Position control method of hydraulic servo system

Abstract

The invention discloses a position control method of a hydraulic servo system, and relates to the technical field of hydraulic servo systems. The method comprises the steps of obtaining an application range of the hydraulic servo system and marking the application range as an initial range; based on the initial range, determining a moving position range of the hydraulic servo system in the initial range and marking the moving position range as an initial search range; based on the initial search range, a final search range is obtained through calculation according to a final range correlation function, and the optimal movement position of the hydraulic servo is determined in the final search range and marked as an execution position; re-determining an execution position according to an execution correlation function based on the final search range; and controlling the hydraulic servo system to move to the execution position by utilizing the super-spiral sliding mode controller based on the execution position. The position control performance of the hydraulic servo system is improved.

Description

Technical Field

[0001] This application relates to the technical field of hydraulic servo systems, and in particular to a position control method for a hydraulic servo system. Background Technology

[0002] Hydraulic servo systems enable the system's output, such as displacement, velocity, or force, to automatically, quickly, and accurately follow changes in the input. Simultaneously, the output power is significantly amplified. Hydraulic servo systems are widely used in industrial control due to their unique advantages such as fast response speed, high load stiffness, and high control power. They are widely applied in aircraft control surfaces, machine tool copying devices, automated production lines, and construction machinery. In these applications, to solve problems more precisely, it is often necessary to control the position of the hydraulic servo system.

[0003] In related technologies, hydraulic servo systems adjust position through sliding mode control. However, due to the high-frequency switching of control in sliding mode systems, the control quantity eventually generates significant jitter, resulting in a decrease in the performance of the hydraulic servo system equipment, which requires improvement. Summary of the Invention

[0004] The purpose of this invention is to provide a position control method for a hydraulic servo system to solve the problems mentioned in the background art.

[0005] This application provides a position control method for a hydraulic servo system, which adopts the following technical solution:

[0006] Obtain the application scope of the hydraulic servo system and mark it as the initial scope;

[0007] Based on the initial range, the range of movement positions of the hydraulic servo system is determined within the initial range and marked as the initial search range;

[0008] Based on the initial search range, the association function is determined according to the final range. The final search range is calculated, where t represents the current iteration number and T is the maximum iteration number. This represents the position information of the i-th point in the j-th dimension. It is the worst position globally. Q is the optimal position currently occupied by the participating points, Q is a random number following a normal distribution, and L is a matrix of all 1s; Let represent a 1×d matrix, where each element is randomly assigned the value 1 or -1, and = ( -1; This indicates that no location requiring hydraulic servo was found among the participating points;

[0009] Based on the final search range, the optimal movement position of the hydraulic servo is determined within the final search range and marked as the execution position;

[0010] Based on the execution location, according to the execution association function The execution location is redefined, whereby... To be the globally optimal position This represents the position information of the i-th point in the j-th dimension. It represents the worst-case position globally; β is the step size control parameter, a random number following a normal distribution with a mean of 0 and a variance of 1; K∈[-1,1] is a random number. This is the fitness value of the current participating point; and These are the current best and worst fitness values ​​globally, respectively; ε is the smallest constant.

[0011] Based on the execution position, the hydraulic servo system is controlled to move to the execution position using a super spiral sliding mode controller.

[0012] By adopting the above technical solution, the improved super-spiral sliding mode controller, combined with the improved sparrow optimization algorithm, can stably and quickly track the system's given information. The overshoot of the tracking response curve is very small, the oscillation amplitude is well controlled, almost no oscillation occurs, and the tracking error of the system is also relatively small, thus improving the position control performance of the hydraulic servo system.

[0013] Preferably, the step of determining the range of movement positions of the hydraulic servo system within the initial range and marking it as the initial search range, based on the initial range, specifically includes:

[0014] Based on the initial range, at least two points are selected within the initial range for searching and marked as the initial population;

[0015] Based on the initial population, each search of the initial population is recorded as an iteration, and the search range is determined after multiple searches.

[0016] Get real-time delay value And set a delay threshold ST, and ∈[0, 1], ST∈[0.5, 1];

[0017] Based on search association function Determine the initial search range Where t represents the current iteration number, and T is the maximum iteration number. This represents the position information of the i-th point in the j-th dimension. This initializes the population parameters, where Q is a normally distributed random number and L is a matrix of all ones. It is the real-time delay value, and ST is the delay threshold.

[0018] By adopting the above technical solution, the points involved in the search are likened to "sparrows," and the initial search range is determined according to the sparrow algorithm. This allows for faster identification of problem points, saving time and improving the convenience of position control in hydraulic servo systems.

[0019] Preferably, obtain the real-time delay value. And set a delay threshold ST, and The steps for ∈[0, 1], ST∈[0.5, 1] ​​are as follows:

[0020] Obtain the input and output change times at different points, and calculate the real-time delay time.

[0021] Based on the aforementioned delay time, the maximum delay time and the minimum delay time are set, and the delay range is calculated.

[0022] Based on the delay range and the real-time delay time, the ratio of the real-time delay time range to the delay range is compared and marked as the real-time delay value. ;

[0023] Based on the aforementioned delay range, a delay time threshold is set, and the difference between the delay time threshold and the minimum delay time is calculated and marked as the delay difference.

[0024] Based on the delay difference, the ratio of the delay difference range to the delay range is compared and marked as the delay threshold ST.

[0025] By adopting the above technical solution and setting a delay threshold, it is possible to more quickly and effectively confirm whether there is a problem at each position point. The judgment method is simpler, the judgment time is shorter, and the speed of position control of the hydraulic servo system is improved.

[0026] Preferred, The steps for initializing population parameters are as follows:

[0027] Based on the initial population, a three-dimensional mapping chaotic initialization population is introduced to obtain the initial population parameters;

[0028] Randomly set parameters to control the uniform distribution of the population and mark them as control parameters;

[0029] The initialization function for population association is:

[0030]

[0031] in, ∈[0,1], The parameter t represents the current iteration number.

[0032] By introducing the above technical solution, a cubic map chaotic initialization population is introduced to ensure a uniform distribution of initial solutions. This allows for a faster determination of the general problem area during the first search, reducing missed areas due to uneven distribution and improving the accuracy of position control in the hydraulic servo system.

[0033] Preferred, The steps for representing the position information of the i-th point in the j-th dimension are as follows:

[0034] When calculating point location information under different iterations, Gaussian mutation is used to improve the convergence rate of the algorithm;

[0035] The correlation function for improving the convergence rate, obtained from the current iteration number m, is as follows:

[0036]

[0037] Here, the current iteration number m serves as the degree of freedom parameter for the adaptive t-distribution, and t represents the current iteration number. This represents the location information of the i-th point.

[0038] By adopting the above technical solution, the current iteration number m of the population is used as the degree of freedom parameter of the adaptive t-distribution. This enhances the algorithm's global and local optimization capabilities in the early and late stages of population iteration, thereby improving the position control performance of the hydraulic servo system.

[0039] Preferably, the step of improving the correlation function of the convergence rate specifically includes:

[0040] The correlation function for the current iteration number t is ,in, p is the scaling factor, and p is the dynamic selection probability;

[0041] The correlation function for the dynamic selection probability p is:

[0042]

[0043] in, , For different scaling factors, M is the maximum number of iterations, and m is the current number of iterations.

[0044] By adopting the above technical solution and using the dynamic selection probability p to adjust the use of the t-distribution mutation operator, the mutation problem caused by iteration can be reduced, the obtained position result will be more accurate, and the position control accuracy of the hydraulic servo system will be improved.

[0045] Preferably, the step of controlling the hydraulic servo system to move to the execution position using a super-spiral sliding mode controller based on the execution position specifically includes:

[0046] Set the stability equation of the sliding surface, and adjust the sliding surface s according to the stability equation of the sliding surface until the sliding surface s satisfies the stability equation of the sliding surface;

[0047] The stability equation of the sliding surface is:

[0048]

[0049] Where s is the sliding surface and v is the sliding velocity. , is the scaling factor, g(s) is the exponential value, and sat(s) is the saturation function;

[0050] Based on the execution position, the state vector of the control system reaches the execution position and records the arrival position;

[0051] Based on the arrival position, the average error range between the arrival position and the execution position is obtained and recorded as the average error;

[0052] Based on the average error, a number of corrections is set. If the number of adjustments reaches the required number of corrections, the average error is accumulated and corrected when the state vector of the control system moves.

[0053] By adopting the above technical solution and adjusting the sliding surface equation, a smoother sliding surface can be obtained, which is conducive to the system's state entering the sliding surface more smoothly, thereby reducing jitter and improving the position control performance of the hydraulic servo system.

[0054] Preferably, the step where g(s) is an exponential value specifically includes:

[0055] Obtain relevant parameters of the sliding surface;

[0056] The relevant parameters of the sliding surface include the sliding surface angle j and the sliding surface smoothness h;

[0057] The correlation function for the exponential value is:

[0058]

[0059] Where j is the sliding surface angle, h is the sliding surface smoothness, and s is the sliding surface.

[0060] By adopting the above technical solution, g(s) is used to replace the fixed exponent 1 / 2 of the original spiral sliding mode control method to form a variable exponential function, which can accelerate the approach rate of the system state, shorten the approach time, and make the system state enter the sliding surface more smoothly, thereby improving the position control performance of the hydraulic servo system.

[0061] Preferably, the step where sat(s) is a saturation function specifically includes:

[0062] Obtain the saturation Δ of the sliding surface and the minimum jitter frequency k of the sliding surface;

[0063] The saturation function is:

[0064]

[0065] Where Δ is the saturation of the sliding surface, k is the minimum jitter frequency of the sliding surface, and s is the sliding surface.

[0066] By adopting the above technical solution, replacing the sign(s) function of the traditional super-spiral sliding mode algorithm with the saturation function sat(s), and replacing the ideal sign function with the saturation function, the system chattering can be further reduced, and the position control performance of the hydraulic servo system can be improved.

[0067] In summary, this application includes at least one of the following beneficial technical effects:

[0068] 1. The improved super-spiral sliding mode controller, combined with the improved sparrow optimization algorithm, can stably and quickly track the system setpoint. The overshoot of the tracking response curve is very small, the oscillation amplitude is well controlled, there is almost no oscillation, and the tracking error of the system is also relatively small, which improves the position control performance of the hydraulic servo system.

[0069] 2. By setting a delay threshold, it is possible to more quickly and effectively confirm whether there is a problem at each position point. The judgment method is simpler and the judgment time is shorter, which improves the speed of position control of the hydraulic servo system.

[0070] 3. A cubic map is introduced to initialize the chaotic population, ensuring a uniform distribution of initial solutions. This allows for a faster determination of the general problem area during the first search, reducing missed areas due to uneven distribution and improving the accuracy of position control in the hydraulic servo system. Attached Figure Description

[0071] Figure 1 This is a schematic diagram illustrating the specific steps of an embodiment of a position control method for a hydraulic servo system according to the present invention;

[0072] Figure 2 This is a schematic diagram illustrating the specific steps of step 2 in an embodiment of the position control method for a hydraulic servo system according to the present invention.

[0073] Figure 3 This is a schematic diagram illustrating the specific steps of step 23 in an embodiment of the position control method for a hydraulic servo system according to the present invention.

[0074] Figure 4 This is a schematic diagram illustrating the specific steps of step 24 in an embodiment of the position control method for a hydraulic servo system according to the present invention.

[0075] Figure 5 This is a schematic diagram illustrating the specific steps of step 6 in an embodiment of the position control method for a hydraulic servo system according to the present invention.

[0076] Figure 6 This is a schematic diagram illustrating the specific steps of step 62 in an embodiment of the position control method for a hydraulic servo system according to the present invention.

[0077] Figure 7 This is a schematic diagram illustrating step 7 of an embodiment of the position control method for a hydraulic servo system according to the present invention. Detailed Implementation

[0078] The following describes the embodiments and appendices. Figure 1-7 The present invention will be described in further detail, but the embodiments of the present invention are not limited thereto.

[0079] Example:

[0080] This invention discloses a position control method for a hydraulic servo system, referring to... Figure 1-7 Specifically, it includes the following steps:

[0081] Step S1: Obtain the application range of the hydraulic servo system and mark it as the initial range;

[0082] Step S2: Based on the initial range, determine the range of movement positions of the hydraulic servo system within the initial range and mark it as the initial search range;

[0083] Step S3: Based on the initial search range, determine the correlation function according to the final range. The final search range is calculated, where t represents the current iteration number and T is the maximum iteration number. This represents the position information of the i-th point in the j-th dimension. It is the worst position globally. Q is the optimal position currently occupied by the participating points, Q is a random number following a normal distribution, and L is a matrix of all 1s; Let represent a 1×d matrix, where each element is randomly assigned the value 1 or -1, and = ( -1; This indicates that no location requiring hydraulic servo was found among the participating points;

[0084] Step S4: Based on the final search range, determine the optimal movement position of the hydraulic servo within the final search range and mark it as the execution position;

[0085] Step S5: Based on the execution location, determine the execution association function. The execution location is redefined, whereby... To be the globally optimal position This represents the position information of the i-th point in the j-th dimension. It represents the worst-case position globally; β is the step size control parameter, a random number following a normal distribution with a mean of 0 and a variance of 1; K∈[-1,1] is a random number. This is the fitness value of the current participating point; and These are the current best and worst fitness values ​​globally, respectively; ε is the smallest constant.

[0086] Step S6: Based on the execution position, the hydraulic servo system is controlled to move to the execution position using a super spiral sliding mode controller.

[0087] In practical applications, the sparrow search algorithm is used to determine the location where the hydraulic servo system needs to perform its work. Then, an improved super-spiral sliding mode control method is used to control the hydraulic servo system's execution system to move to the specific location to perform the work. The sparrow search algorithm has a simple structure and good stability, but it also has some shortcomings. First, the population initialization is too random, resulting in poor initial individual quality. Second, the main method of the sparrow optimization algorithm is a large-scale random search, which involves a significant computational load. Therefore, cubic chaotic initialization is introduced to optimize the first generation of individuals, improving the overall quality. Furthermore, an adaptive t-distribution is used to enhance the algorithm's global search capability in the early stages and its local search capability in the later stages, further shortening the optimization time.

[0088] The steps for determining the range of movement positions of the hydraulic servo system within the initial range and marking it as the initial search range are as follows:

[0089] Step S21: Based on the initial range, set at least two points within the initial range for searching and mark them as the initial population;

[0090] Step S22: Based on the initial population, each search of the initial population is recorded as one iteration. After multiple searches, the search range is determined.

[0091] Step S23: Obtain the real-time delay value And set a delay threshold ST, and ∈[0, 1], ST∈[0.5, 1];

[0092] Step S24, based on the search association function Determine the initial search range Where t represents the current iteration number, and T is the maximum iteration number. This represents the position information of the i-th point in the j-th dimension. This initializes the population parameters, where Q is a normally distributed random number and L is a matrix of all ones. It is the real-time delay value, and ST is the delay threshold.

[0093] In practical applications, the Sparrow Algorithm is a novel intelligent optimization algorithm. In this algorithm, the population is divided into three types of members: discoverers, joiners, and scouts. Discoverers are the leaders of the population, responsible for exploring food sources and guiding the foraging area for the entire population. Joiners rely on the discoverers' guidance to find food. Scouts, upon detecting potential danger signals in the population, warn other members so they can quickly move to safe locations to forage. During each iteration, the discoverers' positions are updated according to a specific formula designed to ensure they have priority access to food sources during the search. Furthermore, because discoverers guide the foraging direction for the entire population, their search range is larger than that of joiners. In a hydraulic servo system, the points participating in the search are like "sparrows." For example, if a discoverer finds a significant delay problem in area A, it needs to find the problem point within area A. Determining the initial range allows for faster location of the problem point, thus enabling problem-solving.

[0094] Get real-time delay value And set a delay threshold ST, and The steps for ∈[0, 1], ST∈[0.5, 1] ​​are as follows:

[0095] Step S231: Obtain the input change time and output change time at different points, and calculate the real-time delay time;

[0096] Step S232: Based on the delay time, set the maximum delay time and the minimum delay time, and calculate the delay range;

[0097] Step S233: Based on the delay range and the real-time delay time, compare the ratio of the real-time delay time range to the delay range and mark it as the real-time delay value. ;

[0098] Step S234: Based on the delay range, set a delay time threshold, and calculate the difference between the delay time threshold and the minimum delay time, which is then marked as the delay difference.

[0099] Step S235: Based on the delay difference, compare the ratio of the delay difference range to the delay range and mark it as the delay threshold ST.

[0100] In practical applications, hydraulic servo systems are designed to ensure that the system's output quickly and accurately follows changes in the input. Therefore, the larger the difference between the time of output change and the time of input change, the more necessary it is to use a hydraulic servo system for adjustment. For example, if the system's input change time is 12:20:19, the output change time at point A is 12:20:20, and the output change time at point B is 12:20:25, it indicates that the output at point B cannot quickly follow the input change, and the hydraulic servo system should be moved to point B for adjustment.

[0101] The steps for initializing population parameters are as follows:

[0102] Step S241: Based on the initial population, introduce a three-dimensional mapping chaotic initialization population to obtain the initial population parameters;

[0103] Step S242: Randomly set the parameters for controlling the uniform distribution of the population and mark them as control parameters;

[0104] Step S243, initialize the population association function as follows:

[0105]

[0106] in, ∈[0,1], The parameter t represents the current iteration number.

[0107] In practical applications, the sparrow positions in the original algorithm are generated through random initialization, which cannot ensure that the initial positions of individuals are uniformly distributed in the search space, affecting the algorithm's search speed and optimization performance. This results in suboptimal performance when solving for the optimal parameters of the active disturbance rejection controller. Therefore, a cubic map is introduced to initialize the population chaotically, ensuring a uniform distribution of initial solutions. For example, if the participating points are concentrated in region A in the original algorithm, even if the problem in region B is more severe, the participating points cannot be located in region B immediately because they are not in region B.

[0108] The steps for representing the position information of the i-th point in the j-th dimension are as follows:

[0109] Step S244: When calculating the point location information under different iterations, Gaussian mutation is used to improve the convergence rate of the algorithm.

[0110] Step S245, obtain the current iteration number m, and the correlation function that improves the convergence rate is as follows:

[0111]

[0112] Here, the current iteration number m serves as the degree of freedom parameter for the adaptive t-distribution, and t represents the current iteration number. This represents the location information of the i-th point.

[0113] In practical applications, Cauchy mutation can improve the global optimization ability of swarm intelligence algorithms, and combining Gaussian mutation with intelligent algorithms can significantly improve the convergence rate. Since the two boundaries of the t-distribution are both Gaussian and Cauchy distributions, combining the t-distribution with... The formula is improved by combining the existing formula with the current iteration number m of the population. This uses the degree of freedom parameter of the adaptive t-distribution, enhancing the algorithm's global and local optimization capabilities in the early and later stages of population iteration.

[0114] The steps to improve the correlation function for convergence rate are as follows:

[0115] Step S2451, the correlation function for the current iteration number t is: ,in, p is the scaling factor, and p is the dynamic selection probability;

[0116] Step S2452, the correlation function for the dynamic selection probability p is:

[0117]

[0118] in, , For different scaling factors, M is the maximum number of iterations, and m is the current number of iterations.

[0119] In practical applications, a dynamic selection probability p is used to adjust the application of the t-distribution mutation operator. Since the bird iteration is not static, more accurate data can be obtained based on the dynamic selection probability. This leads to more accurate final results, pinpointing more precise problem areas and accelerating problem-solving.

[0120] Based on the execution position, the steps for controlling the hydraulic servo system to move to the execution position using a super-spiral sliding mode controller are as follows:

[0121] Step S61: Set the stability equation of the sliding surface, and adjust the sliding surface s according to the stability equation of the sliding surface until the sliding surface s satisfies the stability equation of the sliding surface.

[0122] Step S62, the stability equation of the sliding surface is:

[0123]

[0124] Where s is the sliding surface and v is the sliding velocity. , is the scaling factor, g(s) is the exponential value, and sat(s) is the saturation function;

[0125] Step S63: Based on the execution position, the state vector of the control system reaches the execution position and the arrival position is recorded;

[0126] Step S64: Based on the arrival position, obtain the average error range between the arrival position and the execution position and record it as the average error;

[0127] Step S65: Based on the average error, set the number of corrections. If the number of adjustments reaches the required number of corrections, then accumulate the average error for correction when the state vector of the control system moves.

[0128] In practical applications, sliding mode variable structure control (SMCC) is a type of adaptive algorithm. Its characteristic is that the system's state vector exists in two states during the stabilization process: the approaching state before reaching the sliding surface and the sliding mode state after reaching the sliding surface. The system's state vector is insensitive to disturbances while sliding on the sliding surface, thus exhibiting good error convergence. However, due to modeling errors, the system's state vector does not remain on the sliding surface after reaching it, but rather fluctuates up and down, causing the control law to switch back and forth frequently, ultimately resulting in significant jitter in the control input. This is the biggest drawback of SMCC. Improving the stability equation of the sliding surface can yield a sliding surface more suitable for the system, reducing jitter and improving system performance.

[0129] The steps where g(s) is an exponential value are as follows:

[0130] Step S621: Obtain relevant parameters of the sliding surface;

[0131] Step S622, the relevant parameters of the sliding surface include the sliding surface angle j and the sliding surface smoothness h;

[0132] Step S623, the correlation function for the exponential value is:

[0133]

[0134] Where j is the sliding surface angle, h is the sliding surface smoothness, and s is the sliding surface.

[0135] In practical applications, use Instead of the fixed exponent 1 / 2 in the original formula, a variable exponential function is constructed. The improved nonlinear term value is... When the system is far from the sliding surface s=0, the nonlinear term value is large, which can accelerate the approach rate of the system state and shorten the approach time. When the nonlinear term is smaller, the system state will more smoothly engage with the sliding surface. Adjusting both the sliding surface angle and its smoothness can further ensure the system state engages with the sliding surface, reducing jitter and improving system performance.

[0136] The steps for sat(s) to be a saturation function are as follows:

[0137] Step S624: Obtain the saturation Δ of the sliding surface and the minimum jitter frequency k of the sliding surface;

[0138] Step S625, the saturation function is:

[0139]

[0140] Where Δ is the saturation of the sliding surface, k is the minimum jitter frequency of the sliding surface, and s is the sliding surface.

[0141] In practical applications, replacing the sign(s) function of the traditional superspiral sliding mode algorithm with the saturation function sat(s), and replacing the ideal sign function with the saturation function, can further reduce system chattering. Compared with the sign function, this saturation function causes the velocity to gradually decrease to zero as it approaches the switching plane.

[0142] The implementation principle of this method is as follows: First, the application range of the hydraulic servo system is obtained and marked as the initial range. Based on the initial range, the movement position range of the hydraulic servo system is determined within the initial range and marked as the initial search range. When determining the initial search range, the discoverer position formula in the improved sparrow algorithm is analyzed, where a cubic map chaotic initialization of the population is introduced to ensure a uniform distribution of the initial solution. Furthermore, Gaussian mutation is combined with intelligent algorithms to improve the convergence rate of the algorithm, and the current iteration number m of the population is used as the degree of freedom parameter of the adaptive t-distribution, enabling the algorithm to improve its global and local optimization capabilities in the early and late stages of population iteration. Based on the initial search range, according to the final range correlation function... The final search range is calculated. Based on the final search range, the optimal movement position of the hydraulic servo is determined within the final search range and marked as the execution position. Based on the execution position, according to the execution correlation function... The execution position is then redefined. Based on the execution position, a super-spiral sliding mode controller is used to move the hydraulic servo system to the execution position. This is based on the traditional super-spiral sliding mode control method. Instead of the fixed exponent 1 / 2 in the original formula, a variable exponent function is constructed. Taking into account the sliding surface angle and the smoothness of the sliding surface, the saturation function sat(s) is used to replace the sign(s) function of the traditional super-spiral sliding mode algorithm. Replacing the ideal sign function with the saturation function can further reduce system chattering.

[0143] The above are all preferred embodiments of this application and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.

Claims

1. A position control method for a hydraulic servo system, characterized in that, Includes the following steps: Obtain the application scope of the hydraulic servo system and mark it as the initial scope; Based on the initial range, the range of movement positions of the hydraulic servo system is determined within the initial range and marked as the initial search range; Based on the initial search range, the association function is determined according to the final range. The final search range is calculated, where t represents the current iteration number, T is the maximum iteration number, and X... i,j X represents the position information of the i-th point in the j-th dimension. worst It is the worst position globally, X p It represents the optimal position currently occupied by the participating points, where Q is a random number following a normal distribution, and L is a matrix of all 1s; A + Let A represent a 1×d matrix, where each element is randomly assigned the value 1 or -1, and A + =A T (AA T -1; i>n / 2 indicates that there are participating points but no location requiring hydraulic servo was found; Based on the final search range, the optimal movement position of the hydraulic servo is determined within the final search range and marked as the execution position; Based on the execution location, according to the execution association function The execution location has been redefined, where X best X is the globally optimal position. i,j X represents the position information of the i-th point in the j-th dimension. worst It represents the worst-case position globally; β is the step size control parameter, a random number following a normal distribution with a mean of 0 and a variance of 1; K∈[-1,1] is a random number, f i This is the fitness value of the current participating point; f g and f w These are the current best and worst fitness values ​​globally, respectively; ε is the smallest constant. Based on the execution position, the hydraulic servo system is controlled to move to the execution position using a super spiral sliding mode controller.

2. The position control method for a hydraulic servo system according to claim 1, characterized in that, Based on the initial range, the step of determining the range of movement positions of the hydraulic servo system within the initial range and marking it as the initial search range is as follows: Based on the initial range, at least two points are selected within the initial range for searching and marked as the initial population; Based on the initial population, each search of the initial population is recorded as an iteration, and the search range is determined after multiple searches. Obtain the real-time delay value R2 and set the delay threshold ST, where R2∈[0,1] and ST∈[0.5,1]; Based on search association function Determine the initial search range Where t represents the current iteration number, T is the maximum iteration number, and X i,j X represents the position information of the i-th point in the j-th dimension. t+1 This initializes the population parameters, where Q is a random number following a normal distribution, L is a matrix of all 1s, R2 is the real-time delay value, and ST is the delay threshold.

3. The position control method for a hydraulic servo system according to claim 2, characterized in that, The steps to obtain the real-time delay value R2 and set the delay threshold ST, where R2∈[0,1] and ST∈[0.5,1], are as follows: Obtain the input and output change times at different points, and calculate the real-time delay time. Based on the aforementioned delay time, the maximum delay time and the minimum delay time are set, and the delay range is calculated. Based on the delay range and the real-time delay time, compare the ratio of the real-time delay time range to the delay range and mark it as the real-time delay value R2; Based on the aforementioned delay range, a delay time threshold is set, and the difference between the delay time threshold and the minimum delay time is calculated and marked as the delay difference. Based on the delay difference, the ratio of the delay difference range to the delay range is compared and marked as the delay threshold ST.

4. The position control method for a hydraulic servo system according to claim 3, characterized in that, X t+1 The steps for initializing population parameters are as follows: Based on the initial population, a three-dimensional mapping chaotic initialization population is introduced to obtain the initial population parameters; Randomly set parameters to control the uniform distribution of the population and mark them as control parameters; The initialization function for population association is: Among them, X t+1 ∈[0,1], ρ is the control parameter, and t represents the current iteration number.

5. The position control method for a hydraulic servo system according to claim 4, characterized in that, X i,j The steps for representing the position information of the i-th point in the j-th dimension are as follows: When calculating point location information under different iterations, Gaussian mutation is used to improve the convergence rate of the algorithm; The correlation function for improving the convergence rate, obtained from the current iteration number m, is as follows: Wherein, the current iteration number m serves as the degree of freedom parameter of the adaptive t-distribution, t represents the current iteration number, and X i This represents the location information of the i-th point.

6. The position control method for a hydraulic servo system according to claim 5, characterized in that, The steps for improving the correlation function of the convergence rate are as follows: The correlation function for the current iteration number t is t = a1 × p, where a1 is the scaling factor and p is the dynamic selection probability; The correlation function for the dynamic selection probability p is: Where ω1 and ω2 are different scaling factors, M is the maximum iteration number, and m is the current iteration number.

7. The position control method for a hydraulic servo system according to claim 6, characterized in that, Based on the aforementioned execution position, the steps of controlling the hydraulic servo system to move to the execution position using a super-spiral sliding mode controller are as follows: Set the stability equation of the sliding surface, and adjust the sliding surface s according to the stability equation of the sliding surface until the sliding surface s satisfies the stability equation of the sliding surface; The stability equation of the sliding surface is: Where s is the sliding surface, v is the sliding velocity, λ and α are scaling factors, g(s) is the exponential value, and sat(s) is the saturation function; Based on the execution position, the state vector of the control system reaches the execution position and records the arrival position; Based on the arrival position, the average error range between the arrival position and the execution position is obtained and recorded as the average error; Based on the average error, a number of corrections is set. If the number of adjustments reaches the required number of corrections, the average error is accumulated and corrected when the state vector of the control system moves.

8. The position control method for a hydraulic servo system according to claim 7, characterized in that, The step where g(s) is an exponential value specifically includes: Obtain relevant parameters of the sliding surface; The relevant parameters of the sliding surface include the sliding surface angle j and the sliding surface smoothness h; The correlation function for the exponential value is: Where j is the sliding surface angle, h is the sliding surface smoothness, and s is the sliding surface.

9. A position control method for a hydraulic servo system according to claim 8, characterized in that, The step where sat(s) is a saturation function is specifically as follows: Obtain the saturation Δ of the sliding surface and the minimum jitter frequency k of the sliding surface; The saturation function is: Where Δ is the saturation of the sliding surface, k is the minimum jitter frequency of the sliding surface, and s is the sliding surface.

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