CN118617926A - Damping-adjustable suspension control method and device for multi-axis special vehicle - Google Patents
Damping-adjustable suspension control method and device for multi-axis special vehicle Download PDFInfo
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Abstract
The application discloses a damping-adjustable suspension control method and device for a multi-axis special vehicle, and relates to the field of vehicle design control; the method comprises the following steps: acquiring operation information of a multi-axis special vehicle; determining reference dynamic state information based on a whole-vehicle ceiling damping reference model; determining a sliding mode control quantity according to the actual dynamic state information and the reference dynamic state information; determining the expected damping force of each suspension according to the sliding mode control quantity and the damping force grouping distribution strategy; for any suspension, inputting the expected damping force and suspension compression amount signals into an oil-gas suspension damping force neural network model to obtain control signals; the control signal is used for adjusting the damping force of the suspension of the multi-axle special vehicle in running; the application can improve the running smoothness and the steering stability of the multi-axle vehicle.
Description
Technical Field
The application relates to the field of vehicle design control, in particular to a damping-adjustable suspension control method and device for a multi-axle special vehicle.
Background
The suspension is a connection and force transmission device between the wheel and the vehicle body, and has the main functions of transmitting the vertical, longitudinal and lateral counter forces applied to the wheel by the road surface and the moment formed by the counter forces to the vehicle body, and playing a role of relieving continuous impact from the road surface and inhibiting vibration of the vehicle body.
The semi-active suspension can adjust the damping or rigidity of the suspension according to the working condition change, thereby realizing the effect of improving the running smoothness and the operation stability. Currently, semi-active suspensions used in multi-axle vehicles mainly include damping-adjustable semi-active suspensions and stiffness-adjustable semi-active suspensions.
Compared with a double-axle vehicle, the multi-axle special vehicle has higher requirements on the vibration suppression of the sprung mass due to the specificity of the load, and has higher requirements on the control effect of the damping-adjustable semi-active suspension.
In addition, as multiple-axis semi-active suspension actuators are more, the traditional semi-active suspension control method mainly aims at controlling a single suspension, and the control effect of the actuators at each wheel on the whole vehicle vibration state is difficult to fully play.
In view of the foregoing, it is desirable to design a damping-adjustable semi-active suspension control method capable of adapting to the working condition changes of a multi-axle vehicle and multiple actuating mechanisms of the multi-axle vehicle, so as to further improve the running smoothness and the steering stability of the multi-axle vehicle.
Disclosure of Invention
The application aims to provide a damping-adjustable suspension control method and device for a multi-axle special vehicle, which can improve the running smoothness and the operating stability of the multi-axle vehicle.
In order to achieve the above object, the present application provides the following solutions:
In a first aspect, the present application provides a damping-adjustable suspension control method for a multi-axle special vehicle, including:
Acquiring operation information of a multi-axis special vehicle; the operation information includes: suspension compression amount signals and actual dynamic state information of each suspension; the kinetic state information includes: the sprung mass centroid vertical vibration acceleration, the sprung mass centroid roll vibration angular velocity and the sprung mass centroid pitch vibration angular velocity;
Determining reference dynamic state information based on a whole-vehicle ceiling damping reference model; the whole-vehicle ceiling damping reference model is a differential mathematical model constructed according to the damping coefficient of an ideal damper, the size parameter of a multi-axle special vehicle and actual dynamics state information;
determining a sliding mode control quantity according to the actual dynamic state information and the reference dynamic state information; the slip form control amount includes: a control amount in the vertical direction, a control amount in the roll direction, and a control amount in the pitch direction; the control amount includes: damping force and moment;
Determining the expected damping force of each suspension according to the sliding mode control quantity and the damping force grouping distribution strategy; the damping force grouping allocation strategy is determined according to the control quantity;
For any suspension, inputting the expected damping force and suspension compression amount signals into an oil-gas suspension damping force neural network model to obtain control signals; the control signal includes: controlling the current; the control signal is used for adjusting the damping force of the suspension of the multi-axle special vehicle in running; the hydro-pneumatic suspension damping force neural network model is obtained by training a counter-propagation neural network by adopting a Levenberg-Marquardt algorithm.
Optionally, the whole vehicle ceiling damping reference model specifically includes:
Wherein m s is the multi-axis vehicle sprung mass; The vertical vibration acceleration of the mass center; omega y is the angular velocity of the centroid pitching vibration; u is the longitudinal movement speed of the multiaxial vehicle; j represents a j-th axis of the multi-axis vehicle; f zlj is the suspension force provided by the hydro-pneumatic suspension on the left side of the j-th axis; f zrj is the suspension force provided by the hydro-pneumatic suspension on the right side of the j-th axis; g is gravity acceleration; phi is the centroid camber angle; θ is centroid pitch angle; c sky is the damping coefficient of the canopy; Is the vertical vibration speed of the mass center; j x is the roll moment of inertia of the sprung mass with respect to the roll center; Angular acceleration of the roll vibration for the centroid; omega x is the centroid roll oscillation angular velocity; t is the track width; hz is the height of the sprung mass centroid to the roll center; j y is the moment of inertia of the sprung mass about the centre of pitch; Pitching vibration angular acceleration of the mass center; l j is the distance from the j-th axis to the centroid; hp is the height of the sprung mass centroid to the pitch center; a s is the distance from the centroid to the 1 st axis; b s is the distance of the centroid to the last axis.
Alternatively, the expression of the control amount in the vertical direction is:
Wherein U z is the control quantity in the vertical direction; u z,eq is the equivalent control quantity in the vertical direction; u z,sw is the switching control quantity in the vertical direction; m s is the multi-axle vehicle sprung mass; lambda z is the equivalent control proportionality coefficient of vertical vibration; is the vertical vibration speed of the mass center; The vertical speed is referenced for the mass center of the canopy damping reference model; j represents a j-th axis of the multi-axis vehicle; f zlj is the suspension force provided by the hydro-pneumatic suspension on the left side of the j-th axis; f zrj is the suspension force provided by the hydro-pneumatic suspension on the right side of the j-th axis; g is gravity acceleration; phi is the centroid camber angle; θ is centroid pitch angle; omega x is the centroid roll oscillation angular velocity; u is the longitudinal movement speed of the multiaxial vehicle; The vertical acceleration is referenced for the mass center of the canopy damping reference model; k z is the vertical vibration switching control gain; sat () is a saturation function; s z is a vertical vibration sliding die surface; Θ z is the boundary layer thickness of the vertical slip mode surface quasi-slip mode.
Alternatively, the expression of the control amount of the roll direction is:
wherein, Is a control amount in the roll direction; j x is the roll moment of inertia of the sprung mass with respect to the roll center; The proportional coefficient is equivalently controlled for the rolling vibration; omega x is the centroid roll oscillation angular velocity; omega xs is the reference roll vibration angular velocity of the centroid of the canopy damping reference model; j represents a j-th axis of the multi-axis vehicle; f zlj is the suspension force provided by the hydro-pneumatic suspension on the left side of the j-th axis; f zrj is the suspension force provided by the hydro-pneumatic suspension on the right side of the j-th axis; m s is the multi-axle vehicle sprung mass; g is gravity acceleration; phi is the centroid camber angle; θ is centroid pitch angle; hz is the height of the sprung mass centroid to the roll center; MR provides anti-roll moment for the anti-roll bar; the centroid of the canopy damping reference model is referenced with the rolling vibration angular acceleration; sat () is a saturation function; switching the control gain for roll vibration; Is a side-tipping vibration sliding die surface; boundary layer thickness for the roll-slip mode.
Alternatively, the expression of the control amount in the pitch direction is:
Wherein U θ is the control quantity of the pitching direction; j y is the moment of inertia of the sprung mass about the centre of pitch; lambda θ is the equivalent control proportionality coefficient of pitching vibration; omega y is the actual pitching vibration angular velocity of the mass center of the multi-axis vehicle; omega ys is the reference pitching vibration angular velocity of the mass center of the canopy damping reference model; j represents a j-th axis of the multi-axis vehicle; f zlj is the suspension force provided by the hydro-pneumatic suspension on the left side of the j-th axis; f zrj is the suspension force provided by the hydro-pneumatic suspension on the right side of the j-th axis; l j is the distance from the j-th axis to the centroid; m s is the multi-axle vehicle sprung mass; g is gravity acceleration; hp is the height of the sprung mass centroid to the pitch center; θ is centroid pitch angle; The pitching vibration angular acceleration is referenced for the mass center of the canopy damping reference model; k θ is pitch vibration switching control gain; s θ is a pitching vibration sliding mode surface; Θ θ is the boundary layer thickness of the quasi-sliding mode of the pitching sliding mode surface; sat () is a saturated function.
Optionally, the method for determining the hydro-pneumatic suspension damping force neural network model specifically comprises the following steps:
acquiring a data set; the dataset comprises: suspension compression signal and damping force of known control current;
dividing the data set into a training set, a verification set and a test set;
Constructing a back propagation neural network;
Training parameters of the back propagation neural network according to the training set by adopting a Levenberg-Marquardt algorithm with the aim of meeting the training ending condition to obtain a trained back propagation neural network; the parameters include: weighting; the training ending conditions include: the training times reach the preset cycle times or the error is smaller than the set performance target value; the error is determined according to the control current in the data set and the control signal output by the trained back propagation neural network;
Verifying the trained back propagation neural network by adopting a verification set to obtain a verified back propagation neural network;
And determining the verified counter-propagation neural network as an hydro-pneumatic suspension damping force neural network model, and adopting a test set to test and verify the hydro-pneumatic suspension damping force neural network model.
In a second aspect, the present application provides a damping-adjustable suspension control device for a multi-axle special vehicle, comprising: the damping-adjustable suspension control system comprises a sensor module, an oil-gas suspension and a controller module which is realized by adopting the damping-adjustable suspension control method of the multi-axle special vehicle;
The sensor module and the oil-gas suspension are connected with the controller module;
The sensor module is used for collecting the operation information of the multi-shaft special vehicle; the operation information includes: suspension compression amount signals and actual dynamic state information of each suspension; the kinetic state information includes: the sprung mass centroid vertical vibration acceleration, the sprung mass centroid roll vibration angular velocity and the sprung mass centroid pitch vibration angular velocity;
the controller module is used for:
Acquiring the operation information;
Determining reference dynamic state information based on a whole-vehicle ceiling damping reference model; the whole-vehicle ceiling damping reference model is a differential mathematical model constructed according to the damping coefficient of an ideal damper, the size parameter of a multi-axle special vehicle and actual dynamics state information;
determining a sliding mode control quantity according to the actual dynamic state information and the reference dynamic state information; the slip form control amount includes: a control amount in the vertical direction, a control amount in the roll direction, and a control amount in the pitch direction; the control amount includes: damping force and moment;
Determining the expected damping force of each suspension according to the sliding mode control quantity and the damping force grouping distribution strategy; the damping force grouping allocation strategy is determined according to the control quantity;
For any suspension, inputting the expected damping force and suspension compression amount signals into an oil-gas suspension damping force neural network model to obtain control signals; the control signal includes: controlling the current; the hydro-pneumatic suspension damping force neural network model is obtained by training a counter-propagation neural network by adopting a Levenberg-Marquardt algorithm;
The hydro-pneumatic suspension is used for adjusting the damping force of the suspension of the multi-axle special vehicle in running according to the control signal.
Optionally, the sensor module includes: a vehicle height sensor, a posture sensor and an acceleration sensor;
the vehicle height sensor, the attitude sensor and the acceleration sensor are all connected with the controller module;
The vehicle height sensor is arranged at each suspension of the multi-axle special vehicle; the vehicle height sensor is used for collecting suspension compression quantity signals;
the attitude sensor is used for collecting the sprung mass centroid rolling vibration angular velocity and the sprung mass centroid pitching vibration angular velocity;
the acceleration sensor is used for collecting the vertical vibration acceleration of the mass center of the sprung mass.
According to the specific embodiment provided by the application, the application discloses the following technical effects:
The application discloses a damping-adjustable suspension control method and a damping-adjustable suspension control device for a multi-axis special vehicle, which are used for acquiring the running information of the multi-axis special vehicle; determining reference dynamic state information based on a whole-vehicle ceiling damping reference model; determining a sliding mode control quantity according to the actual dynamic state information and the reference dynamic state information; determining the expected damping force of each suspension according to the sliding mode control quantity and the damping force grouping distribution strategy; for any suspension, inputting the expected damping force and suspension compression amount signals into an oil-gas suspension damping force neural network model to obtain control signals; the control signal is used for adjusting the damping force of the suspension of the multi-axle special vehicle in running; the application can improve the running smoothness and the steering stability of the multi-axle vehicle.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for controlling a damping-adjustable suspension of a multi-axle special vehicle according to an embodiment of the present application;
FIG. 2 is a schematic diagram showing the specific structural components of an apparatus according to an embodiment of the present application;
FIG. 3 is a flowchart of a suspension control algorithm according to an embodiment of the present application;
FIG. 4 is a schematic diagram of a damping reference model of a whole vehicle canopy according to an embodiment of the present application;
FIG. 5 is a schematic diagram of a model reference sliding mode control strategy according to an embodiment of the present application;
FIG. 6 is a schematic diagram of a multi-axis vehicle damping force grouping distribution strategy according to an embodiment of the present application;
FIG. 7 is a flowchart of an inverse model algorithm for a hydro-pneumatic suspension according to an embodiment of the application.
Symbol description:
The system comprises a sensor module-110, a controller module-120, an oil-gas suspension-130, a vehicle height sensor-111, an attitude sensor-112, an acceleration sensor-113, an electric proportional valve-131, a virtual damper-31, a body-32 of a multi-axle vehicle, a damped continuously adjustable oil-gas suspension-33, a vertical vibration sliding mode controller-410, a rolling vibration sliding mode controller-420, a pitching vibration sliding mode controller-430, a sliding mode controller control quantity output-440, a 1-8-axis module-51, a 2-7-axis module-52, a 3-6-axis module-53 and a 4-5-axis module-54.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The foregoing objects, features, and advantages of the application will be more readily apparent from the following detailed description of the application when taken in conjunction with the accompanying drawings and detailed description.
In one exemplary embodiment, as shown in fig. 1, a damping-adjustable suspension control method for a multi-axle special vehicle includes:
step 100: and acquiring the operation information of the multi-axis special vehicle. The operation information includes: suspension compression amount signals and actual dynamic state information of each suspension; the kinetic state information includes: the sprung mass centroid vertical vibration acceleration, the sprung mass centroid roll vibration angular velocity and the sprung mass centroid pitch vibration angular velocity.
Step 200: and determining the reference dynamic state information based on the whole vehicle ceiling damping reference model. The whole-vehicle ceiling damping reference model is a differential mathematical model constructed according to the damping coefficient of an ideal damper, the size parameter of a multi-axle special vehicle and actual dynamics state information.
The whole vehicle ceiling damping reference model specifically comprises:
Wherein m s is the multi-axis vehicle sprung mass; The vertical vibration acceleration of the mass center; omega y is the angular velocity of the centroid pitching vibration; u is the longitudinal movement speed of the multiaxial vehicle; j represents a j-th axis of the multi-axis vehicle; f zlj is the suspension force provided by the hydro-pneumatic suspension on the left side of the j-th axis; f zrj is the suspension force provided by the hydro-pneumatic suspension on the right side of the j-th axis; g is gravity acceleration; phi is the centroid camber angle; θ is centroid pitch angle; c sky is the damping coefficient of the canopy; Is the vertical vibration speed of the mass center; j x is the roll moment of inertia of the sprung mass with respect to the roll center; Angular acceleration of the roll vibration for the centroid; omega x is the centroid roll oscillation angular velocity; t is the track width; hz is the height of the sprung mass centroid to the roll center; j y is the moment of inertia of the sprung mass about the centre of pitch; Pitching vibration angular acceleration of the mass center; l j is the distance from the j-th axis to the centroid; hp is the height of the sprung mass centroid to the pitch center; a s is the distance from the centroid to the 1 st axis; b s is the distance of the centroid to the last axis.
Step 300: and determining the sliding mode control quantity according to the actual dynamic state information and the reference dynamic state information. The slip form control amount includes: a control amount in the vertical direction, a control amount in the roll direction, and a control amount in the pitch direction; the control amount includes: damping force and moment.
The expression of the control amount in the vertical direction is:
Wherein U z is the control quantity in the vertical direction; u z,eq is the equivalent control quantity in the vertical direction; u z,sw is the switching control quantity in the vertical direction; m s is the multi-axle vehicle sprung mass; lambda z is the equivalent control proportionality coefficient of vertical vibration; is the vertical vibration speed of the mass center; The vertical speed is referenced for the mass center of the canopy damping reference model; j represents a j-th axis of the multi-axis vehicle; f zlj is the suspension force provided by the hydro-pneumatic suspension on the left side of the j-th axis; f zrj is the suspension force provided by the hydro-pneumatic suspension on the right side of the j-th axis; g is gravity acceleration; phi is the centroid camber angle; θ is centroid pitch angle; omega x is the centroid roll oscillation angular velocity; u is the longitudinal movement speed of the multiaxial vehicle; The vertical acceleration is referenced for the mass center of the canopy damping reference model; k z is the vertical vibration switching control gain; sat () is a saturation function; s z is a vertical vibration sliding die surface; Θ z is the boundary layer thickness of the vertical slip mode surface quasi-slip mode.
The expression of the control amount in the roll direction is:
wherein, Is a control amount in the roll direction; j x is the roll moment of inertia of the sprung mass with respect to the roll center; The proportional coefficient is equivalently controlled for the rolling vibration; omega x is the centroid roll oscillation angular velocity; omega xs is the reference roll vibration angular velocity of the centroid of the canopy damping reference model; j represents a j-th axis of the multi-axis vehicle; f zlj is the suspension force provided by the hydro-pneumatic suspension on the left side of the j-th axis; f zrj is the suspension force provided by the hydro-pneumatic suspension on the right side of the j-th axis; m s is the multi-axle vehicle sprung mass; g is gravity acceleration; phi is the centroid camber angle; θ is centroid pitch angle; hz is the height of the sprung mass centroid to the roll center; MR provides anti-roll moment for the anti-roll bar; The centroid of the canopy damping reference model is referenced with the rolling vibration angular acceleration; sat () is a saturation function; k φ is the roll vibration switching control gain; Is a side-tipping vibration sliding die surface; boundary layer thickness for the roll-slip mode.
The expression of the control amount in the pitch direction is:
Wherein U θ is the control quantity of the pitching direction; j y is the moment of inertia of the sprung mass about the centre of pitch; lambda θ is the equivalent control proportionality coefficient of pitching vibration; omega y is the actual pitching vibration angular velocity of the mass center of the multi-axis vehicle; omega ys is the reference pitching vibration angular velocity of the mass center of the canopy damping reference model; j represents a j-th axis of the multi-axis vehicle; f zlj is the suspension force provided by the hydro-pneumatic suspension on the left side of the j-th axis; f zrj is the suspension force provided by the hydro-pneumatic suspension on the right side of the j-th axis; l j is the distance from the j-th axis to the centroid; m s is the multi-axle vehicle sprung mass; g is gravity acceleration; hp is the height of the sprung mass centroid to the pitch center; θ is centroid pitch angle; The pitching vibration angular acceleration is referenced for the mass center of the canopy damping reference model; k θ is pitch vibration switching control gain; s θ is a pitching vibration sliding mode surface; Θ θ is the boundary layer thickness of the quasi-sliding mode of the pitching sliding mode surface; sat () is a saturated function.
Step 400: and determining the expected damping force of each suspension according to the sliding mode control quantity and the damping force grouping distribution strategy. The damping force grouping strategy is determined based on the control amount.
Step 500: and inputting the signals of the expected damping force and the suspension compression amount to the hydro-pneumatic suspension damping force neural network model for any suspension to obtain a control signal. The control signal includes: controlling the current; the control signal is used for adjusting the damping force of the suspension of the multi-axle special vehicle in running; the hydro-pneumatic suspension damping force neural network model is obtained by training a counter-propagation neural network by adopting a Levenberg-Marquardt algorithm.
As an optional implementation manner, the method for determining the hydro-pneumatic suspension damping force neural network model specifically comprises the following steps:
acquiring a data set; the dataset comprises: suspension compression signal and damping force of known control current;
dividing the data set into a training set, a verification set and a test set;
Constructing a back propagation neural network;
Training parameters of the back propagation neural network according to the training set by adopting a Levenberg-Marquardt algorithm with the aim of meeting the training ending condition to obtain a trained back propagation neural network; the parameters include: weighting; the training ending conditions include: the training times reach the preset cycle times or the error is smaller than the set performance target value; the error is determined according to the control current in the data set and the control signal output by the trained back propagation neural network;
Verifying the trained back propagation neural network by adopting a verification set to obtain a verified back propagation neural network;
And determining the verified counter-propagation neural network as an hydro-pneumatic suspension damping force neural network model, and adopting a test set to test and verify the hydro-pneumatic suspension damping force neural network model.
The application also provides an application scene, which applies the damping-adjustable suspension control method of the multi-axis special vehicle. Specifically: the damping adjustable suspension control method for the multi-axis special vehicle can be applied to an adjustable suspension control scene. The adjustable suspension control scene comprises an operation information acquisition link, an adjustable suspension control link and an adjustment link; the operation information enters an adjustable suspension control link from an operation information acquisition link, and a control signal is determined through a whole vehicle ceiling damping reference model, a damping force grouping distribution strategy and an oil-gas suspension damping force neural network model, and enters an adjustment link.
Based on the same inventive concept, the embodiment of the application also provides a multi-axle special vehicle damping adjustable suspension control device for realizing the multi-axle special vehicle damping adjustable suspension control method. The implementation scheme of the device for solving the problem is similar to that described in the above method, so the specific limitation in the embodiments of the damping-adjustable suspension control device for one or more multi-axle special vehicles provided below can be referred to the limitation of the damping-adjustable suspension control method for the multi-axle special vehicles hereinabove, and will not be repeated herein.
In one exemplary embodiment, there is provided a damping-adjustable suspension control device for a multi-axle special vehicle including: the system comprises a sensor module, an oil-gas suspension and a controller module which is realized by adopting a damping adjustable suspension control method of a multi-axle special vehicle.
The sensor module and the hydro-pneumatic suspension are both connected with the controller module.
The sensor module is used for collecting the running information of the multi-axle special vehicle; the operation information includes: suspension compression amount signals and actual dynamic state information of each suspension; the kinetic state information includes: the sprung mass centroid vertical vibration acceleration, the sprung mass centroid roll vibration angular velocity and the sprung mass centroid pitch vibration angular velocity.
A controller module for:
Acquiring operation information; determining reference dynamic state information based on a whole-vehicle ceiling damping reference model; the whole-vehicle ceiling damping reference model is a differential mathematical model constructed according to the damping coefficient of an ideal damper, the size parameter of a multi-axle special vehicle and actual dynamics state information.
The controller module determines the sliding mode control quantity according to the actual dynamic state information and the reference dynamic state information; the slip form control amount includes: a control amount in the vertical direction, a control amount in the roll direction, and a control amount in the pitch direction; the control amount includes: damping force and moment.
The controller module determines the expected damping force of each suspension according to the sliding mode control quantity and the damping force grouping distribution strategy; the damping force grouping strategy is determined based on the control quantity.
The controller module is also used for inputting the expected damping force and suspension compression amount signals to any suspension to the hydro-pneumatic suspension damping force neural network model to obtain control signals; the control signal includes: controlling the current; the hydro-pneumatic suspension damping force neural network model is obtained by training a counter-propagation neural network by adopting a Levenberg-Marquardt algorithm.
The hydro-pneumatic suspension is used for adjusting the damping force of the suspension of the multi-axle special vehicle in running according to the control signal.
As an alternative embodiment, the sensor module comprises: a vehicle height sensor, a posture sensor and an acceleration sensor.
The vehicle height sensor, the attitude sensor and the acceleration sensor are all connected with the controller module; the vehicle height sensor is arranged at each suspension of the multi-axle special vehicle; the vehicle height sensor is used for collecting suspension compression quantity signals.
The attitude sensor is used for collecting the sprung mass centroid rolling vibration angular velocity and the sprung mass centroid pitching vibration angular velocity.
The acceleration sensor is used for collecting the vertical vibration acceleration of the mass center of the sprung mass.
In practical applications, the design flow of the suspension control method may include the following blocks. The hydraulic damping system specifically comprises a whole-vehicle canopy damping reference model, a model reference sliding mode controller, a damping force grouping distribution strategy and a hydro-pneumatic suspension inverse model based on a neural network; the whole vehicle ceiling damping reference model calculates and obtains reference values of whole vehicle vibration information in all directions based on dynamic state information of the vehicle; the model reference sliding mode controller calculates and obtains expected damping force and moment of the whole vehicle in all directions according to the reference value; the damping force grouping distribution strategy calculates the expected damping force of each suspension from the expected damping force and moment; the control signal of the damping adjustable suspension is obtained by calculation based on the hydro-pneumatic suspension damping force inverse model of the BP neural network, and the damping force of the damping continuously adjustable suspension in the running process of the multi-axis vehicle is adjusted, so that the multi-axis vehicle can fully utilize the damping force of the adjustable suspension everywhere, and better vibration suppression effects in the vertical direction, the rolling direction and the pitching direction of the vehicle body are realized.
And calculating control signals of the electric proportional valves at all positions of the suspension according to the positions of different execution mechanisms and the whole vehicle dynamics state information obtained by the sensors so as to fully utilize the damping real-time adjustment of all execution mechanisms of the multi-axle vehicle and realize better sprung mass vibration suppression effect.
Specifically, when the multi-axle vehicle with the damping continuously adjustable suspension and the sensor runs on different grades of uneven road surfaces, based on the dynamic state information of the vehicle, the reference value of the vibration information of the whole vehicle in all directions is calculated according to a whole vehicle ceiling damping reference model, the control moment of the whole vehicle in all directions is calculated by a model reference sliding mode controller based on the reference value, the expected damping force of each suspension is calculated by a multi-axle vehicle damping force distribution strategy based on the control moment, the control current of each suspension is calculated by an oil-gas suspension damping characteristic inverse model based on a Back Propagation (BP) neural network based on the expected damping force, and the damping force of the damping continuously adjustable suspension in running of the multi-axle vehicle is adjusted by the control current.
Compared with the traditional local control method of the ceiling damping control, the whole vehicle ceiling damping control belongs to a global control method. The controller obtains information as vertical, rolling and pitching movements of the mass center of the vehicle body, takes the vibration gesture of the whole vehicle as a control target, comprehensively coordinates the control damping force of each suspension, and pursues that the vertical, rolling and pitching vibrations of the vehicle body are all minimum so as to realize the vertical, rolling and pitching vibration suppression effect of the sprung mass better than that of the traditional semi-active suspension control method.
The robustness of the sliding mode control is strong, and the good sprung mass vibration control effect can be realized under various conditions of vehicle speed and road surface unevenness.
The application adopts a multi-axle vehicle damping force distribution strategy of grouping distribution, so that the suspensions of the multi-axle vehicle can be divided into a plurality of modules, the desired control force and the desired control moment of the damping of the whole vehicle, which are output by the reference sliding mode controller, are distributed according to the distance between the suspensions and the mass center, so that the dynamic adjustment capability of the suspensions is fully utilized, and the better vibration suppression effect of the sprung mass is realized.
Because the application adopts the hydro-pneumatic suspension damping force inverse model based on BP neural network, compared with the traditional direct inverse control current, the hydro-pneumatic suspension damping force inverse model realizes better fitting on the test data of the hydro-pneumatic suspension compression speed, damping force and current, improves the hydro-pneumatic suspension control current solving precision under the expected damping force, and realizes more accurate sprung mass vibration control.
As shown in fig. 2, the specific structure of the device is as follows: a sensor module 110, a controller module 120, and a hydro-pneumatic suspension 130.
The sensor module 110 is connected with the controller module 120 and is used for acquiring the dynamics state information of the vehicle;
The controller module 120 is coupled to the hydro-pneumatic suspension 130 for determining control parameters of the hydro-pneumatic suspension based on vehicle dynamics state information.
The hydro-pneumatic suspension 130 is used for providing damping force and elastic force of the vehicle suspension. The hydro-pneumatic suspension 130 includes an electro-proportional valve 131.
The sensor module includes a vehicle height sensor 111, an attitude sensor 112, and an acceleration sensor 113.
The vehicle height sensor 111 is connected to the controller 120 for measuring a suspension compression amount signal of the suspension of the multi-axle vehicle.
The attitude sensor 112 is connected to the controller 120 for measuring the roll and pitch rates at the sprung mass centroid of the multi-axis vehicle.
The acceleration sensor 113 is connected to the controller 120 for measuring vertical acceleration at the sprung mass centroid of the multi-axis vehicle.
Fig. 3 is a flowchart of a suspension control algorithm provided by the present application, and the specific steps of the algorithm include:
step S11: and the whole-vehicle ceiling damping reference model calculates the reference values of the vertical acceleration, the roll and pitch acceleration of the mass center of the sprung mass and the speeds of all directions according to the vehicle dynamics state information obtained by the sensor and the whole-vehicle ceiling damping control model.
Step S12: and the model reference sliding mode controller calculates and obtains the equivalent control quantity and the switching control quantity of the vibration sliding mode controller in each direction according to the dynamic state reference value calculated by the whole vehicle ceiling damping reference model and the actual motion state of the vehicle measured by the sensor, and calculates and obtains the control quantity of the whole vehicle in the vertical direction, the rolling direction and the pitching direction.
Step S13: and the damping force grouping distribution strategy groups the suspensions at different positions according to the suspension force control amounts in the three directions, and calculates the expected damping force of each suspension according to the grouping.
And S14, calculating control current of each hydro-pneumatic suspension proportional valve according to expected damping force of each suspension by using a hydro-pneumatic suspension damping force inverse model based on the BP neural network.
Fig. 4 is a schematic diagram of a damping reference model of a whole vehicle canopy, 31 is a virtual damper connected with a sky reference system at each suspension corresponding to the position of the vehicle body, and the damping coefficient of the virtual damper is C sky. 32 is the body of the multiaxial vehicle, connected to the virtual damper, with a track width t, a center of mass to front axle distance a s, and center of mass to rear axle distance b s. 33 is a damped continuously adjustable hydro-pneumatic suspension for coupling to a multi-axle vehicle body to provide a practically adjustable damping force having a suspension compression and compression rate of z ij-ztij and
When the vehicle body is only presentAt the vertical vibration speed of (2), the canopy damping force provided by the virtual damper isWhen the vehicle body only has the angular velocity of the roll vibration of omega x, the vertical vibration velocity of the left side of the vehicle body is omega x t/2, the canopy damping force provided by the virtual damper is-omega xtCsky, and the anti-roll moment generated by the canopy damping force on the mass center of the vehicle body isSimilarly, the anti-roll moment generated by the damping force provided by the virtual damper for the right side vehicle body on the mass center of the vehicle body isThe total anti-roll moment is-t 2Cskyωx. Similarly, when the vehicle body has only the pitching vibration angular velocity of omega y, the anti-pitching moment generated by the virtual damper on the mass center of the vehicle body isThe differential equation of vertical, roll and pitch motion of the sprung mass of the whole-vehicle canopy damping reference model can be written as:
FIG. 5 is a schematic diagram of a model reference sliding mode control strategy, which comprises the following specific components:
a vertical vibration slip mode controller 410, a roll vibration slip mode controller 420, and a pitch vibration slip mode controller 430, and control amount outputs 440 thereof.
The vertical vibration sliding mode controller 410 is composed of an equivalent control part and a switching control part, and outputs a vertical control quantity U z.
The error vector defining the vertical vibration sliding mode control is:
The designed slip form surface is as follows:
Order the The equivalent control amount of the vertical vibration sliding mode controller is:
in order to eliminate buffeting, the switching control quantity of the vertical vibration sliding mode controller is as follows:
Uz,sw＝-kzsat(Sz/Θz) kz＞0 (7)
where Θ z is the boundary layer thickness of the quasi-sliding mode.
The total control quantity of the vertical vibration sliding mode controller is as follows:
similarly, the sliding surface of the side-tipping vibration sliding mode controller 420 is:
the total control quantity of the rolling vibration sliding mode controller is as follows:
The sliding mode surface of the pitching vibration sliding mode controller 430 is:
Sθ＝λθ·(θ-θs)+(ωy-ωys) (12)
The total control quantity of the pitching vibration sliding mode controller is as follows:
The control amount 440 of the whole vehicle sliding mode controller is obtained,
And the formula (9), the formula (11) and the formula (13) jointly form the whole vehicle model reference sliding mode controller.
FIG. 6 is a schematic diagram of a multi-axle vehicle damping force grouping distribution strategy. The damping force distribution modules include a 1-8 axis module 51, a 2-7 axis module 52, a 3-6 axis module 53, and a 4-5 axis module 54.
51 Is a 1-8 axle module for multi-axle vehicle damping force grouping distribution, which comprises 4-position damping continuously adjustable hydro-pneumatic suspensions of the 1 st axle and the 8 th axle of the vehicle.
52 Is a multi-axle vehicle damping force grouping distributed 2-7 axle module comprising 4-position damping continuously adjustable hydro-pneumatic suspensions for the 2 nd and 7 th axles of the vehicle.
53 Is a 3-6 axle module for multi-axle vehicle damping force grouping distribution comprising 4-position damping continuously adjustable hydro-pneumatic suspensions for the 3 rd and 6 th axles of the vehicle.
54 Is a 4-5 axle module for multi-axle vehicle damping force grouping distribution that includes 4-position damping continuously adjustable hydro-pneumatic suspensions for the 4 th and 5 th axles of the vehicle.
As the same damping force of each module has the same vertical vibration inhibition effect on the mass center of the vehicle body, the multiaxial vehicle damping force grouping and distributing strategy distributes the damping force of the vertical vibration ceiling of the mass center of the vehicle body to 4 modules in average. As the same damping force of each module has the same rolling vibration inhibition effect on the mass center of the vehicle body, the multi-axis vehicle damping force grouping and distributing strategy distributes the rolling vibration zenith damping moment of the mass center of the vehicle body to 4 modules in average. The front and rear positions of the modules away from the mass center of the vehicle body are different, and the inhibition effect of the same damping force of the hydro-pneumatic spring on the pitching vibration of the mass center is proportional to the longitudinal distance of the mass center, so that the pitching vibration zenith damping moment of the mass center of the vehicle can be divided into 4 modules according to the wheel base proportion.
The vertical, roll and pitch damping control forces and moments of the 1-8 modules are:
the vertical, roll and pitch damping control forces and moments of the 2-7 modules are:
The vertical, roll and pitch damping control forces and moments of the 3-6 modules are:
The vertical, roll and pitch damping control forces and moments of the 4-5 modules are:
furthermore, the expected damping control force of the 4-position damping continuously adjustable oil-gas suspension in the module can be obtained through the generalized inverse matrix.
The control current is reversely calculated through the expected damping force of the damping-adjustable hydro-pneumatic suspension at each position, so that the damping continuous control of the damping-adjustable hydro-pneumatic suspension of the multi-axis special vehicle is realized.
Fig. 7 is a flowchart of an inverse model algorithm of an oil-gas suspension based on a BP neural network, and the specific steps of the algorithm are as follows:
Step S21: bench test is carried out on the hydro-pneumatic spring, the hydro-pneumatic spring damping force is obtained under different proportional valve control currents and hydro-pneumatic spring compression speeds, and a data set is obtained.
Step S22: dividing the data set to obtain data of a training set, a verification set and a test set, selecting proper quantity of neurons, performing model training through a common Levenberg-Marquardt algorithm, continuously calling a weight learning function correction weight by an algorithm in least square fitting, judging whether training is finished or not through detecting whether an error calculated by a performance analysis function is smaller than a performance target value or whether the training frequency reaches the maximum circulation frequency, and finally outputting a trained oil-gas suspension damping force neural network model.
Step S23: the hydro-pneumatic suspension damping force neural network model can predict the control current of each suspension according to the expected damping force of each suspension calculated by the multi-axis vehicle damping force grouping distribution strategy and the suspension compression speed calculated by the vehicle height sensor.
And S24, outputting control current of a proportional valve in the damping-adjustable hydro-pneumatic suspension by the algorithm.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The principles and embodiments of the present application have been described herein with reference to specific examples, the description of which is intended only to assist in understanding the methods of the present application and the core ideas thereof; also, it is within the scope of the present application to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the application.
Claims (8)
1. The damping-adjustable suspension control method for the multi-axle special vehicle is characterized by comprising the following steps of:
Acquiring operation information of a multi-axis special vehicle; the operation information includes: suspension compression amount signals and actual dynamic state information of each suspension; the kinetic state information includes: the sprung mass centroid vertical vibration acceleration, the sprung mass centroid roll vibration angular velocity and the sprung mass centroid pitch vibration angular velocity;
Determining reference dynamic state information based on a whole-vehicle ceiling damping reference model; the whole-vehicle ceiling damping reference model is a differential mathematical model constructed according to the damping coefficient of an ideal damper, the size parameter of a multi-axle special vehicle and actual dynamics state information;
determining a sliding mode control quantity according to the actual dynamic state information and the reference dynamic state information; the slip form control amount includes: a control amount in the vertical direction, a control amount in the roll direction, and a control amount in the pitch direction; the control amount includes: damping force and moment;
Determining the expected damping force of each suspension according to the sliding mode control quantity and the damping force grouping distribution strategy; the damping force grouping allocation strategy is determined according to the control quantity;
For any suspension, inputting the expected damping force and suspension compression amount signals into an oil-gas suspension damping force neural network model to obtain control signals; the control signal includes: controlling the current; the control signal is used for adjusting the damping force of the suspension of the multi-axle special vehicle in running; the hydro-pneumatic suspension damping force neural network model is obtained by training a counter-propagation neural network by adopting a Levenberg-Marquardt algorithm.
2. The method for controlling the damping-adjustable suspension of the multi-axis special vehicle according to claim 1, wherein the whole-vehicle ceiling damping reference model specifically comprises:
Wherein m s is the multi-axis vehicle sprung mass; The vertical vibration acceleration of the mass center; omega y is the angular velocity of the centroid pitching vibration; u is the longitudinal movement speed of the multiaxial vehicle; j represents a j-th axis of the multi-axis vehicle; f zlj is the suspension force provided by the hydro-pneumatic suspension on the left side of the j-th axis; f zrj is the suspension force provided by the hydro-pneumatic suspension on the right side of the j-th axis; g is gravity acceleration; phi is the centroid camber angle; θ is centroid pitch angle; c sky is the damping coefficient of the canopy; Is the vertical vibration speed of the mass center; j x is the roll moment of inertia of the sprung mass with respect to the roll center; Angular acceleration of the roll vibration for the centroid; omega x is the centroid roll oscillation angular velocity; t is the track width; hz is the height of the sprung mass centroid to the roll center; j y is the moment of inertia of the sprung mass about the centre of pitch; Pitching vibration angular acceleration of the mass center; l j is the distance from the j-th axis to the centroid; hp is the height of the sprung mass centroid to the pitch center; a s is the distance from the centroid to the 1 st axis; b s is the distance of the centroid to the last axis.
3. The damping-adjustable suspension control method for a multi-axis special vehicle according to claim 1, wherein the expression of the control amount in the vertical direction is:
Wherein U z is the control quantity in the vertical direction; u z,eq is the equivalent control quantity in the vertical direction; u z,sw is the switching control quantity in the vertical direction; m s is the multi-axle vehicle sprung mass; lambda z is the equivalent control proportionality coefficient of vertical vibration; is the vertical vibration speed of the mass center; The vertical speed is referenced for the mass center of the canopy damping reference model; j represents a j-th axis of the multi-axis vehicle; f zlj is the suspension force provided by the hydro-pneumatic suspension on the left side of the j-th axis; f zrj is the suspension force provided by the hydro-pneumatic suspension on the right side of the j-th axis; g is gravity acceleration; phi is the centroid camber angle; θ is centroid pitch angle; omega x is the centroid roll oscillation angular velocity; u is the longitudinal movement speed of the multiaxial vehicle; The vertical acceleration is referenced for the mass center of the canopy damping reference model; k z is the vertical vibration switching control gain; sat () is a saturation function; s z is a vertical vibration sliding die surface; Θ z is the boundary layer thickness of the vertical slip mode surface quasi-slip mode.
4. The damping-adjustable suspension control method for a multi-axis special vehicle according to claim 1, wherein the expression of the control amount in the roll direction is:
wherein, Is a control amount in the roll direction; j x is the roll moment of inertia of the sprung mass with respect to the roll center; The proportional coefficient is equivalently controlled for the rolling vibration; omega x is the centroid roll oscillation angular velocity; omega xs is the reference roll vibration angular velocity of the centroid of the canopy damping reference model; j represents a j-th axis of the multi-axis vehicle; f zlj is the suspension force provided by the hydro-pneumatic suspension on the left side of the j-th axis; f zrj is the suspension force provided by the hydro-pneumatic suspension on the right side of the j-th axis; m s is the multi-axle vehicle sprung mass; g is gravity acceleration; phi is the centroid camber angle; θ is centroid pitch angle; hz is the height of the sprung mass centroid to the roll center; MR provides anti-roll moment for the anti-roll bar; the centroid of the canopy damping reference model is referenced with the rolling vibration angular acceleration; sat () is a saturation function; switching the control gain for roll vibration; Is a side-tipping vibration sliding die surface; boundary layer thickness for the roll-slip mode.
5. The damping-adjustable suspension control method for a multi-axis special vehicle according to claim 1, wherein the expression of the control amount in the pitch direction is:
Wherein U θ is the control quantity of the pitching direction; j y is the moment of inertia of the sprung mass about the centre of pitch; lambda θ is the equivalent control proportionality coefficient of pitching vibration; omega y is the actual pitching vibration angular velocity of the mass center of the multi-axis vehicle; omega ys is the reference pitching vibration angular velocity of the mass center of the canopy damping reference model; j represents a j-th axis of the multi-axis vehicle; f zlj is the suspension force provided by the hydro-pneumatic suspension on the left side of the j-th axis; f zrj is the suspension force provided by the hydro-pneumatic suspension on the right side of the j-th axis; l j is the distance from the j-th axis to the centroid; m s is the multi-axle vehicle sprung mass; g is gravity acceleration; hp is the height of the sprung mass centroid to the pitch center; θ is centroid pitch angle; The pitching vibration angular acceleration is referenced for the mass center of the canopy damping reference model; k θ is pitch vibration switching control gain; s θ is a pitching vibration sliding mode surface; Θ θ is the boundary layer thickness of the quasi-sliding mode of the pitching sliding mode surface; sat () is a saturated function.
6. The method for determining the damping-adjustable suspension control of the multi-axis special vehicle according to claim 1, wherein the method for determining the hydro-pneumatic suspension damping force neural network model specifically comprises the following steps:
acquiring a data set; the dataset comprises: suspension compression signal and damping force of known control current;
dividing the data set into a training set, a verification set and a test set;
Constructing a back propagation neural network;
Training parameters of the back propagation neural network according to the training set by adopting a Levenberg-Marquardt algorithm with the aim of meeting the training ending condition to obtain a trained back propagation neural network; the parameters include: weighting; the training ending conditions include: the training times reach the preset cycle times or the error is smaller than the set performance target value; the error is determined according to the control current in the data set and the control signal output by the trained back propagation neural network;
Verifying the trained back propagation neural network by adopting a verification set to obtain a verified back propagation neural network;
And determining the verified counter-propagation neural network as an hydro-pneumatic suspension damping force neural network model, and adopting a test set to test and verify the hydro-pneumatic suspension damping force neural network model.
7. The damping-adjustable suspension control device for the multi-axle special vehicle is characterized by comprising: a sensor module, a hydro-pneumatic suspension and a controller module realized by the damping-adjustable suspension control method of the multi-axle special vehicle according to any one of claims 1-6;
The sensor module and the oil-gas suspension are connected with the controller module;
The sensor module is used for collecting the operation information of the multi-shaft special vehicle; the operation information includes: suspension compression amount signals and actual dynamic state information of each suspension; the kinetic state information includes: the sprung mass centroid vertical vibration acceleration, the sprung mass centroid roll vibration angular velocity and the sprung mass centroid pitch vibration angular velocity;
the controller module is used for:
Acquiring the operation information;
Determining reference dynamic state information based on a whole-vehicle ceiling damping reference model; the whole-vehicle ceiling damping reference model is a differential mathematical model constructed according to the damping coefficient of an ideal damper, the size parameter of a multi-axle special vehicle and actual dynamics state information;
determining a sliding mode control quantity according to the actual dynamic state information and the reference dynamic state information; the slip form control amount includes: a control amount in the vertical direction, a control amount in the roll direction, and a control amount in the pitch direction; the control amount includes: damping force and moment;
Determining the expected damping force of each suspension according to the sliding mode control quantity and the damping force grouping distribution strategy; the damping force grouping allocation strategy is determined according to the control quantity;
For any suspension, inputting the expected damping force and suspension compression amount signals into an oil-gas suspension damping force neural network model to obtain control signals; the control signal includes: controlling the current; the hydro-pneumatic suspension damping force neural network model is obtained by training a counter-propagation neural network by adopting a Levenberg-Marquardt algorithm;
The hydro-pneumatic suspension is used for adjusting the damping force of the suspension of the multi-axle special vehicle in running according to the control signal.
8. The multi-axle, special-purpose vehicle damping-adjustable suspension control device of claim 7, wherein the sensor module comprises: a vehicle height sensor, a posture sensor and an acceleration sensor;
the vehicle height sensor, the attitude sensor and the acceleration sensor are all connected with the controller module;
The vehicle height sensor is arranged at each suspension of the multi-axle special vehicle; the vehicle height sensor is used for collecting suspension compression quantity signals;
the attitude sensor is used for collecting the sprung mass centroid rolling vibration angular velocity and the sprung mass centroid pitching vibration angular velocity;
the acceleration sensor is used for collecting the vertical vibration acceleration of the mass center of the sprung mass.
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