the intermediate control law of the towed ship in the ahead freedom, denotes the control law of the controller of the towed ship in the ahead freedom, denotes the adaptive law of the intermediate control law of the AT in the ahead freedom, denotes the intermediate control law of the AT in the ahead freedom, denotes the rudder angle of the controller of the AT in the yaw freedom, denotes the adaptive law of the AT in the yaw freedom, denotes the intermediate control law of the AT in the yaw freedom, denotes the adaptive law of the towed ship in the ahead freedom, denotes the intermediate control law of the towed ship in the yaw freedom, denotes the control law of the controller of the towed ship in the yaw freedom, denotes the adaptive law of the towed ship in the yaw freedom.

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