A load-driven front fork shock absorber adaptive control method and system

Abstract

The present invention provides a load-driven front fork shock absorber adaptive control method and system, which relates to the field of shock absorber control technology. The method comprises: constructing a front fork shock absorber dynamic model including a front fork shock absorber load; establishing a state equation of the front fork shock absorber dynamic model; establishing a correlation function between a state variable and a desired control target; establishing a PID controller with a nonlinear disturbance compensation term for a comprehensive error of the state variable; updating the gain parameter of the PID controller by an adaptive update law based on state feedback to obtain an optimized PID controller; obtaining the current front fork shock absorber load; taking the minimum value of the correlation function as a target, inputting the current front fork shock absorber load into the optimized PID controller, outputting the current control variable value, i.e., the current front fork shock absorber damping force; and adjusting the front fork shock absorber according to the current front fork shock absorber damping force. The adaptive adjustment capability and control accuracy of the front fork shock absorber under the load change state are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of shock absorber control, and in particular to a load-driven front fork shock absorber adaptive control method and system. Background Art

[0002] A front fork shock absorber is a mechanical device designed to absorb and reduce shock and vibration from the supporting structure or environment. It is usually composed of springs and dampers. These components work together to effectively reduce the vibration and impact force transmitted to the structure, thereby maintaining overall stability and performance. Currently, active front fork shock absorbers with solenoid valves are becoming increasingly popular. They use an electronic control unit (ECU) to dynamically adjust the damping force. The solenoid valves built into the shock absorber can adjust the flow of fluid through the shock absorber in real time based on data from sensors and preset programs. By controlling the opening and closing of these valves, the system can adjust the liquid circulation speed, thereby changing the damping force and achieving precise control of shock absorption performance.

[0003] Load-driven adaptive control of the front fork shock absorber is a control solution for the front fork shock absorber that automatically adjusts the damping characteristics of the shock absorber by real-time monitoring of changes in load and environmental conditions. This control technology relies on complex sensors and control algorithms to ensure that the shock absorber can provide optimal performance under different load and dynamic conditions. In a changing environment and under different load conditions, through adaptive control, the shock absorber can intelligently adjust its own characteristics to ensure that the best performance and stability can be maintained under various conditions, thereby improving overall efficiency and effectiveness.

[0004] However, the design premise of existing front fork shock absorber control schemes is often based on the condition of constant load, and the real-time changes of the shock absorber suspension mass are not taken into consideration. Once the load changes, the front fork shock absorber control force will be insufficient, thereby reducing the control accuracy, and the damping cannot be adaptively adjusted in real time. The scope of application is narrow, the shock absorption effect is poor, and it is not conducive to product promotion. Summary of the invention

[0005] In order to solve the technical problems in the prior art that the design premise of the front fork shock absorber control scheme is often designed based on the condition of constant load, and the real-time change of the shock absorber suspension mass is not considered, once the load changes, the control force of the front fork shock absorber will be insufficient, thereby reducing the control accuracy, and being unable to adaptively adjust the damping in real time, the application range is narrow, the shock absorption effect is poor, and it is not conducive to product promotion, the present invention provides a load-driven front fork shock absorber adaptive control method and system.

[0006] The technical solution provided by the embodiment of the present invention is as follows:

[0007] First aspect

[0008] An embodiment of the present invention provides a load-driven front fork shock absorber adaptive control method, comprising:

[0009] S1: Construct the front fork shock absorber dynamic model including the front fork shock absorber load;

[0010] S2: Establishing the state equation of the front fork shock absorber dynamics model, wherein the state variables of the state equation are the compression amount and compression speed of the front fork shock absorber, and the control variable of the state equation is the damping force of the front fork shock absorber;

[0011] S3: Establishing a correlation function between the state variable and the expected control target, wherein the expected control target includes an expected load center of gravity fluctuation amplitude and an expected load center of gravity fluctuation duration;

[0012] S4: Establish a PID controller with nonlinear disturbance compensation term for the comprehensive error of the state variable;

[0013] S5: updating the gain parameters of the PID controller by an adaptive update law based on state feedback to obtain an optimized PID controller;

[0014] S6: Get the current front fork shock absorber load;

[0015] S7: Taking the minimum value of the correlation function as the goal, the current front fork shock absorber load is input into the optimization PID controller, and the current control variable value, i.e., the current front fork shock absorber damping force, is output;

[0016] S8: Adjusts the front fork shock absorber according to the current front fork shock absorber damping force.

[0017] Second aspect

[0018] An embodiment of the present invention provides a load-driven front fork shock absorber adaptive control system, comprising:

[0019] processor;

[0020] A memory having computer-readable instructions stored therein, wherein when the computer-readable instructions are executed by the processor, the load-driven front fork shock absorber adaptive control method as described in the first aspect is implemented.

[0021] The third aspect

[0022] An embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the load-driven front fork shock absorber adaptive control method as described in the first aspect is implemented.

[0023] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0024] In the present invention, a dynamic model of the front fork shock absorber is established, and then the front fork shock absorber compression amount and the front fork shock absorber compression speed associated with the expected load center of gravity fluctuation amplitude and the expected load center of gravity fluctuation duration are selected as state variables for monitoring, and the adaptively adjustable front fork shock absorber damping force is used as the control variable. By fixing the expected parameters, when the load changes, the state variables, namely the front fork shock absorber compression amount and the front fork shock absorber compression speed, can be adjusted in a follow-up manner by adjusting the control variables. This can effectively cope with complex application environments and real-time changes in loads, and establish a correlation function between the state variables and the expected control targets, and then minimize the control targets. The goal is to optimize the correlation function value, that is, to achieve the expected control goal. The comprehensive error of the state variable is controlled by a PID controller with a nonlinear disturbance compensation term to increase the environmental adaptability and control accuracy. An adaptive update law based on state feedback is introduced to feedback update the various gain parameters of the controller to further ensure the real-time regulation capability and control accuracy of the PID controller. During the control process, the control variable is adjusted by a constant expected target to quickly adjust the damping force of the front fork shock absorber to respond to the real-time changing load during the control process. It has strong adaptive ability, a wide range of applications, high control accuracy, and improves product competitiveness. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0026] Figure 1 A schematic flow chart of a load-driven front fork shock absorber adaptive control method provided by an embodiment of the present invention;

[0027] Figure 2 A schematic structural diagram of a load-driven front fork shock absorber adaptive control system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0028] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0029] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0030] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same.

[0031] In the embodiments of the present invention, sometimes a subscript such as W1 may be mistakenly written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are consistent.

[0032] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0033] Reference Manual Attached Figure 1 , showing a flow chart of a load-driven front fork shock absorber adaptive control method provided in an embodiment of the present invention.

[0034] The embodiment of the present invention provides a load-driven front fork shock absorber adaptive control method, which can be implemented by a load-driven front fork shock absorber adaptive control device, and the load-driven front fork shock absorber adaptive control device can be a terminal or a server. The processing flow of the load-driven front fork shock absorber adaptive control method can include the following steps:

[0035] S1: Construct a front fork shock absorber dynamic model including the front fork shock absorber load.

[0036] It should be noted that by constructing a dynamic model of the front fork shock absorber, the physical behavior of the shock absorber under different loads and motion states can be obtained, thereby ensuring that the subsequent control strategy can accurately match the actual operating conditions. This modeling method not only improves the predictability and responsiveness of the system, but also makes the control process more refined and efficient, thereby greatly improving system performance and reliability.

[0037] In a possible implementation manner, the dynamic model of the front fork shock absorber is specifically:

[0038]

[0039] in, and They represent the suspended mass, i.e. the front fork shock absorber load and the unsuspended mass, respectively. and Respectively represent the angle between the front fork shock absorber and the ground α The sine and cosine functions of k Indicates the spring stiffness of the front fork shock absorber.x Indicates the compression of the front fork shock absorber. c Indicates the damping coefficient of the front fork shock absorber, Indicates the compression speed of the front fork shock absorber. is the vertical acceleration of the unsprung mass.

[0040] It should be noted that this dynamic model provides a comprehensive mathematical description of the behavior of the front fork shock absorber by accurately integrating factors such as suspended mass, unsprung mass, angle between the shock absorber and the ground, spring stiffness, compression, damping coefficient and acceleration. The advantage of the model is that it can accurately simulate the dynamic response of the shock absorber in various motion states and different load conditions, so that the system design and control strategy can be optimized based on actual physical phenomena.

[0041] S2: Establish the state equation of the front fork shock absorber dynamic model.

[0042] The state variables of the state equation are the compression amount and compression speed of the front fork shock absorber, and the control variable of the state equation is the damping force of the front fork shock absorber.

[0043] Among them, the state equation is a mathematical model used to describe the change of the state of a dynamic system over time. In the field of system control, the state equation usually consists of two parts, one part describes the current state of the system, and the other part describes how these states change over time. State variables are internal variables of the system. They provide enough information to describe the complete state of the system at any point in time. In the front fork shock absorber, the state variables are the compression amount and compression speed of the shock absorber. These variables directly affect the performance of the shock absorber. Control variables are external input variables that are used to affect the state of the system. In the case of the front fork shock absorber, the control variable is the damping force, which is a variable that can be adjusted to change the behavior of the shock absorber to achieve the desired performance.

[0044] It should be noted that establishing the state equation of the front fork shock absorber helps to accurately describe and predict the behavior of the shock absorber under different conditions, so that the control system can understand and predict the dynamic behavior of the shock absorber in a mathematical form, which is crucial to achieving efficient, stable and predictable control.

[0045] In a possible implementation, the state variables and control variables are specifically:

[0046]

[0047] in, and They represent the compression amount and compression speed of the front fork shock absorber respectively. Indicates about t Moment Damping Coefficient andt Moment front fork shock absorber compression speed The control variable is t Damping force at all times.

[0048] The specific state equation is:

[0049]

[0050] in, Indicates the front fork shock absorber compression rate, i.e. the front fork shock absorber compression speed. Indicates the rate of change of the front fork shock absorber compression speed, i.e. the front fork shock absorber compression acceleration.

[0051] Among them, the state equation Convert the resultant force into acceleration.

[0052] Specifically, the state equation accurately defines the compression amount and compression speed of the front fork shock absorber as state variables, and the damping force related to the damping coefficient and compression speed as the control variable. The advantage of this design is that by directly associating physical variables such as compression amount and compression speed with the control variables, the control system can respond more directly and effectively to the actual motion state of the shock absorber. The state equation provides real-time and accurate data support for the control strategy by calculating the rate of change of compression amount and the rate of change of compression speed, that is, the dynamic response of the front fork shock absorber. Such modeling not only improves the response speed and accuracy of the system, but also enables the control strategy to adjust the damping force more finely, optimize the overall shock absorption performance, and ensure stability and efficiency under various motion conditions.

[0053] S3: Establish the correlation function between state variables and desired control objectives.

[0054] The expected control target includes the expected load center of gravity fluctuation amplitude and the expected load center of gravity fluctuation duration.

[0055] Among them, the expected control target is the target set in advance according to requirements.

[0056] It should be noted that the main advantage of establishing a correlation function between state variables and desired control targets is to achieve the goal orientation and responsiveness of the control system. By accurately defining the control targets such as the expected load center of gravity fluctuation amplitude and fluctuation duration, the correlation function can directly correspond the actual performance of the shock absorber to these preset targets. This allows the control system to not only respond to the current state variables (such as compression amount and compression speed), but also actively adjust according to changes in these variables to achieve the optimal performance standards, greatly enhancing the adaptability of the system and enabling it to automatically optimize its response under different conditions to ensure the best control effect and performance stability.

[0057] In a possible implementation, the correlation function is specifically:

[0058]

[0059] in, J represents the value of the correlation function, and are weight coefficients, and They represent the expected load center of gravity fluctuation range and the actual load center of gravity fluctuation range respectively. and They represent the expected load gravity center fluctuation time and the actual load gravity center fluctuation time respectively. express t The compression amount of the front fork shock absorber at the moment, T represents the observation time that is greater than one vibration cycle of the front fork shock absorber, represents the natural frequency of the front fork shock absorber, represents the damping ratio, Indicates the attenuation frequency of the front fork shock absorber.

[0060] It should be noted that the set correlation function provides a comprehensive performance evaluation and control standard for the front fork shock absorber by accurately combining the expected and actual load center of gravity fluctuation amplitude and duration. The correlation function not only quantifies the two most critical parameters in the shock absorber performance, but also allows the influence weights of these two parameters to be adjusted to adapt to different application scenarios and specific performance requirements. By calculating the combined impact of compression and compression speed within a cycle, this function deeply analyzes the real-time dynamic behavior of the shock absorber. In addition, by correlating these dynamic behaviors with the natural frequency and damping ratio of the front fork shock absorber, the correlation function provides a method to directly reflect the physical characteristics and performance of the shock absorber. This method enables the control system to accurately adjust the shock absorber settings in real time to cope with different loads and optimize overall stability.

[0061] S4: Establish a PID controller with nonlinear disturbance compensation terms for the comprehensive error of the state variable.

[0062] Among them, the state variable comprehensive error refers to the difference between the preset target state and the actual measured state, including the deviation of the compression amount and compression speed of the front fork shock absorber. This error is the target that the controller attempts to correct to ensure that the system performance meets the predetermined standard. The nonlinear disturbance compensation term refers to a mechanism added to the controller design to deal with disturbances caused by unexpected external changes or internal parameter changes that may affect the performance of the system. This compensation term is specially designed to deal with those nonlinear and unpredictable effects, thereby enhancing the robustness of the system. The PID controller is a controller commonly used in industrial control systems. It includes three parts: proportional (P), integral (I) and differential (D). It adjusts the control input to minimize the error based on the current value of the error (proportional), the cumulative error sum (integral) and the rate of change of the error (differential).

[0063] It should be noted that the PID controller with nonlinear disturbance compensation term for the comprehensive error of state variables can improve the adaptability and stability of the system to external and internal disturbances. The main advantage of introducing nonlinear disturbance compensation term into the PID controller is that it improves the adaptability of the controller to complex dynamic environments. Through nonlinear disturbance compensation, the controller can effectively identify and respond to behaviors that are difficult to predict with simple linear models, such as sudden shocks or long-term performance deviations. This compensation mechanism offsets these disturbances by precisely adjusting the control output to ensure stable operation of the shock absorber while optimizing its response speed and accuracy. In the ever-changing actual use environment, such a controller can more accurately control state variables, such as compression amount and compression speed, to match the set performance indicators, thereby greatly improving the overall efficiency and reliability of the system.

[0064] In a possible implementation, the PID controller is specifically:

[0065]

[0066] in, express t The comprehensive error of state variables at the moment, , , and Respectively represent proportional gain, integral gain, differential gain and disturbance compensation gain, express t Time disturbance estimation, represents the nonlinear disturbance compensation term, and Respectively t The expected compression amount of the front fork shock absorber at time t and the expected compression speed of the front fork shock absorber at time t, represents the system input including the control variables, Indicates that the system input The actual output of the system including the state variables is obtained as follows, Indicates that the system input The theoretical output of the system based on the nominal model, i.e. the state equation, is: L represents the nonlinear gain function of the disturbance observer, L represents the adjustment coefficient of the nonlinear gain function, Indicates taking the absolute value, n Represents the order of the nonlinear gain function that represents the strength of the disturbance response.

[0067] In actual use, the disturbance compensation mechanism estimates the disturbance by calculating the difference between the actual output and the theoretical output, and further adjusts it by a nonlinear gain function. This design enables the controller to dynamically adapt to complex and changing environments and unmodeled dynamics, which not only improves the control accuracy, but also significantly enhances the stability of the system in the face of highly variable and uncertain conditions. Overall, this PID controller effectively improves the performance and reliability of the system through precise adjustment and disturbance resistance, ensuring excellent performance under changing conditions.

[0068] S5: Update the gain parameters of the PID controller through an adaptive update law based on state feedback to obtain an optimized PID controller.

[0069] It should be noted that the process of updating the gain parameters of the PID controller based on the state feedback adaptive update law can significantly improve the adaptability and efficiency of the control system. This adaptive update law enables the PID controller to adjust its parameters, such as proportional, integral and differential gains, in real time according to the actual system performance. Such dynamic adjustment allows the controller to continue to maintain the optimal control effect in the face of changing environments and internal system dynamics. The optimized PID controller can more accurately match the current needs of the system, thereby improving the response speed and control accuracy, reducing overshoot and oscillation, and ensuring the stability and performance consistency of the system under various operating conditions. In addition, the adaptive mechanism reduces the dependence on the accuracy of the preliminary system model and improves the robustness of the control strategy.

[0070] In a possible implementation manner, the gain parameters include proportional gain, integral gain, differential gain and disturbance compensation gain.

[0071] S5 specifically includes:

[0072] S501: Create a gain vector including various gain parameters:

[0073]

[0074] in, represents the gain vector.

[0075] S502: Establish an adaptive control law of the gain vector by combining the comprehensive error of the state variable and the actual output of the system to update the gain parameter of the PID controller and obtain an optimized PID controller:

[0076]

[0077] in, represents the gain vector θ The adaptive control law, represents the adaptive gain learning rate used to adjust the adaptive speed, express t The actual output of the system at that moment, Indicates the PID function operation after the gain parameter is updated. represents the gain vector θ The transpose of represents the state feedback vector, Represents the optimization of PID controller t The output at any time is t Always control variables.

[0078] It should be noted that the gain parameters (including proportional gain, integral gain, differential gain and disturbance compensation gain) of the PID controller are adjusted through the adaptive update law to ensure that the controller can effectively respond to the changing system state and external disturbances. The creation of the gain vector and the application of the adaptive control law allow the controller to dynamically adjust its behavior to adapt to the current operating environment, improving the control accuracy and system performance. By continuously monitoring the difference between the actual output and the theoretical output of the system, as well as the comprehensive error, the control gain is dynamically adjusted to ensure the timeliness and adaptability of the system response. The adaptive control law allows the controller to maintain optimal performance even in the face of complex dynamic environments and unknown disturbances, thereby significantly improving the stability and efficiency of the entire system. In addition, this method reduces the dependence on the initial control parameter setting, increases the flexibility and robustness of the system, and enables the controller to self-optimize during long-term operation and adapt to various operating conditions.

[0079] S6: Get the current front fork shock absorber load.

[0080] Specifically, the current front fork shock absorber load may be acquired through a sensor.

[0081] S7: Taking the minimum value of the correlation function as the goal, the current front fork shock absorber load is input into the optimization PID controller, and the current control variable value, i.e., the current front fork shock absorber damping force, is output.

[0082] It should be noted that the current front fork shock absorber load is input into the optimized PID controller, and the control is performed with the minimum value of the correlation function as the target, allowing the control system to accurately adjust the damping force of the front fork shock absorber based on real-time data to ensure that the performance of the shock absorber just meets the requirements under the current operating conditions. This precise control can significantly improve the adaptability and efficiency of the system, optimize the damping response, reduce unnecessary wear, and extend the service life of the equipment. In addition, through targeted control (i.e., minimizing the correlation function), the system can automatically adjust to the optimal state to achieve the preset performance goals, i.e., reduce the fluctuation amplitude and duration of the load center of gravity and enhance the overall stability of the load.

[0083] In a possible implementation, S7 specifically includes:

[0084] S701: Input the current front fork shock absorber load into the correlation function.

[0085] S702: Optimizing the correlation function under the current front fork shock absorber load, and outputting a minimum correlation function value.

[0086] S703: Calculate the expected front fork shock absorber compression amount and the expected front fork shock absorber compression speed under the minimum correlation function value.

[0087] S704: Inputting the expected front fork shock absorber compression amount and the expected front fork shock absorber compression speed into the optimized PID controller, and outputting the current control variable value, that is, the current front fork shock absorber damping force.

[0088] Specifically, first, by inputting the actual load into the correlation function, the control system can evaluate the performance indicators under the current operating conditions in real time. Subsequently, in step S702, the correlation function is optimized to ensure that the parameter settings that minimize the performance indicators (such as the amplitude and duration of the load center of gravity fluctuation) are found, which directly improves the efficiency and responsiveness of the shock absorber. The calculated expected compression amount and compression speed provide accurate target values ​​for the PID controller to adjust to the optimal control point. Finally, the optimized PID controller adjusts the damping force according to these target values ​​to accurately achieve the best shock absorption effect, improves the adaptability and accuracy of the shock absorber, and ensures that the predetermined performance goals can be achieved under various load conditions.

[0089] S8: Adjusts the front fork shock absorber according to the current front fork shock absorber damping force.

[0090] In a possible implementation manner, S8 specifically includes:

[0091] The solenoid valve of the front fork shock absorber is adjusted by the electronic control unit to achieve the current front fork shock absorber damping force.

[0092] Among them, the precise control of the damping force by adjusting the solenoid valve of the front fork shock absorber through the electronic control unit allows the damping force to be adjusted quickly and accurately to adapt to various complex working conditions. The use of the electronic control unit and the solenoid valve can achieve real-time feedback and adjustment, thereby enhancing the response speed and adaptability of the entire shock absorption system and ensuring that the shock absorber performance is always maintained in the best condition.

[0093] Specifically, in this adaptive control process, the dynamic model of the front fork shock absorber is first carefully constructed to ensure that the behavior of the shock absorber under various loads and dynamic conditions can be accurately simulated. Next, the state equation is defined in detail, covering the key physical parameters of the shock absorber, compression amount and compression speed, and the damping force is used as the main control variable to lay the foundation for achieving accurate control output. By designing the correlation function, the system can directly associate the actual motion state of the shock absorber with the preset performance target, that is, the fluctuation control of the load center of gravity, to ensure that each adjustment is made in the direction of improving the overall performance. Then, a PID controller with a nonlinear disturbance compensation term is introduced to further improve the system's resistance to external disturbances, and the PID parameters are adaptively adjusted through the state feedback mechanism to respond to environmental changes and load fluctuations to ensure that the control strategy always maintains the optimal state. By real-time monitoring of the current shock absorber load and real-time adjustment of the control output, the system can adjust the damping force in real time to accurately match the changing operating conditions. Finally, accurate shock absorber adjustment is implemented through the electronic control unit, making the entire system not only responsive but also able to maintain control accuracy under various operating environments.

[0094] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0095] In the present invention, a dynamic model of the front fork shock absorber is established, and then the front fork shock absorber compression amount and the front fork shock absorber compression speed associated with the expected load center of gravity fluctuation amplitude and the expected load center of gravity fluctuation duration are selected as state variables for monitoring, and the adaptively adjustable front fork shock absorber damping force is used as the control variable. By fixing the expected parameters, when the load changes, the state variables, namely the front fork shock absorber compression amount and the front fork shock absorber compression speed, can be adjusted in a follow-up manner by adjusting the control variables. This can effectively cope with complex application environments and real-time changes in loads, and establish a correlation function between the state variables and the expected control targets, and then minimize the control targets. The goal is to optimize the correlation function value, that is, to achieve the expected control goal. The comprehensive error of the state variable is controlled by a PID controller with a nonlinear disturbance compensation term to increase the environmental adaptability and control accuracy. An adaptive update law based on state feedback is introduced to feedback update the various gain parameters of the controller to further ensure the real-time regulation capability and control accuracy of the PID controller. During the control process, the control variable is adjusted by a constant expected target to quickly adjust the damping force of the front fork shock absorber to respond to the real-time changing load during the control process. It has strong adaptive ability, a wide range of applications, high control accuracy, and improves product competitiveness.

[0096] Reference Manual Attached Figure 2 , showing a schematic structural diagram of a load-driven front fork shock absorber adaptive control system provided by the present invention.

[0097] The present invention further provides a load-driven front fork shock absorber adaptive control system 20, which is applied to the above-mentioned load-driven front fork shock absorber adaptive control method, comprising:

[0098] Processor 201.

[0099] The memory 202 stores computer-readable instructions. When the computer-readable instructions are executed by the processor 201 , the load-driven front fork shock absorber adaptive control method according to the method embodiment is implemented.

[0100] The load-driven front fork shock absorber adaptive control system 20 provided by the present invention can execute the above-mentioned load-driven front fork shock absorber adaptive control method and achieve the same or similar technical effects. To avoid repetition, the present invention will not elaborate on them.

[0101] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0102] In the present invention, a dynamic model of the front fork shock absorber is established, and then the front fork shock absorber compression amount and the front fork shock absorber compression speed associated with the expected load center of gravity fluctuation amplitude and the expected load center of gravity fluctuation duration are selected as state variables for monitoring, and the adaptively adjustable front fork shock absorber damping force is used as the control variable. By fixing the expected parameters, when the load changes, the state variables, namely the front fork shock absorber compression amount and the front fork shock absorber compression speed, can be adjusted in a follow-up manner by adjusting the control variables. This can effectively cope with complex application environments and real-time changes in loads, and establish a correlation function between the state variables and the expected control targets, and then minimize the control targets. The goal is to optimize the correlation function value, that is, to achieve the expected control goal. The comprehensive error of the state variable is controlled by a PID controller with a nonlinear disturbance compensation term to increase the environmental adaptability and control accuracy. An adaptive update law based on state feedback is introduced to feedback update the various gain parameters of the controller to further ensure the real-time regulation capability and control accuracy of the PID controller. During the control process, the control variable is adjusted by a constant expected target to quickly adjust the damping force of the front fork shock absorber to respond to the real-time changing load during the control process. It has strong adaptive ability, a wide range of applications, high control accuracy, and improves product competitiveness.

[0103] It should be understood that the processor in the embodiment of the present invention may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0104] It should also be understood that the memory in the embodiments of the present invention may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0105] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0106] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0107] In the present invention, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0108] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0109] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0110] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0111] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0112] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0113] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0114] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0115] An embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein when the program is executed by a processor, the load-driven front fork shock absorber adaptive control method as described in the method embodiment is implemented.

[0116] A computer-readable storage medium provided by the present invention can implement the steps and effects of the load-driven front fork shock absorber adaptive control method of the above method embodiment, and to avoid repetition, the present invention will not go into details.

[0117] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0118] In the present invention, a dynamic model of the front fork shock absorber is established, and then the front fork shock absorber compression amount and the front fork shock absorber compression speed associated with the expected load center of gravity fluctuation amplitude and the expected load center of gravity fluctuation duration are selected as state variables for monitoring, and the adaptively adjustable front fork shock absorber damping force is used as the control variable. By fixing the expected parameters, when the load changes, the state variables, namely the front fork shock absorber compression amount and the front fork shock absorber compression speed, can be adjusted in a follow-up manner by adjusting the control variables. This can effectively cope with complex application environments and real-time changes in loads, and establish a correlation function between the state variables and the expected control targets, and then minimize the control targets. The goal is to optimize the correlation function value, that is, to achieve the expected control goal. The comprehensive error of the state variable is controlled by a PID controller with a nonlinear disturbance compensation term to increase the environmental adaptability and control accuracy. An adaptive update law based on state feedback is introduced to feedback update the various gain parameters of the controller to further ensure the real-time regulation capability and control accuracy of the PID controller. During the control process, the control variable is adjusted by a constant expected target to quickly adjust the damping force of the front fork shock absorber to respond to the real-time changing load during the control process. It has strong adaptive ability, a wide range of applications, high control accuracy, and improves product competitiveness.

[0119] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

[0120] There are a few points to note:

[0121] (1) The drawings of the embodiments of the present invention only involve structures related to the embodiments of the present invention. Other structures may refer to conventional designs.

[0122] (2) For the sake of clarity, in the drawings used to describe the embodiments of the present invention, the thickness of layers or regions is exaggerated or reduced, that is, these drawings are not drawn according to the actual scale. It is understood that when an element such as a layer, film, region or substrate is referred to as being "on" or "under" another element, the element may be "directly" "on" or "under" the other element or there may be intermediate elements.

[0123] (3) In the absence of conflict, the embodiments of the present invention and the features therein may be combined with each other to obtain new embodiments.

[0124] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. The protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A load-driven front fork shock absorber adaptive control method, characterized in that: include: S1: Construct the front fork shock absorber dynamic model including the front fork shock absorber load; S2: establishing a state equation of the front fork shock absorber dynamics model, wherein the state variables of the state equation are the compression amount and compression speed of the front fork shock absorber, and the control variable of the state equation is the damping force of the front fork shock absorber; S3: establishing a correlation function between the state variable and the desired control target, wherein the desired control target includes an expected load center of gravity fluctuation amplitude and an expected load center of gravity fluctuation duration; S4: Establish a PID controller with nonlinear disturbance compensation term for the comprehensive error of the state variable; S5: updating the gain parameter of the PID controller by an adaptive update law based on state feedback to obtain an optimized PID controller; S6: Get the current front fork shock absorber load; S7: Taking the minimum value of the correlation function as the target, inputting the current front fork shock absorber load into the optimized PID controller, and outputting the current control variable value, i.e., the current front fork shock absorber damping force; S8: adjusting the front fork shock absorber according to the current damping force of the front fork shock absorber; The front fork shock absorber dynamics model is specifically as follows: Among them, m s and m us denote the suspended mass, i.e., the load of the front fork shock absorber and the unsuspended mass, respectively; sin(α) and cos(α) denote the sine function and cosine function of the angle α between the front fork shock absorber and the ground, respectively; k denotes the spring stiffness of the front fork shock absorber; x denotes the compression of the front fork shock absorber; c denotes the damping coefficient of the front fork shock absorber; Indicates the compression speed of the front fork shock absorber. represents the vertical acceleration of the unsprung mass; The state variables and the control variables are specifically: x1=x Wherein, x1 and x2 represent the compression amount of the front fork shock absorber and the compression speed of the front fork shock absorber, respectively, and u(t) represents the damping coefficient c(t) at time t and the compression speed of the front fork shock absorber at time t. The controlled variable is the damping force at time t; The state equation is specifically: in, represents the compression rate of the front fork shock absorber, i.e., the compression speed of the front fork shock absorber, It indicates the rate of change of compression speed of the front fork shock absorber, i.e. the compression acceleration of the front fork shock absorber; Wherein, the correlation function is specifically: Among them, J represents the correlation function value, λ1 and λ2 are weight coefficients, and A cg represent the expected load gravity center fluctuation amplitude and the actual load gravity center fluctuation amplitude respectively, and τ cg They represent the expected load center of gravity fluctuation time and the actual load center of gravity fluctuation time respectively, x(t) represents the compression of the front fork shock absorber at time t, T represents the observation time greater than one vibration cycle of the front fork shock absorber, ω n represents the natural frequency of the front fork shock absorber, ζ represents the damping ratio, ω d Indicates the attenuation frequency of the front fork shock absorber; Wherein, the PID controller is specifically: L=K L (1+|Y(U)|) n Among them, e(t) represents the comprehensive error of state variables at time t, K p , K i , K d and K dist Respectively represent proportional gain, integral gain, differential gain and disturbance compensation gain, represents the disturbance estimate at time t, represents the nonlinear disturbance compensation term, x desired (t) and They respectively represent the expected front fork shock absorber compression amount and the expected front fork shock absorber compression speed at time t, U represents the system input including the control variable, Y(U) represents the actual system output including the state variable obtained under the system input U, Y'(U) represents the system theoretical output obtained based on the nominal model, i.e., the state equation under the system input U, L represents the nonlinear gain function of the disturbance observer, L represents the adjustment coefficient of the nonlinear gain function, || represents taking the absolute value, and n represents the order of the nonlinear gain function representing the intensity of the disturbance response.

2. The load-driven front fork shock absorber adaptive control method according to claim 1, characterized in that: The gain parameters include proportional gain, integral gain, differential gain and disturbance compensation gain; The S5 specifically includes: S501: Create a gain vector including various gain parameters: θ=(K p ,K i ,K d ,K dist ) Wherein, θ represents the gain vector; S502: Establishing an adaptive control law of the gain vector in combination with the state variable comprehensive error and the actual output of the system to update the gain parameter of the PID controller to obtain the optimized PID controller: u'(t)=f PID (θ T )×φ in, represents the adaptive control law of the gain vector θ, γ represents the adaptive gain learning rate used to adjust the adaptive speed, Y(t) represents the actual output of the system at time t, and f PID Represents the PID function operation after the gain parameter is updated, θ T represents the transpose of the gain vector θ, φ represents the state feedback vector, and u'(t) represents the output of the optimized PID controller at time t, that is, the controlled variable at time t.

3. The load-driven front fork shock absorber adaptive control method according to claim 1, characterized in that: The S7 specifically includes: S701: inputting the current front fork shock absorber load into the correlation function; S702: Optimizing the correlation function under the current front fork shock absorber load, and outputting a minimum correlation function value; S703: Calculating an expected front fork shock absorber compression amount and an expected front fork shock absorber compression speed under the minimum correlation function value; S704: Inputting the expected front fork shock absorber compression amount and the expected front fork shock absorber compression speed into the optimized PID controller, and outputting the current control variable value, that is, the current front fork shock absorber damping force.

4. The load-driven front fork shock absorber adaptive control method according to claim 1, characterized in that: The S8 is specifically: The solenoid valve of the front fork shock absorber is adjusted by an electronic control unit to achieve the current damping force of the front fork shock absorber.

5. A load-driven front fork shock absorber adaptive control system, characterized in that: include: processor; A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the load-driven front fork shock absorber adaptive control method according to any one of claims 1 to 4 is implemented.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the load-driven front fork shock absorber adaptive control method according to any one of claims 1 to 4 is implemented.

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