Self-adaptive vibration control method, system and equipment applied to wheel-legged vehicle

Abstract

The invention discloses a self-adaptive vibration control method, system and device applied to a wheel-legged vehicle, and relates to the technical field of wheel-legged vehicles, and the method comprises the steps: constructing a spring-damping model; determining reference data of the wheel-legged vehicle by adopting a nonlinear model predictive control method; obtaining the stiffness of the spring-damping model and the stiffness of the hip joint by adopting a self-adaptive stiffness control law; a linear quadratic regulator and an inverse kinematics and inverse dynamics method are adopted to split the reference data, and the actual knee joint torque and expected control information of each leg of the wheel-legged vehicle are obtained; based on the expected control information, the hip joint rigidity and the motion state, the actual hip joint moment and the actual wheel moment of each leg of the wheel-legged vehicle are calculated; and controlling the wheel-legged vehicle based on the actual knee joint torque, the actual hip joint torque and the actual wheel torque. According to the invention, the stability, comfort, flexibility and operation efficiency of the wheel-legged vehicle in a complex application scene can be improved.

Description

Technical Field

[0001] This application relates to the field of wheeled vehicle technology, and in particular to an adaptive vibration control method, system and device for wheeled vehicles. Background Technology

[0002] Wheel-legged vehicles, as a novel ground mobility platform, combine the efficient maneuverability of wheeled systems on flat terrain with the agile obstacle-crossing capabilities of legged systems on unstructured terrain. Their motion control systems ensure efficient and stable movement under various operating conditions. However, the inherent trade-off between handling stability and ride comfort remains a key challenge when moving at high speeds or on unstructured terrain.

[0003] In some situations, motion control systems often focus only on one aspect, leading to instability risks under dynamic conditions or sacrificing motion performance for excessive comfort. For example: 1. Most wheel-legged vehicles oversimplify the complex coupling relationships between the leg dynamics, especially during high-speed maneuvers dominated by transient effects. In practical applications requiring both high-speed driving and obstacle-crossing capabilities, oversimplified wheel-legged vehicle dynamics models cannot achieve precise lateral and longitudinal motion stability control and vertical vibration control. 2. Wheel-legged vehicle control strategies often treat motion stability and comfort as independent objectives, performing separate motion stability control and vibration reduction control for the entire vehicle and during driving, respectively, rather than co-optimizing them within a unified framework. This results in high control complexity and low computational efficiency. 3. Most existing technologies lack adaptive parameter adjustment mechanisms based on real-time terrain and dynamic state feedback in their vibration control strategies, leading to inflexible control strategies that cannot dynamically adjust according to motion conditions. Summary of the Invention

[0004] The purpose of this application is to provide an adaptive vibration control method, system, and device for wheel-leg vehicles, which can improve the stability, comfort, flexibility, and computational efficiency of wheel-leg vehicles in complex application scenarios.

[0005] To achieve the above objectives, this application provides the following solution.

[0006] In a first aspect, this application provides an adaptive vibration control method for wheel-legged vehicles, comprising: A spring-damping model is constructed based on the thigh, calf, and knee joints of each leg of a wheel-legged vehicle. Based on the desired driving speed, desired angular velocity and observation data of the wheel-leg vehicle, a nonlinear model predictive control method is used to determine the reference data of the wheel-leg vehicle. Based on the attitude information, an adaptive stiffness control law is used to adjust the reference stiffness of the spring-damping model and the reference stiffness of the hip joint of the wheel-leg vehicle to obtain the stiffness of the spring-damping model and the stiffness of the hip joint. Based on the observed data, the reference data, the spring-damping model stiffness, and the spring-damping model, the reference data is decomposed using a linear quadratic regulator and inverse kinematics and inverse dynamics methods to obtain the actual knee joint torque and desired control information for each leg of the wheel-leg vehicle. Based on the desired control information, the hip joint stiffness, and the motion state, calculate the actual hip joint torque and actual wheel torque of each leg of the wheel-leg vehicle; The wheel-leg vehicle is controlled based on the actual knee joint torque, the actual hip joint torque, and the actual wheel torque.

[0007] Secondly, this application provides an adaptive vibration control system for wheel-legged vehicles, comprising: The building module is used to construct a spring-damping model based on the thigh, calf, and knee joints of each leg of a wheel-legged vehicle; The reference state determination module is used to determine the reference data of the wheel-leg vehicle based on the desired driving speed, desired angular velocity and observation data of the wheel-leg vehicle, using a nonlinear model predictive control method. An adjustment module is used to adjust the reference stiffness of the spring-damping model and the reference stiffness of the hip joint of the wheel-leg vehicle based on the attitude information using an adaptive stiffness control law, so as to obtain the stiffness of the spring-damping model and the stiffness of the hip joint. The splitting module is used to split the reference data based on the observed data, the reference data, the spring-damping model stiffness, and the spring-damping model, using a linear quadratic regulator and inverse kinematics and inverse dynamics methods, to obtain the actual knee joint torque and desired control information of each leg of the wheel-leg vehicle; The calculation module is used to calculate the actual hip joint torque and actual wheel torque of each leg of the wheel-leg vehicle based on the desired control information, the hip joint stiffness, and the motion state. A control module is used to control the wheel-leg vehicle based on the actual knee joint torque, the actual hip joint torque, and the actual wheel torque.

[0008] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described adaptive vibration control method applied to wheel-leg vehicles.

[0009] According to the specific embodiments provided in this application, this application has the following technical effects.

[0010] 1. A nonlinear model predictive control method was adopted. After determining the reference state vector of the wheel-leg vehicle, a linear quadratic regulator and inverse kinematics and inverse dynamics methods were used to decompose the reference state vector. During the decomposition process, local optimization was performed on each leg of the wheel-leg vehicle. Specifically, the linear quadratic regulator locally optimized the actual knee joint torque of each leg of the wheel-leg vehicle, realizing the vertical vibration suppression of the wheel-leg vehicle. The inverse kinematics and inverse dynamics methods participated in the local optimization of the actual hip joint torque and actual wheel torque of each leg of the wheel-leg vehicle, realizing the lateral and longitudinal stability control of the wheel-leg vehicle, effectively improving the stability and comfort control of the wheel-leg vehicle in complex application scenarios.

[0011] 2. After decomposing the reference state vector using a linear quadratic regulator and inverse kinematics and inverse dynamics methods, the actual hip joint torque and actual wheel torque are further calculated based on the desired control information obtained from the decomposition. This application uses the decomposition method for calculation, which effectively reduces the control complexity and improves the computational efficiency when accurately capturing the three-dimensional motion characteristics of wheel-leg vehicles.

[0012] 3. This application introduces an adaptive stiffness control law, which enables the stiffness of the spring-damping model and the stiffness of the hip joint to be adjusted in real time according to the observed state of the wheel-leg vehicle, thereby achieving automatic balance between stability and comfort, and thus completing the adaptive dynamic adjustment of stiffness based on behavior recognition, realizing flexible control of the wheel-leg vehicle. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0014] Figure 1 This is a schematic flowchart of an adaptive vibration control method for wheel-legged vehicles provided in an embodiment of this application.

[0015] Figure 2 This is a detailed flowchart illustrating an adaptive vibration control method for wheel-legged vehicles provided in an embodiment of this application.

[0016] Figure 3 This is a schematic diagram of a spring-damping model provided in an embodiment of this application.

[0017] Figure 4 This is a schematic diagram of a wheel-legged vehicle provided in an embodiment of this application.

[0018] Figure 5 This is a schematic diagram of the functional modules of an adaptive vibration control system for a wheel-legged vehicle, provided as another embodiment of this application.

[0019] Figure 6 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application.

[0020] Figure description: Construction module-1, Reference state determination module-2, Adjustment module-3, Splitting module-4, Calculation module-5, Control module-6. Detailed Implementation

[0021] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0022] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0023] In one exemplary embodiment, such as Figure 1 and Figure 2 As shown, an adaptive vibration control method for wheel-leg vehicles is provided. This method is executed by a computer device, specifically by a terminal or server alone, or by both a terminal and a server. In this embodiment, the method is described using a server as an example, and includes the following steps S1 to S6.

[0024] Step S1: Construct a spring-damping model based on the thigh, calf, and knee joints of each leg of the wheel-legged vehicle. A schematic diagram of the model is shown below. Figure 3 As shown. Figure 3 middle The weight of the hip joint and the upper and lower legs. For the mass of the wheel, This refers to the active control force generated by the spring-damped system.

[0025] Step S2: Based on the desired driving speed, desired angular velocity and observation data of the wheel-leg vehicle, a nonlinear model predictive control method is used to determine the reference data of the wheel-leg vehicle.

[0026] The observation data includes: attitude information, observation state vector, observation angle of the knee joint, and motion state. Attitude information includes: yaw rate and lateral acceleration; the observation state vector includes the observed vertical height of the hip joint rotation center, the observed vertical height of the wheel rotation center, the observed vertical velocity of the hip joint rotation center, and the observed vertical velocity of the wheel rotation center; the motion state includes: hip joint observation position, hip joint observation velocity, and wheel observation velocity.

[0027] The reference data includes: a reference state vector and reference motion states. The reference state vector includes: the reference vertical height of the hip joint rotation center, the reference vertical velocity of the hip joint rotation center, the reference vertical height of the wheel rotation center, and the reference vertical velocity of the wheel rotation center. The reference motion states include: wheel position, acceleration, wheel contact force, and center of mass position.

[0028] Specifically, Nonlinear Model Predictive Control (NMPC) is based on a single rigid body dynamics model and solves a high-dimensional optimization problem of the motion of wheel-legged vehicles online at a frequency of 100Hz to optimize trajectory tracking performance. NMPC outputs the center of gravity height and wheel height to calculate the active suspension force through LQR, and simultaneously outputs the center of gravity height, wheel contact force, wheel position and wheel acceleration. Then, it calculates the joint position, velocity and torque through inverse kinematics and inverse dynamics methods.

[0029] The goal of NMPC is to solve a nonlinear switching system model predictive control problem to handle constraints caused by wheel lift due to vibration during motion. The open-source nonlinear switching system solver OCS2 is used for the solution. By optimizing the overall variables based on a single rigid body dynamics model, NMPC can determine continuous-time motion in the roll time domain, while simultaneously optimizing the trajectories of the vehicle body and wheels. The algorithm requires planning the ground reaction forces to achieve vehicle motion control. Finally, it outputs the center of mass position and wheel positions, and plans the wheel velocities.

[0030] To comprehensively capture the kinematic and dynamic characteristics of wheel-leg vehicles, a state vector is established. The state vector includes the velocity of the center of mass in the world coordinate system. (3D) Angular velocity of the center of mass in the world coordinate system (3D) Position of the center of mass in the world coordinate system (3D) Attitude angle of the center of mass in the world coordinate system (3D) The position of the i-th wheel center in the world coordinate system (3i-dimensional) and the rotational speed of the i-th wheel (i-dimensional) — A total of 28 state variables. Control inputs Includes: the contact force of the i-th wheel in the world coordinate system (3i-dimensional), the velocity of the wheel center in the world coordinate system (3i-dimensional) and wheel acceleration (i-dimensional) — A total of 28 input variables. The state variables and control input variables are defined as follows.

[0031] .

[0032] .

[0033] in, This represents the number of wheels and legs.

[0034] The dynamic equations for wheel-legged vehicles are established as follows.

[0035] .

[0036] in, The derivative of the velocity of the center of mass in the world coordinate system. The derivative of the angular velocity of the center of mass in the world coordinate system. The derivative representing the position of the centroid in the world coordinate system. The derivative of the attitude angle of the center of mass in the world coordinate system. The derivative represents the position of the i-th wheel center in the world coordinate system. The derivative representing the rotational speed of the i-th wheel. Represents gravitational acceleration, and I is the moment of inertia of the wheeled vehicle. The rotation matrix representing the attitude of the wheeled vehicle body relative to the world coordinate system. This represents the Euler angle derivative of the angular velocity when transformed from the base coordinate system to the global coordinate system. The Jacobian matrix represents the rotation of the center of mass relative to the vehicle base coordinate system.

[0037] The constraints for solving the predictive control of the NMPC model are as follows.

[0038] To ensure the physical feasibility of the vehicle's movement, constraints were imposed on the optimization variables. Slip steering kinematics, tire models, and tire dynamics were all implemented as constraints. Other constraints in the NMPC framework include the following.

[0039] Friction cone constraint. During the contact phase, the Coulomb friction limit must be satisfied in order to generate an effective desired contact force.

[0040] .

[0041] in, It is the coefficient of friction of the ground. These represent the ground friction forces along the x, y, and z directions, respectively.

[0042] Zero speed constraint. During the contact phase, the speed of the wheel center perpendicular to the terrain normal must be zero.

[0043] .

[0044] in, The unit normal vector representing the terrain.

[0045] Zero-force constraint. During the oscillation phase, the contact force at the end of the wheel must be zero.

[0046] .

[0047] Roll Steering Constraint. This application introduces roll steering constraint into the traditional NMPC constraint. During high-speed cornering of wheel-leg vehicles, load transfer occurs due to inertial forces. Active roll is employed to redistribute the vertical forces on the left and right wheels. , The wheel torque is specifically aimed at achieving active roll steering. Substituting it into the following equation yields the roll steering constraint. Under conventional constraint conditions, this application enables active roll steering in wheel-leg vehicles by introducing roll steering constraints. Active roll steering enhances the steering performance of wheel-leg platforms, achieving superior wheeled motion.

[0048] .

[0049] in, The roll angle is... These represent the height of the center of mass in both tilted and non-tilted states.

[0050] Wheel motion range constraints. Unlike traditional wheel-legged robots that directly restrict joint angles, NMPC achieves kinematic constraints by limiting the position of the wheels relative to the vehicle body within a rectangular region. Since this algorithm targets wheel-legged vehicles with only hip and knee joints, the wheel motion range is constrained to a plane parallel to the vehicle body's x-axis. The inequality constraints are expressed as follows.

[0051] .

[0052] in, It is the nominal position of the end of the i-th wheel in the vehicle coordinate system. It is a constraint vector. Let represent the position of the i-th wheel in the vehicle body coordinate system, calculated as follows.

[0053] .

[0054] Based on the above constraints and the dynamic equations, NMPC can solve for and plan the center of gravity height, wheel height, and wheel position. acceleration Wheel contact force and the position of the center of mass .

[0055] Step S3: Based on the attitude information, the reference stiffness of the spring-damping model and the reference stiffness of the hip joint of the wheel-leg vehicle are adjusted by an adaptive stiffness control law to obtain the stiffness of the spring-damping model and the stiffness of the hip joint.

[0056] Furthermore, the expression for the adaptive stiffness control law is as follows.

[0057] .

[0058] .

[0059] .

[0060] in, For wheel-leg vehicles Stiffness of the spring-damped model for each leg; For wheel-leg vehicles The reference stiffness of the spring-damped model for each leg; The maximum stiffness adjustment range is designed to enhance stability. For wheel-leg vehicles Hip joint stiffness of each leg; For wheel-leg vehicles The reference stiffness of the hip joint of each leg; A weighting function for evaluating the degree of radicalness of the shift; For wheel-legged vehicles, the yaw rate is... For wheel-legged vehicles, the lateral acceleration is... This represents the maximum yaw rate of the wheel-legged vehicle. This represents the maximum lateral acceleration of the wheel-legged vehicle.

[0061] Specifically, an adaptive stiffness control law was proposed, which innovatively achieves a real-time trade-off between stability and comfort by dynamically relating the vehicle's motion state to its joint impedance characteristics. Using a weighting function based on yaw rate and lateral acceleration, the stiffness of the hip joint and the spring-damping model are automatically adjusted. Stiffness is increased during high-speed cornering to enhance rollover resistance, while stiffness is reduced during smooth driving to optimize vibration filtering performance. This behavior-dependent continuous stiffness mapping replaces the traditional fixed-parameter design, improving the adaptive vibration control capability of wheel-leg vehicles.

[0062] To achieve coordinated optimization of handling stability during high-speed cornering and ride comfort during straight-line driving, this application proposes an adaptive stiffness control law based on vehicle dynamics. Its stiffness is dynamically adjusted according to the expression of the adaptive stiffness control law.

[0063] This control law follows the following characteristics depending on the mode of motion: (1) Under straight driving conditions, ( , ).

[0064] .

[0065] The spring-damping model maintains the baseline stiffness, which satisfies the ride comfort requirements of wheel-leg vehicles.

[0066] (2) Under aggressive steering conditions ( or ).

[0067] .

[0068] The maximum height adjustment is triggered, which satisfies the high-speed stability requirements of wheel-leg vehicles.

[0069] Spring-damped model stiffness and hip joint stiffness The adjustment is accomplished by an adaptive stiffness control law based on the above expression.

[0070] Step S4: Based on the observation data, reference data, spring-damping model stiffness, and spring-damping model, the reference data is decomposed using a linear quadratic regulator and inverse kinematics and inverse dynamics methods to obtain the actual knee joint torque and desired control information for each leg of the wheel-leg vehicle.

[0071] Furthermore, the desired control information includes: the desired position of the hip joint, the desired speed of the hip joint, the desired torque of the hip joint, the desired speed of the wheel, and the desired torque of the wheel.

[0072] Furthermore, step S4 specifically includes steps S41-S42.

[0073] Step S41: Based on the observation data, reference state vector and spring-damping model stiffness, the actual knee joint torque of each leg of the wheel-leg vehicle is determined using a linear quadratic regulator and algebraic Riccati equation.

[0074] Furthermore, step S41 specifically includes steps S411-S416.

[0075] Step S411: Based on the observation data, generate the observation state matrix and the observation input matrix.

[0076] Specifically, the Linear Quadratic Regulator (LQR) operates at a high frequency of 500Hz, significantly reducing vehicle vibration and pitch angle fluctuations by adjusting the actual knee joint torque, while complementing the trajectory planning of the NMPC. While the NMPC ensures overall driving stability, the LQR focuses on the fine adjustment of local vertical motion. Together, they overcome the bottleneck of balancing stability and comfort in traditional wheel-leg vehicles in high-speed dynamic scenarios.

[0077] The specific process of LQR calculation for actual knee joint torque is as follows: To achieve vibration damping control in complex motion scenarios, the leg subsystem of the wheel-leg vehicle is modeled as a spring-damped model with active control force, where vibration damping and tracking are achieved by calculating active suspension force. To simultaneously achieve vibration reduction and leg motion tracking, LQR is used to calculate the actual knee joint torque.

[0078] The centroid height first received by LQR and wheel height Because the hip joint is rigidly connected to the vehicle, the position of the hip joint of each leg is fixed relative to the center of gravity in the center-of-gravity reference frame. The reference vertical height of the hip joint rotation center of each leg can be directly obtained through the height of the center of gravity and the vehicle's geometry. The reference vertical height of the wheel rotation center is the wheel height. The reference state vector is determined based on the reference vertical height of the hip joint rotation center and the reference vertical height of the wheel rotation center.

[0079] Taking a four-wheeled, leg-type vehicle as an example, for a single leg, it is established as a quarter-spring-damped model. One leg ( The dynamic equations of the spring-damped model corresponding to the leg number are as follows.

[0080] .

[0081] .

[0082] in, For the first Weight of each leg For the first The weight of each wheel. For the first The active control force generated by the active suspension of each leg. They represent the first The vertical height, vertical velocity, and vertical acceleration of the center of rotation of the hip joint of each leg were observed. They represent the first The vertical height, vertical velocity, and vertical acceleration observed at the center of rotation of each leg wheel are recorded. For the first Stiffness of the spring-damped model for each leg For the first Damping of a spring-damped model with one leg For the first The tire on one leg was deformed. For the first The rigidity of each leg of the tire.

[0083] Therefore, the state equations for the quarter-spring-damped model represented by each leg are as follows.

[0084] .

[0085] Among them, for the first One leg, Let be the observed state vector of this leg. Let be the input vector for this leg. Let be the perturbation vector of this leg. For wheel-leg vehicles The observation state matrix of each leg, For wheel-leg vehicles The observation input matrix for each leg, For wheel-leg vehicles The perturbation matrix for each leg is given by the formula below.

[0086] .

[0087] .

[0088] .

[0089] Step S412: Solve the algebraic Riccati equation based on the observation state matrix and the observation input matrix to obtain the weight matrix of the control input matrix.

[0090] Furthermore, the expression for the algebraic Riccati equation is as follows.

[0091] .

[0092] in, For wheel-leg vehicles The observation state matrix of each leg; For wheel-leg vehicles The positive definite matrix of the optimal cost function of a linear quadratic regulator with one leg; For wheel-leg vehicles The observation input matrix for each leg; For wheel-leg vehicles The weight matrix of the control input matrix for each leg; For wheel-leg vehicles The weight matrix of the observation state matrix of each leg.

[0093] Step S413: Calculate the optimal gain matrix of the spring-damped model based on the weight matrix of the control input matrix.

[0094] Furthermore, the formula for calculating the optimal gain matrix of the spring-damped model is as follows.

[0095] .

[0096] in, For wheel-leg vehicles The optimal gain matrix of a spring-damped model with one leg.

[0097] Step S414: Based on the optimal gain matrix, the observed state vector, and the reference state vector, the active control force of the spring-damped model is calculated using a linear quadratic regulator.

[0098] Furthermore, the formula for calculating the active control force of the spring-damped model is as follows.

[0099] .

[0100] in, For wheel-leg vehicles Active control force of a spring-damped model with individual legs; For wheel-leg vehicles The observation state vector of each leg; For wheel-leg vehicles The reference state vector of each leg.

[0101] Specifically, the optimal gain matrix of the spring-damped model in step S413 This was obtained by solving the infinite time-domain optimization problem for each suspension system.

[0102] .

[0103] The design of the weight matrix in step S412 follows the principles below.

[0104] Sprung mass tracking: via Matrix penalty .

[0105] Unsprung mass tracking: via Matrix penalty .

[0106] Smoothness optimization: inhibition change.

[0107] Tire ground contact: control change.

[0108] In step S414, the control objective is to track the reference state vectors of each leg of the wheel-legged vehicle. (Generated from the reference vertical height of the hip joint rotation center and the wheel rotation center, calculated by NMPC), while suppressing unmeasured disturbances. The calculation formula for the active control force of the spring-damped model is based on the distributed LQR control law, which is designed as follows.

[0109] .

[0110] Step S415: Calculate the active suspension force of each leg of the wheel-leg vehicle based on the active control force, the observed state vector, and the stiffness of the spring-damping model.

[0111] Furthermore, the formula for calculating the active suspension force of each leg of a wheel-leg vehicle is as follows.

[0112] .

[0113] in, For wheel-leg vehicles The active suspension force of each leg; For wheel-leg vehicles Stiffness of the spring-damped model for each leg; For wheel-leg vehicles The vertical height of the center of rotation of the hip joint of each leg; For wheel-leg vehicles The vertical height of the wheel rotation center of each leg observed; For wheel-leg vehicles Damping of a spring-damped model with one leg; For wheel-leg vehicles The vertical velocity of the center of rotation of the hip joint of each leg was observed. For wheel-leg vehicles The vertical velocity of the wheel at the center of rotation of each leg is observed.

[0114] Specifically, vibration control under complex motion states is achieved by calculating the suspension force based on LQR using a single-leg spring-damped model. This is done for the active suspension fulcrum of each leg. ( The total active suspension force includes the following three components.

[0115] .

[0116] Among them, spring force: Damping force: Active control force from the LQR controller: The above formula provides a formula for calculating the active suspension force of each leg of a wheel-leg vehicle.

[0117] Step S416: Based on the active suspension force, the leg length of the wheel-leg vehicle, and the observation angle of the knee joint, calculate the actual knee joint torque of each leg of the wheel-leg vehicle.

[0118] Furthermore, the formula for calculating the actual knee joint torque of each leg in a wheel-legged vehicle is as follows.

[0119] .

[0120] in, For wheel-leg vehicles The actual knee joint torque of each leg; The length of the leg of a wheel-type vehicle; For wheel-leg vehicles The observation angle of the knee joint of each leg.

[0121] Specifically, by equating a single leg to an active suspension system, active suspension force is obtained using an LQR controller. The active spring force output by the LQR controller... It needs to be converted into the desired torque of the corresponding knee joint. This transformation relationship is derived based on the leg geometry and the principle of virtual work.

[0122] For the suspension geometry of a symmetrical leg structure, the relationship between the knee joint angle θ and the suspension length L (the vertical distance from the hip to the wheel) is as follows, for a symmetrical leg structure where both the thigh and lower leg are of length l.

[0123] .

[0124] Differentiating with respect to θ yields the rate of change of the suspension length.

[0125] .

[0126] Solving for knee joint torque and active suspension force using the principle of virtual work. The work done in the direction of suspension.

[0127] .

[0128] By substituting the derivative of the suspension length, a formula for calculating the actual knee joint torque of each leg in a wheel-leg vehicle can be derived.

[0129] Based on the above steps, when the wheel-leg vehicle is traveling at high speed under complex working conditions, the spring-damping model of a single leg is solved by the LQR controller to obtain the active control force and convert it into knee joint torque output, thereby realizing the vibration control of the wheel-leg vehicle.

[0130] Step S42: Based on the reference motion state, the desired control information for each leg of the wheel-leg vehicle is calculated using inverse kinematics and inverse dynamics methods.

[0131] Furthermore, step S42 specifically includes steps S421-S414.

[0132] Step S421: Based on multi-rigid-body dynamics theory, Lagrange mechanics and Newton-Euler algorithm, establish a rigid-body dynamics model of the floating base.

[0133] Step S422: Based on the theory of single rigid body dynamics, Newton's second law, the theory of single rigid body dynamics and the angular momentum equation, establish the vertical and lateral longitudinal system dynamic models of the wheel-leg vehicle.

[0134] Step S423: Establish a wheel dynamics model for the wheel-legged vehicle based on rotational dynamics.

[0135] Step S424: Based on the reference motion state, the inverse kinematics and inverse dynamics methods are used to solve the dynamic model of the floating base rigid body, the vertical and lateral longitudinal system dynamic models of the wheel-leg vehicle, and the wheel dynamic model of the wheel-leg vehicle to obtain the desired control information for each leg of the wheel-leg vehicle.

[0136] Specifically, inverse kinematics and inverse dynamics methods are also calculated at a frequency of 500Hz to provide accurate reference trajectories and torque compensation for impedance control, ultimately achieving precise execution of wheel-leg coordinated motion and ensuring that the vehicle meets both trajectory tracking accuracy and dynamic stability requirements in complex terrain. Wheel-leg vehicle modeling is as follows... Figure 4 As shown, a floating base coordinate system and a fixed reference system for the wheel-leg vehicle were established, and the generalized coordinates and key reference points were displayed.

[0137] The modeling method employs a floating base, using the six-degree-of-freedom motion of the wheel-leg vehicle body as the floating base coordinate system, and its generalized position vector... and generalized velocity vector definition.

[0138] .

[0139] in, This represents the number of joints (wheel joints and hip joints). This represents the joint position and velocity of the j-th joint.

[0140] The motion state obtained through NMPC optimization is converted into joint space commands through inverse kinematics calculation, as follows.

[0141] .

[0142] .

[0143] in, These correspond to the inverse kinematics and inverse dynamics solution functions, respectively.

[0144] For the calculation of the desired joint torque, the wheel-leg vehicle establishes a floating base rigid body dynamics model based on multi-rigid-body dynamics theory, Lagrange mechanics, and Newton-Euler algorithm. The model expression is as follows.

[0145] .

[0146] In the above equation: matrix The mass matrix is ​​a symmetric positive definite matrix. Includes Coriolis force, centrifugal force, and gravity terms; matrix Choose a matrix for the mapping from the actuator to the generalized coordinates. Represents the actuator torque vector. Let be the Jacobian matrix of the constraint equations.

[0147] Meanwhile, based on the theory of rigid body dynamics, Newton's second law, and the angular momentum equation, a vertical and lateral longitudinal system dynamic model of the wheel-leg vehicle is established (the meaning of the parameters has been given in the NMPC calculation process), and the model expression is as follows.

[0148] .

[0149] .

[0150] A wheel dynamics model for wheel-legged vehicles is established based on rotational dynamics, and the model expression is as follows.

[0151] .

[0152] in, Let x and y be the ground forces acting on the i-th wheel along the x and y directions, respectively. These are the corresponding tire stiffness coefficients along the x and y directions, respectively. Let the torque be that of the i-th wheel. The i-th wheel acceleration is input to the NMPC module. These are the wheel's moment of inertia and radius, respectively. These are the offset angle and slip ratio of the i-th wheel, respectively.

[0153] Based on the model expressions established by the rigid body dynamics model of the floating base, the vertical and lateral longitudinal system dynamics models of the wheel-leg vehicle, and the wheel dynamics model of the wheel-leg vehicle, the desired control information of each leg of the wheel-leg vehicle is finally solved. The desired control information includes: the desired position of the hip joint, the desired velocity of the hip joint, the desired torque of the hip joint, the desired velocity of the wheel, and the desired torque of the wheel.

[0154] Step S5: Based on the desired control information, hip joint stiffness, and motion state, calculate the actual hip joint torque and actual wheel torque of each leg of the wheel-leg vehicle.

[0155] Furthermore, the formula for calculating the actual hip joint torque of a wheel-leg vehicle is as follows.

[0156] .

[0157] in, For wheel-leg vehicles The actual hip joint torque of each leg; For wheel-leg vehicles Hip joint stiffness of each leg; For wheel-leg vehicles The desired position of the hip joint of each leg; For wheel-leg vehicles The observation position of the hip joint of each leg; This refers to the hip joint damping coefficient; For wheel-leg vehicles The expected velocity of the hip joint of each leg; For wheel-leg vehicles The observation speed of the hip joint of each leg; For wheel-leg vehicles The expected torque of the hip joint of each leg.

[0158] The formula for calculating the actual wheel torque of each leg of a wheel-legged vehicle is as follows.

[0159] .

[0160] in, For wheel-leg vehicles The actual wheel torque of each leg; This refers to the wheel damping coefficient; For wheel-leg vehicles The expected speed of a wheel with one leg; For wheel-leg vehicles The observed speed of a wheel with one leg; For wheel-leg vehicles The expected torque of a wheel with one leg.

[0161] Step S6: Control the wheel-leg vehicle based on the actual knee joint torque, actual hip joint torque, and actual wheel torque.

[0162] The beneficial effects of the adaptive vibration control method for wheel-legged vehicles proposed in this application are mainly reflected in the following aspects: 1. Innovatively, the leg structure is modeled as a spring-damping model, using the leg mechanism as a virtual damper, and employing a linear quadratic regulator (LQR) to significantly improve ride comfort. By modeling the legs of wheel-leg vehicles as a two-degree-of-freedom spring-damping model with active control force, it is possible to simultaneously calculate active and passive suspension forces to achieve vibration suppression and trajectory tracking functions, effectively solving the problem of vehicle stability and comfort control in complex application scenarios.

[0163] 2. By modeling a single leg as a spring-damped model, vertical vibration reduction control is achieved based on LQR, and the stiffness adjustment function of the adaptive stiffness control law is added. At the same time, trajectory planning and inverse kinematics and inverse dynamics are used to achieve lateral and longitudinal driving stability control. Thus, it is possible to achieve vertical vibration control with variable damping under complex working conditions while efficiently taking into account lateral and longitudinal driving stability control.

[0164] 3. By fusing linear quadratic regulators and inverse kinematics and inverse dynamics methods, the reference data is decomposed and local optimization is performed on each part. Specifically, the actual knee joint torque is calculated by fusing linear quadratic regulators, and lateral / longitudinal stability control and vertical vibration suppression are achieved by controlling the knee joint torque individually, while also realizing multi-dimensional motion collaborative optimization. Furthermore, the decomposition method for calculation accurately captures the three-dimensional motion characteristics of wheel-leg vehicles while effectively reducing control complexity and improving computational efficiency.

[0165] 4. An adaptive stiffness control law is introduced, which enables the stiffness of the spring-damping model and the stiffness of the hip joint to be adjusted in real time according to the observed state of the wheel-leg vehicle. This achieves an automatic trade-off between stability and comfort, thereby completing the adaptive dynamic adjustment of stiffness based on behavior recognition. This enables flexible control of the wheel-leg vehicle, ensuring that stability is enhanced by increasing stiffness during high-speed cornering and comfort is optimized by reducing stiffness during smooth driving.

[0166] 5. Finally, a millisecond-level dynamic response is achieved at a frequency of 500Hz, effectively coordinating the advantages of wheeled mobility efficiency and legged terrain adaptability.

[0167] Based on the same inventive concept, this application also provides an adaptive vibration control system for wheel-legged vehicles. The solution provided by this system is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the adaptive vibration control system for wheel-legged vehicles provided below can be found in the limitations of the adaptive vibration control method for wheel-legged vehicles described above, and will not be repeated here.

[0168] In one exemplary embodiment, such as Figure 5 As shown, an adaptive vibration control system for wheel-legged vehicles is provided, comprising the following modules.

[0169] Module 1 is used to build a spring-damping model based on the thigh, calf, and knee joints of each leg of a wheel-legged vehicle.

[0170] Reference state determination module 2 is used to determine the reference data of the wheel-leg vehicle based on the desired driving speed, desired angular velocity and observation data of the wheel-leg vehicle, using a nonlinear model predictive control method; the observation data includes: attitude information, observation state vector, observation angle of the knee joint and motion state.

[0171] Adjustment module 3 is used to adjust the reference stiffness of the spring-damped model and the reference stiffness of the hip joint of the wheel-leg vehicle based on attitude information using an adaptive stiffness control law, so as to obtain the stiffness of the spring-damped model and the stiffness of the hip joint.

[0172] The splitting module 4 is used to split the reference data based on the observation data, reference data, spring-damping model stiffness, and spring-damping model, using a linear quadratic regulator and inverse kinematics and inverse dynamics methods, to obtain the actual knee joint torque and desired control information of each leg of the wheel-leg vehicle.

[0173] Calculation module 5 is used to calculate the actual hip joint torque and actual wheel torque of each leg of the wheel-leg vehicle based on the desired control information, hip joint stiffness, and motion state.

[0174] Control module 6 is used to control the wheel-leg vehicle based on the actual knee joint torque, actual hip joint torque and actual wheel torque.

[0175] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 6As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs in the non-volatile storage media. The database stores observation and reference data. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When executed by the processor, the computer program implements an adaptive vibration control method for wheel-legged vehicles.

[0176] Those skilled in the art will understand that Figure 6 The structures shown are merely block diagrams of some structures related to the present application and do not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than shown in the figures, or combine certain components, or have different component arrangements. In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0177] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0178] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0179] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0180] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0181] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. An adaptive vibration control method applied to a wheel-legged vehicle, characterized in that, The adaptive vibration control method applied to the wheel-legged vehicle comprises: Based on the thigh, calf and knee joint of each leg of the wheel-legged vehicle, a spring-damper model is constructed; Based on the expected driving speed, expected angular speed and observation data of the wheel-legged vehicle, a nonlinear model predictive control method is used to determine the reference data of the wheel-legged vehicle; Based on the attitude information, the reference stiffness of the spring-damper model and the reference stiffness of the hip joint of the wheel-legged vehicle are adjusted by using an adaptive stiffness control law, so as to obtain the spring-damper model stiffness and the hip joint stiffness; Based on the observation data, the reference data, the spring-damper model stiffness and the spring-damper model, the reference data is split by using a linear quadratic regulator and inverse kinematics and inverse dynamics method, so as to obtain the actual knee joint torque and expected control information of each leg of the wheel-legged vehicle; Based on the observation data, the reference data, the spring-damper model stiffness and the spring-damper model, the reference data is split by using a linear quadratic regulator and inverse kinematics and inverse dynamics method, so as to obtain the actual knee joint torque and expected control information of each leg of the wheel-legged vehicle; Based on the expected control information, the hip joint stiffness and the motion state, the actual hip joint torque and the actual wheel torque of each leg of the wheel-legged vehicle are calculated; 2. The adaptive vibration control method for a wheel-legged vehicle according to claim 1, wherein Based on the actual knee joint torque, the actual hip joint torque and the actual wheel torque, the wheel-legged vehicle is controlled. The observation data comprises attitude information, an observation state vector, an observation angle of the knee joint and a motion state; the attitude information comprises a yaw angular speed and a lateral acceleration; the observation state vector comprises an observation vertical height of a hip joint rotation center, an observation vertical height of a wheel rotation center, an observation vertical speed of the hip joint rotation center and an observation vertical speed of the wheel rotation center; and the motion state comprises a hip joint observation position, a hip joint observation speed and a wheel observation speed; The reference data comprises a reference state vector and a reference motion state; 3. The adaptive vibration control method for a wheel-legged vehicle according to claim 2, wherein The expected control information comprises an expected position of the hip joint, an expected speed of the hip joint, an expected torque of the hip joint, an expected speed of the wheel and an expected torque of the wheel. ； ； ； wherein, Khip is the hip stiffness of the i-th leg of the wheeled-legged vehicle; Khip is the hip stiffness of the i-th leg of the wheeled-legged vehicle; Khip is the hip stiffness of the i-th leg of the wheeled-legged vehicle; Khip is the hip stiffness of the i-th leg of the wheeled-legged vehicle; Khip is the hip stiffness of the i-th leg of the wheeled-legged vehicle; Khip is the hip stiffness of the i-th leg of the wheeled-legged vehicle; Khip is the hip stiffness of the i-th leg of the wheeled-legged vehicle; Khip is the hip stiffness of the i-th leg of the wheeled-legged vehicle; Khip is the hip stiffness of the i-th leg of the wheeled-legged vehicle; Khip is the hip stiffness of the i-th leg of the wheeled-legged vehicle; Khip is the hip stiffness of the i-th leg of the wheeled-legged vehicle; Khip is the hip stiffness of the i-th leg of the wheeled-legged vehicle; Khip is the hip stiffness of the i-th leg of the wheeled-legged vehicle; Khip is the hip stiffness of the i-th leg of the wheeled-legged vehicle; 4. The adaptive vibration control method for a wheel-legged vehicle according to claim 2, wherein The expression of the adaptive stiffness control law is: Based on the observation data, the reference data, the spring-damper model stiffness and the spring-damper model, the reference data is split by using a linear quadratic regulator and inverse kinematics and inverse dynamics method, so as to obtain the actual knee joint torque and expected control information of each leg of the wheel-legged vehicle, which specifically comprises: Based on the observation data, the reference state vector and the spring-damper model stiffness, a linear quadratic regulator and an algebraic Riccati equation are used to determine the actual knee joint torque of each leg of the wheel-legged vehicle; 5. The adaptive vibration control method for a wheel-legged vehicle according to claim 4, wherein Based on the reference motion state, an inverse kinematics and inverse dynamics method is used to calculate the expected control information of each leg of the wheel-legged vehicle. Based on the observation data, the reference state vector and the spring-damper model stiffness, a linear quadratic regulator and an algebraic Riccati equation are used to determine the actual knee joint torque of each leg of the wheel-legged vehicle, which specifically comprises: Based on the observation data, an observation state matrix and an observation input matrix are generated; Based on the observation state matrix and the observation input matrix, the algebraic Riccati equation is solved to obtain a weight matrix of a control input matrix; Based on the weight matrix of the control input matrix, an optimal gain matrix of the spring-damper model is calculated; calculating an active control force of the spring-damper model based on the optimal gain matrix, the observed state vector and the reference state vector, by using a linear quadratic regulator; calculating an active suspension force of each leg of the wheel-legged vehicle based on the active control force, the observed state vector and the spring-damper model stiffness; calculating an actual knee joint torque of each leg of the wheel-legged vehicle based on the active suspension force, a leg length of the wheel-legged vehicle and an observed angle of the knee joint.

6. The adaptive vibration control method for a wheel-legged vehicle according to claim 5, wherein An expression of the algebraic Riccati equation is: ； in, For wheel-leg vehicles The observation state matrix of each leg; For wheel-leg vehicles The positive definite matrix of the optimal cost function of a linear quadratic regulator with one leg; For wheel-leg vehicles The observation input matrix for each leg; For wheel-leg vehicles The weight matrix of the control input matrix for each leg; For wheel-leg vehicles The weight matrix of the observation state matrix of each leg; A calculation formula of the optimal gain matrix of the spring-damper model is: ； wherein, Optimal gain matrix for a spring-damper model of a leg of a wheel-legged vehicle Optimal gain matrix for a spring-damper model of a leg of a wheel-legged vehicle A calculation formula of the active control force of the spring-damper model is: ； wherein, active control force for a spring-damper model of the nth leg of the wheeled-legged vehicle; active control force for a spring-damper model of the nth leg of the wheeled-legged vehicle; observed state vector for the nth leg of the wheeled-legged vehicle; observed state vector for the nth leg of the wheeled-legged vehicle; reference state vector for the nth leg of the wheeled-legged vehicle; reference state vector for the nth leg of the wheeled-legged vehicle; A calculation formula of the active suspension force of each leg of the wheel-legged vehicle is: ； in, For wheel-leg vehicles The active suspension force of each leg; For wheel-leg vehicles Stiffness of the spring-damped model for each leg; For wheel-leg vehicles The vertical height of the center of rotation of the hip joint of each leg; For wheel-leg vehicles The vertical height of the wheel rotation center of each leg observed; For wheel-leg vehicles Damping of a spring-damped model with one leg; For wheel-leg vehicles The vertical velocity of the center of rotation of the hip joint of each leg was observed. For wheel-leg vehicles The vertical velocity observed at the center of rotation of each wheel; A calculation formula of the actual knee joint torque of each leg of the wheel-legged vehicle is: ； in, For wheel-leg vehicles The actual knee joint torque of each leg; The length of the leg of a wheel-type vehicle; For wheel-leg vehicles The observation angle of the knee joint of each leg.

7. The adaptive vibration control method for a wheel-legged vehicle according to claim 4, wherein Based on the reference motion state, the expected control information of each leg of the wheel-legged vehicle is calculated by using inverse kinematics and inverse dynamics methods, specifically including: Based on multi-rigid-body dynamics theory, Lagrange mechanics and Newton-Euler algorithm, a floating base rigid-body dynamics model is established; Based on single-rigid-body dynamics theory, Newton's second law, single-rigid-body dynamics theory and angular momentum equation, vertical and lateral system dynamics models of the wheel-legged vehicle are established; Based on rotational dynamics, a wheel dynamics model of the wheel-legged vehicle is established; Based on the reference motion state, the floating base rigid-body dynamics model, the vertical and lateral system dynamics models of the wheel-legged vehicle and the wheel dynamics model of the wheel-legged vehicle are solved by using inverse kinematics and inverse dynamics methods to obtain the expected control information of each leg of the wheel-legged vehicle.

8. The adaptive vibration control method for a wheel-legged vehicle according to claim 2, wherein A calculation formula of each actual hip joint torque of the wheel-legged vehicle is: ； in, For wheel-leg vehicles The actual hip joint torque of each leg; For wheel-leg vehicles Hip joint stiffness of each leg; For wheel-leg vehicles The desired position of the hip joint of each leg; For wheel-leg vehicles The observation position of the hip joint of each leg; This refers to the hip joint damping coefficient; For wheel-leg vehicles The expected velocity of the hip joint of each leg; For wheel-leg vehicles The observation speed of the hip joint of each leg; For wheel-leg vehicles The expected torque of the hip joint of each leg; A calculation formula of each actual wheel torque of the wheel-legged vehicle is: ； in, For wheel-leg vehicles The actual wheel torque of each leg; This refers to the wheel damping coefficient; For wheel-leg vehicles The expected speed of a wheel with one leg; For wheel-leg vehicles The observed speed of a wheel with one leg; For wheel-leg vehicles The expected torque of a wheel with one leg.

9. An adaptive vibration control system for a wheel-legged vehicle, characterized by, The adaptive vibration control system applied to the wheel-legged vehicle includes: A construction module is configured to construct a spring-damper model based on the thigh, the shank and the knee joint of each leg of the wheel-legged vehicle; A reference state determination module is configured to determine reference data of the wheel-legged vehicle by using a nonlinear model predictive control method based on expected driving speed, expected angular velocity and observation data of the wheel-legged vehicle; An adjustment module is configured to adjust benchmark stiffness of the spring-damper model and benchmark stiffness of the hip joint of the wheel-legged vehicle based on the attitude information by using an adaptive stiffness control law to obtain the spring-damper model stiffness and the hip joint stiffness; A splitting module is configured to split the reference data by using a linear quadratic regulator and inverse kinematics and inverse dynamics methods based on the observation data, the reference data, the spring-damper model stiffness and the spring-damper model to obtain the actual knee joint torque and the expected control information of each leg of the wheel-legged vehicle; A calculation module is configured to calculate the actual hip joint torque and the actual wheel torque of each leg of the wheel-legged vehicle based on the expected control information, the hip joint stiffness and the motion state; A control module is configured to control the wheel-legged vehicle based on the actual knee joint torque, the actual hip joint torque and the actual wheel torque.

10. A computer device comprising: Memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that the processor executes the computer program to implement the adaptive vibration control method applied to the wheel-legged vehicle according to any one of claims 1-8.

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