CN114859700B - Control method, device and system for two-wheeled intelligent balance trolley - Google Patents

Abstract

The invention discloses a control method, a device and a system of a two-wheel intelligent balance trolley, wherein the control method combines three PID control algorithms, wherein a PD control is used for an upright ring, a PI control is used for a speed ring, a P control is used for a steering ring, the method breaks away from the traditional single data information to carry out the upright control, the three-axis angular speed and the linear speed information are fused, and the data are fused by adopting a twice filtering algorithm, so that the steering and straight-going accuracy of the two-wheel intelligent balance trolley is ensured while the balance of the two-wheel intelligent balance trolley is maintained, and the control precision of the intelligent balance trolley is greatly improved.

Description

A control method, device and system for a two-wheeled intelligent balancing vehicle

Technical Field

The present invention belongs to the field of balancing cars, and in particular relates to a control method, device and system for a two-wheeled intelligent balancing car.

Background Art

With the continuous progress of science and technology, the field of robotics has ushered in a revolutionary development. As an important branch of robotics, mobile robots have good mobility and stability and are suitable for various complex environments. Mobile robots can be divided into legged robots, tracked robots and wheeled robots. Wheeled robots are a type of mobile robots, which are widely used in logistics, factories and other scenes, greatly saving the cost of manpower and material resources. The two-wheeled intelligent balancing car has become a hot topic of research due to its small size, simple structure and low cost, providing an application platform for theoretical and experimental research. However, the intelligent balancing car has problems such as low stability and reliability, high cost and complex design.

Summary of the invention

The purpose of the present invention is to overcome the shortcomings of the above-mentioned prior art and provide a control method, device and system for a two-wheeled intelligent balancing car to solve the problems in the prior art that the balancing car has low stability and reliability, high cost and complex design.

In order to achieve the above object, the present invention adopts the following technical solutions:

A control method for a two-wheeled intelligent balancing vehicle, characterized in that:

Obtain the linear velocity and angular velocity, and calculate the PD control amount of the upright ring through the linear velocity and angular velocity;

Obtain the speed information, calculate the speed information and the given speed to obtain the speed deviation, input the speed deviation and the speed deviation at the previous moment into the first-order filtering algorithm to obtain a stable speed deviation, integrate the stable speed deviation value to obtain the integral value of the speed deviation, and calculate the speed loop PI control amount through the integral value of the speed deviation and the control speed amount;

The steering deviation value is obtained by subtracting the z-axis angular velocity from the expected value 0, and the steering deviation value is input into the steering ring P. The output of the steering ring P is added to the steering value to obtain the steering ring control amount;

The vertical loop PD control quantity, the speed loop PI control quantity and the steering loop control quantity are added and subtracted to obtain EPWMA and EPWMB of the EPWM comparison register, and the intelligent balancing car is controlled by EPWMA and EPWMB.

A further improvement of the present invention is:

Preferably, the calculation formula of the linear velocity is:

Among them, accelY and accelZ are the linear velocities in the Y and Z directions respectively;

The calculation formula of the angular velocity is:

GyroX is the angular velocity in the X direction, gyroX is the output value of the X axis, and the setting of k is related to the initialization of the posture detection module;

The tilt angle of the car relative to the X direction is obtained by linear velocity and angular velocity:

angle=(1-W)*angleY+W*(GyroX*dt+angle) (3).

Preferably, the calculation formula of the upright ring PD control amount is:

PD_Control=angle*kp+gryoX*kd (4)

Among them, PD_Control is the output control value of the upright ring PD, angle is the inclination angle of the car relative to the X direction, kp and kd are constants, and grayoX is the measured angular velocity of the X axis.

Preferably, the calculation formula of the speed deviation is:

EncoderNew＝0-(EnocderL+EncoderR) (5)

Wherein, EncoderNew is the deviation value of the latest speed, 0 is the given speed value, EncoderL and EncoderR are the information collected by the first incremental Hall encoder (2) and the second incremental Hall encoder (3) respectively;

The latest speed deviation and the speed deviation at the previous moment are calculated to obtain a stable speed deviation. The calculation formula is:

Encoder＝Encoder _last ×a+(1-a)×EncoderNew

Among them, Encdoer is the speed deviation value, Encoder _last is the speed deviation value at the previous moment, and a is the coefficient.

Preferably, the calculation formula for obtaining the steering deviation value by subtracting the z-axis angular velocity from the expected value 0 is:

TurnBias = 0-GryoZ

Among them, TurnBias is the steering bias value, GryoZ is the z-axis angular velocity;

If the mobile phone controls the left turn, the left and right turn values are set to positive values; if the mobile phone controls the right turn, the left and right turn values are set to negative values; if there is no turn signal, the left and right turn values are set to zero;

The calculation formula for adding the output of the steering ring P to the steering value is:

Turn＝TurnBias*kp2 (11)

Turn＝Turn+Turn _LR (12)

Among them, Turn is the output value of the steering ring P control, TurnBias is the input value of the P control, kp2 is a constant value, and Turn _LR is the left and right steering value.

Preferably, the calculation process of EPWMA and EPWMB of the comparison register of EPWM is:

M1＝|PD_Control+PI_Control-Turn| (13)

M2＝|PD_Control+PI_Control+Turn| (14)

The value of M1 calculated by formula (13) is assigned to EPWMA of the EPWM comparison register, and the value of M2 calculated by formula (14) is assigned to EPWMB of the EPWM comparison register.

Preferably, when the inclination angle of the intelligent balancing car is controlled to be greater than 45°, the motor is turned off and the EPWM output is prohibited.

A control device for a two-wheeled intelligent balancing car, comprising:

The vertical ring PD control module is used to obtain the linear velocity and angular velocity, and obtain the vertical ring PD control amount through the linear velocity and angular velocity calculation;

The speed loop PI control module is used to obtain the speed information, calculate the speed information and the given speed to obtain the speed deviation, and calculate the speed loop PI control amount through the speed deviation, the integral value of the speed deviation and the control speed amount;

A steering ring control module is used to obtain a steering deviation value by subtracting the z-axis angular velocity from the expected value 0, and the steering deviation value is input into the steering ring P. The output of the steering ring P is added to the steering value to obtain a steering ring control amount;

The EPWM module is used to perform addition and subtraction operations on the upright loop PD control quantity, the speed loop PI control quantity and the steering loop control quantity to obtain EPWMA and EPWMB of the EPWM comparison register, and control the intelligent balancing car through the EPWM duty cycle.

Preferably, it comprises a main control board, the main control board is connected to a motor drive module, the motor drive module is simultaneously connected to a first DC brush motor and a second DC brush motor; the first DC brush motor is provided with a first incremental Hall encoder, and the second DC brush motor is provided with a second incremental Hall encoder;

The main control board is also connected to a posture detection module and a debugging module.

Preferably, the main control board, the first incremental Hall encoder and the second incremental Hall encoder are powered by a 12V battery.

Compared with the prior art, the present invention has the following beneficial effects:

The present invention discloses a control method for a two-wheeled intelligent balancing car. The control method combines three PID control algorithms, wherein the upright ring uses PD control, the speed ring uses PI control, and the steering ring uses P control. The method breaks away from the traditional single data information for upright control, integrates the three-axis angular velocity and linear velocity information, and uses a twice filtering algorithm to fuse the data. While maintaining the balance of the two-wheeled intelligent balancing car, the accuracy of its steering and straight driving is also guaranteed, thereby greatly improving the control accuracy of the intelligent balancing car.

Furthermore, the intelligent balancing car control system uses DSP C2000 series chips. Compared with STM32 chips, this chip has more pin functions, stronger numerical processing capabilities, and is more professional in motor control. Its enhanced EPWM can easily output complex PWM waveforms.

Furthermore, the two-wheeled intelligent balancing car and its control system have a Bluetooth module, an LED display, a JTAG interface and a USB interface, which greatly facilitates the balance control and debugging of the intelligent balancing car.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a structural block diagram of a smart balancing car provided by a specific embodiment of the present invention;

FIG2 is a control principle block diagram of an intelligent balancing car provided by a specific embodiment of the present invention;

FIG3 is a flow chart of a control program of an intelligent balancing car provided in a specific embodiment of the present invention.

See Figure 1, 1-main control board, 2-first incremental Hall encoder, 3-second incremental Hall encoder, 4-first DC brush electrode, 5-second DC brush motor, 6-motor driver chip, 7-12V battery, 8-12V to 5V voltage regulator module, 9-OLED display, 10-posture detection module, 11-debugging module, 12-Bluetooth module, 13-host computer software, 14-CCS software, 15-mobile phone.

DETAILED DESCRIPTION

The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments:

In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention; the terms "first", "second", and "third" are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance; in addition, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection or a detachable connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the internal communication of two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

In order to achieve the above-mentioned purpose, the present invention has the following technical scheme: a two-wheeled intelligent balancing car and its control system, including a main control board 1, two incremental Hall encoders, a posture detection module 10, two DC brush motors, a motor drive chip 6, a mobile phone 15, a Bluetooth module 12 and a debugging module 11. The two incremental Hall encoders are respectively a first incremental Hall encoder 2 and a second incremental Hall encoder 3, and the two DC brush electrodes are respectively a first DC brush electrode 4 and a second DC brush motor 5.

Specifically, the input end of the main control board 1 is simultaneously connected to the 12V to 5V voltage stabilizing module 8, the posture detection module 10 and two incremental Hall encoders. The output end of the main control board 1 is simultaneously connected to the motor drive chip 6 and the OLED display screen 9. The input and output ends of the main control board 1 are both connected to the debugging module 11, and the debugging module 11 is the host computer software 13 or the CCS software 14, so that the main control board 1 can exchange information with the host computer software 13 or the CCS software 14. The input and output ends of the main control board 1 can exchange information with the Bluetooth module 12.

The input end of the 12V to 5V voltage stabilizing module 8 is connected to the output end of the 12V mobile power supply 7, the output end of the 12V to 5V voltage stabilizing module 8 is connected to the main control board 1, and the output end of the 12V to 5V voltage stabilizing module 8 is simultaneously connected to the first incremental Hall encoder 2 and the second incremental Hall encoder 3, so that the 12V to 5V voltage stabilizing module 8 can simultaneously power the two incremental Hall encoders and the main control board 1.

The input end of the electrode driving module 6 is connected to the output end of the main control board 1, and the output end of the motor driving chip 6 is connected to the input end of the first DC brushed motor 4 and the input end of the second DC brushed motor 5 at the same time. The output end of the first DC brushed motor 4 is connected to the input end of the first incremental Hall encoder 2, and the output end of the first incremental Hall encoder 2 is connected to the input end of the main control board 1; the output end of the second DC brushed motor 5 is connected to the input end of the second incremental Hall encoder 3, and the output end of the second incremental Hall encoder 3 is connected to the input end of the main control board 1. The motor driving module 6 uses the motor driving chip TB6612FNG, which is a DC motor driver device. It has a large current MOSFET-H bridge structure, a dual-channel circuit output voltage and pulse width, which are used for power supply and motor speed regulation respectively, and can drive two DC brushed motors to work at the same time. The mobile phone 15 is connected to the main control board 1 through the Bluetooth module 12, and the debugging module 11 is connected to the JTAG interface of the main control board 1 through the USB interface of the computer where it is located. The USB interface is used to connect to the debugging module 11, and the JTAG interface is used to connect to the main control board 1.

The main control board 1 adopts a DSPTMS320F2806X chip. The DSPTMS320F2806X main control board 1 is connected to modules such as a posture detection module 10 and a motor drive chip 6. The main control board 1 calculates the collected data to obtain the control amount of the motor.

The two incremental Hall encoders are respectively installed at the tails of the two DC brush motors to measure the motor speed. More specifically, the two incremental Hall encoders measure the speed signals A and B respectively during the rotation of the code disk of the DC brush motor. The two incremental Hall encoders transmit the speed signals to the EQEP module combined with DSPTMS320F2806X respectively, and can respectively measure the rotation direction and the speed of the first DC brush motor 4 and the second DC brush motor 5.

The posture detection module 10 uses the MPU6050 chip, which integrates a 3-axis gyroscope, a 3-axis accelerometer and an expandable digital motion processor DMP, and communicates with the IIC protocol interface. The IIC bus is a two-wire serial bus, one is the data line SDA, and the other is the clock line SCL, which is used to measure the gyroscope signals and acceleration signals in three directions.

The Bluetooth module uses the CC5241 chip to realize the communication between the smart balancing car and the Android phone. The chip supports the UART interface and the SPP Bluetooth serial port protocol. The chip is used to realize the function of the mobile phone controlling the smart balancing car.

The debugging module 11 is provided with CCS software 14 including host computer software 13, which is used to debug and compile the program in the main control board 1 by using the simulator in combination with CCS (code composer studio) software, and the host computer software 13 is used to observe the changes of the relevant parameters in the PID adjustment, so as to determine whether the intelligent balancing car has reached stability.

The 12V battery 7 is a portable mobile power source, which can store electric energy and is mainly used to supply power to mobile devices such as electronic products.

The 12V to 5V voltage regulator module 8 uses the LM2596 series chip, which contains a reference voltage regulator and a fixed frequency oscillator. The fixed output has different versions of 3.3V, 5V and 12V. The version can be adjusted to output various voltages less than 37V. This chip can be used to form an efficient voltage regulator circuit. The smart balancing car uses a fixed output 5V version.

The OLED display screen 9 uses a 0.96-inch 6-pin OLED display screen and uses the SPI communication protocol to communicate with the main control board 1, so that the corresponding data information can be viewed.

The intelligent balancing car control system uses DSP C2000 series chips. Compared with STM32 chips, this chip has more pin functions, stronger numerical processing capabilities, and is more professional in motor control. Its enhanced EPWM can easily output complex PWM waveforms.

The two-wheeled intelligent balancing car and its control system have a Bluetooth module, an LED display, a JTAG interface and a USB interface, which greatly facilitates the balance control and debugging of the intelligent balancing car.

The working principle of the present invention is described below:

See Figure 2. Figure 3 is the control principle diagram of the intelligent balancing car, with the wheel axis as the y-axis, the forward direction of the intelligent balancing car as the x-axis, and the vertical direction of the midpoint of the axle as the z-axis. The vertical ring and the speed ring are connected in series, the output of the speed ring control is used as the input of the vertical ring, and the output of the vertical ring control is used as the output of the system. Through integration, the control principle diagram of Figure 2 can be obtained.

First, the upright ring PD control is required. The linear velocity and angular velocity information of the intelligent balancing car are detected by the posture detection module 10. The corresponding inclination angle information of the intelligent balancing car can be obtained by calculating the linear velocity. The inclination angle information is calculated by calculating the linear velocity, and then the inclination angle relative to the y-axis is obtained. However, the accumulation of acceleration in the linear velocity will affect the measurement accuracy, so the inclination angle information can be calculated with the help of the angular velocity information. The specific expression is as follows:

Among them, accelY and accelZ are the linear velocities in the Y and Z directions obtained by the posture detection module 10, and angleY is the inclination information in the Y-axis direction. GyroX is the angular velocity in the X direction, and gyroX is the angular velocity of the posture detection module 10 about the X axis, which ranges from -32768 to +32768. The setting of k is related to the initialization of the posture detection module 10. When it is initialized to ±2000 degrees/s, k=16.4.

Since the angle obtained by the posture detection module will be affected by its own zero drift, the error will gradually increase over time. The first-order complementary filtering algorithm is used to fuse the angleY obtained by the linear velocity calculation and the GyroX obtained by the angular velocity calculation to obtain a more stable tilt angle relative to the X direction. The expression is as follows:

angle＝(1-W)*angleY+W*(GyroX*dt+angle) (3)

Among them, angle is the inclination information after fusion of the first-order complementary filtering algorithm, angleY is the inclination information in the Y-axis direction, angle+GyroX*dt is the angle information obtained by integrating the posture detection module, dt is the sampling period, and W is the filter coefficient, which ranges from [0,1].

The fused angle angle and the X-axis angular velocity measured by the posture detection module are input into the upright ring PD. The expression is as follows:

PD_Control=angle*kp+gryoX*kd (4)

Among them, PD_Control is the output of PD control, and kp and kd are constants.

Then the speed loop PI control is performed, and the speed information collected by the two incremental Hall encoders is subtracted from the given speed value to obtain the deviation value of the latest speed. The expression is as follows:

EncoderNew＝0-(EnocderL+EncoderR) (5)

Wherein, EncoderNew is the deviation value of the latest speed, 0 is the given speed value, and EncoderL and EncoderR are the information collected by the first incremental Hall encoder 2 and the second incremental Hall encoder 3 .

The latest speed deviation value and the speed deviation value at the previous moment are input into the first-order filtering algorithm to obtain a stable speed deviation. The expression is as follows:

Encoder＝Encoder _last ×a+(1-a)×EncoderNew (6)

Among them, Encoder is the speed deviation value, Encoder _last is the speed deviation value at the previous moment, and a is the coefficient.

The speed deviation values are accumulated to obtain the integral value of the speed deviation. The expression is as follows:

EncoderI＝EncoderI+Encoder (7)

Among them, EncoderI is the integral value of the speed deviation, and Encoder is the speed deviation value.

At the same time, when the mobile phone controls the forward and backward movement of the smart balancing car, when the smart balancing car receives a command to move forward, the speed is set to a positive value, and when the smart balancing car receives a command to move backward, the speed is set to a negative value. If the smart balancing car does not move forward or backward, the speed controlled by the mobile phone is zero. The expression is as follows:

EncoderI＝EncoderI+MoveControl (8)

Among them, MoveControl is the speed controlled by the mobile phone. Finally, the speed deviation value and the integral of the speed deviation value are input into the speed loop PI control to obtain the control amount of the speed loop. The expression is as follows:

PI_Control＝Encoder*kp1+EncoderI*ki (9)

Among them, PI_Control is the output of PI control, kp1 and ki are constants.

If only the upright ring and the speed ring are controlled, the intelligent balancing car will deviate during driving. At this time, it is necessary to add mobile phone software to control the steering, that is, the control of the steering ring P. The function of the steering ring P control is to make the driving trajectory of the intelligent balancing car close to a straight line. Use the posture detection module 10 to measure the angular velocity of the z-axis. If the mobile phone controls the left turn, the left and right steering values are set to positive values. If the mobile phone controls the right turn, the left and right steering values are set to negative values. If there is no steering signal, the left and right steering values are set to zero. The steering deviation value is obtained by subtracting the measured z-axis angular velocity from the expected value 0. The expression is as follows:

TurnBias＝0-GryoX (10)

Among them, TurnBias is the steering bias value, and GryoX is the z-axis angular velocity.

The steering deviation is used as the input of the steering ring P control. The output of the steering ring P control is added to the left and right steering values to obtain the steering ring control amount. The expression is as follows:

Turn＝TurnBias*kp2 (11)

Turn＝Turn+Turn _LR (12)

Among them, Turn is the output value of the steering ring P control, TurnBias is the input value of the P control, kp2 is a constant value, and Turn _LR is the left and right steering value.

Finally, in the interrupt program, the control quantities of the vertical loop, speed loop, and steering loop are added and subtracted. The expressions are as follows:

M1＝|PD_Control+PI_Control-Turn| (13)

M2＝|PD_Control+PI_Control+Turn| (14)

By limiting the EPWM register value, if the values of M1 and M2 do not exceed the limit conditions, the values of M1 and M2 are assigned to EPWMA and EPWMB of the EPWM comparison register, and the duty cycle (how much high voltage occupies a cycle) is adjusted by these two values, which is equivalent to adjusting different voltages. Finally, the EPWM waveform is output to the motor drive module 6 through the main control board 1. The motor drive module 6 drives the two DC brush motors 4 and 5. The intelligent balancing car can be maintained in a balanced state and the two DC brush motors 4 and 5 can move as required. The accuracy of the system is improved by combining the three PID control methods.

Refer to Figure 3, which is a control program flow chart of the intelligent balancing car. First, the intelligent balancing car is powered on and run, and each device and function is initialized, such as: initializing the system clock, initializing the PIE control register, initializing the QEP function pin, initializing the timer and initializing the IIC module, etc. The main control board 1 receives the acceleration and angular velocity information of the posture detection module 10 and the information of the two incremental Hall encoders 2 and 3, and waits to receive the start control instruction. After receiving the start control instruction, it starts to calculate the control amount of the upright ring, speed ring, and steering ring, and sets the final control amount to the duty cycle of EPWM. The intelligent car can maintain a balanced state. When the inclination angle is greater than 45 degrees, the motor is turned off and the EPWM output is prohibited.

The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (5)

1. A control method of a two-wheeled intelligent balance trolley is characterized in that,

Acquiring a linear speed and an angular speed, and calculating to acquire a control quantity of the vertical ring PD through the linear speed and the angular speed;

obtaining rotation speed information, calculating the rotation speed information and a given speed to obtain a speed deviation, inputting the speed deviation and the speed deviation at the previous moment into a first-order filtering algorithm to obtain a stable speed deviation, integrating the stable speed deviation value to obtain an integral value of the speed deviation, and calculating to obtain a speed loop PI control quantity through the integral value of the speed deviation and a control speed quantity;

the z-axis angular velocity is differenced with an expected value 0 to obtain a steering deviation value, the steering deviation value is input into a steering ring P, and the output of the steering ring P is added with the steering value to obtain a steering ring control quantity;

the control quantity of the vertical ring PD, the control quantity of the speed ring PI and the control quantity of the steering ring are subjected to addition and subtraction to obtain EPWMA and EPWMB of EPWM comparison registers, and the intelligent balance trolley is controlled through EPWMA and EPWMB;

the calculation formula of the linear velocity is as follows:

wherein accelY and accelZ are linear velocities in the Y and Z directions, respectively;

the calculation formula of the angular velocity is as follows:

GyroX is the angular velocity in the X direction, gyroX is the output value of the X axis, and the setting of k is related to the initialization of the pose detection module;

the inclination angle of the trolley relative to the X direction is obtained by the linear velocity and the angular velocity:

angle＝(1-W)*angleY+W*(GyroX*dt+angle) (3)；

The calculation formula of the control amount of the vertical ring PD is as follows:

PD_Control＝angle*kp+gryoX*kd (4)

Wherein, PD_control is the output Control quantity of the vertical ring PD, angle is the angle of inclination of the trolley relative to the X direction, kp and kd are constants, gryoX is the angular velocity of the X axis obtained by measurement;

the calculation formula of the speed deviation is as follows:

EncoderNew＝0-(EnocderL+EncoderR) (5)

Wherein EncoderNew is the deviation value of the latest speed, 0 is a given speed value, encoderL and EncoderR are the information acquired by the first incremental Hall encoder (2) and the second incremental Hall encoder (3) respectively;

Calculating the latest speed deviation and the speed deviation at the last moment to obtain stable speed deviation, wherein the calculation formula is as follows:

Encoder＝Encoder_last×a+(1-a)×EncoderNew

wherein Encdoer is a speed deviation value, encoder _last is a speed deviation value at the last moment, and a is a coefficient;

The calculation formula for obtaining the steering deviation value by making the difference between the z-axis angular velocity and the expected value 0 is as follows:

TurnBias＝0-GryoZ

Wherein TurnBias is the steering bias value, gryoZ is the z-axis angular velocity;

If the mobile phone controls left steering, setting a left steering value and a right steering value to be positive values; if the mobile phone controls the right steering, setting the left steering value and the right steering value as negative values; if no steering signal exists, setting a left steering value and a right steering value to be zero;

the calculation formula of the addition of the output of the steering ring P and the steering value is:

Turn＝TurnBias*kp2 (11)

Turn＝Turn+Turn_LR (12)

Wherein Turn is the output value of the steering ring P control, turnBias is the input value of the P control, kp2 is a constant value, turn _LR is the left and right steering value;

The EPWMA and EPWMB calculation process for the compare register of EPWM is:

M1＝|PD_Control+PI_Control-Turn| (13)

M2＝|PD_Control+PI_Control+Turn| (14)

the value of M1 calculated in equation (13) is assigned to EPWMA of the EPWM compare register, and the value of M2 calculated in equation (14) is assigned to EPWMB of the EPWM compare register.

2. The control method of the two-wheeled intelligent balance car according to claim 1, wherein when the inclination angle of the intelligent balance car is controlled to be larger than 45 degrees, the motor is turned off, and EPWM is forbidden to output.

3. The utility model provides a controlling means of balanced dolly of two-wheeled intelligence which characterized in that includes:

the vertical ring PD control module is used for acquiring the linear speed and the angular speed, and calculating the vertical ring PD control quantity through the linear speed and the angular speed;

The speed loop PI control module is used for acquiring the rotation speed information, calculating the rotation speed information and a given speed to acquire speed deviation, and calculating the speed loop PI control quantity through the speed deviation, an integral value of the speed deviation and a control speed quantity;

The steering ring control module is used for obtaining a steering deviation value by making a difference between the z-axis angular speed and an expected value 0, inputting the steering deviation value into a steering ring P, and obtaining a steering ring control quantity by adding the output of the steering ring P and the steering value;

EPWM module for adding and subtracting the control quantity of the vertical ring PD, the control quantity of the speed ring PI and the control quantity of the steering ring to obtain EPWMA and EPWMB of the comparison register of EPWM, and controlling the intelligent balance trolley through the duty ratio of EPWM;

the calculation formula of the linear velocity is as follows:

wherein accelY and accelZ are linear velocities in the Y and Z directions, respectively;

the calculation formula of the angular velocity is as follows:

GyroX is the angular velocity in the X direction, gyroX is the output value of the X axis, and the setting of k is related to the initialization of the pose detection module;

the inclination angle of the trolley relative to the X direction is obtained by the linear velocity and the angular velocity:

angle＝(1-W)*angleY+W*(GyroX*dt+angle) (3)；

The calculation formula of the control amount of the vertical ring PD is as follows:

PD_Control＝angle*kp+gryoX*kd (4)

Wherein, PD_control is the output Control quantity of the vertical ring PD, angle is the angle of inclination of the trolley relative to the X direction, kp and kd are constants, gryoX is the angular velocity of the X axis obtained by measurement;

the calculation formula of the speed deviation is as follows:

EncoderNew＝0-(EnocderL+EncoderR) (5)

Wherein EncoderNew is the deviation value of the latest speed, 0 is a given speed value, encoderL and EncoderR are the information acquired by the first incremental Hall encoder (2) and the second incremental Hall encoder (3) respectively;

Calculating the latest speed deviation and the speed deviation at the last moment to obtain stable speed deviation, wherein the calculation formula is as follows:

Encoder＝Encoder_last×a+(1-a)×EncoderNew

wherein Encdoer is a speed deviation value, encoder _last is a speed deviation value at the last moment, and a is a coefficient;

The calculation formula for obtaining the steering deviation value by making the difference between the z-axis angular velocity and the expected value 0 is as follows:

TurnBias＝0-GryoZ

Wherein TurnBias is the steering bias value, gryoZ is the z-axis angular velocity;

If the mobile phone controls left steering, setting a left steering value and a right steering value to be positive values; if the mobile phone controls the right steering, setting the left steering value and the right steering value as negative values; if no steering signal exists, setting a left steering value and a right steering value to be zero;

the calculation formula of the addition of the output of the steering ring P and the steering value is:

Turn＝TurnBias*kp2 (11)

Turn＝Turn+Turn_LR (12)

Wherein Turn is the output value of the steering ring P control, turnBias is the input value of the P control, kp2 is a constant value, turn _LR is the left and right steering value;

The EPWMA and EPWMB calculation process for the compare register of EPWM is:

M1＝|PD_Control+PI_Control-Turn| (13)

M2＝|PD_Control+PI_Control+Turn| (14)

the value of M1 calculated in equation (13) is assigned to EPWMA of the EPWM compare register, and the value of M2 calculated in equation (14) is assigned to EPWMB of the EPWM compare register.

4. A control system of a two-wheeled intelligent balance trolley controlled by the device of claim 3, which is characterized by comprising a main control board (1), wherein the main control board (1) is connected with a motor driving module (6), and the motor driving module (6) is simultaneously connected with a first direct current brush motor (4) and a second direct current brush motor (5); a first incremental Hall encoder (2) is arranged on the first direct-current brush motor (4), and a second incremental Hall encoder (3) is arranged on the second direct-current brush motor (5);

the main control board (1) is also connected with a pose detection module (10) and a debugging module (11).

5. The control system of the two-wheeled intelligent balance car according to claim 4, wherein the main control board (1), the first incremental hall encoder (2) and the second incremental hall encoder (3) are powered by a 12V battery (7).