WO2018176597A1 - 一种基于速率陀螺仪的无人自行车平衡控制方法 - Google Patents

一种基于速率陀螺仪的无人自行车平衡控制方法 Download PDF

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WO2018176597A1
WO2018176597A1 PCT/CN2017/084511 CN2017084511W WO2018176597A1 WO 2018176597 A1 WO2018176597 A1 WO 2018176597A1 CN 2017084511 W CN2017084511 W CN 2017084511W WO 2018176597 A1 WO2018176597 A1 WO 2018176597A1
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bicycle
rate gyroscope
control
motor
gyroscope
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PCT/CN2017/084511
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French (fr)
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吴建国
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深圳市靖洲科技有限公司
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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/08—Control of attitude, i.e. control of roll, pitch, or yaw
    • G05D1/0891—Control of attitude, i.e. control of roll, pitch, or yaw specially adapted for land vehicles
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/04—Programme control other than numerical control, i.e. in sequence controllers or logic controllers
    • G05B19/042—Programme control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/20—Pc systems
    • G05B2219/26—Pc applications
    • G05B2219/2612—Data acquisition interface
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/20—Pc systems
    • G05B2219/26—Pc applications
    • G05B2219/2637—Vehicle, car, auto, wheelchair

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  • the invention relates to an unmanned bicycle technology, in particular to an unmanned bicycle balance control method based on a rate gyroscope.
  • Baidu has announced the development of a complex artificial intelligence unmanned bicycle.
  • This product is an unmanned bicycle with complex artificial intelligence such as environmental awareness, planning and self-balancing control. It mainly integrates Baidu in artificial intelligence.
  • the achievements of deep learning, big data and cloud computing technologies however, there is no disclosure of technical details.
  • most of the sports intervention service systems with wide coverage, low cost and high specificity are adopted, and the intervention of the unmanned bicycles in accordance with the actual situation is expected to solve the problems of bicycle erect.
  • the unmanned bicycle is a typical unstable system. It is based on bicycles, and is equipped with rotating handles and actuators for driving the rear wheels. In order to achieve stable balance of unmanned bicycles during riding, an anthropomorphic control strategy is adopted. According to the daily experience, the handlebar is turned upwards in the direction of the dumping, so that the gravity component of the dumping of the car is transferred to the centripetal force of the car body as a curve motion, avoiding the dumping of the car body, and the rider adjusts the center of gravity of the car, and can assist in The car body is corrected from the dumping direction to the balance point.
  • the bicycle is similar to the inverted pendulum. However, the dynamic characteristics of the former are more complicated.
  • An object of the present invention is to provide a rate gyroscope-based unmanned bicycle balance control method, comprising the following steps:
  • the control torque is calculated by the control algorithm and then supplied to the rate gyro frame, thereby generating a corresponding recovery torque on the bicycle frame.
  • the bicycle comprises four rigid bodies of the front wheel, the rear wheel, the vehicle body and the front fork, the wheel is sufficiently thin, and there is only one contact point with the ground.
  • the wheels do not slide relative to each other during rolling and all angles of the system are small.
  • the rate gyroscope in the step (1) is driven by a DC gear motor to generate an accurate torque on the bicycle frame, which is derived by the mechanical and electrical characteristics of the rate gyroscope and the tributary motor.
  • the Grande method obtains the equation of motion for bicycles and rate gyroscopes:
  • m b represents the moment of inertia of the bicycle relative to the shaft
  • m g represents the mass of the gyroscope
  • h g represents the vertical height of the bicycle centroid
  • I b represents the moment of inertia of the bicycle relative to the shaft
  • m g represents the mass of the rate gyro
  • h g represents the vertical height of the centroid of the gyroscope
  • I P represents the moment of inertia with respect to the centroid
  • Ir represents the radial moment with respect to the centroid
  • ⁇ represents the mass of the rate gyroscope
  • R m represents the impedance of the DC motor
  • L m represents Inductive reactance of DC motor
  • B m represents motor friction
  • K m represents torque voltage constant
  • ⁇ represents bicycle inclination
  • ⁇ represents gyroscope frame angle
  • i represents DC motor current
  • v DC motor voltage.
  • the step (2) combines the idea of the fuzzy output feedback system with the conventional linear control theory, and combines the use of the gyroscope to realize the balance control of the system when the vehicle speed changes.
  • the step (2) defines the "speed" v as a linguistic variable according to the basic idea of the fuzzy state space model, and divides the bicycle dynamics model into a plurality of fuzzy subspace sets for each fuzzy subspace system dynamics.
  • the characteristics are described by a local linear state equation.
  • the overall system dynamics is the weighted sum of the local linear models.
  • the control rules of the whole system are the weighted sum of the local feedback control of each subsystem, and the linguistic variable "speed" is assigned to multiple language values.
  • each language value is described by a triangle membership function, and a fuzzy implicit conditional sentence is used to describe the fuzzy feedback model of the system:
  • the step (2) uses a single value fuzzer and a center of gravity defuzzifier to obtain a state equation of the entire system, and the state equation of the whole system is related to the applicability after the normalization of the i th rule.
  • the step (2) can obtain a fuzzy state equation of the entire closed-loop system according to the fuzzy dynamic model and the control law of the entire system.
  • the step (3) is based on the offset signal collected by the offset sensor, and is obtained by the control algorithm to obtain the control torque, and then supplied to the rate gyro frame, so as to generate a corresponding recovery torque on the bicycle frame, including: determining the vehicle Put the adjustment properties and determine the adjustment amount of the handlebar in two steps.
  • the step of determining the adjustment property of the handlebar is determined by the universal interface communication of the single chip microcomputer, the interface level is determined by reading the pin to determine the adjustment property of the handlebar, and the motor of the control handlebar is forwardly reversed or the motor is kept not rotated through the communication interface. information.
  • the step of determining the adjustment amount of the handlebar comprises sampling the output signal of the rate gyro around the stable axis by using a single-chip microcomputer, and sampling the high-level time length and the given value.
  • the deviation is obtained by the difference, and the deviation is integrated to obtain the angle rotated by the unmanned bicycle around the stable shaft, thereby determining the adjustment amount of the handlebar.
  • the upright balance of the driverless bicycle can be effectively controlled.
  • FIG. 1 is a flow chart of a rate gyro-based unmanned bicycle balance control method according to an embodiment of the present invention
  • FIG. 2 is a schematic diagram of an unmanned bicycle balance control system based on a rate gyro according to an embodiment of the invention.
  • a method for controlling an unmanned bicycle balance based on a rate gyroscope is described in detail below with reference to FIG. 1, which includes the following steps:
  • the control torque is calculated by the control algorithm and then supplied to the rate gyro frame, thereby generating a corresponding recovery torque on the bicycle frame.
  • the momentum of the particle point relative to the moving point is equal to the vector sum of the moments of the external force of the principal moment of the point and the traction inertial force applied to the centroid to the moment of the point, according to the above theorem, for each rigid body part constituting the bicycle system, respectively.
  • the bicycle consists of four rigid bodies of the front wheel, the rear wheel, the car body and the front fork. The wheels are thin enough to have only one contact point with the ground. There is no relative slip during scrolling and all angles of the system are small.
  • the rate gyro is driven by a DC gear motor that produces an accurate torque on the bicycle frame that is derived from the mechanical and electrical characteristics of the rate gyroscope and the tributary motor.
  • the Lagrangian method is used to obtain the equation of motion for the bicycle and the rate gyro. :
  • m b represents the moment of inertia of the bicycle relative to the shaft
  • m g represents the mass of the gyroscope
  • h g represents the vertical height of the bicycle centroid
  • I b represents the moment of inertia of the bicycle relative to the shaft
  • m g represents the mass of the rate gyroscope
  • h g represents the vertical height of the centroid of the gyroscope
  • I P represents the moment of inertia with respect to the centroid
  • Ir represents the radial moment with respect to the centroid
  • ⁇ represents the mass of the rate gyroscope
  • R m represents the impedance of the DC motor
  • L m represents Inductive reactance of DC motor
  • B m represents motor friction
  • K m represents torque voltage constant
  • ⁇ represents bicycle inclination
  • ⁇ represents gyroscope frame angle
  • i represents DC motor current
  • v DC motor voltage
  • the dynamic characteristics of the bicycle have a very close relationship with the speed.
  • the general controller is difficult to achieve good control effects at various speeds, and the idea of the fuzzy output feedback system needs to be
  • the combination of conventional linear control theory and the use of gyroscopes achieves balanced control of the system as the vehicle speed changes.
  • M i represents the membership function of the i-th element in the fuzzy feedback set T, and a set of fuzzy implied conditional sentences is used to describe the fuzzy feedback model of the system:
  • a i A (v i ), v i M i is the membership function value corresponding to the velocity values 1, M i (x) represents x belongs to the membership function M i, and also indicates the i-th The applicability of the rules.
  • the single value fuzzer and the center of gravity defuzzifier are used to obtain the state equation of the whole system:
  • fuzzy controller can be represented as a fuzzy model as follows:
  • control rules of the entire system are the weighted sum of the local feedback control of each subsystem, namely:
  • step (3) is based on the offset signal collected by the offset sensor, and is controlled by a control algorithm to obtain a control torque and then supplied to the rate gyro frame to generate a corresponding recovery torque on the bicycle frame.
  • the step is divided into the general-purpose interface communication through the single-chip microcomputer to determine the interface level by reading the pin to determine the adjustment property of the handlebar, and the communication interface is used to convey the information that the motor of the handlebar is reversed or the motor is not rotated, and the rate gyro is adopted by the single-chip microcomputer.
  • the instrument samples the output signal when it is rotated by the stable axis, and the difference between the sampled high-level time length and the given value is obtained, and the deviation is integrated to obtain the angle that the unmanned bicycle rotates around the stable axis. To determine the amount of adjustment of the handlebars.
  • the single chip is used to shape the enable pulse of the rate gyroscope and the output PWM signal, and the external interrupt of the MCU is interrupted.
  • 0 is the falling edge trigger mode, which is used to input the shaped PWM signal
  • the external interrupt 1 of the MCU is used as the rising edge start mode for inputting the inverted PWM signal.

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • Aviation & Aerospace Engineering (AREA)
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  • Remote Sensing (AREA)
  • Feedback Control In General (AREA)
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Abstract

一种基于速率陀螺仪的无人自行车平衡控制方法，包括如下步骤：（1）建立无人自行车的平衡系统模型；（2）设计直立姿态平衡控制器；（3）根据偏移传感器采集到的偏移信号，经过控制算法运算得到控制力矩后提供给速率陀螺仪框架，从而在自行车车架上产生对应的恢复力矩。采用本发明的方法，能够有效控制无人驾驶自行车的直立平衡。

Description

一种基于速率陀螺仪的无人自行车平衡控制方法 技术领域
本发明涉及无人自行车技术，特别是一种基于速率陀螺仪的无人自行车平衡控制方法。
背景技术
自20世纪60年代移动机器人诞生以来，研究人员一直梦想研究无人智能交通工具，作为智能交通系统的重要组成部分，无人自行车排除了人为不确定因素的影响，不仅可以提高驾驶安全性，而且可以解决交通拥堵，提高能源利用率，百度曾宣布开发复杂人工智能无人自行车，该产品是具备环境感知、规划和自平衡控制等复杂人工智能的无人自行车，主要集合了百度在人工智能、深度学习、大数据和云计算技术的成就，然而对技术细节没有任何披露。目前大多采用采用覆盖面广、成本低，且针对性强的运动干预服务系统，对无人自行车的运动进行符合实际情况的干预，有望解决自行车直立等问题。
无人自行车是一个典型的不稳定系统，是以自行车为基础，再配以转动手把和驱动后轮的执行装置构成，为了实现骑行过程中无人自行车的稳定平衡，采用拟人控制策略，根据日常经验向倾倒方向同向上转动车把，使得车倾倒的重力分力转用于车体作为曲线运动的向心力，避免车体倾倒，而骑车人调节自身的重心作用，可以辅助起到将车体从倾倒的方向纠正回平衡点处的作用自行车与倒立摆相似,然而前者动力学特性更加复杂,是研究非线性控制\智能控制等控制方法理想的实验平台,理论上具有重大的意义。并且目前大多数自行车车体 平衡控制器的设计是在车速不变的条件下进行的，而自行车的动力学行为与速度有非常大的关系，因此，需要根据速度的变化结合速率陀螺仪的使用优化无人自行车的平衡控制方法。
发明内容
本发明的目的一方面在于提供一种基于速率陀螺仪的无人自行车平衡控制方法，包括如下步骤：
(1)建立无人自行车的平衡系统模型；
(2)设计直立姿态平衡控制器；
(3)根据偏移传感器采集到的偏移信号，经过控制算法运算得到控制力矩后提供给速率陀螺仪框架，从而在自行车车架上产生对应的恢复力矩。
优选的，所述步骤(1)中的平衡系统模型建立之前，需要作如下假设：自行车包含前轮、后轮、车体和前叉四个刚体，车轮足够细，与地面仅有一个接触点，车轮在滚动过程中没有相对滑动以及系统所有角度都是小量。
优选的，所述步骤(1)中的所述速率陀螺仪由一个直流齿轮电机驱动，在自行车框架上产生一个精确力矩，该力矩通过速率陀螺仪和支流电机的机械和电学特性推导，采用拉格朗日方法获得自行车和速率陀螺仪的运动方程：
θ轴和β轴方向上的力矩和分别为：
Figure PCTCN2017084511-appb-000001
Figure PCTCN2017084511-appb-000002
直流齿轮电机运动方程为：
Figure PCTCN2017084511-appb-000003
其中，mb表示自行车相对于轴的转动惯量，mg表示陀螺仪的质量，hg表示 自行车质心的垂直高度，Ib表示自行车相对于轴的转动惯量，mg表示速率陀螺仪的质量，hg表示陀螺仪质心的垂直高度，IP表示相对于质心的极惯性矩，Ir表示相对于质心的径向力矩，ω表示速率陀螺仪的质量，Rm表示直流电机阻抗，Lm表示直流电机的感抗，Bm表示电机摩擦，Km表示力矩电压常量，θ表示自行车倾角，β表示陀螺仪框架角，i表示直流电机电流，v表示直流电机电压。
优选的，所述步骤(2)将模糊输出反馈系统的思想与常规的线性控制理论相结合，并结合陀螺仪的使用实现了车速发生变化时系统的平衡控制。
优选的，所述步骤(2)根据如下模糊状态空间模型的基本思想定义“速度”v作为语言变量，将自行车动力学模型分为多个模糊子空间集合，对于每个模糊子空间系统动力学特性用一个局部线性状态方程描述，整个系统动力学特性为局部线性模型的加权和，整个系统的控制规则为各个子系统局部反馈控制的加权和，将语言变量“速度”赋予多个语言值，根据日常生活中的经验，每个语言值用一个三角形隶属度函数来描述后，采用一组模糊蕴含条件句描述系统的模糊反馈模型：
优选的，所述步骤(2)采用单值模糊器和重心解模糊器得到整个系统的状态方程，所述整个系统的状态方程与第i条规则归一化后的适用度相关。
优选的，所述步骤(2)根据整个系统的模糊动态模型及控制规律，可以得到整个闭环系统的模糊状态方程。
优选的，所述步骤(3)根据偏移传感器采集到的偏移信号，经过控制算法运算得到控制力矩后提供给速率陀螺仪框架，从而在自行车车架上产生对应的恢复力矩包括：确定车把调节性质和确定车把的调节量两个步骤。
优选的，所述确定车把调节性质的步骤通过单片机的通用接口通讯通过读引脚确定接口电平确定车把调节性质，通过通讯接口传达控制车把的电机正反转或者保持电机不转动的信息。
优选的，所述确定车把的调节量的步骤包括采用单片机将速率陀螺仪绕其被稳定轴转动时的输出信号进行采样，将采样得到的高电平时间长度与给定值 做差得到偏差值，对偏差之进行积分获得无人自行车绕稳定轴所转过的角度，从而确定车把的调节量。
采用本发明的方法，能够有效控制无人驾驶自行车的直立平衡。
根据下文结合附图对本发明具体实施例的详细描述，本领域技术人员将会更加明了本发明的上述以及其他目的、优点和特征。
附图说明
后文将参照附图以示例性而非限制性的方式详细描述本发明的一些具体实施例。附图中相同的附图标记标示了相同或类似的部件或部分。本领域技术人员应该理解，这些附图未必是按比例绘制的。本发明的目标及特征考虑到如下结合附图的描述将更加明显，附图中：
图1为根据本发明实施例的基于速率陀螺仪的无人自行车平衡控制方法的流程图；
图2为根据本发明实施例的基于速率陀螺仪的无人自行车平衡控制系统示意图。
具体实施方式
结合附图1如下详细说明一种基于速率陀螺仪的无人自行车平衡控制方法，包括如下步骤：
(1)建立无人自行车的平衡系统模型；
(2)设计直立姿态平衡控制器，并构建隶属度函数；
(3)根据偏移传感器采集到的偏移信号，经过控制算法运算得到控制力矩后提供给速率陀螺仪框架，从而在自行车车架上产生对应的恢复力矩。
其中，步骤(1)具体实施过程中根据一个定理：质点系相对于动点地动量 矩对时间的导数等于外力系对该点的主矩与加在质心上的牵引惯性力的合力对该点之矩的矢量和，根据上述定理，对于组成自行车系统的每一个刚体部分，分别进行当量方程的运算。因此，步骤(1)中的平衡系统模型建立之前，需要作如下假设：自行车包含前轮、后轮、车体和前叉四个刚体，车轮足够细，与地面仅有一个接触点，车轮在滚动过程中没有相对滑动以及系统所有角度都是小量。速率陀螺仪由一个直流齿轮电机驱动，在自行车框架上产生一个精确力矩，该力矩通过速率陀螺仪和支流电机的机械和电学特性推导，采用拉格朗日方法获得自行车和速率陀螺仪的运动方程：
θ轴和β轴方向上的力矩和分别为：
Figure PCTCN2017084511-appb-000004
Figure PCTCN2017084511-appb-000005
直流齿轮电机运动方程为：
Figure PCTCN2017084511-appb-000006
其中，mb表示自行车相对于轴的转动惯量，mg表示陀螺仪的质量，hg表示自行车质心的垂直高度，Ib表示自行车相对于轴的转动惯量，mg表示速率陀螺仪的质量，hg表示陀螺仪质心的垂直高度，IP表示相对于质心的极惯性矩，Ir表示相对于质心的径向力矩，ω表示速率陀螺仪的质量，Rm表示直流电机阻抗，Lm表示直流电机的感抗，Bm表示电机摩擦，Km表示力矩电压常量，θ表示自行车倾角，β表示陀螺仪框架角，i表示直流电机电流，v表示直流电机电压
其中，在控制器设计方面，自行车的动力学特性与速度有着非常密切的联系，一般的控制器很难在各种不同的速度下都达到良好的控制效果，需要将模糊输出反馈系统的思想与常规的线性控制理论相结合，并结合陀螺仪的使用实现了车速发生变化时系统的平衡控制。
首先，根据如下模糊状态空间模型的基本思想定义“速度”v作为语言变量， 这种思想在于，将自行车动力学模型分为5个模糊子空间集合，对于每个模糊子空间系统动力学特性用一个局部线性状态方程描述，整个系统动力学特性是局部线性模型的加权和，v范围在[0，25m/s]，即论域U＝[0，25]，模糊集合T(速度)＝{很慢，慢速，中速，快速，很快}，因此，语言变量“速度”具有5个语言值，根据日常生活中的经验，每个语言值用一个三角形隶属度函数来描述，其表达式如下：
Figure PCTCN2017084511-appb-000007
式中，i＝1,2,...,5；，Mi表示模糊反馈集合T中的第i个元素的隶属度函数，采用一组模糊蕴含条件句描述系统的模糊反馈模型：
如果v是Mi，则x＝Aix+Bu  (5)
其中，Ai＝A(vi)，vi是Mi的隶属度函数值为1时对应的速度值，Mi(x)表示x属于Mi的隶属度函数，同时也表示第i条规则的适用度。
采用单值模糊器和重心解模糊器，从而得到整个系统的状态方程：
Figure PCTCN2017084511-appb-000008
式中，
Figure PCTCN2017084511-appb-000009
其中，μi(x)表示第i条规则归一化后的适用度。
从而模糊控制器可以表示为如下的模糊模型：
如果x(t)是Mi，则：
u(t)＝-Lix(t)  (9)，
式中，i＝1,2,...,5，
整个系统的控制规则为各个子系统局部反馈控制的加权和，即：
Figure PCTCN2017084511-appb-000010
结合整个系统的模糊动态模型及控制规律，可以得到整个闭环系统的模糊状态方程：
Figure PCTCN2017084511-appb-000011
采用李亚普诺夫方法，找到合适的Li从而保证整个系统是闭环稳定的。
参见附图2，步骤(3)根据偏移传感器采集到的偏移信号，经过控制算法运算得到控制力矩后提供给速率陀螺仪框架，从而在自行车车架上产生对应的恢复力矩。该步骤分为通过单片机的通用接口通讯通过读引脚确定接口电平确定车把调节性质，通过通讯接口传达控制车把的电机正反转或者保持电机不转动的信息，并且采用单片机将速率陀螺仪绕其被稳定轴转动时的输出信号进行采样，将采样得到的高电平时间长度与给定值做差得到偏差值，对偏差之进行积分获得无人自行车绕稳定轴所转过的角度，从而确定车把的调节量。
由于计算机只能处理数字信号，因此需要对数字信号进行变换采用离散求和代替几分，然后进行递推，采用单片机对速率陀螺仪的使能脉冲和输出的PWM信号进行整形，将单片机外部中断0为下降沿触发模式，用于输入经过整形的PWM信号，将单片机外部中断1为上升沿出发模式，用于输入经过整型的反向的PWM信号。
虽然本发明已经参考特定的说明性实施例进行了描述，但是不会受到这些实施例的限定而仅仅受到附加权利要求的限定。本领域技术人员应当理解可以在不偏离本发明的保护范围和精神的情况下对本发明的实施例能够进行改动和修改。

Claims (10)

  1. 一种基于速率陀螺仪的无人自行车平衡控制方法，其特征在于，包括如下步骤：
    (1)建立无人自行车的平衡系统模型；
    (2)设计直立姿态平衡控制器；
    (3)根据偏移传感器采集到的偏移信号，经过控制算法运算得到控制力矩后提供给速率陀螺仪框架，从而在自行车车架上产生对应的恢复力矩。
  2. 根据权利要求1所述的一种基于速率陀螺仪的无人自行车平衡控制方法，其特征在于，所述步骤(1)中的平衡系统模型建立之前，需要作如下假设：自行车包含前轮、后轮、车体和前叉四个刚体，车轮足够细，与地面仅有一个接触点，车轮在滚动过程中没有相对滑动以及系统所有角度都是小量。
  3. 根据权利要求1所述的一种基于速率陀螺仪的无人自行车平衡控制方法，其特征在于，所述步骤(1)中的所述速率陀螺仪由一个直流齿轮电机驱动，在自行车框架上产生一个精确力矩，该力矩通过速率陀螺仪和支流电机的机械和电学特性推导，采用拉格朗日方法获得自行车和速率陀螺仪的运动方程：
    θ轴和β轴方向上的力矩和分别为：
    Figure PCTCN2017084511-appb-100001
    Figure PCTCN2017084511-appb-100002
    直流齿轮电机运动方程为：
    Figure PCTCN2017084511-appb-100003
    其中，mb表示自行车相对于轴的转动惯量，mg表示陀螺仪的质量，hg表示自行车质心的垂直高度，Ib表示自行车相对于轴的转动惯量，mg表示速率陀螺仪的质量，hg表示陀螺仪质心的垂直高度，IP表示相对于质心的极惯性矩，Ir表示相对于质心的径向力矩，ω表示速率陀螺仪的质量，Rm表示直流电机阻抗，Lm 表示直流电机的感抗，Bm表示电机摩擦，Km表示力矩电压常量，θ表示自行车倾角，β表示陀螺仪框架角，i表示直流电机电流，v表示直流电机电压。
  4. 根据权利要求1所述的一种基于速率陀螺仪的无人自行车平衡控制方法，其特征在于，所述步骤(2)将模糊输出反馈系统的思想与常规的线性控制理论相结合，并结合陀螺仪的使用实现了车速发生变化时系统的平衡控制。
  5. 根据权利要求4所述的一种基于速率陀螺仪的无人自行车平衡控制方法，其特征在于，所述步骤(2)根据如下模糊状态空间模型的基本思想定义“速度”v作为语言变量，将自行车动力学模型分为多个模糊子空间集合，对于每个模糊子空间系统动力学特性用一个局部线性状态方程描述，整个系统动力学特性为局部线性模型的加权和，整个系统的控制规则为各个子系统局部反馈控制的加权和，将语言变量“速度”赋予多个语言值，根据日常生活中的经验，每个语言值用一个三角形隶属度函数来描述后，采用一组模糊蕴含条件句描述系统的模糊反馈模型：
  6. 根据权利要求4所述的一种基于速率陀螺仪的无人自行车平衡控制方法，其特征在于，所述步骤(2)采用单值模糊器和重心解模糊器得到整个系统的状态方程，所述整个系统的状态方程与第i条规则归一化后的适用度相关。
  7. 根据权利要求4所述的一种基于速率陀螺仪的无人自行车平衡控制方法，其特征在于，所述步骤(2)根据整个系统的模糊动态模型及控制规律，得到整个闭环系统的模糊状态方程。
  8. 根据权利要求1所述的一种基于速率陀螺仪的无人自行车平衡控制方法，其特征在于，所述步骤(3)根据偏移传感器采集到的偏移信号，经过控制算法运算得到控制力矩后提供给速率陀螺仪框架，从而在自行车车架上产生对应的恢复力矩包括：确定车把调节性质和确定车把的调节量两个步骤。
  9. 根据权利要求8所述的一种基于速率陀螺仪的无人自行车平衡控制方法，其特征在于，所述确定车把调节性质的步骤通过单片机的通用接口通讯通过读引脚确定接口电平确定车把调节性质，通过通讯接口传达控制车把的电机正反 转或者保持电机不转动的信息。
  10. 根据权利要求8所述的一种基于速率陀螺仪的无人自行车平衡控制方法，其特征在于，所述确定车把的调节量的步骤包括采用单片机将速率陀螺仪绕其被稳定轴转动时的输出信号进行采样，将采样得到的高电平时间长度与给定值做差得到偏差值，对偏差之进行积分获得无人自行车绕稳定轴所转过的角度，从而确定车把的调节量。。
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