A method and system for synchronous control of multiple drive wheels of a ship washing machine based on SMC-CCC-LESO

Abstract

This invention proposes a synchronous control method and system for multiple drive wheels of a ship washing machine based on SMC-CCC-LESO, relating to the field of ship cleaning technology. Addressing the problem of poor disturbance rejection in existing ship washing machine control methods, this invention configures a linear extended state observer for each drive wheel of the ship washing machine. The linear extended state observer tracks the operating state of each drive wheel in real time and outputs the estimated speed and total disturbance of each drive wheel. The estimated speed of each drive wheel is input into a cross-coupled control model, which calculates the coupling error between multiple drive wheels based on the estimated speed. The coupling error and the estimated total disturbance are then input into a sliding mode control model, which outputs an initial control command based on the coupling error and the estimated total disturbance. The synchronous control method for multiple drive wheels of the ship washing machine of this invention exhibits better disturbance rejection.

Description

Technical Field

[0001] This invention relates to the field of ship cleaning technology, and in particular to a method and system for synchronous control of multiple drive wheels in a ship washing machine based on SMC-CCC-LESO. Background Technology

[0002] With the continuous development of the global shipping industry, the number and tonnage of ships are increasing year by year. Biofouling such as barnacles and algae, as well as rust adhering to the hull surface, significantly increase navigation resistance and accelerate hull corrosion. Therefore, efficient and precise hull cleaning has become a key factor in ensuring shipping efficiency and ship lifespan. In dock and port operations, ship washing machines need to adapt to complex terrain and harsh environments with high humidity and high salinity. Hydraulic drive systems, with their advantages of low-speed, high-torque output and high power-to-weight ratio, have become the core power choice for driving ship washing machines. Designing a multi-wheel synchronous control method adapted to the characteristics of the hydraulic system for the four-wheel independent drive structure of hydraulic ship washing machines is crucial for achieving efficient and reliable cleaning operations.

[0003] In existing technologies, hydraulic ship washing machines generally employ sliding mode control to control the drive wheels. This involves designing a sliding surface and switching control laws to force the system state to converge to the sliding surface within a finite time. The insensitivity of sliding mode control to parameter changes and disturbances improves the robustness of single-wheel speeds. To achieve synchronous control of multiple drive wheels, cross-coupling control from CNC machine tools is directly applied to hydraulic ship washing machines. This involves calculating the average speed of the four wheels and using the deviation between the individual wheel speed and the average speed as a correction term to adjust the individual wheel. However, existing hydraulic ship washing machines do not optimize coupling gain and error compensation logic for the nonlinear characteristics of the hydraulic system, resulting in poor disturbance rejection.

[0004] Therefore, developing a synchronous control method and system for multiple drive wheels of a ship washing machine based on SMC-CCC-LESO is of great significance for improving the anti-disturbance performance of the control method. Summary of the Invention

[0005] To address the problem of poor disturbance resistance in existing ship washing machine control methods, this invention proposes a synchronous control method for multiple drive wheels of a ship washing machine based on SMC-CCC-LESO, which specifically includes the following steps: S1. Configure a linear expansion state observer for each drive wheel of the ship washing machine; S2. The linear expansion state observer tracks the operating state of each drive wheel in real time and outputs the estimated speed of each drive wheel and the estimated total disturbance. S3. Input the estimated speed of each drive wheel into the cross-coupling control model, which obtains the coupling error between multiple drive wheels based on the estimated speed. S4. Input the coupling error and the total disturbance estimate into the sliding mode control model, and the sliding mode control model outputs the initial control command based on the coupling error and the total disturbance estimate.

[0006] Furthermore, in S1, for each drive wheel of the ship washing machine, a linear expansion state observer is configured, including: associating the linear expansion state observer with the hydraulic drive link of the corresponding drive wheel; and preset the observer gain and sampling period for the linear expansion state observer.

[0007] Furthermore, in step S2, the linear expansion state observer tracks the operating state of each drive wheel in real time and outputs the estimated speed and total disturbance of each drive wheel, including: acquiring the shaft speed signal in the hydraulic drive link of the drive wheel in real time through the linear expansion state observer to determine the estimated speed; and analyzing the changes in hydraulic system oil parameters and external load in real time through the linear expansion state observer to determine the estimated total disturbance.

[0008] Furthermore, the linear expansion state observer collects the shaft speed signal in the hydraulic drive link of the drive wheel in real time to determine the speed estimate, including: calculating the drive wheel speed estimate by discretization iterative algorithm on the shaft speed signal; The hydraulic parameters are hydraulic oil viscosity and compressibility, and the external loads include dock surface friction and cleaning head operating load. The linear expansion state observer analyzes the changes in hydraulic system hydraulic parameters and external loads in real time to determine the total disturbance estimate. This includes comprehensively quantifying the disturbances caused by changes in hydraulic oil viscosity and compressibility, as well as the load torque fluctuations caused by uneven dock surface friction and the disturbances caused by changes in cleaning head operating load, to obtain the total disturbance estimate of the drive wheel.

[0009] Furthermore, in step S4, the sliding mode control model outputs control commands based on the coupling error and the total disturbance estimate, including: substituting the coupling error into the basic sliding surface formula to calculate the degree of deviation between the current system state and the sliding surface; using an exponential reaching law to adjust the output rate of the control quantity according to the degree of deviation and the total disturbance estimate to ensure that the system state converges to the sliding surface; and calculating the control commands based on the convergence process.

[0010] Furthermore, the formula for the sliding surface is: ; Where s is the sliding surface variable, used to characterize the degree of deviation between the current operating state of the drive wheel and the ideal sliding surface, c1 and c2 are the sliding surface coefficients, and e is the coupling error.

[0011] Furthermore, in step S3, the cross-coupling control model obtains the coupling error between multiple drive wheels based on the estimated rotational speed, including: calculating the arithmetic mean of the estimated rotational speeds of the four drive wheels and using the arithmetic mean as the average rotational speed of the four drive wheels; performing a difference calculation between the estimated rotational speed of each drive wheel and the average rotational speed of the four drive wheels to obtain the deviation value between the estimated rotational speed of each drive wheel and the average rotational speed; performing a difference calculation between the deviation values ​​of two adjacent drive wheels and between the deviation values ​​of two diagonally opposite drive wheels to obtain the relative deviation between all wheels.

[0012] Furthermore, after receiving the control command, the method also includes validating the control command, specifically: obtaining the rated operating parameter range of the hydraulic actuator of the ship washing machine; comparing the control command with the rated operating parameter range; and adjusting the control command to the boundary value of the rated operating parameter range when the control command exceeds the rated operating parameter range.

[0013] This invention also provides a synchronous control system for multiple drive wheels of a ship washing machine based on SMC-CCC-LESO, the system being used to execute the synchronous control method for multiple drive wheels of a ship washing machine based on SMC-CCC-LESO described above, the system comprising: A configuration module for configuring a linear expansion state observer for each drive wheel of the ship washing machine; The state tracking module is used to track the operating state of each drive wheel in real time through the linearly extended state observer, and output the estimated speed of each drive wheel and the estimated total disturbance. The coupling error calculation module is used to input the estimated speed of each drive wheel into the cross-coupling control model, which calculates the coupling error between multiple drive wheels based on the estimated speed. The control command output module is used to input the coupling error and the total disturbance estimate into the sliding mode control model, and the sliding mode control model outputs control commands based on the coupling error and the total disturbance estimate.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention configures a linearly expanded state observer for each drive wheel of a ship washing machine. The linearly expanded state observer tracks the operating state of each drive wheel in real time and outputs the estimated speed and total disturbance of each drive wheel. The estimated speed of each drive wheel is input into a cross-coupled control model, which calculates the coupling error between multiple drive wheels based on the estimated speed. The coupling error and the estimated total disturbance are input into a sliding mode control model, which outputs an initial control command based on the coupling error and the estimated total disturbance. Specifically, the linearly expanded state observer for each drive wheel tracks its operating state in real time, accurately sensing the estimated speed and total disturbance of each drive wheel. Since the coupling error accurately reflects the speed deviation between drive wheels and the deviation between a single drive wheel and the control target, and the estimated total disturbance is a disturbance factor affecting speed deviation, the sliding mode control model combines both the coupling error and the estimated total disturbance to simultaneously control single-wheel tracking and adjust the speed difference between multiple wheels, thus balancing single-wheel tracking and multi-wheel synchronization. In addition, it can correct speed deviations while specifically compensating for the effects of disturbances, thus improving the disturbance resistance of the multi-drive wheel synchronization control method. Attached Figure Description

[0015] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0016] Figure 1 This is a flowchart of a multi-drive wheel synchronization control method for a ship washing machine based on SMC-CCC-LESO, provided by an embodiment of the present invention; Figure 2 This is a graph showing the simulation results provided in the embodiments of the present invention; Figure 3 This is a schematic diagram of the structure of a multi-drive wheel synchronous control system for a ship washing machine based on SMC-CCC-LESO, provided in an embodiment of the present invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0018] The specific embodiments of the present invention will be described below.

[0019] To address the poor disturbance rejection of existing ship washing machine control methods, this invention configures a linear extended state observer for each drive wheel of the ship washing machine. The linear extended state observer tracks the operating state of each drive wheel in real time and outputs the estimated speed and total disturbance of each drive wheel. The estimated speed of each drive wheel is input into a cross-coupled control model, which calculates the coupling error between multiple drive wheels based on the estimated speed. The coupling error and the estimated total disturbance are then input into a sliding mode control model, which outputs an initial control command based on the coupling error and the estimated total disturbance. This invention provides a multi-drive wheel synchronous control method for ship washing machines with improved disturbance rejection.

[0020] Example 1 This invention provides a method for synchronous control of multiple drive wheels in a ship washing machine based on SMC-CCC-LESO. Figure 1 This is a flowchart of a multi-drive wheel synchronization control method for a ship washing machine based on SMC-CCC-LESO, provided by an embodiment of the present invention. Figure 1 As shown, the specific steps include the following: S1. Configure a linear expansion state observer for each drive wheel of the ship washing machine.

[0021] Each drive wheel is the core execution unit for realizing equipment movement, driven by an independent hydraulic drive link, which includes a hydraulic motor, proportional valve, etc. The Linear Extended State Observer (LESO) is an observation device used for real-time estimation of system state and disturbances. It can extend unknown disturbances of the system into new state variables, track and output key information in real time through discretization algorithms, and provide data support for subsequent control.

[0022] Each linear expansion state observer is bound to the hydraulic drive link of its corresponding drive wheel to ensure that the observer can acquire data such as the shaft speed signal of the hydraulic motor of that drive wheel and changes in hydraulic system pressure / flow. Dedicated observer parameters are set for each linear expansion state observer, including observer gain for adjusting response speed and filtering performance, and sampling period for controlling data acquisition frequency, ensuring that the linear expansion state observer is adapted to the hydraulic dynamic characteristics of the corresponding drive wheel.

[0023] Specifically, for each drive wheel of the ship washing machine, a linear expansion state observer is configured, including: associating the linear expansion state observer with the hydraulic drive link of the corresponding drive wheel; and preset the observer gain and sampling period for the linear expansion state observer.

[0024] The hydraulic drive link refers to the dedicated hydraulic control loop for each drive wheel, which is the source of the observation signal for the linear extended state observer. The observer gain, as a core parameter of the linear extended state observer, is used to adjust the observer's response sensitivity to speed estimation errors, directly affecting the accuracy of disturbance estimation. The observer gain is determined based on the dynamic response characteristics of the hydraulic system, ensuring rapid convergence and oscillation-free operation. The sampling period refers to the time interval for the discretization calculation of the linear extended state observer, i.e., the frequency at which the linear extended state observer updates the speed estimate and the total disturbance estimate. For example, a sampling period of 0.01s adapts to the dynamic characteristics of the hydraulic system and avoids observation lag caused by excessively slow sampling.

[0025] For the four-wheel independent hydraulic drive architecture of the ship washing machine, a dedicated LESO is deployed for each drive wheel, and each LESO is bound to the hydraulic drive link of its corresponding drive wheel, clearly defining the signal acquisition range of the LESO. The observer gain and sampling period are set for each LESO to complete its initialization. Due to the nonlinear characteristics of the hydraulic system, such as oil compressibility and valve dead zone, as well as disturbances such as uneven ground friction and sudden changes in the load on the washing head, the disturbance of a single link can be accurately estimated by associating the hydraulic link with a dedicated LESO for each drive wheel.

[0026] S2. The linear expansion state observer tracks the operating state of each drive wheel in real time and outputs the estimated speed of each drive wheel and the estimated total disturbance.

[0027] The operating state of each drive wheel refers to the dynamic performance of a single drive wheel under hydraulic drive, specifically manifested in its speed change characteristics and its state affected by disturbances. LESO continuously monitors and updates the operating state of the drive wheels at a preset sampling period, continuously updating the monitoring results of the estimated drive wheel speed and the estimated total disturbance. Furthermore, it converts the continuous signal output by LESO into a discrete signal adapted to the digital controller.

[0028] Specifically, the linear extended state observer tracks the operating status of each drive wheel in real time and outputs the estimated speed and total disturbance of each drive wheel. This includes: acquiring the shaft speed signal in the hydraulic drive link of the drive wheel in real time through the linear extended state observer to determine the estimated speed; and analyzing the changes in hydraulic system oil parameters and external load in real time through the linear extended state observer to determine the estimated total disturbance.

[0029] The shaft speed signal directly reflects the actual speed of the drive wheel and serves as the reference feedback signal for LESO's speed estimation. This signal can be acquired in real-time by a speed encoder installed on the output shaft of the hydraulic motor. Changes in hydraulic system fluid parameters constitute internal disturbances, including variations in fluid viscosity with temperature, differences in fluid compressibility due to pressure fluctuations, and changes in leakage caused by fluid aging. These directly affect the speed output accuracy of the hydraulic motor. External load changes constitute external disturbances, such as increased load due to sudden contact between the cleaning head and a hull protrusion, fluctuations in drive wheel friction load due to wet or dry dock surfaces, and changes in cleaning resistance caused by variations in hull curvature.

[0030] For example, the shaft speed signal in the hydraulic drive link of the drive wheel is acquired in real time by a linear expansion state observer, and the speed estimate is determined, including: calculating the speed estimate of the drive wheel by discretization iterative algorithm on the shaft speed signal.

[0031] The discretization iterative algorithm refers to iteratively updating the current observation result by using the observation state of the previous cycle and the current feedback error at a fixed sampling period, thus converting the continuous signal output by LESO into a discrete signal adapted to the digital controller. The speed estimate is a value calculated by LESO using the discretization iterative algorithm that approximates the actual speed of the drive wheels; this value serves as the final speed estimate output by LESO. Replacing the potentially disturbed original shaft speed signal with the final speed estimate output by LESO can reduce the interference error of the original shaft speed signal.

[0032] The hydraulic parameters include hydraulic oil viscosity and compressibility, and the external loads include dock surface friction and cleaning head operation load. The changes in hydraulic system hydraulic parameters and external loads are analyzed in real time by a linear expansion state observer to determine the total disturbance estimate. This includes comprehensively quantifying the disturbances caused by changes in hydraulic oil viscosity and compressibility, as well as the load torque fluctuations caused by uneven dock surface friction and the disturbances caused by changes in cleaning head operation load, to obtain the total disturbance estimate of the drive wheel.

[0033] The total disturbance estimate refers to the quantified result of the sum of internal and external disturbances output by LESO. By monitoring the dynamic changes in the error between the estimated and actual speeds using LESO, abnormal fluctuations can be identified as being caused by changes in oil viscosity, compressibility, or the load on the ground friction cleaning head. The actual speed of the drive wheel is acquired by a speed encoder, and the difference between the actual speed and the speed estimate output by LESO in the previous cycle is calculated to determine the speed estimation error. For example, when a decrease in oil viscosity causes an abnormal increase in hydraulic motor speed, or a decrease in ground friction causes a sudden increase in drive wheel speed, the actual speed is greater than the speed estimate, and the error shows a negative change. Conversely, when increased oil compressibility causes a lag in motor speed, or an increase in the cleaning head load causes a sudden drop in drive wheel speed, the actual speed is less than the speed estimate, and the error shows a positive change. The dynamic trend of the speed estimation error directly reflects the combined influence of all disturbance sources, including oil parameters and external loads.

[0034] By integrating the effects of dispersed disturbance sources into a single quantitative index using LESO, and employing the discretization iterative algorithm described in the above embodiments, the impact of various disturbances on the rotational speed is transformed into a numerical value of a unified dimension, eliminating the differences in physical quantities among the disturbance sources. Changes in oil viscosity, oil compressibility, uneven ground friction, and changes in the load on the cleaning head will ultimately cause the drive wheel rotational speed to deviate from the target value. There is no need to distinguish whether the speed estimation error is caused by viscosity or friction; the total influence intensity of all disturbances can be directly quantified by the change in the speed estimation error. Since internal and external disturbances do not occur independently during ship washing machine operation but coexist and are coupled, directly quantifying the total influence intensity of all disturbances is more consistent with engineering practice and offers a faster response speed.

[0035] S3. Input the estimated speed of each drive wheel into the cross-coupling control model, which obtains the coupling error between multiple drive wheels based on the estimated speed.

[0036] Cross-Coupled Control (CCC) is a control model used to solve the synchronization problem of multiple independently driven wheels. Its core function is to calculate the speed differences between wheels and generate coupling errors to adjust the speed consistency of multiple wheels. The coupling error between multiple drive wheels includes the coupling result of single-wheel speed tracking error and inter-wheel synchronization error, which is the core basis for adjusting multi-wheel synchronization.

[0037] Specifically, the cross-coupling control model obtains the coupling error between multiple drive wheels based on the estimated rotational speed, including: calculating the arithmetic mean of the estimated rotational speeds of the four drive wheels and using the arithmetic mean as the average rotational speed of the four drive wheels; performing a difference calculation between the estimated rotational speed of each drive wheel and the average rotational speed of the four drive wheels to obtain the deviation value between the estimated rotational speed of each drive wheel and the average rotational speed; performing a difference calculation between the deviation values ​​of two adjacent drive wheels and between the deviation values ​​of two diagonally opposite drive wheels to obtain the relative deviation between all wheels.

[0038] The cross-coupling control model calculates the average speed of the four drive wheels based on the received speed estimates. This average speed represents the overall speed level of the four drive wheels. Subtracting the average speed from the speed estimate of each drive wheel yields four deviation values. These deviation values ​​reflect the difference between the speed of each drive wheel and the overall average speed. A positive deviation indicates that the drive wheel's speed is above average, while a negative deviation indicates that it is below average. The larger the absolute value of the deviation, the worse the synchronization between the individual drive wheel and the overall system. For adjacent drive wheels, the deviation value of one drive wheel is subtracted from the deviation value of the other to obtain the relative deviation of the adjacent drive wheels. Similarly, for diagonal drive wheels, the deviation value of one drive wheel is subtracted from the deviation value of the other to obtain the relative deviation of the diagonal wheels. These relative deviations of adjacent and diagonal wheels cover all wheel relationships. Even if the speed signal of one group of wheels temporarily becomes abnormal, the system can still determine the overall synchronization status through the relative deviations of other groups, reducing synchronization control failures caused by local signal problems. By simultaneously calculating the relative deviations of adjacent drive wheels and diagonal drive wheels, it covers all possible wheel-to-wheel relationships in a four-wheel drive system, which can more accurately reflect the overall synchronization status and improve the synchronization accuracy of multiple wheels.

[0039] S4. Input the coupling error and the total disturbance estimate into the sliding mode control model, and the sliding mode control model outputs control commands based on the coupling error and the total disturbance estimate.

[0040] Sliding Mode Control (SMC) is a nonlinear robust control algorithm for a single drive wheel. Its core objective is to force the drive wheel speed to move along a preset sliding surface by frequently switching the control quantity, thereby achieving accurate speed tracking of the target value and suppressing the influence of external disturbances and changes in system parameters.

[0041] Specifically, the sliding mode control model outputs control commands based on the coupling error and the total disturbance estimate, including: substituting the coupling error into a preset sliding surface formula to calculate the degree of deviation between the current system state and the sliding surface; using an exponential reaching law to adjust the output rate of the control quantity according to the degree of deviation and the total disturbance estimate to ensure that the system state converges to the sliding surface; and calculating the control commands based on the convergence process.

[0042] The sliding surface formula is a mathematical expression defining the ideal trajectory of a system, quantifying the deviation between the actual state and the ideal trajectory. The exponential reaching law is a dynamic adjustment law that forces the system state to converge towards the sliding surface. Its purpose is to ensure that the deviation between the current system state and the sliding surface gradually decreases over time, eventually achieving complete conformity and precise speed tracking of the target value. This includes a dual adjustment mechanism: exponential decay adjustment and sign-assisted adjustment. For example, exponential decay adjustment dynamically adjusts the convergence speed based on the magnitude of the current deviation; the greater the deviation, the faster the convergence speed; as the deviation decreases, the convergence speed automatically slows down to avoid overshoot or oscillation due to excessive speed.

[0043] The formula for the sliding surface is: ; Where s is the sliding surface variable, which characterizes the degree of deviation between the current operating state of the drive wheel and the ideal sliding surface; c1 and c2 are sliding surface coefficients, which are used to adjust the weight of the deviation degree and the convergence speed; and e is the coupling error, which is calculated by the subsequent cross-coupling control model.

[0044] Introducing the total disturbance estimate into the sliding mode control model enables the output control commands to compensate for disturbances in real time, improving disturbance rejection capability and adapting to complex loads. Furthermore, incorporating coupling error into the sliding surface formula allows for simultaneous control of single-wheel tracking and adjustment of the speed difference between the four wheels, achieving multi-wheel synchronous control while maintaining single-wheel tracking performance.

[0045] After receiving the control command, the method also includes validating the control command, specifically: obtaining the rated operating parameter range of the hydraulic actuator of the ship washing machine; comparing the control command with the rated operating parameter range; and adjusting the control command to the boundary value of the rated operating parameter range when the control command exceeds the rated operating parameter range.

[0046] The control commands obtained after adjustment via sliding mode orifice and cross-coupling are compared one by one with the acquired rated operating parameter range to determine whether the commands are within the allowable value range. Control commands include current commands for controlling proportional valves and speed commands for driving hydraulic motors. If a control command exceeds the rated range, it is adjusted to the boundary value of the rated parameter range to ensure that the control commands always fall within the safe range that the actuator can withstand.

[0047] Based on the above embodiments, to verify the feasibility and control effect of the above method in a four-wheel independent drive boat washing machine system, a simulation platform was used to conduct simulation analysis on the control method. In actual operation, due to factors such as road friction, differences in the structure of the boat washing device, or fluctuations in the hydraulic system, uneven loads on each wheel are often caused, thus affecting wheel speed synchronization and vehicle running stability. To verify the coordination capability of the control system, a load change scenario of uniform speed operation + sudden change in inter-wheel load was designed: the system initially runs at a constant speed of 1 m / s, and then at a certain moment, the load conditions of each wheel are suddenly changed. Figure 2 As shown, the four curves represent the actual rotational speeds of the four drive wheels. The red curve represents the drive wheel with its original load remaining constant, serving as a baseline. The other three drive wheels are subjected to sudden load increases of 150%, 100%, and 60% of the original load, respectively. Under sudden load changes, the time for wheel speed adjustment and recovery to stability is approximately 0.025 seconds, verifying the control strategy's rapid response and inter-wheel coordination capabilities under load disturbances. In this embodiment, the speed control system based on sliding mode and cross-coupling control exhibits rapid response, precise synchronization, and robust operation, validating the engineering feasibility and superior performance of the aforementioned control method in a four-wheel independent drive boat washing machine application.

[0048] In this embodiment, a linear extended state observer is configured for each drive wheel of the ship washing machine. The linear extended state observer tracks the operating state of each drive wheel in real time and outputs the estimated speed and total disturbance of each drive wheel. The estimated speed of each drive wheel is input into a cross-coupled control model, which calculates the coupling error between multiple drive wheels based on the estimated speed. The coupling error and the estimated total disturbance are input into a sliding mode control model, which outputs an initial control command based on the coupling error and the estimated total disturbance. Specifically, a linear extended state observer is configured for each drive wheel to track its operating state in real time and accurately perceive the estimated speed and total disturbance of each drive wheel. Since the coupling error accurately reflects the speed deviation between drive wheels and the deviation between a single drive wheel and the control target, and the estimated total disturbance is a disturbance factor affecting the speed deviation, the sliding mode control model combines both the coupling error and the estimated total disturbance to simultaneously control single-wheel tracking and adjust the speed difference between multiple wheels, thus balancing single-wheel tracking and multi-wheel synchronization. In addition, it can correct speed deviations while specifically compensating for the effects of disturbances, thus improving the disturbance resistance of the multi-drive wheel synchronization control method.

[0049] Example 2 This invention also provides a multi-drive wheel synchronous control system for a ship washing machine based on SMC-CCC-LESO. Figure 3 This is a schematic diagram of a multi-drive wheel synchronous control system for a ship washing machine based on SMC-CCC-LESO, provided by an embodiment of the present invention. Figure 3 As shown, the system includes: A configuration module for configuring a linear expansion state observer for each drive wheel of the ship washing machine; The state tracking module is used to track the operating state of each drive wheel in real time through the linearly extended state observer, and output the estimated speed of each drive wheel and the estimated total disturbance. The coupling error calculation module is used to input the estimated speed of each drive wheel into the cross-coupling control model, which calculates the coupling error between multiple drive wheels based on the estimated speed. The control command output module is used to input the coupling error and the total disturbance estimate into the sliding mode control model, and the sliding mode control model outputs control commands based on the coupling error and the total disturbance estimate.

[0050] The multi-drive wheel synchronous control system for a ship washing machine based on SMC-CCC-LESO provided in this embodiment executes the multi-drive wheel synchronous control method for a ship washing machine based on SMC-CCC-LESO described in any of the above embodiments, and has the beneficial effects of any of the above embodiments, which will not be repeated here.

[0051] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the technical solutions of the embodiments of the present invention.

Claims

1. A method for SMC-CCC-LESO-based synchronous control of multiple drive wheels of a ship washing machine, characterized in that, include: S1. Configure a linear expansion state observer for each drive wheel of the ship washing machine; S2. The linear expansion state observer tracks the operating state of each drive wheel in real time and outputs the estimated speed of each drive wheel and the estimated total disturbance. Specifically, it includes: The estimated rotational speed is determined by acquiring the shaft speed signal in the hydraulic drive link of the drive wheel in real time through the linear expansion state observer. The linear expansion state observer analyzes the changes in hydraulic system oil parameters and external loads in real time to determine the estimated total disturbance value. This includes: calculating the estimated speed of the drive wheel from the shaft speed signal using a discretization iterative algorithm; the oil parameters include hydraulic oil viscosity and compressibility, and the external loads include dock surface friction and cleaning head operation load. The linear expansion state observer analyzes the changes in hydraulic system oil parameters and external loads in real time to determine the estimated total disturbance value, including: comprehensively quantifying the disturbances caused by changes in hydraulic oil viscosity and compressibility, as well as the load torque fluctuations caused by uneven dock surface friction and the disturbances caused by changes in cleaning head operation load, to obtain the estimated total disturbance value of the drive wheel. S3. Input the estimated speed of each drive wheel into the cross-coupling control model, which obtains the coupling error between multiple drive wheels based on the estimated speed. S4. Input the coupling error and the total disturbance estimate into the sliding mode control model, and the sliding mode control model outputs control commands based on the coupling error and the total disturbance estimate; Specifically, this includes: substituting the coupling error into the basic sliding surface formula to calculate the degree of deviation between the current system state and the sliding surface; using an exponential reaching law to adjust the output rate of the control quantity according to the degree of deviation and the total disturbance estimate to ensure that the system state converges to the sliding surface; and calculating the control command based on the convergence process.

2. The method for synchronous control of multiple drive wheels of a ship washing machine based on SMC-CCC-LESO according to claim 1, characterized in that, In step S1, a linear expansion state observer is configured for each drive wheel of the ship washing machine, including: Associate the linear expansion state observer with the hydraulic drive link of the corresponding drive wheel; The observer gain and sampling period are preset for the linearly extended state observer.

3. The SMC-CCC-LESO-based synchronized control method of the multiple drive wheels of the washing machine according to claim 1, characterized by, The formula for the sliding surface is: ； Where s is the sliding surface variable, used to characterize the degree of deviation between the current operating state of the drive wheel and the ideal sliding surface, c1 and c2 are the sliding surface coefficients, and e is the coupling error.

4. The method for synchronous control of multiple drive wheels of a ship washing machine based on SMC-CCC-LESO according to claim 1, characterized in that, In step S3, the cross-coupling control model obtains the coupling error between multiple drive wheels based on the estimated rotational speed, including: Calculate the arithmetic mean of the estimated rotational speeds of the four drive wheels, and use the arithmetic mean as the average rotational speed of the four drive wheels; The estimated speed of each drive wheel is calculated by subtracting the average speed of the four drive wheels to obtain the deviation between the estimated speed of each drive wheel and the average speed. The difference between the deviation values ​​of two adjacent drive wheels and the difference between the deviation values ​​of two diagonally opposite drive wheels are calculated to obtain the relative deviation between all wheels.

5. The method for synchronous control of multiple drive wheels of a ship washing machine based on SMC-CCC-LESO according to claim 1, characterized in that, After receiving the control command, the process also includes validating the control command, specifically: Obtain the rated operating parameter range of the hydraulic actuator of the ship washing machine; Compare the control command with the rated operating parameter range; When the control command exceeds the rated operating parameter range, the control command is adjusted to the boundary value of the rated operating parameter range.

6. A synchronous control system for multiple drive wheels of a ship washing machine based on SMC-CCC-LESO, characterized in that, The system is used to execute the multi-drive wheel synchronization control method for a ship washing machine based on SMC-CCC-LESO as described in any one of claims 1-5. The system includes: A configuration module for configuring a linear expansion state observer for each drive wheel of the ship washing machine; The state tracking module is used to track the operating state of each drive wheel in real time through the linearly extended state observer, and output the estimated speed of each drive wheel and the estimated total disturbance. The coupling error calculation module is used to input the estimated speed of each drive wheel into the cross-coupling control model, which calculates the coupling error between multiple drive wheels based on the estimated speed. The control command output module is used to input the coupling error and the total disturbance estimate into the sliding mode control model, and the sliding mode control model outputs control commands based on the coupling error and the total disturbance estimate.

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