Anti-sway control method and system for shipborne crane

Abstract

The application relates to the field of anti-swing technology of ship-mounted cranes, and discloses a ship-mounted crane anti-swing control method and system, which comprises the following steps: establishing a ship coordinate system and calculating the relative kinetic energy of the crane according to the ship coordinate system, adding inertial force to the generalized force of each degree of freedom, analyzing the dynamic model of the swing of the crane boom and the load, combining the RBF neural network model and the sliding mode control to construct adaptive sliding mode anti-swing control, analyzing the continuous uncertain upper bound disturbance existing in the under-actuated ship-mounted crane load based on the adaptive sliding mode anti-swing control, approximating the continuous disturbance of the uncertain upper bound, simplifying the dynamic model, introducing the backstepping sliding mode control to compensate the observation error of the nonlinear disturbance observer, judging the position of the load, and switching the LQR control and the adaptive sliding mode anti-swing control according to the judgment result. The application has the characteristics of good anti-swing control and dynamic adjustment.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of anti-swing technology of ship-mounted cranes, in particular to an anti-swing control method and system for ship-mounted cranes. BACKGROUND

[0002] The shipbuilding industry is a modern comprehensive and strategic industry that provides technical equipment for water transportation, marine resource development and national defense construction. Ship-mounted cranes are one of the core equipment in the field of shipbuilding industry, and are mainly used for cargo transfer, submarine tunnel construction, shipwreck salvage, underwater hoisting operations in cross-sea bridge projects and underwater supply tasks. Ship-mounted cranes usually work in harsh sea conditions, and external disturbances will cause the movement of the ship to be random, which not only amplifies the swing angle of the load, but also disturbs the positioning process of the load.

[0003] At present, many researches apply nonlinear control methods to the anti-swing control of ship-mounted cranes, but the traditional control has many parameters, which leads to an increase in the positioning error of the load and the load can only change in a small range near the balance point to maintain stability, and cannot select effective anti-swing control for other positions.

[0004] Therefore, how to provide an anti-swing control method and system for ship-mounted cranes is a technical problem that technicians in the field urgently need to solve. SUMMARY

[0005] In view of this, the present application provides an anti-swing control method and system for ship-mounted cranes, aiming to solve the problem that the traditional control has many parameters, which leads to an increase in the positioning error of the load and the load can only change in a small range near the balance point to maintain stability.

[0006] In one aspect, the present application provides an anti-swing control method for ship-mounted cranes, comprising:

[0007] A ship coordinate system is established, and the relative kinetic energy of the crane is calculated according to the ship coordinate system. The inertia force is added to the generalized force of each degree of freedom, and the dynamic model of the swing of the load and the movement of the boom is analyzed;

[0008] According to the combination of the RBF neural network model and the sliding mode control, an adaptive sliding mode anti-swing control is constructed, and the persistent disturbance existing in the load of the underactuated ship-mounted crane is analyzed based on the adaptive sliding mode anti-swing control, and the persistent disturbance of the uncertain upper bound is approximated;

[0009] The dynamic model is simplified, a nonlinear disturbance observer is used to observe the total disturbance, and the observation error of the nonlinear disturbance observer is compensated by introducing backstepping sliding mode control;

[0010] The adaptive sliding mode anti-sway control and the LQR control are compared, the position of the load is judged according to the comparison result and the observation of the nonlinear disturbance observer, and the LQR control and the adaptive sliding mode anti-sway control are switched according to the judgment result.

[0011] Furthermore, when establishing a ship coordinate system and calculating the relative kinetic energy of the crane based on the ship coordinate system, the method includes:

[0012] The center of mass of the ship is used as the origin of the ship coordinate system, and the bow direction is set as x S The positive direction of the axis is set as z, with the vertical deck upward direction S The positive direction of the axis, x G Axis and x S Axis parallel, z G The axis is perpendicular to the ground;

[0013] Establish the ship coordinate system I S ={x S y S z S}, establish inertial coordinate system I G ={x G y G z G};

[0014] Assuming the hook and the load are a point mass, and the boom has a uniform mass distribution, the cable is strained and the load is not positioned higher than the highest point of the boom, measure the angle of the boom and the load relative to the z S The swing angle of the shaft;

[0015] The relative kinetic energy of the crane is given by the following formula:

[0016]

[0017] Where E represents the relative kinetic energy of the crane, m represents the mass of the boom, and P L Indicates the length of the boom, represents the generalized speed of the boom, m P Indicates the mass of the load, Represents the row vector from the origin to the payload, dp p Represents the column vector from the origin to the payload.

[0018] Furthermore, when the inertia force is added to the generalized forces of each degree of freedom, the dynamic model of the boom motion and load swing is analyzed, including:

[0019] The generalized forces include boom gravity, load gravity, boom inertia force and load inertia force;

[0020] The pitch angle of the boom, the cable length, and the load swing angle are set as generalized coordinates, and the control torque of the boom pitch and the cable tension are set as control inputs to construct the dynamic model.

[0021] Furthermore, when constructing the dynamic model, it includes:

[0022] A dynamic model is established in generalized coordinates based on the Lagrange equation of the relative kinetic energy of the crane. The dynamic model is derived from the following formula:

[0023]

[0024] Where M(q) represents the inertia matrix, represents the Coriolis centripetal matrix, U c represents the input control vector, represents the vector of boom inertia force and load inertia force, f represents external force disturbance, represents the acceleration in generalized coordinates, represents the generalized velocity of generalized coordinates;

[0025] According to the dynamic model, it is determined that the shipborne crane is an under-actuated shipborne crane.

[0026] Furthermore, when constructing an adaptive sliding mode anti-sway control based on the combination of the RBF neural network model and the sliding mode control, it includes:

[0027] The adaptive sliding mode anti-sway control divides the nominal into several sub-nominals, defines the sliding mode surface of each sub-nominal, establishes a hierarchical sliding mode controller on each sub-nominal sliding mode surface, and establishes a lumped sliding mode compensator on the last sliding mode surface. The Lyapunov function is used to determine if any state deviates from the sub-sliding mode surface, and the total switching control input pulls it back to the detached sliding mode surface. The RBF neural network model is used to approximate the disturbances to the hierarchical sliding mode controller and the lumped sliding mode compensator.

[0028] Furthermore, when simplifying the kinetic model, it includes:

[0029] Create a new state vector and transform the dynamic model:

[0030] eta1=φ-α, eta2=L, eta3=θ-α;

[0031] η=[η1η2η3] T ;

[0032]

[0033] Among them, η represents the generalized coordinate, φ represents the angle of the boom, and α represents the ship's rotation around x. SThe axis rotation angle, L is the cable length, θ is the load relative to z S The swing of the axis, η1, η2 and η3 represent the generalized coordinates, [η1η2η3] T Represents vector transpose, M t (η) represents the inertia matrix, represents the Coriolis centripetal matrix, G t (η) represents the gravity vector, U c represents the input control vector, f represents the external force disturbance, represents the generalized acceleration of generalized coordinates, represents the generalized velocity in generalized coordinates.

[0034] Furthermore, when a nonlinear disturbance observer is used to observe the total disturbance and a backstepping sliding mode control is introduced to compensate for the observation error of the nonlinear disturbance observer, the method includes:

[0035] defining an observation error using a nonlinear disturbance observer, setting dynamic characteristics relative to the nonlinear disturbance observer, and determining an error dynamic equation of the nonlinear disturbance observer;

[0036] Establishing a matrix to converge the error dynamic equation exponentially and converting the observed disturbance into the control quantity of the corresponding input channel;

[0037] The output error vector is defined and derived, the Lyapunov function and adaptive feedback control law are used to obtain the law, and the input is obtained according to the Lyapunov function.

[0038] Furthermore, when determining the position of the load, the following steps are included:

[0039] Counting all historical load positions and establishing a position set, and when a load position exists in the position set, determining not to correct the load position, and determining the load position as the target position;

[0040] When the position of the load does not exist in the position set, it is determined that the position of the load should be corrected, and the position set is aggregated using an aggregation algorithm;

[0041] Taking the location set as the data set to be aggregated;

[0042] Determine the expected number of clusters k as 2 and initialize the parameters of the Gaussian distribution;

[0043] Calculating the probability that each data in the to-be-aggregated data set belongs to each Gaussian distribution to obtain a responsibility value;

[0044] obtaining a data set according to the responsibility value, and using the data set as a close set;

[0045] The average of the positions of all historical loads in the similar set is taken as the optimization factor, and the product value of the optimization factor and the position of the load is taken as the target position.

[0046] Furthermore, when switching between the LQR control and the adaptive sliding mode anti-sway control according to the judgment result, the method includes:

[0047] Pre-set the target position distance threshold;

[0048] When the target position is greater than or equal to the target position distance threshold, the adaptive sliding mode anti-sway control is adopted;

[0049] When the target position is less than the target position distance threshold, the LQR control is adopted.

[0050] Compared with the existing technology, the present invention has the following advantages: The calculation of the crane's relative kinetic energy in the ship's coordinate system accurately describes the dynamic response of the boom and load, improving the control accuracy and stability of the load motion. A dynamic model is established based on the boom motion and load swing, clearly demonstrating the actual conditions of each state and enhancing the scientific and rigorous nature of the shipborne crane's anti-sway control. The use of an RBF neural network to approximate disturbances and the introduction of sliding mode control creates an adaptive sliding mode anti-sway control system. This adaptive sliding mode anti-sway control system can effectively compensate for ship sway and wind and wave interference based on external disturbances and motion changes, ensuring the smooth operation of the shipborne crane in complex environments. A nonlinear disturbance observer is used to observe the total disturbance, identify the load's swing trend, and compensate for the observation error through inverse sliding mode control. Active control of load swing is achieved, effectively reducing the load swing amplitude in the horizontal and vertical directions. According to the load position and the observation results of the nonlinear disturbance observer, intelligent switching is performed between LQR control and adaptive sliding mode anti-swing control. When there is a large disturbance or uncertainty, it switches to adaptive sliding mode control, overcoming the disturbance of the hull in a complex environment, thereby extending the service life of the shipborne crane and reducing operating costs and maintenance expenses.

[0051] On the other hand, the present application also provides a ship-borne crane anti-sway control system for applying the above-mentioned ship-borne crane anti-sway control method, comprising:

[0052] a model building unit for establishing a ship coordinate system and calculating the relative kinetic energy of the crane based on the ship coordinate system, adding the inertial force to the generalized force of each degree of freedom, and analyzing and obtaining a dynamic model of the boom movement and load swing;

[0053] An adaptive sliding mode unit is used to construct an adaptive sliding mode anti-sway control based on a combination of an RBF neural network model and a sliding mode control, and to analyze a persistent uncertain upper bound disturbance of the underactuated shipborne crane load based on the adaptive sliding mode anti-sway control, thereby approximating the persistent disturbance of the uncertain upper bound;

[0054] An inversion unit simplifies the dynamic model and uses a nonlinear disturbance observer to observe the total disturbance; an inversion sliding mode control is introduced to compensate for the observation error of the nonlinear disturbance observer;

[0055] A switching unit compares the adaptive sliding mode anti-sway control and the LQR control, judges the position of the load based on the comparison result and the observation of the nonlinear disturbance observer, and switches between the LQR control and the adaptive sliding mode anti-sway control according to the judgment result.

[0056] It is understandable that the above-mentioned ship-borne crane anti-sway control method and system have the same beneficial effects, which will not be described in detail here. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present invention. The same reference symbols are used throughout the drawings to represent the same components. In the drawings:

[0058] Figure 1 A flow chart of an anti-sway control method for a ship-borne crane provided in an embodiment of the present invention;

[0059] Figure 2 This is a functional block diagram of a ship-borne crane anti-sway control system provided by an embodiment of the present invention;

[0060] Figure 3 Trajectory diagram of the adaptive neural network sliding mode control provided by the embodiment of the present invention;

[0061] Figure 4 A trajectory diagram of each state variable under the inverse sliding mode control provided by an embodiment of the present invention;

[0062] Figure 5 This is a graph of input under the inverse sliding mode control provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0063] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, unless there is a conflict, the embodiments of the present disclosure and the features in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in conjunction with the embodiments.

[0064] In some embodiments of this application, see Figure 1 As shown, a ship-borne crane anti-sway control method includes:

[0065] S100: Establishing a ship coordinate system and calculating the relative kinetic energy of the crane based on the ship coordinate system. Adding the inertial force to the generalized force of each degree of freedom, the dynamic model of the boom motion and load swing is analyzed.

[0066] S200: Based on the combination of RBF neural network model and sliding mode control, an adaptive sliding mode anti-sway control is constructed. Based on the adaptive sliding mode anti-sway control, the persistent uncertain upper bound disturbance of the underactuated shipborne crane load is analyzed and the persistent disturbance with the uncertain upper bound is approximated.

[0067] S300: The dynamic model is simplified and a nonlinear disturbance observer is used to observe the total disturbance. Inverse sliding mode control is introduced to compensate for the observation error of the nonlinear disturbance observer.

[0068] S400: Compare the adaptive sliding mode anti-sway control and the LQR control, judge the position of the load based on the comparison result and the observation of the nonlinear disturbance observer, and switch between the LQR control and the adaptive sliding mode anti-sway control based on the judgment result.

[0069] Specifically, the dynamics of the load of the ship-mounted crane is analyzed by using the Lagrange equation, the ship coordinate system is established according to the actual position of the crane on the ship, and the state variables are transformed, and the dynamics model of the swing of the load and the swing of the load is obtained, which lays a foundation for the subsequent research on the load swing control of the ship-mounted crane. In view of the problem that the under-actuated ship-mounted crane has a continuous uncertain upper bound disturbance, an adaptive radial basis (Radial Basis Function) neural network sliding mode anti-swing control is established, the dynamics model is converted into a nominal form conforming to the under-actuated characteristics, the uncertainty of the parameters of the under-actuated ship-mounted crane is compensated according to the control law of the sliding mode control, the adaptive RBF neural network model is used to approximate and compensate the disturbance of the uncertain upper bound caused by the continuous and nonlinear disturbance of the ship-mounted crane, and the dual control of the effective load positioning and swing suppression under the continuous unknown disturbance is realized. The position of the load of the ship-mounted crane is tracked, the adaptive backstepping sliding mode anti-swing control strategy is adopted, the observable part of the disturbance signal is observed online by using the nonlinear disturbance observer, and the observation error is exponentially convergent by designing the parameters; the adaptive backstepping sliding mode method is used to construct the load anti-swing controller after the nonlinear disturbance observer is introduced, the disturbance that cannot be observed is compensated again by using the adaptive law, the position tracking accuracy of the load of the ship-mounted crane is improved, the position uncertainty of the load and the random disturbance of the external environment are overcome, and then the control performance is improved.

[0070] It can be understood that the sliding mode control is adopted when the load is far away from the target position, and the LQR (Linaer Quadratic Regulator) control is switched when the load is close to the target position, the switching of the LQR control and the sliding mode control is realized, the simulation verification is carried out by using MATLAB, it is proved that the ship-mounted crane has consistent ultimate bounded stability, low state deviation and output deviation can be obtained, and the swing of the load is suppressed, and the ship-mounted crane has good effectiveness and feasibility.

[0071] In some embodiments of the application, when the ship coordinate system is established and the relative kinetic energy of the crane is calculated according to the ship coordinate system, the ship coordinate system is established by taking the center of mass of the ship as the origin of the ship coordinate system, taking the bow direction as the positive direction of the x S axis, and taking the direction perpendicular to the deck upward as the positive direction of the z S axis, the x G axis and the x S axis are parallel, and the z G axis is perpendicular to the ground, the ship coordinate system I S ={x S y S z S} is established, and the inertial coordinate system I G ={x G yG z G}, assuming the hook and the load as a point mass, and the boom as having a uniform mass distribution, the cable being strained and the load not being higher than the highest point of the boom, the angle of the boom and the position of the load relative to z S the swing angle of the axis, the relative kinetic energy of the crane is given by the following formula:

[0072]

[0073] where E represents the relative kinetic energy of the crane, m represents the mass of the boom, P L represents the length of the boom, represents the generalized velocity of the boom, m P represents the mass of the load, represents the row vector from the origin to the payload, dp p represents the column vector from the origin to the payload.

[0074] It can be understood that by establishing the ship coordinate system, only the heave motion and roll motion of the ship in the vertical plane are considered, thereby reducing the complexity of obtaining the dynamic model, represents the rotation between I S and I G , and is expressed as a rotation matrix as

[0075]

[0076] where α represents the rotation angle of the ship around the x S axis, S α and C α represent sinα and cosα, and similarly S φ and C φ represent sinφ and cosφ, the displacement between I S and I G is represented by P G :

[0077] p G = [0 p y p z ] T ;

[0078] d s represents the vector from the coordinate origin O S of the ship coordinate system to the crane base, d represents the distance from the boom center of gravity to the coordinate origin O S ;

[0079] d s = [0 d y d z ]T ;

[0080] Load in inertial coordinate system I G The position coordinates are expressed as:

[0081]

[0082] Where y represents the inertial coordinate system I G middle y G Axis value, z represents the inertial coordinate system I G Middle z G Axis value, P L represents the length of the boom, φ represents the angle of the boom, and θ represents the load relative to z S The swing of the axis, L is the length of the cable, d is the distance from the center of gravity of the boom to the coordinate origin O S The distance, O S The origin of the ship coordinate system, P p Indicates that from O S The displacement vector to the payload, g represents the acceleration due to gravity, m represents the mass of the boom, m P Indicates the mass of the load. G Other fixed positions in the inertial coordinate system I G The target position (a, b) in d , L d and θ d Denote the expected values ​​of φ, L, and θ at the equilibrium point. To eliminate the swing in the inertial coordinate system, θ d Equal to the ship's rotation x S Axis rotation angle α,

[0083]

[0084] Thus the relative kinetic energy of the crane is obtained.

[0085] In some embodiments of the present application, when constructing a dynamic model, the process includes: establishing a dynamic model in generalized coordinates according to the Lagrange equation of the relative kinetic energy of the crane, wherein the dynamic model is derived from the following formula:

[0086]

[0087] Where M(q) represents the inertia matrix, represents the Coriolis centripetal matrix, U c represents the input control vector, represents the vector of boom inertia force and load inertia force, f represents external force disturbance, represents the acceleration in generalized coordinates, The generalized velocity of the generalized coordinates is expressed. According to the dynamic model, the shipborne crane is judged to be an underactuated shipborne crane.

[0088] It is understandable that defining q as [q1 q2 q3] T =[φ L θ] T The generalized coordinates of M(q)∈R 3×3 , U c ∈R 3×1 , f∈R 3×1 , the control torque of the boom is M c , cable tension F L , the friction force f of the boom pitching φ , cable friction f l , the disturbance force f exerted by the sea breeze on the load θ , the expanded expression is:

[0089]

[0090]

[0091] U c =[M c F L 0] T ;

[0092]

[0093] f=[f φ f l f θ ] T ;

[0094] The Lagrange equation for the generalized coordinate q = φ is expanded as:

[0095]

[0096] The Lagrange equation for the generalized coordinate q=L is expanded as:

[0097]

[0098] The Lagrange equation for the generalized coordinate q = θ is expanded as:

[0099]

[0100] According to the dynamic model, it is concluded that the shipborne crane is an underactuated shipborne crane system, which has strongly coupled nonlinear characteristics and is also disturbed by the ship motion caused by the sea breeze.

[0101] In some embodiments of the present application, when constructing an adaptive sliding mode anti-sway control based on a combination of an RBF neural network model and a sliding mode control, it includes: the adaptive sliding mode anti-sway control divides the nominal into several sub-nominals, defines the sliding mode surface of each sub-nominal, establishes a hierarchical sliding mode controller on the sliding mode surface of each sub-nominal, and establishes a lumped sliding mode compensator on the last sliding mode surface, uses a Lyapunov function to determine whether any state deviates from the sub-sliding mode surface, and uses a total switching control input to pull it back to the detached sliding mode surface, and uses an RBF neural network model to approximate the disturbances to the hierarchical sliding mode controller and the lumped sliding mode compensator.

[0102] It is understandable that the tracking error is defined as:

[0103] e L =LL d , e1=φ-φ d , e2=θ-θ d ;

[0104] Take two equivalent inputs u and u L Control the tracking desired trajectory of the three states φ, L and θ, input u L To control L, input u to control φ and θ simultaneously. L The control law is:

[0105]

[0106] k L2 and k L1 Represents a positive real number, and its mathematical model is composed of φ and θ and is written in the form of the following state space expression:

[0107]

[0108] in, is the state variable vector, u is a single control input, d2 is the upper bound of the uncertainty of the payload, d2 is the disturbance of the payload caused by the ship motion, g i and b i (i=1,2) are nonlinear functions of state variables, b1(x)=1, b2(x)=-P L S θ-φ / L,

[0109]

[0110] Without considering the uncertainty disturbance, the first layer of sliding surface is constructed, and each sliding surface is:

[0111] s1=c1x1+x2;

[0112] s2=c2x3+x4;

[0113] Where c1 and c2 are positive constants, and the second sliding surface is:

[0114] S=a1s1+a2s2;

[0115] Where a1 and a2 are positive constants, and the exponential trending law is used:

[0116]

[0117] Where k and ε are positive constants. A lumped sliding mode compensator is established on the last sliding mode surface to obtain the lumped sliding mode control law. The total control input is:

[0118] u=u eq1 +u eq2 +u sw ;

[0119] Among them, u eq1 and u eq2 Denotes the hierarchical sliding mode control law of each sub-nominal sliding surface, u sw It represents the switching control of the sliding surface, and the overall control law is:

[0120]

[0121] Using Lyapunov function, Lyapunov function is:

[0122]

[0123] Use RBF neural network model to approximate disturbance, f = W *T h(x)+δ,h=[h j ] T represents the output of the Gaussian basis function, δ represents the neural network approximation error, W * represents the ideal neural network weight, f represents the external force disturbance, according to Add the total control input u = u eq1 +u eq2 +u sw , and the neural network input is z = [x1, x3] T , we get:

[0124]

[0125] According to the Lyapunov function and LaSalle invariant set theorem, the RBF neural network will continuously approach the disturbance of the uncertain upper bound, thereby suppressing the swing of the load and improving the stability of the anti-sway system.

[0126] In some embodiments of the present application, when simplifying the dynamic model, the process includes: establishing a new state vector and converting the dynamic model:

[0127] eta1=φ-α, eta2=L, eta3=θ-α;

[0128] η=[η1η2η3] T ;

[0129]

[0130] Among them, η represents the generalized coordinate, φ represents the angle of the boom, and α represents the ship's rotation around x. S The axis rotation angle, L is the cable length, θ is the load relative to z S The swing of the axis, η1, η2 and η3 represent the generalized coordinates, [η1 η2 η3] T Represents vector transpose, M t (η) represents the inertia matrix, represents the Coriolis centripetal matrix, G t (η) represents the gravity vector, U c represents the input control vector, f represents the external force disturbance, represents the generalized acceleration of generalized coordinates, represents the generalized velocity in generalized coordinates.

[0131] Understandably, M t (η)∈R 3×3 , G t (η)∈R 3×1 , U c ∈R 3×1 , f∈R 3×1 , the expanded expression is:

[0132]

[0133]

[0134] U c =[M c F L 0] T ;

[0135]

[0136] f=[f φ f l f θ ] T ;

[0137] The Lagrange equation for the generalized coordinate η=η1 is expanded as:

[0138]

[0139] The Lagrange equation of generalized coordinate η = η2 is expanded as:

[0140]

[0141] The Lagrange equation of generalized coordinate η = η3 is expanded as:

[0142]

[0143] The virtual input is set as u = [u1 u2 u3], which is converted from the dynamic model, and lays a foundation for observing the total disturbance by using the nonlinear disturbance observer subsequently, and the heave motion of the ship is set as p = 0.4sin(πt) s z = 0.4sin(πt), α = π / 36sin(πt) rad, if the load reaches the target position (4 m, 3.5 m) in the case of no sea wave and other disturbances, which is equivalent to the case of the crane on land, the expected values of φ, L and θ are: φ = 1.04 rad, L = 1 m and θ = 0 rad, considering the case on the sea, the rest of the parameters are shown in the following table, d d d

[0144]

[0145] The parameters of the adaptive neural network sliding mode control are selected as: c1 = 1.7, c2 = 20, a1 = 1.7, a2 = 1, k = 10 and ε = 0.5, the trajectories of the cable length, the angle of the boom and the swing angle of the load under the adaptive neural network sliding mode control are simulated and verified in matlab, and refer to the figure as shown in Figure 3

[0146] In some embodiments of the present application, when the nonlinear disturbance observer is used to observe the total disturbance, and the backstepping sliding mode control is used to compensate for the observation error of the nonlinear disturbance observer, the method comprises the following steps: defining the observation error of the nonlinear disturbance observer, setting the dynamic characteristics relative to the nonlinear disturbance observer, determining the error dynamic equation of the nonlinear disturbance observer, establishing a matrix to make the error dynamic equation converge exponentially, converting the observed disturbance into the control amount of the corresponding input channel, defining the output error vector and deriving, using the Lyapunov function and the adaptive feedback control law to take the law, and obtaining the input according to the Lyapunov function.

[0147] It can be understood that the observation error of the nonlinear disturbance observer is defined as:

[0148] ​​​​​

[0149] Among them, e f represents the observation error, Expressed as the estimated value of f, f represents the external force disturbance, set the dynamic characteristics relative to the nonlinear disturbance observer, and define the process of the ship-borne crane being disturbed as slow. From this we can conclude that:

[0150]

[0151] Given a nonlinear observer, the gain satisfies:

[0152]

[0153] The error dynamic equation of the nonlinear disturbance observer is obtained:

[0154]

[0155] Create a matrix ρ 11 =ρ and is greater than 0, so that the error dynamic equation converges exponentially, and the output of the nonlinear disturbance observer is transmitted to the gain Converting the observed disturbance into the control quantity of the corresponding input channel yields:

[0156]

[0157] By adopting a nonlinear disturbance observer, the external force disturbance f is transformed into e f , thereby reducing the impact of disturbances, using nonlinear disturbance observer, reorganizing the state space expression into the form:

[0158]

[0159] The output error vector is defined as

[0160] z1=η-η d ;

[0161] Among them, η d Represents the given position command of the shipborne crane. Derivative of the above formula yields:

[0162]

[0163] Define the Lyapunov function as:

[0164]

[0165] Set the virtual control amount a1=c1z1, where c1 represents c1∈R 3×3 Be a symmetric and positive constant matrix, let:

[0166]

[0167] Substitute z2 into the formula The formula is

[0168]

[0169] Substitute the above formula into Lyapunov function V1, and the formula is

[0170]

[0171] When z2 = 0, then V1≤0, and the Lyapunov function is defined as:

[0172]

[0173] Where s = [s1 s2 s3] is the sliding surface, and is defined as:

[0174] s = k1z1 + z2;

[0175] Where k1∈R 3×3 is a positive definite symmetric matrix, and the derivative of the above formula is obtained by combining the formula:

[0176]

[0177] The derivative of Lyapunov function V2 is obtained as:

[0178]

[0179] The estimated observation error is defined as is the estimated value of the observation error of the nonlinear disturbance observer, and the formula is obtained as:

[0180]

[0181] The Lyapunov function is defined as:

[0182]

[0183] Where γ represents a normal number, and the derivative of the above formula is obtained as:

[0184]

[0185] The adaptive feedback control law is:

[0186]

[0187] wherein β, c2 represent normal number matrix, similar β3 and c3 also represent normal number matrix, according to the adaptive law of the above formula:

[0188]

[0189] The total sliding mode surface function of η1 and η3 is defined as:

[0190] S = a1s1 + a2s3;

[0191] According to the above formula, the Lyapunov function is:

[0192]

[0193] The above formula is derived as:

[0194]

[0195] According to the above formula:

[0196]

[0197] Substitute into formula, the input of the ship-mounted crane as an underactuated ship-mounted crane system is:

[0198]

[0199] F L = u2;

[0200] Set m = 10 kg, m p = 1 kg, p L = 0.7m, g = 9.8m / s 2 , d = 0.05m, φ = 0.17 rad, L = 0.1m and θ = 0 rad, c1 = 80, c2 = 25, k1 = 10, β = 10, γ = 12, verify the backstepping sliding mode control, each state variable and input is simulated and verified in matlab, refer to Figure 4 and Figure 5 After introducing the nonlinear disturbance observer in the underactuated ship-mounted crane system, the influence of the output disturbance is further reduced, which not only improves the tracking performance of the ship-mounted crane load position, but also further eliminates the disturbance.

[0201] In some embodiments of the present application, when the position of the load is determined, the position of all historical loads is counted and a position set is established, when the position of the load exists in the position set, it is determined that the position of the load is not corrected, the target position is determined according to the position of the load, when the position of the load does not exist in the position set, it is determined that the position of the load is corrected, the position set is aggregated by using an aggregation algorithm, the position set is taken as a data set to be aggregated, the expected cluster number k is determined as 2, and the parameters of the Gaussian distribution are initialized, the probability of each data in the data set to be aggregated belonging to each Gaussian distribution is calculated, the responsibility value is obtained, the data set is obtained according to the responsibility value, the data set is taken as a similar set, and the position of all historical loads in the similar set is averaged to obtain an optimization factor, and the product value of the optimization factor and the position of the load is taken as the target position.

[0202] It can be understood that by counting the positions of historical loads and establishing a position set, the reliability of the data source is ensured, and the risk of misjudgment is reduced. The position set represents a reasonable range of the load within a certain range. When it is determined that there is a position that does not exist, there may be a human-set error. By using the aggregation algorithm for correction, the influence of random fluctuations is reduced, and the optimization factor dynamically adjusts the position of the load, which improves the accuracy of position determination and also takes into account the real-time correction capability. The complexity of calculation is reduced and the robustness is enhanced.

[0203] In some embodiments of the present application, when the LQR control and the adaptive sliding mode anti-swing control are switched according to the determination result, the target position distance threshold is set in advance, when the target position is greater than or equal to the target position distance threshold, the adaptive sliding mode anti-swing control is used, and when the target position is less than the target position distance threshold, the LQR control is used.

[0204] It can be understood that the underactuated ship-mounted crane system is linearized near the equilibrium point, and an approximate transformation is made so that η1≈η 1d , η2≈η 2d , η3≈0, sinη3≈η3, cosη3≈1, The formula is Taylor expanded at the equilibrium point and high-order terms are ignored, and the target position distance threshold Ω c

[0205]

[0206] Ω c ={x,y<0.17};

[0207] wherein E represents the linearization result near the equilibrium point, E1 represents the adaptive sliding mode anti-swing control, E * ​LQR control, x, y represent target position, LQR control is based on minimizing the quadratic performance index of system state, represented as the weighted sum of control performance and state deviation, represented by the quadratic function of system state. And the gain matrix is determined by the weight matrix Q and R, the two weight matrices are used to adjust the system state and control input, the state weight matrix Q reflects the importance of state deviation, and the input weight matrix R reflects the importance of control input change, LQR control considers the optimization of the entire state space, so its tracking performance will not change much when facing system disturbance, and lower state deviation and output deviation can be obtained, thereby achieving the effect of suppressing load swing. And the adaptive sliding mode anti-swing control has good robustness and fast response characteristics, by introducing the sliding mode surface, it can quickly respond and resist external disturbance, and ensure that the ship-mounted crane can still maintain stability when facing uncertain disturbance. When the disturbance amplitude is large, it is easy to cause the tracking performance to decline, therefore, when pursuing to improve the control accuracy and anti-swing ability, the control can be dynamically selected according to the actual situation, and the characteristics of the two controls can be maximized.

[0208] In summary, the beneficial effects of the present application are that the calculation of the relative kinetic energy of the crane in the ship coordinate system can accurately describe the dynamic response of the boom and the load, improve the control accuracy and stability of the load movement, and the dynamic model is established according to the boom movement and the load swing, the actual situation of each state is clearly shown, the scientificity and rigor of the anti-swing control of the ship-mounted crane are improved, the RBF neural network is used to approximate the disturbance and the sliding mode control is introduced, and the adaptive sliding mode anti-swing control is constructed. The adaptive sliding mode anti-swing control can effectively compensate for the ship body sway and wave disturbance according to the change of external disturbance and motion, and ensure the smooth operation of the ship-mounted crane in complex environment. The total disturbance is observed by using the nonlinear disturbance observer, the swing trend of the load is identified, and the observation error is compensated by the backstepping sliding mode control. The active control of the load swing is realized, the swing amplitude of the load in the horizontal and vertical directions is effectively reduced, and the intelligent switching is carried out between the LQR control and the adaptive sliding mode anti-swing control according to the position of the load and the observation result of the nonlinear disturbance observer. When there is a large disturbance or uncertainty, switch to adaptive sliding mode control, overcome the disturbance of the ship body in complex environment, thereby prolonging the service life of the ship-mounted crane, reducing the operation cost and maintenance cost.

[0209] In another preferred mode based on the above embodiment, referring to Figure 2 The present embodiment provides a ship-mounted crane anti-swing control system for applying the above ship-mounted crane anti-swing control method, which comprises:

[0210] The model building unit is used to establish the ship coordinate system and calculate the relative kinetic energy of the crane based on the ship coordinate system. The inertial force is added to the generalized force of each degree of freedom to analyze and obtain the dynamic model of the boom movement and load swing;

[0211] An adaptive sliding mode unit is used to construct an adaptive sliding mode anti-sway control based on the RBF neural network model and sliding mode control. Based on the adaptive sliding mode anti-sway control, the continuous uncertain upper bound disturbance of the underactuated shipborne crane load is analyzed and the continuous disturbance with the uncertain upper bound is approximated.

[0212] The inversion unit simplifies the dynamic model and uses a nonlinear disturbance observer to observe the total disturbance; the inversion sliding mode control is introduced to compensate for the observation error of the nonlinear disturbance observer;

[0213] The switching unit compares the adaptive sliding mode anti-sway control and the LQR control, judges the load position based on the comparison result and the observation of the nonlinear disturbance observer, and switches between the LQR control and the adaptive sliding mode anti-sway control according to the judgment result.

[0214] It is understandable that the model building unit establishes the ship coordinate system as the benchmark for dynamic analysis. Based on this coordinate system, the relative kinetic energy of the crane can be accurately derived. The influence of inertial force on the load is taken into account and introduced into the generalized force of each degree of freedom of the crane to establish a dynamic model. Based on the dynamic model, the dynamic relationship between the boom motion and the load swing can be analyzed, thereby obtaining an accurate kinematic description, which provides a reliable theoretical basis for subsequent control strategies. The adaptive sliding mode unit combines the RBF neural network model with sliding mode control to construct an adaptive sliding mode anti-sway control. This control can adaptively adjust the control parameters to respond to the continuous uncertain upper bound disturbance of the load in real time. During the control process, the adaptive sliding mode unit accurately estimates and compensates for the continuous disturbance of the uncertain upper bound by approximating the disturbance in real time. The robustness and anti-interference ability of the system are improved, so that the ship-borne crane can operate stably in the complex environment of waves and sea breeze. The inversion unit realizes accurate observation of the system by simplifying the dynamic model and applying the nonlinear disturbance observer. The nonlinear disturbance observer can accurately evaluate the total disturbance of the system, improve the accuracy of the dynamic response of the load swing and boom movement, and reduce the risk of error accumulation and disturbance uncertainty in the system. The switching unit compares the adaptive sliding mode anti-sway control with the linear quadratic regulator LQR control, and judges the switching of the control strategy according to the observation results of the nonlinear disturbance observer and the load position. When the load swings within the allowable range, the system adopts LQR control to achieve precise positioning and energy consumption optimization; when the load swing exceeds the control range, the system automatically switches to adaptive sliding mode anti-sway control to ensure the stability of the load, improve the control flexibility of the system, and ensure the safe and efficient operation of the ship-borne crane.

[0215] 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 take the form of a complete hardware embodiment, a complete software embodiment, or a combination of software and hardware embodiments. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0216] 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 produce 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 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0217] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0218] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0219] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A ship-borne crane anti-sway control method, characterized in that: include: Establishing a ship coordinate system and calculating the relative kinetic energy of the crane based on the ship coordinate system, adding the inertial force to the generalized forces of each degree of freedom, and analyzing the dynamic model of the boom movement and load swing; An adaptive sliding mode anti-sway control system is constructed by combining an RBF neural network model with sliding mode control. Based on the adaptive sliding mode anti-sway control system, the persistent uncertain upper bound disturbance of the underactuated shipborne crane load is analyzed, and the persistent disturbance with the uncertain upper bound is approximated. The dynamic model is simplified, and a nonlinear disturbance observer is used to observe the total disturbance; and a backstepping sliding mode control is introduced to compensate for the observation error of the nonlinear disturbance observer. comparing the adaptive sliding mode anti-sway control and the LQR control, judging the position of the load based on the comparison result and the observation of the nonlinear disturbance observer, and switching between the LQR control and the adaptive sliding mode anti-sway control according to the judgment result; When building a kinetic model, include: A dynamic model is established in generalized coordinates based on the Lagrange equation of the relative kinetic energy of the crane. The dynamic model is derived from the following formula: ； in represents the inertia matrix, represents the Coriolis centripetal matrix, represents the input control vector, represents the vector of boom inertia force and load inertia force, f represents external force disturbance, represents the acceleration in generalized coordinates, represents the generalized velocity of generalized coordinates; determining, according to the dynamic model, that the shipborne crane is an underactuated shipborne crane; When constructing an adaptive sliding mode anti-sway control based on the combination of the RBF neural network model and the sliding mode control, it includes: The adaptive sliding mode anti-sway control divides the nominal into several sub-nominals, defines a sliding mode surface for each sub-nominal, establishes a hierarchical sliding mode controller on each sub-nominal sliding mode surface, and establishes a lumped sliding mode compensator on the last sliding mode surface. A Lyapunov function is used to determine if any state deviates from the sub-sliding mode surface, and a total switching control input is used to pull it back to the deviated sliding mode surface. An RBF neural network model is used to approximate disturbances to the hierarchical sliding mode controller and the lumped sliding mode compensator. When simplifying the kinetic model, it includes: Create a new state vector and transform the dynamic model: ； ； ； Among them, η represents the generalized coordinate, Φ represents the angle of the boom, and α represents the ship's rotation around x. s The axis rotation angle, L is the cable length, θ is the load relative to z s The swing of the axis, η1, η2 and η3 represent the generalized coordinates, [η1, η2, η3] T represents the vector transpose, represents the inertia matrix, represents the Coriolis centripetal matrix, represents the gravity vector, represents the input control vector, f represents the external force disturbance, represents the generalized acceleration of generalized coordinates, represents the generalized velocity of generalized coordinates; When a nonlinear disturbance observer is used to observe the total disturbance and a backstepping sliding mode control is introduced to compensate for the observation error of the nonlinear disturbance observer, the method includes: defining an observation error using a nonlinear disturbance observer, setting dynamic characteristics relative to the nonlinear disturbance observer, and determining an error dynamic equation of the nonlinear disturbance observer; Establishing a matrix to converge the error dynamic equation exponentially and converting the observed disturbance into the control quantity of the corresponding input channel; Define the output error vector and perform derivative, adopt Lyapunov function and adaptive feedback control law to obtain the law, and obtain the input according to Lyapunov function; When determining the position of the load, include: Counting all historical load positions and establishing a position set, and when a load position exists in the position set, determining not to correct the load position, and determining the load position as the target position; When the position of the load does not exist in the position set, it is determined that the position of the load should be corrected, and the position set is aggregated using an aggregation algorithm; Taking the location set as the data set to be aggregated; Determine the expected number of clusters k as 2 and initialize the parameters of the Gaussian distribution; Calculating the probability that each data in the to-be-aggregated data set belongs to each Gaussian distribution to obtain a responsibility value; obtaining a data set according to the responsibility value, and using the data set as a close set; Taking the average of the positions of all historical loads in the similar set as an optimization factor, and taking the product of the optimization factor and the position of the load as the target position; When switching between the LQR control and the adaptive sliding mode anti-sway control according to the judgment result, the method includes: Pre-set the target position distance threshold; When the target position is greater than or equal to the target position distance threshold, the adaptive sliding mode anti-sway control is adopted; When the target position is less than the target position distance threshold, the LQR control is adopted.

2. The anti-sway control method for a shipborne crane according to claim 1, characterized in that: When establishing a ship coordinate system and calculating the relative kinetic energy of the crane based on the ship coordinate system, it includes: The center of mass of the ship is used as the origin of the ship coordinate system, and the bow direction is set as x s The positive direction of the axis is set as z, with the vertical deck upward direction s The positive direction of the axis, x G Axis and x s Axis parallel, z G The axis is perpendicular to the ground; Establish the ship coordinate system , establish an inertial coordinate system ; Assuming the hook and the load are a point mass, and the boom has a uniform mass distribution, the cable is strained and the load is not positioned higher than the highest point of the boom, measure the angle of the boom and the load relative to the z s The swing angle of the shaft; The relative kinetic energy of the crane is given by the following formula: ； Where E represents the relative kinetic energy of the crane, m represents the mass of the boom, and P L Indicates the length of the boom, represents the generalized speed of the boom, m p Indicates the mass of the load, Represents the row vector from the origin to the payload, dp p Represents the column vector from the origin to the payload.

3. The anti-sway control method for a shipborne crane according to claim 2, characterized in that: When the inertia force is added to the generalized forces of each degree of freedom, the dynamic model of boom motion and load swing is analyzed, including: The generalized forces include boom gravity, load gravity, boom inertia force and load inertia force; The pitch angle of the boom, the cable length, and the load swing angle are set as generalized coordinates, and the control torque of the boom pitch and the cable tension are set as control inputs to construct the dynamic model.

4. A shipborne crane anti-sway control system, used to apply the shipborne crane anti-sway control method according to any one of claims 1 to 3, characterized in that: include: a model building unit for establishing a ship coordinate system and calculating the relative kinetic energy of the crane based on the ship coordinate system, adding the inertial force to the generalized force of each degree of freedom, and analyzing and obtaining a dynamic model of the boom movement and load swing; An adaptive sliding mode unit is used to construct an adaptive sliding mode anti-sway control based on a combination of an RBF neural network model and a sliding mode control, and to analyze a persistent uncertain upper bound disturbance of the underactuated shipborne crane load based on the adaptive sliding mode anti-sway control, thereby approximating the persistent disturbance of the uncertain upper bound; Inversion unit, simplifies the dynamic model and uses nonlinear disturbance observer to observe the total disturbance; introduces Backstepping sliding mode control compensates for the observation error of the nonlinear disturbance observer; A switching unit compares the adaptive sliding mode anti-sway control and the LQR control, judges the position of the load based on the comparison result and the observation of the nonlinear disturbance observer, and switches between the LQR control and the adaptive sliding mode anti-sway control according to the judgment result.

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