CN108298011B - Model-based self-balancing unmanned bicycle and data acquisition driving control method thereof - Google Patents
Model-based self-balancing unmanned bicycle and data acquisition driving control method thereof Download PDFInfo
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- CN108298011B CN108298011B CN201810081435.1A CN201810081435A CN108298011B CN 108298011 B CN108298011 B CN 108298011B CN 201810081435 A CN201810081435 A CN 201810081435A CN 108298011 B CN108298011 B CN 108298011B
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B62—LAND VEHICLES FOR TRAVELLING OTHERWISE THAN ON RAILS
- B62M—RIDER PROPULSION OF WHEELED VEHICLES OR SLEDGES; POWERED PROPULSION OF SLEDGES OR SINGLE-TRACK CYCLES; TRANSMISSIONS SPECIALLY ADAPTED FOR SUCH VEHICLES
- B62M6/00—Rider propulsion of wheeled vehicles with additional source of power, e.g. combustion engine or electric motor
- B62M6/40—Rider propelled cycles with auxiliary electric motor
- B62M6/45—Control or actuating devices therefor
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
Abstract
The invention discloses a model-based self-balancing unmanned bicycle and a control method for data acquisition driving of the self-balancing unmanned bicycle. The control method comprises a self-balancing control part and an unmanned control part. Self-balancing control is based on data acquisition drive, builds the model through data, through finding the relevance of people's control and controller control, turns into the control model that the controller controlled the bicycle with the control model of people's bicycle that learns out, realizes the self-balancing of bicycle. The unmanned bicycle can have a self-balancing function in various motion states through a coupling control method, and meanwhile, an indirect driving method is adopted, so that the self-balancing and unmanned driving of the bicycle can be realized only by installing three controller modules on the common bicycle without further modification of the common bicycle.
Description
Technical Field
The invention relates to the field of traffic, in particular to a model-based self-balancing unmanned bicycle and a control method for data acquisition driving of the self-balancing unmanned bicycle.
Background
As a traditional vehicle, the bicycle has the advantages of narrow and small body, simple mechanism, small-radius rotation, convenience, flexibility, no pollution, no noise, no energy source, low selling price and the like, and plays a significant role in modern life with increasingly serious problems of road congestion, air pollution, oil price rise and the like. The unmanned bicycle can provide driving balance assistance for special people such as children and the old, and is expected to be widely applied to disaster rescue and forest operation.
As people's attention to intelligent vehicles and unmanned technologies continues to increase, unmanned bicycles or bicycle robots have been developed primarily based on this intelligent vehicle concept. At present, most researchers of unpiloted bicycles are around both aspects of dynamic modeling and new control algorithm, and the research on the unpiloted bicycles mostly stays in the stages of theoretical discussion and preliminary experiments. Due to the complex dynamic characteristics and certain lateral instability of the bicycle, the self-balancing of the bicycle still has many troublesome problems, and how to solve the self-balancing problem of the bicycle running at a static or low speed is the key point for the unmanned bicycle to break through the current development limitation.
The existing balance system applied to the motorcycle or the electric bicycle is essentially the superposition of a monocycle balance system (namely, an inverted pendulum balance system) and a two-foot balance system. The front handle of the bicycle has high degree of freedom, and the two wheels have no direct driving force. Therefore, the driving force on a motorcycle or an electric bicycle that causes the balance thereof is not present on the bicycle, and the balancing method thereof is not effective on the bicycle, which brings more difficulty to the self-balancing and unmanned driving of the bicycle.
There have been some studies on bicycle models, but no one has considered how to cooperate with corresponding controllers for balance control, so there has been no study on a mechanism model of a bicycle with multiple balance controllers.
Disclosure of Invention
The invention aims to provide a model-based self-balancing unmanned bicycle and a control method for data acquisition driving thereof, aiming at the defects of the prior art.
The purpose of the invention is realized by the following technical scheme: a model-based self-balancing unmanned bicycle comprises a bicycle, a sensor module, a handlebar control module, a bicycle body middle control module and a bicycle body rear control module; the handlebar control module, the middle part control module and the rear part control module control each mechanism of the modules according to the information provided by the sensor module, thereby carrying out the indirect control of the balance and the advancing of the bicycle; the control variables of the mechanisms of the handlebar control module, the middle part control module of the vehicle body and the rear part control module of the vehicle body are coupled with each other;
the sensor module is used for measuring bicycle variables and human body variables, wherein the bicycle variables comprise a bicycle handlebar deflection angle α, a bicycle body deflection angle β and a bicycle rear wheel rotation angle phi;
the handlebar control module is positioned on a handlebar of the bicycle, and the center of gravity of the handlebar is adjusted through the center of gravity adjusting mechanism, so that the adjustment of the handlebar deflection angle α is realized;
the middle control module of the bicycle body is positioned in the middle of the bicycle body, and the center of gravity of the middle of the bicycle body is adjusted through the center of gravity adjusting mechanism;
the rear part control module of the bicycle body is positioned at the rear part of the bicycle, the gravity center of the rear part of the bicycle body is adjusted through the gravity center adjusting mechanism, and the balance control and the rear wheel rotation control of the rear part of the bicycle are performed through the rotating wheel mechanism; the adjustment of the rotation angle phi of the rear wheel of the bicycle is realized through the rotation control of the rear wheel;
the gravity center adjusting mechanisms of the handlebar control module, the middle control module and the rear control module are respectively controlled by the balance of the rear control module, so that the adjustment of the bicycle body deflection angle β is realized together;
the sensor module measures bicycle variables and human body variables when ordinary people ride the bicycles; taking bicycle variables as input and human body variables as output, and obtaining a control network or rule of a person for the bicycle through machine learning;
and establishing a mapping relation between the control variables of the handlebar control module, the middle part control module of the bicycle body and the rear part control module of the bicycle body and the human body variables, and obtaining the control rules of the handlebar control module, the middle part control module of the bicycle body and the rear part control module of the bicycle body on the bicycle through secondary mapping to realize the balance control of the unmanned bicycle.
Further, handlebar control module's focus adjustment mechanism is for transversely placing the slide bar mechanism on the handlebar, and handlebar control module carries out handlebar focus's regulation through the slider position of adjusting handlebar slide bar mechanism.
Furthermore, the gravity center adjusting mechanism of the vehicle body middle control module is an eccentric wheel, and the vehicle body middle control module adjusts the gravity center of the vehicle body middle part by adjusting the rotating angle of the eccentric wheel.
Furthermore, the gravity center adjusting mechanism of the vehicle body rear control module is an eccentric wheel, and the vehicle body rear control module adjusts the gravity center of the vehicle body rear part by adjusting the rotating angle of the eccentric wheel.
Further, the rotating wheel mechanism of the vehicle body rear control module is two rotating wheels which are perpendicular to each other: the vertical rotating wheel is tangent to the horizontal rotating wheel and is parallel to the rear wheel of the bicycle; the rear control module of the bicycle body performs balance control and rear wheel rotation control on the rear part of the bicycle by adjusting the rotating speeds of the two rotating wheels.
A control method of data acquisition drive of a model-based self-balancing unmanned bicycle comprises a balance control part and an unmanned control part;
the implementation method of the balance control part comprises the following steps:
1) data acquisition: the sensor module measures bicycle variables and human body variables when ordinary people ride the bicycles;
2) primary mapping: taking bicycle variables as input and human body variables as output, and obtaining a control network or rule of a person for the bicycle through machine learning;
3) secondary mapping and controller establishment: establishing a mapping relation between control variables of the handlebar control module, the vehicle body middle control module and the vehicle body rear control module and human body variables, obtaining control rules of the handlebar control module, the vehicle body middle control module and the vehicle body rear control module on the bicycle through secondary mapping of the established control network or rule of the person on the bicycle, and obtaining controller parameters;
4) self-balancing is realized: the controller parameters obtained by secondary mapping are respectively input into an actual handlebar control module, a vehicle body middle control module and a vehicle body rear control module, fine adjustment is carried out, and three controllers of the bicycle are built, so that self-balancing of the bicycle is realized;
the unmanned control part comprises the following implementation methods: and selecting a desired bicycle variable according to the target motion state to realize the unmanned control of the bicycle.
Further, in step 3), the mapping relationship between the control variables of the handlebar control module, the vehicle body middle control module and the vehicle body rear control module and the human body variables is as follows:
the secondary mapping relation between the control variable of the handlebar control module and the human body variable is as follows:
the secondary mapping relation between the control variable of the vehicle body middle control module and the human body variable is as follows:
the secondary mapping relation between the control variable of the vehicle body rear control module and the human body variable is as follows:
wherein F1 and F2 are respectively the pressure on the left side and the right side of the handlebar detected by the pressure sensor array of the handlebar, x1 and x2 are respectively the distance between the center of the pressure on the left side and the center of the handlebar, F3 and F4 are respectively the pressure of the left pedal and the pressure of the right pedal detected by the pressure sensor array of the left pedal and the pressure sensor array of the right pedal, F5 and F6 are respectively the pressure on the left side and the pressure on the right side detected by the pressure sensor array of the saddle, α1The included angle between the projection of the human body spine deflection direction detected by a posture sensor arranged on the human body spine on the horizontal plane and the vehicle body is formed; r is the center-of-gravity deflection radius of the human vertebra on a horizontal plane, which is detected by a posture sensor arranged on the human vertebra; x, m3When the gravity center adjusting mechanism of the handlebar control module is a slide bar mechanism, the position of the handlebar slide block and the mass of the slide block counterweight are theta1When the gravity center adjusting mechanism of the vehicle body middle control module is an eccentric wheel, the rotating angle theta of the eccentric wheel is2When the gravity center adjusting mechanism of the control module at the rear part of the vehicle body is an eccentric wheel, the rotating angle, omega, of the eccentric wheel1、ω2When the rotating wheel mechanisms of the control module at the rear part of the vehicle body are two rotating wheels which are vertical to each other, the rotating speeds of the two rotating wheels are equal.
Further, the machine learning method is a neural network or a fuzzy neural network method.
Further, the implementation of the unmanned control portion includes: selecting a target motion state, and controlling the bicycle in the target motion state; the motion state includes: starting, advancing, turning and retreating;
the bicycle control under the starting state comprises the following specific steps:
1) the integral adjustment is that the bicycle handlebar deflection angle α tends to be a constant through the variable adjustment of the handlebar control module, the vehicle body middle control module and the vehicle body rear control module, even if the bicycle tends to be an integral body from a running vehicle;
2) the center of gravity is adjusted through the variable adjustment of the handlebar control module, the middle part control module of the bicycle body and the rear part control module of the bicycle body, so that the bicycle handlebar deflection angle α tends to 0, and the bicycle body deflection angle β tends to 0, even if the bicycle is in a vertical standing state from a certain deflection angle;
the bicycle control under the advancing state comprises the following specific steps:
1) the integral adjustment, namely the bicycle handlebar deflection angle α tends to 0 through the variable adjustment of the handlebar control module, the middle part control module of the bicycle body and the rear part control module of the bicycle body, even if the bicycle is obtained and tends to an integral body when the handlebar does not rotate;
2) the center of gravity is adjusted through the variable adjustment of the handlebar control module, the middle part control module of the bicycle body and the rear part control module of the bicycle body, so that the deflection angle β of the bicycle body tends to 0, even if the bicycle is balanced by the bicycle;
3) indirect drive: the rear wheel of the bicycle is indirectly driven through the variable adjustment of a rotating wheel mechanism of a control module at the rear part of the bicycle body, so that the rotating angle phi of the rear wheel of the bicycle changes at a certain angular speed, even if the bicycle is driven to move forwards at a certain speed;
the bicycle control under the turning state comprises the following specific steps:
1) the integral adjustment, namely the bicycle handlebar deflection angle α tends to turn direction through the variable adjustment of the handlebar control module, the middle part control module of the bicycle body and the rear part control module of the bicycle body, even if the bicycle tends to be an integral body when the handlebar rotates;
2) the center of gravity is adjusted through the variable adjustment of the handlebar control module, the middle part control module of the bicycle body and the rear part control module of the bicycle body, so that the deflection angle β of the bicycle body tends to 0, even if the bicycle is balanced by the bicycle;
3) indirect drive: the rear wheel of the bicycle is indirectly driven through the variable adjustment of a rotating wheel mechanism of a control module at the rear part of the bicycle body, so that the rotating angle phi of the rear wheel of the bicycle changes at a certain angular speed, even if the bicycle turns at a certain speed;
the bicycle control method in the backward state comprises the following specific steps:
1) the bicycle is characterized in that a rear wheel of the bicycle is indirectly driven through variable adjustment of a rotating wheel mechanism of a control module at the rear part of the bicycle body, so that the rotating angle phi of the rear wheel of the bicycle is reversely changed at a certain angular speed, even if the rear wheel of the bicycle is reversely rotated at a certain speed, a front-back relation exists at the ground contact part of a handlebar and a front wheel, when the bicycle is in a backward state, the handlebar and the front wheel are in a dragged state, the dragging force at the joint of the handlebar is in the front, the handlebar rotating torque generated when the bicycle advances is eliminated, the adjustment of the handlebar deflection angle α of the bicycle can be simplified, and the bicycle tends to be a whole in the backward;
2) and (4) adjusting the center of gravity of the bicycle by variable adjustment of the handlebar control module, the middle part control module of the bicycle body and the rear part control module of the bicycle body, so that the deflection angle β of the bicycle body of the bicycle tends to be 0 even if the bicycle is balanced by the bicycle.
Further, the selecting the target motion state specifically includes:
1) macroscopic route determination: determining the integral traveling route of the bicycle in modes of navigation, manual selection and the like;
2) road surface control and obstacle avoidance: monitoring the road surface through a sensor module; carrying out terrain scanning, judging the terrain and selecting a control method corresponding to the terrain; and judging whether an obstacle exists or not, and if so, avoiding the obstacle, namely obtaining the traveling direction of the bicycle to be adjusted according to road surface information such as distance, obstacle width, obstacle motion condition and the like so as to adjust.
The invention has the beneficial effects that:
(1) the self-balancing bicycle has a self-balancing function when the bicycle is static.
(2) The self-balancing bicycle has a self-balancing function under various motion conditions.
(3) The unmanned bicycle controls a multivariable coupling system through a coupling control method, and control variables of the three controller modules are coupled with each other, so that the unmanned bicycle becomes a self-balancing whole.
(4) The unmanned bicycle adopts an indirect driving method, can realize the unmanned driving of the common bicycle only by installing the three controller modules on the common bicycle, and does not need to further modify the common bicycle.
(5) The unmanned bicycle adopts a self-balancing control method of data acquisition, establishes the mapping relation between a human body and the bicycle and between the bicycle and the three controllers, replaces the human body with the three controllers for self-balancing control, and provides a new idea for system simulation and realization of the self-balancing unmanned bicycle.
Drawings
FIG. 1 is an overall block diagram of the unmanned bicycle of the present invention;
FIG. 2 is a top plan view of the drone bicycle of this invention;
FIG. 3 is a rear elevational view of the unmanned bicycle of the present invention;
FIG. 4 is a block diagram of the steps of the control method of the bicycle data acquisition drive of the present invention;
FIG. 5 is a schematic view of the vehicle seat sensor array of the present invention;
FIG. 6 is a schematic diagram showing the relationship between the rear wheel eccentric wheel mechanism variable and the human body posture variable of the present invention;
FIG. 7 is a block diagram of the steps of the bicycle model drive control method of the present invention;
fig. 8 is a rear wheel driving schematic view of the unmanned bicycle of the present invention.
Detailed Description
In order to explain the present invention in more detail, an unmanned bicycle with a self-balancing function will be described in detail with reference to the accompanying drawings.
As shown in fig. 1, the present invention provides an unmanned bicycle with self-balancing function, which comprises a bicycle, a sensor module, a handlebar control module, a middle control module of a bicycle body, and a rear control module of the bicycle body.
The bicycle is a common bicycle on the market and comprises a front wheel (mass m1, radius r), a rear wheel (mass m1, radius r) and a frame (mass m 2).
The sensor module is used for measuring a bicycle handlebar deflection angle α, a bicycle body deflection angle β and a bicycle rear wheel rotation angle phi as shown in fig. 2, the bicycle handlebar deflection angle α is an included angle between a bicycle front wheel and a bicycle body, the bicycle handlebar deflection angle α is a positive number, indicates that a bicycle handlebar deflects rightward, and indicates that a bicycle handlebar deflects leftward as a negative number, the bicycle body deflection angle β is an included angle between a bicycle body and a vertical plane, the bicycle body deflection angle β is a positive number, indicates that a bicycle body tilts rightward, and indicates that a bicycle body tilts leftward as a negative number, the bicycle rear wheel rotation angle phi is a rotation angle of a bicycle rear wheel along a rear wheel axis, the bicycle rear wheel rotation angle phi is a positive number, indicates that a bicycle rear wheel rotates forward, and the bicycle rear wheel rotates backward, the bicycle rear wheel rotation angle phi is a negative number, the sensor module can be installed at a handlebar control module, or can be installed at a handlebar control module, a vehicle body deflection angle phi and a vehicle body deflection angle F2, a left-right-left-right pressure sensor array F84, a pressure sensor F3, a pressure sensor array F, a left-right pressure sensor F3, a pressure sensor F7, a pressure sensor array F3, a pressure sensor array F, a pressure sensor array F3, a pressure sensor array F, a pressure sensor;
handlebar control module be located the bicycle handlebar, including transversely placing in the electric slide bar mechanism on the handlebar (slider counter weight m 3.) handlebar control module carry out the regulation of handlebar focus and handlebar deflection angle α through adjusting handlebar slider position x be the distance at slider and handlebar center, handlebar slider position x indicate the slider to be located handlebar center right side when being the positive number, indicate the slider to be located handlebar center left side when being the negative number.
The bicycle body middle control module is positioned on a bicycle body and comprises a bicycle body electric eccentric wheel mechanism (radius r1, a bicycle body eccentric wheel counterweight m 4). The vehicle body middle control module adjusts the gravity center of the vehicle body by adjusting the rotation angle theta 1 of the vehicle body eccentric wheel. When the rotation angle theta 1 of the eccentric wheel of the vehicle body is positive, the eccentric wheel counterweight is positioned on the right side of the vehicle body, and when the rotation angle theta 1 of the eccentric wheel of the vehicle body is negative, the eccentric wheel counterweight is positioned on the left side of the vehicle body.
The control module at the rear part of the bicycle body is positioned above a rear wheel of the bicycle and comprises a rear seat electric eccentric wheel mechanism (radius r2, rear seat eccentric wheel counterweight m5) and an electric rotating wheel mechanism m 6. The backseat electric eccentric wheel mechanism adjusts the gravity center of the backseat by adjusting the rotation angle theta 2 of the backseat eccentric wheel, when the rotation angle theta 2 of the backseat eccentric wheel is positive, the eccentric wheel counterweight is positioned on the right side of the vehicle body, and when the rotation angle theta 2 of the backseat eccentric wheel is negative, the eccentric wheel counterweight is positioned on the left side of the vehicle body. The electric rotating wheel mechanism m6 comprises two rotating wheels which are perpendicular to each other: the bicycle comprises a horizontal rotating wheel and a vertical rotating wheel, wherein the horizontal rotating wheel is positioned right above a rear seat eccentric wheel, the center of the horizontal rotating wheel and the center of the rear seat eccentric wheel are positioned on the same vertical plane, and the vertical rotating wheel is tangent to the horizontal rotating wheel and is parallel to a rear wheel of the bicycle; the electric rotating wheel mechanism m6 performs the auxiliary balance of the rear seat part of the bicycle and the indirect control of the rotation of the rear wheel by adjusting the rotating speeds of the two rotating wheels.
The handlebar control module, the vehicle body middle control module and the vehicle body rear control module control each mechanism of the modules according to the information provided by the sensor module, thereby carrying out the indirect control of the balance and the advancing of the bicycle. The control variables of the handlebar control module, the middle control module of the vehicle body and the rear control module of the vehicle body are coupled with each other.
The control method of the unmanned bicycle with the self-balancing function comprises two parts, namely a self-balancing control method and an unmanned control method.
The self-balancing control method comprises but is not limited to a data acquisition driving control method, a bicycle model driving control method, a behavior driving control method, a key balance decomposition control method, an equivalent mapping control method, a self-evolution control method, an environment evolution self-adaptive evolution control method and a competition and cooperation control method.
The control method of the data acquisition driving provides a method for learning to obtain the balance control model of the unmanned bicycle based on data of the bicycle, and the learned control model of the bicycle is converted into a control model of the bicycle controlled by the controller by finding out the association between the human control and the controller control.
As shown in fig. 4, the control method of the data acquisition driver includes the following specific steps:
1) the data acquisition comprises the steps that a sensor module measures bicycle variables and human body variables when a common person rides a bicycle, wherein the bicycle variables comprise a bicycle handlebar deflection angle α, a bicycle body deflection angle β, a bicycle rear wheel rotation angle phi and primary and secondary derivatives thereof, the human body variables comprise pressures F1 and F2 on the left side and the right side of a handlebar detected by a pressure sensor array of the handlebar, distances x1 and x2 between pressure centers on the left side and the right side and the handlebar center, pressures F3 and F4 of left pedals and right pedals detected by pressure sensors of the left pedals and right pedals, pressures F5 and F6 on the left side and the right side detected by a pressure sensor array of a saddle shown in figure 5, an included angle α 1 between a projection of a human body spinal deflection direction on a horizontal plane and a bicycle body, a gravity center deflection radius R on the horizontal plane and the primary and secondary derivatives thereof detected by a posture sensor arranged on a human body spine, and the included angle α 1 of the human body and the bicycle body represent that the human body incline to the right side of the bicycle when the human body is positive number and represents that the;
2) the method comprises the following steps of obtaining a human-to-bicycle control network or rule through means such as machine learning, wherein the human-to-bicycle control network is a mapping network obtained through neural network learning by taking bicycle variables as input and human body variables as output, the human-to-bicycle control rule is a mapping fuzzy rule obtained through fuzzy neural network learning by taking the bicycle variables as input and the human body variables as output, and the human-to-bicycle control rule takes a bicycle body deflection angle β as an example and can be as follows:
3) secondary mapping and controller establishment: establishing a mapping relation between control variables of the handlebar control module, the middle part control module of the bicycle body and the rear part control module of the bicycle body and human body variables, and obtaining a control rule of the controller for the bicycle through secondary mapping of the established control network or rule of the person for the bicycle; the control variables comprise a handlebar slide block position x, a rotation angle theta 1 of the eccentric wheel of the bicycle body, a rotation angle theta 2 of the eccentric wheel of the rear seat and primary and secondary derivatives thereof, a rotation speed omega 1 of the horizontal rotating wheel of the rear seat, a rotation speed omega 2 of the vertical rotating wheel of the rear seat and primary derivatives thereof;
the secondary mapping relation between the control variable of the handlebar control module and the human body variable is as follows:
the secondary mapping relation between the control variable of the vehicle body middle control module and the human body variable is as follows:
as shown in fig. 6, the secondary mapping relationship between the control variable of the rear control module of the vehicle body and the human body variable is as follows:
wherein R ismaxIs the maximum value of R;
4) self-balancing is realized: the controller parameters obtained by secondary mapping are respectively input into an actual handlebar control module, a vehicle body middle control module and a vehicle body rear control module, fine adjustment is carried out, and three controllers of the bicycle are built, so that self-balancing of the bicycle is realized;
the control method of the bicycle model drive constructs a mechanism model of the unmanned bicycle based on three control modules, provides a method for carrying out balance control on the unmanned bicycle based on the mechanism model, and carries out controller design by establishing simulation of the mechanism model.
As shown in fig. 7, the control method of the bicycle model driving comprises the following specific steps:
1) selecting controllable considerable key variables, wherein the controllable considerable key variables comprise bicycle variables and control variables of a handlebar control module, a bicycle body middle control module and a bicycle body rear control module, the bicycle variables comprise a bicycle handlebar deflection angle α, a bicycle body deflection angle β, a bicycle rear wheel rotation angle phi and primary and secondary derivatives thereof, and the control variables comprise a handlebar slide block position x, a bicycle body eccentric wheel rotation angle theta 1, a rear seat eccentric wheel rotation angle theta 2 and primary and secondary derivatives thereof;
2) physical modeling: establishing a physical model of the unmanned bicycle based on the selected key variables; the physical model of the unmanned bicycle can be as follows:
wherein m is1Mass of front and rear wheels, m2Mass of the frame, m3Mass of the handle-bar slide counterweight, m4Mass of eccentric wheel of vehicle body, m5Mass m of eccentric wheel counterweight of backseat6The mass of the rear seat rotating wheel, r is the radius of the front wheel and the rear wheel, r1Radius of eccentric wheel of vehicle body, r2Radius of the eccentric wheel of the backseat, h2Height of center of mass of frame, h3The height of the balance weight of the sliding block of the handlebar,4height of the eccentric wheel of the vehicle body, h5Height of the eccentric wheel of the backseat6The height of the rear seat rotating wheel;
3) system simulation and controller establishment: performing system simulation of the unmanned bicycle control by using a control method including but not limited to PID control and neural network fuzzy control;
4) self-balancing is realized: and inputting the controller parameters obtained by system simulation into the handlebar control module, the middle part control module of the bicycle body and the rear part control module of the bicycle body, carrying out fine adjustment, and establishing three controllers of the bicycle, thereby realizing the self-balance of the bicycle.
The bicycle model driving control method and/or the data acquisition driving control method can be further generalized to a model-based self-balancing control method of the unmanned bicycle with a self-balancing function, and a model is constructed through mechanism and/or data; the behavior driving control method and/or the key balance decomposition control method and/or the equivalent mapping control method can be further generalized to a behavior driving based self-balancing control method of the unmanned bicycle with a self-balancing function, and the behavior driving control method and/or the key balance decomposition control method and/or the equivalent mapping control method are directly used for controlling the balance of the bicycle; the self-evolution control method and/or the environmental evolution self-adaptive evolution control method and/or the competition and cooperation control method can be further generalized to be a self-balancing control method of the unmanned bicycle with the self-balancing function based on the intelligent evolution, the self-balancing control method is used for carrying out balanced learning through off-line and/or on-line evolution, and meanwhile, the unmanned bicycle with the self-balancing function based on the intelligent evolution also has an application and an application method of the unmanned bicycle with habit correction.
The habit-correcting unmanned bicycle application and application method provides the habit-correcting unmanned bicycle application and application method, and after healthy riding habits of athletes or coaches are learned, habit correction is performed through a bicycle variable control method that the superposition effect of three controllers and a user on bicycle control tends to be healthy.
The unmanned control method comprises a bicycle control method and method selection under various running states of starting, advancing, turning, backing and the like.
The bicycle control method under the starting state comprises the following specific steps:
1) the integral adjustment is that the bicycle handlebar deflection angle α tends to be a constant through the variable adjustment of the handlebar control module, the vehicle body middle control module and the vehicle body rear control module, even if the bicycle tends to be an integral body from a running vehicle;
2) and (3) adjusting the center of gravity of the bicycle by variable adjustment of the handlebar control module, the middle part control module of the bicycle body and the rear part control module of the bicycle body, so that the handlebar deflection angle α of the bicycle tends to 0, and the bicycle body deflection angle β of the bicycle tends to 0, even if the bicycle is in a vertical standing state from a certain deflection angle.
The bicycle control method under the forward state comprises the following specific steps:
1) the integral adjustment, namely the bicycle handlebar deflection angle α tends to 0 through the variable adjustment of the handlebar control module, the middle part control module of the bicycle body and the rear part control module of the bicycle body, even if the bicycle is obtained and tends to an integral body when the handlebar does not rotate;
2) the center of gravity is adjusted through the variable adjustment of the handlebar control module, the middle part control module of the bicycle body and the rear part control module of the bicycle body, so that the deflection angle β of the bicycle body tends to 0, even if the bicycle is balanced by the bicycle;
3) indirect drive: as shown in fig. 8, the rear wheel of the bicycle is indirectly driven by the variable adjustment of the rear seat rotation wheel mechanism of the rear body control module, so that the rotation angle of the rear wheel of the bicycle is adjusted
At a certain angular velocity, even if the bicycle is moving forward at a certain speed.
The bicycle control method under the turning state comprises the following specific steps:
1) the integral adjustment, namely the bicycle handlebar deflection angle α tends to turn direction through the variable adjustment of the handlebar control module, the middle part control module of the bicycle body and the rear part control module of the bicycle body, even if the bicycle tends to be an integral body when the handlebar rotates;
2) the center of gravity is adjusted through the variable adjustment of the handlebar control module, the middle part control module of the bicycle body and the rear part control module of the bicycle body, so that the deflection angle β of the bicycle body tends to 0, even if the bicycle is balanced by the bicycle;
3) indirect drive: as shown in fig. 8, the rear wheel of the bicycle is indirectly driven by the variable adjustment of the rear seat rotation wheel mechanism of the rear body control module, so that the rotation angle of the rear wheel of the bicycle is adjusted
At a certain angular velocity even when the vehicle is derived to turn at a certain speed.
The bicycle control method in the backward state comprises the following specific steps:
1) indirect drive: the rear wheel of the bicycle is indirectly driven through the variable adjustment of the rotating wheel mechanism of the control module at the rear part of the bicycle body, so that the rotating angle of the rear wheel of the bicycle is adjusted
Because there is a fore-and-aft relation in the place where handlebar contacts the ground with front wheel, in the backward state of bicycle, handlebar and front wheel are in the state of being dragged, the drag force in the handlebar junction is in the front, has dispelled the handlebar rotation torque produced while the bicycle advances, can simplify the regulation of the bicycle handlebar deflection angle α, the bicycle will tend to a whole in the backward state;
2) and (4) adjusting the center of gravity of the bicycle by variable adjustment of the handlebar control module, the middle part control module of the bicycle body and the rear part control module of the bicycle body, so that the deflection angle β of the bicycle body of the bicycle tends to be 0 even if the bicycle is balanced by the bicycle.
The specific steps of the selection of the bicycle control method under the various running states are as follows:
1) macroscopic route determination: determining the integral traveling route of the bicycle in modes of navigation, manual selection and the like;
2) road surface control and obstacle avoidance: monitoring the road surface through a sensor module; carrying out terrain scanning, judging the terrain and selecting a control method corresponding to the terrain; judging whether an obstacle exists or not, if so, avoiding the obstacle, namely obtaining the traveling direction of the bicycle to be adjusted through road surface information such as distance, obstacle width, obstacle motion condition and the like so as to adjust;
example 1
The following describes an unmanned bicycle with self-balancing function, taking a control method of data acquisition driving to perform balance control of the unmanned bicycle as an example.
At time t0, before the bicycle is put into use, three devices, namely a handlebar control module, a middle body control module and a rear body control module (including a sensor module), are installed on a common bicycle, a controller is established based on a data acquisition driving control method, and an unmanned control method is added.
As shown in fig. 4, the control method of the data acquisition driver includes the following specific steps:
1) the data acquisition comprises the steps that a sensor module measures bicycle variables and human body variables when a common person rides a bicycle, wherein the bicycle variables comprise a bicycle handlebar deflection angle α, a bicycle body deflection angle β, a bicycle rear wheel rotation angle phi and primary and secondary derivatives thereof, the human body variables comprise pressures F1 and F2 on the left side and the right side of a handlebar detected by a pressure sensor array of the handlebar, distances x1 and x2 between pressure centers on the left side and the right side and the handlebar center, pressures F3 and F4 of left pedals and right pedals detected by pressure sensors of the left pedals and right pedals, pressures F5 and F6 on the left side and the right side detected by a pressure sensor array of a saddle shown in figure 5, an included angle α 1 between a projection of a human body spinal deflection direction on a horizontal plane and a bicycle body, a gravity center deflection radius R on the horizontal plane and the primary and secondary derivatives thereof detected by a posture sensor arranged on a human body spine, and the included angle α 1 of the human body and the bicycle body represent that the human body incline to the right side of the bicycle when the human body is positive number and represents that the;
2) the method comprises the following steps of obtaining a human-to-bicycle control network or rule through means such as machine learning, wherein the human-to-bicycle control network is a mapping network obtained through neural network learning by taking bicycle variables as input and human body variables as output, the human-to-bicycle control rule is a mapping fuzzy rule obtained through fuzzy neural network learning by taking the bicycle variables as input and the human body variables as output, and the human-to-bicycle control rule takes a bicycle body deflection angle β as an example and can be as follows:
3) secondary mapping and controller establishment: establishing a mapping relation between control variables of the handlebar control module, the middle part control module of the bicycle body and the rear part control module of the bicycle body and human body variables, and obtaining a control rule of the controller for the bicycle through secondary mapping of the established control network or rule of the person for the bicycle; the control variables comprise a handlebar slide block position x, a rotation angle theta 1 of the eccentric wheel of the bicycle body, a rotation angle theta 2 of the eccentric wheel of the rear seat and primary and secondary derivatives thereof, a rotation speed omega 1 of the horizontal rotating wheel of the rear seat, a rotation speed omega 2 of the vertical rotating wheel of the rear seat and primary derivatives thereof;
the secondary mapping relation between the control variable of the handlebar control module and the human body variable is as follows:
the secondary mapping relation between the control variable of the vehicle body middle control module and the human body variable is as follows:
as shown in fig. 6, the secondary mapping relationship between the control variable of the rear control module of the vehicle body and the human body variable is as follows:
wherein R ismaxIs the maximum value of R;
4) self-balancing is realized: the controller parameters obtained by secondary mapping are respectively input into an actual handlebar control module, a vehicle body middle control module and a vehicle body rear control module, fine adjustment is carried out, and three controllers of the bicycle are built, so that self-balancing of the bicycle is realized;
and at the time t1, the controller with the self-balancing function and the unmanned function is built and then is put into use by the user. A user turns on a power switch, and the unmanned bicycle with the self-balancing function is started based on the bicycle control method in the starting state.
The bicycle control method under the starting state comprises the following specific steps:
1) the integral adjustment is that the bicycle handlebar deflection angle α tends to be a constant through the variable adjustment of the handlebar control module, the vehicle body middle control module and the vehicle body rear control module, even if the bicycle tends to be an integral body from a running vehicle;
2) and (3) adjusting the center of gravity of the bicycle by variable adjustment of the handlebar control module, the middle part control module of the bicycle body and the rear part control module of the bicycle body, so that the handlebar deflection angle α of the bicycle tends to 0, and the bicycle body deflection angle β of the bicycle tends to 0, even if the bicycle is in a vertical standing state from a certain deflection angle.
At time t2, the user rides the self-balancing unmanned bicycle, and the self-balancing unmanned bicycle automatically assists in balancing.
At the time of t3, a user sets a certain distance of automatic driving by himself, and the unmanned bicycle with the self-balancing function adjusts the bicycle advancing based on the selection of bicycle control methods under various running states, and is driven in an indirect driving mode.
The specific steps of the selection of the bicycle control method under the various running states are as follows:
1) macroscopic route determination: determining the integral traveling route of the bicycle in modes of navigation, manual selection and the like;
2) road surface control and obstacle avoidance: monitoring the road surface through a sensor module; carrying out terrain scanning, judging the terrain and selecting a control method corresponding to the terrain; judging whether an obstacle exists or not, if so, avoiding the obstacle, namely obtaining the traveling direction of the bicycle to be adjusted through road surface information such as distance, obstacle width, obstacle motion condition and the like so as to adjust;
the indirect drive is realized by rotating a rear seat of a control module at the rear part of the vehicle bodyVariable adjustment of the wheel mechanism indirectly drives the rear wheel of the bicycle, so that the rotation angle of the rear wheel of the bicycle
Varying at a certain angular velocity even when the bicycle is moving forward at a certain speed, as shown in fig. 8.
And at the time t4, the pilotless bicycle with the self-balancing function arrives at a specified place and waits for a next command.
Example 2
Next, a self-balancing control of the unmanned bicycle by a model-driven control method will be described in detail.
At time t0, before the bicycle is put into use, three devices, namely a handlebar control module, a middle body control module and a rear body control module (including a sensor module), are installed on a common bicycle, a controller is built based on a model-driven control method, and an unmanned control method is added.
As shown in fig. 7, the control method of the bicycle model driving comprises the following specific steps:
1) selecting controllable considerable key variables, wherein the controllable considerable key variables comprise bicycle variables and control variables of a handlebar control module, a bicycle body middle control module and a bicycle body rear control module, the bicycle variables comprise a bicycle handlebar deflection angle α, a bicycle body deflection angle β, a bicycle rear wheel rotation angle phi and primary and secondary derivatives thereof, and the control variables comprise a handlebar slide block position x, a bicycle body eccentric wheel rotation angle theta 1, a rear seat eccentric wheel rotation angle theta 2 and primary and secondary derivatives thereof;
2) physical modeling: establishing a physical model of the unmanned bicycle based on the selected key variables; the physical model of the unmanned bicycle can be as follows:
wherein m is1Mass of front and rear wheels, m2Mass of the frame, m3Mass of the handle-bar slide counterweight, m4Mass of eccentric wheel of vehicle body, m5Mass m of eccentric wheel counterweight of backseat6The mass of the rear seat rotating wheel, r is the radius of the front wheel and the rear wheel, r1Radius of eccentric wheel of vehicle body, r2Radius of the eccentric wheel of the backseat, h2Height of center of mass of frame, h3The height of the balance weight of the sliding block of the handlebar,4height of the eccentric wheel of the vehicle body, h5Height of the eccentric wheel of the backseat6The height of the rear seat rotating wheel;
3) system simulation and controller establishment: performing system simulation of the unmanned bicycle control by using a control method including but not limited to PID control and neural network fuzzy control;
4) self-balancing is realized: the controller parameters obtained by secondary mapping are respectively input into an actual handlebar control module, a vehicle body middle control module and a vehicle body rear control module, fine adjustment is carried out, and three controllers of the bicycle are built, so that self-balancing of the bicycle is realized;
and at the time t1, the controller with the self-balancing function and the unmanned function is built and then is put into use by the user. A user turns on a power switch, and the unmanned bicycle with the self-balancing function is started based on the bicycle control method in the starting state.
The bicycle control method under the starting state comprises the following specific steps:
1) the integral adjustment is that the bicycle handlebar deflection angle α tends to be a constant through the variable adjustment of the handlebar control module, the vehicle body middle control module and the vehicle body rear control module, even if the bicycle tends to be an integral body from a running vehicle;
2) and (3) adjusting the center of gravity of the bicycle by variable adjustment of the handlebar control module, the middle part control module of the bicycle body and the rear part control module of the bicycle body, so that the handlebar deflection angle α of the bicycle tends to 0, and the bicycle body deflection angle β of the bicycle tends to 0, even if the bicycle is in a vertical standing state from a certain deflection angle.
At time t2, the user rides the self-balancing unmanned bicycle, and the self-balancing unmanned bicycle automatically assists in balancing.
At the time of t3, a user sets a certain distance of automatic driving by himself, and the unmanned bicycle with the self-balancing function adjusts the bicycle advancing based on the selection of bicycle control methods under various running states, and is driven in an indirect driving mode.
The specific steps of the selection of the bicycle control method under the various running states are as follows:
1) macroscopic route determination: determining the integral traveling route of the bicycle in modes of navigation, manual selection and the like;
2) road surface control and obstacle avoidance: monitoring the road surface through a sensor module; carrying out terrain scanning, judging the terrain and selecting a control method corresponding to the terrain; judging whether an obstacle exists or not, if so, avoiding the obstacle, namely obtaining the traveling direction of the bicycle to be adjusted through road surface information such as distance, obstacle width, obstacle motion condition and the like so as to adjust;
the indirect drive indirectly drives the rear wheel of the bicycle through the variable adjustment of a backseat rotating wheel mechanism of a control module at the rear part of the bicycle body, so that the rotating angle phi of the rear wheel of the bicycle changes at a certain angular speed, even if the bicycle is driven to move forwards at a certain speed, as shown in figure 8.
And at the time t4, the pilotless bicycle with the self-balancing function arrives at a specified place and waits for a next command.
Example 3
The following describes an unmanned bicycle with a self-balancing function, taking the driving habit learning of the unmanned bicycle as an example.
At time t0, before the bicycle is put into use, three devices, namely a handlebar control module, a vehicle body middle control module and a vehicle body rear control module (including a sensor module), are installed on a common bicycle and are set through a self-balancing control method and an unmanned control method.
At time t1, the bicycle is used by the athlete or coach for a period of time, and the healthy driving habit with the least damage is learned, so as to obtain a healthy bicycle variable control method, namely, a good riding habit.
At time t2, the user is invested in practice and habit correction is performed. The habit correction is a bicycle variable control method which is implemented by superposing the control of a bicycle by three controllers on the control of a user on the bicycle so that the superposition effect tends to be healthy; thus, if the riding habit of the user is not good, the controller gives an additional disturbance, and the user feels hard, so that the user tends to use the healthy riding habit to ride the bicycle, and the user has a good riding habit.
Example 4
The following describes an unmanned bicycle with a self-balancing function, taking the driving habit learning of the unmanned bicycle as an example.
At time t0, before the bicycle is put into use, three devices, namely a handlebar control module, a vehicle body middle control module and a vehicle body rear control module (including a sensor module), are installed on a common bicycle and are set through a self-balancing control method and an unmanned control method.
At time t1, the user is invested in the learning of the personalized driving habits. The riding habit learning is the learning of the driving habit of the user who uses the unmanned bicycle for a long time.
At time t2, another person (or a thief) rides the user's bicycle, and the bicycle also continues to learn the driving habits of the other person, thereby determining the change of the riding person. And then, the unmanned bicycle can contact the user through the server terminal for confirmation, judge whether the user borrows or rents the bicycle, and further contact police or related mechanisms through the server terminal if the user does not borrow or rents the bicycle for a period exceeding the renting period, and provide positioning for the user. If the user trades the unmanned bicycle, the driving habit of any previous user needs to be cleared through related authorization.
Example 5
In the following, an application of the shared unmanned bicycle to intelligent taxi calling and returning is taken as an example, and an unmanned bicycle with a self-balancing function is specifically described.
At time t0, before the bicycle is put into use, the three devices, namely the handlebar control module, the middle body control module and the rear body control module (including the sensor module), are installed on a common shared bicycle and are set through a self-balancing control method and an unmanned control method.
At time t1, the bicycle is directly released to the street for the user, and each unmanned bicycle should have its own parking space and support.
At time t2, the user calls the car through the mobile phone software on the street, the server searches the nearest shared unpiloted bicycle to the car-calling place, and the shared unpiloted bicycle is started and automatically driven to the car-calling place. If this unmanned bicycle is in non-vertical state, then need the unmanned aerial vehicle housekeeper to go out, hang just bicycle with the hook, make it get back to vertical state to start and autopilot. The vertical state is the state when the deflection angle of the bicycle body is less than or equal to the deflection angle of the bicycle body when the rear wheel of the bicycle is supported.
At time t3, the shared unpiloted bicycle arrives at the location of the call and is available to the user.
At the time t4, after the shared unpiloted bicycles are used up by the user, the server can automatically screen out the area with the lowest density of the shared unpiloted bicycles in a certain range, and the shared unpiloted bicycles can automatically drive to the place suitable for parking in the area and park for the next use requirement.
Claims (10)
1. A model-based self-balancing unmanned bicycle comprises a bicycle and a sensor module, and is characterized in that the bicycle further comprises a handlebar control module, a middle body control module and a rear body control module;
the sensorThe module is used for measuring bicycle variables and human body variables, wherein the bicycle variables comprise a bicycle handlebar deflection angle α, a bicycle body deflection angle β and a bicycle rear wheel rotation angle
The human body variables comprise handlebar pressure, pedal pressure, saddle pressure and human body posture of a person in the riding process;
the handlebar control module is positioned on a handlebar of the bicycle, and the center of gravity of the handlebar is adjusted through the center of gravity adjusting mechanism, so that the adjustment of the handlebar deflection angle α is realized;
the middle control module of the bicycle body is positioned in the middle of the bicycle body, and the center of gravity of the middle of the bicycle body is adjusted through the center of gravity adjusting mechanism;
the rear part control module of the bicycle body is positioned at the rear part of the bicycle, the gravity center of the rear part of the bicycle body is adjusted through the gravity center adjusting mechanism, and the balance control and the rear wheel rotation control of the rear part of the bicycle are performed through the rotating wheel mechanism; realizing the rotation angle of the rear wheel of the bicycle by the rotation control of the rear wheel
(iii) adjustment of (c);
the gravity center adjusting mechanisms of the handlebar control module, the middle control module and the rear control module are respectively controlled by the balance of the rear control module, so that the adjustment of the bicycle body deflection angle β is realized together;
the self-balancing unmanned bicycle needs to be matched and adjusted by three modules, namely a handlebar control module, a middle control module of the bicycle body and a rear control module of the bicycle body, which are respectively distributed at three positions, in a working state so as to realize self-balancing of the bicycle;
the sensor module measures bicycle variables and human body variables when ordinary people ride the bicycles; taking bicycle variables as input and human body variables as output, and obtaining a control network or rule of a person for the bicycle through machine learning;
and establishing a mapping relation between the control variables of the handlebar control module, the middle part control module of the bicycle body and the rear part control module of the bicycle body and the human body variables, and obtaining the control rules of the handlebar control module, the middle part control module of the bicycle body and the rear part control module of the bicycle body on the bicycle through secondary mapping to realize the balance control of the unmanned bicycle.
2. The model-based self-balancing unmanned bicycle of claim 1, wherein the handlebar control module center of gravity adjustment mechanism is a slide bar mechanism placed transversely on the handlebar, and the handlebar control module adjusts the handlebar center of gravity by adjusting a slide block position of the handlebar slide bar mechanism.
3. The model-based self-balancing unmanned bicycle of claim 1, wherein the center of gravity adjusting mechanism of the middle bicycle body control module is an eccentric wheel, and the middle bicycle body control module adjusts the center of gravity of the middle bicycle body by adjusting the rotation angle of the eccentric wheel.
4. The model-based self-balancing unmanned bicycle of claim 1, wherein the center of gravity adjusting mechanism of the rear body control module is an eccentric wheel, and the rear body control module adjusts the center of gravity of the rear body by adjusting the rotation angle of the eccentric wheel.
5. The model-based self-balancing unmanned bicycle of claim 1, wherein the rotating wheel mechanism of the rear body control module is two rotating wheels perpendicular to each other: the vertical rotating wheel is tangent to the horizontal rotating wheel and is parallel to the rear wheel of the bicycle; the rear control module of the bicycle body performs balance control and rear wheel rotation control on the rear part of the bicycle by adjusting the rotating speeds of the two rotating wheels.
6. A control method of data acquisition driving of a model-based self-balancing unmanned bicycle is characterized by comprising a balance control part and an unmanned control part;
the implementation method of the balance control part comprises the following steps:
1) data acquisition: the sensor module measures bicycle variables and human body variables when ordinary people ride the bicycles;
2) primary mapping: taking bicycle variables as input and human body variables as output, and obtaining a control network or rule of a person for the bicycle through machine learning;
3) secondary mapping and controller establishment: establishing a mapping relation between control variables of the handlebar control module, the vehicle body middle control module and the vehicle body rear control module and human body variables, obtaining control rules of the handlebar control module, the vehicle body middle control module and the vehicle body rear control module on the bicycle through secondary mapping of the established control network or rule of the person on the bicycle, and obtaining controller parameters;
4) self-balancing is realized: the controller parameters obtained by secondary mapping are respectively input into an actual handlebar control module, a vehicle body middle control module and a vehicle body rear control module, fine adjustment is carried out, and three controllers of the bicycle are built, so that self-balancing of the bicycle is realized;
the unmanned control part comprises the following implementation methods: and selecting a desired bicycle variable according to the target motion state to realize the unmanned control of the bicycle.
7. The method according to claim 6, wherein, in the step 3),
the secondary mapping relation between the control variable of the handlebar control module and the human body variable is as follows:
the secondary mapping relation between the control variable of the vehicle body middle control module and the human body variable is as follows:
the secondary mapping relation between the control variable of the vehicle body rear control module and the human body variable is as follows:
wherein F1 and F2 are respectively the pressure on the left side and the right side of the handlebar detected by the pressure sensor array of the handlebar, x1 and x2 are respectively the distance between the center of the pressure on the left side and the center of the handlebar, F3 and F4 are respectively the pressure of the left pedal and the pressure of the right pedal detected by the pressure sensor array of the left pedal and the pressure sensor array of the right pedal, F5 and F6 are respectively the pressure on the left side and the pressure on the right side detected by the pressure sensor array of the saddle, α1The included angle between the projection of the human body spine deflection direction detected by a posture sensor arranged on the human body spine on the horizontal plane and the vehicle body is formed; r is the center-of-gravity deflection radius of the human vertebra on a horizontal plane, which is detected by a posture sensor arranged on the human vertebra; x, m3When the gravity center adjusting mechanism of the handlebar control module is a slide bar mechanism, the position of the handlebar slide block and the mass of the slide block counterweight are theta1When the gravity center adjusting mechanism of the vehicle body middle control module is an eccentric wheel, the rotating angle theta of the eccentric wheel is2When the gravity center adjusting mechanism of the control module at the rear part of the vehicle body is an eccentric wheel, the rotating angle, omega, of the eccentric wheel1、ω2When the rotating wheel mechanisms of the control module at the rear part of the vehicle body are two rotating wheels which are vertical to each other, the rotating speeds of the two rotating wheels are equal.
8. The method of claim 6, wherein the machine learning method is a neural network or a fuzzy neural network method.
9. The method of claim 6, wherein the implementation of the drone control portion includes: selecting a target motion state, and controlling the bicycle in the target motion state; the motion state includes: starting, advancing, turning and retreating;
the bicycle control under the starting state comprises the following specific steps:
1) the integral adjustment is that the bicycle handlebar deflection angle α tends to be a constant through the variable adjustment of the handlebar control module, the vehicle body middle control module and the vehicle body rear control module, even if the bicycle tends to be an integral body from a running vehicle;
2) the center of gravity is adjusted through the variable adjustment of the handlebar control module, the middle part control module of the bicycle body and the rear part control module of the bicycle body, so that the bicycle handlebar deflection angle α tends to 0, and the bicycle body deflection angle β tends to 0, even if the bicycle is in a vertical standing state from a certain deflection angle;
the bicycle control under the advancing state comprises the following specific steps:
1) the integral adjustment, namely the bicycle handlebar deflection angle α tends to 0 through the variable adjustment of the handlebar control module, the middle part control module of the bicycle body and the rear part control module of the bicycle body, even if the bicycle is obtained and tends to an integral body when the handlebar does not rotate;
2) the center of gravity is adjusted through the variable adjustment of the handlebar control module, the middle part control module of the bicycle body and the rear part control module of the bicycle body, so that the deflection angle β of the bicycle body tends to 0, even if the bicycle is balanced by the bicycle;
3) indirect drive: the rear wheel of the bicycle is indirectly driven through the variable adjustment of the rotating wheel mechanism of the control module at the rear part of the bicycle body, so that the rotating angle of the rear wheel of the bicycle is adjusted
Varying at a certain angular speed, even if the bicycle is moving forward at a certain speed;
the bicycle control under the turning state comprises the following specific steps:
1) the integral adjustment, namely the bicycle handlebar deflection angle α tends to turn direction through the variable adjustment of the handlebar control module, the middle part control module of the bicycle body and the rear part control module of the bicycle body, even if the bicycle tends to be an integral body when the handlebar rotates;
2) the center of gravity is adjusted through the variable adjustment of the handlebar control module, the middle part control module of the bicycle body and the rear part control module of the bicycle body, so that the deflection angle β of the bicycle body tends to 0, even if the bicycle is balanced by the bicycle;
3) indirect drive: the rear wheel of the bicycle is indirectly driven through the variable adjustment of the rotating wheel mechanism of the control module at the rear part of the bicycle body, so that the rotating angle of the rear wheel of the bicycle is adjusted
Change at a certain angular velocity even if the vehicle is turning at a certain velocity;
the bicycle control method in the backward state comprises the following specific steps:
1) the bicycle is characterized in that a rear wheel of the bicycle is indirectly driven through variable adjustment of a rotating wheel mechanism of a control module at the rear part of the bicycle body, so that the rotating angle phi of the rear wheel of the bicycle is reversely changed at a certain angular speed, even if the rear wheel of the bicycle is reversely rotated at a certain speed, a front-back relation exists at the ground contact part of a handlebar and a front wheel, when the bicycle is in a backward state, the handlebar and the front wheel are in a dragged state, the dragging force at the joint of the handlebar is in the front, the handlebar rotating torque generated when the bicycle advances is eliminated, the adjustment of the handlebar deflection angle α of the bicycle can be simplified, and the bicycle tends to be a whole in the backward;
2) and (4) adjusting the center of gravity of the bicycle by variable adjustment of the handlebar control module, the middle part control module of the bicycle body and the rear part control module of the bicycle body, so that the deflection angle β of the bicycle body of the bicycle tends to be 0 even if the bicycle is balanced by the bicycle.
10. The method according to claim 9, wherein the selecting a target motion state is specifically:
1) macroscopic route determination: determining the integral traveling route of the bicycle in a navigation and manual selection mode;
2) road surface control and obstacle avoidance: monitoring the road surface through a sensor module; carrying out terrain scanning, judging the terrain and selecting a control method corresponding to the terrain; and judging whether an obstacle exists or not, and if so, avoiding the obstacle, namely obtaining the traveling direction of the bicycle to be adjusted according to the distance, the width of the obstacle and the movement condition of the obstacle so as to adjust.
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