A bicycle trajectory tracking sliding mode control method based on disturbance observer

Abstract

The application discloses a bicycle trajectory tracking sliding mode control method based on an interference observer, comprising the following steps: S10, adding different sliding mode controllers and expected path information; S20, collecting motion parameters through a sensor group; S30, integrating and calculating the difference of the motion parameters to obtain tracking errors of each motion parameter; S40, bringing the tracking errors into a preset sliding surface to act on a reaction wheel driving motor, a forward motor and a steering engine; S50, judging whether saturation overflow occurs, and ending operation if an abnormality occurs; otherwise, returning to S20 to cyclically operate. The application controls a handlebar and a rear wheel driving force of the bicycle, uses a transformed coordinate system as a reference error, and adopts a stable converging reaching law to obtain a reference steering and a reference speed. The rear wheel driving force is controlled through a terminal sliding mode design reached within a predetermined time to follow the expected speed, so that the overall trajectory tracking method based on the bicycle framework is finally realized.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of robot balance control, and relates to a bicycle trajectory tracking sliding mode control method based on a disturbance observer. BACKGROUND

[0002] The sliding mode control method has a wide range of uses in the control discipline due to its excellent tracking performance and strong robustness to external disturbances. Nowadays, various robots gradually develop in the research direction of movement, tracking, distribution and the like in life. However, the control effect of the products on the market often has problems such as slow convergence, difficult parameter adjustment and large shaking. Therefore, based on the defects existing in the prior art, a series of methods are provided to solve the bicycle path tracking problem.

[0003] As for the structure of the bicycle, it largely depends on the human being's continuous learning and skilled habit. In the current research on the balance bicycle robot, the main control schemes include stabilization through handlebars, stabilization through an inverted pendulum model and stabilization through a reaction wheel. Among them, the reaction wheel system independently controls the balance of the bicycle robot. This scheme relies on the torque generated by the rotation of the reaction wheel to make the bicycle tend to be balanced. Its control unit is independent and easy to control, although it is more power-consuming to control, but the effect is the most intuitive and effective. In the current research, the control of the reaction wheel uses control methods such as fuzzy control, neural network control and disturbance observer control. These control methods have application prospects such as anti-chattering and prevention of actuator saturation. In the research on bicycle trajectory tracking control, the primary prerequisite is often to ensure that the bicycle maintains a certain balance angle under any condition of driving. Then consider the tracking strategy, which needs to consider the handlebar turning angle and the rear wheel driving force. In this strategy, the effect of using robust control or fuzzy control is poor, and frequent shaking often occurs. In view of the defects existing in the prior art, it is necessary to make further research to provide a better solution to improve the defects in the prior art. SUMMARY

[0004] To solve the above problems, the technical scheme of the present application is a bicycle trajectory tracking terminal sliding mode control method based on a disturbance observer. The bicycle trajectory tracking terminal sliding mode control system comprises a front wheel, a handlebar, a rear wheel, a frame and a reaction wheel installed at the middle position of the frame, and further comprises a microcontroller, a sensor group and a motor part connected with the microcontroller respectively, wherein,

[0005] The sensor group is used to collect the motion parameters of the bicycle, including the yaw angle, the reaction wheel speed, the rear wheel speed and the handlebar turning angle. The motor part includes a reaction wheel driving motor, a forward motor and a steering engine.

[0006] Based on the above control system, the control method comprises the following steps:

[0007] S10, adding different sliding mode controllers and reference path information;

[0008] S20, the sensor group collects motion parameters, position information;

[0009] S30, the motion parameters are integrated and the tracking errors of each motion parameter are obtained;

[0010] S40, the tracking error is brought into the preset sliding surface and acts on the reaction wheel driving motor, the forward motor and the steering engine;

[0011] S50, it is judged whether the saturation overflow condition appears, and the running is ended if the abnormality appears; otherwise, it is returned to S20 and the running is circularly performed.

[0012] Preferably, the balance driving equation for the bicycle model in S10 comprises:

[0013]

[0014] Wherein, u σ is the vehicle swing input torque, τ Δ is the uncertain term generated by the front fork angle, u φ is the torque generated by the reaction wheel;

[0015] The equation is modified to:

[0016]

[0017] l(t) represents the concentrated uncertain disturbance, which is composed of the unobserved term of the complex model, the external disturbance and the measurement error, simply speaking, J0 is the observed mh 2 +J1, f 10 (t) is the observed mhσcosθ, f 20 (t) is the observed mhbVcosθ, f 30 (t) is the observed mhbσcosθ, M0 is the observed mgh, in such a nominal model, And is a positive number large enough; Table 1 shows the specific meaning of each parameter in the bicycle model. The angle of the front wheel relative to the positive direction of the bicycle is denoted as β, b is the distance from the rear wheel to the center of mass of the bicycle, and L is the distance from the rear wheel to the front wheel. The reaction wheel is installed in the middle of the bicycle frame, and the frame inclination angle is defined as θ. When the inclination angle is zero, the height of the overall center of gravity from the ground is h. When the bicycle is driving and turning, the bicycle performs a circular motion with a curvature of σ = 1 / R. The moment of inertia of the bicycle as a whole in the y-axis direction is J1, the moment of inertia of the reaction wheel is J2, the front fork angle of the front wheel is η, the yaw angle is ψ, the handlebar turning angle is φ, the total mass of the bicycle is m, the driving track is Δ, the front fork angle of the front wheel is η, and the driving speed is v.

[0018] Preferably, the output equation of the motor control including the reaction wheel in S40 is composed of three parts:

[0019] u = u eq + u k + u sto (3)

[0020] Wherein:

[0021]

[0022]

[0023]

[0024] The formula contains two sliding surfaces, one is a non-singular fast terminal sliding surface s * , and the other is a specially set integral sliding surface s and the adaptive function

[0025]

[0026]

[0027]

[0028] Wherein, the inclination error e = θ - θ d , θ d is the desired inclination value, and e0 is the initial inclination error value. For any turning bicycle, the above control can keep the bicycle stable and balanced; in the selection of parameters, different parameter values are selected according to different bicycle model sizes, and the selection is limited to λ > 0, 2 > γ > 1, k1 > 0, k2 > 0, α > 0, and η > 0. The larger η is, the better the convergence speed is.

[0029] Preferably, in the path tracking strategy in S50, the tracking error of the inertial system needs to be converted to the reference coordinate:

[0030]

[0031] Reference speed v d Reference curvature σ d is set as:

[0032]

[0033] wherein v r is the reference speed when the reference path is proposed, σ r is the reference curvature in the reference path, which is obtained by the formula:

[0034]

[0035] The bicycle travels according to the reference speed and the reference curvature, and tracks the expected continuous curve path.

[0036] In terms of the handle control of the bicycle, the steering can be controlled by giving a rudder reference current signal, while in terms of the speed, accurate control needs to be adopted so as to enable it to track the given trajectory;

[0037] In this regard, the dynamic model of the bicycle is modeled as:

[0038]

[0039] Since the parameters are obtained directly or indirectly by the sensor group, the dynamic model is simplified as a nominal model:

[0040]

[0041] wherein m(t) is m+mσ 2 b 2 g1(t) is g2(t) is g3(t) is

[0042] Preferably, the S50 further includes setting an integral terminal sliding mode surface s v that reaches a predetermined time and an adaptive control law u v that reaches a predetermined time:

[0043]

[0044]

[0045] wherein the speed tracking error e v = v-v d , l1>0, l2>0, l3>0, l4>0, and the selection of the parameters m1, m2, m3, m4 needs to satisfy wherein n1>1, n3>1 and 1 / 2

[0046]

[0047]

[0048] Preferably, the sensor group comprises an encoder and a motion sensor MPU6050 chip.

[0049] Preferably, the microcontroller comprises an STM32F103 chip.

[0050] The present application has at least the following beneficial effects: systematically solve the problem of the reference position in the coordinate system starting from the bicycle body. First, the bicycle structure needs to be modeled, and the uncertain parameters and unknown disturbances are concentrated into the overall uncertain disturbance. The non-singular terminal sliding mode control is used, and the barrier function is used as the control law to control the error within a certain range. The super-helix algorithm is used to quickly compensate for the overall uncertain disturbance, and the robustness and accuracy of the system are enhanced. Under the premise of ensuring that the bicycle can be stable within the inclination angle that meets the kinematics under any driving condition. Under this premise, the handlebar and rear wheel driving force of the bicycle are controlled, the transformed coordinate system is used as the reference error, and the stable convergence approach law is used to obtain the reference steering and reference speed. The terminal sliding mode design is used to control the rear wheel driving force to follow the reference speed within a predetermined time, and finally the overall path tracking method based on the bicycle structure is realized. BRIEF DESCRIPTION OF DRAWINGS

[0051] Figure 1 The step flow chart of the bicycle trajectory tracking terminal sliding mode control method based on the disturbance observer of the embodiment of the present application is shown in the figure.

[0052] Figure 2 The system structure diagram corresponding to the bicycle trajectory tracking terminal sliding mode control method based on the disturbance observer of the embodiment of the present application is shown in the figure.

[0053] Figure 3 The bicycle model diagram corresponding to the bicycle trajectory tracking terminal sliding mode control method based on the disturbance observer of the embodiment of the present application is shown in the figure.

[0054] Figure 4 The trajectory tracking effect diagram of the bicycle trajectory tracking terminal sliding mode control method based on the disturbance observer of the embodiment of the present application is shown in the figure.

[0055] Figure 5This is a diagram showing the tilt angle tracking error of the bicycle trajectory tracking terminal sliding mode control method based on an interference observer, according to an embodiment of the present invention.

[0056] Figure 6 This is a diagram illustrating the tilt angle tracking effect of the sliding mode control method for bicycle trajectory tracking terminals based on interference observers, according to an embodiment of the present invention.

[0057] Figure 7 This is a reaction wheel control input diagram for a bicycle trajectory tracking terminal sliding mode control method based on an interference observer, according to another embodiment of the present invention.

[0058] Figure 8 This is a tracking accuracy effect diagram of the bicycle trajectory tracking terminal sliding mode control method based on interference observer according to an embodiment of the present invention;

[0059] Figure 9 A tracking accuracy diagram of existing control methods;

[0060] Figure 10 These are approximation effect diagrams of the bicycle trajectory tracking terminal sliding mode control method based on the interference observer in different situations according to embodiments of the present invention;

[0061] Figure 11 The diagram shows the effect of the bicycle trajectory tracking terminal sliding mode control method based on the interference observer on the rear wheel speed drive according to an embodiment of the present invention. Detailed Implementation

[0062] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0063] Conversely, this invention encompasses any substitutions, modifications, equivalent methods, and solutions made within the spirit and scope of the invention as defined in the claims. Furthermore, to provide a better understanding of the invention, certain specific details are described in detail below. However, those skilled in the art will fully understand the invention even without these detailed descriptions.

[0064] See Figure 1 This is a flowchart illustrating the steps of the sliding mode control method for a bicycle trajectory tracking terminal based on an interference observer, according to an embodiment of the present invention. Figure 2 , Figure 3 The diagram shows the control system structure and model of the method of the present invention. The control system includes a front wheel 42, handlebars 44, rear wheel 43, frame, and a reaction wheel 41 mounted in the middle of the frame. It also includes a microcontroller 10, a sensor group and a motor unit respectively connected to the microcontroller 10.

[0065] The sensor group is used to collect the motion parameters of the self-balancing bicycle, including angular velocity, acceleration and motor part rotating speed; the motor part includes reaction wheel driving motor 31, forward motor 33 and steering rudder 32; in structure, the steering rudder 32 is installed at the position of the vehicle head, behind the handlebar 44, for controlling the steering angle of the front wheel 42, i.e. the steering angle of the vehicle head. The reaction wheel 41 is driven by the reaction wheel driving motor 31, installed in the middle of the vehicle frame, perpendicular to the vehicle frame. An STM32 microcontroller is carried at the tail of the vehicle, which is the microcontroller 10 of the system. The 11.1V lithium battery installed below the STM32 supplies power for the whole balancing bicycle system. The sensor group includes an encoder for measuring the speed of the reaction wheel 41; MPU-6050 motion sensor chip can be used to obtain the yaw angle of the balancing bicycle. Figure 3 The model diagram of the bicycle is shown respectively.

[0066] Based on the above control system, the control method includes the following steps:

[0067] S10, adding different sliding mode controllers and expected path information;

[0068] S20, the sensor group collects the motion parameters;

[0069] S30, integrating and subtracting the motion parameters to obtain the tracking error of each motion parameter;

[0070] S40, bringing the tracking error into the preset sliding surface, and acting on the reaction wheel driving motor, the forward motor and the steering rudder;

[0071] S50, judging whether the saturation overflow occurs, and ending the running if the abnormality occurs; otherwise, returning to S20 to run in a loop.

[0072] Based on the above control system, the sliding mode control method based on the combination of observer and self-adaptation adopts nominal model for modeling, and centrally processes the uncertain parameters.

[0073] In the specific embodiment, the balance driving equation for the bicycle model in S10 is:

[0074]

[0075] Wherein, u σ is the vehicle swing input torque, τ Δ is the uncertain term generated by the front fork angle, u φ is the torque generated by the reaction wheel;

[0076] The equation is modified to:

[0077]

[0078] l(t) represents the lumped uncertainty disturbance, which consists of unobserved terms of the complex model, external disturbances and measurement errors. In simple terms, J0is the observed mh 2 +J1, f 10 (t) is the observed mhbVcosθ, f 20 (t) is the observed mhbVcosθ, f 30 (t) is the observed mhbVcosθ, f and is a sufficiently large positive number; Table 1 explains the specific meaning of each parameter in the bicycle model. The angle of the front wheel relative to the positive direction of the bicycle is denoted as β, b is the distance from the rear wheel to the center of mass of the bicycle, L is the distance from the rear wheel to the front wheel. The reaction wheel is installed in the middle of the bicycle frame, and the frame inclination angle is defined as θ. When the inclination angle is zero, the height of the overall center of gravity from the ground is h. When the bicycle is driving and turning, the bicycle performs a circular motion with a curvature of σ = 1 / R. The moment of inertia of the bicycle in the y-axis direction is J1, the moment of inertia of the reaction wheel is J2, the front fork angle of the front wheel is η, the yaw angle is ψ, the handlebar turning angle is φ, the total mass of the bicycle is m, the driving trajectory is Δ, the front fork angle of the front wheel is η, and the driving speed is v.

[0079] The output equation of the motor control including the reaction wheel in S40 is composed of three parts:

[0080] u = u eq + u k + u sto (3)

[0081] wherein:

[0082]

[0083]

[0084]

[0085] The formula contains two sliding surfaces, one is a non-singular fast terminal sliding surface s * , and the other is an integral sliding surface s specially set and an adaptive function :

[0086]

[0087]

[0088]

[0089] wherein, the inclination angle error e = θ - θ d, θ d is the desired tilt angle value, e0 is the initial tilt angle error value, the above control can make the bicycle keep stable balance for any steering condition; in the selection of parameters, different parameter values are selected according to different bicycle model sizes, and the selection is limited to λ>0, 2>γ>1, k1>0, k2>0, α>0, η>0, wherein the larger η is, the better the convergence speed is.

[0090] In S50, the tracking error of the inertial system needs to be converted into the reference coordinate in the path tracking strategy:

[0091]

[0092] Reference speed v d , reference curvature σ d is set to:

[0093]

[0094] wherein v r is the reference speed when the reference path is proposed, σ r is the reference curvature in the reference path, which is obtained from the formula:

[0095]

[0096] The bicycle travels according to the reference speed and the reference curvature, and tracks the desired continuous curve path.

[0097] In terms of handle control of the bicycle, the steering can be controlled by giving a reference current signal of the steering engine, and in terms of speed, accurate control needs to be adopted to enable it to track the given trajectory;

[0098] In this regard, the dynamic model of the bicycle is modeled:

[0099]

[0100] Since the parameters are obtained directly or indirectly by the sensor group, the dynamic model is simplified to a nominal model:

[0101]

[0102] wherein m(t) is m+mσ 2 b 2 , g1(t) is g2(t) is g3(t) is

[0103] In S50, an integral terminal sliding surface s vand the control law u v :

[0104]

[0105] u v = -(g1(t) + g2(t) + g3(t))

[0106]

[0107] where the velocity tracking error e v = v - v d , l1>0, l2>0, l3>0, l4>0, and the parameters m1, m2, m3, m4 need to be chosen to satisfy where n1>1, n3>1, and 1 / 2

[0108]

[0109]

[0110] The core is the torque output of the sliding mode control method based on the observer and the adaptive combination:

[0111]

[0112] The sliding mode control method based on the disturbance observer estimates the uncertain parameters in the system through the high-order approximation characteristics of the super-spiral method.

[0113] The adaptive function of the adaptive sliding mode control method adopts a piecewise function, where The barrier function used at this time is:

[0114]

[0115] The terminal sliding mode method based on the convergence within a predetermined time is:

[0116]

[0117]

[0118] The barrier function of the sliding mode control method based on the combination of the disturbance observer and the adaptive changes according to the change of the disturbance: the larger the disturbance, the larger the barrier function, and the smaller the disturbance, the smaller the barrier function.

[0119] The terminal sliding mode method based on convergence within a predetermined time must converge within a predetermined time t=t1+t2

[0120] In the face of uncertain parameters in the system, the conventional sliding mode control method only compensates for the upper bound of the uncertain parameters, rather than finding a more accurate observation value, so the output gain of the controller is a large constant value. Even when the uncertain disturbance is small, the controller still maintains a large gain, which can cause severe chattering. Compared with the prior art, the present application adds a disturbance observation term and an adaptive term in the control method. When there are varying external disturbances or the bicycle robot system cannot be accurately modeled, the uncertain parameters are accurately compensated using the super-helix method, which has a high-order stable characteristic. With the accurate disturbance observation term, the adaptive term can be more accurately added to the system as a stable term: when the time-varying uncertain parameter is large, the error increases, and the output gain of the controller increases; when the time-varying uncertain parameter is small, the error decreases, and the output gain of the controller decreases. Under this scheme, not only can the unknown error be frequently observed, but also the controller input can be smooth, continuous, and without frequent chattering. This is more effective in scenarios where the amplitude and rate of input control of a DC motor have constraints (MRC).

[0121] In path tracking control, the dynamic control is often controlled by a simple strong robustness control, which only considers the convergence within a limited time, which can cause frequent repeated chattering, actuator output saturation, and other situations in path tracking. The present application uses a sliding mode control with a predetermined time convergence, which can make the reference error in any state converge to the terminal sliding mode surface within a predetermined time through the selection of two parameters m1 and m2, and can make the reference error approach zero on the sliding mode surface within a predetermined time through the selection of two other parameters m3 and m4, which can effectively control the bicycle to start tracking and stably track the reference speed under any condition.

[0122] Referring to Figure 4 , Figure 5 , Figure 6 and Figure 7 are signal graphs of experimental simulation, showing the balance controller output and the inclination angle of the bicycle during balancing. As can be seen from the graph, the present application has a smooth and continuous control amount in the controller input, which can control the error accuracy to a very small range and control the chattering to be small, and has strong adaptability to changes in external parameters.

[0123] Next, experimental examples will be used to illustrate the superiority of the present application compared with the prior art in the field of bicycle robot self-path tracking. In the experiment, an 8-shaped curve is used as the tracking target, and under the condition of ensuring the balance of the bicycle, the reference curve is followed as much as possible.

[0124] In the example, from Figure 4 It can be seen that even if the initial swing angle is not close to the reference path, the bicycle can still track the reference trajectory in the subsequent tracking. Through the reverse deduction of the reference inclination angle, the driving of the reaction wheel motor is Figure 5 、 Figure 6 It can be seen that the approximation effect is stable and the interference ability is strong. Figure 7 The output control item can be seen that since the patent adopts high-order continuous super-helix method as interference observation compensation and barrier function as control law, the combination is smooth and continuous. This control method is very suitable for application in the scene with limited controller output amplitude and rate. In the control effect comparison, Figure 8 and Figure 9 The two figures respectively represent the control effect comparison chart of the method proposed in the patent and the adaptive barrier function method control. It can be seen that the control accuracy error of the method proposed in the patent is smaller, and the chattering generated is weaker than other methods, which can make the overall system more stable.

[0125] In the rear wheel driving control, the patent proposes a terminal sliding mode control that reaches within a predetermined time, Figure 10 It can be seen that the three different speeds can approach the reference speed within 0.4s of the predetermined time under the control of this method. And in Figure 11 In the comparison of the effect chart of other methods, it can be found that it has a faster approaching rate and can follow the reference signal faster.

[0126] The above only describes the preferred embodiments of the present application and is not used to limit the present application. Any modification, equivalent replacement and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A disturbance observer based bicycle trajectory tracking sliding mode control method, characterized in that, The bicycle trajectory tracking terminal sliding mode control system comprises a front wheel, a handlebar, a rear wheel, a frame and a reaction wheel installed at the middle position of the frame, and further comprises a microcontroller, a sensor group and a motor part connected with the microcontroller respectively, wherein, The sensor group is used to collect motion parameters of the bicycle, including a yaw angle, a reaction wheel rotating speed, a rear wheel rotating speed and a handlebar steering angle; the motor part comprises a reaction wheel driving motor, a forward motor and a steering rudder; Based on the above control system, the control method comprises the following steps: S10, adding different sliding mode controllers and reference path information; S20, the sensor group collects motion parameters and position information; S30, the motion parameters are integrated and differenced to obtain tracking errors of each motion parameter; S40, the tracking errors are brought into a preset sliding mode surface and applied to the reaction wheel driving motor, the forward motor and the steering rudder; S50, judging whether saturation overflow occurs, if yes, ending the operation; otherwise, returning to S20 and circulating the operation; The balance driving equation for the bicycle model in S10 is: wherein, u σ is the input torque of the steering wheel, τ Δ is the uncertain term generated by the front fork angle, u φ is the torque generated by the reaction wheel; The equation is modified by a nominal model as follows: l(t) represents concentrated uncertain disturbance, which consists of unobserved items of complex model, external disturbance and measurement error. In brief, J0 is the observed mh 2 +J1, f 10 (t) is the observed mhσcosθ, f 20 (t) is the observed mhbVcosθ, f 30 (t) is the observed mhbσcosθ, M0 is the observed mgh, in such nominal model, it can be obtained that, is a positive number; the angle of the front wheel relative to the positive direction of the bicycle is denoted as β, b is the distance from the rear wheel to the center of mass of the bicycle, L is the distance from the rear wheel to the front wheel, the reaction wheel is installed in the middle of the vehicle body frame, the vehicle body inclination angle is defined as θ, when the inclination angle is zero, the height of the overall center of gravity from the ground is h, when the bicycle is driving and steering, the bicycle performs a circular motion, the curvature is σ=1 / R, the moment of inertia of the bicycle overall in the y-axis direction is J1, the moment of inertia of the reaction wheel is J2, the front fork angle of the front wheel is η, the yaw angle is ψ, the handlebar turning angle is φ, the overall mass of the bicycle is m, the driving track is Δ, the front fork angle of the front wheel is η, and the driving speed is v; The output equation of the motor control of the reaction wheel in S40 is composed of three parts: u = u eq +u k +u sto (3) Wherein: The formula contains two sliding surfaces, one is a nonsingular fast terminal sliding surface s * , and the other is a specially set integral sliding surface s and an adaptive function : Wherein, the tilt angle error e = θ - θ d , θ d is the desired tilt angle value, e0is the initial tilt angle error value, the above control can make the bicycle keep stable balance for any steering condition; wherein the selection of parameters, according to the size of different car model selection of different parameter values, the selection limit is λ>0, 2>γ>1, k1>0, k2>0, α>0, η>0, wherein, the larger the convergence speed is better.

2. The method of claim 1, wherein, In S50, in the path tracking strategy, the tracking error of the inertial system needs to be converted into a reference coordinate: Reference speed v d Reference curvature σ d is set to: where v r is the reference speed at the time of proposing the reference path, σ r is the reference curvature in the reference path, which is derived from the formula The bicycle travels according to the reference speed and the reference curvature, and tracks the expected continuous curve path; The handlebar control of the bicycle can control the steering by giving the rudder reference current signal, and the speed needs to be accurately controlled to track the given trajectory; For this, the bicycle power model is modeled as follows: Since the parameters are obtained directly or indirectly by the sensor group, the power model is simplified as a nominal model as follows: where m(t) is m + mσ 2 b 2 , g1(t) is g2(t) is g3(t) is 3. The method of claim 2, wherein, The S50 also includes setting an integral terminal sliding surface s of a predetermined time arrival v And an adaptive control law u of predetermined time arrival v : Wherein, the speed tracking error e v = v - v d l1>0, l2>0, l3>0, l4>0, and the selection of parameters m1, m2, m3, m4 needs to meet Wherein, n1>1, n3>1, and 1 / 2<n2<1, 1 / 2<n4<1; for the sake of simplicity, n1=n3, n2=n4 can also be used; no matter the initial state, the front wheel power control of the bicycle can reach convergence within T=t1+t2, wherein:

4. The method of claim 1, wherein, The sensor group comprises an encoder and a motion sensor MPU6050 chip.

5. The method of claim 1, wherein, The microcontroller comprises an STM32F103 chip.

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