Control method for electro-hydraulic multi-cylinder synchronous stretching

Abstract

The invention relates to the technical field of hydraulic systems, in particular to an electro-hydraulic multi-cylinder synchronous stretching control method which comprises the following steps: S1, constructing a multi-cylinder hydraulic system of a hydraulic forging press; s2, establishing an optimized fuzzy PID control module based on an improved NRBO algorithm, and configuring the optimized fuzzy PID control module in the multi-cylinder hydraulic system of the hydraulic forging press; s3, configuring a joint simulation interface for the multi-cylinder hydraulic system of the forging hydraulic press through the joint simulation model; s4, on the basis of the coordinated game model, synchronous position control characteristics of the multiple hydraulic cylinders are constructed in the multi-cylinder hydraulic system of the hydraulic forging press, and synchronous control over the telescopic positions of the multiple hydraulic cylinders is conducted; and S5, setting a control group and an experimental group, and performing simulation verification on the multi-cylinder hydraulic system of the hydraulic forging press through the simulation model. According to the system, the fuzzy PID control module is optimized, nonlinear interference is adjusted and inhibited on line, response and robustness are improved, displacement synchronization precision is improved, multi-cylinder collaborative optimal balance is achieved through the coordination game model, and adaptability and stability are enhanced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of hydraulic systems, in particular to a control method for electro-hydraulic multi-cylinder synchronous extension and retraction. BACKGROUND

[0002] As a common linear driving element, hydraulic cylinders are widely used in forging, injection molding, shipbuilding, engineering machinery and other fields. In a multi-cylinder driving system, due to the influence of non-linear factors such as uneven load distribution, hydraulic oil temperature change, leakage, friction, etc., the synchronization control accuracy of multiple cylinders is difficult to guarantee, especially under the conditions of large load, fast response and long stroke, the synchronization error is easy to accumulate and affect the stability of the system.

[0003] The traditional fuzzy PID controller introduces fuzzy logic into the PID parameters, so that the proportional, integral and derivative coefficients can be dynamically adjusted according to the error and its rate of change, but the performance of the existing fuzzy PID controller depends on the reasonable setting of membership functions, rule base and initial PID parameters. Improper parameter selection will lead to slow system response, large overshoot or increased steady-state error. In addition, its rule base is fixed and cannot adapt to the time-varying characteristics of the hydraulic system, resulting in poor working condition adaptability. SUMMARY

[0004] In view of the problem that improper parameter selection in the prior art leads to slow system response, large overshoot or increased steady-state error, the present application provides a control method for electro-hydraulic multi-cylinder synchronous extension and retraction,

[0005] The present application provides a control method for electro-hydraulic multi-cylinder synchronous extension and retraction, comprising:

[0006] S1: Construct a multi-cylinder hydraulic system of a forging hydraulic press, which comprises an oil tank, a hydraulic pump in liquid communication with the oil tank, an electric motor for driving the hydraulic pump to work, n hydraulic cylinders in liquid communication with the oil tank, an electro-hydraulic proportional reversing valve in liquid communication with the oil tank for controlling the extension and retraction of the n hydraulic cylinders, an overflow valve installed on the main oil inlet, a back pressure valve installed on the main oil return, and a displacement sensor.

[0007] S2: Establish an optimized fuzzy PID control module based on the improved NRBO algorithm, and configure the optimized fuzzy PID control module in the multi-cylinder hydraulic system of the forging hydraulic press constructed in step S1.

[0008] S3: Configure a joint simulation interface for the multi-cylinder hydraulic system of the forging hydraulic press through a joint simulation model, and realize real-time interaction between the optimized fuzzy PID control module established in step S2 and the multi-cylinder hydraulic system of the forging hydraulic press.

[0009] S4: Based on the coordination game model, the synchronous position control features of multiple hydraulic cylinders are constructed in the multi-cylinder hydraulic system of the forging hydraulic press. The multi-cylinder hydraulic system of the forging hydraulic press is started to synchronously control the extension and retraction positions of the multiple hydraulic cylinders.

[0010] S5: Set up a control group and an experimental group, and use a simulation model to verify the multi-cylinder hydraulic system of the forging hydraulic press obtained in step S4.

[0011] Furthermore, in step S2, the optimized fuzzy PID control module includes:

[0012] An adaptive step size adjustment model is obtained by combining multi-scale factors, search intensity, and random weights.

[0013] Real-time monitoring of the success rate of trap avoidance operations, obtaining an adaptive trap avoidance model, and dynamically adjusting the avoidance value.

[0014] Establish elite and exploration populations, optimize the allocation of search resources, and automatically switch search modes based on search status.

[0015] The dimensionality correlation factor and dynamic decay factor are obtained, and the improved NRBO algorithm formula is obtained by combining the adaptive step size adjustment model with the ordinary NRBO algorithm formula.

[0016] By improving the NRBO algorithm formula, we can comprehensively obtain the position update model of n hydraulic cylinders across different dimensions.

[0017] A success rate-based intelligent trap avoidance strategy is proposed, which establishes an intelligent trap avoidance operator and uses a comprehensive hydraulic cylinder position update model to plan the synchronous deviation displacement of n hydraulic cylinders.

[0018] Furthermore, by improving the NRBO algorithm formula, a comprehensive model for updating the positions of n hydraulic cylinders across different dimensions is obtained, including:

[0019] The improved NRBO algorithm calculates the fitness function value by calling the dynamic system model, and obtains the system performance index based on the time-weighted absolute error integral. The calculation formula is as follows: ,in, The fitness function; To control the deviation signal; that is, the synchronous displacement deviation signal of the two main working cylinders of the forging hydraulic press; The end time; This refers to the current moment.

[0020] Furthermore, the hydraulic cylinder position update model includes:

[0021] Input the synchronous displacement deviation and displacement deviation change rate of the hydraulic cylinder, and obtain the adjustment variable coefficient and fuzzy control rule coefficient of the PID controller.

[0022] The subsets of synchronous displacement deviation and displacement deviation change rate are set as: {Negative large NB, Negative medium NM, Negative small NS, Zero ZO, Positive small PS, Positive medium PM, Positive large PB}; the subsets of adjustment variable coefficients are set as: {B, M, S}.

[0023] Set the input function to a trigonometric function, set the output negative NB and positive PB to Gaussian functions, and set all others to trigonometric functions. Obtain the mapping table using an if-then statement.

[0024] Furthermore, in step S2, the improved NRBO algorithm formula is as follows:

[0025] ,

[0026] in, This represents the x-th PID parameter value during the current iteration. For the next generation of PID parameter values, Let x be the x-th parameter value of the globally best historical individual. Let x be the x-th parameter value of the current optimal individual. Let x be the parameter value of the worst individual. To avoid the probability of traps, As a leading item, To avoid disturbances in the trap.

[0027] Furthermore, in step S3, the co-simulation model includes:

[0028] Configure Amesim and Matlab with the same compiler and set appropriate environment variables, embed the Amesim model into Simulink to run as an independent subsystem, and configure an independent solver.

[0029] Furthermore, in step S4, the coordination game model includes:

[0030] Real-time status parameters are collected for each of the n hydraulic cylinders. These status parameters include displacement, velocity, reference trajectory, and error.

[0031] Based on the utility function, the optimal control input quantities u1, u2...u of the hydraulic cylinder are obtained by solving for the extreme values. n That is, it satisfies the Nash equilibrium condition.

[0032] Based on the magnitude of the real-time synchronization error e and the control inputs u1, u2...u n The status is adjusted in real time according to the synchronization judgment conditions. Precision Cost Weights Control the cost weight.

[0033] Furthermore, the synchronization determination conditions include:

[0034] when When, increase Reduce synchronization error

[0035] when When, increase To reduce control costs, among which, express, represents the control cost weight, and e represents the synchronization error.

[0036] Furthermore, the control group and experimental group are set up, and the multi-cylinder hydraulic system of the forging hydraulic press obtained in step S4 is simulated and verified using a simulation model:

[0037] The simulation model of the control group includes: a pulse signal unit, an amplifier module, an integrator module, a derivative module, and a traditional PID module.

[0038] The simulation model of the experimental group includes: a clock module, a product operation module, an adaptive function module, an amplifier module, an integral module, a differential module, and a traditional PID module.

[0039] Compared with the prior art, the beneficial effects of the present invention are:

[0040] This invention constructs a highly efficient, stable, and synchronized multi-cylinder hydraulic system for forging hydraulic presses by integrating an optimized fuzzy PID control module, an improved NRBO algorithm, and coordination game theory. Specifically, a fuzzy PID control module based on the improved NRBO algorithm is used to overcome the shortcomings of traditional fuzzy PID controllers, such as poor adaptability and insufficient dynamic accuracy. It can dynamically adjust control parameters in real time, effectively suppressing nonlinear interference caused by load changes, oil compressibility, and leakage. It exhibits fast response speed and strong robustness, thereby significantly improving the synchronization accuracy of multi-cylinder displacement and ensuring the coordinated movement of each mechanism during forging. By constructing the synchronization position characteristics of each hydraulic cylinder through a coordination game model, the hydraulic system can achieve optimal balanced allocation in multi-cylinder collaborative operation, quickly respond to command changes, reduce the accumulation of synchronization errors, and further optimize the dynamic performance of the control system by combining real-time interactive verification with a co-simulation interface, enhancing its adaptability and stability under different working conditions.

[0041] It should be understood that the description in the Summary of the Invention is not intended to limit the key or essential features of the embodiments of the present invention, nor is it intended to restrict the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0042] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0043] Figure 1 The overall flowchart of the control method for synchronous extension and retraction of multiple electro-hydraulic cylinders.

[0044] Figure 2 A schematic block diagram of a multi-cylinder hydraulic system for a forging hydraulic press.

[0045] Figure 3 A schematic block diagram of the fuzzy PID control module structure is provided for optimization.

[0046] Figure 4 A schematic block diagram illustrating the structure of a coordinated game model.

[0047] Figure 5 To optimize the operation flowchart of the fuzzy PID control module.

[0048] Figure 6 This is a comparison chart of the synchronization errors between the control group and the experimental group. Detailed Implementation

[0049] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, illustrating only the basic structure of the invention, and therefore only show the components relevant to the invention.

[0050] It should be noted that when a component is referred to as being "fixed to" or "set on" another component, it can be directly on or indirectly set on the other component; when a component is referred to as being "connected to" another component, it can be directly connected to or indirectly connected to the other component.

[0051] It should be understood that the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", and "outer" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or component referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.

[0052] Please refer to Figures 1-6 This invention provides a control method for synchronous extension and retraction of multiple electro-hydraulic cylinders, comprising:

[0053] S1: Construct a multi-cylinder hydraulic system for a forging hydraulic press. The multi-cylinder hydraulic system for a forging hydraulic press includes: an oil tank, a hydraulic pump connected to the oil tank's hydraulic circuit, a motor for driving the hydraulic pump, n hydraulic cylinders connected to the oil tank's hydraulic circuit, an electro-hydraulic proportional directional valve connected to the oil tank's hydraulic circuit for controlling the extension and retraction of the n hydraulic cylinders, an overflow valve installed on the main oil inlet line, a back pressure valve installed on the main oil return line, and a displacement sensor.

[0054] S2: Establish an optimized fuzzy PID control module based on the improved NRBO algorithm, and configure the optimized fuzzy PID control module in the multi-cylinder hydraulic system of the forging hydraulic press constructed in step S1.

[0055] S3: Configure a joint simulation interface for the multi-cylinder hydraulic system of the forging hydraulic press through a joint simulation model, and enable the optimized fuzzy PID control module established in step S2 to interact with the multi-cylinder hydraulic system of the forging hydraulic press in real time.

[0056] S4: Based on the coordination game model, the synchronous position control characteristics of multiple hydraulic cylinders are constructed in the multi-cylinder hydraulic system of the forging hydraulic press. The multi-cylinder hydraulic system of the forging hydraulic press is started to synchronously control the extension and retraction positions of multiple hydraulic cylinders.

[0057] S5: Set up a control group and an experimental group, and use a simulation model to verify the multi-cylinder hydraulic system of the forging hydraulic press obtained in step S4.

[0058] This embodiment constructs a highly efficient, stable, and synchronized multi-cylinder hydraulic system for a forging hydraulic press by integrating an optimized fuzzy PID control module, an improved NRBO algorithm, and coordination game theory. Specifically, a fuzzy PID control module based on the improved NRBO algorithm is adopted to overcome the shortcomings of traditional fuzzy PID controllers, such as poor adaptability and insufficient dynamic accuracy. It can dynamically adjust control parameters in real time, effectively suppressing nonlinear interference caused by load changes, oil compressibility, and leakage. It has a fast response speed and strong robustness, thereby significantly improving the synchronization accuracy of multi-cylinder displacement and ensuring the coordinated movement of each mechanism during forging. By constructing the synchronization position characteristics of each hydraulic cylinder through a coordination game model, the hydraulic system can achieve optimal balanced allocation in multi-cylinder collaborative operation, quickly respond to command changes, reduce the accumulation of synchronization errors, and further optimize the dynamic performance of the control system by combining real-time interactive verification with a co-simulation interface, enhancing its adaptability and stability under different working conditions.

[0059] In this embodiment, a multi-cylinder hydraulic system for a forging hydraulic press, comprising multiple hydraulic cylinders, an electro-hydraulic proportional directional valve, and a sensing and detection unit, is first constructed. Then, the fuzzy PID controller is self-tuned by improving the NRBO algorithm, which significantly improves the system's adaptability to nonlinear and time-varying loads and its dynamic adjustment accuracy. Real-time interaction between the control module and the hydraulic system is achieved through the establishment of a joint simulation model. A coordination game model is introduced to describe the synchronous coupling relationship between the multiple cylinders, realizing the coordinated control of the extension and retraction positions of multiple hydraulic cylinders. Finally, the system performance is verified through simulation comparison.

[0060] Furthermore, this embodiment abstracts the mechanical and hydraulic coupling relationships between multiple cylinders into the cooperation and competition of game participants, thereby intelligently coordinating the control commands of each cylinder and achieving true collaborative position control. Simulations and experiments show that this fusion method can effectively suppress asynchronous errors, further improve synchronization accuracy, and accelerate the system response speed. Moreover, it exhibits extremely strong robustness and stability under off-center load conditions, providing an efficient and reliable control solution for achieving smooth and precise synchronous movements in large forging equipment.

[0061] To further explain, the working process of the multi-cylinder hydraulic system of the forging hydraulic press is a closed-loop, controlled power transmission and position adjustment process, specifically as follows: The motor drives the hydraulic pump to draw oil from the tank, converting mechanical energy into hydraulic energy and outputting high-pressure oil. The hydraulic system pressure is set to an upper limit by the relief valve to protect system safety. Based on the target displacement command, the control hydraulic system sends a control signal to the electro-hydraulic proportional directional valve, causing the valve core to open proportionally, precisely distributing and delivering the hydraulic oil output by the pump to the rodless or rod chamber of each hydraulic cylinder. When hydraulic oil is output to the rodless chamber, the hydraulic cylinder piston rod retracts; when hydraulic oil is output to the rod chamber, the hydraulic cylinder piston rod extends. At this time, the displacement sensor installed on each hydraulic cylinder detects the actual position of the piston rod and feeds the signal back to the control system.

[0062] The multi-cylinder hydraulic system of the forging hydraulic press integrates an optimized fuzzy PID control module, which continuously compares the target position of each cylinder with the actual position fed back by the displacement sensor to calculate the dynamic error and the rate of change of error. After the work is completed, the electro-hydraulic proportional directional valve controls the hydraulic oil to enter the rod chamber of the hydraulic cylinder, while the hydraulic oil in the rodless chamber flows back to the oil tank through the back pressure valve, and the piston rod retracts, completing one work cycle.

[0063] Furthermore, in step S1, the main mathematical model of the multi-cylinder hydraulic system of the forging hydraulic press includes:

[0064] Single hydraulic cylinder transmission: ,in, The transfer function model of the hydraulic cylinder is represented. The effective working area of ​​the piston. The effective bulk modulus of the oil. For the total controlled volume, For the Laplace operator.

[0065] Electro-hydraulic proportional directional valve transmission function: ,in, The transfer function model of the electro-hydraulic proportional directional valve is represented. For the Laplace operator, This is the valve core displacement-flow gain coefficient. The natural angular frequency of the valve core. The valve core damping ratio, The valve orifice flow coefficient, This refers to the pressure drop at the valve port.

[0066] Displacement sensor transfer function: ,in, The transfer function model of the displacement sensor. The Laplace transform of the sensor output voltage, The Laplace transform of the input displacement.

[0067] like Figure 3 and Figure 5 As shown, in step S2, optimizing the fuzzy PID control module includes obtaining an adaptive step size adjustment model by comprehensively considering multi-scale factors, search intensity, and random weights.

[0068] Real-time monitoring of the success rate of trap avoidance operations, obtaining an adaptive trap avoidance model, and dynamically adjusting the avoidance value.

[0069] Establish elite and exploration populations, optimize the allocation of search resources, and automatically switch search modes based on search status.

[0070] The dimensionality correlation factor and dynamic decay factor are obtained, and the improved NRBO algorithm formula is obtained by combining the adaptive step size adjustment model with the ordinary NRBO algorithm formula.

[0071] By improving the NRBO algorithm formula, we can comprehensively obtain the position update model of n hydraulic cylinders across different dimensions.

[0072] A success rate-based intelligent trap avoidance strategy is proposed, which establishes an intelligent trap avoidance operator and uses a comprehensive hydraulic cylinder position update model to plan the synchronous deviation displacement of n hydraulic cylinders.

[0073] In this embodiment, compared to the traditional fixed-step-size method, the step-size adjustment model can dynamically adjust the step size according to the search state, ensuring both local search accuracy and enhancing global exploration capabilities. The calculation formula for the step-size adjustment model is:

[0074] ,in, This is the step size factor, which controls the search movement distance; is the scaling factor, with values ​​ranging from {0.1, 0.5, 1.0, 2.0}. This is the search intensity coefficient.

[0075] Furthermore, the success rate of trap avoidance operations is inversely proportional to the value of the interference factor. By monitoring the success rate of trap avoidance operations in real time, the fluctuation of the interference factor value is dynamically adjusted, with the fluctuation range within 0.1 to 0.9. The calculation formula for the adaptive trap avoidance model is as follows:

[0076] ,in, For the probability of avoiding traps in the current iteration, The success rate of avoiding trap operations in the last 10 times. This is the indicator of whether the trap avoidance attempt was successful in the kth iteration.

[0077] To further explain, an elite population and an exploration population are established, and search resources are allocated preferentially. The elite population adopts a refined search strategy, while the exploration population adopts a broad search strategy. The calculation formula is as follows:

[0078] ,in For the search intensity parameter, For population size, Let be the rank of the i-th particle in the population.

[0079] Search modes include: fine-grained mode, broad mode, and default mode. The calculation formula for selecting the search mode is as follows:

[0080] ,in, Let Newton-Raphson search rule vector be the vector. This is the position vector of the current worst solution. This represents the current iteration number. The maximum number of iterations, This is the position vector of the current particle.

[0081] To further explain, the dimensionality correlation factor and dynamic decay factor are obtained, and the improved NRBO algorithm formula is obtained by combining the adaptive step size adjustment model with the ordinary NRBO algorithm formula.

[0082] The formula for calculating the dimensional correlation factor is as follows: ,in, Dimensional correlation factor; These are random numbers that follow a standard normal distribution.

[0083] The formula for calculating the dynamic attenuation factor is as follows: ,in, It is a dynamic decay factor that decreases with the number of iterations; This represents the current iteration number; This represents the maximum number of iterations.

[0084] The standard NRBO iterative formula is as follows:

[0085] ,in, For the specific location of the next generation, For the current individual position, The optimal position globally. This is the worst position globally. For random perturbation vectors, It is a random number. and For the search space boundary, and For random individual locations.

[0086] The improved NRBO algorithm formula can then be obtained through synthesis. The improved NRBO algorithm formula is as follows: ;

[0087] ;

[0088] in, This represents the x-th PID parameter value during the current iteration. For the next generation of PID parameter values, Let x be the x-th parameter value of the globally best historical individual. Let x be the x-th parameter value of the current optimal individual. Let x be the parameter value of the worst individual. To avoid the probability of traps, As a leading item, To avoid disturbances in the trap.

[0089] The guiding term combines the globally optimal direction and the stochastic difference direction.

[0090] Furthermore, in the improved NRBO algorithm formula, the particle motion space dimension is 8, which corresponds to the proportional factor K in the fuzzy PID control model. pu K iu K du And quantification factor K e K ec and the fuzzy rule control coefficient K c1 K c2 K c3 .

[0091] Furthermore, the NRBO algorithm formula is improved to comprehensively obtain the position update model of n hydraulic cylinders across different dimensions.

[0092] The basic iterative formula for optimizing the fuzzy PID control module is as follows:

[0093]

[0094] in, Let be the individual's position at the t-th iteration. Let NRSR be the new position in the (t+1)th iteration, and let NRSR be the search term in the Newton-Raphson method. For individuals who are considered "poor" within the current group, As a "better" individual within the current group, The guiding term is based on the global optimum and the difference between two random individuals. The optimal position globally. , For two randomly selected individual locations, , Uniformly random numbers, This is a coefficient that decays with each iteration. It is a binary trigger. The perturbation vector generated for trap avoidance.

[0095] To further explain, the optimized fuzzy PID control module calculates the fitness function value by calling the parameters of the dynamic system model. Based on the time-weighted absolute error integral, the fitness function value is used as the system performance index. The calculation formula is as follows: ,in, The fitness function; To control the deviation signal; that is, the synchronous displacement deviation signal of the two main working cylinders of the forging hydraulic press; The end time; This refers to the current moment.

[0096] To further clarify, the calculation formula for the position update model of n hydraulic cylinders is: , For the middle position variable; Let i be the position of the i-th particle in the j-th dimension; It is a dynamic decay factor that decreases with the number of iterations; This is a dimensional correlation factor.

[0097] A success rate-based intelligent trap avoidance strategy is proposed, which establishes an intelligent trap avoidance operator and uses a comprehensive hydraulic cylinder position update model to plan the synchronous deviation displacement of n hydraulic cylinders. The calculation formula is as follows:

[0098] ,in, To avoid the trap's strength coefficient, The new position vector after the trap avoidance operation. and For random weights, This is the updated position vector. This is the average position vector of the population.

[0099] To further explain, the hydraulic cylinder position update model includes: inputting the synchronous displacement deviation and displacement deviation change rate of the hydraulic cylinder, and obtaining the adjustment variable coefficients and fuzzy control rule coefficients of the PID controller;

[0100] The subsets of synchronous displacement deviation and displacement deviation change rate are set as: {Negative large NB, Negative medium NM, Negative small NS, Zero ZO, Positive small PS, Positive medium PM, Positive large PB}; the subsets of adjustment variable coefficients are set as: {B, M, S}.

[0101] The input function is set to a trigonometric function, the output negative NB and positive PB are set to Gaussian functions, and the rest are set to trigonometric functions. The mapping table is obtained through if-then statements.

[0102] In this embodiment, the synchronous displacement deviation e and the synchronous displacement deviation change rate ec are tuned by an optimized fuzzy PID control module to obtain a correction value K. p K i K d Before fuzzy processing, the fundamental universes of discourse of e and ec need to be mapped to the corresponding fuzzy set universes using a quantization factor, ΔK. p ΔK i ΔK d It also needs to be mapped to its universe of discourse via a scaling factor. When the universe of discourse of the input quantities e and ec is [-1,1], the output quantity ΔK p ΔK i ΔK d The domains of discourse are [-0.01, 3], [-0.01, 1], and [-0.3, 0.3], respectively. Let K... p K i K d The value to be adjusted is K p ′、K i ′、K d Then, the optimized fuzzy PID parameters are obtained through parameter calculation, and the calculation formula is:

[0103]

[0104] The mapping table for ΔKp is as follows:

[0105] e\ec NB NM NS ZO PS PM PB NB PB PB PM PM PS ZO ZO NM PB PB PM PS PS ZO NS NS PM PM PM PS ZO NS NS ZO PM PM PS ZO NS NM NM PS PS PS ZO NS NS NM NM PM PS ZO NS NM NM NM NB PB ZO ZO NM NM NM NB NB

[0106] The mapping table for ΔKi is as follows:

[0107] e\ec NB NM NS ZO PS PM PB NB NB NB NM NM NS ZO ZO NM NB NB NM NS ZO ZO PS NS NM NM NS ZO PS PM PM ZO NM NS ZO PS PM PM PB PS NS ZO PS PM PM PB PB PM ZO ZO PM PM PB PB PB PB ZO PS PM PB PB PB PB

[0108] The mapping relationship of ΔKd is:

[0109] e\ec NB NM NS ZO PS PM PB NB PS NS NB NB NB NM PS NM PS NS NB NM NM NS ZO NS ZO NS NM NM NS NS ZO ZO ZO NS NS NS NS NS ZO PS ZO ZO ZO NM NM NS ZO PM PB NS NS NM NM NS PS PB PB PM PS PS PS PS PS

[0110] Optimize the output ΔK of the fuzzy PID control module p ΔK i ΔK d Optimizing the proportional parameter K of the fuzzy PID p Integral parameter K i Differential parameter K d The settings are adjusted, and the output signal is sent to the multi-cylinder hydraulic system of the forging hydraulic press.

[0111] By improving the NRBO algorithm, the proportional factor K in the fuzzy PID control module is adjusted. pu K iu K du Quantification factor K e K ec and the fuzzy rule control coefficient K c1 K c2 K c3 Joint optimization is performed, and the optimal value is input into the fuzzy PID control module. Let K... p K i K d The value to be adjusted is K p ′、K i ′、K d K is obtained by fuzzy inference using fuzzy rules. p K i K d The setpoint is ΔK p ΔK i ΔK d The PID coefficients are obtained through parameter calculation, and the calculation formula is as follows:

[0112]

[0113] The specific parameters in the main function of the improved NRBO algorithm are as follows: the number of particles is 100, the maximum number of iterations is 30, and the dimension of the particle motion space is 8, which corresponds to the proportional factor K in the fuzzy PID control module. pu K iu K du And quantification factor K e K ec and the fuzzy rule control coefficient K c1 K c2 K c3 ;and K pu K iu K du K e K ec K c1 Kc2 K c3 The initial values ​​are set to 1, 0.01, 1, 1, 1, 1, 0.05, and 1 respectively; then the scaling factor K pu K iu K du The ranges are set to [0,60], [0,10], and [0,10] respectively, with a quantization factor K. e K ec The range is set to [0, 3], and the fuzzy rule control coefficient K c1 K c2 K c3 The ranges are set to [0, 240], [0, 10], and [0, 20], respectively.

[0114] To further explain, an improved NRBO algorithm is used to optimize the parameters of the fuzzy PID control module. Specifically, the system error is used as the fitness function input to the improved NRBO algorithm. The value of the fitness function is calculated, and then the scaling factor K of the fuzzy PID control module is adjusted based on the fitness of the function. pu K iu K du And quantification factor K e K ec and the fuzzy rule control coefficient K c1 K c2 K c3 These eight parameters are used to find the optimal values ​​in the parameter space of the variables, so that the control performance of the system can reach its best.

[0115] In step S3, the co-simulation model includes: configuring the same compiler for Amesim and Matlab and setting appropriate environment variables, embedding the Amesim model into Simulink to run as an independent subsystem, and configuring an independently running solver.

[0116] like Figure 4 As shown, in step S4, the coordination game model includes:

[0117] Real-time status parameters are collected for each of the n hydraulic cylinders. These parameters include displacement, velocity, reference trajectory, and error.

[0118] Based on the utility function, the optimal control input quantities u1, u2...u of the hydraulic cylinder are obtained by solving for the extreme values. n That is, it satisfies the Nash equilibrium condition.

[0119] Based on the magnitude of the real-time synchronization error e and the control inputs u1, u2...u n The status is adjusted in real time according to the synchronization judgment conditions. Precision Cost Weights Control the cost weight.

[0120] To further clarify, the synchronization determination conditions include:

[0121] when When, increase Reduce synchronization error

[0122] when When, increase To reduce control costs, among which, express, represents the control cost weight, and e represents the synchronization error.

[0123] To further clarify, the real-time displacement values ​​of the n hydraulic cylinders are set as: x1, x2...x n The real-time speed values ​​are set to: u1, u2...u n Reference trajectory ;

[0124] The synchronization error value is calculated using the following formula: ;

[0125] The tracking error value is calculated using the following formula: , ;

[0126] The formula for calculating the utility function is: ,in, Weights for tracking error; For synchronization error weights; To control cost weights.

[0127] Based on the utility function, the optimal control input values ​​for n hydraulic cylinders are obtained through the extreme value solution method. , ... Furthermore, this input value must satisfy the Nash equilibrium condition, meaning that changing the control quantity of any hydraulic cylinder alone cannot improve its own utility. The calculation formula is as follows:

[0128] ,in, Let be the sensitivity of the displacement of the i-th hydraulic cylinder to its control input.

[0129] Adjust in real time based on the synchronization error amplitude and the saturation state of the control input value. and ;when Time increases Prioritize reducing synchronization errors;

[0130] when Time increases Limit the control cost of hydraulic cylinders;

[0131] Subsequently, the optimal control input is applied to each of the n hydraulic cylinders, and the above steps are repeated to achieve dynamic synchronous control.

[0132] like Figure 6 As shown, in step S5, a control group and an experimental group are set up, and the multi-cylinder hydraulic system of the forging hydraulic press obtained in step S4 is simulated and verified using a simulation model:

[0133] The simulation model of the control group includes: a pulse signal unit, an amplifier module, an integrator module, a derivative module, and a traditional PID module;

[0134] The simulation model of the experimental group includes: clock module, product operation module, fitness function module, amplifier module, integral module, differential module, and traditional PID module.

[0135] In this embodiment, the process of outputting the simulation model of the control group is as follows: the pulse signal output by the given pulse signal unit is used as the input signal, the input signal is processed by the optimized fuzzy PID control module and combined with the hydraulic system state to generate the output signal, the output signal is used as the negative feedback signal, and together with the input signal, it forms an error signal e which is sent to the fuzzy PID control module.

[0136] The process of outputting the simulation model of the experimental group is as follows: the output pulse signal of the given pulse signal unit is used as the input signal. The input signal is processed by the optimized fuzzy PID control module and combined with the hydraulic system state to generate the output signal. The output signal is used as the negative feedback signal and together with the input signal to form the error signal e, which is sent to the fuzzy PID control module. The parameters of the optimized fuzzy PID control module are optimized by the improved NRBO algorithm, and finally the optimized fuzzy PID control module obtained by the fusion of the improved NRBO algorithm is constructed.

[0137] Further explanation is provided by comparing simulations to verify the effectiveness of the optimized multi-cylinder position synchronization control. The experimental group employed an optimized fuzzy PID control module based on the improved NRBO algorithm. Compared to the control group, this module added clock, product operation, and adaptive function modules, ensuring that the system error e is not only input to the fuzzy PID control module but also serves as input to the improved NRBO algorithm. This algorithm dynamically optimizes the control parameters of the fuzzy PID online, thereby adjusting the controller behavior in real time. This significantly improves the synchronization tracking accuracy of the multi-cylinder system under nonlinear and strongly coupled conditions, exhibiting stronger robustness. It effectively suppresses disturbances and parameter changes, ensuring a fast and stable dynamic response and forming a more adaptive closed-loop optimized control structure.

[0138] It should be understood that the specific embodiments described above are for illustrative purposes only and are not intended to limit the scope of the invention. Obvious variations or modifications derived from the spirit of the invention are still within the protection scope of the invention.

Claims

1. A control method for synchronous extension and retraction of multiple electro-hydraulic cylinders, characterized in that, include: S1: Construct a multi-cylinder hydraulic system for a forging hydraulic press. The multi-cylinder hydraulic system of the forging hydraulic press includes: an oil tank, a hydraulic pump connected to the oil tank via a hydraulic circuit, a motor for driving the hydraulic pump, n hydraulic cylinders connected to the oil tank via a hydraulic circuit, an electro-hydraulic proportional directional valve connected to the oil tank via a hydraulic circuit for controlling the extension and retraction of the n hydraulic cylinders, an overflow valve installed on the main oil inlet line, a back pressure valve installed on the main oil return line, and a displacement sensor. S2: Establish an optimized fuzzy PID control module based on the improved NRBO algorithm, and configure the optimized fuzzy PID control module in the multi-cylinder hydraulic system of the forging hydraulic press constructed in step S1; S3: Configure a joint simulation interface for the multi-cylinder hydraulic system of the forging hydraulic press through a joint simulation model, and allow the optimized fuzzy PID control module established in step S2 to interact with the multi-cylinder hydraulic system of the forging hydraulic press in real time. S4: Based on the coordination game model, the synchronous position control features of multiple hydraulic cylinders are constructed in the multi-cylinder hydraulic system of the forging hydraulic press. The multi-cylinder hydraulic system of the forging hydraulic press is started to synchronously control the extension and retraction positions of the multiple hydraulic cylinders. S5: Set up a control group and an experimental group, and use a simulation model to verify the multi-cylinder hydraulic system of the forging hydraulic press obtained in step S4.

2. The control method for synchronous extension and retraction of multiple electro-hydraulic cylinders according to claim 1, characterized in that, In step S2, the optimized fuzzy PID control module includes: An adaptive step size adjustment model is obtained by combining multi-scale factors, search intensity, and random weights. Real-time monitoring of the success rate of trap avoidance operations; obtaining an adaptive trap avoidance model; and dynamically adjusting the avoidance value. Establish elite and exploration populations, optimize and allocate search resources, and automatically switch search modes based on search status; The dimensionality correlation factor and dynamic decay factor are obtained, and the improved NRBO algorithm formula is obtained by combining the adaptive step size adjustment model with the ordinary NRBO algorithm formula. By improving the NRBO algorithm formula, a comprehensive update model of n hydraulic cylinder positions across different dimensions is obtained. A success rate-based intelligent trap avoidance strategy is proposed, which establishes an intelligent trap avoidance operator and uses a comprehensive hydraulic cylinder position update model to plan the synchronous deviation displacement of n hydraulic cylinders.

3. The control method for synchronous extension and retraction of multiple electro-hydraulic cylinders according to claim 2, characterized in that, The method of comprehensively obtaining the update model of n hydraulic cylinder positions across different dimensions by improving the NRBO algorithm formula includes: The improved NRBO algorithm calculates the fitness function value by calling the dynamic system model, and obtains the system performance index based on the time-weighted absolute error integral. The calculation formula is as follows: ,in, The fitness function; To control the deviation signal; that is, the synchronous displacement deviation signal of the two main working cylinders of the forging hydraulic press; The end time; This refers to the current moment.

4. The control method for synchronous extension and retraction of multiple electro-hydraulic cylinders according to claim 2, characterized in that, The hydraulic cylinder position update model includes: Input the synchronous displacement deviation and displacement deviation change rate of the hydraulic cylinder, and obtain the adjustment variable coefficient and fuzzy control rule coefficient of the PID controller; The subsets of synchronous displacement deviation and displacement deviation change rate are set as: {Negative large NB, Negative medium NM, Negative small NS, Zero ZO, Positive small PS, Positive medium PM, Positive large PB}; the subsets of adjustment variable coefficients are set as: {B, M, S}. Set the input function to a trigonometric function, set the output negative NB and positive PB to Gaussian functions, and set all others to trigonometric functions. Obtain the mapping table using an if-then statement.

5. The control method for synchronous extension and retraction of multiple electro-hydraulic cylinders according to claim 1, characterized in that, In step S2, the improved NRBO algorithm formula is as follows: ， in, This represents the x-th PID parameter value during the current iteration. For the next generation of PID parameter values, Let x be the x-th parameter value of the globally best historical individual. Let x be the x-th parameter value of the current optimal individual. Let x be the parameter value of the worst individual. To avoid the probability of traps, As a leading item, To avoid disturbances in the trap.

6. The control method for synchronous extension and retraction of multiple electro-hydraulic cylinders according to claim 1, characterized in that, In step S3, the co-simulation model includes: Configure Amesim and Matlab with the same compiler and set appropriate environment variables, embed the Amesim model into Simulink to run as an independent subsystem, and configure an independent solver.

7. The control method for synchronous extension and retraction of multiple electro-hydraulic cylinders according to claim 1, characterized in that, In step S4, the coordination game model includes: Real-time status parameters are collected for each of the n hydraulic cylinders. These status parameters include: displacement, velocity, reference trajectory, and error. Based on the utility function, the optimal control input quantities u1, u2...u of the hydraulic cylinder are obtained by solving for the extreme values. n That is, it satisfies the Nash equilibrium condition; Based on the magnitude of the real-time synchronization error e and the control inputs u1, u2...u n The status is adjusted in real time according to the synchronization judgment conditions. Precision Cost Weights Control the cost weight.

8. The control method for synchronous extension and retraction of multiple electro-hydraulic cylinders according to claim 7, characterized in that, The synchronization determination conditions include: when When, increase This reduces synchronization errors. when When, increase To reduce control costs, among which, express, represents the control cost weight, and e represents the synchronization error.

9. The control method for synchronous extension and retraction of multiple electro-hydraulic cylinders according to claim 1, characterized in that, The control group and experimental group were set up, and the multi-cylinder hydraulic system of the forging hydraulic press obtained in step S4 was simulated and verified using a simulation model: The simulation model of the control group includes: a pulse signal unit, an amplifier module, an integrator module, a derivative module, and a traditional PID module; The simulation model of the experimental group includes: a clock module, a product operation module, an adaptive function module, an amplifier module, an integral module, a differential module, and a traditional PID module.

Images