Unmanned ship trajectory tracking dynamic surface control method based on preset performance

Abstract

The application relates to a preset-performance-based unmanned ship trajectory tracking dynamic surface control method, which comprises the following steps: setting an expected trajectory, an initial position and an initial speed of an unmanned ship, establishing a kinematics and dynamics model of the unmanned ship; obtaining a current motion state of the unmanned ship through a sensor on the unmanned ship; comparing an actual trajectory of the unmanned ship with the expected trajectory to obtain a tracking error of the unmanned ship; constructing a preset performance function, setting preset performance constraint parameters, and determining an error range allowed by the tracking error; establishing a fuzzy state observer to estimate the position and speed of the unmanned ship; constraining the tracking error in the error range, performing nonlinear transformation on the constrained tracking error, combining dynamic surface technology, and establishing a virtual control law; and calculating an expected control torque and transmitting the expected control torque to an actuator, so that the unmanned ship can quickly and accurately track the expected trajectory. The method is beneficial to improving the transient performance and steady-state performance of the unmanned ship trajectory tracking.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of unmanned ship control, and particularly relates to a trajectory tracking dynamic surface control method for unmanned ship based on preset performance. BACKGROUND

[0002] In recent years, with the rapid development of artificial intelligence, big data, cloud computing, and Internet of Things technologies, unmanned ship technology has also been significantly improved. As an autonomous marine unmanned device, unmanned ships have the advantages of high cost performance, high concealment, high speed, flexibility, and avoidance of personnel casualties, and therefore have attracted more and more attention. An intelligent control system is the core of the motion of an unmanned ship, which adjusts the speed, heading, position, and other parameters of the unmanned ship in real time according to the perceived environmental information and navigation plan, to ensure that the unmanned ship can stably and accurately sail along the predetermined route.

[0003] Various control algorithms have been used to solve the trajectory tracking control problem of unmanned ships, such as backstepping control, sliding mode control, robust adaptive control, etc. However, the above control schemes can only guarantee the stability performance of the tracking error, but cannot guarantee the transient performance, which is very important in maritime practice. Excessive transient control error may lead to accidents. Prescribed performance control technology is an effective method to guarantee transient performance, and limited time control technology is also of great concern due to its powerful and simple features in adjusting settling time. Therefore, considering both transient performance and settling time is a direction worthy of further research.

[0004] Trajectory tracking control of unmanned ships has become a key problem in the field of marine engineering and control. Various control algorithms have been used to solve the trajectory tracking control problem of unmanned ships, such as backstepping control, sliding mode control, neural network control, and fuzzy control. Backstepping control may encounter the so-called "derivative explosion" problem in some cases, so a filter link needs to be introduced to solve the problem of deriving the virtual control quantity. In the process of sliding mode control, when the state trajectory reaches the sliding mode surface, chattering phenomenon may occur. Chattering can damage the performance of the system, affect the accuracy of the control system, and cause energy waste. In addition, due to the rapid development of artificial intelligence, neural network control and fuzzy control have attracted more and more attention, but the algorithm is relatively complex, which increases the difficulty of its practical application. Complex algorithms may lead to low computational efficiency, making it difficult to achieve fast response in real-time control systems.

[0005] From the practical point of view, it is becoming increasingly important to consider the specified transient and steady-state control performance in the design of the controller. The preset performance control method is widely used in the trajectory tracking control of unmanned ships, which ensures that the tracking error converges to a preset arbitrary small area while ensuring that the convergence speed and overshoot meet the preset conditions. The performance function design converts the preset performance into an equivalent unrestricted range, and combines the dynamic surface technology to achieve the preset performance. However, the corresponding logarithmic inverse function conversion is required in the design process of the performance function, and therefore, the logarithmic error mapping function used in the related work leads to potential singularity problem of the designed control law. In addition, the preset performance control lacks unified and strict stability analysis, and the guess value of the unknown parameter is used in the design process, which is difficult to obtain.

[0006] If the unmanned ship cannot accurately track the desired trajectory, it will inevitably travel a longer distance, resulting in an increase in the stabilization time. In order to ensure the rapid convergence of the tracking error, a finite time unmanned ship control scheme is proposed. However, the convergence time of these finite time methods is affected by the initial conditions and control parameters, making it almost impossible to accurately estimate. SUMMARY

[0007] The purpose of the present application is to provide a preset performance-based unmanned ship trajectory tracking dynamic surface control method, which is beneficial to improve the transient performance and steady-state performance of the unmanned ship trajectory tracking.

[0008] In order to achieve the above purpose, the technical scheme adopted by the present application is as follows: a preset performance-based unmanned ship trajectory tracking dynamic surface control method, comprising the following steps:

[0009] Step 1, setting the desired trajectory η of the unmanned ship d , the initial position η0, the initial speed v0, establishing the kinematics and dynamics model of the unmanned ship;

[0010] Step 2, obtaining the current motion state of the unmanned ship through the sensor on the unmanned ship;

[0011] Step 3, comparing the actual trajectory of the unmanned ship with the desired trajectory to obtain the tracking error of the unmanned ship;

[0012] Step 4, constructing a preset performance function, setting a preset performance constraint parameter, and determining the allowable error range of the tracking error;

[0013] Step 5, according to the kinematics and dynamics model of the unmanned ship, establishing a fuzzy state observer to estimate the position and speed of the unmanned ship;

[0014] Step 6, the tracking error is constrained in the error range according to the preset performance function, and the constrained tracking error is nonlinearly transformed, and then a virtual control law is established by combining the dynamic surface technology;

[0015] Step 7, the expected control torque is obtained by analyzing and calculating the expected trajectory and the actual position state information of the unmanned ship, and is transmitted to the unmanned ship master control; the unmanned ship master control receives and processes the data, and sends the control torque command to the bottom controller, and the bottom controller controls the propeller through the PWM wave, so that the unmanned ship can quickly and accurately track the expected trajectory.

[0016] Further, in step 1, the nonlinear kinematics and dynamics models of the full-drive unmanned ship considering environmental disturbance are established as follows:

[0017]

[0018] wherein, is the actual trajectory vector of the unmanned ship in the inertial coordinate (x, y) and the bow angle ψ, is the actual speed vector of the unmanned ship in the body coordinate system (u, v, and r), J(ψ) is the coordinate transformation matrix, M is the positive definite inertia matrix, C(v) is the Coriolis centripetal matrix, D(v) is the damping matrix, τ is the actual control input vector provided by the unmanned ship propulsion system, and d is the unknown time-varying environmental disturbance vector.

[0019] Further, in step 2, the current position, attitude and speed of the unmanned ship are obtained through the sensors on the unmanned ship.

[0020] Further, in step 3, the actual trajectory of the unmanned ship is compared with the expected trajectory to obtain the trajectory tracking error e of the unmanned ship:

[0021] e = η - η d

[0022] wherein, η d = [x d , y d , ψ d ] T is the reference trajectory.

[0023] Further, in step 4, the preset performance function is constructed by combining the finite time and the preset performance, and the expression of the preset performance function is as follows:

[0024]

[0025] wherein, e i and k i(t) are the tracking errors and the preset performance function in x, y, ψ directions respectively, l > 0, ζ > 0, n is the system order, i, λ are all to-be-designed constants; and are k i the initial and final values of (t) respectively, t is a time constant; is a preset convergence time parameter, i = x, y, ψ;

[0026] The tracking error is constrained, and the expression is as follows:

[0027] -k i (t) < e i < k i (t), i = x, y, ψ.

[0028] Further, in step 5, a fuzzy state observer is established according to the kinematics and dynamics model of the unmanned ship to estimate the position and speed of the unmanned ship, and the implementation method is as follows:

[0029] Let be an unknown function, a fuzzy logic system is used to identify the unknown function, so that:

[0030]

[0031] where w T is an ideal weight matrix, ζ(v) is a fuzzy basis function vector, is an approximation error, is an upper bound of the approximation error;

[0032] The kinematics and dynamics model of the unmanned ship is transformed to obtain:

[0033]

[0034] where, and are the first derivatives of η and v respectively, y is an output item; let the composite disturbance There exists an unknown constant such that

[0035] The fuzzy state observer is established as follows:

[0036]

[0037] In the formula, k1, k2, k3 > 0 are all observer gains, where and respectively, η, v, the estimated value of w and q, is the first derivative of

[0038] Further, in step 6, the tracking error is constrained in the error range according to the preset performance function, and the constrained tracking error is nonlinearly transformed, and then a virtual control law is established according to the position and speed of the unmanned ship estimated by the fuzzy state observer and combined with the dynamic surface technology, and the implementation method is as follows:

[0039] The tracking error is nonlinearly transformed as follows:

[0040]

[0041] wherein k(t) = [k x (t), k y (t), k ψ (t)] T , e = [e x , e y , e ψ ] T S1 is the new error vector after nonlinear transformation, and the derivative of S1 is obtained as follows:

[0042]

[0043] wherein, S1 = [S 1,x , S 1,y , S 1,ψ ] T , and are the first derivatives of e i , k i (t) and S 1,i , i = x, y, ψ;

[0044] It is further obtained that:

[0045]

[0046] wherein, is the first derivative of η d ;

[0047] The virtual control law α ∈ R3 is established as follows:

[0048]

[0049] wherein χ = diag(χ x , χ y , χ ψ ), κ = diag(κx ,κ y ,κ ψ ), K1 is a diagonal matrix of positive definite parameters.

[0050] Further, in step 7, the established virtual control law a is passed through a first-order low-pass filter as follows:

[0051]

[0052] where v d is the state vector of the first-order filter, T d > 0 is the filter time constant of the filter;

[0053] Define the speed error surface vector as follows:

[0054]

[0055] The controller analyzes and calculates according to the obtained expected trajectory and the actual position state information of the unmanned ship, and obtains the expected control torque as follows:

[0056]

[0057] where K2 is a diagonal matrix of positive definite parameters;

[0058] Then, the expected control torque is sent to the controller to realize the operation of the unmanned ship according to the expected trajectory.

[0059] Further, the working process of the unmanned ship is as follows:

[0060] 1) The control center sends a request instruction to the unmanned ship master control to request communication with the unmanned ship master control and obtain the state information of the unmanned ship;

[0061] 2) The unmanned ship master control receives the request instruction, collects and verifies the sensor information, and sends it to the control center in a set format through the TCP / IP protocol after packaging;

[0062] 3) The control center receives the data and sets the format for verification and unpacking, and then classifies and matches the data according to the data type to obtain the corresponding state information of the unmanned ship;

[0063] 4) The control center sends the state information of the unmanned ship to the controller, and outputs the expected control torque through the unmanned ship trajectory tracking dynamic surface control method based on the preset performance;

[0064] 5) The control center packages the obtained expected control torque into a control command according to the set format and sends it to the unmanned ship master control;

[0065] 6) The unmanned ship master control obtains the expected control torque according to the control command, and sends the control torque instruction to the bottom controller based on the expected control torque, and the bottom controller controls the propeller through the PWM wave, so that the unmanned ship can quickly and accurately track the expected trajectory.

[0066] Compared with the prior art, the application has the following beneficial effects: the application constructs an unmanned ship trajectory tracking dynamic surface control method based on a preset performance, and a finite time is added to improve the preset performance function. A simple non-logarithmic performance function is used to ensure that the tracking error converges to a neighborhood of the origin, and the inverse function is no longer needed, so that the potential singularity problem of the designed control law can be avoided. Secondly, the dynamic surface technology is used, that is, the negative influence of the differential operation on the intermediate control law is avoided. In addition, a fuzzy state observer is used to estimate the position and speed of the unmanned ship system, and finally a stable output is obtained. The application proposes a strategy with preset performance constraints, and adds a finite time preset performance function. Compared with the control method using the traditional preset performance function, the error convergence speed of the method is faster, and the overshoot problem is solved, so that the tracking error of the unmanned ship can converge to a preset small range boundary and the performance function can be adjusted arbitrarily to meet the actual demand, while meeting the transient performance and steady-state performance requirements. The final experimental results show that the method has good trajectory tracking capability, and can stably and accurately travel on the specified trajectory under certain disturbance. BRIEF DESCRIPTION OF DRAWINGS

[0067] Figure 1 is a method implementation flowchart of an embodiment of the application;

[0068] Figure 2 is an implementation schematic diagram of the unmanned ship control system in the embodiment of the application;

[0069] Figure 3 is an implementation schematic diagram of the controller module in the embodiment of the application. DETAILED DESCRIPTION

[0070] The application will be further described below in combination with the drawings and embodiments.

[0071] It should be pointed out that the following detailed description is exemplary and is intended to provide further description of the present application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as generally understood by those skilled in the art to which the present application belongs.

[0072] It is to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments in accordance with the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and / or "comprising," when used in this specification, specify the presence of stated features, steps, operations, elements, components, and / or groups thereof, but do not preclude the presence or addition of one or more other features, steps, operations, elements, components, and / or groups thereof.

[0073] In the motion control of unmanned ships, trajectory tracking control has always been a hot spot in this field. In actual operation, unmanned ships are always sailing in unpredictable environments, there are unknown wind, wave, flow interference, which seriously affects the stability of ship trajectory tracking control. In this case, it is very necessary to improve the robustness and anti-disturbance ability of the system to provide a reliable unmanned ship control system. At the same time, the uncertainty of the dynamic model and the unknown speed are also difficult problems to overcome. The approximation ability of fuzzy logic system or neural network has become an effective tool to solve the control problem of nonlinear unmanned ship system. From the practical point of view, the specified transient and steady state control performance is considered in the design of the controller. The preset performance control method is adopted to ensure that the tracking error converges to a pre-set small area while ensuring the convergence speed and overshoot, which requires the simultaneous satisfaction of transient performance and steady state performance, and directly improves the system performance as the target. Based on the research of these problems, a fuzzy state observer is used to estimate the position and speed of the unmanned ship system, and a preset performance function with finite time is introduced, and a preset performance based unmanned ship trajectory tracking dynamic surface control method is proposed.

[0074] As Figure 1 shown, the embodiment provides a preset performance based unmanned ship trajectory tracking dynamic surface control method, comprising the following steps:

[0075] Step 1, setting the desired trajectory η of the unmanned ship d , the initial position η0, the initial speed v0, establishing the kinematics and dynamics model of the unmanned ship.

[0076] The nonlinear kinematics and dynamics model of the full-drive unmanned ship considering environmental disturbance is established as follows:

[0077]

[0078] Wherein, is the actual trajectory vector of the unmanned ship in the inertial coordinate (x, y) and the bow angle ψ, The actual velocity vector of the unmanned ship in the body coordinate system is composed of the surge velocity u, the sway velocity v and the yaw angle velocity r; J(ψ) is the coordinate transformation matrix, M is a positive definite inertia matrix; C(v) is the Coriolis centripetal matrix; D(v) is the damping matrix, τ is the actual control input vector provided by the propulsion system of the unmanned ship, and d is the unknown time-varying environmental disturbance vector.

[0079] Step 2, obtaining the current motion state of the unmanned ship through sensors on the unmanned ship, including the current position, attitude and velocity.

[0080] Step 3, comparing the actual trajectory of the unmanned ship with the desired trajectory to obtain the tracking error of the unmanned ship.

[0081] Specifically, the actual trajectory of the unmanned ship is compared with the desired trajectory to obtain the trajectory tracking error e of the unmanned ship:

[0082] e = η - η d

[0083] η d = [x d , y d , ψ d ] T is the reference trajectory, i.e. the desired trajectory.

[0084] Step 4, constructing a preset performance function, setting a preset performance constraint parameter, and determining an error range allowed by the tracking error.

[0085] Specifically, a preset performance function is constructed by combining finite time and preset performance, and the expression of the preset performance function is as follows:

[0086]

[0087] wherein e i and k i (t) are the tracking errors and the preset performance function in the x, y and ψ directions respectively, ι>0, n is the order of the system, ι, λ are to-be-designed constants; and are the initial value and the final value of k i (t) respectively, and t is a time constant; is a preset convergence time parameter, i = x, y, ψ;

[0088] The tracking error is constrained, and the expression is as follows:

[0089] -k i (t)<e i <k i(t), i = x, y, ψ.

[0090] Step 5, according to the kinematic and dynamic model of the unmanned ship, a fuzzy state observer is established to estimate the position and velocity of the unmanned ship. The specific implementation method is:

[0091] Let f(v) = -M -1 C(v)v - M -1 D(v)v = [f x , f y , f ψ ] T is an unknown function, a fuzzy logic system is used to identify the unknown function, so that:

[0092]

[0093] where w T is the ideal weight matrix, ζ(v) is the fuzzy basis function vector, is the approximation error, and is a constant, i = x, y, ψ, is the upper bound of the approximation error.

[0094] Transform the kinematic and dynamic model of the unmanned ship to get:

[0095]

[0096] where and are the first derivatives of η and v, respectively, y is the output term; Let the composite disturbance There exists an unknown constant such that

[0097] The fuzzy state observer is established as follows:

[0098]

[0099] where k1, k2, k3 > 0 are all observer gains, where and are the estimated values of η, ν, w and q, respectively, is the first derivative of .

[0100] Step 6, according to the preset performance function, the tracking error is constrained within the error range, and the nonlinear transformation is performed on the constrained tracking error, and then combined with the dynamic surface technology, a virtual control law is established.

[0101] Specifically, the tracking error is constrained in the error range according to a preset performance function, and the constrained tracking error is nonlinearly transformed, and then a virtual control law is established according to the unmanned ship position and speed estimated by the fuzzy state observer and in combination with the dynamic surface technology, and the implementation method is as follows:

[0102] The tracking error is nonlinearly transformed as follows:

[0103]

[0104] Wherein, k(t)=[k x (t),k y (t),k ψ (t)] T , e=[e x , e y , e ψ ] T S1 is a new error vector after nonlinear transformation, and the derivative of S1 is obtained as follows:

[0105]

[0106] Wherein, and are the first derivatives of e i , k i (t) and S 1,i , i=x, y, ψ.

[0107] Further obtained:

[0108]

[0109] Wherein, is the first derivative of η d .

[0110] The virtual control law α∈R3 is established as follows:

[0111]

[0112] Wherein, χ=diag(χ x , χ y , χ ψ ), κ=diag(κ x , κ y , κ ψ ), K1 is a positive definite parameter diagonal matrix.

[0113] Step 7, according to the obtained desired trajectory, the actual position state information of the unmanned ship is analyzed and calculated, the desired control torque is obtained and transmitted to the unmanned ship master control; the unmanned ship master control receives the data and processes it, and sends the control torque command to the bottom layer controller, and the bottom layer controller realizes the control of the thruster through the PWM wave, so that the unmanned ship can quickly and accurately track the desired trajectory.

[0114] Let the established virtual control law a pass through the following first-order low-pass filter:

[0115]

[0116] Where, v d is the state vector of the first-order filter, T d >0 is the filter time constant of the filter. Define the heading error vector:

[0117]

[0118] The controller analyzes and calculates according to the obtained desired trajectory and the actual position state information of the unmanned ship, and obtains the desired control torque as follows:

[0119]

[0120] Where, K2 is a positive definite parameter diagonal matrix.

[0121] Then, the desired control torque is sent to the controller to realize the operation of the unmanned ship according to the desired trajectory.

[0122] As shown in Figure 2 , in this embodiment, the unmanned ship includes:

[0123] 1) Unmanned ship surrounding information acquisition module: through millimeter wave radar detection and ranging, providing the distance, speed and angle of the obstacles around the unmanned ship, etc. Information can be used for obstacle avoidance and navigation during unmanned ship driving; through the remote monitoring and data transmission function of the camera, the unmanned ship can identify the camera to transmit the navigation situation to the shore control center in real time, realizing the omnidirectional navigation monitoring and management.

[0124] 2) Unmanned ship itself information acquisition module: through GPS signal reception to determine the real-time position and speed of the unmanned ship.

[0125] 3) Control center: receiving the state information sent by the unmanned ship, and calculating the desired control torque through the controller set in the control center, and then packaging into control command and sending to the unmanned ship.

[0126] 4) Unmanned ship master control (master control module): receiving control command, sending control torque command to bottom layer controller based on desired control torque.

[0127] 5) Bottom layer controller: receive the expected control torque sent by the unmanned ship master control, then control the thruster through PWM wave.

[0128] 6) Thruster: used to control the unmanned ship to travel along the expected route.

[0129] The working process of the unmanned ship is as follows:

[0130] 1) The control center sends a request instruction to the unmanned ship master control, requesting communication with the unmanned ship master control and obtaining the state information of the unmanned ship;

[0131] 2) The unmanned ship master control receives the request instruction, collects and verifies the sensor information, and sends it to the control center in a set format through TCP / IP protocol after packaging;

[0132] 3) The control center receives the data and sets the format for verification and unpacking, then classifies and matches the data according to the data type to obtain the corresponding state information of the unmanned ship;

[0133] 4) The control center sends the state information of the unmanned ship to the controller, and outputs the expected control torque based on the preset performance of the unmanned ship trajectory tracking dynamic surface control method;

[0134] 5) The control center packs the expected control torque obtained into a control command according to the set format and sends it to the unmanned ship master control;

[0135] 6) The unmanned ship master control obtains the expected control torque according to the control command, and sends the control torque instruction to the bottom layer controller based on the expected control torque, and the bottom layer controller controls the thruster through the PWM wave, so that the unmanned ship can quickly and accurately track the expected trajectory.

[0136] The controller module based on the above unmanned ship trajectory tracking dynamic surface control method in this embodiment is further described in detail.

[0137] 1. Algorithm implementation part:

[0138] 1.1 Unmanned ship mathematical model

[0139] Firstly, the ship mathematical model proposed by Fossen is adopted in the present application. Under normal circumstances, the trajectory tracking problem of the unmanned ship can be simplified to consider the forward, lateral drift and yaw three degrees of freedom motion, and its three degrees of freedom model expression is as follows:

[0140]

[0141] Among them, is the position vector composed of the actual position (x, y) and the yaw angle ψ of the ship in the inertial coordinate system, is the velocity vector of the ship in surge u, sway v and yaw rate r in the body-fixed coordinate system. Meanwhile, J(ψ) is the coordinate transformation matrix, M is the positive definite inertia matrix; C(v) is the Coriolis centripetal matrix; D(v) is the damping matrix, and their expressions are as follows:

[0142]

[0143] where d = [d1, d2, d3] T is the unknown time-varying environmental disturbance vector. τ is the actual control input vector provided by the propulsion system, which is composed of τ u (surge), τ v (roll) and τ r (yaw). Wherein, d 11 (u) = -X u -X |u|u |u|, d 22 (v, r) = -Y v -Y |v|v |v| -Y |r|v |r|, d 23 (v, r) = -Y r -Y |v|r |v| -Y |r|r |r|, d 32 (v, r) = -N v -N |v|v |v| -N |r|v |r|, d 33 (v, r) = -N r -N |v|r |v| -N |r|r |r|, m is the mass of the unmanned ship, I z is the moment of inertia, x g is the distance between the center of gravity of the unmanned ship and the origin of the body-fixed coordinate system.

[0144] 1.2 Finite-time Prescribed Function Design

[0145] The finite-time prescribed function will be discussed below to ensure the convergence and stability of the tracking error.

[0146] Definition: If the smooth function k(t) has the following properties:

[0147] 1) k(t) ∈ R and k(t) > 0;

[0148] 2)

[0149] 3) is an arbitrary design parameter;

[0150] 4) when t≥T s , t is the settling time.

[0151] Then k(t) is called a finite-time prescribed function.

[0152] Based on the above definition, the present application proposes an improved finite-time prescribed function.

[0153]

[0154] where ι>0, n is the system order,

[0155] The finite-time prescribed function proposed in the present application can achieve faster convergence speed. On the other hand, although neural networks or fuzzy logic systems are used, the stable time is independent of the initial conditions and design parameters, and the tracking error can still asymptotically converge to a pre-specified region.

[0156] The error performance function can be expressed as:

[0157] -k i (t)<e i (t)<k i (t), i=x, y, ψ#(8)

[0158] e=η-η d #(9)

[0159] where, η d =[x d ,y d ,ψ d ] T is the reference trajectory.

[0160] 1.3 Fuzzy State Observer Design

[0161] Then a fuzzy state observer is designed to estimate the velocity. Since the function f(v)=-M -1 C(v)v-M -1 D(v)v is unknown, a fuzzy logic system is used to identify the unknown function f(v)=[f1,f2,f3] T , so that

[0162]

[0163] where, and are constants.

[0164] From equation (10), the system (1) and (2) can be written as:

[0165]

[0166] where, The composite disturbance considered in (11) can be obtained as i.e. there exists an unknown constant q * such that and

[0167] The mathematical form of the fuzzy state observer design is as follows:

[0168]

[0169] where k1>0, k2>0 and k3>0 are the observer gains.

[0170] 1.4 Controller design

[0171] Construct the following nonlinear transformation:

[0172]

[0173] Step 1: Differentiate (13) with respect to time:

[0174]

[0175] From (1), (2), (9), (14) we have

[0176]

[0177] Design the virtual control law a e R3:

[0178]

[0179] where χ = diag(χ x , χ y , χ z ), κ = diag(κ1, κ2, κ3), K1 is a positive definite diagonal matrix of parameters.

[0180] Let the designed virtual control law a pass through the following first-order low-pass filter.

[0181]

[0182] where v d is the state vector of the first-order filter, T d >0 is the filter time constant.

[0183] Step 2: Define the airspeed error surface vector S2 e R3:

[0184]

[0185] Differentiate (18) using (1), (2), (18):

[0186]

[0187] The control law can be designed by (19):

[0188]

[0189] Where K2 is a positive definite parameter diagonal matrix.

[0190] 2. Controller system building and implementation steps:

[0191] In the simulation implementation part, firstly, the dynamics and kinematics model of the unmanned ship is constructed; secondly, the traditional preset performance function is further optimized under the fusion of the finite time, and the dynamic surface control is introduced, so that the control precision of the system is improved and the error convergence speed is accelerated. The control design process is simple and clear, and is convenient for practical application.

[0192] Through the above description, the specific process of the unmanned ship trajectory tracking control method and the control system thereof is described. Firstly, by designing the preset performance function with finite time, the position error is limited in a certain range, the error convergence speed is accelerated, and stability is maintained; then, the fuzzy logic state observer is used to estimate the speed, so that the system runs more smoothly; finally, a dynamic surface control algorithm based on the preset performance unmanned ship trajectory tracking is proposed, which increases the tracking accuracy of the system and improves the robustness of the system.

[0193] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can adopt a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer usable program code.

[0194] The present application is described with reference to flowcharts and / or block diagrams according to the method, device (system) and computer program product of the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can be realized by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a machine that implements the functions described in the flowcharts and / or block diagrams. Figure 1one or more processes and / or blocks Figure 1 an apparatus for performing the functions specified in the flowchart or multiple flows and / or blocks.

[0195] These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instructions which implement the flowchart or multiple flows and / or blocks. Figure 1 one or more processes and / or blocks Figure 1 an apparatus for performing the functions specified in the flowchart or multiple flows and / or blocks.

[0196] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the flowchart or multiple flows and / or blocks. Figure 1 one or more processes and / or blocks Figure 1 an apparatus for performing the functions specified in the flowchart or multiple flows and / or blocks.

[0197] The above descriptions are only preferred embodiments of the present application, and are not intended to limit the present application in other forms. Any skilled person in the art can modify or change the above-mentioned technical content into equivalent embodiments with equivalent changes. However, any simple modification, equivalent change and modification of the above-mentioned embodiments within the technical solution of the present application, according to the technical essence of the present application, still belongs to the protection scope of the technical solution of the present application.

Claims

1. A trajectory tracking dynamic surface control method for unmanned surface vehicle based on preset performance, characterized in that, The method comprises the following steps: Step 1, set the desired trajectory of the unmanned ship , initial position , initial speed , establish the kinematics and dynamics model of the unmanned ship; Step 2, obtaining the current motion state of the unmanned ship through sensors on the unmanned ship; Step 3, comparing the actual trajectory of the unmanned ship with the expected trajectory to obtain the tracking error of the unmanned ship; Step 4, constructing a preset performance function, setting preset performance constraint parameters, and determining an error range allowed by the tracking error; Step 5, according to the kinematics and dynamics model of the unmanned ship, a fuzzy state observer is established to estimate the position and speed of the unmanned ship; Step 6, according to the preset performance function, the tracking error is constrained within the error range, and the constrained tracking error is nonlinearly transformed, and then combined with the dynamic surface technology, a virtual control law is established; Step 7, according to the expected trajectory and the actual position state information of the unmanned ship, the expected control torque is obtained and transmitted to the unmanned ship master control; the unmanned ship master control receives and processes the data, and then sends the control torque command to the bottom controller, and the bottom controller controls the propeller through the PWM wave, so that the unmanned ship can quickly and accurately track the expected trajectory; In step 5, according to the kinematics and dynamics model of the unmanned ship, a fuzzy state observer is established to estimate the position and speed of the unmanned ship, and the implementation method is as follows: Let is an unknown function, a fuzzy logic system is used to identify the unknown function such that: wherein, is an ideal weight matrix, is a fuzzy basis function vector, is an approximation error, , , is an approximation error upper bound; The kinematics and dynamics model of the unmanned ship is transformed to obtain: where and are the first derivatives of and respectively, is the output term; let the composite disturbance ; there exist unknown constants such that ; The fuzzy state observer is established as follows: wherein are observer gains, wherein , , , and are the estimated values of , , , , and , is the first derivative of . In step 6, according to the preset performance function, the tracking error is constrained within the error range, and the constrained tracking error is nonlinearly transformed, and then combined with the dynamic surface technology, a virtual control law is established, and the implementation method is as follows: The tracking error is nonlinearly transformed as follows: wherein , , is the new error vector after the non-linear transformation, and the derivation of which gives wherein , , , , and are the first derivatives of , and respectively, ; Further obtained: wherein is a first derivative; Establishing a virtual control law As follows: wherein , , is a diagonal matrix of positive definite parameters.

2. The trajectory tracking dynamic surface control method for unmanned ship based on preset performance according to claim 1, characterized in that, In step 1, the kinematics and dynamics model of the full-drive unmanned ship considering environmental disturbance is established as follows: wherein is the actual position of the USV in the inertial coordinate system and the yaw angle consisting of the actual trajectory vector, is the surge velocity of the USV in the body coordinate system , the sway velocity and the yaw angular velocity consisting of the actual velocity vector, and are the first derivatives of and respectively; is the coordinate transformation matrix, is the positive definite inertia matrix; is the Coriolis centripetal matrix; is the damping matrix, is the actual control input vector provided by the USV propulsion system, is the unknown time-varying environmental disturbance vector. 3.The method of claim 1, wherein, In step 2, the current position, attitude and speed of the unmanned ship are obtained through sensors on the unmanned ship.

4. The trajectory tracking dynamic surface control method for unmanned ship based on preset performance according to claim 1, characterized in that, In step 3, the actual trajectory of the unmanned ship is compared with the expected trajectory to obtain a trajectory tracking error of the unmanned ship : wherein is the reference trajectory.

5. The trajectory tracking dynamic surface control method for unmanned ship based on preset performance according to claim 1, characterized in that, In step 4, the preset performance function is constructed by combining finite time and preset performance, and the expression of the preset performance function is as follows: wherein, and are respectively the tracking errors of the three directions and a preset performance function, , , , is the system order, are all to-be-designed constants; and are respectively the initial value and the terminal value of is a time constant; is a preset convergence time parameter, ; The tracking error is constrained, and the expression is as follows: 。 6. The trajectory tracking dynamic surface control method for unmanned ship based on preset performance according to claim 1, characterized in that, In step 7, the established virtual control law is passed through the following first order low pass filter: wherein is a state vector of a first order filter, is a filter time constant of the filter; The heading speed error vector is defined as: The controller analyzes and calculates the expected control torque according to the expected trajectory and the actual position state information of the unmanned ship as follows: wherein is a diagonal matrix of positive definite parameters; Then, the expected control torque is sent to the controller to realize the operation of the unmanned ship according to the expected trajectory.

7. The preset performance-based dynamic surface control method for trajectory tracking of unmanned surface vehicle according to any one of claims 1-6, characterized in that, The working process of the unmanned ship is as follows: 1) The control center sends a request command to the unmanned ship master control to request communication with the unmanned ship master control and obtain the state information of the unmanned ship; 2) The unmanned ship master control receives the request command, collects and verifies the sensor information, packages according to the set format, and sends to the control center through the TCP / IP protocol; 3) The control center receives the data and sets the format for verification and unpacking, and then classifies and matches the data according to the data type to obtain the corresponding state information of the unmanned ship; 4) The control center sends the state information of the unmanned ship to the controller, and outputs the expected control torque through the preset performance based unmanned ship trajectory tracking dynamic surface control method; 5) The control center packages the expected control torque obtained into a control command according to the set format and sends it to the unmanned ship master control; 6) The unmanned ship master control gets the desired control torque according to the control command, and sends the control torque instruction to the bottom controller based on the desired control torque, and the bottom controller controls the propeller through the PWM wave, so that the unmanned ship can quickly and accurately track the desired trajectory.

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