Displacement precision control method for ship block attitude adjusting mechanism

Abstract

The invention discloses a displacement precision control method for a ship block attitude adjusting mechanism. The method comprises the following steps: S1, constructing a mathematical model of the ship block attitude adjusting mechanism; s2, constructing a sliding mode controller model; and S3, constructing an improved sliding mode controller model added with the RBF neural network. The method has the beneficial effects that according to ship block attitude adjusting equipment, aiming at the difficulties of poor position tracking precision and slow control response speed caused by interference of mechanical vibration and ship block gravity center shift of a motion shaft in a jacking direction, firstly, a mathematical model of a motion shaft in a Z-axis direction is established in combination with motor structural characteristics and a mechanism transmission form; secondly, optimizing the tracking precision of the motion axis by adopting a trajectory tracking control method based on a sliding mode control method; an RBF neural network method is adopted to eliminate the problem that the sliding mode method is easy to buffet; by designing an RBF neural network sliding mode controller, the position tracking precision and the control response speed of the motion axis in the jacking direction are improved.

Description

Technical Field

[0001] The invention relates to a method for controlling the displacement accuracy of a ship section attitude adjustment mechanism, and belongs to the technical field of ship section attitude adjustment control. Background Art

[0002] In recent years, there have been many innovations and advances in the research field of control systems for ship section attitude adjustment and docking technology. The ship section attitude adjustment equipment is a six-degree-of-freedom parallel mechanism that controls multiple attitude adjustment trolleys to jointly support the moving ship section, and is a type of split-type attitude adjustment equipment. The split-type ship section attitude adjustment system has the problems of low attitude adjustment accuracy and slow response speed, which is essentially the problem of low motion axis position control accuracy and slow response speed.

[0003] As a very effective nonlinear control method, sliding mode control has shown significant advantages in the field of motion control of attitude adjustment mechanisms. Its characteristics are that it can be customized according to actual needs and can show extremely high robustness to parameter changes and external disturbances of the control object. As a control strategy that does not rely on an accurate model, the implementation of sliding mode control is relatively simple. Compared with other control algorithms, it is more adaptable to complex and changeable working environments. In recent years, research on sliding mode control of motion systems has made significant progress. For example, fuzzy sliding mode adaptive control has been studied for AC position servo systems, mainly focusing on suppressing chattering, but the improvement of dynamic performance is limited; and the application of sliding mode control to a type of planar parallel mechanism for dynamic and control analysis is proposed. Simulation and experimental results show that the sliding mode controller is superior to most controller models in performance. However, the discontinuous switching characteristics of traditional sliding mode control can cause chattering. This chattering not only reduces control accuracy and increases energy consumption, but also causes system oscillation or instability in severe cases, causing damage to controller components. How to reduce the defect of sliding mode control itself that is prone to chattering has become a difficulty in the application research of sliding mode control. Summary of the invention

[0004] Purpose of the invention: In view of the shortcomings of the prior art, the present invention provides a method for controlling the displacement accuracy of a ship section attitude adjustment mechanism. The present invention controls the ship section attitude adjustment mechanism by designing a traditional sliding mode controller, and adjusts the switching gain in the sliding mode controller by an RBF neural network method, thereby improving the position tracking accuracy and control response speed of the moving axis in the jacking direction of the ship.

[0005] Technical solution: A method for controlling the displacement accuracy of a ship section attitude adjustment mechanism, comprising the following steps:

[0006] S1: Construct the mathematical model of the ship's overall section attitude adjustment mechanism;

[0007] S2: Construct sliding mode controller model;

[0008] S3: Construct an improved sliding mode controller model by adding RBF neural network.

[0009] Preferably, the S1 specifically includes:

[0010] S101: Construct servo drive mathematical model;

[0011] S102: Construct a mechanical transmission mathematical model and obtain a single-axis overall transfer function of the ship's overall section attitude adjustment mechanism.

[0012] Preferably, the S101 is specifically:

[0013] The stator and rotor coordinate systems have the following transformation relationship:

[0014]

[0015] Among them, i A 、i B 、i C is the stator three-phase current; C is the coefficient; i o is the zero-sequence current; i d with i q Indicates the d-axis and q-axis currents;

[0016] This transformation directly transforms the quantity in the rotor two-phase rotating coordinate system into the stator three-phase stationary coordinate system, thus we can get:

[0017]

[0018] Where: i d with i q represents the d-axis and q-axis currents; i A 、i B 、i C is the stator three-phase current; i o is the zero-sequence current, the induced electromotive force of the stator winding can be regarded as the back electromotive force, the stator applied voltage u and the back electromotive force e and the stator resistance voltage drop R is Phase balance, thus the voltage balance equation of phase A is written as u A :

[0019]

[0020] Where: A is the flux linkage of phase A; R s is the stator phase resistance;

[0021] Substituting the variables in formula (1) into formula (3), we can get:

[0022]

[0023] Where: 0is the rated magnetic flux; u 0 Stator external rated voltage;

[0024] After sorting, we get:

[0025]

[0026] Where: d With q represents the magnetic flux between the d-axis and the q-axis; R s represents the stator phase resistance;

[0027] The above formula should hold true for any value of the angle θ, so the stator voltage equation can be obtained:

[0028]

[0029] Among them: U d with U q represents the d-axis and q-axis voltages, and:

[0030]

[0031] Where: L d With L q represents the d-axis and q-axis inductance, Ψt is the armature flux;

[0032] The electromagnetic torque formula of the motor is:

[0033] T em =P n (ψ d i q -ψ q i d ) (8)

[0034] Where: P n is the number of motor pole pairs,

[0035] In summary, the stator voltage equation of the permanent magnet synchronous motor in the rotating coordinate system can be obtained:

[0036]

[0037] Where: P is the differential operator; Ψ r Represents the flux linkage of the permanent magnet fundamental wave excitation magnetic field through the stator winding; ω r is the electronic angular speed;

[0038] Stator flux equation:

[0039]

[0040] Electromagnetic torque equation:

[0041] Tem =P n (ψ d i q -ψ q i d )=P n [ψ r i q +(L d -L q )i d i q ] (11)

[0042] Motor motion equation:

[0043]

[0044] Where: T em is the electromagnetic torque; T L is the load torque; B is the viscous friction coefficient; ω is the rotor electrical angular velocity; J is the total moment of inertia,

[0045] Since the stator quadrature-axis current component and the rotor flux determine the motor torque, a rotor flux orientation method is used in the permanent magnet synchronous motor to control the permanent magnet synchronous motor.

[0046] Preferably, the S102 is specifically:

[0047] Motor speedω:

[0048]

[0049] Motor shaft rotation angle θ:

[0050] q=K 1 x 0 (14)

[0051] Where: K 1 is the reduction ratio coefficient of the reducer; x 0 is the displacement of the end of the screw (mm),

[0052] Let J be the total moment of inertia converted to the motor shaft:

[0053]

[0054] Where: J 1 is the moment of inertia on the motor rotor shaft; J2 is the moment of inertia on the reducer output shaft; i is the reduction ratio of the reducer; P is the pitch of the ball screw; m is the torque required to move the ship section,

[0055] Let the load torque ML(S) = 0, and the transfer function under the input of current Xi(S) can be obtained as follows:

[0056]

[0057] Let the current Xi(S) = 0, the input can get the load torque ML(S) = 0. The transfer function under the input is:

[0058]

[0059] Where: Ka is the transfer function of the given link; Kb is the transfer function of the amplification link; Kc is the transfer function of the feedback link including the detection device, k m is the moment constant, L is the wheelbase, s is the displacement of the motion axis, and R is the radius of the worm gear;

[0060] When the inductance L of the armature winding is ignored, the transfer function under the load torque input ML(S) = 0 is:

[0061]

[0062] Formula (19) is combined with formula (12) and Laplace transform is performed to obtain the transfer function from the control voltage to the displacement of the motion axis in the Z-axis direction of the ship section:

[0063]

[0064] In the formula, ω n is the speed, l 0 is the length of the screw, M is the lifting mass, and B is the coefficient of hysteresis friction.

[0065] Preferably, the S2 specifically includes:

[0066] S201: Sliding mode control condition analysis;

[0067] S202: Construct a sliding mode controller model.

[0068] Preferably, the S201 is specifically:

[0069] Assume the sliding surface is S, and the sufficient condition for the existence of sliding is:

[0070]

[0071] Formula (21) can also be expressed as:

[0072]

[0073] Formula (22) is the existence condition of sliding control, which can ensure that the system motion will reach the sliding surface within a finite time in any initial state.

[0074] The reachability condition of sliding mode control is:

[0075]

[0076] Formula (23) can be equivalent to:

[0077]

[0078] Sliding mode control needs to ensure that the moving point of the system moves stably on the sliding surface. The stability of sliding mode control is judged according to the Lyapunov criterion, and the Lyapunov function is designed:

[0079]

[0080] Derivative of (25):

[0081]

[0082] From the reachability of the system, ss<0, we can get V<0. The reachability of sliding mode control can ensure the stability of the sliding mode motion of the system.

[0083] Preferably, the S202 is specifically:

[0084] It is known that the dynamic system of the Z-axis motion axis of the ship section docking equipment is a second-order system. The transfer function (20) is converted into the state equation form:

[0085]

[0086] y is the output of system (27), i.e., the final motion axis position, x 1 is the motor rotation angle output, x 2 is the displacement output of the mechanical structure, d(t) is the disturbance caused by the center of gravity offset of the ship during the attitude adjustment process, d(t) is the upper limit of the disturbance, let |dt|≤D, u is the voltage signal input, and the expressions of f(x), g(x), and d(t) are as follows:

[0087]

[0088] Design a control input u so that the output y tracks the reference trajectory y d , assuming the ideal displacement of the motion axis is θ d , then e = θ - θ d is the tracking error,

[0089] The designed sliding surface s is:

[0090]

[0091] Among them, c>0 then:

[0092]

[0093] f(x) and g(x) are known, and the designed sliding mode control rate is:

[0094]

[0095] To verify the stability of the system, the Lyapunov function is taken as:

[0096]

[0097] but:

[0098]

[0099] Where f is f(x), g is g(x), and d is d(t); η≥D,η=D+η 0 ,η 0 >0, η is the actual interference, η 0 is the maximum interference, D is the interference boundary, then:

[0100]

[0101] According to the LaSalle invariance principle, the closed-loop system is asymptotically stable. In order to alleviate the designed system chattering, the function sat(s) is used in the controller instead of the function sgn(s), that is:

[0102]

[0103] Where Δ is the boundary layer,

[0104] Therefore, the control input of the system with mechanical vibration uncertainty is designed as:

[0105]

[0106] Preferably, S3 is specifically:

[0107] Since the RBF network has universal approximation characteristics, the network method of using RBF neural network to approximate f(x) is:

[0108]

[0109] Among them, x is the network input; j is the jth node of the network hidden layer, the network's Gaussian basis function output, w* is the weight of the ideal network approximating f and g respectively; ε is the approximation error of the network, |ε|≤ε N , b j is the function width parameter, h(x) is the nonlinear activation function, N is the number of output nodes, c j is the center point.

[0110] The network input is x = [x 1x 2 ], the network output is:

[0111]

[0112] Then the input control rate is:

[0113]

[0114] Substituting the input control rate formula (40) into formula (30), we get:

[0115]

[0116] in,

[0117] Define the Lyapunov function as:

[0118]

[0119] Among them, γ>0, γ is a stability parameter,

[0120] From equation (40) and equation (41), we can get

[0121]

[0122] Take the adaptive rate as

[0123]

[0124] but:

[0125]

[0126] Since the RBF neural network approximation error ε is a very small positive real number, we take η≥Ε N +D+η 0 , η 0 >0

[0127]

[0128] When L≡0, according to the Lasalle invariant set principle, the closed-loop system is asymptotically stable, so the RBF neural network sliding mode control system is stable.

[0129] Beneficial effects: According to the ship section attitude adjustment equipment, the present invention aims at the difficulty that the moving axis in the jacking direction has poor position tracking accuracy and slow control response speed under the interference of mechanical vibration and the center of gravity offset of the ship section. Firstly, a mathematical model of the moving axis in the Z-axis direction is established by combining the motor structure characteristics and the mechanism transmission form, and then a trajectory tracking control method based on the sliding mode control method is adopted to optimize the tracking accuracy of the moving axis; and an RBF neural network method is adopted to eliminate the problem that the sliding mode method itself is prone to chattering; by designing an RBF neural network sliding mode controller, the position tracking accuracy and control response speed of the moving axis in the jacking direction are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0130] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0131] Figure 1 is a flow chart of the method of the present invention;

[0132] Figure 2 This is a schematic diagram of the Z-axis transmission structure of the posture adjustment vehicle of the present invention;

[0133] Figure 3 It is the structure diagram of RBF neural network;

[0134] Figure 4 (a, b) are the traditional sliding mode control diagram and RBF neural network control tracking curve diagram respectively;

[0135] Figure 5 (a, b) are the traditional sliding mode control diagram and RBF neural network control tracking error diagram respectively;

[0136] Figure 6 (a, b) are the traditional sliding mode control diagram and the RBF neural network control sudden disturbance signal simulation diagram respectively;

[0137] Figure 7 (a, b) are the traditional sliding mode control diagram and the RBF neural network control output simulation tracking diagram of car No. 1 respectively;

[0138] Figure 8 (a, b) are the traditional sliding mode control diagram and the RBF neural network control output simulation tracking diagram of car No. 2 respectively;

[0139] Fig. 9 (a, b) are the traditional sliding mode control diagram and the RBF neural network control output simulation tracking diagram of car No. 3 respectively;

[0140] Fig.10 (a, b) are the traditional sliding mode control diagram and the RBF neural network controlled output simulation tracking diagram of car No. 4 respectively. DETAILED DESCRIPTION

[0141] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0142] In the description of the present invention, it is necessary to understand that the terms "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating the orientation or position relationship are based on the orientation or position relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention.

[0143] In the present invention, unless otherwise clearly specified and limited, a first feature being "above" or "below" a second feature may include that the first and second features are in direct contact, or may include that the first and second features are not in direct contact but are in contact through another feature between them. Moreover, a first feature being "above", "above" and "above" a second feature includes that the first feature is directly above and obliquely above the second feature, or simply indicates that the first feature is higher in level than the second feature. A first feature being "below", "below" and "below" a second feature includes that the first feature is directly below and obliquely below the second feature, or simply indicates that the first feature is lower in level than the second feature.

[0144] like Figure 1 As shown, a method for controlling the displacement accuracy of a ship section attitude adjustment mechanism comprises the following steps:

[0145] S1: Construct the mathematical model of the ship's overall section attitude adjustment mechanism;

[0146] S101: Construct servo drive mathematical model;

[0147] The stator and rotor coordinate systems have the following transformation relationship:

[0148]

[0149] Among them, i A 、i B 、i C is the stator three-phase current; C is the coefficient; i o is the zero-sequence current; id with i q Indicates the d-axis and q-axis currents;

[0150] This transformation directly transforms the quantity in the rotor two-phase rotating coordinate system into the stator three-phase stationary coordinate system, thus we can get:

[0151]

[0152] Where: i d with i q represents the d-axis and q-axis currents; i A 、i B 、i C is the stator three-phase current; i o is the zero-sequence current, the induced electromotive force of the stator winding can be regarded as the back electromotive force, the stator applied voltage u and the back electromotive force e and the stator resistance voltage drop R is Phase balance, thus the voltage balance equation of phase A is written as u A :

[0153]

[0154] Where: A is the flux linkage of phase A; R s is the stator phase resistance;

[0155] Substituting the variables in formula (1) into formula (3), we can get:

[0156]

[0157] Where: 0 is the rated magnetic flux; u 0 Stator external rated voltage;

[0158] After sorting, we get:

[0159]

[0160] Where: d With q represents the magnetic flux between the d-axis and the q-axis; R s represents the stator phase resistance;

[0161] The above formula should hold true for any value of the angle θ, so the stator voltage equation can be obtained:

[0162]

[0163] Among them: U d with U q represents the d-axis and q-axis voltages, and:

[0164]

[0165] Where: L d With L q represents the d-axis and q-axis inductance, Ψt is the armature flux;

[0166] The electromagnetic torque formula of the motor is:

[0167] T em =P n (ψ d i q -ψ q i d ) (8)

[0168] Where: P n is the number of motor pole pairs,

[0169] In summary, the stator voltage equation of the permanent magnet synchronous motor in the rotating coordinate system can be obtained:

[0170]

[0171] Where: P is the differential operator; Ψ r Represents the flux linkage of the permanent magnet fundamental wave excitation magnetic field through the stator winding; ω r is the electronic angular speed;

[0172] Stator flux equation:

[0173]

[0174] Electromagnetic torque equation:

[0175] T em =P n (ψ d i q -ψ q i d )=P n [ψ r i q +(L d -L q )i d i q ] (11)

[0176] Motor motion equation:

[0177]

[0178] Where: T em is the electromagnetic torque; T L is the load torque; B is the viscous friction coefficient; ω is the rotor electrical angular velocity; J is the total moment of inertia,

[0179] Since the stator quadrature-axis current component and the rotor flux determine the motor torque, a rotor flux orientation method is used in the permanent magnet synchronous motor to control the permanent magnet synchronous motor.

[0180] S102: construct a mechanical transmission mathematical model and obtain a single-axis overall transfer function of the ship's overall section attitude adjustment mechanism;

[0181] In this embodiment, the Z-axis transmission of the posture adjustment mechanism is composed of a reducer, a lead screw, and a worm gear, and its structural diagram is as follows: Figure 2 As shown in the figure, since the mechanical mechanism of the Z-axis motion axis is mainly a lead screw and a worm gear, the viscous damping torque with each motion pair is ignored; in order to reduce the complexity of the controller calculation, the lead screw is regarded as a rigid body motion, so the elastic torque with the motion axis is ignored;

[0182] Motor speedω:

[0183]

[0184] Motor shaft rotation angle θ:

[0185] q=K 1 x 0 (14)

[0186] Where: K 1 is the reduction ratio coefficient of the reducer; x 0 is the displacement of the end of the screw (mm),

[0187] Let J be the total moment of inertia converted to the motor shaft:

[0188]

[0189] Where: J 1 is the moment of inertia on the motor rotor shaft; J2 is the moment of inertia on the reducer output shaft; i is the reduction ratio of the reducer; P is the pitch of the ball screw; m is the torque required to move the ship section,

[0190] Let the load torque ML(S) = 0, and the transfer function under the input of current Xi(S) can be obtained as follows:

[0191]

[0192] Let the current Xi(S) = 0, the input can get the load torque ML(S) = 0. The transfer function under the input is:

[0193]

[0194] Where: Ka is the transfer function of the given link; Kb is the transfer function of the amplification link; Kc is the transfer function of the feedback link including the detection device, km is the moment constant, L is the wheelbase, s is the displacement of the motion axis, and R is the radius of the worm gear;

[0195] When the inductance L of the armature winding is ignored, the transfer function under the load torque input ML(S) = 0 is:

[0196]

[0197] Formula (19) is combined with formula (12) and Laplace transform is performed to obtain the transfer function from the control voltage to the displacement of the motion axis in the Z-axis direction of the ship section:

[0198]

[0199] In the formula, ω n is the speed, l 0 is the length of the screw, M is the lifting mass, and B is the coefficient of hysteresis friction.

[0200] Among them, the parameters of the Z-axis transmission part are shown in Table 1:

[0201] Table 1 Transmission part structural parameters

[0202] Parameter name Parameter Value Screw transmission ratio 40 Screw lead 10mm Reducer reduction ratio 40

[0203] S2: Construct sliding mode controller model;

[0204] S201: Sliding mode control condition analysis;

[0205] Assume the sliding surface is S, and the sufficient condition for the existence of sliding is:

[0206]

[0207] Formula (21) can also be expressed as:

[0208]

[0209] Formula (22) is the existence condition of sliding control, which can ensure that the system motion will reach the sliding surface within a finite time in any initial state.

[0210] The reachability condition of sliding mode control is:

[0211]

[0212] Formula (23) can be equivalent to:

[0213]

[0214] Sliding mode control needs to ensure that the moving point of the system moves stably on the sliding surface. The stability of sliding mode control is judged according to the Lyapunov criterion, and the Lyapunov function is designed:

[0215]

[0216] Derivative of (25):

[0217]

[0218] From the reachability of the system, ss<0, we can get V<0. The reachability of sliding mode control can ensure the stability of the sliding mode motion of the system.

[0219] S202: construct a sliding mode controller model;

[0220] It is known that the dynamic system of the Z-axis motion axis of the ship section docking equipment is a second-order system. The transfer function (20) is converted into the state equation form:

[0221]

[0222] y is the output of system (27), i.e., the final motion axis position, x 1 is the motor rotation angle output, x 2 is the displacement output of the mechanical structure, d(t) is the disturbance caused by the center of gravity offset of the ship during the attitude adjustment process, d(t) is the upper limit of the disturbance, let |dt|≤D, u is the voltage signal input, and the expressions of f(x), g(x), and d(t) are as follows:

[0223]

[0224] Design a control input u so that the output y tracks the reference trajectory y d , assuming the ideal displacement of the motion axis is θ d , then e = θ - θ d is the tracking error,

[0225] The designed sliding surface s is:

[0226]

[0227] Among them, c>0 then:

[0228]

[0229] f(x) and g(x) are known, and the designed sliding mode control rate is:

[0230]

[0231] To verify the stability of the system, the Lyapunov function is taken as:

[0232]

[0233] but:

[0234]

[0235] Where f is f(x), g is g(x), and d is d(t); η≥D,η=D+η 0 ,η 0 >0, η is the actual interference, η 0 is the maximum interference, D is the interference boundary, then:

[0236]

[0237] According to the LaSalle invariance principle, the closed-loop system is asymptotically stable. In order to alleviate the designed system chattering, the function sat(s) is used in the controller instead of the function sgn(s), that is:

[0238]

[0239] Where Δ is the boundary layer,

[0240] Therefore, the control input of the system with mechanical vibration uncertainty is designed as:

[0241]

[0243] like Figure 3 As shown, S3: Construct the sliding mode controller model after adding the improved RBF neural network:

[0244] Since the RBF network has universal approximation characteristics, the network method of using RBF neural network to approximate f(x) is:

[0245]

[0246] Among them, x is the network input; j is the jth node of the network hidden layer, the network's Gaussian basis function output, w* is the weight of the ideal network approximating f and g respectively; ε is the approximation error of the network, |ε|≤ε N , b j is the function width parameter, h(x) is the nonlinear activation function, N is the number of output nodes, c j is the center point.

[0247] The network input is x = [x 1 x 2 ], the network output is:

[0248]

[0249] Then the input control rate is:

[0250]

[0251] Substituting the input control rate formula (40) into formula (30), we get:

[0252]

[0253] in,

[0254] Define the Lyapunov function as:

[0255]

[0256] Among them, γ>0, γ is a stability parameter,

[0257] From equation (40) and equation (41), we get

[0258]

[0259] Take the adaptive rate as

[0260]

[0261] but:

[0262]

[0263] Since the RBF neural network approximation error ε is a very small positive real number, we take η≥Ε N +D+η 0 , η 0 >0

[0264]

[0265] When L≡0, according to the Lasalle invariant set principle, the closed-loop system is asymptotically stable, so the RBF neural network sliding mode control system is stable.

[0266] Simulation analysis of the improved sliding mode controller:

[0267] The input module is the expected displacement of the ship section docking equipment, and the controlled object is controlled through the control rate control module. Among them, the simulation parameters involved in the RBF network and the sliding mode control method are simulated multiple times with different values, and the simulation parameter value finally selected after optimization is: sliding mode switching function c = 5. The network input is taken The RBF neural network structure is 2-5-1, c i =[-1.0 -0.5 0 0.5 1.0], width b j=0.50, and the network weight is 0.10. The control system parameters of the Z-axis of the ship's general section attitude adjustment trolley are brought in to perform simulation in the Z-axis direction.

[0268] At the beginning of the simulation, a sinusoidal signal with an amplitude of 10 and 1° and a frequency of 1Hz was first input for different controllers, and a step interference signal with an interference of 2 was added to simulate the traditional sliding mode control and the neural network sliding mode control respectively. The position tracking accuracy of the two was compared, and the tracking curve was obtained as shown in the figure. Figure 4 As shown in (a, b), the tracking error is Figure 5 As shown in (a, b).

[0269] Depend on Figure 4 (a) It can be seen that under the traditional sliding mode control, the output curve is basically consistent with the planned motion trajectory curve, indicating that the traditional sliding mode control can meet the predetermined trajectory tracking task. Figure 5 The real-time tracking error of (a) reflects that the traditional sliding mode control has more error fluctuations during the control process, reflecting the defect that the traditional sliding mode control is prone to chattering. Figure 4 (b) is the tracking curve under RBF neural network sliding mode control, which shows that RBF neural network sliding mode control can also meet the predetermined trajectory tracking task; Figure 5 (b) shows that the error change frequency of RBF neural network sliding mode control is low, and it only appears at the point where the velocity and acceleration direction of the moving axis change, and the average tracking error is smaller than that of traditional sliding mode control. Based on the above phenomenon, it can be concluded that both traditional sliding mode control and RBF neural network sliding mode control can well complete the task of tracking the predetermined trajectory of the moving axis. However, when the moving axis tracks the target trajectory, traditional sliding mode control will cause more jitter. RBF neural network sliding mode control will reduce the jitter phenomenon. At the same time, under RBF neural network sliding mode control, the average tracking error of the moving axis is smaller than that of traditional sliding mode control. Therefore, in terms of the effect of moving axis trajectory tracking, RBF neural network sliding mode control is better than traditional sliding mode control.

[0270] For the sudden change of control signal, the control response speed of the simulation controller is simulated. Similarly, a step signal with an amplitude of 10 is input into the system, and a step interference signal with an interference of 2 is added. The traditional sliding mode control and the neural network sliding mode control are simulated respectively, and the speed of control signal response when the control signal suddenly changes is compared. The step response curve is as follows Figure 6 As shown in (a, b).

[0271] Comparing the step response of traditional sliding mode control and RBF neural network sliding mode control, we can get the following results: Figure 6(a, b) Simulation results. The following conclusion can be drawn from the figure: both can quickly reach the steady-state position. However, the local enlarged figure shows that when approaching the steady-state position, the traditional sliding mode control will produce a large jitter. The maximum deviation is 4mm. The RBF neural network sliding mode control will quickly return after exceeding the steady-state control, with a maximum deviation of 0.5mm. And by Figure 6 (a) It can be seen that the traditional sliding mode reaches the steady-state position at 0.23 seconds. The RBF neural network sliding mode control reaches the steady-state position at 0.17s. The above simulation results show that when disturbances occur, the RBF neural network sliding mode control can respond faster than the traditional sliding mode control. It can reach the steady-state position in a short time. The RBF neural network sliding mode control can approach the steady-state position more smoothly. The traditional sliding mode control will produce jitter. Based on the above simulation results, it is proved that the designed RBF neural network sliding mode control has better response speed and control stability than the traditional sliding mode control when random interference occurs.

[0272] Simulation analysis of attitude adjustment displacement accuracy control:

[0273] Taking the motion axis along the Z axis as an example, in an attitude adjustment task, the Z axis motion axes of four small vehicles need to cooperate to adjust the attitude of the ship section. Input the motion axis displacement control command calculated by the ship section attitude adjustment motion planning software, and the traditional sliding mode controller and the RBF neural network sliding mode controller control the Z axis motion axis to track the displacement signal. The actual motion curve and target motion curve of the motion axis are obtained as follows Figure 7 (a, b)— Fig.10 (a, b) are shown in Table 2. The performance parameters of different controllers are shown in Table 2.

[0274] Table 2 Performance parameters of different controllers

[0275] Performance parameters Traditional sliding mode control RBF neural network sliding mode control Maximum error (mm) 0.16 0.12 Average error (mm) 0.13 0.09 Maximum response time (s) 0.36 0.28 Average response time (s) 0.32 0.23

[0276] The following conclusions can be drawn from the simulation experiment: During the posture adjustment process, the Z-axis motion axis can complete the task of tracking the preset trajectory of the motion axis under both the traditional sliding mode control and the RBF neural network sliding mode control. The average position error is controlled within 0.15 mm, and the control response time is within 0.3 s.

[0277] In terms of control response speed, traditional sliding mode control responds slowly, with an average response speed of 0.32s. When encountering a control signal of direction change, the direction will change only after exceeding the predetermined position. The RBF neural network control response speed is extremely fast, with an average response time of 0.23s. Compared with traditional sliding mode control, the control response time is improved by 28.125%. When encountering a control signal of sudden change in direction, it can respond in time.

[0278] In terms of displacement tracking accuracy, the displacement tracking accuracy of traditional sliding mode control is low, with an average tracking error of 0.13mm. The average tracking error of RBF neural network sliding mode control is 0.09mm, which is 30.76% higher than that of traditional sliding mode control.

[0279] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0280] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for controlling displacement accuracy of a ship section attitude adjustment mechanism, characterized in that: The following steps are involved: S1: Construct the mathematical model of the ship's overall section attitude adjustment mechanism; S2: Construct sliding mode controller model; S3: Construct an improved sliding mode controller model by adding RBF neural network.

2. The method for controlling displacement accuracy of a ship section attitude adjustment mechanism according to claim 1 is characterized in that: The S1 specifically includes: S101: Construct servo drive mathematical model; S102: Construct a mechanical transmission mathematical model and obtain a single-axis overall transfer function of the ship's overall section attitude adjustment mechanism.

3. The method for controlling displacement accuracy of a ship section attitude adjustment mechanism according to claim 2 is characterized in that: The S101 is specifically: The stator and rotor coordinate systems have the following transformation relationship: Among them, i A 、i B 、i C is the stator three-phase current; C is the coefficient; i o is the zero-sequence current; i d with i q Indicates d-axis and q-axis currents; This transformation directly transforms the quantity in the rotor two-phase rotating coordinate system into the stator three-phase stationary coordinate system, thus we can get: Where: i d with i q represents the d-axis and q-axis currents; i A 、i B 、i C is the stator three-phase current; i o is the zero-sequence current, the induced electromotive force of the stator winding can be regarded as the back electromotive force, the stator applied voltage u and the back electromotive force e and the stator resistance voltage drop R is Phase balance, thus the voltage balance equation of phase A is written as u A : Where: A is the flux linkage of phase A; R s is the stator phase resistance; Substituting the variables in formula (1) into formula (3), we can get: Where: Ψ0 is the rated flux; u0 is the rated voltage applied to the stator; After sorting, we get: Where: d With q represents the magnetic flux between the d-axis and the q-axis; R s represents the stator phase resistance; The above formula should hold true for any value of the angle θ, so the stator voltage equation can be obtained: Among them: U d with U q represents the d-axis and q-axis voltages, and: Where: L d With L q represents the d-axis and q-axis inductance, Ψt is the armature flux; The electromagnetic torque formula of the motor is: T em =P n (ψ d I q -ψ q I d ) (8) Where: P n is the number of motor pole pairs, In summary, the stator voltage equation of the permanent magnet synchronous motor in the rotating coordinate system can be obtained: Where: P is the differential operator; Ψ r Represents the flux linkage of the permanent magnet fundamental wave excitation magnetic field through the stator winding; ω r is the electronic angular speed; Stator flux equation: Electromagnetic torque equation: T em =P n (ψ d i q -ψ q i d )=P n [ψ r i q +(L d -L q )i d i q ] (11) Motor motion equation: Where: T em is the electromagnetic torque; T L is the load torque; B is the viscous friction coefficient; ω is the rotor electrical angular velocity; J is the total moment of inertia, Since the stator quadrature-axis current component and the rotor flux determine the motor torque, a rotor flux orientation method is used in the permanent magnet synchronous motor to control the permanent magnet synchronous motor.

4. The method for controlling displacement accuracy of a ship section attitude adjustment mechanism according to claim 3 is characterized in that: The S102 is specifically: Motor speedω: Motor shaft rotation angle θ: q=K1x0 (14) Where: K1 is the reduction ratio coefficient of the reducer; x0 is the displacement of the end of the screw (mm), Let J be the total moment of inertia converted to the motor shaft: Where: J1 is the moment of inertia on the motor rotor shaft; J2 is the moment of inertia on the reducer output shaft; i is the reduction ratio of the reducer; P is the pitch of the ball screw; m is the torque required to move the ship section, Let the load torque ML(S) = 0, and the transfer function under the input of current Xi(S) can be obtained as follows: Let the current Xi(S) = 0, the input can get the load torque ML(S) = 0. The transfer function under the input is: Where: Ka is the transfer function of the given link; Kb is the transfer function of the amplification link; Kc is the transfer function of the feedback link including the detection device, k m is the moment constant, L is the wheelbase, s is the displacement of the motion axis, and R is the radius of the worm gear; When the inductance L of the armature winding is ignored, the transfer function under the load torque input ML(S) = 0 is: Formula (19) is combined with formula (12) and Laplace transform is performed to obtain the transfer function from the control voltage to the displacement of the motion axis in the Z-axis direction of the ship section: In the formula, ω n is the rotation speed, l0 is the screw length, M is the lifting mass, and B is the coefficient of friction.

5. The method for controlling displacement accuracy of a ship section attitude adjustment mechanism according to claim 4 is characterized in that: The S2 specifically includes: S201: Sliding mode control condition analysis; S202: Construct a sliding mode controller model.

6. The method for controlling displacement accuracy of a ship section attitude adjustment mechanism according to claim 5 is characterized in that: The S201 is specifically: Assume the sliding surface is S, and the sufficient condition for the existence of sliding is: Formula (21) can also be expressed as: Formula (22) is the existence condition of sliding control, which can ensure that the system motion will reach the sliding surface within a finite time in any initial state. The reachability condition of sliding mode control is: Formula (23) can be equivalent to: Sliding mode control needs to ensure that the moving point of the system moves stably on the sliding surface. The stability of sliding mode control is judged according to the Lyapunov criterion, and the Lyapunov function is designed: Derivative of (25): From the reachability of the system, ss<0, we can get V<0. The reachability of sliding mode control can ensure the stability of the sliding mode motion of the system.

7. The method for controlling displacement accuracy of a ship section attitude adjustment mechanism according to claim 6 is characterized by: The S202 is specifically as follows: It is known that the dynamic system of the Z-axis motion axis of the ship section docking equipment is a second-order system. The transfer function (20) is converted into the state equation form: y is the output of system (27), i.e., the final motion axis position, x1 is the motor rotation angle output, x2 is the mechanical structure displacement output, d(t) is the interference caused by the center of gravity offset of the ship during the attitude adjustment process, d(t) is the upper limit of the interference, let |dt|≤D, u is the voltage signal input, and the expressions of f(x), g(x), and d(t) are as follows: Design a control input u so that the output y tracks the reference trajectory y d , assuming the ideal displacement of the motion axis is θ d , then e = θ - θ d is the tracking error, The designed sliding surface s is: Among them, c>0 then: f(x) and g(x) are known, and the designed sliding mode control rate is: To verify the stability of the system, the Lyapunov function is taken as: but: Where f is f(x), g is g(x), and d is d(t); let η≥D, η=D+η0, η0>0, η is the actual interference, η0 is the maximum interference, and D is the interference boundary, then: According to the LaSalle invariance principle, the closed-loop system is asymptotically stable. In order to alleviate the designed system chattering, the function sat(s) is used in the controller instead of the function sgn(s), that is: Where Δ is the boundary layer, Therefore, the control input of the system with mechanical vibration uncertainty is designed as:

8. The method for controlling displacement accuracy of a ship section attitude adjustment mechanism according to claim 7 is characterized in that: The S3 is specifically: Since the RBF network has universal approximation characteristics, the network method of using RBF neural network to approximate f(x) is: f=W *T h(x)+ε (38) Among them, x is the network input; j is the jth node of the network hidden layer, the network's Gaussian basis function output, w* is the weight of the ideal network approximating f and g respectively; ε is the approximation error of the network, |ε|≤ε N , b j is the function width parameter, h(x) is the nonlinear activation function, N is the number of output nodes, c j is the center point; The network input is x = [x1 x2], then the network output is: Then the input control rate is: Substituting the input control rate formula (40) into formula (30), we get: in, Define the Lyapunov function as: Among them, γ>0, γ is a stability parameter, From equation (40) and equation (41), we can get Take the adaptive rate as but: Since the RBF neural network approximation error ε is a very small positive real number, we take η≥Ε N +D+η0,η0>0 When L≡0, according to the Lasalle invariant set principle, the closed-loop system is asymptotically stable, so the RBF neural network sliding mode control system is stable.

Images