Intelligent bicycle body inclination angle fuzzy control method and system based on variable universe

Abstract

The invention provides an intelligent bicycle body dip angle fuzzy control method and system based on a variable universe, and belongs to the field of bicycle body design, and the method comprises the steps: building a bicycle control model and a bicycle body dip angle fuzzy PID control model according to a bicycle structure and a control principle; a bicycle control model is simulated, stability analysis is carried out, a fuzzy PID controller is defined, and a variable universe theory is configured into a bicycle body inclination angle fuzzy PID control module; and dynamically adjusting discourse domain expansion and contraction factors of the variable discourse domain theory, and realizing optimization of fuzzy control of the inclination angle of the bicycle body on the premise of not changing a fuzzy rule. According to the invention, based on the variable universe fuzzy PID controller, intelligent bicycle body inclination angle control has better response speed and anti-interference capability, and a better control effect can be realized for an intelligent bicycle nonlinear system.

Description

Technical Field

[0001] The invention belongs to the technical field of bicycle body design, and in particular relates to an intelligent bicycle body inclination angle fuzzy control method and system based on variable domain. Background Art

[0002] Bicycles are a widely used means of transportation. They have unique dynamic characteristics, which are reflected in the fact that they are easy to fall over at low speeds, but can run stably when the speed reaches a certain level.

[0003] With the rapid development of artificial intelligence and autonomous driving technology, driverless bicycles have been put into use. Under this trend, the nonlinear and time-varying mechanical properties of bicycles have attracted much attention. In driverless bicycles, bicycle control models are involved. Existing bicycle control models often oversimplify the structure of actual bicycles and complex control principles, resulting in a large deviation between the model and the actual situation, and unable to accurately reflect the actual motion state of the bicycle. Only simple linear dynamic models are considered, ignoring the nonlinear factors of the bicycle during steering, acceleration, and deceleration, as well as the coupling between various components, making the controllers designed based on these models ineffective in practical applications.

[0004] Moreover, some existing bicycle body tilt angle control methods generally use PID controllers, which are composed of three parts: proportional (P), integral (I), and differential (D). This method calculates the error between the control quantity and the expected value, and dynamically adjusts the control output according to the current system error and its rate of change.

[0005] This method is relatively fixed once its control rules and parameters are determined, and cannot be adjusted quickly and flexibly according to real-time changes in the actual operating status. This results in unstable control performance under different working conditions, making it difficult to always maintain a good body tilt angle control effect, and also reduces the reliability and stability of the system. Summary of the invention

[0006] The present invention provides a fuzzy control method for the inclination angle of an intelligent bicycle body based on a variable domain. The method dynamically adjusts the expansion factor of the domain to better adapt to changes in the system without changing the fuzzy rules, thereby improving the control accuracy, response speed and robustness of the bicycle body inclination angle control system.

[0007] Methods include: S101: According to the bicycle structure and control principle, build the bicycle control model, bicycle body inclination fuzzy PID control model and bicycle dynamics equation; S102: Simulate the bicycle control model and perform stability analysis to obtain a balance condition that meets the minimum speed of the bicycle; then define a fuzzy PID controller and configure the variable domain theory into the bicycle body inclination fuzzy PID control module; S103: Dynamically adjust the domain expansion factor of the variable domain theory to optimize the fuzzy control of the bicycle body inclination angle without changing the fuzzy rules.

[0008] It should be further explained that in step S101, the bicycle control model is constructed based on the centrifugal force viewpoint. The control model and the dynamic equation specifically include: The torque balance theorem is used to obtain the dynamic equation of the bicycle when it is balanced in a static state.

[0009] Construct the moment balance equation for a stationary bicycle: =

[0010] Among them, the contact point between the wheel and the ground is O, and the straight line perpendicular to the contact surface is the Z axis. is the angle between the body and the Z axis. Assume that the bicycle is only subject to gravity acting on the center of gravity, m is the total mass of the bicycle, is the centroid height, is the external force applied in the Y-axis direction, The point of action is M, is the distance between point M and point O.

[0011] Furthermore, according to the force and torque balance equation of the bicycle, the bicycle dynamics equation is obtained in the following form:

[0012] Among them, m is the overall mass of the bicycle body, g is the acceleration of gravity, h is the height of the bicycle's center of gravity, a is the distance between the projection point of the center of gravity on the horizontal plane and point P, b is the bicycle wheelbase, and c is the distance between the intersection point K of the front fork axis and the ground and point Q. is the fork angle of the bicycle, is the inclination angle of the bicycle body, is the vehicle speed, is the front handlebar angle, is the rate at which the bicycle handlebar angle changes with time, is the acceleration of the bicycle body inclination angle changing with time.

[0013] It should be further explained that the stability analysis in step S102 includes: The obtained dynamic model and equation are Laplace transformed, and the relationship is:

[0014] The transfer function of the system is obtained as:

[0015] Furthermore, stability analysis is performed and the equilibrium condition for the minimum speed of the bicycle is obtained.

[0016] Assuming a fixed handlebar angle value After dynamic adjustment, the body inclination angle is stable at ; and The ratio of If it means:

[0017] in, For a given fixed handlebar angle value, To dynamically adjust the stable value of the caster angle, for and ratio.

[0018] exist In the case of , the equilibrium condition that satisfies the minimum speed of the bicycle is: .

[0019] It should be further noted that the method further comprises: defining a fuzzy PID controller; The deviation between the expected and actual value of the bicycle body inclination angle e and the rate of change of deviation ec As the input variable, the fuzzy input quantity is fuzzy reasoned through the fuzzy rule base in the fuzzy PID controller to obtain the adjustment value of the PID parameter , , As the output variable; the PID parameter adjustment formula is:

[0020] in, , , Corresponding to the proportional coefficient, integral coefficient, and differential coefficient, , , Corresponding to the preset value of PID control parameter, , , The corresponding value is the correction value of the PID control parameter.

[0021] It should be further explained that in the method, the centroid method is used to solve the fuzzy problem, and its expression is:

[0022] in, is a fuzzy set; P for The number of elements in ; f is the exact value after defuzzification; Furthermore, we define the input error e and error rate of change ec The initial domain of the variable is [-E, E] and [-EC, EC], and the initial domain of the output variable is [-U, U]. The domain formula containing the scaling factor is:

[0023] Where: E represents the input error e The upper limit of the basic domain range, -E represents the input error e The upper limit of the basic domain range of ; EC represents the upper limit of the basic domain range of the error change rate ec, and -EC represents the lower limit of the basic domain range of the error change rate ec; U represents the upper limit of the initial domain range of the output variable, and -U represents the lower limit of the initial domain range of the output variable; α(e) and α(ec) are the scaling factors of the input variables; β(y) is the scaling factor of the output variable; X(e) and X(ec) are the transformed input and output domains.

[0024] It should be further noted that the method further includes: determining a scaling factor; The exponential function of the scaling factor of the input and output domains is expressed as:

[0025] In the formula, , are all constants; P is a constant vector; is the fuzzy parameter of the bicycle fuzzy controller, The scaling factor for the function input domain, To adjust the parameters of the fuzzy controller output intensity, A constant greater than 0 to adjust the input value stretch strength. is the accumulated system error from time 0 to y, is the initial state of the controller, i is the sum index of multiple accumulations, n The total sum of multiple items.

[0026] The expression of the scaling factor is: ; Where e is the error between the system output and the expected output, is the scaling factor, Integral scaling factor, Differential scaling factor.

[0027] The present application also provides a fuzzy control system for the inclination angle of an intelligent bicycle body based on a variable domain, the system comprising: A model building module is used to build a bicycle control model and a bicycle body inclination angle fuzzy PID control model according to the bicycle structure and control principle; The controller configuration module is used to simulate the bicycle control model and perform stability analysis, then define the fuzzy PID controller and configure the variable domain theory into the bicycle body inclination fuzzy PID control module; The control optimization module is used to dynamically adjust the universe expansion factor of the variable universe theory to optimize the fuzzy control of the bicycle body inclination angle without changing the fuzzy rules.

[0028] It can be seen from the above technical solutions that the present invention has the following advantages: The fuzzy control method for the body inclination of an intelligent bicycle based on a variable domain provided in this application ensures the accuracy of the fuzzy control of the body inclination by considering the actual structure of the bicycle and the model built by the control principle, making the simulation results closer to the actual riding situation and improving the authenticity and practicality of the control model. In addition, the fuzzy PID control model is introduced to control the body inclination, combining the good adaptability of fuzzy control to nonlinear systems and the accuracy of PID control. The fuzzy control part can dynamically adjust the PID parameters according to the fuzzy information such as the body inclination deviation and its rate of change, overcoming the limitations of the traditional PID control parameters that are fixed, improving the adaptability and robustness of the body inclination control, and better coping with complex and changeable riding environments.

[0029] This application also simulates and analyzes the stability of the bicycle control model, configures the variable domain theory into the fuzzy PID control module, and realizes the dynamic adjustment of the domain. During the bicycle driving process, when the body inclination angle deviation is large, the domain range is automatically expanded, so that the fuzzy controller can output a larger range of PID parameter adjustment values, enhancing the control ability of large deviation conditions; and when the body inclination angle is close to a stable state, the domain range is reduced to improve the accuracy and sensitivity of the control.

[0030] The present application can also dynamically adjust the domain expansion factor of the variable domain theory, and realize the real-time optimization of the fuzzy control of the bicycle body inclination angle without changing the fuzzy rules. The domain range of the fuzzy controller is automatically adjusted, and then the PID parameters are adjusted, so that the controller can quickly adapt to the changes in the body inclination angle under different working conditions, maintain the stable driving of the bicycle, and improve the bicycle's adaptive ability and real-time control performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solution of the present invention, the accompanying drawings required for use in the description will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0032] Figure 1 It is the flow chart of the fuzzy control method of the inclination angle of the intelligent bicycle body based on variable domain; Figure 2 This is the force diagram of the bicycle in a stationary state; Figure 3 Schematic diagram of applying torque to maintain balance; Figure 4 To provide schematic diagrams for bicycle parameters and their symbols; Figure 5 This is the schematic diagram of the fuzzy PID control system; Figure 6 This is the principle diagram of domain change; Figure 7 This is the principle diagram of the variable universe fuzzy PID control system; Figure 8 It is the variable domain fuzzy PID simulation curve diagram; Fig. 9 Schematic diagram of the fuzzy control system for the inclination angle of an intelligent bicycle based on variable domain. DETAILED DESCRIPTION

[0033] The method for intelligent bicycle body inclination fuzzy control based on variable domain provided by the present application is to solve the problem of low control precision of bicycle body inclination. The method of the present application builds a control model according to the structural characteristics and control principle of the bicycle system, and uses MATLAB / Simulink software to simulate the control system. The present application introduces the theory of variable domain into the fuzzy PID control of bicycle body inclination. By dynamically adjusting the expansion factor of the domain, better adaptation to the changes of the system can be achieved without changing the fuzzy rules, and the control precision, response speed and robustness of the bicycle body inclination control system are improved, thereby improving the control effect.

[0034] The steps of the intelligent bicycle body inclination angle fuzzy control method based on variable domain will be described in detail below. For the purpose of illustration rather than limitation, specific details such as specific system structure and technology are proposed to facilitate a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application can also be implemented in other embodiments without these specific details.

[0035] The phrases such as "one embodiment" or "some embodiments" described in the present application mean that the specific features, structures or characteristics described in the embodiment are included in one or more embodiments of the present application. Therefore, the phrases such as "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments" etc. that appear in different places in the present application do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways.

[0036] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0037] See also Figure 1 Shown is a flow chart of a fuzzy control method for the inclination angle of an intelligent bicycle body based on a variable domain in a specific embodiment.

[0038] The smart bicycle involved in this embodiment is based on a mountain bike structure, and can be equipped with a driving device, a detection device, a communication device, a control device, etc.

[0039] Specifically, the driving device includes a front vehicle handlebar steering drive and a bicycle motion drive, which meet the requirements of flexible control of the bicycle handlebar angle and vehicle speed control. The detection device includes a vehicle body inclination detection device, a handlebar angle detection device, and a vehicle speed measurement device.

[0040] The vehicle body tilt detection module uses the LPMS-IG1P high-precision attitude sensor as the tilt sensor. The communication device involves data communication between the drive device, the detection device and the mobile control device. The control device and the drive device are connected by EtherCAT industrial Ethernet communication; the control device and the attitude sensor are connected by USB; and the control device and the mobile controller are connected by a smartphone with Bluetooth function. The control device part uses an industrial PC as the control system of the bicycle.

[0041] According to actual needs, in addition to the above-mentioned devices, the bicycle body inclination control system also includes: a transformer module responsible for system voltage conversion and a relay module for switching driving modes.

[0042] In this embodiment, a smart bicycle is modeled and analyzed. The bicycle involved in this application can be understood as a smart bicycle, or a smart mountain bike, etc. It can be expressed as a bicycle or a smart bicycle, which has the same meaning.

[0043] This embodiment analyzes the mechanics of a bicycle in a stationary state. Before analyzing the dynamics of a bicycle in motion, a static analysis is performed on a bicycle that conforms to the Whipple configuration. The Whipple configuration here can be based on a conventional front plate, gap, and rear plate. The front plate is the first line of defense against impact and is used to initially crush the impacted object; the gap provides expansion space for the broken fragments and slows down their speed; the rear plate further blocks and absorbs the remaining energy of the fragments. Based on this approach, assuming that there is no other external force except gravity, the force is as follows Figure 2 and Figure 3 shown.

[0044] In this embodiment, the contact point O between the wheel and the ground is defined, the straight line perpendicular to the contact surface is the Z axis, and the angle between the vehicle body and the Z axis is Assume that the bicycle is only subject to gravity acting on the center of gravity, the total mass of the vehicle is m, and the height of the center of mass is . Apply an external force in the Y-axis direction , The point of action is M, and the distance between point M and point O is , the force The torque is specified as .

[0045] The torque balance equation of a bicycle at rest is: =

[0046] Among them, the contact point between the wheel and the ground is O, and the straight line perpendicular to the contact surface is the Z axis. is the angle between the body and the Z axis. Assume that the bicycle is only subject to gravity acting on the center of gravity, m is the total mass of the bicycle, is the centroid height, is the external force applied in the Y-axis direction, The point of action is M, is the distance between point M and point O.

[0047] In combination with the above bicycle system, the intelligent bicycle body inclination angle fuzzy control method based on variable domain includes the following steps.

[0048] S101: According to the bicycle structure and control principle, a bicycle control model and a bicycle body inclination angle fuzzy PID control model are constructed.

[0049] In some embodiments, a bicycle control model is constructed based on the above-mentioned intelligent bicycle structure using MATLAB modeling software. The bicycle control model describes the mathematical relationship between the rider's control force, the body inclination angle, the speed, and the actual driving trajectory of the bicycle.

[0050] This embodiment builds a fuzzy PID control model based on the bicycle body inclination control quantity. To determine the input of the fuzzy PID control model, the body inclination deviation and its change rate can be selected as input variables, and the three parameters of the PID controller, namely the proportional coefficient, the integral coefficient and the differential coefficient, are used as outputs, and fuzzy subsets of the input and output variables are defined. The fuzzy subsets can involve (negative large, negative medium, negative small, zero, positive small, positive medium, positive large).

[0051] It can be seen that the bicycle control model constructed in this embodiment can reflect the actual operating condition of the bicycle; the constructed body inclination fuzzy PID control model can utilize the advantages of fuzzy control to deal with the body inclination control problem with uncertainty and nonlinearity. Compared with traditional PID control, it can adjust the control parameters according to the actual situation and improve the control accuracy.

[0052] S102: Simulate the bicycle control model and perform stability analysis to obtain a balance condition that meets the minimum speed of the bicycle; then define a fuzzy PID controller and configure the variable domain theory into the bicycle body inclination fuzzy PID control module.

[0053] In this embodiment, different working condition parameters are set based on the established bicycle control model, such as simulating different road surface flatness, bumpy road surface, different riding speeds, and different rider control conditions. The simulation function of the software is used to run the model, observe the state changes of the bicycle under these different working conditions, such as the change of the body inclination angle, whether the driving trajectory is stable, etc., and collect relevant simulation data.

[0054] Based on the collected simulation data, the qualitative analysis method of stability analysis is used to determine whether the bicycle can maintain a stable driving state under different working conditions, analyze whether the body inclination angle will show divergence or abnormal fluctuations and other unstable phenomena, and determine whether certain parameters affecting stability are set unreasonably or the control effect is poor under specific working conditions.

[0055] In this embodiment, the specific structure and function of the fuzzy PID controller are first clarified, and parameters such as the quantization factor and proportional factor of the input and output variables are defined to ensure that the numerical range and accuracy of the input and output meet the control requirements.

[0056] According to the variable universe theory, the initial value range of the universe expansion factor and its correlation with the bicycle state variables are determined.

[0057] For example, when the body tilt angle deviation is large, the corresponding domain expansion factor can be set to increase, thereby expanding the domain range of the fuzzy controller input and output variables, so that the control strategy can be adjusted in a wider range; conversely, when the body tilt angle deviation is small, the domain range is reduced to improve the refinement of the control. The variable domain theory can be embedded in the control module through programming or module configuration in the modeling software.

[0058] The simulation and stability analysis of this embodiment can discover problems existing in the bicycle control model in advance and provide a basis for optimizing the control strategy; defining a fuzzy PID controller can improve the accuracy and reliability of control; and introducing the variable domain theory can enable the fuzzy PID control to dynamically adjust the domain range according to actual conditions, thereby enhancing the adaptability of the control strategy to different working conditions and different vehicle body inclination states, and avoiding problems such as insufficient control accuracy or control failure that may occur under a fixed domain.

[0059] S103: Dynamically adjust the domain expansion factor of the variable domain theory to optimize the fuzzy control of the bicycle body inclination angle without changing the fuzzy rules.

[0060] In this embodiment, during the actual operation of the bicycle, relevant status information of the bicycle is monitored in real time, especially key indicators such as the body inclination angle and its change rate. According to the pre-set rules and algorithms, the value of the domain expansion factor is calculated and updated in real time according to the magnitude of the current body inclination angle deviation, the speed of the deviation change, etc.

[0061] For example, if the body inclination angle deviation increases rapidly in a short period of time, it indicates that the bicycle may be in an unstable state. At this time, the domain expansion factor is quickly increased according to the set adjustment strategy to expand the domain range of the fuzzy controller so that the PID parameters can be adjusted more quickly to correct the body inclination angle; if the body inclination angle deviation gradually decreases and tends to be stable, the domain expansion factor is reduced accordingly to make the control more precise.

[0062] Without changing the fuzzy rules, that is, keeping the previously set fuzzy control rules unchanged, the domain range of the input and output variables is changed through the dynamic adjustment of the domain expansion factor, thereby affecting the results of fuzzy reasoning and the final output PID controller parameters. Then, the control parameters are continuously optimized dynamically according to the actual operating status, so that the bicycle body inclination angle can be stabilized within the desired range, improving the stability and comfort of riding.

[0063] It can be seen that this embodiment collects the state data of the bicycle in real time through the inclination sensor, angular velocity sensor, etc. installed on the bicycle, transmits the collected data to the control device, runs the pre-written domain expansion factor adjustment algorithm program in the control device, realizes the real-time update of the domain expansion factor, and thus completes the optimization of the vehicle body inclination fuzzy control. In addition, the dynamic adjustment of the domain expansion factor can make the fuzzy control strategy adapt to the real-time running state changes of the bicycle and always maintain a good control effect; and the optimization is performed without changing the fuzzy rules, avoiding the complexity and uncertainty caused by frequent modification of the rules, and ensuring the stability and reliability of the control strategy.

[0064] Furthermore, as a refinement and extension of the specific implementation method of the above embodiment, in order to fully illustrate the specific implementation process in this embodiment, a specific execution method and simulation method are given below.

[0065] like Figure 4 As shown, in this embodiment, in combination with various parameters of the bicycle and their symbol regulations, points P and Q are defined as the contact points of the rear wheel and the front wheel with the ground, b is the wheelbase, point G is the center of gravity, h is the height of the center of gravity from the ground, and a is the distance from the projection of G on the horizontal plane to point P. is the front fork angle, point K is the intersection of the front fork axis and the ground, and c is the distance between K and Q. is the body inclination angle, For the handlebar angle.

[0066] The dynamic analysis is based on the following assumptions: (1) There is no side slip when the bicycle is moving; (2) The turning radius is much larger than the wheelbase; (3) When the body inclination angle is When , the turning radius is assumed to be the geometric distance between the focus of the ellipse and the nearest point on the circumference.

[0067] Combined with the above assumptions, through the force and torque balance equation of the bicycle, the following form of bicycle dynamics equation is obtained:

[0068] This embodiment also performs a stability analysis of the bicycle system.

[0069] Specifically, the inclination angle of the bicycle body can be defined as satisfy According to the above assumptions, after Laplace transformation of the above formula, the formula is expressed as:

[0070]

[0071] From the above formula, the system transfer function is:

[0072] Given a fixed handlebar angle value After dynamic adjustment, the body inclination angle is stable at . and The ratio of If it means:

[0073] In this embodiment, the control method can be set only This method is effective in the case of , which means that this method needs to meet certain speed conditions, namely:

[0074] It can be seen that the speed of the bicycle in the above formula is related to the parameters a, c, g, h, and After measuring the basic parameters of the automatic driving bicycle body control system designed in this embodiment, the basic parameters are obtained: m is 32.7kg, h is 0.65m, is 70°, a is 0.4m, b is 1.1m, and c is 0.09m.

[0075] Substituting the above parameters into the speed condition formula, we get the following results: (1-15) The above formula shows that for a bicycle with basic parameters and no one is driving, when the speed is greater than 0.714 When you are riding, you can control the bicycle to keep it balanced.

[0076] For the variable universe fuzzy PID controller of this embodiment, the expansion factor and fuzzy PID control are combined to achieve effective control of the inclination angle of the bicycle body.

[0077] The general mathematical expression for a PID controller is:

[0078] in, is the control quantity, is the difference between the expected value and the actual value, is the proportional gain, is the integral gain, is the differential gain.

[0079] The fuzzy PID controller involved in this embodiment is based on the traditional PID controller, with a fuzzy controller added, fuzzy reasoning is used to adjust the PID parameters, and the signal is output through the PID algorithm. It combines the advantages of the PID controller and the fuzzy controller well, realizes adaptive adjustment of control parameters, and is more suitable for nonlinear and time-varying systems such as bicycles.

[0080] The fuzzy PID controller of this system is based on the inclination angle of the bicycle body. The difference between the expected value and the actual value e and the rate of change of deviation ec As the input variable, the fuzzy input quantity is fuzzy inferred through the fuzzy rule base in the fuzzy controller to obtain the adjustment value of the PID parameter , , As the output variable. The PID parameter adjustment formula is:

[0081] in, , , Corresponding to the proportional coefficient, integral coefficient, and differential coefficient, , , Corresponding to the preset value of PID control parameter, , , The corresponding value is the correction value of the PID control parameter.

[0082] The schematic diagram of the fuzzy PID controller is as follows: Figure 5 As shown in the figure, the fuzzy domain of the input variable is set to [-6, 6], and the basic domain and fuzzy domain of the output variables Kp, Ki, and Kd are all [2.8, 3.7], [0, 0.04], and [0.01, 0.09]. The scale factor of the input variable and the quantization factor of the output variable are both set to 1.

[0083] In this embodiment, 7 fuzzy subsets can be set, namely {negative large, negative medium, negative small, zero, positive small, positive medium, positive large}, denoted as {NB, NM, NS, ZO, PS, PM, PB}.

[0084] This embodiment uses the centroid method to solve the fuzzy problem, and its expression is:

[0085] in, — fuzzy sets; P —— The number of elements in ; f ——The exact value after defuzzification.

[0086] For the variable universe fuzzy PID controller of this embodiment, a telescoping factor module is added to the fuzzy PID controller. The initial universe telescoping transformation process is as follows: Figure 6 shown.

[0087] Assuming that the initial domains of the input error e and the error change rate ec are [-E, E] and [-EC, EC], and the initial domain of the output variable is [-U, U], the domain formula containing the scaling factor is:

[0088] Among them, α(e) and α(ec) are the scaling factors of the input variables; β(y) is the scaling factor of the output variable; X(e), X(ec) and β(y) are the transformed input and output domains.

[0089] According to the above analysis, the principle diagram of the variable domain PID controller is shown in Figure 7.

[0090] This embodiment uses an exponential function based on a function form as a scaling factor. The scaling factor function of the input and output domains is:

[0091] In the formula, , are all constants; P is a constant vector; is the fuzzy parameter of the bicycle fuzzy controller, The scaling factor for the function input domain, To adjust the parameters of the fuzzy controller output intensity, A constant greater than 0 to adjust the input value stretch strength. is the accumulated system error from time 0 to y, is the initial state of the controller, i is the sum index of multiple accumulations, n The total sum of multiple items.

[0092] After adding variable universe fuzzy PID to the original system, the bicycle body inclination control system was simulated through MATLAB, and a step signal with an amplitude of 5m was selected as the input signal to obtain the simulation curve, such as Figure 8 shown.

[0093] from Figure 8 It can be seen that when Kp is 3.5, Ki is 0.02 and Kd is 0.03, the system responds quickly and has a small steady-state error.

[0094] Based on the above simulation process, this application solves the problem of low accuracy and instability in the inclination control of the bicycle body, and this application uses MATLAB / Simulink to build a body inclination control system based on variable universe fuzzy PID, and simulates its response characteristics and adaptive capabilities. According to the simulation results, when the Kp value is 3.5, the KI value is 0.02, and the Kd value is 0.03, compared with the traditional PID and fuzzy PID, the designed variable universe fuzzy PID has better response speed and anti-interference ability, and can achieve better control effects for nonlinear systems such as bicycles.

[0095] The following is an embodiment of the intelligent bicycle body inclination fuzzy control system based on variable domain provided by the embodiments of the present disclosure. The system and the intelligent bicycle body inclination fuzzy control method based on variable domain in the above-mentioned embodiments belong to the same inventive concept. For details not fully described in the embodiment of the intelligent bicycle body inclination fuzzy control system based on variable domain, reference can be made to the above-mentioned embodiment of the intelligent bicycle body inclination fuzzy control method based on variable domain.

[0096] like Fig. 9 As shown, the system includes: A model building module is used to build a bicycle control model and a bicycle body inclination angle fuzzy PID control model according to the bicycle structure and control principle; The controller configuration module is used to simulate the bicycle control model and perform stability analysis, then define the fuzzy PID controller and configure the variable domain theory into the bicycle body inclination fuzzy PID control module; The control optimization module is used to dynamically adjust the universe expansion factor of the variable universe theory to optimize the fuzzy control of the bicycle body inclination angle without changing the fuzzy rules.

[0097] The present application also provides an electronic device, comprising a display module, a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps of the fuzzy control method for the inclination angle of an intelligent bicycle body based on a variable domain are implemented.

[0098] In embodiments of the present invention, electronic devices include, but are not limited to, laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic devices may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the embodiments of the present application described and / or required herein.

[0099] In the embodiment of the present application, the processor can be implemented by using at least one of a special purpose integrated circuit, a programmable logic device, a field programmable gate array, a processor, a controller, a microcontroller, a microprocessor, and an electronic unit designed to perform the functions described herein. In some cases, such an implementation can be implemented in a controller. For software implementation, implementations such as processes or functions can be implemented with separate software modules that allow execution of at least one function or operation. The software code can be implemented by a software application (or program) written in any appropriate programming language, and the software code can be stored in a memory and executed by a controller.

[0100] The display module is used to display information input by the user or information provided to the user. The display module may include a display panel, which may be configured in the form of a liquid crystal display, an organic light emitting diode, etc.

[0101] The memory can be used to store software programs and various data. The memory can include high-speed random access memory and can also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0102] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A fuzzy control method for the inclination angle of an intelligent bicycle body based on a variable universe, characterized in that: Methods include: S101: According to the bicycle structure and control principle, build the bicycle control model, bicycle body inclination fuzzy PID control model and dynamic equation; S102: simulating a bicycle control model and performing stability analysis to obtain a balance condition that meets the minimum speed of the bicycle; Re-define the fuzzy PID controller and configure the variable universe theory into the bicycle body inclination angle fuzzy PID control module; S103: Dynamically adjust the domain expansion factor of the variable domain theory to optimize the fuzzy control of the bicycle body inclination angle without changing the fuzzy rules.

2. The fuzzy control method for the inclination angle of an intelligent bicycle body based on a variable universe according to claim 1 is characterized in that: In step S101, the torque balance theorem is used to obtain the dynamic equation of the bicycle when it is balanced in a static state; Construct the moment balance equation for a stationary bicycle: = Among them, the contact point between the wheel and the ground is O, and the straight line perpendicular to the contact surface is the Z axis. is the angle between the bicycle body and the Z axis; assuming that the bicycle is only subject to gravity acting on the center of gravity, m is the total mass of the bicycle, is the centroid height, is the external force applied in the Y-axis direction, The point of action is M, is the distance between point M and point O.

3. The fuzzy control method for the inclination angle of an intelligent bicycle body based on a variable universe according to claim 2 is characterized in that: In the method, according to the force and torque balance equation of the bicycle, the bicycle dynamics equation of the following form is obtained: Among them, m is the overall mass of the bicycle body, g is the acceleration of gravity, h is the height of the bicycle's center of gravity, a is the distance between the projection point of the center of gravity on the horizontal plane and point P, b is the bicycle wheelbase, and c is the distance between the intersection point K of the front fork axis and the ground and point Q. is the fork angle of the bicycle, is the inclination angle of the bicycle body, is the vehicle speed, is the front handlebar angle, is the rate at which the bicycle handlebar angle changes with time, is the acceleration of the bicycle body inclination angle changing with time.

4. The method for fuzzy control of the inclination angle of an intelligent bicycle body based on a variable universe according to claim 1 or 2, characterized in that: The stability analysis of step S102 includes: The obtained dynamic model and equation are Laplace transformed, and the relationship is: The transfer function of the system is obtained as: Perform stability analysis process and obtain the equilibrium condition for the bicycle's minimum speed; Assuming a fixed handlebar angle value After dynamic adjustment, the body inclination angle is stable at ; and The ratio of Indicates that: in, For a given fixed handlebar angle value, To dynamically adjust the stable value of the caster angle, for and The ratio of exist In the case of , the equilibrium condition that satisfies the minimum speed of the bicycle is: 。 5. The fuzzy control method for the inclination angle of an intelligent bicycle body based on a variable universe according to claim 1 or 2, characterized in that: The method also includes: defining a fuzzy PID controller; The deviation between the expected and actual value of the bicycle body inclination angle e and the rate of change of deviation ec As the input variable, the fuzzy input quantity is fuzzy reasoned through the fuzzy rule base in the fuzzy PID controller to obtain the adjustment value of the PID parameter , , As the output variable; the PID parameter adjustment formula is: in: , , Corresponding to the proportional coefficient, integral coefficient, and differential coefficient, , , Corresponding to the preset value of PID control parameter, , , The corresponding value is the correction value of the PID control parameter.

6. The fuzzy control method for the inclination angle of an intelligent bicycle body based on a variable universe according to claim 5 is characterized in that: In this method, the center of the fuzzy set membership function curve and the area enclosed by the horizontal coordinate are obtained based on the centroid method, and the horizontal coordinate corresponding to the center is selected as the representative value of the fuzzy set. The expression is: in, is a fuzzy set; P for The number of elements in ; f is the exact value after defuzzification; Defining input error e and error rate of change ec The initial domain of the variable is [-E, E] and [-EC, EC], and the initial domain of the output variable is [-U, U]. The domain formula containing the scaling factor is: Where E represents the input error e The upper limit of the basic domain range, -E represents the input error e The upper limit of the basic domain range of ; EC represents the upper limit of the basic domain range of the error change rate ec, and -EC represents the lower limit of the basic domain range of the error change rate ec; U represents the upper limit of the initial domain range of the output variable, and -U represents the lower limit of the initial domain range of the output variable; α(e) and α(ec) are the scaling factors of the input variables; β(y) is the scaling factor of the output variable; X(e) and X(ec) are the transformed input and output domains.

7. The fuzzy control method for the inclination angle of an intelligent bicycle body based on a variable universe according to claim 6 is characterized in that: The method also includes: determining a scaling factor; The exponential function of the scaling factor of the input and output domains is expressed as: in, , are all constants; P is a constant vector; is the fuzzy parameter of the bicycle fuzzy controller, The scaling factor for the function input domain, To adjust the parameters of the fuzzy controller output intensity, A constant greater than 0 to adjust the input value stretch strength. is the accumulated system error from time 0 to y, is the initial state of the controller, i is the sum index of multiple accumulations, n It is the total sum of multiple accumulations; The expression of the scaling factor is: ； Where e is the error between the system output and the expected output, is the scaling factor, Integral scaling factor, Differential scaling factor.

8. A fuzzy control system for the inclination angle of an intelligent bicycle body based on a variable universe, characterized in that: The system is used to implement the fuzzy control method of the inclination angle of the intelligent bicycle body based on the variable domain as claimed in any one of claims 1 to 7; The system includes: A model building module is used to build a bicycle control model and a bicycle body inclination angle fuzzy PID control model according to the bicycle structure and control principle; The controller configuration module is used to simulate the bicycle control model and perform stability analysis, then define the fuzzy PID controller and configure the variable domain theory into the bicycle body inclination fuzzy PID control module; The control optimization module is used to dynamically adjust the universe expansion factor of the variable universe theory to optimize the fuzzy control of the bicycle body inclination angle without changing the fuzzy rules.

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