Preset performance course keeping fault-tolerant control method for unmanned surface ship

Abstract

The invention discloses a preset performance course keeping fault-tolerant control method for an unmanned surface ship, and belongs to the field of fault-tolerant control, and the method comprises the steps: firstly constructing a course keeping system which comprises a self-adaptive fault-tolerant controller, a steering engine and an unmanned ship; and then introducing a performance function, constructing a self-adaptive fault-tolerant controller, performing course control of the unmanned ship, and completing preset performance course keeping fault-tolerant control of the unmanned ship. According to the method, the expected course can be stably tracked, the course error is controlled within the preset range under the constraint of the specified performance function, the designed controller has the good convergence speed and tracking precision, and the control requirement in the complex environment is met.

Description

Technical Field

[0001] The present invention belongs to the field of fault-tolerant control, and in particular relates to a preset performance heading maintenance fault-tolerant control method for an unmanned surface ship. Background Art

[0002] With the continuous deepening of ocean exploration, unmanned systems have gradually begun to be applied in various industries. Against the background of the rapid development of technologies such as artificial intelligence and the Internet of Things, unmanned surface vehicles (USVs) have become a hot field in ship research due to their irreplaceable advantages. At present, surface unmanned ships have been used in marine fields such as natural gas exploration and coastal monitoring, and their heading tracking control has become a hot issue in the control and marine engineering communities. The kinematics of unmanned ships under the action of wind, waves, and currents has the characteristics of nonlinearity and uncertainty of the internal parameters of the model, which brings many difficulties to the design of the controller. Therefore, it is of great significance to study the motion control of the uncertain heading keeping system of unmanned ships.

[0003] The servo is the core component of the USV to control its direction, attitude and navigation trajectory, and controls the heading by adjusting the rudder angle. However, due to the complex working environment and long-term operation, the servo system is prone to partial failures such as mechanical wear, electrical failure or external physical damage. Such failures will cause the USV's control ability to drop significantly, or even cause it to lose control, seriously affecting mission execution and navigation safety. Therefore, how to ensure the USV's continued navigation capability and navigation safety in the event of partial failure of the servo has become a technical problem that needs to be solved urgently.

[0004] Most existing control systems assume that the steering gear is in normal working condition, and often lack effective control mechanisms in the event of partial steering gear failure. Some traditional control methods usually rely on preset failure models, which may not be able to make timely and accurate adjustments when faced with complex and unknown steering gear failures, thereby reducing the reliability and mission completion capability of the USV.

[0005] To address these issues, adaptive fault-tolerant control technology was introduced, which can be dynamically adjusted according to the real-time status of the system without relying on a predetermined fault model. In the application of USV, the adaptive fault-tolerant control method for partial failure of the servo can monitor the status of the vehicle in real time when the failure occurs, and adjust the control strategy to ensure the continuous and stable operation of the vehicle in the failure environment. This method can not only effectively deal with known failure modes, but also make adaptive adjustments to unknown faults, greatly improving the robustness of the USV. Summary of the invention

[0006] In view of the shortcomings of the prior art, the present invention studies the problem of adaptive fault-tolerant control of heading keeping of an unmanned vehicle system with partial failure of a steering gear. Mainly based on the kinematic characteristics of the ship, the Nomoto model is used to describe the system under consideration, and a second-order underdamped system is given to illustrate the structure of the actual steering gear system; a real-time adaptive law for fault coefficient is established and incorporated into the controller design; the prescribed performance function is incorporated into the controller design, and combined with the adaptive control law, the heading tracking error has the prescribed performance; by designing the corresponding lyapunov function, it is proved that all signals in the closed-loop system are semi-globally always bounded, and the transformed tracking errors are limited to the prescribed performance bounds.

[0007] Step (1) constructs a heading keeping system, which includes an adaptive fault-tolerant controller, a steering gear, and an unmanned ship. The deviation signal generated by the desired heading and the actual heading is output to the adaptive fault-tolerant controller, and a control signal u1(t) is generated. The control signal acts on the steering gear to generate an actual steering angle δ(t), so that the actual heading is consistent with the desired heading.

[0008] Step (2) introduces the performance function, constructs an adaptive fault-tolerant controller, performs the unmanned ship heading control, and completes the preset performance heading maintenance fault-tolerant control of the unmanned ship.

[0009] Introduce the following definitions:

[0010] First, the choice of performance function ρ(t) satisfies the following two requirements:

[0011] The performance function ρ(t) is always positive and monotonically decreasing in the defined time domain;

[0012]

[0013] The following function satisfies the above two requirements:

[0014] ρ(t)=(ρ0-ρ ∞ ) -βt +ρ ∞ (1)

[0015] Where ρ0,ρ ∞ , β is a positive real number, β is the lower bound of the rate of convergence of the tracking error; then, the boundary function shown in the following formula is constructed by the performance function:

[0016] -σ min ρ(t)<z(t)<σ max ρ(t),t≥0 (2)

[0017] Where z(t) is the tracking error subject to performance constraints, σ∈(0,1] is the constraint on the overshoot of the system response, and σ min , σ maxRepresent the maximum and minimum values ​​of σ respectively. Perform error transformation:

[0018] z(t)=ρ(t)S(ε(t)) (3)

[0019]

[0020] Among them, ε(t) is the transformed error, and S(ε(t)) is a smooth and strictly monotonically increasing function. From the definition of S(ε(t)), we can get:

[0021]

[0022] The mapping relationship is S -1 :(-σ min ,σ max )→(-∞,+∞).

[0023] An adaptive fault-tolerant controller is designed to stabilize the transformed rudder angle and heading angle errors. The transformed heading angle error is defined as follows:

[0024] e2(t)=ψ d (t)-ψ(t) (6)

[0025] Among them, ψ d (t) is the desired heading, and ψ(t) is the actual heading.

[0026] To facilitate subsequent derivation, ε(t) in the original formula is represented as ε1(t), S is represented as S1, z(t) is represented as z1(t), and ρ(t) is represented as ρ1(t).

[0027] As mentioned above, the error after conversion can be defined as follows:

[0028]

[0029] in,

[0030]

[0031] From equations (5) and (7), we can see that if ε(t) is bounded, then the specified performance constraints are met for all t ≥ 0. In addition, σ min and σ max It can be regarded as the overshoot parameter of the error system. In this case, if z(0)>0, the maximum overshoot is specified to be less than σ min ρ(0); if z(0)<0, the maximum overshoot is specified to be less than σ max ρ(0). ∞ and β can also guarantee the lower bounds of steady-state error and convergence rate.

[0032] Then the derivative of the transformed error can be obtained:

[0033]

[0034] Where z1(t)=e2(t), χ1(t) is the adaptive adjustment coefficient, and:

[0035]

[0036] Taking the derivative of ε1(t), we can get:

[0037]

[0038] Based on this, an adaptive fault-tolerant controller is constructed. The control law u1(t) shown below is designed to obtain:

[0039]

[0040] Where λ is the partial failure coefficient, u(t) is the original control signal, is the estimated value of the adaptive gain, is the rate of change of heading angle, e2(t) is the quadratic error term, ρ1(t) is the reference signal inside the adaptive system, is the estimated value of system uncertainty, is the hyperbolic tangent function used to provide nonlinear correction, k1 is the control gain, τ1 is a negative constant, It obeys the following adaptive update law:

[0041]

[0042]

[0043] Where k2 and k3 are control gains, λ is the partial failure coefficient estimated at the previous moment and satisfies 0<λ<1.

[0044] Beneficial effects of the present invention: The present invention focuses on the heading control problem of unmanned surface vehicles (USVs), especially in the case of partial failure of the servo, and proposes a heading keeping method that combines a prescribed performance function with adaptive control. First, the dynamic behavior of the USV is modeled by the Nomoto model, the dynamic characteristics of the servo system are further analyzed, and a prescribed performance function is introduced to ensure that the dynamic behavior of the heading error can meet the predetermined performance indicators. In order to ensure the robustness of the system in the case of partial failure of the servo, the present invention designs an adaptive control law so that the controller can be adaptively adjusted according to the actual fault conditions of the system, thereby compensating for the performance degradation caused by the failure of the servo. The simulation results show that under the proposed control scheme, all signals in the closed-loop system are bounded, the system can stably track the desired heading, and control the heading error within a predetermined range under the constraints of the prescribed performance function. In addition, the experiment also shows that the designed controller has good convergence speed and tracking accuracy, which meets the control requirements in complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 This is the block diagram of the heading keeping system;

[0046] Figure 2 It is a mathematical model of unmanned ship;

[0047] Figure 3 is the actual heading angle change;

[0048] Figure 4 is the attenuated sinusoidal wave disturbance function;

[0049] Figure 5 is the actual heading diagram of the USV with specified performance constraints;

[0050] Figure 6 This is a comparative test of the adaptive rate without adding failure coefficient. DETAILED DESCRIPTION

[0051] The present invention will be described in further detail below with reference to the examples shown in the accompanying drawings. Figure 1 As shown, a preset performance heading keeping fault-tolerant control method for a surface unmanned ship is as follows:

[0052] Step (1) Take a heading keeping system, which includes an adaptive fault-tolerant controller, a steering gear, and an unmanned ship. The mathematical model of the unmanned ship is as follows: Figure 2 As shown, the deviation signal generated by the desired heading and the actual heading is output to the controller, and the control signal u(t) is generated. The control signal acts on the steering gear to generate the actual steering angle δ(t), so that the actual heading is consistent with the desired heading. The actual heading angle changes as shown in Figure 3 shown.

[0053] Step (2) combines the unmanned ship motion transfer model and the steering gear characteristics, introduces the specified performance function to construct an adaptive fault-tolerant controller for heading control, and introduces the following definition:

[0054] First, the choice of performance function ρ(t) satisfies the following two requirements:

[0055] The performance function ρ(t) is always positive and monotonically decreasing in the defined time domain;

[0056]

[0057] The following function satisfies the above two requirements:

[0058] ρ(t)=(ρ0-ρ ∞ ) -βt +ρ ∞ (1)

[0059] Where ρ0,ρ ∞ , β is a positive real number, β is the lower bound of the rate of convergence of the tracking error; then, the boundary function shown in the following formula is constructed by the performance function:

[0060] -σ min ρ(t)<z(t)<σ max ρ(t),t≥0 (2)

[0061] Where z(t) is the tracking error subject to performance constraints, σ∈(0,1] is the constraint on the overshoot of the system response, and σ min , σ max Represent the maximum and minimum values ​​of σ respectively. Perform error transformation:

[0062] z(t)=ρ(t)S(ε(t)) (3)

[0063]

[0064] Among them, ε(t) is the transformed error, and S(ε(t)) is a smooth and strictly monotonically increasing function. From the definition of S(ε(t)), we can get:

[0065]

[0066] The mapping relationship is S -1 :(-σ min ,σ max )→(-∞,+∞).

[0067] Design a controller to stabilize the transformed rudder angle and heading angle errors. Define the transformed heading angle error as follows:

[0068] e2(t)=ψ d(t)-ψ(t) (6)

[0069] Among them, ψ d (t) is the desired heading, and ψ(t) is the actual heading.

[0070] To facilitate subsequent derivation, ε(t) in the original formula is represented as ε1(t), S is represented as S1, z(t) is represented as z1(t), and ρ(t) is represented as ρ1(t).

[0071] As mentioned above, the error after conversion can be defined as follows:

[0072]

[0073] in,

[0074]

[0075] From equations (5) and (7), we can see that if ε(t) is bounded, then the specified performance constraints are met for all t ≥ 0. In addition, σ min and σ max It can be regarded as the overshoot parameter of the error system. In this case, if z(0)>0, the maximum overshoot is specified to be less than σ min ρ(0); if z(0)<0, the maximum overshoot is specified to be less than σ max ρ(0). ρ(0) is the initial value of the performance function, z(0) is the initial value of the tracking error subject to performance constraints, ρ ∞ and β can also guarantee the lower bounds of steady-state error and convergence rate.

[0076] Then the derivative of the transformed error can be obtained:

[0077]

[0078] Where z1(t)=e2(t), χ1(t) is the adaptive adjustment coefficient, and:

[0079]

[0080] Taking the derivative of ε1(t), we can get:

[0081]

[0082] Based on this, an adaptive fault-tolerant controller is constructed. The control law u1(t) shown below is designed to obtain:

[0083]

[0084] Where λ is the partial failure coefficient, u(t) is the original control signal, is the estimated value of the adaptive gain, is the rate of change of heading angle, e2(t) is the quadratic error term, ρ1(t) is the reference signal inside the adaptive system, is the estimated value of system uncertainty, is the hyperbolic tangent function used to provide nonlinear correction, k1 is the control gain, τ1 is a negative constant, It obeys the following adaptive update law:

[0085]

[0086]

[0087] Where k2 and k3 are control gains, λ is the partial failure coefficient estimated at the previous moment and satisfies 0<λ<1.

[0088] In the model verification phase, the unmanned ship motion mathematical model and steering gear characteristics are required, as described below:

[0089] A mathematical model of unmanned ship motion with internal parameter uncertainty is established. Assuming that the unmanned ship is completely symmetrical and the origin of the coordinate system is set at the center of gravity of the unmanned ship, the mathematical model of the unmanned ship motion is:

[0090]

[0091] Among them, u(t) and v(t) are the components of the unmanned ship's motion speed in the x and y directions, and r(t) is the bow angular velocity; is the impact acceleration; To affect acceleration; I is the flight acceleration; zz is the inertia of the unmanned ship along the Z axis; J zz is the additional inertia; m, m x 、m y Respectively represent the mass, longitudinal and lateral additional mass of the unmanned ship; X H , Y H is the force acting on the unmanned ship, N H is the torque acting on the unmanned ship, and the variables with subscripts P and R represent the force and torque acting on the propeller and steering gear, respectively.

[0092] During heading control, the drift angle is very small, and equation (15) can be linearized as:

[0093]

[0094] Where Δu(t) is the change in longitudinal velocity; Longitudinal acceleration; X uThe damping coefficient in the longitudinal direction; u0 is the linear representation of u(t); Y υ is the hydrodynamic coefficient generated by the lateral velocity; Y r Y is the hydrodynamic coefficient generated by the yaw angular velocity; δ is the influence coefficient of rudder angle on lateral force; N υ is the yaw moment coefficient generated by the lateral velocity; N r is the yaw moment coefficient generated by the yaw angular velocity; N δ is the influence coefficient of rudder angle on longitudinal force.

[0095] The first equation of equation (16) and the second and third equations are decoupled, indicating that the linear motion of the unmanned ship system can be controlled separately from the steering motion. In actual operation, engineers pay more attention to the angular velocity of the bow than to the drift motion. Let Δu = u s (t)-u0≈0, where u s (t) is the expected longitudinal velocity, which means that the system assumes that the change of longitudinal velocity is not significant or can be ignored under this assumption. Then the transfer function of rudder angle δ(s) and yaw angular velocity r(s) can be described as:

[0096]

[0097] Since the motion of the unmanned ship has low-frequency characteristics, the transfer function can be reduced to a first-order model at low frequency:

[0098]

[0099] T, T1, T2, T3 are ship time constants, T = T1 + T2 - T3; s is the frequency in the Laplace transform domain; T4 is the gain of the heading keeping system; Formula (18) is expressed in the time domain as:

[0100]

[0101] definition When there are internal parameter uncertainties and external disturbances, equation (19) can be rewritten to obtain the unmanned ship motion transfer model:

[0102]

[0103] Among them, ω(t) is the disturbance angle and δ(t) is the rudder angle.

[0104] Study the characteristics of the steering gear. There is an unavoidable delay from the control rudder angle output to the actual rudder angle signal. Usually on large ships, this rudder characteristic can be regarded as a first-order inertia link. However, due to the high efficiency and high speed of the rudder, the first-order inertia link is difficult to fully describe. The second-order underdamped system is used to describe the steering gear characteristics, and the description is as follows:

[0105]

[0106] Among them, T E3 represents the controller gain, T E1 、T E2 is the controller time constant,

[0107] The main conclusion is given by the following theorem:

[0108] Theorem: For a heading keeping system with control law and adaptive law. If there is ρ1(0), such that -σ min ρ1(0)<δ e (0)<σ max ρ1(0), where ρ1(0) is the initial value of the reference signal in the adaptive controller, δ e (0) is the range of the initial error, then all signals in the resulting closed-loop system are ultimately semi-globally uniformly bounded, and the transformed tracking error is confined to the specified performance bound for all t ≥ 0.

[0109] The Lyapunov function related to the rudder angle error is defined as:

[0110]

[0111] in, is the estimated error of uncertainty, is the adaptive gain estimation error.

[0112] Then the time derivative of V1 along equation (12) is:

[0113]

[0114] in, is the uncertainty of the observation error, is the adaptive gain observation error, is the expected angular velocity.

[0115] Substituting (12) into (23) we obtain:

[0116]

[0117] According to Lemma 1, we can get the following inequality:

[0118]

[0119] Since the partial failure coefficient satisfies 0<λ<1, equation (25) can be changed to:

[0120]

[0121] Where k0 = 0.2785. Substituting the adaptive law (14) into (26) yields:

[0122]

[0123] Substituting the adaptive law (13) into (27), we can obtain:

[0124]

[0125]

[0126] Depend on We can get:

[0127]

[0128] Depend on We can get:

[0129]

[0130] Among them, μ is the controller related parameter, Let k2 = 2a p , k3=2b p have to:

[0131]

[0132] Among them, a p 、b p is the controller gain, which is used to adjust the system response and stability.

[0133] Let a = λ min{k1,a p}, then (31) can be transformed into the following formula:

[0134]

[0135] Rewrite (32) as:

[0136]

[0137] Then multiply both sides by the integration factor e 2at :

[0138]

[0139] Integrating both sides of the inequality gives:

[0140]

[0141] From the above inequality we can get:

[0142]

[0143] From formula (36), we can see that all variables are semi-globally uniformly bounded, and the tracking error satisfies all specified performance constraints for t ≥ 0, and the proof is complete.

[0144] The effectiveness of the design method is verified by simulation examples. First, the system parameters (8) are determined using real experimental data, and its feasibility is verified by experiments. In the heading keeping experiment, the "Qingshan" submarine is taken as an example, and the specified performance control is simulated using Matlab. The model parameters of the USV second-order underdamped system (8) and its rudder are T4 = 0.1267, T = 1.9493, T E1 =0.19,T E2 =0.3446,T E3 =0.28. Figure 4 The decaying sinusoidal wave disturbance function shown is W(t)=10·e -0.035·t ·sin(0.7π·(t+0.1)), the control parameters are as follows: k1=1.159, k2=0.689, k3=1.724, τ1=-9.57, For the performance function, the parameters are selected as follows: ρ0=28,ρ ∞ =2,β=0.1,σ min =0.18,σ max =1.01. Figure 5 is the actual heading diagram of the USV with specified performance constraints, Figure 6 This is a comparative experiment without adding the adaptive law of failure coefficient. The simulation results show that the proposed control scheme achieves the control target.

[0145] The present invention studies the control problem of an unmanned ship with a partial failure of the steering gear. A mathematical model of the unmanned ship is established, and the model is matched using measured data to verify the rationality and accuracy of the model. The steering gear failure model is discussed, and a prescribed performance function is introduced to ensure that the dynamic behavior of the heading error can meet the predetermined performance indicators. The present invention designs an adaptive control law so that the controller can be adaptively adjusted according to the actual fault conditions of the system, thereby compensating for the performance degradation caused by the failure of the steering gear. The effectiveness of the adaptive controller is verified through numerical experiments and comparative experiments.

Claims

1. A method for fault-tolerant control of preset performance and heading maintenance of a surface unmanned vessel, characterized in that: The process includes: Build a heading keeping system that includes an adaptive fault-tolerant controller, a steering gear, and an unmanned vessel; The performance function is introduced, an adaptive fault-tolerant controller is constructed, the unmanned ship heading control is performed, and the preset performance heading maintenance fault-tolerant control of the unmanned ship is completed.

2. The preset performance heading keeping fault-tolerant control method of a surface unmanned vessel according to claim 1 is characterized in that: In the heading keeping system, the deviation signal generated by the desired heading and the actual heading is output to the adaptive fault-tolerant controller to generate a control signal u1(t). The control signal acts on the steering gear to generate an actual rudder angle δ(t), so that the actual heading of the unmanned ship is consistent with the desired heading.

3. The preset performance heading keeping fault-tolerant control method of a surface unmanned vessel according to claim 2 is characterized in that: The performance functions are as follows: The choice of performance function ρ(t) satisfies the following two requirements: The performance function ρ(t) is always positive and monotonically decreasing in the defined time domain; The following function satisfies the above two requirements. ρ(t)=(ρ0-ρ ∞ )e -βt +r ∞ (1) Where ρ0,ρ ∞ ,β is a positive real number, and β is the lower bound of the rate of convergence of the tracking error.

4. The preset performance heading keeping fault-tolerant control method of an unmanned surface ship according to claim 3 is characterized in that: The specific implementation process of constructing the adaptive fault-tolerant controller is as follows: The boundary function is constructed from the performance function as shown below: -s min ρ(t)<z(t)<σ max ρ(t),t≥0 (2) Where z(t) is the tracking error subject to performance constraints, σ∈(0,1] is the constraint on the overshoot of the system response, and σ min , σ max Respectively represent the maximum and minimum values ​​of σ; Perform error transformation: z(t)=ρ(t)S(ε(t)) (3) Among them, ε(t) is the transformed error, S(ε(t)) is a smooth and strictly monotonically increasing function; from the definition of S(ε(t)), we get: The mapping relationship is S -1 :(-σ min ,σ max )→(-∞,+∞); Design an adaptive fault-tolerant controller to stabilize the transformed rudder angle and heading angle errors; define the transformed heading angle error as follows: e2(t)=ψ d (t)-ψ(t) (6) Among them, ψ d (t) is the desired heading, ψ(t) is the actual heading; Represent ε(t) in the original formula as ε1(t), S as S1, z(t) as z1(t), and ρ(t) as ρ1(t). The error after conversion is defined as follows: in, From equations (5) and (7), we can see that if ε(t) is bounded, then the specified performance constraints are met for all t ≥ 0; in addition, σ min and σ max is regarded as the overshoot parameter of the error system; in this case, if z(0)>0, the maximum overshoot is specified to be less than σ min ρ(0); if z(0)<0, the maximum overshoot is specified to be less than σ max ρ(0); Then the transformed error is differentiated to obtain: Where z1(t)=e2(t), χ1(t) is the adaptive adjustment coefficient, and: Taking the derivative of ε1(t) we get: Based on this, an adaptive fault-tolerant controller is constructed and the control law u1(t) is designed as follows: Where λ is the partial failure coefficient, u(t) is the original control signal, is the estimated value of the adaptive gain, is the rate of change of heading angle, e2(t) is the quadratic error term, ρ1(t) is the reference signal inside the adaptive system, is the estimated value of uncertainty, is the hyperbolic tangent function used to provide nonlinear correction, k1 is the control gain, τ1 is a negative constant, It obeys the following adaptive update law: Where k2 and k3 are control gains, λ satisfies 0<λ<1.

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