Under-actuated unmanned ship trajectory tracking control method

Abstract

The invention provides an under-actuated unmanned ship trajectory tracking control method, which is used for under-actuated unmanned ship trajectory tracking control with input quantization and output limitation based on an event trigger mechanism, and comprises the following steps: acquiring sea condition information of a surrounding environment and other ships, and establishing a trajectory tracking control model of an unmanned ship; a composite quantizer is adopted to quantize control input of a control system, and a linear analysis model is used to describe an input quantization process; designing a barrier Lyapunov function on the basis of an output limited theory, compensating uncertain items and external interference in the under-actuated unmanned ship model by using a neural network system, and obtaining a trajectory tracking controller and an adaptive rate of the under-actuated unmanned ship; an event triggering mechanism is introduced, and the control rate of the under-actuated unmanned ship system is updated based on an event triggering strategy; it is proved that the stability of the designed under-actuated unmanned ship trajectory tracking control system is finally bounded when priori information of parameters does not need to be quantized, and all signals are finally bounded, so that Zeno behaviors are avoided.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to a trajectory tracking control method for an underactuated unmanned ship. Background Art

[0002] With the rapid development of automation technology and intelligent ships, trajectory tracking control of underactuated unmanned ships has become one of the hot topics in the field of marine engineering. Compared with traditional large ships, the development and application of unmanned ships have shown many advantages in the fields of marine resource exploration, military mission execution, remote environmental monitoring, etc.

[0003] However, in a complex and changing marine environment, maintaining ship stability and performing trajectory tracking control is one of the key challenges in achieving autonomous navigation of unmanned ships. In navigation practice, the trajectory tracking control method based on traditional modeling is often limited due to the fact that the degrees of freedom of underactuated unmanned ships are not fully controllable and the difficulty of uncertain parameter selection during modeling; on the other hand, the components of unmanned ships transmit information through communication channels, and the bandwidth of the communication channels is very limited, so unmanned ships face the challenge of insufficient communication bandwidth resources. By using quantization technology before signal transmission and considering the event triggering mechanism, not only the execution frequency of the actuator can be reduced, but also the communication rate within the bandwidth can be reduced. Therefore, considering input quantization technology and event triggering strategy in studying the trajectory tracking control problem of underactuated unmanned ships has attracted more and more attention.

[0004] Simply relying on quantization technology and event triggering for trajectory tracking control cannot fully meet the requirements of the development of under-actuated unmanned ships. On the one hand, although the use of quantization technology in control systems has certain advantages, the introduction of quantization technology will introduce quantization errors. If these errors are too obvious, it may seriously affect the performance of the unmanned ship's trajectory tracking control. On the other hand, when unmanned ships perform tasks in complex marine environments, they need to overcome challenges such as wind, current, and waves, making it more difficult to design controllers, especially when there are two controllable inputs for under-actuated unmanned ships. Therefore, how to overcome the above difficulties and design innovative controllers to achieve good trajectory tracking control of under-actuated unmanned ships is still a hot topic in the current development of unmanned ships. Summary of the invention

[0005] According to the above-mentioned technical problems of limited communication bandwidth of unmanned ships at sea and avoiding collisions caused by position errors, a trajectory tracking control method for underactuated unmanned ships with input quantization and output limitation based on event triggering mechanism is provided. The present invention uses a neural network system to compensate for model uncertainty and external interference in the control system, and adopts a linear model to describe the quantization process. At the same time, considering the event triggering strategy, it significantly reduces the execution frequency of the actuator, saves communication resources, and reduces the communication burden. In addition, based on the output limitation theory, the obstacle Lyapunov function is designed to obtain an adaptive control law, which successfully minimizes the tracking error and achieves good trajectory tracking of the underactuated unmanned ship.

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

[0007] A trajectory tracking control method for an underactuated unmanned ship, comprising:

[0008] S1. Obtain the surrounding environment and sea condition information of other ships around, and establish a mathematical model for the trajectory tracking control of the unmanned ship;

[0009] S2, using a composite quantizer to quantize the control input in the control system, and using a linear analytical model to describe the input quantization process;

[0010] S3. Based on the output-constrained theory, the obstacle Lyapunov function is designed, and the neural network system is used to compensate for the uncertainties and external disturbances in the underactuated unmanned ship model, and the trajectory tracking controller and adaptive rate of the underactuated unmanned ship are obtained;

[0011] S4, introduce an event trigger mechanism, and update the control rate of the underactuated unmanned ship system based on the event trigger strategy;

[0012] S5. Based on Lyapunov stability theory, the stability of the designed underactuated unmanned ship trajectory tracking control system with event-triggered mechanism, input quantization and output constraint is proved when no prior information of quantized parameters is required. All signals in the entire closed-loop control system are ultimately bounded and Zeno behavior is avoided.

[0013] Furthermore, step S1 specifically includes:

[0014] S11. Establish a mathematical model of the motion of the underactuated unmanned ship as follows:

[0015]

[0016] in, is the lateral position of the unmanned ship, The longitudinal direction of the unmanned ship is unknown. is the heading angle of the unmanned ship, u, v, r are the forward speed, lateral speed and yaw speed of the unmanned ship respectively;

[0017] S12. Establish a nonlinear dynamic mathematical model of the underactuated unmanned ship as follows:

[0018]

[0019] in, All represent the quality of the unmanned ship. Both represent the hydrodynamic derivative term, I z represents the moment of inertia around the z-axis; f u (·), f v (·) and f r (·) represent the nonlinear functions of centripetal force, Coriolis force and hydrodynamic damping effect, respectively; τ uω , τ vω , τ rω Both represent the time-varying disturbance caused by wind currents; Q(τ u ) and Q(τ r ) represents the control input τ u and τ r Quantified value of .

[0020] Further, step S2 specifically includes:

[0021] S21. Use a composite quantizer to quantize the control input in the system. The specific quantization process is described as follows:

[0022]

[0023] Where q1(·) represents the logarithmic quantizer; f i (·) is defined as a rounding function less than or equal to (·); τ i,s >0, indicating the switching threshold between logarithmic quantization and uniform quantization; σ i represents the quantization density of the uniform quantizer;

[0024] S22. Define the ideal trajectory as follows:

[0025] U d (t) = [x d (t),y d (t)] T

[0026] Among them, x d and d It represents the ideal trajectory of the unmanned ship in the inertial coordinate system, and the trajectory parameter U d (t) represents a continuously differentiable vector;

[0027] S23. The design satisfies both the actual position U of the unmanned ship and the expected trajectory U d (t), and ensure that no violation and z e The controller of the limit is described as follows:

[0028]

[0029] Among them, U=[x,y] T represents the actual position of the unmanned ship in the inertial coordinate system, l0 represents a small constant and satisfies l0>0;

[0030] S24. According to step S12, obtain:

[0031]

[0032] S25. Set the control target of the dynamic system as follows:

[0033]

[0034] Among them, κ1 and κ2 are both small positive integers;

[0035] S26. According to step S24, obtain:

[0036]

[0037] in,

[0038] S27. For the quantized control input in the unmanned ship model, let Q(τ u )=q 1u (t)τ u +q 2u (t), Q(τ r )=q 1r (t)τ r +q 2r (t), take:

[0039]

[0040] Among them, q 1u (t) and q 1r (t) are all unknown, since q 1u and q 1r The positive or negative value of will not change during the quantization process, so we can get q from the above formula 1u (t)>0,q 1r (t)>0; when τ u (t)<a,τ r When (t)<a, ​​Q(τ u(t)), Q(τ r (t)) is bounded, so q 2u (t), q 2r (t) is bounded,

[0041] Further, step S3 specifically includes:

[0042] S31. Define the position error as follows:

[0043]

[0044] Among them, z e represents the position error, sgn(·) represents the sign function, and sgn(0)=1;

[0045] S32. For the design of unmanned ship sloshing motion controller, define the Lyapunov function as follows:

[0046]

[0047] Where log(·) represents the natural logarithm of (·), k a =k d -B0; limit z e Satisfy | e |<k a , we get z e is positive; then according to step S31, we get

[0048] S33, deriving the Lyapunov function defined in step S32, and obtaining:

[0049]

[0050] S34, let α u =u e +u, define the stability function α as follows:

[0051]

[0052] where k ze >0, and k ze is a constant, so we have:

[0053]

[0054] S35, due to u e Without constraints, define the Lyapunov function as follows:

[0055]

[0056] S36, deriving the Lyapunov function defined in step S35, and obtaining:

[0057]

[0058] make get:

[0059]

[0060] S37, due to F u Unknown, according to the universal approximation theorem, for any small constant ε u , there exists a neural network system W u * T h u (X), Definition Among them, W u * is the ideal weight of the fuzzy logic system, h u (x) is the fuzzy basis vector, ε u is the approximation error of the fuzzy system and |ε u |≤ε; definition Yes W u * The estimated value of is:

[0061]

[0062] in, Due to q 1u (t) and q 1r (t) are unknown and time-varying, so we use adaptive estimation to estimate their bounds. To prevent singular problems when the estimated value is zero, we use q 1u (t) and q 1r (t) is estimated by lower bound and the time-varying gain is defined as follows:

[0063]

[0064]

[0065] Among them, q 1u (t) min Yes 1u The lower bound of (t), q 1r (t) min Yes 1r The lower bound of (t);

[0066] S38. Design the system control law and adaptive law as follows:

[0067]

[0068]

[0069]

[0070] in, is for μ u The estimate of θ u is a constant, γ1, γ2, σ u is a positive number,

[0071] S39. For the design of the unmanned ship swaying motion controller, define the Lyapunov function as follows:

[0072]

[0073] S310, deriving the Lyapunov function defined in step S39, and obtaining:

[0074]

[0075] S311, let α r =r e +r, define the stability function α r for: According to step S310, it is obtained:

[0076]

[0077] S312, due to r e Without constraints, define the Lyapunov function as follows:

[0078]

[0079] S313, deriving the Lyapunov function defined in step S312, and obtaining:

[0080]

[0081] make get:

[0082]

[0083] S314, Order Design the system control law and adaptive law as follows:

[0084]

[0085]

[0086]

[0087] in, Indicates μ r The estimate of θ r represents a constant, γ3, γ4, σ r All represent positive numbers,

[0088] Further, step S4 specifically includes:

[0089] S41. Define the event triggering strategy as follows:

[0090]

[0091]

[0092] Where Y represents the time constant and satisfies e τu (t) and e τr (t) satisfies e τu (t) = ρ u (t)-τ u (t), e τr (t) = ρ r (t)-τ r (t);

[0093] S42. The analysis of the defined event triggering strategy is as follows:

[0094] when or hour, in The value of |e τu (t)|≥Y and|e τr (t)|≥Y value;

[0095] when|e τu (t)|<Y or|e τr When (t)|<Y, no control input signal is transmitted and the controller value remains unchanged;

[0096] There exists a continuous time-varying coefficient φ(t) that satisfies and |φ(t)|≤1; and |φ(t)|≤1; so that ρ u (t) = τ u (t)+φ(t)Y,ρ r (t) = τ r (t)+φ(t)Y, that is:

[0097]

[0098] There are three specific situations:

[0099] When φ(t)=0, |e τu (t|)=|ρ u (t)-τ u (t)|=0, at this time When φ(t)=0, |e τr (t)|=|ρ r (t)-τ r (t)|=0, at this time

[0100] When |φ(t)|<1, |e τu (t)|=|ρ u (t)-τ u (t)|<Y, at this time When |φ(t)|<1, |e τr (t)|=|ρ r (t)-τ r (t)|<Y, at this time

[0101] When |φ(t)|=1, |e τu (t)|=|ρ u (t)-τ u (t)|=Y, determine the current time at this time When |φ(t)|=1, |e τr (t)|=|ρ r (t)-τ r (t)|=Y, determine the current time at this time

[0102] S43. Based on the event-driven strategy, the control law of the underactuated unmanned ship system is updated as follows:

[0103]

[0104]

[0105] Furthermore, step S5 specifically includes:

[0106] S51. Define the Lyapunov function as follows:

[0107]

[0108] S52, deriving the Lyapunov function defined in step S51, and obtaining:

[0109]

[0110] S53, bring in the control rate and the adaptive rate, and get:

[0111]

[0112] S54. Due to Then we have:

[0113]

[0114] make Then we have:

[0115]

[0116] S55, according to Then we get:

[0117]

[0118] Combining steps S53 and S55, we obtain:

[0119]

[0120] S56. Due to

[0121] Thus we get:

[0122]

[0123] S57. According to the output-constrained lemma, for the error system where η=[w,z1] T , there exist continuously differentiable and positive definite functions V1 and V2, k b >0, the position output is x1, and the position error z1 is defined as x1-y d , satisfying: when z1→-k b Or z1→k b When V1(z1)→∞;γ1(||η||)≤U(η)≤γ2(||η||), where γ1 and γ2 are K ∞ Class functions;

[0124] S58. Assume |z1(0)|<k b , take V(η):=V1(z1)+U(ω), if it satisfies: but K b >0, then: Thus we can get:

[0125]

[0126] S59, according to step S56, obtain:

[0127]

[0128] S510, consider the event triggering mechanism, update the control rate, and obtain:

[0129]

[0130] Due to q 1u >0,q 1r >0, according to the lemma: for any unknown real variable e, there exists a constant n that satisfies the following inequality: Then we get:

[0131]

[0132]

[0133] Among them, c>0, d>0, and satisfy:

[0134]

[0135] in,

[0136] S511. According to step S510, obtain Therefore, according to Lyapunov stability theory, all signals in the entire closed-loop system are ultimately bounded, and the tracking error of the system can converge to a smaller residual set.

[0137] Compared with the prior art, the present invention has the following advantages:

[0138] 1. The present invention provides an underactuated unmanned ship trajectory tracking control method, which uses an event-triggered mechanism with input quantization and output limitation for underactuated unmanned ship trajectory tracking control, which is more in line with navigation practice.

[0139] 2. The present invention provides an under-actuated unmanned ship trajectory tracking control method, which addresses the problem of limited maritime communication bandwidth of unmanned ships. It uses quantization technology before signal transmission and considers event triggering strategies, thereby saving communication resources and reducing the execution frequency of actuators.

[0140] 3. The underactuated unmanned ship trajectory tracking control method provided by the present invention adopts a composite quantizer to linearly describe the quantization process, that is, the designed controller does not require prior information of the quantization parameters.

[0141] Based on the above reasons, the present invention can be widely promoted in fields such as artificial intelligence. BRIEF DESCRIPTION OF THE DRAWINGS

[0142] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0143] Figure 1 The figure is a flow chart of the method of the present invention.

[0144] Figure 2 This is a diagram of the unmanned ship trajectory tracking results provided by an embodiment of the present invention.

[0145] Figure 3 An unmanned ship trajectory tracking error diagram provided for an embodiment of the invention.

[0146] Figure 4 A comparison diagram of control input curves without considering input quantization and event triggering provided in an embodiment of the present invention.

[0147] Figure 5 A comparison diagram of control input curves considering input quantization and event triggering provided in an embodiment of the present invention.

[0148] Figure 6 The time interval between two consecutive sampling moments provided in the embodiment of the present invention. DETAILED DESCRIPTION

[0149] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0150] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0151] like Figure 1 As shown, the present invention provides a trajectory tracking control method for an underactuated unmanned ship, which uses an event-triggered mechanism with input quantization and output limitation for trajectory tracking control of an underactuated unmanned ship, including:

[0152] S1. Obtain the surrounding environment and sea condition information of other ships around, and establish a mathematical model for the trajectory tracking control of the unmanned ship;

[0153] S2, using a composite quantizer to quantize the control input in the control system, and using a linear analytical model to describe the input quantization process;

[0154] S3. Based on the output-constrained theory, the obstacle Lyapunov function is designed, and the neural network system is used to compensate for the uncertainties and external disturbances in the underactuated unmanned ship model, and the trajectory tracking controller and adaptive rate of the underactuated unmanned ship are obtained;

[0155] S4, introduce an event trigger mechanism, and update the control rate of the underactuated unmanned ship system based on the event trigger strategy;

[0156] S5. Based on Lyapunov stability theory, the stability of the designed underactuated unmanned ship trajectory tracking control system with event-triggered mechanism, input quantization and output constraint is proved when no prior information of quantized parameters is required. All signals in the entire closed-loop control system are ultimately bounded and Zeno behavior is avoided.

[0157] In specific implementation, as a preferred embodiment of the present invention, step S1 specifically includes:

[0158] S11. Establish a mathematical model of the motion of the underactuated unmanned ship as follows:

[0159]

[0160] in, is the lateral position of the unmanned ship, The longitudinal direction of the unmanned ship is unknown. is the heading angle of the unmanned ship, u, v, r are the forward speed, lateral speed and yaw speed of the unmanned ship respectively;

[0161] S12. Establish a nonlinear dynamic mathematical model of the underactuated unmanned ship as follows:

[0162]

[0163] in, All represent the quality of the unmanned ship. Both represent the hydrodynamic derivative term, I z represents the moment of inertia around the z-axis; f u (·), f v (·) and f r (·) represent the nonlinear functions of centripetal force, Coriolis force and hydrodynamic damping effect, respectively; τ uω , τ vω , τ rω Both represent the time-varying disturbance caused by wind currents; Q(τ u ) and Q(τ r ) represents the control input τ u and τ r Quantified value of .

[0164] In specific implementation, as a preferred embodiment of the present invention, step S2 specifically includes:

[0165] S21. Use a composite quantizer to quantize the control input in the system. The specific quantization process is described as follows:

[0166]

[0167] Where q1(·) represents the logarithmic quantizer; f i (·) is defined as a rounding function less than or equal to (·); τ i,s >0, indicating the switching threshold between logarithmic quantization and uniform quantization; σ i represents the quantization density of the uniform quantizer;

[0168] S22. Define the ideal trajectory as follows:

[0169] U d (t) = [x d (t),y d (t)] T

[0170] Among them, x d and d It represents the ideal trajectory of the unmanned ship in the inertial coordinate system, and the trajectory parameter U d (t) represents a continuously differentiable vector;

[0171] S23. The design satisfies both the actual position U of the unmanned ship and the expected trajectory U d (t), and ensure that no violation and z e The controller of the limit is described as follows:

[0172]

[0173] Among them, U=[x,y] T represents the actual position of the unmanned ship in the inertial coordinate system, l0 represents a small constant and satisfies l0>0;

[0174] S24. According to step S12, obtain:

[0175]

[0176] S25. Set the control target of the dynamic system as follows:

[0177]

[0178] Among them, κ1 and κ2 are both small positive integers;

[0179] S26. According to step S24, obtain:

[0180]

[0181] in,

[0182] S27. For the quantized control input in the unmanned ship model, let Q(τ u )=q 1u (t)τ u +q 2u (t), Q(τ r )=q 1r (t)τ r +q 2r (t), take:

[0183]

[0184] Among them, q 1u (t) and q 1r (t) are all unknown, since q 1u and q 1r The positive or negative value of will not change during the quantization process, so we can get q from the above formula 1u (t)>0,q 1r (t)>0; when |τ u (t)|<a,|τ r(t)|<a, Q(τ u (t)), Q(τ r (t)) is bounded, so q 2u (t), q 2r (t) is bounded,

[0185] In specific implementation, as a preferred embodiment of the present invention, step S3 specifically includes:

[0186] S31. Define the position error as follows:

[0187]

[0188] Among them, z e represents the position error, sgn(·) represents the sign function, and sgn(0)=1;

[0189] S32. For the design of unmanned ship sloshing motion controller, define the Lyapunov function as follows:

[0190]

[0191] Where log(·) represents the natural logarithm of (·), k a =k d -B0; limit z e Satisfy | e |<k a , we get z e is positive; then according to step S31, we get

[0192] S33, deriving the Lyapunov function defined in step S32, and obtaining:

[0193]

[0194] S34, let α u =u e +u, define the stability function α as follows:

[0195]

[0196] where k ze >0, and k ze is a constant, so we have:

[0197]

[0198] S35, due to u e Without constraints, define the Lyapunov function as follows:

[0199]

[0200] S36, deriving the Lyapunov function defined in step S35, and obtaining:

[0201]

[0202] make get:

[0203]

[0204] S37, due to F u Unknown, according to the universal approximation theorem, for any small constant ε u , there exists a neural network system W u * T h u (X), Definition Among them, W u * is the ideal weight of the fuzzy logic system, h u (x) is the fuzzy basis vector, ε u is the approximation error of the fuzzy system and |ε u |≤ε; definition Yes W u * The estimated value of is:

[0205]

[0206] in, Due to q 1u (t) and q 1r (t) are unknown and time-varying, so we use adaptive estimation to estimate their bounds. To prevent singular problems when the estimated value is zero, we use q 1u (t) and q 1r (t) is estimated by lower bound and the time-varying gain is defined as follows:

[0207]

[0208]

[0209] Among them, q 1u (t) min Yes 1u The lower bound of (t), q 1r (t) min Yes 1r The lower bound of (t);

[0210] S38. Design the system control law and adaptive law as follows:

[0211]

[0212]

[0213]

[0214] in, is for μ u The estimate of θ u is a constant, γ1, γ2, σ u is a positive number,

[0215] S39. For the design of the unmanned ship swaying motion controller, define the Lyapunov function as follows:

[0216]

[0217] S310, deriving the Lyapunov function defined in step S39, and obtaining:

[0218]

[0219] S311, let α r =r e +r, define the stability function α r for: According to step S310, it is obtained:

[0220]

[0221] S312, due to r e Without constraints, define the Lyapunov function as follows:

[0222]

[0223] S313, deriving the Lyapunov function defined in step S312, and obtaining:

[0224]

[0225] make get:

[0226]

[0227] S314, Order Design the system control law and adaptive law as follows:

[0228]

[0229]

[0230]

[0231] in, Indicates μ r The estimate of θ r represents a constant, γ3, γ4, σ r All represent positive numbers,

[0232] In specific implementation, as a preferred embodiment of the present invention, step S4 specifically includes:

[0233] S41. While considering input quantization, in order to save communication resources and effectively reduce the execution frequency of the actuator, this paper adopts an event trigger strategy and defines the event trigger strategy as follows:

[0234]

[0235]

[0236] Where Y represents the time constant and satisfies e τu (t) and e τr (t) satisfies e τu (t) = ρ u (t)-τ u (t), e τr (t) = ρ r (t)-τ r (t);

[0237] S42. The analysis of the defined event triggering strategy is as follows:

[0238] when or hour, in The value of e τu (t)≥Y and e τr (t)≥Y value;

[0239] when|e τu (t)|<Y or|e τr When (t)|<Y, no control input signal is transmitted and the controller value remains unchanged;

[0240] There exists a continuous time-varying coefficient φ(t) that satisfies and |φ(t)|≤1; and |φ(t)|≤1; so that ρ u (t) = τu (t)+φ(t)Y,ρ r (t) = τ r (t)+φ(t)Y, that is:

[0241]

[0242] There are three specific situations:

[0243] When φ(t)=0, |e τu (t)|=|ρ u (t)-τ u (t)|=0, at this time When φ(t)=0, |e τr (t)|=|ρ r (t)-τ r (t)|=0, at this time

[0244] When |φ(t)|<1, |e τu (t)|=|ρ u (t)-τ u (t)|<Y, at this time When |φ(t)|<1, |e τr (t)|=|ρ r (t)-τ r (t)|<Y, at this time

[0245] When |φ(t)|=1, |e τu (t)|=|ρ u (t)-τ u (t)|=Y, determine the current time at this time When |φ(t)|=1, |e τr (t)|=|ρ r (t)-τ r (t)|=Y, determine the current time at this time

[0246] S43. Based on the event-driven strategy, the control law of the underactuated unmanned ship system is updated as follows:

[0247]

[0248]

[0249] In specific implementation, as a preferred embodiment of the present invention, step S5 specifically includes:

[0250] S51. Define the Lyapunov function as follows:

[0251]

[0252] S52, deriving the Lyapunov function defined in step S51, and obtaining:

[0253]

[0254] S53, bring in the control rate and the adaptive rate, and get:

[0255]

[0256] S54. Due to Then we have:

[0257]

[0258] make Then we have:

[0259]

[0260] S55, according to Then we get:

[0261]

[0262] Combining steps S53 and S55, we obtain:

[0263]

[0264] S56. Due to

[0265] Thus we get:

[0266]

[0267] S57. According to the output-constrained lemma, for the error system where η=[w,z1] T , there exist continuously differentiable and positive definite functions V1 and V2, k b >0, the position output is x1, and the position error z1 is defined as x1-y d , satisfying: when z1→-k b Or z1→k b When V1(z1)→∞;γ1(||η||)≤U(η)≤γ2(||η||), where γ1 and γ2 are K ∞ Class functions;

[0268] S58. Assume |z1(0)|<k b , take V(η):=V1(z1)+U(ω), if it satisfies: but K b >0, then: Thus we can get:

[0269]

[0270] S59, according to step S56, obtain:

[0271]

[0272] S510, consider the event triggering mechanism, update the control rate, and obtain:

[0273]

[0274] Due to q 1u >0,q 1r >0, according to the lemma: for any unknown real variable e, there exists a constant n that satisfies the following inequality: Then we get:

[0275]

[0276]

[0277] Among them, c>0, d>0, and satisfy:

[0278]

[0279] in,

[0280] S511. According to step S510, obtain Therefore, according to Lyapunov stability theory, all signals in the entire closed-loop system are ultimately bounded, and the tracking error of the system can converge to a smaller residual set.

[0281] Example

[0282] In order to verify the effectiveness of the solution of the present invention, this embodiment uses MATLAB to perform computer simulation research, and the parameters are set as follows:

[0283] The simulation object is the "Lanxin" unmanned ship of Dalian Maritime University. The parameters of the unmanned ship are as follows: length 7.02m, width 2.6m, speed 35kn, full load draft 0.32m, full load displacement 2.73m 3 , square coefficient 0.6976. The parameters of the unmanned ship are shown in Table 1:

[0284] Table 1 Parameters of unmanned ship

[0285]

[0286] Set the ideal course of the unmanned ship to U d =[3t,2sin(t)] T , the initial state of the actual controlled object is [0,1.8], and the controller designed in step S4 is used to control the unmanned ship. The design parameters are as follows: k ze =0.05, k ue =150, k re =0.6, γ1 = γ3 = 1, γ2 = γ4 = 2, θ u =0.03,θ r =0.02,σ u =σ i = 0.1, quantization density σ of uniform quantizer i is 1.5.

[0287] The experimental results are as follows Figure 2-Figure 6 As shown, Figure 2 The unmanned ship trajectory tracking results are displayed, and it can be seen that the controller designed by the present invention has good control performance and can realize the unmanned ship trajectory tracking relatively quickly. Figure 3 This is the unmanned ship tracking error result diagram. It can be seen that the tracking error can quickly converge to a smaller residual set, which further verifies the effectiveness of the designed controller. Figure 4 is the control input curve without considering input quantization and event triggering. Figure 5 In order to consider the control input curve before and after input quantization and event triggering, it can be seen from the comparison that after comprehensive consideration of input quantization and event triggering, the execution frequency of the actuator is reduced, and the control system can remain within the safety range when executing commands, ensuring the effectiveness of the control system. Figure 6 is the time interval between two consecutive sampling moments. It can be seen that the event will only occur at certain moments when it is triggered, and there is no Zeno behavior within the triggering time.

[0288] The simulation results verify the effectiveness of the underactuated unmanned ship trajectory tracking control method based on the event trigger mechanism with input quantization and output limitation. The control system can effectively respond to changes and disturbances in the external environment and maintain stable trajectory tracking control. Considering the input quantization technology and event trigger mechanism, it can not only reduce the communication burden of the unmanned ship, but also reduce the execution frequency of the actuator, while avoiding Zeno behavior; the controller designed based on the output limitation theory ensures that the control signal of the unmanned ship is within a safe range, avoiding the adverse consequences that may be caused by excessive or too small output signals; further verifying the effectiveness of the present invention.

[0289] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A trajectory tracking control method for an underactuated unmanned ship, characterized in that: include: S1. Obtain the surrounding environment and sea condition information of other ships around, and establish a mathematical model for the trajectory tracking control of the unmanned ship; S2, using a composite quantizer to quantize the control input in the control system, and using a linear analytical model to describe the input quantization process; S3. Based on the output-constrained theory, the obstacle Lyapunov function is designed, and the neural network system is used to compensate for the uncertainties and external disturbances in the underactuated unmanned ship model, and the trajectory tracking controller and adaptive rate of the underactuated unmanned ship are obtained; S4, introduce an event trigger mechanism, and update the control rate of the underactuated unmanned ship system based on the event trigger strategy; S5. Based on Lyapunov stability theory, the stability of the designed underactuated unmanned ship trajectory tracking control system with event-triggered mechanism, input quantization and output constraint is proved when no prior information of quantized parameters is required. All signals in the entire closed-loop control system are ultimately bounded and Zeno behavior is avoided.

2. The trajectory tracking control method of an underactuated unmanned ship according to claim 1, characterized in that: Step S1 specifically includes: S11. Establish a mathematical model of the motion of the underactuated unmanned ship as follows: in, is the lateral position of the unmanned ship, The longitudinal direction of the unmanned ship is unknown. is the heading angle of the unmanned ship, u, v, r are the forward speed, lateral speed and yaw speed of the unmanned ship respectively; S12. Establish a nonlinear dynamic mathematical model of the underactuated unmanned ship as follows: in, All represent the quality of the unmanned ship. Both represent the hydrodynamic derivative term, I z represents the moment of inertia around the z-axis; f u (·), f v (·) and f r (·) represent the nonlinear functions of centripetal force, Coriolis force and hydrodynamic damping effect, respectively; τ uω , τ vω , τ rω Both represent the time-varying disturbance caused by wind currents; Q(τ u ) and Q(τ r ) represents the control input τ u and τ r Quantified value of .

3. The trajectory tracking control method of an underactuated unmanned ship according to claim 1, characterized in that: Step S2 specifically includes: S21. Use a composite quantizer to quantize the control input in the system. The specific quantization process is described as follows: Where q1(·) represents the logarithmic quantizer; f i (·) is defined as a rounding function less than or equal to (·); τ i,s >0, indicating the switching threshold between logarithmic quantization and uniform quantization; σ i represents the quantization density of the uniform quantizer; S22. Define the ideal trajectory as follows: U d (t)=[x d (t),y d (t)] T Among them, x d and d It represents the ideal trajectory of the unmanned ship in the inertial coordinate system, and the trajectory parameter U d (t) represents a continuously differentiable vector; S23. The design satisfies both the actual position U of the unmanned ship and the expected trajectory U d (t), and ensure that no violation and z e The controller of the limit is described as follows: Among them, U=[x,y] T represents the actual position of the unmanned ship in the inertial coordinate system, l0 represents a small constant and satisfies l0>0; S24. According to step S12, obtain: S25. Set the control target of the dynamic system as follows: Among them, κ1 and κ2 are both small positive integers; S26. According to step S24, obtain: in, S27. For the quantized control input in the unmanned ship model, let Q(τ u )=q 1u (t)τ u +q 2u (t), Q(τ r )=q 1r (t)τ r +q 2r (t), take: Among them, q 1u (t) and q 1r (t) are all unknown, since q 1u and q 1r The positive or negative value of will not change during the quantization process, so we can get q from the above formula 1u (t)>0,q 1r (t)>0; when τ u (t)|<a,τ r (t)|<a, Q(τ u (t)), Q(τ r (t)) is bounded, so q 2u (t), q 2r (t) is bounded, 4. The trajectory tracking control method of an underactuated unmanned ship according to claim 1, characterized in that: Step S3 specifically includes: S31. Define the position error as follows: Among them, z e represents the position error, sgn(·) represents the sign function, and sgn(0)=1; S32. For the design of unmanned ship sloshing motion controller, define the Lyapunov function as follows: Where log(·) represents the natural logarithm of (·), k a =k d -B0; limit z e Satisfy | e |<k a , we get z e is positive; then according to step S31, we get S33, deriving the Lyapunov function defined in step S32, and obtaining: S34, let α u =u e +u, define the stability function α as follows: where k ze >0, and k ze is a constant, so we have: S35, due to u e Without constraints, define the Lyapunov function as follows: S36, deriving the Lyapunov function defined in step S35, and obtaining: make get: S37, due to F u Unknown, according to the universal approximation theorem, for any small constant ε u , there exists a neural network system W u *T h u (X), Definition Among them, W u * is the ideal weight of the fuzzy logic system, h u (x) is the fuzzy basis vector, ε u is the approximation error of the fuzzy system and |ε u |≤ε; Definition Yes W u * The estimated value of is: in, Due to q 1u (t) and q 1r (t) are unknown and time-varying, so we use adaptive estimation to estimate their bounds. To prevent singular problems when the estimated value is zero, we use q 1u (t) and q 1r (t) is estimated by lower bound and the time-varying gain is defined as follows: Among them, q 1u (t) min Yes 1u The lower bound of (t), q 1r (t) min Yes 1r The lower bound of (t); S38. Design the system control law and adaptive law as follows: in, is for μ u The estimate of θ u is a constant, γ1, γ2, σ u is a positive number, S39. For the design of the unmanned ship swaying motion controller, define the Lyapunov function as follows: S310, deriving the Lyapunov function defined in step S39, and obtaining: S311, let α r =r e +r, define the stability function α r for: According to step S310, it is obtained: S312, due to r e Without constraints, define the Lyapunov function as follows: S313, deriving the Lyapunov function defined in step S312, and obtaining: make get: S314, Order Design the system control law and adaptive law as follows: in, Indicates μ r The estimate of θ r represents a constant, γ3, γ4, σ r All represent positive numbers, 5. The trajectory tracking control method of an underactuated unmanned ship according to claim 1, characterized in that: Step S4 specifically includes: S41. Define the event triggering strategy as follows: Where Y represents the time constant and satisfies e τu (t) and e τr (t) satisfies e τu (t) = ρ u (t)-τ u (t), e τr (t) = ρ r (t)-τ r (t); S42. The analysis of the defined event triggering strategy is as follows: when or hour, in The value of |e τu (t)|≥Y and |e τr (t)|≥Y value; when|e τu (t)|<Y or|e τr When (t)|<Y, no control input signal is transmitted and the controller value remains unchanged; There exists a continuous time-varying coefficient φ(t) that satisfies and |φ(t)|≤1; and |φ(t)|≤1; so that ρ u (t) = τ u (t)+φ(t)Y,ρ r (t) = τ r (t)+φ(t)Y, that is: There are three specific situations: When φ(t)=0, |e τu (t)|=|ρ u (t)-τ u (t)|=0, at this time When φ(t)=0, |e τr (t)|=|ρ r (t)-τ r (t)|=0, at this time When |φ(t)|<1, |e τu (t)|=|ρ u (t)-τ u (t)|<Y, at this time When |φ(t)|<1, |e τr (t)|=|ρ r (t)-τ r (t)|<Y, at this time When |φ(t)|=1, |e τu (t)|=|ρ u (t)-τ u (t)|=Y, determine the current time at this time When |φ(t)|=1, |e τr (t)|=|ρ r (t)-τ r (t)|=Y, determine the current time at this time S43. Based on the event-driven strategy, the control law of the underactuated unmanned ship system is updated as follows:

6. The trajectory tracking control method of an underactuated unmanned ship according to claim 1, characterized in that: Step S5 specifically includes: S51. Define the Lyapunov function as follows: S52, deriving the Lyapunov function defined in step S51, and obtaining: S53, bring in the control rate and the adaptive rate, and get: S54. Due to Then we have: make Then we have: S55, according to Then we get: Combining steps S53 and S55, we obtain: S56. Due to Thus we get: S57. According to the output-constrained lemma, for the error system where η=[w,z1] T , there exist continuously differentiable and positive definite functions V1 and V2, k b >0, the position output is x1, and the position error z1 is defined as x1-y d , satisfying: when z1→-k b Or z1→k b When V1(z1)→∞;γ1(||η||)≤U(η)≤γ2(||η||), where γ1 and γ2 are K ∞ Class functions; S58. Assume |z1(0)|<k b , take V(η):=V1(z1)+U(ω), if it satisfies: Then |z1(t)|<k b , K b >0, then: Thus we can get: S59, according to step S56, obtain: S510, consider the event triggering mechanism, update the control rate, and obtain: Due to q 1u >0,q 1r >0, according to the lemma: for any unknown real variable e, there exists a constant n that satisfies the following inequality: Then we get: Among them, c>0, d>0, and satisfy: in, S511. According to step S510, obtain Therefore, according to Lyapunov stability theory, all signals in the entire closed-loop system are ultimately bounded, and the tracking error of the system can converge to a smaller residual set.

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