A distributed IESO-PD delay control method for multiple underactuated unmanned vessel systems

Abstract

The present invention belongs to the technical field of unmanned vessel control and specifically discloses a distributed IESO-PD delay control method for a multi-underactuated unmanned vessel system, comprising the following steps: Step S1, establishing a full-drive model of the multi-underactuated unmanned vessel system; Step S2, determining the system's collaborative control objectives and network topology; Step S3, designing an extended state observer improved by low-pass filtering; Step S4, constructing a collaborative formation tracking error system; Step S5, designing a distributed PD delay controller, solving the control parameters of the distributed PD delay controller based on the distribution theorem of quasi-polynomial roots and pole configuration; and implementing the system's collaborative formation tracking control through the output instructions of the distributed PD delay controller. The present invention solves the problem of comprehensive control of underactuated unmanned vessel systems in harsh sea conditions; the detection information involved is only the position information of the underactuated unmanned vessel system; and the desired performance index requirements can be met by optimizing the control parameters.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned vessel control, and in particular to a distributed IESO-PD delay control method for a multiple underactuated unmanned vessel system. Background Art

[0002] With the development of marine resource development and intelligent shipping, the research on collaborative control of multiple underactuated (the number of independent control inputs is less than the number of degrees of freedom) unmanned vessel systems has broad application value in the fields of ocean exploration, search and rescue, resource extraction, cargo transportation, and collaborative operations.

[0003] However, designing appropriate control schemes for nonlinear underactuated unmanned vessel systems in complex and harsh marine environments (such as wind, wave, and current interference, sensor measurement noise, and communication delays) has always been a significant challenge. In particular, research on cooperative control in the presence of communication delays is relatively limited, and the following issues exist:

[0004] Existing control schemes often focus on delay analysis, that is, the control scheme designed is effective only when the delay size meets certain constraints, and does not substantially solve the delay control problem; existing nonlinear delay control schemes are extremely complex, not only with poor practicality but also with poor control effects; existing results have not further considered the impact of other complex sea conditions on the design of nonlinear delay control schemes, such as wind, wave and current disturbances and sensor measurement noise, and related research results are still blank; existing control schemes usually require the system to be able to directly detect a lot of information for control services, such as in addition to the posture information of the unmanned ship system, its speed and even acceleration information are also required to be directly detected, which will significantly increase the detection cost and is not practical.

[0005] In summary, a distributed IESO-PD delay control method for a multiple underactuated unmanned vessel system is needed to solve the above problems in the prior art. Summary of the Invention

[0006] The present invention provides a distributed IESO-PD (IESO-Improved Extended State Observer, P-Proportional, D-Differential) delay control method for a multiple underactuated unmanned vessel system, which solves the communication delay control problem of the multiple underactuated unmanned vessel system.

[0007] In order to achieve the purpose of solving the above technical problems, the present invention adopts the following technical solutions:

[0008] A distributed IESO-PD delay control method for a multi-underactuated unmanned vessel system includes the following steps:

[0009] Step S1, establishing an all-wheel drive model of a multi-underactuated unmanned vessel system;

[0010] Step S2: determining the coordinated control target and network topology of the multiple underactuated unmanned vessel system;

[0011] Step S3: designing an extended state observer improved by low-pass filtering to achieve measurement noise filtering and disturbance suppression;

[0012] Step S4: constructing a collaborative formation tracking error system;

[0013] Step S5: designing a distributed PD delay controller based on the estimated value output by the extended state observer in step S3 and the tracking error in step S4, and solving the control parameters of the distributed PD delay controller based on the distribution theorem of quasi-polynomial roots and pole placement;

[0014] The coordinated formation tracking control of multiple underactuated unmanned ship systems is realized through the output instructions of the distributed PD time-delay controller.

[0015] In some embodiments of the present invention, step S1 includes the following steps:

[0016] Establish the kinematic and dynamic models of a three-degree-of-freedom multi-underactuated unmanned vessel system under external disturbances;

[0017] The motion state parameters in the kinematic model are upgraded and dimensionally reduced to achieve conversion between the kinematic model and the dynamic model to obtain an all-wheel drive model.

[0018] In some embodiments of the present invention, the formula of the all-wheel drive model is:

[0019] ;

[0020] Among them, p k (t)=[ x k (t), y k (t)] T is the position vector, B k is the state transfer matrix, τ k (t)=[τ u k (t),τ r k (t)] T is the control input vector, f k (t)=[f x k (t), f y k (t)] T is a known continuous vector function, d k (t)=[f xdis k (t), f ydisk (t)] T is an unknown continuous vector function, k =1,2,…, N It is the first in the multi-underactuated unmanned ship system k An unmanned boat.

[0021] In some embodiments of the present invention, the equation of the improved extended state observer in step S3 is as follows:

[0022] ;

[0023] in: 、 、 They are position information p k (t), speed information q k (t), disturbance information d k (t) estimated value; is the virtual control input; y 1k (t) is the state of the low-pass filter; τ f is the time constant of the low-pass filter; y 0k (t) The position information detected by the unmanned vessel; is the output information of the improved extended state observer; β=[β1,β2,β3] T is the control parameter of the improved extended state observer.

[0024] In some embodiments of the present invention, the collaborative tracking error in step S4 is:

[0025]

[0026]

[0027] Among them: e 1k (t) is the k Unmanned boat t Position error at the moment, e 2k (t) is the k Unmanned boat t The velocity error at the moment, α kj is the graph adjacency matrix k Rank j Column elements, τ is the communication delay, p k (t-τ) and q k (t-τ) are the k An unmanned ship t The actual position and speed information detected at the moment, p j (t-τ) and q j (t-τ) are thej An unmanned ship t The actual position and velocity information detected at the moment, p0(t-τ) and q0(t-τ) are respectively t The actual position and velocity information of the desired trajectory received at the moment, Δ k and Δ j Respectively k The first unmanned ship and the j The expected formation information of unmanned ships, b k For the k The containment gain of the unmanned ship.

[0028] In some embodiments of the present invention, the control protocol of the distributed PD delay controller in step S5 is for:

[0029]

[0030] Among them, k p is the proportional control parameter of the distributed PD delay controller, k d is the differential control parameter of the distributed PD delay controller, and Respectively k An unmanned ship t The time-delayed observation position and time-delayed observation speed information at the moment, and Respectively j An unmanned ship t The delayed observation position and delayed observation speed information at the moment; p0(t-τ) and q0(t-τ) are respectively t The actual position and velocity information of the desired trajectory received at the moment, Δ k and Δ j Respectively k The first unmanned ship and the j The expected formation information of unmanned ships, α kj is the graph adjacency matrix k Rank j Column elements, τ is the communication delay; b k To contain the gain.

[0031] In some embodiments of the present invention, the proportional control parameter k of the distributed PD delay controller is p and the differential control parameter k of the distributed PD delay controller d The calculation process is:

[0032] The dynamic equations of the overall closed-loop error system are constructed using the all-wheel drive model of the multi-underactuated unmanned vessel system, the improved extended state observer and the distributed PD time-delay controller.

[0033] Analyze the characteristic polynomial of the error state based on the frequency domain method;

[0034] Separate the error state vector into IESO disturbance compensation error and PD delay control error;

[0035] The characteristic function corresponding to the PD delay control error is transformed into the corresponding quasi-polynomial form, and the proportional control parameter k is solved based on the necessary and sufficient conditions for the distribution of the quasi-polynomial roots and the pole placement method. p and the differential control parameter k d .

[0036] In some embodiments of the present invention, a distributed IESO-PD delay control system for a multi-underactuated unmanned vessel system is provided to implement the above-mentioned distributed IESO-PD delay control method, including:

[0037] A model conversion module, which is used to convert the mathematical model of the multiple underactuated unmanned ship system into an all-wheel drive model;

[0038] A filtering and suppression module is designed to filter measurement noise and suppress disturbances by improving the extended state observer with low-pass filtering;

[0039] The controller design execution module is used to design a distributed PD delay controller; it is also used to calculate the control parameters of the distributed PD delay controller; it is also used to output control instructions to realize the coordinated formation tracking control of multiple underactuated unmanned ships;

[0040] Communication module, used for communicating with external devices.

[0041] In some embodiments of the present invention, an electronic device is provided, comprising:

[0042] a processor, and a memory and a transceiver communicatively connected to the processor;

[0043] The memory stores computer-executable instructions; the transceiver is used to transmit and receive data;

[0044] The processor executes the computer-executable instructions stored in the memory to implement the above-mentioned distributed IESO-PD delay control method.

[0045] In some embodiments of the present invention, a computer-readable storage medium is provided, characterized in that:

[0046] The computer-readable storage medium stores computer-executable instructions, which are used to implement the above-mentioned distributed IESO-PD delay control method when executed by a processor.

[0047] The technical solution of the present invention has the following technical effects compared with the prior art:

[0048] The present invention adopts an improved extended state observer and a distributed PD delay controller to solve the comprehensive control problem of an underactuated unmanned vessel system in the presence of wind, wave and current disturbances, measurement noise and communication delay. The detection information involved in the present invention is only the position information of the unmanned vessel system, and there is no need to detect its speed, acceleration and other information. In addition, the control method of the present invention is highly practical and can meet the desired performance index requirements by optimizing the configuration of control parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0050] Figure 1 The present invention is a flowchart of the control method.

[0051] Figure 2 The present invention relates to a network topology structure of a multi-underactuated unmanned vessel system.

[0052] Figure 3 The present invention provides a cooperative tracking route map for a multiple underactuated unmanned vessel system when the desired trajectory is circular.

[0053] Figure 4 The invention relates to a multi-underactuated unmanned vessel system, and discloses an estimation error of the disturbance in the x-axis direction when the expected trajectory is circular.

[0054] Figure 5 It is the estimation error of the disturbance in the y-axis direction of the multiple underactuated unmanned vessel system involved in the present invention when the expected trajectory is circular.

[0055] Figure 6 It is the estimated error of the position in the x-axis direction of the multiple underactuated unmanned vessel system involved in the present invention when the expected trajectory is circular.

[0056] Figure 7 It is the estimated error of the position in the y-axis direction of the multiple underactuated unmanned vessel system involved in the present invention when the expected trajectory is circular.

[0057] Figure 8 Schematic diagram of the structure of the control system.

[0058] Figure 9 Schematic diagram of the structure of the electronic device.

[0059] Figure numerals: 100, control system; 110, model conversion module; 120, filtering and suppression module; 130, controller design execution module; 140, communication module; 200, electronic device; 210, processor; 220, memory; 230, transceiver. DETAILED DESCRIPTION

[0060] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0061] In the description of the present invention, it should be noted that, unless otherwise expressly specified or limited, the terms "installed," "connected," and "connected" should be understood in a broad sense. For example, they can refer to fixed connections, detachable connections, or integral connections. A person of ordinary skill in the art will understand the specific meanings of the above terms in the present invention in specific circumstances. In the description of the above embodiments, specific features, structures, materials, or characteristics may be combined in any appropriate manner in any one or more embodiments or examples.

[0062] Example 1: Reference Figure 1 As shown, this embodiment provides a distributed IESO-PD delay control method for a multi-underactuated unmanned vessel system, including the following steps:

[0063] Step S1, establishing an all-wheel drive model of a multi-underactuated unmanned vessel system;

[0064] Step S11: The multi-underactuated unmanned vessel system of this embodiment is composed of N It consists of three isomorphic three-DOF underactuated unmanned vessels. k strip( k =1,…, N ) The mathematical model of the underactuated unmanned ship is (for convenience, some variables will be omitted in the following text. t Explicit expression; in this embodiment, the formula is labeled using the # (number) notation format):

[0065]

[0066] where η k =[x k ,y k ,ψ k ] T is the posture state variable of the system, ν k =[u k ,vk ,r k ] T is the velocity state variable of the system; specifically, x k 、y k , ψ k They represent the longitudinal displacement, transverse displacement and yaw angle in the earth coordinate system, u k 、v k 、r k represent the longitudinal velocity, transverse velocity and yaw angular velocity in the inertial coordinate system respectively; τ k =[τ u k , 0,τ r k ] T represents the control input; τ k dis =[τ k udis ,τ k vdis ,τ k rdis ] T Represents the external environment disturbance. The state transfer matrix J(ψ k ), inertia matrix M k , Coriolis force and centripetal force matrix C k (ν k ), damping matrix D k They are as follows:

[0067]

[0068] .

[0069] where d 11 d 22 d 23 d 32 d 33 represents the fluid dynamic damping, m 11 、m 22 、m 23 、m 32 、m 33 represents inertia and additional mass, and both are positive; and there exists m 23 =m 32 , c k 13 =m 32 r k -m 22 v k , c k 23 =m 11 u k .

[0070] Step S12: Based on the idea of upgrading and reducing dimensionality, the first-order underactuated unmanned vessel dynamic model is derived and converted into a second-order all-wheel drive form through a series of variable substitutions and sorting, as follows:

[0071] According to formula (1), we can get:

[0072]

[0073] According to the dynamic equation in formula (1) and m in the actual system 23 m 32 -m 22 m 33 >>0 facts, we can get:

[0074]

[0075] in:

[0076]

[0077] According to formula (2) and formula (3), we can get:

[0078]

[0079] in:

[0080] .

[0081] Definition k Unmanned boat driven by axle t The position state in the inertial coordinate system at the moment is p k (t)=[ x k (t), y k (t)] T , then the converted all-wheel drive model is obtained:

[0082]

[0083] where τ k (t)=[τ u k (t), τ r k (t)] T is the control input vector, f k (t)=[f x k (t), f y k (t)] Tis a known continuous vector function, d k (t)=[f xdis k (t), f ydis k (t)] T is an unknown continuous vector function, and the state transfer matrix of the system is

[0084]

[0085] Obviously there is: detB k ≠0, detB k ≠∞, that is, formula (5) is expressed as a second-order all-wheel drive mathematical model.

[0086] In step S1, based on the idea of order-up and dimension-down, a series of variable substitutions are performed to transform the first-order underactuated unmanned ship dynamic model into a second-order all-wheel drive form, laying the foundation for the subsequent transformation of nonlinear control into linear control.

[0087] Step S2: determining the coordinated control target and network topology of the multiple underactuated unmanned vessel system;

[0088] Step S21: Determine the coordinated control target of the multiple underactuated unmanned vessel system:

[0089] Define p k (t)=[x k (t),y k (t)] T It is k An unmanned ship t Location information at all times, It is k An unmanned ship t The speed information at the moment, given the expected formation position information is Δ=[Δ1 T ,Δ2 T ,…,Δ N T ] T , the expected tracking trajectory position information is q0(t)=[x0(t),y0(t)] T , the expected tracking trajectory velocity information is .

[0090] For the k unmanned ships, in any initial state, have

[0091]

[0092] Step S22: determining the network topology of the multiple underactuated unmanned vessel system;

[0093] Reference Figure 2 As shown,N The interactive communication topology formed by the underactuated unmanned vessels contains at least one directed spanning tree, and the root node can directly obtain the desired trajectory information.

[0094] Step S3: designing an extended state observer improved by low-pass filtering to achieve measurement noise filtering and disturbance suppression;

[0095] Step S31: Based on the measurement noise problem in reality, the all-wheel drive model formula (5) can be rewritten as:

[0096]

[0097] where y 0k (t) is the actual output position information of the unmanned ship system, ω k (t) is the measurement noise,

[0098] is the virtual control input.

[0099] Step S32: Design an extended state observer (IESO) with improved low-pass filtering. k For an unmanned ship, the improved extended state observer based on the low-pass filter design is as follows:

[0100]

[0101] in 、 、 They are position information p k (t), speed information q k (t), disturbance information d k Estimated value of (t); y 1k (t) is the state of the low-pass filter; τ f is the time constant of the low-pass filter; y 0k (t) is the position information detected by the unmanned ship, is the output information of IESO, β=[β1,β2, β3] T is the control parameter of IESO;

[0102] Step S33: To verify the stability of the IESO output, based on formulas (6) and (7), the IESO error dynamics equation is obtained as follows:

[0103]

[0104] in: is the error state vector of system (7), and , , are the differences between the IESO estimated position and the true position, the estimated velocity and the true velocity, and the estimated total disturbance and the true total disturbance, respectively.

[0105] is the external disturbance vector of system (8), is a 2×2 matrix whose elements are all 0, h k (t) is d k The derivative of (t) and satisfies , is an unknown bounded positive number.

[0106] A 0k The state transition matrix is a Hurwitz matrix:

[0107]

[0108] The I2-order unit matrix is used to filter measurement noise and suppress complex disturbances through IESO.

[0109] In step S3, an extended state observer (IESO) based on a low-pass filter is proposed. While filtering measurement noise, it treats nonlinear disturbances as estimated system states and estimates them using the designed extended state observer. This ultimately achieves both filtering measurement noise and suppressing complex disturbances. Compared to existing disturbance compensation control schemes, the IESO design only requires direct detection of the UAV's position, significantly reducing measurement costs and making it more practical.

[0110] Step S4: constructing a collaborative formation tracking error system;

[0111] Based on the expected formation information and tracking signals, a cooperative formation tracking error system is constructed:

[0112] According to the cooperative control target, the cooperative tracking error is obtained as:

[0113]

[0114]

[0115] Among them: e 1k (t) is the k Unmanned boat t Position error at the moment, e 2k (t) is the k Unmanned boat t The velocity error at the moment, α kj is the graph adjacency matrix k Rank j Column elements, τ is the communication delay, p k (t-τ) and q k(t-τ) are respectively k An unmanned ship t The actual position and speed information detected at the moment, p j (t-τ) and q j (t-τ) are the j An unmanned ship t The actual position and velocity information detected at the moment, p0(t-τ) and q0(t-τ) are respectively t The actual position and velocity information of the desired trajectory received at the moment, Δ k and Δ j Respectively k The first unmanned ship and the j The expected formation information of unmanned ships, b k For the k The containment gain of the unmanned ship.

[0116] Step S5: designing a distributed PD delay controller based on the estimated value output by the extended state observer in step S3 and the tracking error in step S4, and solving the control parameters of the distributed PD delay controller based on the distribution theorem of quasi-polynomial roots and pole placement;

[0117] The coordinated formation tracking control of multiple underactuated unmanned ship systems is realized through the output instructions of the distributed PD time-delay controller.

[0118] Step S51: Design a distributed PD delay controller:

[0119] Define the error system state variables:

[0120]

[0121] For the k Unmanned ships, according to formulas (7)-(10), design a distributed PD delay control protocol as follows:

[0122]

[0123] Among them, the proportional control parameter of the distributed PD delay controller, k d is the differential control parameter of the distributed PD delay controller, and Respectively k An unmanned ship t The time-delayed observation position and time-delayed observation speed information at the moment, and Respectively j An unmanned ship t The time-delay observation position and time-delay observation speed information at the moment.

[0124] Specifically, the input information used by the distributed PD delay controller is estimated by IESO. Based on formula (7) and formula (11), it can be seen that The design only needs to detect the position information of the unmanned ship, which will greatly reduce the detection cost and reduce the introduction of measurement error information.

[0125] Step S52: Calculate the proportional control parameter k of the distributed PD delay controller p and the differential control parameter k of the distributed PD delay controller d .

[0126] S521. Using the all-wheel drive model of the multi-underactuated unmanned vessel system, the improved extended state observer and distributed PD time-delay controller are used to construct the dynamic equation of the overall closed-loop error system:

[0127]

[0128] in, is the error state vector of the overall closed-loop error system,

[0129] and is the position error state vector,

[0130] is the velocity error state vector,

[0131] is the IESO error state vector; is the delay error state vector; is the external disturbance input of the overall closed-loop error system; A F , B F , D F is the coefficient matrix.

[0132] S522. Analyze the characteristic polynomial of the error state based on the frequency domain method;

[0133] because is a bounded disturbance input, so the stability control problem of the overall closed-loop error system can be transformed into the stability control problem of the time-delay system, which satisfies the following formula:

[0134]

[0135] Based on the frequency domain method, Laplace transform is performed on Equation (13) to obtain:

[0136] in s is a complex frequency domain variable, e -τs Represents the time delay link, when the characteristic polynomial matrix A F +B F e-τs When it is Hurwitz stable, the time-delay system is stable.

[0137] S523, separating the error state vector into IESO disturbance compensation error and PD delay control error;

[0138]

[0139] The matrix E 11 、E 12 、E 22 is not a 0 matrix, and Only determined by IESO, E 11 It is only determined by the distributed PD delay controller. F +B F e -τs The special upper diagonal matrix structure of , if and only if the matrix E 11 、 E 22 When the Hurwitz stability condition is satisfied at the same time, the system (13) is stable, so the design of the PD delay controller and the design of the IESO are separable.

[0140] In order to verify the stability of PD control delay error, Split into two parts, namely and e0(t), where It is only related to the design of PD delay controller, and e0(t) is only related to the design of IESO. Based on Laplace transform, we can get The corresponding characteristic function δ(s) is as follows:

[0141]

[0142] Among them, μ k is the eigenvalue of the matrix L+B, L is the graph Laplacian matrix, B By b k A purely diagonal matrix.

[0143] S524, transform the characteristic function corresponding to the PD delay control error into a corresponding quasi-polynomial form, and solve the proportional control parameter k based on the necessary and sufficient conditions for the distribution of the quasi-polynomial roots and the pole placement method p and the differential control parameter k d .

[0144] Based on the desired performance index requirements, the critical position of the pole configuration s = -σ is determined, where σ is a positive number, to ensure that all poles in formula (15) are on the left side of s = -σ.

[0145] Transform formula (15) to obtain the corresponding quasi-polynomial form, and solve k based on the distribution theorem of quasi-polynomial roots. p 、k d The specific steps are as follows:

[0146] Step 1: Eliminate e -τs , that is, δ(s)Í e τs .

[0147] Step 2: Scale and translate the variable s to obtain a new variable λ=s / τ-σ.

[0148] Step 3: Name the quasi-polynomial obtained in step 2 as H k (λ). Substitute λ=iz to get the real part H r (z) and the imaginary part H i (z), where Represents an imaginary unit, satisfying i 2 =-1, z is a complex variable.

[0149] Step 4: Variable Substitution ,in for The conjugate of . According to the necessary conditions for the distribution of quasi-polynomial roots, we can get k d range.

[0150] Step 5: Variable Substitution , according to the necessary conditions for the distribution of quasi-polynomial roots, we can get k p range.

[0151] Step 6: Use iterative algorithm to find the p 、k d The effective area of parameters within the range that satisfies the necessary and sufficient conditions for the quasi-polynomial root distribution.

[0152] According to the above calculation method, the PD controller parameters can be directly calculated by the pole configuration method according to the delay size, without repeated debugging. In addition, the configured control parameter k p and k d It can meet the expected performance index requirements and achieve the optimal control effect.

[0153] Effect verification:

[0154] Reference Figure 2 As shown in the figure, a multi-underactuated unmanned ship system consisting of five isomorphic underactuated unmanned ships is considered. k The relevant parameters of the unmanned ship are as follows:

[0155] ,

[0156] External disturbances: ,

[0157] in , ,

[0158] ;

[0159] Noise: ω k (t) Use white noise with a power of 0.0000002 and a sampling time of 0.001s, and the noise amplitude is limited to [ 0.05,0.05].

[0160] Communication delay: τ=0.06s.

[0161] Communication topology diagram Figure 2 shown.

[0162] Only ship 1 can directly receive the information of the desired trajectory, and the Laplacian matrix is:

[0163]

[0164] Control Objective: Multiple underactuated UAVs coordinately track a circular trajectory based on the following formation information. The trajectory is centered at (0, 0), has an angular velocity of 0.2 rad / s, and a radius of 50 m. The initial position and velocity information of each UAV, as well as the formation information, are as follows:

[0165]

[0166]

[0167]

[0168] Controller parameter settings:

[0169] Improved extended state observer parameter β=[90,2700, 81000] T , the time constant τ of the low-pass filter f =0.01. b1=1, b2=b3=b4=b5=0. Pole configuration σ=0.2. Proportional control parameter k p =6, differential control parameter k d = 6. The simulation duration is set to 50s and the sampling period is set to 0.01s.

[0170] Reference Figure 3The figure below shows the collaborative tracking process of multiple underactuated unmanned vessels (UAVs) in a set formation when the desired trajectory is circular. Arrows represent the direction of motion during the collaborative tracking process. The simulation results in this figure visually demonstrate how multiple UAVs, starting from an initial state, gradually adjust their positions to ultimately achieve collaborative tracking of the desired trajectory.

[0171] Figure 4 and Figure 5 The disturbance observation error evolution of the improved extended state observer is shown. It can be clearly seen from the figure that the estimation error converges smoothly within 5 seconds in both the x-axis and y-axis directions.

[0172] Figure 6 and Figure 7 The figure shows the evolution of the position observation error of the improved extended state observer. It can be clearly seen from the figure that the estimation error converges smoothly within 5 seconds in both the x-axis and y-axis directions.

[0173] The technical solution of the present invention has the following technical effects compared with the prior art:

[0174] The present invention adopts an improved extended state observer and a distributed PD delay controller to solve the comprehensive control problem of an underactuated unmanned vessel system in the presence of wind, wave and current disturbances, measurement noise and communication delay. The detection information involved in the present invention is only the position information of the unmanned vessel system, and there is no need to detect its speed, acceleration and other information. In addition, the control method of the present invention is highly practical and can meet the desired performance index requirements by optimizing the configuration of control parameters.

[0175] Example 2: This example will be based on Figure 8 and Figure 9 A distributed IESO-PD delay control system 100 and an electronic device 200 of a multiple underactuated unmanned vessel system are described.

[0176] Among them, reference Figure 8 As shown, a distributed IESO-PD delay control system 100 for a multi-underactuated unmanned vessel system is provided, comprising:

[0177] The model conversion module 110 is used to convert the mathematical model of the multi-underactuated unmanned ship system into an all-wheel drive model; based on the idea of order increase and dimensionality reduction, after a series of variable substitutions, the first-order underactuated unmanned ship dynamic model is converted into a second-order all-wheel drive form, laying the foundation for the subsequent conversion of nonlinear control into linear control.

[0178] The filtering and suppression module 120 is used to filter measurement noise and suppress disturbances by designing an extended state observer improved by low-pass filtering. That is, while filtering the measurement noise, the nonlinear disturbance is regarded as the system state to be estimated and estimated by the designed extended state observer, ultimately achieving the filtering of measurement noise and the suppression of complex disturbances.

[0179] The controller design execution module 130 is used to design a distributed PD delay controller; it is also used to calculate the control parameters of the distributed PD delay controller; it is also used to output control instructions to realize the collaborative formation tracking control of multiple underactuated unmanned ships; the PD controller parameters can be directly calculated by the pole configuration method according to the delay size, without repeated debugging, and the configured control parameters and It can meet the expected performance index requirements and achieve the optimal control effect.

[0180] The communication module 140 is used to communicate with external devices.

[0181] It should be understood that the control system 100 here is embodied in the form of a functional module. The term "module" here can refer to an application specific integrated circuit (ASIC), an electronic circuit, a processor (such as a shared processor, a proprietary processor or a group processor, etc.) and a memory for executing one or more software or firmware programs, a combined logic circuit and / or other suitable components that support the described functions. In an optional example, those skilled in the art will understand that the control system 100 can be specifically the electronic device 200 in the above embodiment, or the functions of the electronic device 200 in the above embodiment can be integrated in the control system 100, and the control system 100 can be used to execute the various processes and / or steps corresponding to the electronic device 200 in the above method embodiment. To avoid repetition, they will not be described here.

[0182] The control system 100 has the functions of implementing the corresponding steps performed by the electronic device 200 of the control method in Example 1. These functions can be implemented through hardware or through hardware executing corresponding software. The hardware or software includes one or more modules corresponding to the above functions. For example, the acquisition module can be a communication interface, such as a transceiver interface.

[0183] Reference Figure 9 As shown, in this embodiment, an electronic device 200 is provided, including:

[0184] A processor 210, and a memory 220 and a transceiver 230 communicatively connected to the processor;

[0185] The memory 220 stores computer-executable instructions; the transceiver 230 is used to send and receive data;

[0186] The processor 210 executes the computer-executable instructions stored in the memory 220 to implement the control method in embodiment 1.

[0187] It should be understood that the electronic device 200 can be used to execute the corresponding steps and / or processes in the above-mentioned method embodiments. Optionally, the memory 220 may include a read-only memory and a random access memory, and provide instructions and data to the processor. A portion of the memory 220 may also include a non-volatile random access memory. For example, the memory 220 may also store device type information. The processor 210 can be used to execute the instructions stored in the memory 220, and when the processor 210 executes the instructions, the processor 210 may perform the corresponding steps and / or processes in the above-mentioned method embodiments.

[0188] It should be understood that in the embodiment of the present application, the processor 210 may be a central processing unit (CPU), or other general-purpose processors, DSP digital signal processors, ASIC application-specific integrated circuits, FPGA field programmable gate arrays or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0189] During implementation, each step of the above method can be completed by an integrated logic circuit of the hardware in the processor 210 or by instructions in the form of software. The steps of the method disclosed in conjunction with the embodiments of the present application can be directly embodied as being executed by a hardware processor, or can be executed by a combination of hardware and software modules in the processor 210. The software module can be located in a storage medium mature in the art, such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor executes the instructions in the memory, and completes the steps of the above method in combination with its hardware. To avoid repetition, it will not be described in detail here.

[0190] Example 3: In this example, a computer-readable storage medium is provided, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the control method in Example 1.

[0191] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0192] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0193] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0194] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0195] In the description of the above embodiments, specific features, structures, materials or characteristics may be combined in an appropriate manner in any one or more embodiments or examples.

[0196] The above are only specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A distributed IESO-PD delay control method for a multi-underactuated unmanned vessel system, characterized in that: The following steps are involved: Step S1, establishing an all-wheel drive model of a multi-underactuated unmanned vessel system; The formula of the all-wheel drive model is: ； Among them, p k (t)=[ x k (t), y k (t)] T is the position vector, B k is the state transfer matrix, τ k (t)=[τ u k (t), τ r k (t)] T is the control input vector, f k (t)=[f x k (t), f y k (t)] T is a known continuous vector function, d k (t)=[f xdis k (t),f ydis k (t)] T is an unknown continuous vector function, k =1,2,…, N It is the first in the multi-underactuated unmanned ship system k unmanned boats; Step S2: determining the coordinated control target and network topology of the multiple underactuated unmanned vessel system; Step S3: designing an extended state observer improved by low-pass filtering to achieve measurement noise filtering and disturbance suppression; The equation of the improved extended state observer is as follows: ； in: 、 、 They are position information p k (t), speed information q k (t), disturbance information d k (t) estimated value; is the virtual control input; y 1k (t) is the state of the low-pass filter; τ f is the time constant of the low-pass filter; y 0k (t) The position information detected by the unmanned vessel; is the output information of the improved extended state observer; β=[β1,β2, β3] T is the control parameter of the improved extended state observer; Step S4: constructing a collaborative formation tracking error system; Step S5: designing a distributed PD delay controller based on the estimated value output by the extended state observer in step S3 and the tracking error in step S4, and solving the control parameters of the distributed PD delay controller based on the distribution theorem of quasi-polynomial roots and pole placement; The coordinated formation tracking control of multiple underactuated unmanned ship systems is realized through the output instructions of the distributed PD time delay controller. The control protocol of the distributed PD delay controller for: ； Among them, k p is the proportional control parameter of the distributed PD delay controller, k d is the differential control parameter of the distributed PD delay controller, and Respectively k An unmanned ship t The time-delayed observation position and time-delayed observation speed information at the moment, and Respectively j An unmanned ship t The delayed observation position and delayed observation speed information at the moment; p0(t-τ) and q0(t-τ) are respectively t The actual position and velocity information of the desired trajectory received at the moment, Δ k and Δ j Respectively k The first unmanned ship and the j The expected formation information of unmanned ships, α kj is the graph adjacency matrix k Rank j Column elements, τ is the communication delay; b k To contain the gain, is the estimated value of the disturbance information.

2. The distributed IESO-PD delay control method according to claim 1, characterized in that: The step S1 comprises the following steps: Establish the kinematic and dynamic models of a three-degree-of-freedom multi-underactuated unmanned vessel system under external disturbances; The motion state parameters in the kinematic model are upgraded and dimensionally reduced to achieve conversion between the kinematic model and the dynamic model to obtain an all-wheel drive model.

3. The distributed IESO-PD delay control method according to claim 1, characterized in that: The collaborative tracking error in step S4 is: ； ； Among them: e 1k (t) is the k Unmanned boat t Position error at the moment, e 2k (t) is the k Unmanned boat t The velocity error at the moment, α kj is the graph adjacency matrix k Rank j Column elements, τ is the communication delay, p k (t-τ) and q k (t-τ) are respectively k An unmanned ship t The actual position and speed information detected at the moment, p j (t-τ) and q j (t-τ) are the j An unmanned ship t The actual position and velocity information detected at the moment, p0(t-τ) and q0(t-τ) are respectively t The actual position and velocity information of the desired trajectory received at the moment, Δ k and Δ j Respectively k The first unmanned ship and the j The expected formation information of unmanned ships, b k For the k The containment gain of the unmanned ship.

4. The distributed IESO-PD delay control method according to claim 1, characterized in that: The proportional control parameter k of the distributed PD delay controller p and the differential control parameter k of the distributed PD delay controller d The calculation process is: The dynamic equations of the overall closed-loop error system are constructed using the all-wheel drive model of the multi-underactuated unmanned vessel system, the improved extended state observer and the distributed PD time-delay controller. Analyze the characteristic polynomial of the error state based on the frequency domain method; Separate the error state vector into IESO disturbance compensation error and PD delay control error; The characteristic polynomial corresponding to the PD delay control error is transformed into the corresponding quasi-polynomial form, and the proportional control parameter k is solved based on the necessary and sufficient conditions for the distribution of the quasi-polynomial roots and the pole placement method. p and the differential control parameter k d .

5. A distributed IESO-PD delay control system for a multi-underactuated unmanned vessel system, characterized in that: To implement the distributed IESO-PD delay control method according to any one of claims 1 to 4, comprising: A model conversion module, which is used to convert the mathematical model of the multiple underactuated unmanned ship system into an all-wheel drive model; A filtering and suppression module is designed to filter measurement noise and suppress disturbances by improving the extended state observer with low-pass filtering; The controller design execution module is used to design a distributed PD delay controller; it is also used to calculate the control parameters of the distributed PD delay controller; it is also used to output control instructions to realize the coordinated formation tracking control of multiple underactuated unmanned ships; Communication module, used for communicating with external devices.

6. An electronic device, characterized in that: include: a processor, and a memory and a transceiver communicatively connected to the processor; The memory stores computer-executable instructions; the transceiver is used to transmit and receive data; The processor executes the computer-executable instructions stored in the memory to implement the distributed IESO-PD delay control method according to any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the distributed IESO-PD delay control method according to any one of claims 1 to 4.

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