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

Abstract

The invention relates to an unmanned ship trajectory tracking dynamic surface control method based on preset performance, and the method comprises the steps: setting an expected trajectory, an initial position and an initial speed of an unmanned ship, and building a kinematics and dynamics model of the unmanned ship; obtaining the current motion state of the unmanned ship through a sensor on the unmanned ship; comparing the 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 a preset performance constraint parameter, 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; the tracking error is constrained in an error range, nonlinear transformation is performed on the constrained tracking error, and a virtual control law is established in combination with a dynamic surface technology; and expected control torque is calculated and transmitted to an actuator, so that the unmanned ship can quickly and accurately track an expected track. The method is beneficial for improving transient performance and steady-state performance of trajectory tracking of the unmanned ship.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned ship control, and in particular to an unmanned ship trajectory tracking dynamic surface control method based on preset performance. Background Art

[0002] In recent years, with the rapid development of technologies such as artificial intelligence, big data, cloud computing, and the Internet of Things, unmanned ship technology has also been significantly improved. As autonomous unmanned marine equipment, unmanned ships have the advantages of high cost performance, high concealment, high speed, flexibility, and avoidance of casualties. Therefore, unmanned ships have received more and more attention. The intelligent control system is the core of the unmanned ship's movement. It 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 sail stably and accurately along the predetermined route.

[0003] Various control algorithms are 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 stable performance of the tracking error, but not the transient performance, which is very important in navigation practice. Excessive transient control errors may lead to accidents. Preset performance control technology is an effective method to ensure transient performance. At the same time, finite time control technology has attracted much attention for its powerful and simple features in adjusting the settling time. Therefore, considering both transient performance and settling time at the same time is a direction worthy of further research.

[0004] Trajectory tracking control of unmanned ships has become a key issue in the marine engineering and control community. Various control algorithms are 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 "differential explosion" problem in some cases, so it is necessary to introduce a filtering link to solve the problem of derivation of the virtual control quantity. In the sliding mode control process, when the state trajectory reaches the sliding surface, chattering may occur. Chattering will destroy 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 received more and more attention, but the algorithm is relatively complex, which increases its difficulty in practical application. Complex algorithms may lead to low computational efficiency and difficulty in achieving fast response in real-time control systems.

[0005] From a practical point of view, it is becoming increasingly important to take the specified transient and steady-state control performance into account in the design of the controller. The preset performance control method ensures that the convergence speed and overshoot meet the preset conditions while ensuring that the tracking error converges to a preset arbitrarily small area. Therefore, it has been widely used in the trajectory tracking control of unmanned ships. The performance function design converts the preset performance into an equivalent unrestricted range and combines 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. Therefore, the logarithmic error mapping function used in related work leads to potential singularity problems in the designed control law. In addition, the preset performance control lacks a unified and rigorous stability analysis, and its design process uses guessed values ​​of unknown parameters, which are 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, finite-time unmanned ship control schemes have been 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 of the invention

[0007] The object of the present invention is to provide a dynamic surface control method for unmanned ship trajectory tracking based on preset performance, which is beneficial to improving the transient performance and steady-state performance of unmanned ship trajectory tracking.

[0008] In order to achieve the above object, the technical solution adopted by the present invention is: a method for controlling a dynamic surface of an unmanned ship trajectory tracking based on preset performance, comprising the following steps:

[0009] Step 1: Set the desired trajectory η of the unmanned ship d , initial position η0, initial velocity v0, establish the kinematic and dynamic models of the unmanned ship;

[0010] Step 2: Obtain the current motion state of the unmanned ship through the sensors on the unmanned ship;

[0011] Step 3: Compare the actual trajectory of the unmanned ship with the expected trajectory to obtain the tracking error of the unmanned ship;

[0012] Step 4: Construct a preset performance function, set preset performance constraint parameters, and determine the allowable error range of the tracking error;

[0013] Step 5: According to the kinematic and dynamic models of the unmanned ship, a fuzzy state observer is established to estimate the position and speed of the unmanned ship;

[0014] Step 6: Constrain the tracking error within the error range according to the preset performance function, perform nonlinear transformation on the constrained tracking error, and then establish a virtual control law in combination with the dynamic surface technology;

[0015] Step 7: Analyze and calculate the expected trajectory and the actual position status information of the unmanned ship to obtain the expected control torque and transmit it to the unmanned ship main control; after the unmanned ship main control receives and processes the data, it sends the control torque instruction to the underlying controller, and the underlying controller controls the thruster through the PWM wave, so that the unmanned ship can quickly and accurately track the expected trajectory.

[0016] Furthermore, in step 1, the nonlinear kinematic and dynamic models of the fully driven unmanned ship considering environmental interference are established as follows:

[0017]

[0018] in, is the actual trajectory vector composed of the actual position (x, y) of the unmanned ship in inertial coordinates and the bow angle ψ, is the actual velocity vector composed of the longitudinal velocity u, lateral velocity v and bow angular velocity r of the unmanned ship in the appendage coordinate system; J(ψ) is the coordinate system 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] Furthermore, in step 2, the current position, attitude and speed of the unmanned ship are obtained through sensors on the unmanned ship.

[0020] Furthermore, 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] Among them, η d =[x d ,y d ,ψ d ] T is the reference trajectory.

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

[0024]

[0025] Among them, e i and k i(t) are the tracking errors and preset performance functions in the three directions of x, y, and ψ, respectively, l>0, ζ>0, n is the system order, ι, λ are all constants to be designed; and k i (t) initial and final values, t is the time constant; is the 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] Furthermore, in step 5, according to the kinematic and dynamic models 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:

[0029] set up is an unknown function, and a fuzzy logic system is used to identify the unknown function, so that:

[0030]

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

[0032] The kinematic and dynamic models of the unmanned ship are transformed to obtain:

[0033]

[0034] in, and are the first-order derivatives of η and v respectively, y is the output term; let the composite disturbance There are unknown constants Make

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

[0036]

[0037] In the formula, k1, k2, k3>0 are all observer gains, in and are η, ν, The estimated values ​​of w and q, for The first derivative of .

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

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

[0040]

[0041] Where 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 then S1 is derived to obtain:

[0042]

[0043] in, S1=[S 1,x ,S 1,y ,S 1,ψ ] T , and e i , k i (t) and S 1,i The first derivative of , i = x, y, ψ;

[0044] Further conclusion:

[0045]

[0046] in, is η d The first derivative of

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

[0048]

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

[0050] Furthermore, in step 7, the established virtual control law α is passed through the following first-order low-pass filter:

[0051]

[0052] Among them, 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:

[0054]

[0055] The controller analyzes and calculates the desired trajectory and the actual position status information of the unmanned ship, and obtains the desired control torque as follows:

[0056]

[0057] Among them, K2 is a diagonal matrix with positive definite parameters;

[0058] Then, the desired control torque is sent to the controller to enable the unmanned ship to run according to the desired trajectory.

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

[0060] 1) The control center sends a request command to the unmanned ship master control, requesting to communicate with the unmanned ship master control and obtain the unmanned ship status information;

[0061] 2) The unmanned ship master receives the request command, collects and verifies the sensor information, packages it according to the set format, and sends it to the control center through the TCP / IP protocol;

[0062] 3) The control center receives the data and sets it to be checked and unpacked according to the format, and then classifies and matches the data according to the data type to obtain the corresponding status information of the unmanned ship;

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

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

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

[0066] Compared with the prior art, the present invention has the following beneficial effects: the present invention constructs a dynamic surface control method for unmanned ship trajectory tracking based on preset performance, and improves it by adding finite time to the preset performance function. A simple non-logarithmic performance function is used to ensure that the tracking error converges to a certain neighborhood of the origin, and an inverse function is no longer required. Therefore, the potential singularity problem of the designed control law can be avoided. Secondly, the dynamic surface technology is adopted, that is, the negative impact of differential operation on the intermediate control law is avoided. In addition, the fuzzy state observer is used to estimate the position and speed of the unmanned ship system, and a stable output can be obtained in the end. The present invention proposes a strategy with preset performance constraints, and adds a preset performance function with finite time. Compared with the control method using the traditional preset performance function, the method has a faster error convergence speed and solves the overshoot problem, so that the tracking error of the unmanned ship can converge to a preset small range and the performance function used can arbitrarily adjust the convergence time to meet the actual needs, while meeting the transient performance and steady-state performance requirements. The final experimental results show that the method has good trajectory tracking ability and can stably and accurately travel on the specified trajectory under certain interference. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 is a flow chart of a method implementation of an embodiment of the present invention;

[0068] Figure 2 is a schematic diagram of the implementation principle of the unmanned ship control system in an embodiment of the present invention;

[0069] Figure 3 It is a schematic diagram of the implementation principle of the controller module in the embodiment of the present invention. DETAILED DESCRIPTION

[0070] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0071] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present application belongs.

[0072] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0073] In the motion control of unmanned ships, trajectory tracking control has always been a hot topic in this field. In actual operation, unmanned ships always sail in unpredictable environments, with unknown wind, wave and flow interference, which seriously affect the stability of ship trajectory tracking control. In this case, it is very necessary to improve the robustness and anti-interference 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 to overcome. The approximation ability of fuzzy logic systems or neural networks has become an effective tool for solving nonlinear unmanned ship system control problems. From a practical point of view, the specified transient and steady-state control performance is taken into account in the design of the controller. The preset performance control method is adopted to ensure that the tracking error converges to a pre-set arbitrarily small area while ensuring the convergence speed and overshoot. It requires the simultaneous satisfaction of transient performance and steady-state performance, and directly aims to improve system performance. Based on the study of these problems, the present invention uses a fuzzy state observer to estimate the position and speed of the unmanned ship system, and introduces a preset performance function with a finite time, and proposes a dynamic surface control method for unmanned ship trajectory tracking based on preset performance.

[0074] like Figure 1 As shown, this embodiment provides a method for controlling an unmanned ship's trajectory tracking dynamic surface based on preset performance, comprising the following steps:

[0075] Step 1: Set the desired trajectory η of the unmanned ship d , initial position η0, initial velocity v0, and establish the kinematic and dynamic models of the unmanned ship.

[0076] The nonlinear kinematic and dynamic models of the fully driven unmanned ship considering environmental interference are established as follows:

[0077]

[0078] in, is the actual trajectory vector composed of the actual position (x, y) of the unmanned ship in inertial coordinates and the bow angle ψ, is the actual velocity vector composed of the longitudinal velocity u, lateral velocity v and bow angular velocity r of the unmanned ship in the appendage coordinate system; J(ψ) is the coordinate system 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.

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

[0080] Step 3: Compare the actual trajectory of the unmanned ship with the expected trajectory to obtain the tracking error of the unmanned ship.

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

[0082] e=η-η d

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

[0084] Step 4: Construct a preset performance function, set preset performance constraint parameters, and determine the allowable error range of the tracking error.

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

[0086]

[0087] Among them, e i and k i (t) are the tracking errors and preset performance functions in the three directions of x, y, and ψ, respectively, ι>0, n is the system order, ι, λ are all constants to be designed; and k i (t) initial and final values, t is the time constant; is the 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: Based on the kinematic and dynamic models of the unmanned ship, a fuzzy state observer is established to estimate the position and speed of the unmanned ship. The specific implementation method is:

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

[0092]

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

[0094] The kinematic and dynamic models of the unmanned ship are transformed to obtain:

[0095]

[0096] in, and are the first-order derivatives of η and v respectively, y is the output term; let the composite disturbance There are unknown constants Make

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

[0098]

[0099] In the formula, k1, k2, k3>0 are all observer gains, in and are η, ν, The estimated values ​​of w and q, for The first derivative of .

[0100] Step 6: Constrain the tracking error within the error range according to the preset performance function, perform nonlinear transformation on the constrained tracking error, and then combine the dynamic surface technology to establish a virtual control law.

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

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

[0103]

[0104] Where 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 then S1 is derived to obtain:

[0105]

[0106] in, and e i , k i (t) and S 1,i The first derivative of , i = x, y, ψ.

[0107] Further conclusion:

[0108]

[0109] in, is η d The first derivative of .

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

[0111]

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

[0113] Step 7: Analyze and calculate the expected trajectory and the actual position status information of the unmanned ship to obtain the expected control torque and transmit it to the unmanned ship main control; after the unmanned ship main control receives and processes the data, it sends the control torque instruction to the underlying controller, and the underlying controller controls the thruster through the PWM wave, so that the unmanned ship can quickly and accurately track the expected trajectory.

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

[0115]

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

[0117]

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

[0119]

[0120] Where K2 is a diagonal matrix with positive definite parameters.

[0121] Then, the desired control torque is sent to the controller to enable the unmanned ship to run according to the desired trajectory.

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

[0123] 1) Unmanned ship surrounding information acquisition module: Through millimeter wave radar detection and ranging, it provides information such as the distance, speed and angle of obstacles around the unmanned ship, which can be used for tasks such as obstacle avoidance and navigation during the unmanned ship's travel; through the camera's remote monitoring and data transmission functions, the unmanned ship recognition camera can transmit the navigation situation to the shore control center in real time, realizing all-round navigation monitoring and management.

[0124] 2) Unmanned ship’s own information acquisition module: Determine the real-time position and speed of the unmanned ship through GPS receiving signals.

[0125] 3) Control center: Receives status information sent by the unmanned ship, calculates the expected control torque through the controller set in the control center, and then packages it into a control command and sends it to the unmanned ship.

[0126] 4) Unmanned ship main control (main control module): receives control commands and sends control torque instructions to the underlying controller based on the expected control torque.

[0127] 5) Bottom-level controller: receives the desired control torque from the unmanned ship main control, and then controls the thruster through PWM waves.

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

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

[0130] 1) The control center sends a request command to the unmanned ship master control, requesting to communicate with the unmanned ship master control and obtain the unmanned ship status information;

[0131] 2) The unmanned ship master receives the request command, collects and verifies the sensor information, packages it according to the set format, and sends it to the control center through the TCP / IP protocol;

[0132] 3) The control center receives the data and sets it to be checked and unpacked according to the format, and then classifies and matches the data according to the data type to obtain the corresponding status information of the unmanned ship;

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

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

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

[0136] The controller module implemented based on the unmanned ship trajectory tracking dynamic surface control method in this embodiment is further elaborated below.

[0137] 1. Algorithm implementation part:

[0138] 1.1 Mathematical model of unmanned ship

[0139] First, the present invention adopts the ship mathematical model proposed by Fossen. Under normal circumstances, the trajectory tracking problem of the unmanned ship can be simplified to consider the three-degree-of-freedom motion of forward movement, horizontal drift and bow pitch. The three-degree-of-freedom model expression is as follows:

[0140]

[0141] in, is the position vector composed of the actual position (x, y) of the ship in inertial coordinates and the yaw angle ψ, is the velocity vector composed of the ship's sway velocity u, sway velocity v and bow angular velocity r in the appendage coordinate system. At the same time, J(ψ) is the coordinate system 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:

[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 given by τ u (Surge), τ v (swing) and τ r (Yaw) composition. Among them, 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 attached coordinate system.

[0144] 1.2 Design of finite time preset function

[0145] The following will discuss the finite time preset function to ensure that the tracking error converges and stabilizes.

[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 any design parameter;

[0150] 4) When t ≥ T s hour, t is the sedimentation time.

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

[0152] Based on the above definition, the present invention proposes an improved finite time preset function.

[0153]

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

[0155] The finite time preset function proposed in the present invention can achieve a faster convergence speed. On the other hand, despite the use of a neural network or fuzzy logic system, whose stabilization time is independent of initial conditions and design parameters, the tracking error can still converge asymptotically 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] Among them, 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 speed. Since the function f(v) = -M -1 C(v)vM -1 D(v)v is unknown, so a fuzzy logic system is used to identify the unknown function f(v) = [f1, f2, f3] T , so that

[0162]

[0163] In the formula, And is a constant.

[0164] According to formula (10), systems (1) and (2) can be written as:

[0165]

[0166] In the formula, The composite perturbation considered in (11) can be obtained That is, there is an unknown constant q * , so that and

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

[0168]

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

[0170] 1.4 Controller Design

[0171] Construct the following nonlinear transformation:

[0172]

[0173] Step 1: Derivative (13):

[0174]

[0175] Through (1), (2), (9), (14) we can get

[0176]

[0177] Design virtual control law α∈R3:

[0178]

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

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

[0181]

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

[0183] Step 2: Define the speed error surface vector S2∈R3:

[0184]

[0185] Deriving (18), using equations (1), (2), and (18):

[0186]

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

[0188]

[0189] where K2 is a diagonal matrix with positive definite parameters.

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

[0191] In the simulation implementation part, the unmanned ship dynamics and kinematics model is first constructed; secondly, the traditional preset performance function is further optimized under the fusion of finite time, and dynamic surface control is introduced to improve the control accuracy of the system and accelerate the error convergence speed. Its control design process is simple and clear, which is convenient for practical application.

[0192] Through the above description, the present invention describes the specific process of the unmanned ship trajectory tracking control method and its control system. First, by designing a preset performance function with a limited time, the position error is limited to a certain range, the error convergence speed is accelerated and maintained stable; then, the fuzzy logic state observer is used to estimate the speed, making the system run more smoothly; finally, a dynamic surface control algorithm for unmanned ship trajectory tracking based on preset performance is proposed, which increases the system tracking accuracy while improving the robustness of the system.

[0193] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0194] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0195] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0196] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0197] The above is only a preferred embodiment of the present invention, and does not limit the present invention in other forms. Any technician familiar with the profession may use the above disclosed technical content to change or modify it into an equivalent embodiment with equivalent changes. However, any simple modification, equivalent change and modification made to the above embodiment according to the technical essence of the present invention without departing from the technical solution of the present invention still belongs to the protection scope of the technical solution of the present invention.

Claims

1. A method for controlling the trajectory of an unmanned ship based on preset performance and a dynamic surface, characterized in that: The following steps are involved: Step 1: Set the desired trajectory η of the unmanned ship d , initial position η0, initial velocity v0, establish the kinematic and dynamic models of the unmanned ship; Step 2: Obtain the current motion state of the unmanned ship through the sensors on the unmanned ship; Step 3: Compare the actual trajectory of the unmanned ship with the expected trajectory to obtain the tracking error of the unmanned ship; Step 4: Construct a preset performance function, set preset performance constraint parameters, and determine the allowable error range of the tracking error; Step 5: According to the kinematic and dynamic models of the unmanned ship, a fuzzy state observer is established to estimate the position and speed of the unmanned ship; Step 6: Constrain the tracking error within the error range according to the preset performance function, perform nonlinear transformation on the constrained tracking error, and then establish a virtual control law in combination with the dynamic surface technology; Step 7: Analyze and calculate the expected trajectory and the actual position status information of the unmanned ship to obtain the expected control torque and transmit it to the unmanned ship main control; after the unmanned ship main control receives and processes the data, it sends the control torque instruction to the underlying controller, and the underlying controller controls the thruster through the PWM wave, so that the unmanned ship can quickly and accurately track the expected trajectory.

2. The unmanned ship trajectory tracking dynamic surface control method based on preset performance according to claim 1, characterized in that: In step 1, the nonlinear kinematic and dynamic models of the fully driven unmanned ship considering environmental interference are established as follows: in, is the actual trajectory vector composed of the actual position (x, y) of the unmanned ship in inertial coordinates and the bow angle ψ, is the actual velocity vector composed of the longitudinal velocity u, lateral velocity v and bow angular velocity r of the unmanned ship in the appendage coordinate system; J(ψ) is the coordinate system 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.

3. The unmanned ship trajectory tracking dynamic surface control method based on preset performance according to claim 1, characterized in that: In step 2, the current position, attitude and speed of the unmanned ship are obtained through the sensors on the unmanned ship.

4. The unmanned ship trajectory tracking dynamic surface control method 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 the trajectory tracking error e of the unmanned ship: e=η-η d Among them, η d =[x d ,y d , ψ d ] T is the reference trajectory.

5. The unmanned ship trajectory tracking dynamic surface control method based on preset performance according to claim 1, characterized in that: In step 4, a preset performance function is constructed by combining the limited time and the preset performance. The expression of the preset performance function is as follows: Among them, e i and k i (t) are the tracking errors and preset performance functions in the three directions of x, y, and ψ, respectively, ι>0, n is the system order, All are constants to be designed; and k i (t) initial and final values, t is the time constant; is the preset convergence time parameter, i = x, y, ψ; The tracking error is constrained and the expression is as follows: -k i (t)<e i <k i (t),i=x,y,ψ。 6. The unmanned ship trajectory tracking dynamic surface control method based on preset performance according to claim 1, characterized in that: In step 5, according to the kinematic and dynamic models of the unmanned ship, a fuzzy state observer is established to estimate the position and speed of the unmanned ship. The implementation method is as follows: Let f(v) = -M -1 C(v)vM -1 D(v)v=[f x , f y , f ψ ] T is an unknown function, and a fuzzy logic system is used to identify the unknown function, so that: Among them, w T is the ideal weight matrix, ζ(v) is the fuzzy basis function vector, is the approximate error, i=x,y,ψ, is the upper bound of the approximation error; The kinematic and dynamic models of the unmanned ship are transformed to obtain: in, and are the first-order derivatives of η and v respectively, y is the output term; let the composite disturbance M -1 d=[q x ,q y ,q ψ ] T ; There are unknown constants Make The fuzzy state observer is established as follows: In the formula, k1, k2, k3>0 are all observer gains, in and are η, v, The estimated values ​​of w and q, for The first derivative of .

7. The unmanned ship trajectory tracking dynamic surface control method based on preset performance according to claim 1, characterized in that: In step 6, the tracking error is constrained within the error range according to the preset performance function, and the constrained tracking error is nonlinearly transformed. Then, according to the position and speed of the unmanned ship estimated by the fuzzy state observer, combined with the dynamic surface technology, a virtual control law is established. The implementation method is as follows: The tracking error is transformed nonlinearly as follows: Where 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 then S1 is derived to obtain: in, S1=[S 1，x , S 1，y , S 1，ψ ] T , and e i , k i (t) and S 1，i The first derivative of , i = x, y, ψ; Further conclusion: in, is η d The first derivative of The virtual control law α∈R3 is established as follows: Where χ=diag(χ x , χ y , χ ψ ), κ=diag(κ x , κ y , κ ψ ), K1 is a diagonal matrix with positive definite parameters.

8. The unmanned ship trajectory tracking dynamic surface control method based on preset performance according to claim 1, characterized in that: In step 7, the established virtual control law α is passed through the following first-order low-pass filter: Among them, v d is the state vector of the first-order filter, T d >0 is the filter time constant of the filter; Define the speed error surface vector: The controller analyzes and calculates the desired trajectory and the actual position status information of the unmanned ship, and obtains the desired control torque as follows: Among them, K2 is a diagonal matrix with positive definite parameters; Then, the desired control torque is sent to the controller to enable the unmanned ship to run according to the desired trajectory.

9. The unmanned ship trajectory tracking dynamic surface control method based on preset performance according to any one of claims 1 to 8, 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, requesting to communicate with the unmanned ship master control and obtain the unmanned ship status information; 2) The unmanned ship master receives the request command, collects and verifies the sensor information, packages it according to the set format, and sends it to the control center through the TCP / IP protocol; 3) The control center receives the data and sets it to be checked and unpacked according to the format, and then classifies and matches the data according to the data type to obtain the corresponding status information of the unmanned ship; 4) The control center sends the state information of the unmanned ship to the controller, and outputs the desired control torque through the unmanned ship trajectory tracking dynamic surface control method based on preset performance; 5) The control center packages the desired control torque obtained into a control command according to the set format and sends it to the unmanned ship main control; 6) The unmanned ship main control obtains the expected control torque according to the control command, and sends the control torque instruction to the underlying controller based on the expected control torque. The underlying controller controls the thruster through PWM waves, so that the unmanned ship can quickly and accurately track the expected trajectory.

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