Layered stability control method for multi-configuration motion of unmanned transformation vehicle

Abstract

The invention discloses a stability hierarchical control method for multi-configuration motion of an unmanned transformation vehicle, which comprises the following steps: 1, establishing an electromechanical dynamical model for adjusting a center-of-mass mechanism, 2, establishing an upper-layer decision controller, 3, establishing a lower-layer vehicle-state steady-state steering stability controller and a human-like-state walking stability controller, and 4, establishing a lower-layer vehicle-state steady-state steering stability controller and a human-like-state walking stability controller. 4, inputting the obtained control voltage into the electromechanical dynamic model of the mass center adjusting mechanism to obtain the displacement of the mass center adjusting sliding block, and outputting the displacement of the mass center adjusting sliding block; and 5, calculating the stability factor of the vehicle state of the unmanned deformation vehicle or the zero moment point of the human-like state by adjusting the displacement of the mass center sliding block, and feeding back to a lower-layer stability controller to form closed-loop control. The working condition can be judged according to the motion parameters of the unmanned transformation vehicle, the working condition is converted into the corresponding working mode to control and adjust the movement of the sliding block in the mass center mechanism, the mass center position of the unmanned transformation vehicle is further changed, and therefore the stability of multi-configuration motion of the unmanned transformation vehicle is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of robot stability control, and in particular relates to a stability hierarchical control method for multi-configuration motion of an unmanned transformable vehicle. Background Art

[0002] The unmanned transformable vehicle is a wheeled-legged robot that can transform between two configurations: a car state and a humanoid state. It combines the advantages of wheeled and legged mobility, has strong flexibility and environmental applicability, and is one of the current research hotspots in the field of robotics. At present, wheeled-legged robots mainly include the following research aspects: design and optimization of mechanical structure, nonlinear motion planning and control, perception and navigation, and artificial intelligence and machine learning. The walking stability control methods of wheeled-legged robots are mostly based on the zero-torque point method to readjust and plan the motion trajectory of the robot's leg joints, redesign the angular motion laws of the ankle and hip joints according to the three-dimensional inverted pendulum model, and control the robot's motion posture in real time, thereby improving the robot's walking stability. However, the robot's walking is a multi-degree-of-freedom, strongly coupled motion process. The leg joints must not only perform their own walking motion control, but also take into account the real-time control of walking stability. Therefore, the entire walking control process is highly dependent on the kinematic and dynamic models, and the stability control effect in complex environments is relatively limited. In addition, in the research on wheeled driving stability control of wheeled robots, most of them are based on sensors to collect information such as the robot's speed, acceleration and tilt angle, and control the speed and movement direction of the wheels through decision modules and control modules. Few studies treat wheeled robots as a special vehicle and use automobile theory to control the stability of wheeled driving. At present, the steering driving stability control of automobiles is mostly achieved through differential braking or active front wheel steering technology to control the transient steering of the vehicle, and there is little research on the steady-state steering control of the vehicle. Summary of the invention

[0003] The present invention aims to solve the deficiencies of the above-mentioned prior art and provides a stability hierarchical control method for the multi-configuration motion of an unmanned transformable vehicle, so as to judge the working condition according to the motion parameters of the unmanned transformable vehicle, convert it into a corresponding working mode to control the movement of the slider in the center of mass adjustment mechanism, so as to change the center of mass position of the unmanned transformable vehicle, thereby improving the stability of the multi-configuration motion of the unmanned transformable vehicle.

[0004] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme:

[0005] The present invention provides a stability hierarchical control method for multi-configuration motion of an unmanned transformable vehicle. The unmanned transformable vehicle has two configurations: a car state and a humanoid state. The whole vehicle structure includes: a front body, a rear body, and a mass center adjustment mechanism. Two mechanical legs and a lifting mechanism are arranged on the bottom plate of the rear body. Each mechanical leg includes: a thigh, a calf, a supporting foot, an ankle joint, a knee joint, and a hip joint. The mass center adjustment mechanism includes a slide rail, a mass center adjustment slider that moves in two degrees of freedom in the horizontal and vertical directions, a ball screw, and a drive motor. The stability hierarchical control method is performed in the following steps:

[0006] Step 1: Establish the coordinate system of the unmanned transformable vehicle;

[0007] Step 1.1: Establish a basic coordinate system on the horizontal plane where the support foot of the unmanned transforming vehicle is located The origin of the basic coordinate system is set at the middle position of the support foot. The positive direction of the axis is the forward direction of the unmanned transformable vehicle, and the basic coordinate system The positive direction of the axis is perpendicular to the horizontal plane and upward. The positive direction of the axis is perpendicular to Axis and The axis is in the plane and points to the outside of the vehicle;

[0008] Step 1.2, sequentially establish the appendage coordinate system of the right leg ankle joint, the appendage coordinate system of the knee joint and the appendage coordinate system of the hip joint, the appendage coordinate system of the left leg ankle joint, the appendage coordinate system of the knee joint and the appendage coordinate system of the hip joint, and the appendage coordinate system of the lifting mechanism, and record any one of the appendage coordinate systems as the first Attached coordinate system , ;

[0009] Step 2: Establish the displacement of the adjustment center of mass slider according to formula (1): The control voltage of the drive motor Electromechanical dynamics model between;

[0010] (1)

[0011] In formula (1), represents the equivalent current constant of the mass center adjustment mechanism; represents the equivalent torque constant of the mass center adjustment mechanism; is the transmission ratio of the ball screw, and , is the lead of the ball screw; and are the equivalent moment of inertia and equivalent viscous damping coefficient of the adjusted mass center mechanism respectively; To adjust the acceleration of the center of mass slider, To adjust the speed of the center of mass slider;

[0012] Step 3: Establish the upper-level decision control strategy according to formula (2) ;

[0013] (2)

[0014] In formula (2), are the motion parameters of the unmanned transformable vehicle in each configuration, and ,in, Indicates the speed of the unmanned transforming car, The front wheel turning angle of the unmanned transforming car, To adjust the speed of the center of mass slider, is the angular velocity of the three joints; is a set of discrete operating modes, and ,in, This is the working mode of the unmanned transformable car in the car-like steering state. This is the working mode of the unmanned transformable vehicle in a human-like walking state. This is the working mode of the unmanned transformable vehicle when driving in a straight line. It is the working mode of the unmanned transformable vehicle when it is parked and stationary; is a finite set of input variables, and , Indicates the front wheel angle state, when season ,when season ; Indicates vehicle speed state, when season ,when season ; Represents the joint angular velocity state, when season ,when season ; Indicates adjusting the speed of the center of mass slider state, when season ,when season ; means and; Indicates the transition between two working modes;

[0015] Step 4: Determine the working condition according to the motion parameters of the unmanned transformable vehicle, and perform stability control under the corresponding working mode;

[0016] Make the unmanned transforming car in working mode at the beginning ;

[0017] When the motion parameters of the unmanned transformable vehicle are obtained When the unmanned transforming car is switched to working mode ;

[0018] When the motion parameters of the unmanned transformable vehicle are obtained When the unmanned transforming car is switched to working mode ;

[0019] When the motion parameters of the unmanned transformable vehicle are obtained When the unmanned transforming car is switched to working mode ;

[0020] When the motion parameters of the unmanned transformable vehicle are obtained When the unmanned transforming car is switched to working mode ;

[0021] Step 5: Establish the lower-level stability control strategy and adjust the working mode or working mode Stability control of the unmanned transformable vehicle under the vehicle;

[0022] Step 5.1: In working mode Stability control of the unmanned transformable vehicle under the vehicle;

[0023] Step 5.1.1: Turn the unmanned transformable vehicle into a car-like state. The actual stability factor at the moment and expected stability factor The deviation between and the rate of change of deviation The input is processed by the Fuzzy-PID controller and the output is used to adjust the center of mass slider. Control voltage at the moment ;

[0024] Step 5.1.2: Substituting into equation (1), we can get the value of adjusting the center of mass slider: After the displacement of time, Substitute the displacement at time into equation (3) to obtain The unmanned transforming car is in the basic coordinate system at all times The centroid coordinates of ;

[0025] (3)

[0026] In formula (3), is the mass of any e-th component among the lifting mechanism, the slider for adjusting the center of mass, the thighs of the two mechanical legs, the calves of the two mechanical legs, and the supporting feet of the two mechanical legs, m is the total mass of the unmanned transformable vehicle, num is the number of components, and num=8, for The centroid of the e-th component of the unmanned transformable vehicle at time Axis coordinates;

[0027] Step 5.1.3: Use equation (4) to obtain the unmanned transformable vehicle in the car state turning state The actual stability factor at the moment ;

[0028] (4)

[0029] In formula (4), are the cornering stiffness of the front and rear wheels, yes The distance from the center of mass of the vehicle to the front axle at the time, and ; yes The distance from the center of mass of the vehicle to the rear axle at the time, and ; It is the wheelbase of the unmanned transforming vehicle;

[0030] Step 5.1.4: +1 assigned to After that, return to step 5.1.1 and execute in sequence until the steering driving state of the unmanned transforming vehicle in the car state changes or the deviation is zero, thereby realizing the unmanned transforming vehicle in the working mode. stability control under

[0031] Step 5.2: Based on the K-means clustering algorithm, The unmanned transformable vehicle is under stability control;

[0032] Step 5.2.1. Establish the stability level of K-means clustering algorithm ;

[0033] Step 5.2.2: According to the stability level Determine whether the unmanned transformable car is in a human-like walking state The stability level corresponding to the actual zero moment point position at the moment , and input into the expansion factor fuzzy controller for processing, thus outputting Deviation scaling factor at time , The deviation change rate scaling factor at the moment , The expected displacement scaling factor at time As the control parameters of the basic fuzzy controller;

[0034] Step 5.2.3: Make the unmanned transformable car walk in a humanoid manner The expected zero moment point position at time The actual zero moment point position Deviation and the rate of change of deviation The input is processed into the basic fuzzy controller and the output is used to adjust the centroid slider. Expected displacement at time ;

[0035] Step 5.2.4: Move the slider to adjust the center of mass. Expected displacement at time The input is processed by the ADRC and the output is the center of mass slider of the unmanned vehicle in the human-like walking state. Control voltage at the moment ;

[0036] Step 5.2.5: Substituting into equation (1), we can get the value of adjusting the center of mass slider: After the displacement of time, Substitute the displacement at the time into equation (7) to calculate the displacement of the unmanned transformable vehicle in the basic coordinate system: Moment Towards zero moment point and Towards zero moment point , thus obtaining the unmanned transformable vehicle in a human-like walking state The actual zero moment point position at time ;

[0037] (7)

[0038] In formula (7), They are The e-th component of the unmanned transformable vehicle at time Towards, Xianghe The acceleration of the center of mass toward is the acceleration due to gravity, They are At the moment, the center of mass of the e-th component of the unmanned transformable vehicle is in the basic coordinate system Axis coordinates, Axis coordinates and Axis coordinates;

[0039] Step 5.2.6: Assign to After that, return to step 5.2.2 and execute in sequence until the walking state of the unmanned transformable vehicle in the human-like state changes or the deviation is zero, thereby realizing the unmanned transformable vehicle in the working mode. Stability control under.

[0040] The characteristic of the stability hierarchical control method for multi-configuration motion of an unmanned transformable vehicle described in the present invention is that step 5.2.1 includes:

[0041] Step 5.2.1.1. Calculate the deviation sequence between the j actual zero moment point positions and the expected zero moment point positions of the unmanned transformable vehicle in the humanoid walking state and the deviation change rate series , we obtain the stability clustering data set of the unmanned deformable vehicle in the human-like walking state;

[0042] Step 5.2.1.2: Define the maximum number of iterations , the current number of iterations is R, and R=1 is initialized. N data points are randomly selected from the stability clustering data set as the initial cluster center points { | },in, Indicates the Rth iteration Initial cluster centers; Indicates the Rth iteration The deviation of the cluster centers; Indicates the Rth iteration The deviation change rate of the cluster center points;

[0043] Step 5.2.1.3, define and initialize variable i = 1;

[0044] Step 5.2.1.4: Calculate the i-th data point in the stability clustering data set according to formula (6) ) and the nth cluster center point at the Rth iteration Distance , so that the i-th data point ( ) is assigned to the nearest cluster center;

[0045] (6)

[0046] In formula (6), represents the bias weight factor, represents the weight factor of the deviation change rate;

[0047] Step 5.2.1.5: After assigning i+1 to i, return to step 5.2.1.4 and execute sequentially until i > j; thereby all data points in the stability clustering data set are assigned to the nearest cluster center point, and N clusters are obtained at the Rth iteration;

[0048] Step 5.2.1.6: Calculate the average value of the deviation and deviation change rate of the data points contained in the nth cluster at the Rth iteration, and use it as the nth cluster center point at the R+1th iteration. ;in, Indicates the R+1th iteration. The deviation of the cluster centers; Indicates the R+1th iteration. The deviation change rate of the cluster center points;

[0049] Step 5.2.1.7, after assigning R+1 to R, return to step 5.2.1.3 and execute sequentially until R > So far, we get The N clusters and their cluster centers at the iteration; The cluster center point at the iteration is used as the stability level of the unmanned deformable vehicle in the walking state .

[0050] An electronic device of the present invention includes a memory and a processor, wherein the memory is used to store a program that supports the processor to execute the stability stratification control method, and the processor is configured to execute the program stored in the memory.

[0051] The present invention provides a computer-readable storage medium, wherein a computer program is stored on the computer-readable storage medium, and the computer program executes the steps of the stability stratification control method when the computer program is executed by a processor.

[0052] Compared with the prior art, the present invention has the following beneficial effects:

[0053] 1. The present invention takes the adjustment center of mass mechanism installed on the rear body of the unmanned transformable vehicle as the control object, and designs a hierarchical control method for the multi-configuration motion stability control of the unmanned transformable vehicle. Among them, the upper decision controller based on the hybrid theory makes control decisions according to the motion parameters of the unmanned transformable vehicle to select the appropriate working mode, and the lower stability controller uses the fuzzy-PID controller to perform steady-state steering control in the vehicle state, or uses variable domain fuzzy control and anti-disturbance control to perform walking stability control in the human-like state according to the control decision issued by the upper controller, thereby improving the stability of the unmanned transformable vehicle in multiple configurations.

[0054] 2. The present invention innovatively integrates the K-means clustering method with the zero-moment point stability criterion method, which not only realizes the quantitative rating of the stability of the unmanned transformable vehicle during walking, but also further improves the control precision and adaptability of the variable domain fuzzy controller, and improves the accuracy and real-time performance of walking stability control.

[0055] 3. The control system of the present invention is completely independent of the power system of the unmanned transformable vehicle. The forward walking and turning walking in the humanoid state are completed by mechanical legs, the turning driving in the car state is completed by the wire-controlled steering mechanism, and the stability control of the system is completed by the adjustment of the center of mass mechanism. The two functions are completed by two actuators respectively. The stability control will not affect the normal motion planning of the unmanned transformable vehicle. It not only broadens the control method of the steering stability in the car state, but also improves the stability and flexibility of walking and turning in the humanoid state. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 A schematic diagram of the structure and configuration of the unmanned transformable vehicle involved in the present invention;

[0057] Figure 2 It is a structural schematic diagram of a mass center adjustment mechanism of the unmanned transformable vehicle involved in the present invention;

[0058] Figure 3 This is a schematic diagram of the stability hierarchical control principle of the multi-configuration motion of the unmanned transformable vehicle based on the adjustment of the center of mass mechanism of the present invention;

[0059] Figure 4 A schematic diagram of a coordinate system of an unmanned transformable vehicle according to the present invention;

[0060] Figure 5 It is a stability factor simulation curve diagram of the front and rear systems of the unmanned transformable vehicle in steady-state steering stability control according to the present invention;

[0061] Figure 6a The present invention relates to the unmanned deformable vehicle humanoid walking stability control front and rear system Simulation curve diagram towards zero moment point;

[0062] Figure 6b The present invention relates to the unmanned deformable vehicle humanoid walking stability control front and rear system Simulation curve towards zero moment point. DETAILED DESCRIPTION

[0063] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments:

[0064] The unmanned transformable vehicle involved in the present invention is a nonlinear mechanical system with multiple components and motion coupling, which has two configurations: a car state and a humanoid state, and has different motion modes and stability characteristics in different configurations. Specifically, the whole vehicle structure and configuration of the unmanned transformable vehicle of the present invention are as follows: Figure 1 The unmanned transformable vehicle includes: a front body, a rear body, and a mass center adjustment mechanism; two mechanical legs and a lifting mechanism are arranged on the bottom plate of the rear body; each mechanical leg includes: a thigh, a calf, a supporting foot, an ankle joint, a knee joint, and a hip joint;

[0065] The front and rear bodies of the unmanned transforming car have wheel hub motors, a steering trapezoidal mechanism, disc brakes and McPherson suspension, which enable the unmanned transforming car to travel at high speed, steer and brake in a car-like state; the front body is connected to the rear body through a lifting mechanism. When the unmanned transforming car is reconstructed into a humanoid state, the lifting mechanism is driven by the motor and the electric push rod to realize the horizontal lifting of the front body. The mechanical legs are installed on the bottom plate of the rear body. In the car state, they are folded to the bottom of the body. In the humanoid state, they are unfolded to the ground to support the entire body, realizing striding walking and steering.

[0066] The mass center adjustment mechanism includes a slide rail, a mass center adjustment slider that moves in two degrees of freedom in the horizontal and vertical directions, a ball screw, and a drive motor. Specifically, the structure and arrangement of the mass center adjustment mechanism are as follows: Figure 2 Its main working principle is to convert the rotation of the driving motor into the movement of the center of mass slider on the slide rail through the ball screw, thereby changing the center of mass position of the unmanned transformable vehicle, thereby improving the stability of the multi-configuration motion of the unmanned transformable vehicle.

[0067] In this embodiment, a stability hierarchical control method for multi-configuration motion of an unmanned transformable vehicle is provided. Specifically, Figure 3 As shown. The method consists of an upper-level decision controller and a lower-level motion stability controller. The upper-level decision controller is based on hybrid theory. It determines the motion condition of the unmanned deformable vehicle according to its motion parameters and converts it into the corresponding working mode for motion stability control. The lower-level controller consists of a steady-state steering stability control module in the car state and a walking stability control module in the human-like state. The motion stability of the unmanned deformable vehicle is achieved by controlling the movement of the center of mass slider. The stability hierarchical control method is carried out in the following steps:

[0068] Step 1: Establish the coordinate system of the unmanned transformable vehicle;

[0069] Step 1.1: Establish a basic coordinate system on the horizontal plane where the support foot of the unmanned transforming vehicle is located The origin of the basic coordinate system is set at the middle position of the support foot. The positive direction of the axis is the forward direction of the unmanned transformable vehicle, and the basic coordinate system The positive direction of the axis is perpendicular to the horizontal plane and upward. The positive direction of the axis is perpendicular to Axis and The axis is in the plane and points to the outside of the vehicle;

[0070] Step 1.2, sequentially establish the appendage coordinate system of the right leg ankle joint, the appendage coordinate system of the knee joint and the appendage coordinate system of the hip joint, the appendage coordinate system of the left leg ankle joint, the appendage coordinate system of the knee joint and the appendage coordinate system of the hip joint, and the appendage coordinate system of the lifting mechanism, and record any one of the appendage coordinate systems as the wth appendage coordinate system , , specifically, if Figure 4 As shown;

[0071] Define the attached coordinate system relatively Coordinate system along axis, axis, The rotation angle and translation distance of the axis are: , , , then the homogeneous transformation matrix between adjacent attached coordinate systems is It can be expressed as:

[0072]

[0073] The homogeneous transformation matrix of any attached coordinate system relative to the basic coordinate system can be expressed as: ;

[0074] Step 2: According to formula (1), establish the displacement of the adjustment center of mass slider The control voltage of the drive motor Electromechanical dynamics model between;

[0075] (1)

[0076] In formula (1), represents the equivalent current constant of the mass center adjustment mechanism; represents the equivalent torque constant of the mass center adjustment mechanism; is the transmission ratio of the ball screw, and , is the lead of the ball screw; and are the equivalent moment of inertia and equivalent viscous damping coefficient of the adjusted mass center mechanism respectively; To adjust the acceleration of the center of mass slider, To adjust the speed of the center of mass slider;

[0077] Step 3: Establish the upper-level decision control strategy according to formula (2) ;

[0078] (2)

[0079] In formula (2), are the motion parameters of the unmanned transformable vehicle in each configuration, and ,in, Indicates the speed of the unmanned transforming car, The front wheel turning angle of the unmanned transforming car, To adjust the speed of the center of mass slider, is the angular velocity of the three joints; is a discrete set of operating modes, and ,in, This is the working mode of the unmanned transformable car in the car-like steering state. This is the working mode of the unmanned transformable vehicle in a human-like walking state. This is the working mode of the unmanned transformable vehicle when driving in a straight line. It is the working mode of the unmanned transformable vehicle when it is parked and stationary; is a finite set of input variables, and , Indicates the front wheel angle state, when season ,when season ; Indicates vehicle speed state, when season ,when season ; Represents the joint angular velocity state, when season ,when season ; Indicates adjusting the speed of the center of mass slider state, when season ,when season ; means and; Indicates the transition between two operating modes.

[0080] Step 4: Determine the working condition according to the motion parameters of the unmanned transformable vehicle and switch to the corresponding working mode for stability control;

[0081] For the multi-configuration motion of the unmanned transformable vehicle, on the one hand, the motion parameters (vehicle speed , front wheel angle , adjust the speed of the center of mass slider , joint angular velocity ) all show the characteristics of changing with time. On the other hand, the motion under different configurations constitutes a series of discrete motion events. Therefore, the motion of the unmanned transforming vehicle under multiple configurations is a hybrid dynamic system. When controlling the hybrid system, when the unmanned transforming vehicle detects changes in motion parameters, it drives the upper-level decision controller to select the corresponding working mode according to the characteristics of discrete events, and performs motion stability control under the corresponding working mode.

[0082] Make the unmanned transforming car in working mode at the beginning ;

[0083] When the motion parameter input of the unmanned transformable vehicle is When the unmanned transforming car is switched to working mode ;

[0084] When the motion parameter input of the unmanned transformable vehicle is When the unmanned transforming car is switched to working mode ;

[0085] When the motion parameter input of the unmanned transformable vehicle is When the unmanned transforming car is switched to working mode ;

[0086] When the motion parameter input of the unmanned transformable vehicle is When the unmanned transforming car is switched to working mode .

[0087] Step 5: Establish the lower-level stability control strategy and adjust the working mode or working mode Stability control of the unmanned transformable vehicle under the vehicle;

[0088] Step 5.1: In working mode Stability control of the unmanned transformable vehicle under the vehicle;

[0089] Step 5.1.1: Turn the unmanned transformable vehicle into a car-like state. The actual stability factor at the moment and expected stability factor The deviation between and the rate of change of deviation The input is processed by the Fuzzy-PID controller and the output is used to adjust the center of mass slider. Control voltage at the moment In the steady-state steering stability control of automobiles, the control parameters of conventional PID controllers are fixed and have poor robustness, so fuzzy PID controllers are used as the working mode. The stability controller below.

[0090] The actual stability factor and expected stability factor The deviation between and the rate of change of deviation As the input of the fuzzy controller, the output is , , First, put and Fuzzy processing, and the processed data is input into the fuzzy controller; after fuzzification, fuzzy reasoning and clarification, the , , , and then get the new PID control parameter value.

[0091] Step 5.1.1.1. Determine the domain of the fuzzy controller: Input variables The domain of is [-0.1, 0.1], The domain of is [-0.02, 0.02], and the output variable The domain of is [-15, 15], The domain of is [-1, 1], The domain of is [-15, 15];

[0092] Step 5.1.1.2, determine the fuzzy set: The fuzzy set of fuzzy control input and output is , respectively represent {negative large, negative medium, negative small, zero, positive small, positive medium, positive large};

[0093] Step 5.1.1.3. Determine the membership function: triangular membership function.

[0094] Step 5.1.1.4, establish fuzzy rules;

[0095] , , The fuzzy rules are shown in Table 1;

[0096] Table 1 , , Fuzzy rule table

[0097]

[0098] Step 5.1.2: Substituting into equation (1), we can get the value of adjusting the center of mass slider: After the displacement of time, Substitute the displacement at time into equation (3) to obtain The unmanned transforming car is in the basic coordinate system at all times The centroid coordinates of ;

[0099] (3)

[0100] In formula (3), is the mass of any e-th component among the lifting mechanism, the slider for adjusting the center of mass, the thighs of the two mechanical legs, the calves of the two mechanical legs, and the supporting feet of the two mechanical legs, m is the total mass of the unmanned transformable vehicle, num is the number of components, and num=8, for The centroid of the e-th component of the unmanned transformable vehicle at time Axis coordinates.

[0101] Step 5.1.3: Use equation (4) to obtain the unmanned transformable vehicle in the car state turning state The actual stability factor at the moment ;

[0102] (4)

[0103] In formula (4), are the cornering stiffness of the front and rear wheels, yes The distance from the center of mass of the vehicle to the front axle at the time, and ; yes The distance from the center of mass of the vehicle to the rear axle at the time, and ; It is the wheelbase of the unmanned transforming vehicle;

[0104] Step 5.1.4: +1 assigned to After that, return to step 5.1.1 and execute in sequence until the steering driving state of the unmanned transforming vehicle in the car state changes or the deviation is zero, thereby realizing the unmanned transforming vehicle in the working mode. Stability control under.

[0105] Step 5.2: Based on the K-means clustering algorithm, Stability control of the unmanned transformable vehicle under the vehicle;

[0106] Step 5.2.1. Establish the stability level of K-means clustering algorithm ;

[0107] Step 5.2.1.1: Calculate the speed of the unmanned deformable vehicle in a humanoid walking state. The deviation sequence between the actual zero moment point position and the expected zero moment point position and the deviation change rate series , we obtain the stability clustering data set of the unmanned deformable vehicle in the human-like walking state;

[0108] Step 5.2.1.2, define the maximum number of iterations as 1000, the current number of iterations as R, and initialize R=1, randomly select 5 data points in the stability clustering data set as the initial cluster center points { | },in, Indicates the Rth iteration Initial cluster centers; Indicates the Rth iteration The deviation of the cluster centers; Indicates the Rth iteration The deviation change rate of the cluster center.

[0109] Step 5.2.1.3, define and initialize variable i = 1;

[0110] Step 5.2.1.4: Calculate the i-th data point in the stability clustering data set according to formula (6) ) and the nth cluster center point at the Rth iteration Distance , so that the i-th data point ( ) is assigned to the nearest cluster center;

[0111] (6)

[0112] In formula (6), represents the bias weight factor, Represents the weight factor of the deviation change rate.

[0113] Step 5.2.1.5, after assigning i+1 to i, return to step 5.2.1.4 and execute sequentially until i > So far; all data points in the stability clustering data set are assigned to the nearest cluster center point, and 5 clusters are obtained at the Rth iteration;

[0114] Step 5.2.1.6: Calculate the average value of the deviation and deviation change rate of the data points contained in the nth cluster at the Rth iteration, and use it as the nth cluster center point at the R+1th iteration. ;in, Indicates the R+1th iteration. The deviation of the cluster centers; Indicates the R+1th iteration. The deviation change rate of the cluster center.

[0115] Step 5.2.1.7, after assigning R+1 to R, return to step 5.2.1.3 and execute sequentially until R > So far, we get The five clusters and their cluster centers at the first iteration; The cluster center point at the iteration is used as the stability level of the unmanned deformable vehicle in the walking state ,Specifically, as shown in Table 2;

[0116] Table 2 Cluster center points and stability levels during walking

[0117]

[0118] Step 5.2.2, the domain range and the number of fuzzy quantity level divisions have a great influence on the control effect of the fuzzy controller. When the number of fuzzy quantity levels is more and the domain range is smaller, the level division is finer and the control accuracy of the controller is higher. However, the finer the level division, the slower the controller solution speed will be, and the negative effect of measurement error in classification will be greater. In order to improve the adaptability and accuracy of the fuzzy controller without increasing the number of fuzzy levels, a variable domain fuzzy controller is used to control the walking stability of the unmanned deformable vehicle.

[0119] According to the stability level Determine whether the unmanned transformable car is in a human-like walking state The stability level corresponding to the actual zero moment point position at the moment , which is input into the telescopic factor fuzzy controller for processing and output Deviation scaling factor at time , The deviation change rate scaling factor at the moment , The expected displacement scaling factor at time As the control parameters of the basic fuzzy controller;

[0120] Step 5.2.2.1. Determine the fuzzy domain: Input variables of the telescoping factor fuzzy controller The domain is [0, 1], and the output variable is the deviation scaling factor , Deviation change rate scaling factor , expected displacement expansion factor , the output domain is [0.5, 1.5];

[0121] Step 5.2.2.2, determine the fuzzy set: The input fuzzy set of the telescopic factor fuzzy controller is { }, representing {very stable, relatively stable, metastable, critically stable, unstable}, and the output fuzzy set is , representing {very small, small, medium, large, very large} respectively;

[0122] Step 5.2.2.3, determine the membership function: triangle membership function;

[0123] Step 5.2.2.4, establish the fuzzy rules of the scaling factor, specifically, as shown in Table 3;

[0124] Table 3. Table of fuzzy rules for scaling factors

[0125]

[0126] Step 5.2.3: Make the unmanned transformable car walk in a humanoid manner The expected zero moment point position at time The actual zero moment point position Deviation and the rate of change of deviation The input is processed into the basic fuzzy controller and the output is used to adjust the centroid slider. Expected displacement at time .

[0127] Step 5.2.3.1. Determine the fuzzy domain: Input variable deviation The domain of [- , ]; Input variable deviation change rate The domain of [- ], output variable expected slider displacement The domain of [- , ];

[0128] Step 5.2.3.2, determine the fuzzy set: The input and output fuzzy sets of the basic fuzzy controller are , respectively represent {negative large, negative medium, negative small, zero, positive small, positive medium, positive large};

[0129] Step 5.2.3.3, determine the membership function: triangle membership function;

[0130] Step 5.2.3.4, establish the fuzzy rules of the expected slider displacement, specifically, as shown in Table 4;

[0131] Table 4 Fuzzy rules for expected slider displacement

[0132]

[0133] Step 5.2.4: Move the slider to adjust the center of mass. Expected displacement at time The input is processed by the ADRC and the output is the center of mass slider of the unmanned vehicle in the human-like walking state. Control voltage at the moment .

[0134] Step 5.2.4.1, design a linear expansion observer;

[0135] definition is the total disturbance of the system and is differentiable, ; is the input coefficient of the control system, , rewrite the electromechanical dynamics model established in formula (1) according to formula (7);

[0136] (7)

[0137] In formula (7), is the control voltage for driving the motor, To adjust the acceleration of the center of mass slider.

[0138] According to formula (8), the augmented state space expression of the electromechanical dynamics model is obtained:

[0139] (8)

[0140] In formula (8), represents the augmented state variable, and ,in ; represents the differential of the state variable, Indicates the displacement of adjusting the center of mass slider; represents the augmented coefficient matrix, and , represents the augmented input matrix and , represents the augmented perturbation matrix, and , represents the differential of the total disturbance of the system, represents the augmented output matrix, and .

[0141] According to formula (9), the Lumberg state observer is established;

[0142] (9)

[0143] In formula (9), is the observer vector, and ; is the observation error feedback gain matrix, and ; Represents the estimated adjustment center of mass slider displacement.

[0144] Defining Observation Error , subtracting equation (8) from equation (9) to obtain:

[0145] (10)

[0146] According to modern control theory, the observation error The condition is that the matrix The eigenvalues ​​of have negative real parts. The matrix The expression is:

[0147] (11)

[0148] matrix The characteristic polynomial of is:

[0149] (12)

[0150] According to the bandwidth method, the observer bandwidth is defined as , the three poles of the observer are placed at ,Right now:

[0151] (13)

[0152] According to formula (14), the observer error feedback gain is obtained. hour, ;

[0153] (14)

[0154] Step 5.2.4.2. Design the tracking differentiator.

[0155] In the process of stability adjustment, the expected displacement of the slider of the center of mass adjusted by the basic fuzzy controller output is a very complex nonlinear curve, and a large overshoot will occur in the process of fast tracking. In order to suppress the overshoot of the control, the fastest tracking differentiator is used to smooth the expected displacement. The fastest tracking differentiator can well balance the contradiction between overshoot and rapidity, and enhance the robustness and noise suppression ability of the system.

[0156] Therefore, the fastest tracking differentiator is used to optimize the tracking effect of the ADRC on the input signal. According to formula (15), the discrete form of the fastest tracking differentiator is obtained:

[0157] (15)

[0158] In formula (15), is the desired displacement of the slider The tracking signal, yes The approximate differential signal of is the expected displacement of the slider The discrete points of is the discrete sampling period; is the fastest tracking function; is the speed factor; is the filtering factor.

[0159] Step 5.2.4.3, the anti-disturbance controller uses the error feedback control law to actively compensate for the total disturbance estimated by the state observer, which not only achieves the anti-disturbance effect, but also transforms the controlled object into an integral series structure. Therefore, the control voltage is designed according to formula (16): The state error feedback control law is:

[0160] (16)

[0161] In formula (16), is the proportional control parameter, is the differential control parameter.

[0162] Expected slider displacement of variable universe fuzzy controller output using tracking differentiator After smoothing, the state observer and the error feedback control law are used to calculate the interference-free control voltage according to equation (17). , complete the real-time tracking of the desired slider displacement;

[0163] (17)

[0164] Step 5.2.5: Substituting into equation (1), we can get the value of adjusting the center of mass slider: After the displacement of time, Substitute the displacement at the time into equation (18) to calculate the displacement of the unmanned transformable vehicle in the basic coordinate system: Moment Towards zero moment point and Towards zero moment point , thus obtaining the unmanned transformable vehicle in a human-like walking state The actual zero moment point position at time ;

[0165] (18)

[0166] In formula (18), They are The e-th component of the unmanned transformable vehicle at time Towards, Xianghe The acceleration of the center of mass toward is the acceleration due to gravity, They are At the moment, the center of mass of the e-th component of the unmanned transformable vehicle is in the basic coordinate system Axis coordinates, Axis coordinates and Axis coordinates.

[0167] Step 5.2.6: Assign to After that, return to step 5.2.2 and execute in sequence until the walking state of the unmanned transformable vehicle in the human-like state changes or the deviation is zero, thereby realizing the unmanned transformable vehicle in the working mode. Stability control under.

[0168] In this embodiment, an electronic device includes a memory and a processor, wherein the memory is used to store a program that supports the processor to execute the above method, and the processor is configured to execute the program stored in the memory.

[0169] In this embodiment, a computer-readable storage medium stores a computer program on the computer-readable storage medium, and the computer program executes the steps of the above method when executed by a processor.

[0170] Stability factor before and after adjusting the center of mass mechanism for vehicle steady-state steering stability control The change curve of Figure 5 Without stability control, the slider of the mass center self-adjusting mechanism is located in the center of the slide rail and remains stationary. Since the center of mass position of the vehicle has not changed, the stability factor It is always 0.00097. When performing stability control, both PID control and fuzzy PID control methods can make the stability factor curve converge to the expected value of 0.0024. However, the three control coefficients of fuzzy PID control are variable, so compared with PID control, fuzzy PID has a better control effect, as shown in Table 5.

[0171] Table 5 Control system performance indicators

[0172]

[0173] The unmanned deformable vehicle is controlled by adjusting the center of mass mechanism for humanoid walking stability. Direction and The zero moment point change curve of the direction is as follows Figure 6a and Figure 6b shown. Figure 6a The PID control algorithm tracks When the desired trajectory is toward the zero moment point, not only is there a more obvious control mutation, but the tracking effect is also poor, and the maximum tracking error can reach 0.037m. Therefore, under the PID stability control algorithm, the walking stability margin of the unmanned deformable vehicle is insufficient, and it is easy to tip over and become unstable under external interference. In contrast, the two Fuzzy-ADRC control algorithms (with and without K-means clustering algorithm) can track the desired zero moment point more quickly and smoothly, showing better control effect. However, in the transition stage between the single-foot support period and the double-foot support period, after the K-means clustering algorithm quantifies the stability level preprocessing, the overshoot of the Fuzzy-ADRC control algorithm is reduced by 14.86%, and the steady-state error is also reduced by 3.6%. Therefore, the K-means clustering algorithm can not only more accurately quantify the stability changes of the unmanned deformable vehicle during walking, but also further improve the control effect of the Fuzzy-ADRC control algorithm. Figure 6b Similar control results were also shown. When tracking the expected trajectory of the zero moment point, the PID control effect is the worst and there is a large mutation, while the Fuzzy-ADRC control algorithm with the K-means clustering algorithm shows the least overshoot and steady-state error. Therefore, all three control strategies can ensure the stability of the unmanned transformable vehicle during walking, but the Fuzzy-ADRC control algorithm with the K-means clustering algorithm performs best in controlling the zero moment point, thereby ensuring the optimal walking stability of the unmanned transformable vehicle.

Claims

1. A stability hierarchical control method for multi-configuration motion of an unmanned transformable vehicle, characterized in that: The unmanned transformable vehicle has two configurations: a car state and a humanoid state. The whole vehicle structure includes: a front body, a rear body, and a mass center adjustment mechanism. Two mechanical legs and a lifting mechanism are arranged on the bottom plate of the rear body. Each mechanical leg includes: a thigh, a calf, a supporting foot, an ankle joint, a knee joint, and a hip joint. The mass center adjustment mechanism includes a slide rail, a mass center adjustment slider that moves in two degrees of freedom in the horizontal and vertical directions, a ball screw, and a drive motor. The stability hierarchical control method is performed in the following steps: Step 1: Establish the coordinate system of the unmanned transformable vehicle; Step 1.1: Establish a basic coordinate system on the horizontal plane where the support foot of the unmanned transforming vehicle is located The origin of the basic coordinate system is set at the middle position of the support foot. The positive direction of the axis is the forward direction of the unmanned transformable vehicle, and the basic coordinate system The positive direction of the axis is perpendicular to the horizontal plane and upward. The positive direction of the axis is perpendicular to Axis and The axis is in the plane and points to the outside of the vehicle; Step 1.2, sequentially establish the appendage coordinate system of the right leg ankle joint, the appendage coordinate system of the knee joint and the appendage coordinate system of the hip joint, the appendage coordinate system of the left leg ankle joint, the appendage coordinate system of the knee joint and the appendage coordinate system of the hip joint, and the appendage coordinate system of the lifting mechanism, and record any one of the appendage coordinate systems as the wth appendage coordinate system , ; Step 2: Establish the displacement of the adjustment center of mass slider according to formula (1): The control voltage of the drive motor Electromechanical dynamics model between; (1) In formula (1), represents the equivalent current constant of the mass center adjustment mechanism; represents the equivalent torque constant of the mass center adjustment mechanism; is the transmission ratio of the ball screw, and , is the lead of the ball screw; and are the equivalent moment of inertia and equivalent viscous damping coefficient of the adjusted mass center mechanism respectively; To adjust the acceleration of the center of mass slider, To adjust the speed of the center of mass slider; Step 3: Establish the upper-level decision control strategy according to formula (2) ; (2) In formula (2), are the motion parameters of the unmanned transformable vehicle in each configuration, and ,in, Indicates the speed of the unmanned transforming car, The front wheel turning angle of the unmanned transforming car, To adjust the speed of the center of mass slider, is the angular velocity of the three joints; is a discrete set of operating modes, and ,in, This is the working mode of the unmanned transformable car in the car-like steering state. This is the working mode of the unmanned transformable vehicle in a human-like walking state. This is the working mode of the unmanned transformable vehicle when driving in a straight line. It is the working mode of the unmanned transformable vehicle when it is parked and stationary; is a finite set of input variables, and , Indicates the front wheel angle state, when season ,when season ; Indicates vehicle speed state, when season ,when season ; Represents the joint angular velocity state, when season ,when season ; Indicates adjusting the speed of the center of mass slider state, when season ,when season ; means and; Indicates the transition between two working modes; Step 4: Determine the working condition according to the motion parameters of the unmanned transformable vehicle, and perform stability control under the corresponding working mode; Make the unmanned transforming car in working mode at the beginning ; When the motion parameters of the unmanned transformable vehicle are obtained When the unmanned transforming car is switched to working mode ; When the motion parameters of the unmanned transformable vehicle are obtained When the unmanned transforming car is switched to working mode ; When the motion parameters of the unmanned transformable vehicle are obtained When the unmanned transforming car is switched to working mode ; When the motion parameters of the unmanned transformable vehicle are obtained When the unmanned transforming car is switched to working mode ; Step 5: Establish the lower-level stability control strategy and adjust the working mode or working mode Stability control of the unmanned transformable vehicle under the vehicle; Step 5.1: In working mode The unmanned transformable vehicle is under stability control; Step 5.1.1: Turn the unmanned transformable vehicle into a car-like state. The actual stability factor at the moment and expected stability factor The deviation between and the rate of change of deviation The input is processed by the Fuzzy-PID controller and the output is used to adjust the center of mass slider. Control voltage at the moment ; Step 5.1.2: Substituting into equation (1), we can get the value of adjusting the center of mass slider: After the displacement of time, Substitute the displacement at time into equation (3) to obtain The unmanned transforming car is in the basic coordinate system at all times The centroid coordinates of ; (3) In formula (3), is the mass of any e-th component among the lifting mechanism, the slider for adjusting the center of mass, the thighs of the two mechanical legs, the calves of the two mechanical legs, and the supporting feet of the two mechanical legs, m is the total mass of the unmanned transformable vehicle, num is the number of components, and num=8, for The centroid of the e-th component of the unmanned transformable vehicle at time Axis coordinates; Step 5.1.3: Use equation (4) to obtain the unmanned transformable vehicle in the car state turning state The actual stability factor at the moment ; (4) In formula (4), are the cornering stiffness of the front and rear wheels, yes The distance from the center of mass of the vehicle to the front axle at the time, and ; yes The distance from the center of mass of the vehicle to the rear axle at the time, and ; It is the wheelbase of the unmanned transforming car; Step 5.1.4: +1 assigned to After that, return to step 5.1.1 and execute in sequence until the steering driving state of the unmanned transforming vehicle in the car state changes or the deviation is zero, thereby realizing the unmanned transforming vehicle in the working mode. stability control under Step 5.2: Based on the K-means clustering algorithm, Stability control of the unmanned transformable vehicle under the vehicle; Step 5.2.

1. Establish the stability level of K-means clustering algorithm ; Step 5.2.2: According to the stability level Determine whether the unmanned transformable vehicle is in a human-like walking state The stability level corresponding to the actual zero moment point position at the moment , and input into the expansion factor fuzzy controller for processing, thus outputting Deviation scaling factor at time , The deviation change rate scaling factor at the moment , The expected displacement scaling factor at time As the control parameters of the basic fuzzy controller; Step 5.2.3: Make the unmanned transformable car walk in a humanoid manner The expected zero moment point position at time The actual zero moment point position Deviation and the rate of change of deviation The input is processed into the basic fuzzy controller and the output is used to adjust the centroid slider. Expected displacement at time ; Step 5.2.

4. Move the slider to adjust the center of mass. Expected displacement at time The input is processed by the ADRC and the output is the center of mass slider of the unmanned vehicle in the human-like walking state. Control voltage at the moment ; Step 5.2.5: Substituting into equation (1), we can get the value of adjusting the center of mass slider: After the displacement of time, Substitute the displacement at the moment into equation (7) to calculate the displacement of the unmanned transformable vehicle in the basic coordinate system: Moment Towards zero moment point and Towards zero moment point , thus obtaining the unmanned transformable vehicle in a human-like walking state The actual zero moment point position at time ; (7) In formula (7), They are The e-th component of the unmanned transformable vehicle at time Towards, Xianghe The acceleration of the center of mass toward is the acceleration due to gravity, They are At the moment, the center of mass of the e-th component of the unmanned transformable vehicle is in the basic coordinate system Axis coordinates, Axis coordinates and Axis coordinates; Step 5.2.6: Assign to After that, return to step 5.2.2 and execute in sequence until the walking state of the unmanned transformable vehicle in the human-like state changes or the deviation is zero, thereby realizing the unmanned transformable vehicle in the working mode. Stability control under.

2. A stability hierarchical control method for multi-configuration motion of an unmanned transformable vehicle according to claim 1, characterized in that: The step 5.2.1 includes: Step 5.2.1.

1. Calculate the deviation sequence between the j actual zero moment point positions and the expected zero moment point positions of the unmanned transformable vehicle in the humanoid walking state and the deviation change rate series , we obtain the stability clustering data set of the unmanned deformable vehicle in human-like walking state; Step 5.2.1.2: Define the maximum number of iterations , the current number of iterations is R, and R=1 is initialized. N data points are randomly selected from the stability clustering data set as the initial cluster center points { | },in, Indicates the Rth iteration Initial cluster centers; Indicates the Rth iteration The deviation of the cluster centers; Indicates the Rth iteration The deviation change rate of the cluster center points; Step 5.2.1.3, define and initialize variable i = 1; Step 5.2.1.4: Calculate the i-th data point in the stability clustering data set according to formula (6) ) and the nth cluster center point at the Rth iteration Distance , so that the i-th data point ( ) is assigned to the nearest cluster center; (6) In formula (6), represents the bias weight factor, represents the weight factor of the deviation change rate; Step 5.2.1.5: After assigning i+1 to i, return to step 5.2.1.4 and execute sequentially until i > j; thereby all data points in the stability clustering data set are assigned to the nearest cluster center point, and N clusters are obtained at the Rth iteration; Step 5.2.1.6: Calculate the average value of the deviation and deviation change rate of the data points contained in the nth cluster at the Rth iteration, and use it as the nth cluster center point at the R+1th iteration. ;in, Indicates the R+1th iteration. The deviation of the cluster centers; Indicates the R+1th iteration. The deviation change rate of the cluster center points; Step 5.2.1.7, after assigning R+1 to R, return to step 5.2.1.3 and execute sequentially until R > So far, we get The N clusters and their cluster centers at the iteration; The cluster center point at the iteration is used as the stability level of the unmanned deformable vehicle in the walking state .

3. An electronic device, comprising a memory and a processor, characterized in that: The memory is used to store a program that supports the processor to execute the stability stratification control method according to claim 1 or 2, and the processor is configured to execute the program stored in the memory.

4. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the stability stratification control method according to claim 1 or 2 are executed.

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