Torque control method of tracked vehicle independently driven by motors on two sides

Abstract

The invention discloses a motor torque control method applied to a bilateral independent electrically-driven tracked vehicle. The method comprises the following steps: determining an instantaneous center position of a tracked vehicle during steering based on a kinematic model of the tracked vehicle independently driven by motors on two sides; determining a tracking position by searching a target point on a path, and calculating a current transverse deviation and a current course deviation in combination with the current position and course information of the tracked vehicle; the transverse deviation and the course deviation obtained through calculation serve as input, the yaw velocity of the vehicle during steering serves as output, and then the yaw velocity of the vehicle is obtained; then, the linear speeds of the two sides of the tracked vehicle are obtained through the established kinematics model of the tracked vehicle, and then the expected rotating speeds of motors on the two sides during steering are calculated; and finally, optimizing by adopting a fractional order PI lambda D mu controller and an improved particle swarm optimization algorithm, and calculating the optimal output torques of the motors on the left side and the right side of the tracked vehicle.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned tracked vehicle control, and in particular to a motor torque control method for a double-sided independently electrically driven tracked vehicle. Technical Background

[0002] Tracked vehicles play an important role on a variety of unpaved roads and terrains due to their excellent ground adaptability and outstanding off-road capabilities.

[0003] The power system of a traditional tracked vehicle consists of several key components, including the engine, transmission, steering mechanism, drive shaft, and side reduction gears. The engine is responsible for generating power, while the transmission adjusts the speed and torque of the engine output by switching different gear ratios. The steering mechanism uses transverse links to achieve differential movement of the tracks to facilitate vehicle steering. The drive shaft is responsible for transmitting the power generated by the transmission to the side reduction gears. The side reduction gears further reduce speed and increase torque through a series of gear meshing, and ultimately transmit power to the drive wheels. The vehicle's traction performance curve is determined by the cooperation of the engine and the reduction gears.

[0004] Compared with traditional tracked vehicles, electric-drive tracked vehicles have the advantages of compact structure, good maneuverability, power redundancy, and clean energy. Steering is an important working condition of tracked vehicles, which directly affects the maneuverability and off-road performance of high-speed tracked vehicles. Traditional high-speed tracked vehicles use steering mechanisms to complete steering, but the steering mechanism has a series of problems such as complex structure, large space occupation, low power utilization, high processing difficulty, and high cost. With the development of electric drive technology, electric-drive tracked vehicles can use electronic controllers to directly control the tracks on both sides to achieve electronic differential steering, but the driving resistance varies nonlinearly over a large range with the driving state, so the tracking performance and anti-interference ability of the algorithm are required to be high.

[0005] The steering control mechanism of the dual-sided independent electric drive tracked vehicle is mainly based on the differential drive principle, that is, by controlling the speed and torque of the motors on both sides to generate a speed difference to achieve the vehicle's steering function. This steering control method does not require a complex mechanical steering mechanism, simplifies the structure of the vehicle, and improves the flexibility and response speed of the steering. Moreover, the dual-sided independent electric drive tracked vehicle has the advantages of simple structure, fast response speed, high energy efficiency and strong adaptability, and is an important direction for the development of tracked vehicle technology.

[0006] Unmanned tracked vehicles can be remotely controlled or autonomous driving technology to plan paths and require closed-loop control to meet kinematic requirements. Since these vehicles often need to operate under special conditions, precise control of the vehicle's dynamic torque is critical to meet kinematic requirements.

[0007] At present, the dynamic torque distribution of unmanned tracked vehicles with independent dual-side drive is usually based on parameter calibration configuration. This method can obtain a relatively reasonable torque distribution scheme, but it has obvious limitations, that is, it is difficult to cover different working conditions using the same set of parameters, which will cause the vehicle to encounter insufficient power or overshoot problems in actual operation. With the increasing requirements for motion control accuracy, the traditional dynamic torque calculation method has been unable to meet the needs of precise control. Summary of the invention

[0008] In view of the problems existing in the prior art, the present invention provides a motor torque control method for a tracked vehicle with two motors independently driving the tracked vehicle. λ D μ The controller and the improved particle swarm optimization algorithm perform global optimization to obtain the optimal output torque of the left and right motors, improve the control accuracy of the tracked vehicle drive motor and reduce the overshoot, thereby improving the robustness and reliability of tracked vehicle motion control.

[0009] To achieve the above objectives, the present invention is implemented by the following technical solutions: A torque control method for a tracked vehicle independently driven by double-sided motors, comprising the following steps:

[0010] Step 1. Establish a kinematic model of a tracked vehicle driven by two motors on both sides, determine the XOY coordinate axis, the position of the vehicle's centroid, the center distance between the left and right tracks, the instantaneous position of the steering center, the centroid speed of the vehicle, the movement radius Rt of the wheels on both sides of the tracked vehicle, etc.;

[0011] Step 2. Establish a preview tracking model for double-sided tracked vehicles, determine the tracking position by searching for target points on the path, and calculate the current lateral deviation based on the current position and heading information of the tracked vehicle.

[0012] According to the geometric relationship, the vehicle geometric centroid C to the straight line planning path L n L n+1 The lateral deviation is as follows:

[0013]

[0014] Then, a preview tracking model of a double-sided tracked vehicle is established to calculate the preview point of the tracked vehicle. The process of calculating the preview point is as follows:

[0015]

[0016] L OS =k*v (3)

[0017] According to L n Point coordinates and L n+1Point coordinates We can get:

[0018]

[0019] and

[0020]

[0021] According to the above formulas (4) and (5), the X of the preview point T during the tracking process of the tracked vehicle is T The coordinate equation is:

[0022]

[0023] Solve the preview point equations (6) and (7) to calculate the preview point coordinates T(X T ,Y T ), and select the minimum distance point as the final preview point of the vehicle. According to the coordinates of the preview point of the vehicle, combined with the current position and heading information of the tracked vehicle, the current heading deviation of the tracked vehicle is calculated as follows:

[0024]

[0025] f(X,Y) is the azimuth angle solution function, and the azimuth angle solution function calculation formula is as follows:

[0026]

[0027] Step 3. Design a fuzzy controller using the fuzzy control algorithm, and use the lateral deviation and heading deviation calculated in step 2 as input after fuzzification, and use the yaw rate when the tracked vehicle turns as output. First, fuzzify the input and output of the fuzzy controller, and then confirm the membership functions of the lateral deviation, heading deviation and yaw rate according to the fuzzy rules. After being processed by the established fuzzy control rules, the output value of the yaw rate is defuzzified by the maximum membership method, so as to obtain the yaw rate when the tracked vehicle turns.

[0028] Step 4. Collect the centroid velocity v of the tracked vehicle and the yaw angular velocity ω of the vehicle when turning calculated in step 3. According to the kinematic model of the tracked vehicle, the linear velocity on both sides of the tracked vehicle can be obtained.

[0029]

[0030] According to the known radius Rt of the wheels on both sides of the tracked vehicle and the calculated linear velocity v of the left and right tracks L 、v R , we can get the rotation speed of the wheels on both sides of the tracked vehicle. Since the motor directly drives the wheels to move, the rotation speed of the wheels on both sides is the expected rotation speed of the left and right motors:

[0031]

[0032] Collect the actual speed n of the motors on the left and right sides L and n R , and the expected speed n of the left and right motors obtained by calculation reqL and n reqR The difference between the two, using fractional PI λ D μ The control algorithm performs feedback control to obtain the output torque of the left and right motors:

[0033]

[0034] Step 5. For the fractional-order PI in step 4 above λ D μ The proportional coefficient, integral coefficient, differential coefficient, integral order and differential order of the control are all globally optimized through the improved particle swarm optimization algorithm. λ D μ The parameter proportional coefficient, integral coefficient, differential coefficient, integral order and differential order are taken as the five components of one particle, and the parameters are optimized in five-dimensional space.

[0035] Assume that the velocity and position of particle i at the k+1th iteration are updated according to the following formula:

[0036] V in (k+1)=w(k+1)V in (k)+c1r1(P in (k)-X in (k)+c2r2(P gn (k)-X in (k))) (13)

[0037] X in (k+1)=X in (k)+V in (k+1) (14)

[0038] The calculation formula of the inertia weight after adaptive adjustment of the improved particle swarm optimization algorithm is:

[0039] w(i)=0.4+0.5*ww(i) / Num (15)

[0040] The objective function of the particle swarm optimization algorithm is:

[0041]

[0042] The fitness function adjusts the integral of the deviation, the controller input and the overshoot according to the corresponding weight coefficient to design the fitness function and calculate the fitness function value of each particle:

[0043] Fitness=J (17)

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] ① The present invention provides a motor torque control method for a double-sided independent electric drive tracked vehicle, which can be applied to unmanned platforms, improve the adaptability of the drive motor torque for different driving conditions, improve the response capability of the whole vehicle, and reduce overshoot.

[0046] ② The fractional-order PI provided by the present invention λ D μ Fuzzy controller is innovatively combined with improved particle swarm optimization algorithm to perform fractional-order PI λ D μ The proportional coefficient, integral coefficient, differential coefficient, integral order and differential order of the control are all globally optimized through an improved particle swarm optimization algorithm, which improves the robustness of the tracked vehicle in torque control.

[0047] ③ The present invention helps to realize adaptive torque regulation of tracked vehicles, improves the efficiency and safety of field operations and transfer, saves manpower and material resources, and has broad market prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 Kinematic model of the tracked vehicle independently driven by double-sided motors of the present invention;

[0049] Figure 2 The preview tracking model of the double-tracked vehicle of the present invention;

[0050] Figure 3 Composition diagram of the fuzzy controller of the present invention;

[0051] Figure 4 Overall flow chart of the present invention. DETAILED DESCRIPTION

[0052] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Figure 1As shown in the figure, a kinematic model of a tracked vehicle with independent drive of two motors on both sides is established to determine the XOY coordinate axis, the position of the vehicle's centroid, the center distance between the left and right tracks, the instantaneous position of the turning center, the centroid speed of the vehicle, and the movement radius Rt of the wheels on both sides of the tracked vehicle. Figure 2 As shown, the established preview tracking model of the double-sided tracked vehicle determines the tracking position by searching the target point on the path, and calculates the current lateral deviation, the preview point of the tracked vehicle and the current heading deviation in combination with the current position and heading information of the tracked vehicle.

[0053] The following steps are involved:

[0054] Step 1. According to the geometric relationship, the vehicle geometric centroid C to the straight line planning path L n L n+1 The lateral deviation is as follows:

[0055]

[0056] Based on the attached Figure 2 The preview tracking model of the double-sided tracked vehicle is established, and the process of calculating the preview point of the tracked vehicle is as follows:

[0057]

[0058] L OS =k*v (3)

[0059] According to L n Point coordinates and L n+1 Point coordinates We can get:

[0060]

[0061] and

[0062]

[0063] According to the above formulas (4) and (5), the X of the preview point T during the tracking process of the tracked vehicle is T The coordinate equation is:

[0064]

[0065] Solve the preview point equations (6) and (7) to calculate the preview point coordinates T(X T ,Y T ), and select the minimum distance point as the final preview point of the vehicle. According to the coordinates of the preview point of the vehicle, combined with the current position and heading information of the tracked vehicle, the current heading deviation of the tracked vehicle is calculated as follows:

[0066]

[0067] f(X,Y) is the azimuth angle solution function, and the azimuth angle solution function calculation formula is as follows:

[0068]

[0069] Step 2. Figure 3 As shown in the figure, a fuzzy controller is designed using a fuzzy control algorithm with lateral deviation and heading deviation as input and yaw rate when the vehicle is turning as output. The input and output of the fuzzy controller are first fuzzified, and then the membership functions of the lateral deviation, heading deviation and yaw rate are respectively confirmed according to the fuzzy rules. After being processed by the established fuzzy control rules, the output value of the yaw rate is defuzzified by the maximum membership method, thereby obtaining the yaw rate when the tracked vehicle is turning.

[0070] Step 3. Collect the centroid velocity v of the tracked vehicle and the yaw angular velocity ω of the vehicle when turning calculated in step 2. According to the kinematic model of the tracked vehicle, the linear velocity on both sides of the tracked vehicle can be obtained.

[0071]

[0072] According to the known radius Rt of the wheels on both sides of the tracked vehicle and the calculated linear velocity v of the left and right tracks L 、v R , we can get the rotation speed of the wheels on both sides of the tracked vehicle. Since the motor directly drives the wheels to move, the rotation speed of the wheels on both sides is the expected rotation speed of the left and right motors:

[0073]

[0074] Then collect the actual speed n of the motors on the left and right sides L and n R , and the expected speed n of the left and right motors obtained by calculation reqL and n reqR The difference between the two, using fractional PI λ D μ The control algorithm performs feedback control to obtain the output torque of the left and right motors:

[0075]

[0076] Step 4. For the fractional-order PI in step 3 above λ D μ The proportional coefficient, integral coefficient, differential coefficient, integral order and differential order of the control are all globally optimized through the improved particle swarm optimization algorithm to output the optimal output torque of the tracked vehicle when turning.

[0077] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A fuzzy control method for path tracking of a dual-motor tracked vehicle considering slip and rotation, characterized in that: The following steps are involved: Step (1): When the dual-motor tracked vehicle is working, the on-board computer uses the dual-antenna RTK-GNSS to obtain the real-time position and heading information of the tracked vehicle, and compares it with the target path generated by the pre-planned path starting point, thereby calculating the current lateral deviation d and heading deviation θ of the vehicle. The calculation process is as follows: First, the real-time position P(x0, y0) of the tracked vehicle is obtained through RTK-GNSS, the lateral deviation and heading deviation of the tracked vehicle are determined, and the left deviation is defined as positive and the right deviation as negative. When a dual-motor tracked vehicle tracks a straight path, it is necessary to determine a straight line AB in advance. First, convert the WGS-84 coordinate system coordinates to the ENU station center coordinate system coordinates. The starting point is A (x1, y1) and the end point is B (x2, y2). The equation of the navigation line AB is as follows: (1) in (2) The lateral deviation d between the dual-motor tracked vehicle and the navigation line AB is calculated as follows: (3) When determining the heading angle deviation, it is stipulated that the north direction is the starting direction, the clockwise direction is positive, and the heading angle of the navigation line AB varies from 0° to 360°. The formula for calculating the target navigation line heading angle α is as follows: (4) The difference between the chassis headings β and α obtained by dual-antenna RTK-GNSS is the heading angle deviation θ: θ=β-α (5) Step (2): The dual-motor tracked vehicle is approximately in planar motion during operation. The kinematic model of the dual-motor tracked vehicle is established to determine the coordinate axis, the vehicle geometric center, the distance between the left and right track centers, the instantaneous position of the steering center, etc. The reference system X C O C Y C Origin O C It coincides with the centroid position of the vehicle and changes with the movement of the tracked vehicle. According to the kinematic model of the dual-motor tracked vehicle, the relationship between the track speed on both sides of the dual-motor tracked chassis and the chassis speed and heading angle can be calculated; The position of the dual-motor tracked vehicle at time t can be expressed by the following plane motion equation: Where x(t) is the X-axis position coordinate of the dual-motor tracked vehicle at time t, m; y(t) is the Y-axis position coordinate of the dual-motor tracked vehicle at time t, m; γ(t) is the X-axis position coordinate of the dual-motor tracked vehicle at time t, m; C O C Y C The angle between the reference system and the XOY reference system, (°), Derivative the motion equation of the upper tracked vehicle at time t: in is the linear velocity in the X-axis direction in the XOY reference system, m / s; is the linear velocity in the Y-axis direction under the fixed reference system, m / s; is the angular velocity of rotation, rad / s; Step (3): When a dual-motor tracked vehicle is operating in a wet and soft field in a hilly or mountainous area, there is a relative displacement between the ground contacting part of the track and the ground. To improve the accuracy of vehicle navigation, it is necessary to consider the slippage of the track during path tracking. Measuring the centroid angular velocity ω of a tracked vehicle using an MTi-30 inertial sensor C ,The steering radius correction coefficient is derived through the kinematic model of the tracked vehicle, and then the slip and turn characteristics of the tracked chassis are indirectly characterized, When the tracked vehicle turns, the theoretical speed of the left and right tracks is V l 、V r The calculation formula is as follows: Where n1 is the left driving wheel speed, rad / s; n2 is the right driving wheel speed, rad / s; l is the track pitch, m; z is the number of driving wheel teeth, Actual speed of the left and right tracks V ls 、V rs The calculation formula is as follows: Where V c is the absolute velocity of the vehicle's centroid relative to the ground, m / s, which can be measured by RTK-GNSS; The slip rate of the tracks on both sides can be expressed by the actual speed and the theoretical speed. The slip of the track during the operation of the tracked vehicle causes the actual speed of the track to change compared with the theoretical speed. When the chassis is turning, the slip rate of the low-speed track is δ1, and the slip rate of the high-speed track is δ2. The calculation formula is as follows: The theoretical speed of the tracks on both sides determines the theoretical turning radius when turning, and the actual speed of the tracks on both sides determines the actual turning radius when turning. When a dual-motor tracked vehicle turns, the calculation formula for the theoretical turning radius and the actual turning radius is as follows: The relationship between the actual turning radius and the theoretical turning radius when the dual-motor tracked vehicle turns can also reflect the slip rate of the tracks on both sides of the chassis. The correction coefficient K of the tracked vehicle turning radius is defined as r The steering radius correction coefficient is used to characterize the chassis's slip and turn characteristics, and the steering radius correction coefficient K is defined as r The formula is as follows: Step (4): Design a three-input fuzzy controller. The conventional fuzzy controller for tracked vehicles uses the lateral deviation d and the heading deviation θ as input parameters, and the difference in the motor speed control amount on both sides of the tracked vehicle as output. Based on the conventional fuzzy controller, the present invention introduces a steering radius correction coefficient K r As the third input parameter to design the three-input fuzzy controller, First, the input and output data are fuzzy processed, as shown in Table 1: Table 1 Fuzzy processing Reconfirm the membership function: The triangular membership function has better flexibility and higher computational efficiency. Then formulate fuzzy control rules: According to the relationship between lateral deviation, heading deviation and steering radius correction coefficient under different deviations, set the following control rules: The control rules are designed to reduce the deviation when the three parameters have large deviations, and to quickly approach the target reference line when the deviations are not large, so as to formulate the lateral deviation d, heading deviation θ, and turning radius correction coefficient K. r 147 control rules for the difference between the motor speed control quantity PWM (duty cycle) on both sides of the output; Step (5): According to the fuzzy control algorithm of step (4), the decision of the difference between the speed control quantities PWM of the motors on both sides is obtained, and then the decision information is output to the lower computer controller through the serial communication method. The photoelectric speed sensor is used to measure the real-time speed of the crawler drive wheels on both sides, and the speed is fed back to the lower computer controller and PID feedback adjustment is performed to keep the target speed of the motors on both sides constant. After receiving the PWM duty cycle information from the lower computer controller, the drive system motor controller controls the speed of the motors on both sides, outputs power to the crawler drive wheels on both sides, performs start, stop, turn and other operations, and completes automatic tracking of the operation path.

2. The path tracking fuzzy control method of a dual-motor driven crawler vehicle considering slip and sliding according to claim 1, characterized in that: In step (3) and step (4), the steering radius correction coefficient Kr of the tracked vehicle is defined to characterize the slip and turn characteristics of the chassis, and a three-input fuzzy controller is designed with the lateral deviation d, heading deviation θ and steering radius correction coefficient Kr as input parameters.

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