An electro-hydraulic actuator simulation method, device, medium and product based on RBF neural network control

Abstract

The application discloses an electro-hydraulic actuator simulation method and device based on RBF neural network control, a medium and a product, and relates to electro-hydraulic actuator simulation. The method comprises the following steps: establishing an electro-hydraulic actuator mathematical model, acquiring important parameters of a plunger pump and a hydraulic cylinder, establishing an electro-hydraulic actuator simulation model, a signal and interference module and an RBF neural network control model in Matlab / Simulink; inputting the position input curve and the interference input of the electro-hydraulic actuator under different working conditions into the RBF neural network control model, realizing permanent magnet synchronous motor speed control, and then obtaining the tracking curve of the electro-hydraulic actuator under different working conditions; and inputting the tracking curve into the electro-hydraulic actuator simulation model to realize the simulation of the electro-hydraulic actuator. The application can realize the tracking of the position signal under different working conditions, fully consider the influence of disturbance and the like, improve the anti-interference performance and tracking accuracy, optimize the control parameters, and then achieve the optimal control effect.

Description

Technical Field

[0001] This application relates to the simulation of electro-hydraulic actuators, and in particular to a simulation method, equipment, medium, and product for electro-hydraulic actuators based on RBF neural network control. Background Technology

[0002] Electro-hydrostatic actuators (EHAs) are devices that convert electrical energy into hydraulic energy and are widely used in industrial automation, aerospace, and other fields. They offer advantages such as fast response, high force density, and high precision, making them widely applicable in applications requiring high precision and responsiveness. However, controlling electro-hydrostatic actuators is a complex problem. Traditional PID (proportional-integral-derivative) controllers struggle to meet the requirements for control accuracy and system dynamic response. To overcome these issues, neural network control technology has been gradually introduced into the control of electro-hydrostatic actuators in recent years. For example, a passive fault-tolerant control for EHAs has been designed using the evolutionary control method, but this method lacks specificity and therefore has only moderate overall control performance. Based on the application of adaptive Kalman filtering for synchronous estimation of system state and faults, state feedback is performed using fault information and state estimation to perform pole placement on the closed-loop system, thereby correcting system errors caused by actuator faults and achieving fault-tolerant control. However, this method does not consider factors such as parameter uncertainty and time-varying parameters, thus leaving room for improvement. The EHA was controlled using a radial basis function (RBF) neural network combined with a robust observer, but the considerations for the selection and optimization of control parameters need to be improved. Summary of the Invention

[0003] The purpose of this application is to provide a simulation method, device, medium, and product for electro-hydraulic actuators based on RBF neural network control, which can track position signals under different working conditions, fully consider the effects of disturbances, improve anti-interference and tracking accuracy, optimize control parameters, and thus achieve the best control effect.

[0004] To achieve the above objectives, this application provides the following solution:

[0005] In a first aspect, this application provides a simulation method for an electro-hydraulic actuator based on RBF neural network control, including:

[0006] A mathematical model of an electro-hydraulic actuator is established based on the working principles of a permanent magnet synchronous motor, a piston pump, a hydraulic cylinder, and a load.

[0007] Obtain key parameters of the plunger pump and hydraulic cylinder; these key parameters include at least: total load mass, viscous damping coefficient, hydraulic cylinder piston area, average volume of hydraulic lines and cylinder, equivalent elastic modulus, leakage coefficient, and pump displacement.

[0008] Based on the mathematical model of the electro-hydraulic actuator and the key parameters, a simulation model of the electro-hydraulic actuator was established in Matlab / Simulink.

[0009] Based on the operating conditions of electro-hydraulic actuators, a signal and interference module was built in Matlab / Simulink to obtain the position input curves and interference of the electro-hydraulic actuators under different operating conditions.

[0010] Based on the mathematical model of the electro-hydraulic actuator, an RBF neural network control model was built in Matlab / Simulink;

[0011] The position input curves and disturbances of the electro-hydraulic actuator under different working conditions are input into the RBF neural network control model, and the speed of the permanent magnet synchronous motor is used as the control quantity to realize the speed control of the permanent magnet synchronous motor.

[0012] Based on the speed control results of the permanent magnet synchronous motor, the tracking results of the electro-hydraulic actuator under different operating conditions are obtained to generate tracking curves;

[0013] The tracking curve is input into the electro-hydraulic actuator simulation model to obtain the simulation results of the electro-hydraulic actuator.

[0014] Optionally, a mathematical model of the electro-hydraulic actuator is established based on the working principles of the permanent magnet synchronous motor, piston pump, hydraulic cylinder, and load, including:

[0015] Under the condition of neglecting magnetic circuit saturation, eddy current and hysteresis losses and high-order harmonics of the magnetic field, a mathematical model of the permanent magnet synchronous motor is performed to obtain the permanent magnet synchronous motor model.

[0016] Based on Newton's laws of motion, a mathematical model is performed on the piston pump, hydraulic cylinder, and load to obtain the piston pump and hydraulic cylinder model.

[0017] The mathematical model of the electro-hydraulic actuator is established based on the permanent magnet synchronous motor model and the plunger pump and hydraulic cylinder model.

[0018] Optionally, based on the mathematical model of the electro-hydraulic actuator and the key parameters, a simulation model of the electro-hydraulic actuator is established in Matlab / Simulink, including:

[0019] Based on the mathematical model of the electro-hydraulic actuator, a simulation model of a permanent magnet synchronous motor was established in Matlab / Simulink, and the speed loop and current loop were adjusted by PI control to enable the permanent magnet synchronous motor to complete speed tracking.

[0020] Based on the aforementioned key parameters, build Simulink models of the hydraulic pump, hydraulic cylinder, and load in Matlab / Simulink.

[0021] The simulation model of the electro-hydraulic actuator is obtained based on the simulation model of the permanent magnet synchronous motor and the Simulink models of the hydraulic pump, hydraulic cylinder and load.

[0022] Optionally, referring to the operating conditions of the electro-hydraulic actuator, a signal and interference module can be built in Matlab / Simulink to obtain the position input curves and interferences of the electro-hydraulic actuator under different operating conditions, including:

[0023] Referring to the operating conditions of the electro-hydraulic actuator, a first operating condition is set, and under the first operating condition, the first sinusoidal position curve, nonlinear term and fault phase are tracked to obtain the first position input curve and disturbance of the electro-hydraulic actuator;

[0024] Referring to the operating conditions of the electro-hydraulic actuator, a second operating condition is set, and under the second operating condition, the second sinusoidal position curve, nonlinear term, and fault phase are tracked to obtain the second position input curve and disturbance of the electro-hydraulic actuator; wherein, a hydraulic pipeline blockage fault occurs within the set time period of the second operating condition.

[0025] The first position input curve and interference, as well as the second position input curve and interference, form the position input curve and interference of the electro-hydraulic actuator under different operating conditions.

[0026] Optionally, the first sinusoidal position curve is represented as:

[0027] x 1d =0.05 + 0.02 × sin(0.5πt)(1 - e -0.5t );

[0028] The nonlinear term is expressed as:

[0029] d = 0.35x1x2 + 2×10 -5 ;

[0030] The fault phase is represented as follows:

[0031]

[0032] The second sine position curve is represented as follows:

[0033] x 1d=0.05 + 0.02 × sin(0.5πt)(1 - e -0.5t );

[0034] In the formula, x 1d Given a displacement signal, x1 is the displacement tracking signal output by the actual electro-hydraulic actuator module, x2 is the velocity output by the actual electro-hydraulic actuator module, t is time, f(x) is the fault state, and d is the disturbance state.

[0035] Optionally, the process of obtaining the tracking results of the electro-hydraulic actuator under different operating conditions includes:

[0036] The control parameters were optimized using a particle swarm optimization algorithm, and the optimal tracking result of the electro-hydraulic actuator under different operating conditions was determined based on the optimized control parameters.

[0037] Optionally, the mathematical model of the electro-hydraulic actuator, the signal and interference module, and the RBF neural network control model are integrated to obtain an integrated simulation model of the electro-hydraulic actuator.

[0038] In a second aspect, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the electro-hydraulic actuator simulation method based on RBF neural network control as described above.

[0039] Thirdly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described electro-hydraulic actuator simulation method based on RBF neural network control.

[0040] Fourthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described electro-hydraulic actuator simulation method based on RBF neural network control.

[0041] According to the specific embodiments provided in this application, this application has the following technical effects:

[0042] This application provides a simulation method, device, medium, and product for an electro-hydraulic actuator based on RBF neural network control. By constructing an RBF neural network control model, position signal tracking can be achieved under different operating conditions. By building a signal and interference module in Matlab / Simulink, the position input curves and interference of the electro-hydraulic actuator under different operating conditions are obtained, which can fully consider the effects of disturbances, improve anti-interference capability, and enhance tracking accuracy. Attached Figure Description

[0043] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1 A flowchart illustrating a simulation method for an electro-hydraulic actuator based on RBF neural network control, provided in an embodiment of this application;

[0045] Figure 2 A schematic diagram of a permanent magnet synchronous motor model provided for an embodiment of this application;

[0046] Figure 3 This is a schematic diagram of an EHA structure provided in an embodiment of this application;

[0047] Figure 4 A schematic diagram of a plunger pump and hydraulic cylinder model provided for an embodiment of this application;

[0048] Figure 5 A schematic diagram of an RBF neural network controller module provided in an embodiment of this application;

[0049] Figure 6 This is a schematic diagram of a signal generation module provided in an embodiment of this application;

[0050] Figure 7 This is a schematic diagram of an interference generation module provided in an embodiment of this application;

[0051] Figure 8 This is a schematic diagram of an electro-hydraulic actuator module provided in an embodiment of this application;

[0052] Figure 9 An algorithm flowchart provided for one embodiment of this application;

[0053] Figure 10 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0054] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0055] To make the objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0056] RBF neural networks are a commonly used neural network structure with good approximation and generalization capabilities, and are widely used in function approximation and system modeling. By rationally designing the structure and parameters of RBF neural networks, efficient modeling and control of electro-hydraulic actuator systems can be achieved. Simulation models of electro-hydraulic actuators based on RBF neural networks can more accurately describe the dynamic characteristics and nonlinear behavior of the system, achieving precise modeling and control. Compared with traditional control methods, control based on RBF neural networks has better robustness and adaptability, effectively improving the control performance and stability of the system. Therefore, developing simulation models of electro-hydraulic actuators based on RBF neural network control has significant theoretical and practical application value, and is crucial for improving the control accuracy and response speed of electro-hydraulic actuator systems. Based on this, in an exemplary embodiment, such as... Figure 1 As shown, a simulation method for an electro-hydraulic actuator based on RBF neural network control is provided. This method is executed by a computer device, specifically by a terminal or server alone, or by both a terminal and a server. In this embodiment, the method is described using a server as an example, including steps 100 to 107. Wherein:

[0057] Step 100: Establish a mathematical model of the electro-hydraulic actuator based on the working principles of the permanent magnet synchronous motor, piston pump, hydraulic cylinder and load.

[0058] Step 101: Obtain key parameters for the piston pump and hydraulic cylinder. Specifically, based on actual conditions, obtain key parameters for the hydraulic cylinder such as the total mass of the piston and load, viscous damping coefficient, piston area, hydraulic lines, and average cylinder volume. Also, based on actual conditions, obtain key parameters for the hydraulic pump such as equivalent elastic modulus, leakage coefficient, and pump displacement.

[0059] Step 102: Based on the mathematical model and key parameters of the electro-hydraulic actuator, establish a simulation model of the electro-hydraulic actuator in Matlab / Simulink.

[0060] Step 103: Referring to the operating conditions of the electro-hydraulic actuator, build a signal and interference module in Matlab / Simulink to obtain the position input curve and interference of the electro-hydraulic actuator under different operating conditions.

[0061] Step 104: Based on the mathematical model of the electro-hydraulic actuator, build an RBF neural network control model in Matlab / Simulink. The established RBF neural network control model consists of, for example: Figure 5The RBF neural network controller module shown is implemented.

[0062] Step 105: Using the position input curves and disturbance input RBF neural network control model of the electro-hydraulic actuator under different operating conditions, and with the speed of the permanent magnet synchronous motor as the control variable, the speed control of the permanent magnet synchronous motor is realized.

[0063] Step 106: Based on the speed control results of the permanent magnet synchronous motor, obtain the tracking results of the electro-hydraulic actuator under different operating conditions to generate a tracking curve.

[0064] Step 107: Input the tracking curve into the electro-hydraulic actuator simulation model to obtain the simulation results of the electro-hydraulic actuator.

[0065] In another exemplary embodiment of this application, the implementation process of step 100 may include:

[0066] Step 1001: Perform mathematical modeling of the permanent magnet synchronous motor. The EHA structure used in this embodiment is as follows: Figure 3 As shown, a permanent magnet synchronous motor (PMSM) is a multi-input, strongly coupled, and nonlinear system with highly complex electromagnetic relationships. Therefore, neglecting magnetic circuit saturation, eddy current and hysteresis losses, and higher harmonics of the magnetic field, a mathematical model of the PMSM is performed, resulting in the PMSM model (e.g., ...). Figure 2 (As shown).

[0067] In practical applications, the mathematical equations of the permanent magnet synchronous motor in the dp rotating coordinate system are used as the model of the permanent magnet synchronous motor, and are expressed as follows:

[0068]

[0069] In the formula, t represents time. Let ω be the derivative of (·) with respect to time. θ is the rotational angle of the motor. ω is the rotational speed of the motor. p ψ represents the number of pole pairs of the motor. f denoted by , where J is the flux linkage amplitude of the rotor permanent magnet. J is the rotor moment of inertia. B is the coefficient of viscous friction. T is... L R represents the load torque. s This represents the resistance of the stator armature winding. d i q These represent the stator armature currents, respectively. L is the equivalent stator inductance. d u q These are the stator armature voltages, respectively.

[0070] In electro-hydraulic actuators, the following is adopted: Decoupling control enables speed regulation of the permanent magnet synchronous motor (hereinafter referred to as the motor system). For i dThe desired value is obtained by comparing the actual position feedback value with the desired value and obtaining the ideal speed control signal through the position controller. The ideal speed control signal is then compared with the actual speed output, and the desired value of the stator armature current q-axis component is obtained through the speed controller. A velocity loop is formed. Finally, the current loop uses feedback to make i... d and i q The difference between the actual value and the expected value is calculated, and then the torque is controlled by a current controller. This control process primarily utilizes a control system that includes signal acquisition modules for position, speed, rotational speed, and current; an intelligent power module; a control algorithm module; and a permanent magnet synchronous AC servo motor. The control algorithm module can include a speed loop controller, a current loop controller, a coordinate transformation module, and a space vector pulse width modulation module. The specific working process of the control system is as follows: The three-phase current is acquired and transformed using Clarke and Park transformations to obtain the d-axis component i of the stator armature current in the dq coordinate system. d and q-axis component i q The desired value of the q-axis component of the stator armature current is obtained through the velocity loop. Given the expected value of the d-axis component The error between the d-axis and q-axis components of the stator armature current and their expected values ​​is used as the input to the speed loop to obtain the stator armature voltage u. d and u q After the Park inverse transformation, the various control signals in the IPM are calculated through SVPWM to drive the motor to rotate.

[0071] Step 1002: Based on Newton's laws of motion, mathematically model the piston pump, hydraulic cylinder, and load to obtain the piston pump and hydraulic cylinder model (e.g., Figure 4 For example, the piston pump and hydraulic cylinder model is represented as:

[0072]

[0073] In the formula: P1 and P2 are the pressures at the oil inlet and outlet of the oil cylinder, respectively. A c Let be the piston area of ​​the hydraulic cylinder. M be the total mass of the hydraulic cylinder piston and load. B1 be the viscous damping coefficient. k be the stiffness coefficient. x be the piston rod displacement. This represents the piston rod speed. F is the acceleration of the piston rod. f For load changes caused by faults, F d This refers to load changes caused by nonlinearity.

[0074]

[0075] In the formula: Q1 and Q2 are the hydraulic oil flow rates near the oil port and the oil outlet, respectively. D PThis represents the pump displacement. ω P This refers to the pump speed, i.e., the actual controlled variable. L P P is the external leakage coefficient of the hydraulic pump. a P b These are the pressures at the hydraulic pump's outlet and return ports, respectively. P0 is the pressure at the hydraulic pump's internal leakage port.

[0076] Based on the relationship between flow rate and pressure, we can conclude that:

[0077]

[0078] In the formula: Q i Q0 and Q1 represent the oil inlet and return flow rates of the hydraulic cylinder, respectively. V a This represents the average volume of the hydraulic lines and cylinders. β e L is the equivalent elastic modulus. ep Let be the leakage coefficient. Assume the cylinder and piping are well-sealed and there is no hydraulic oil leakage to the outside of the system, i.e.:

[0079] Q i =Q1,Q0=Q2,L a =L ep +L p .

[0080] Summarized as follows:

[0081]

[0082] In the formula: f and d represent the fault state and the disturbance state, respectively.

[0083] Step 1003: Establish a mathematical model of the electro-hydraulic actuator based on the permanent magnet synchronous motor model and the piston pump and hydraulic cylinder models. This mathematical model of the electro-hydraulic actuator is essentially the state equation of the electro-hydraulic actuator, expressed as:

[0084]

[0085] In the formula, X represents the input state of the electro-hydraulic actuator. Let X be the derivative of the input state X of the electro-hydraulic actuator, Y be the output state of the electro-hydraulic actuator, and A, B2, U, F, Y, and C be state matrices. a The total leakage coefficient is denoted by , and u is the controlled variable, i.e., the given speed of the motor.

[0086] In another exemplary embodiment of this application, the implementation process of step 102 may include:

[0087] Step 1021: Based on the mathematical model of the electro-hydraulic actuator, establish a simulation model of the permanent magnet synchronous motor in Matlab / Simulink, and adjust the speed loop and current loop through PI control to enable the permanent magnet synchronous motor to complete speed tracking.

[0088] Step 1022: Based on the important parameters, build Simulink models of the hydraulic pump, hydraulic cylinder and load in Matlab / Simulink.

[0089] Step 1023: Obtain the electro-hydraulic actuator simulation model based on the permanent magnet synchronous motor simulation model and the Simulink models of the hydraulic pump, hydraulic cylinder and load.

[0090] In another exemplary embodiment of this application, the implementation process of step 103 may include:

[0091] Step 1031: Referring to the electro-hydraulic actuator operating conditions, set the first operating condition, and under the first operating condition, track the first sinusoidal position curve, nonlinear term and fault phase to obtain the first position input curve and disturbance of the electro-hydraulic actuator.

[0092] Step 1032: Referring to the electro-hydraulic actuator operating condition, set a second operating condition, and under the second operating condition, track the second sinusoidal position curve, nonlinear term, and fault phase to obtain the second position input curve and disturbance of the electro-hydraulic actuator. Specifically, a hydraulic line blockage fault occurs within the set time period of the second operating condition.

[0093] Step 1033: The first position input curve and interference, as well as the second position input curve and interference, form the position input curve and interference of the electro-hydraulic actuator under different operating conditions.

[0094] The first sine position curve is represented as:

[0095] x 1d =0.05 + 0.02 × sin(0.5πt)(1 - e -0.5t ).

[0096] The nonlinear term is represented as:

[0097] d = 0.35x1x2 + 2×10 -5 .

[0098] The faulty phase is represented as follows:

[0099]

[0100] The second sine position curve is represented as:

[0101] x 1d =0.05 + 0.02 × sin(0.5πt)(1 - e -0.5t ).

[0102] In the formula, x 1dGiven a displacement signal, x1 is the displacement tracking signal output by the actual electro-hydraulic actuator module, x2 is the velocity output by the actual electro-hydraulic actuator module, t is time, f(x) is the fault state, and d is the disturbance state.

[0103] In another exemplary embodiment of this application, steps 104 and 105 involve building an RBF neural network control module in Matlab / Simulink based on the mathematical model of the electro-hydraulic actuator and the principle of the RBF neural network control method. The RBF neural network control module approximates the disturbance, using the permanent magnet synchronous motor speed as the control variable to achieve speed control of the permanent magnet synchronous motor. Specifically:

[0104] Step 1: Define the position error z1 = x1 - x 1d ,but

[0105] Where z1 is the position error, and x1 is the displacement tracking signal output by the actual electro-hydraulic actuator module. The derivative of z1, The derivative of x1, Given a displacement signal x 1d The derivative of .

[0106] Step 2: Define Lyapunov functions but

[0107] Where V1 is a Lyapunov function. x1 is the derivative of the Lyapunov function V1. x2 is the derivative of x1.

[0108] Step 3, take Right now but

[0109] Step 4: Define Lyapunov functions because get

[0110] Where V2 is a Lyapunov function. The derivative of z2, Let x be the derivative of V², and x³ be the second derivative of x. For x 1d The second derivative of .

[0111] Step 5, take Get it now:

[0112] but

[0113] Where c2 is a control parameter.

[0114] Step 6: Due to the universal approximation property of the RBF neural network, the RBF neural network is used to approximate the fault state f(x). The network algorithm is as follows:

[0115]

[0116] f = W *T h(x)+ξ.

[0117] Where x is the network input, j is the j-th node in the hidden layer of the network, and h = [h j ] T W is the Gaussian function output of the network. * Let ξ be the ideal weights of the network, and ξ be the approximation error of the network, where ξ ≤ ξ. N c j Let b be the center vector of the j-th node in the hidden layer. j Let be the base width parameter of the j-th node.

[0118] The network input is x = [x² x³]. T The network output will then be:

[0119]

[0120] but:

[0121]

[0122] Where f(x) represents linear disturbance. It is an intermediate parameter.

[0123] Step 7: Based on the RBF neural network principle in Step 6, define the Lyapunov function, which is:

[0124] From step 6 ,have to:

[0125]

[0126] Therefore, we can conclude that:

[0127]

[0128] have to:

[0129]

[0130] The control law is designed as follows:

[0131]

[0132] have to:

[0133]

[0134] Summarized as follows:

[0135]

[0136] Take the adaptive law as:

[0137]

[0138] Therefore, we can conclude that:

[0139]

[0140] It can be proven that it is semi-globally uniformly bounded within a certain compact set, and as t→∞, z1→0, z2→0, z3→0, thus

[0141] Where γ is 0.5, Let be the derivative of z3, d be the nonlinear disturbance term, and c3 be the control parameter.

[0142] In another exemplary embodiment of this application, for the parameter selection problem of the RBF neural network control algorithm, the particle swarm optimization algorithm can be used to optimize the control parameters to achieve the optimal control effect. Based on this, the particle swarm optimization algorithm is used to iteratively optimize control parameters c1, c2, and c3 to achieve the optimal tracking control effect. Specifically:

[0143] (1): Set the parameters for the movement range, set the learning factors z1 = 1.3, z2 = 1.7, the maximum number of evolutionary generations G = 500, kg represents the current number of evolutionary generations, the particle movement position range is min = 0, max = 1000, and the particle movement speed range is V. max =1; V min =-1. In a D-dimensional search space, the population size of the particles is Size=100, and each particle represents a candidate solution in the solution space, where the position of the i-th particle in the entire space is represented by X. i The speed is V i The optimal solution generated by the i-th particle from the initial to the current iteration number is given by the individual value P. i The current optimal solution for the entire population is BestS. Randomly generate Size particles and randomly generate the initial population's position and velocity matrices.

[0144] (2): Individual fitness evaluation: Using the initial position of each particle as the individual value, calculate the initial fitness value f(X) of each particle in the population. i ), f(X i ) is defined as Where x1 is a given position signal, x d The position signal output by the electro-hydraulic actuator is used to determine the optimal position for the population.

[0145] (3): Update the particle velocity and position, generate a new population, and reduce the number of particles whose velocity and position exceed the limit to avoid the algorithm getting stuck in a local optimum. Add a local adaptive mutation operator for adjustment.

[0146]

[0147] Where kg = 1, 2, ..., G, i = 1, 2, ..., Size, r1 and r2 are random numbers from 0 to 1, z1 is the local learning factor, and z2 is the global learning factor.

[0148] (4): Compare the current fitness value f(X) of the particles. i ) and its own historical best value p i If f(X) i ) is better than p i Then set p i For the current f(X) i ), and update the particle positions.

[0149] (5): Compare the current fitness value f(X) of the particle. i ) and the population optimum BestS, if f(X) i If f(X) is better than BestS, then set BestS to the current value f(X). i Update the global optimum of the population.

[0150] (6): Check the termination condition. If it is met, the optimization ends. Otherwise, kg′=kg+1, and go to step (3). The termination condition is that the optimization reaches the maximum number of generations or is less than the given precision.

[0151] In another exemplary embodiment of this application, the mathematical model of the electro-hydraulic actuator, the signal and interference module, and the RBF neural network control model are integrated to obtain an integrated simulation model of the electro-hydraulic actuator. This integrated simulation model can be modularized into an integrated simulation system for the electro-hydraulic actuator, which includes, for example, […]. Figure 6 The signal generation module shown is as follows: Figure 7 The interference generation module shown is as follows: Figure 5 The RBF neural network controller module shown, and as follows Figure 8 The electro-hydraulic actuator module is shown. The implementation process of the integrated simulation system for the entire electro-hydraulic actuator is as follows: Figure 9 As shown. Figures 2 to 9Since all the chips used are existing chips, the chip unit name or pin interface name represented by each English symbol can be obtained by simply looking up the corresponding manual of each chip, so it will not be described in detail here.

[0152] In summary, compared with known electro-hydraulic actuator simulation methods, this application has the following advantages:

[0153] 1. The simulation model of the electro-hydraulic actuator based on RBF neural network control provided in this application establishes a total parameter model of the electro-hydraulic actuator based on the actual physical model, which can realize the tracking of position signals under different working conditions.

[0154] 2. This application fully considers the effects of disturbances, and the proposed control method has stronger anti-interference ability and higher tracking accuracy compared with other classical control methods.

[0155] 3. To address the parameter selection problem of the RBF neural network control algorithm, the particle swarm optimization algorithm is used to optimize the control parameters to achieve the best control effect.

[0156] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 10 As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs stored in the non-volatile storage media. The database stores simulation data for electro-hydraulic actuators. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a simulation method for electro-hydraulic actuators based on RBF neural network control.

[0157] Those skilled in the art will understand that Figure 10The structures shown are merely block diagrams of some structures related to the present application and do not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than shown in the figures, or combine certain components, or have different component arrangements. In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0158] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0159] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0160] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0161] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0162] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0163] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0164] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A simulation method for an electro-hydraulic actuator based on RBF neural network control, characterized in that, include: A mathematical model of an electro-hydraulic actuator is established based on the working principles of a permanent magnet synchronous motor, a piston pump, a hydraulic cylinder, and a load. Obtain key parameters of the plunger pump and hydraulic cylinder; these key parameters include at least: total load mass, viscous damping coefficient, hydraulic cylinder piston area, average volume of hydraulic lines and cylinder, equivalent elastic modulus, leakage coefficient, and pump displacement. Based on the mathematical model of the electro-hydraulic actuator, a simulation model of a permanent magnet synchronous motor was established in Matlab / Simulink, and the speed loop and current loop were adjusted by PI control to enable the permanent magnet synchronous motor to complete speed tracking. Based on the aforementioned key parameters, Simulink models of the piston pump, hydraulic cylinder, and load were built in Matlab / Simulink. Based on the simulation model of the permanent magnet synchronous motor and the Simulink models of the plunger pump, hydraulic cylinder and load, the simulation model of the electro-hydraulic actuator is obtained. Referring to the operating conditions of electro-hydraulic actuators, a signal and interference module was built in Matlab / Simulink to obtain the position input curves and interference of electro-hydraulic actuators under different operating conditions. Based on the mathematical model of the electro-hydraulic actuator, an RBF neural network control model was built in Matlab / Simulink; The position input curves and disturbances of the electro-hydraulic actuator under different working conditions are input into the RBF neural network control model. The control law is derived based on the Lyapunov function and embedded into the RBF neural network control model. The disturbance is approximated by the RBF neural network control model, and the speed of the permanent magnet synchronous motor is used as the control quantity to realize the speed control of the permanent magnet synchronous motor. Based on the results of the speed control of the permanent magnet synchronous motor, the tracking results of the electro-hydraulic actuator under different operating conditions are obtained to generate a tracking curve. In the process of obtaining the tracking results of the electro-hydraulic actuator under different operating conditions, the particle swarm optimization algorithm is used to optimize the control parameters, and the optimal tracking result of the electro-hydraulic actuator under different operating conditions is determined based on the optimized control parameters. The tracking curve is input into the electro-hydraulic actuator simulation model to obtain the simulation results of the electro-hydraulic actuator.

2. The simulation method for electro-hydraulic actuators based on RBF neural network control according to claim 1, characterized in that, A mathematical model of an electro-hydraulic actuator is established based on the working principles of a permanent magnet synchronous motor, a piston pump, a hydraulic cylinder, and a load, including: Under the condition of neglecting magnetic circuit saturation, eddy current and hysteresis losses and high-order harmonics of the magnetic field, a mathematical model of the permanent magnet synchronous motor is performed to obtain the permanent magnet synchronous motor model. Based on Newton's laws of motion, a mathematical model is performed on the piston pump, hydraulic cylinder, and load to obtain the piston pump and hydraulic cylinder model. The mathematical model of the electro-hydraulic actuator is established based on the permanent magnet synchronous motor model and the plunger pump and hydraulic cylinder model.

3. The simulation method for electro-hydraulic actuators based on RBF neural network control according to claim 1, characterized in that, Referring to the operating conditions of electro-hydraulic actuators, a signal and interference module was built in Matlab / Simulink to obtain the position input curves and interference of the electro-hydraulic actuator under different operating conditions, including: Referring to the operating conditions of the electro-hydraulic actuator, a first operating condition is set, and under the first operating condition, the first sinusoidal position curve, nonlinear term and fault phase are tracked to obtain the first position input curve and disturbance of the electro-hydraulic actuator; Referring to the operating conditions of the electro-hydraulic actuator, a second operating condition is set, and under the second operating condition, the second sinusoidal position curve, nonlinear term, and fault phase are tracked to obtain the second position input curve and disturbance of the electro-hydraulic actuator; wherein, a hydraulic pipeline blockage fault occurs within the set time period of the second operating condition. The first position input curve and interference, as well as the second position input curve and interference, form the position input curve and interference of the electro-hydraulic actuator under different operating conditions.

4. The simulation method for electro-hydraulic actuators based on RBF neural network control according to claim 3, characterized in that, The first sinusoidal position curve is represented as follows: ； The nonlinear term is expressed as: ； The fault phase is represented as follows: ； The second sine position curve is represented as follows: ； In the formula, Given a displacement signal, This is the displacement tracking signal output by the actual electro-hydraulic actuator module. This refers to the actual output speed of the electro-hydraulic actuator module. For time, This is a fault condition. d It is in a perturbation state.

5. The simulation method for electro-hydraulic actuators based on RBF neural network control according to claim 1, characterized in that, The mathematical model of the electro-hydraulic actuator, the signal and interference module, and the RBF neural network control model are integrated to obtain an integrated simulation model of the electro-hydraulic actuator.

6. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the electro-hydraulic actuator simulation method based on RBF neural network control as described in any one of claims 1-5.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the electro-hydraulic actuator simulation method based on RBF neural network control as described in any one of claims 1-5.

8. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the electro-hydraulic actuator simulation method based on RBF neural network control as described in any one of claims 1-5.

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