CN118358311B - Magnetorheological semi-active suspension control method based on model reference self-adaptive control - Google Patents
Magnetorheological semi-active suspension control method based on model reference self-adaptive control Download PDFInfo
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- 239000000725 suspension Substances 0.000 title claims abstract description 141
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60G—VEHICLE SUSPENSION ARRANGEMENTS
- B60G17/00—Resilient suspensions having means for adjusting the spring or vibration-damper characteristics, for regulating the distance between a supporting surface and a sprung part of vehicle or for locking suspension during use to meet varying vehicular or surface conditions, e.g. due to speed or load
- B60G17/015—Resilient suspensions having means for adjusting the spring or vibration-damper characteristics, for regulating the distance between a supporting surface and a sprung part of vehicle or for locking suspension during use to meet varying vehicular or surface conditions, e.g. due to speed or load the regulating means comprising electric or electronic elements
- B60G17/018—Resilient suspensions having means for adjusting the spring or vibration-damper characteristics, for regulating the distance between a supporting surface and a sprung part of vehicle or for locking suspension during use to meet varying vehicular or surface conditions, e.g. due to speed or load the regulating means comprising electric or electronic elements characterised by the use of a specific signal treatment or control method
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60G—VEHICLE SUSPENSION ARRANGEMENTS
- B60G17/00—Resilient suspensions having means for adjusting the spring or vibration-damper characteristics, for regulating the distance between a supporting surface and a sprung part of vehicle or for locking suspension during use to meet varying vehicular or surface conditions, e.g. due to speed or load
- B60G17/015—Resilient suspensions having means for adjusting the spring or vibration-damper characteristics, for regulating the distance between a supporting surface and a sprung part of vehicle or for locking suspension during use to meet varying vehicular or surface conditions, e.g. due to speed or load the regulating means comprising electric or electronic elements
- B60G17/0152—Resilient suspensions having means for adjusting the spring or vibration-damper characteristics, for regulating the distance between a supporting surface and a sprung part of vehicle or for locking suspension during use to meet varying vehicular or surface conditions, e.g. due to speed or load the regulating means comprising electric or electronic elements characterised by the action on a particular type of suspension unit
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Abstract
The application provides a magneto-rheological semi-active suspension control method based on model reference self-adaptive control, which comprises the following steps: establishing a dynamic model with a semi-active suspension variable load and a reference model; determining a parameter updating law and a self-adaptive control law by a Lyapunov method based on the difference value of state variables of the dynamic model and the reference model; and determining the optimal control force of the magnetorheological damper based on the parameter updating law and the self-adaptive control law, and calculating the optimal input current through an inverse model of the magnetorheological damper based on the optimal control force so as to control the magnetorheological damper. According to the application, the influence of the vehicle load on the semi-active suspension is considered, the variable load control of the semi-active suspension is realized, the robustness, the accuracy and the anti-interference capability of the semi-active suspension control can be improved, and the riding comfort and the operation stability of the vehicle can be improved.
Description
Technical Field
The invention relates to the field of magnetorheological semi-active suspension control, in particular to a magnetorheological semi-active suspension control method based on model reference self-adaptive control.
Background
Under complex road conditions, conventional passive suspensions cannot meet the pursuit of people for vehicle comfort and operational stability. In order to improve the comprehensive performance of the vehicle, the active suspension can improve riding comfort and running safety at the same time, but the active suspension has the problems of high production cost, high energy consumption, complex system, poor reliability and the like. The semi-active suspension adopts a damper with adjustable damping, so that the performance of the active suspension can be almost realized, and the cost, the energy consumption, the complexity and the reliability of the semi-active suspension are all better than those of the active suspension, so that the semi-active suspension is more and more favored by vehicle manufacturers.
In view of the great potential of semi-active suspensions, in recent years, they have become a common focus of academia and industry, and numerous semi-active suspension control methods such as zenith control, geodetic control, PID control, neural network control, and fuzzy control have emerged. The control method can effectively eliminate the influence of the impact of the uneven road surface on the vehicle passengers and improve the running stability of the vehicle on the uneven road surface. But these control methods do not take into account the dynamics of the vehicle load.
At present, in semi-active suspension control, when the mass of a vehicle load reaches a certain degree relative to the mass of a vehicle body, the mass of the load cannot be ignored, and the accuracy of a mathematical model can be directly influenced, so that the control strategy is influenced for controlling the semi-active suspension.
Disclosure of Invention
Based on the above, the invention aims to provide a magneto-rheological semi-active suspension control method based on model reference self-adaptive control so as to solve the defects in the prior art.
In order to achieve the above object, the present invention provides a magnetorheological semi-active suspension control method based on model reference adaptive control, the method comprising:
Establishing a dynamic model with a semi-active suspension variable load;
Solving required parameters based on target constraint conditions, and establishing a reference model based on the required parameters and through a random pavement excitation model;
determining a parameter updating law and an adaptive control law by a Lyapunov method based on the difference value of the state variables of the dynamic model and the reference model;
And determining the optimal control force of the magneto-rheological damper based on the parameter updating law and the self-adaptive control law, and calculating to obtain the optimal input current through an inverse model of the magneto-rheological damper based on the optimal control force so as to control the magneto-rheological damper.
Preferably, the expression of the kinetic model is as follows:
Wherein, In the form of a sprung mass,For the mass of the wheel of the vehicle,In order to provide a spring rate,For the rigidity of the wheel of the vehicle,For the axial displacement excitation of the road surface,For the axial displacement of the tyre,For the sprung mass to be displaced axially,For the dynamic travel of the suspension,For the dynamic load of the tire, the tire is provided with a plurality of grooves,In order to be able to carry the force,In order to control the damping force of the damper,Is the vertical acceleration of the vehicle body,For the vertical acceleration of the wheel,Is the opposite number of suspension strokes.
Preferably, the expression of the reference model is as follows:
Wherein, ，，AndA state feedback gain matrix and a feedforward gain matrix for the reference model,In order for the road surface to interfere,In order for the output to be controlled,Is the first derivative of the state variable of the reference model,For the system matrix of the reference model,As a state variable of the reference model,As an input matrix for road disturbances,For the input matrix of the reference model,In order to have a non-turbulent input,In order to output the matrix of the matrix,Is a direct transmission matrix of road surface disturbances,For the direct transmission matrix of the reference model,For the input matrix of the kinetic model,A system matrix for the kinetic model.
Preferably, the method further comprises:
defining a plurality of specific parameters, and acquiring state space equations based on the specific parameters and the dynamics model, wherein the state space equations and the expressions of the specific parameters are respectively as follows:
Wherein, Is the first derivative of the state variable of the state space equation,For the system matrix of the kinetic model,As an input matrix for road disturbances,For the input matrix of the kinetic model,As an input matrix for an external load,In order to output the matrix of the matrix,Is a direct transmission matrix of road surface disturbances,Direct transmission matrix for the reference modelIs a direct transmission matrix of an external load,In order for the road surface to interfere,For the external input to be made,For a defined amount of the external load,Is the vertical speed of the vehicle body,Is the vertical speed of the wheel.
Preferably, the required parameters include a state feedback gain matrix and a feedforward gain matrix, and the step of solving the required parameters based on the target constraint condition includes:
Taking three performance indexes of vehicle body acceleration, suspension dynamic travel and tire dynamic load as weights, establishing a half positive definite matrix and a positive definite matrix, and constructing a cost function of the reference model based on the positive definite matrix and the half positive definite matrix;
And solving to obtain a state feedback gain matrix of the reference model based on the minimum value of the cost function, and obtaining a feedforward gain matrix based on debugging when the state of the suspension system is stable.
Preferably, the step of determining the parameter updating law and the adaptive control law by the lyapunov method includes:
determining an interval value of the variable load of the semi-active suspension, and acquiring a matrix form of the variable load of the semi-active suspension;
combining the matrix form of the semi-active suspension variable load with the difference value of the state variables of the dynamic model and the reference model to obtain a corresponding error equation;
and calculating the parameter updating law and the self-adaptive control law of the error equation by using a Lyapunov method.
Preferably, the expression of the error equation is as follows:
Wherein, As the first derivative of the difference of the state variables of the reference model and the kinetic model,In order to suppress the input of the disturbance,For the system matrix of the reference model,For the difference in state variables of the reference model and the kinetic model,Is a matrix of correlation functions when external loads change.
Preferably, the expression of the inverse model of the magnetorheological damper is as follows:
Wherein, For the input current of the magnetorheological damper,X h is the stroke displacement of the magnetorheological damper for the output damping force of the magnetorheological damper,The stroke displacement change rate of the magnetorheological damper is the piston speed of the magnetorheological damper.
Preferably, before the step of establishing the actual model and the reference model based on the required parameters and by means of a random road surface excitation model, the method further comprises:
random road surface is used as road surface excitation to build a random road surface excitation model.
The beneficial effects of the invention are as follows: the dynamic model with the semi-active suspension variable load is established, the required parameters are solved according to target constraint conditions, a reference model is established according to the required parameters and through a random road surface excitation model, a parameter update law and a self-adaptive control law are determined through a Lyapunov method based on the difference value of state variables of the dynamic model and the reference model, and then the optimal input current is obtained through calculation of the inverse model of the magnetorheological damper determined based on the parameter update law and the self-adaptive control law, so that the magnetorheological damper can be controlled based on the optimal input current.
Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
FIG. 1 is a flow chart of a method for controlling a magnetorheological semi-active suspension based on model reference adaptive control provided by an embodiment of the invention;
FIG. 2 is a schematic diagram of a semi-active suspension provided by an embodiment of the present invention;
FIG. 3 is a general flow chart of reference adaptive control of a magnetorheological semi-active suspension model provided by an embodiment of the present invention;
Fig. 4 is a schematic time domain diagram of a vehicle vertical acceleration of a semi-active suspension and a passive suspension under an empty condition according to an embodiment of the present invention;
fig. 5 is a schematic diagram of a time domain diagram of suspension travel of a semi-active suspension and a passive suspension in an idle condition according to an embodiment of the present invention;
FIG. 6 is a schematic time-domain plot of tire dynamic loads for a semi-active suspension and a passive suspension in an empty condition provided by an embodiment of the present invention;
FIG. 7 is a schematic diagram of a time domain plot of vertical acceleration of a semi-active suspension and a passive suspension under a 1kN load condition according to an embodiment of the present invention;
FIG. 8 is a schematic diagram of a time domain plot of suspension travel for a semi-active suspension and a passive suspension under a 1kN load provided by an embodiment of the present invention;
FIG. 9 is a schematic diagram of a time domain plot of tire dynamic loads for a semi-active suspension and a passive suspension for a 1kN load provided by an embodiment of the present invention;
FIG. 10 is a schematic diagram of a time domain plot of the vertical acceleration of a semi-active suspension and a passive suspension under a 2kN load condition according to an embodiment of the present invention;
FIG. 11 is a schematic diagram of a time domain plot of suspension travel of a semi-active suspension and a passive suspension under a 2kN load condition according to an embodiment of the present invention;
fig. 12 is a schematic time-domain diagram of tire dynamic load of the semi-active suspension and the passive suspension under a 2kN load condition according to an embodiment of the present invention.
The invention will be further described in the following detailed description in conjunction with the above-described figures.
Detailed Description
In order that the invention may be readily understood, a more complete description of the invention will be rendered by reference to the appended drawings. Several embodiments of the invention are presented in the figures. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.
Referring to fig. 1, a flow chart of a magnetorheological semi-active suspension control method based on model reference adaptive control according to an embodiment of the present invention is shown in fig. 2, and a schematic diagram of a semi-active suspension is shown in fig. 3, and a general flow chart of a magnetorheological semi-active suspension model reference adaptive control is shown in fig. 3. The method comprises the following steps:
Step S101, establishing a dynamic model with a semi-active suspension variable load;
The method comprises the steps of firstly establishing an original dynamic model without a semi-active suspension variable load, then determining the size range of the semi-active suspension variable load according to corresponding vehicle model parameters, adding the semi-active suspension variable load into the original dynamic model, thereby obtaining a dynamic model with the semi-active suspension variable load, and defining specific parameters after establishing the dynamic model to obtain a state space equation of the semi-active suspension.
Step S102, solving required parameters based on target constraint conditions, and establishing a reference model based on the required parameters and through a random pavement excitation model;
The target constraint condition is a weighted square sum of three performance indexes of vehicle body acceleration, suspension dynamic range and tire dynamic load, and for the semi-active suspension, the sizes of the three performance indexes of the vehicle body acceleration, the suspension dynamic range and the tire dynamic load are used as standards for evaluating the performance quality of the semi-active suspension system.
It can be appreciated that the smaller the body acceleration, the more comfortable the vehicle ride experience; the dynamic travel of the suspension is considered in the aspect of vehicle safety, and cannot exceed the travel of the used magnetorheological damper so as to ensure the safety of the semi-active suspension; the dynamic load of the tire is mainly used for measuring the operation stability of the vehicle, and in semi-active suspension control, the three performance indexes cannot be simultaneously met, and the three performance indexes cannot be simultaneously considered. Therefore, weighting is required to optimize the overall performance of the suspension.
Step S103, determining a parameter updating law and an adaptive control law by a Lyapunov method based on the difference value of the state variables of the dynamic model and the reference model;
And step S104, determining the optimal control force of the magneto-rheological damper based on the parameter updating law and the self-adaptive control law, and calculating to obtain the optimal input current through an inverse model of the magneto-rheological damper based on the optimal control force so as to control the magneto-rheological damper.
The optimal input current is controlled to change the output damping force of the magnetorheological shock absorber, so that the rigidity of the semi-active suspension is adjusted flexibly and hard, and the riding comfort and the driving stability of the vehicle are ensured.
Through the steps, a dynamic model with a semi-active suspension variable load is established, a required parameter is solved according to a target constraint condition, a reference model is established according to the required parameter and through a random road surface excitation model, a parameter update law and an adaptive control law are determined through a Lyapunov method based on the difference value of state variables of the dynamic model and the reference model, and then an optimal input current is obtained through calculation of an inverse model of the magnetorheological damper determined based on the parameter update law and the adaptive control law, so that the magnetorheological damper can be controlled based on the optimal input current.
In some of these embodiments, the expression of the kinetic model is as follows:
Wherein, In the form of a sprung mass,For the mass of the wheel of the vehicle,In order to provide a spring rate,For the rigidity of the wheel of the vehicle,For the axial displacement excitation of the road surface,For the axial displacement of the tyre,For the sprung mass to be displaced axially,For the dynamic travel of the suspension,For the dynamic load of the tire, the tire is provided with a plurality of grooves,In order to be able to carry the force,In order to control the damping force of the damper,Is the vertical acceleration of the vehicle body,For the vertical acceleration of the wheel,Is the opposite number of suspension strokes.
Selecting state variablesOutput variableRoad surface excitation variableControllable damping force input variableExternal variable load input variableThe state equation expression of the dynamics model is as follows:
Wherein: a is a system matrix of the dynamic model, B 1 is a pavement excitation input matrix, B 2 is a controllable damping force input matrix, B 3 is an external variable load input matrix, C is an output matrix of the dynamic model, D 1 is a pavement excitation direct output matrix, D 2 is a controllable damping force direct output matrix, D 3 is an external variable load direct output matrix, and the values of matrix parameters are as follows:
In some of these embodiments, the expression of the reference model is as follows:
Wherein, ，，AndA state feedback gain matrix and a feedforward gain matrix for the reference model,Is the first derivative of the state variable of the reference model,For the system matrix of the reference model,As a state variable of the reference model,For the road surface excitation input matrix,For a non-perturbed input matrix of the reference model,In order for the input variable to be non-turbulent,In order to output the matrix of the matrix,The matrix is directly output for road surface excitation,For the direct transmission matrix of the reference model,Is an input matrix for the kinetic model.
In some of these embodiments, the step of solving for the required parameters based on the target constraints includes:
Taking three performance indexes of vehicle body acceleration, suspension dynamic travel and tire dynamic load as weights, establishing a semi-positive definite matrix and a positive definite matrix, and constructing a cost function of the reference model based on the positive definite matrix and the semi-positive definite matrix, wherein the expression of the cost function J is as follows:
In the formula (I)
Wherein,In order to be a matrix of weighting coefficients,、、The weight coefficients of the vehicle body acceleration, the suspension dynamic travel and the tire dynamic load are respectively. The expressions of the semi-positive definite matrix Q and the positive definite matrix R are as follows:
the cost function When the minimum value is reached, a state feedback gain matrix K 1 is obtained at this time.
Where M is the solution of the following Riccati equation.
Determining the cost function J based on the evaluation index of the magneto-rheological semi-active suspension, and minimizing the cost function J to calculate and obtain a state feedback gain matrix K 1 of the reference model; it should be noted that, the feedforward gain matrix K 2 is obtained by debugging when the system state is stable.
The required parameters are compared according to the output y, y is the value of three performance indexes for measuring the acceleration of the vehicle body, the dynamic travel of the suspension and the dynamic load of the tire, namely the target of optimization, and the expression of the required parameters is as follows:
Wherein, AndFor the state feedback gain matrix and the feedforward gain matrix,For the system matrix of the kinetic model,Is an input matrix for the kinetic model.
In some embodiments, the step of determining the parameter update law and the adaptive control law by the lyapunov method includes:
determining an interval value of the variable load of the semi-active suspension, and acquiring a matrix form of the variable load of the semi-active suspension;
The size range of the variable load of the semi-active suspension is between 0 and 2kN, and the equivalent treatment is carried out by adopting the following modes:
And then the semi-active suspension variable load is regarded as disturbance on the damping force of the magnetorheological shock absorber, so that the formula expression is transformed into the following matrix form:
Wherein,
Wherein,For the initial value of the unknown disturbance weight W,Is a correlation function matrix when external load changes, and，In order to have no input damping to suppress load variations,Input damping to suppress load variation.
Combining the matrix form of the semi-active suspension variable load with the difference value of the state variables of the dynamic model and the reference model to obtain a corresponding error equation;
Wherein,
Order the
Order the
Then
Wherein,Is an estimated value of the weight of the unknown disturbance, w is the weight of the unknown disturbance,By the difference between the disturbance weight estimated value and the actual valueInfinitely close to 0, thereby satisfying the estimated valueIs used for the accuracy of the (c) in the (c),Is a correlation function matrix when external load changes, and。
The following relationship is satisfied for the Hurwitz matrix: for the Hurwitz matrix, there is a matrix P satisfying the following relationship:
。
and calculating the parameter updating law and the self-adaptive control law of the error equation by using a Lyapunov method.
Wherein, using Lyapunov method, selecting Lyapunov function
Selecting
Wherein,Is a constant value, and is used for the treatment of the skin,In the form of a square matrix, the square matrix,Is a trace of a square matrix; b 2 is the above value, P isAnd solving an equation, wherein R is an identity matrix.
By Barbalat theorem
Is bounded by
The method can obtain the following steps:
Law of parameter update
Adaptive control law
In some of these embodiments, the expression of the error equation is as follows:
Wherein, As the first derivative of the difference of the state variables of the reference model and the kinetic model,In order to suppress the input of the disturbance,For the system matrix of the reference model,For the difference in state variables of the reference model and the kinetic model,Is a matrix of correlation functions when external load changes,Is the second derivative of the lyapunov function.
In some of these embodiments, the inverse model of the magnetorheological damper is expressed as follows:
wherein F is the output damping force of the magneto-rheological damper, I is the input current of the magneto-rheological damper, Is the first derivative of the state variable of the state space equation,As a state variable of the state space equation, X h is the stroke displacement of the magnetorheological damper,The stroke displacement change rate of the magnetorheological damper is the piston speed of the magnetorheological damper.
In some of these embodiments, prior to the step of establishing the actual model and the reference model based on the desired parameters and by the random road surface excitation model, the method further comprises:
random road surface is used as road surface excitation to build a random road surface excitation model.
The formula expression of the random pavement excitation model is as follows:
In the method, in the process of the invention, The lower cut-off angle frequency is indicated,Is the speed of the vehicle, and is,Is a constant that is related to the road surface,Is a white noise which is a white noise,As the road surface unevenness coefficient,Is the vertical jolt change rate of the road surface,Is vertical jolt of the road surface.
In one embodiment, in order to simulate and test time domain curves of vertical acceleration of a semi-active suspension body under different control parameters, firstly, a positive model and a reverse model of the magnetorheological damper are established according to tensile experimental data of the magnetorheological damper, then a random road surface excitation model, a reference model and a dynamic model are established, the random road surface excitation model is respectively input into the reference model and the actual model, feedback is carried out according to errors obtained by the reference model and the actual model, expected damping force of the magnetorheological damper is obtained through self-adaptive control, and different damping forces are generated when different currents are introduced into the magnetorheological damper, so that corresponding currents are required to be obtained according to the expected damping forces and are input into the magnetorheological damper; the positive model of the magneto-rheological shock absorber obtains damping force according to current; the inverse model of the magneto-rheological shock absorber obtains corresponding control current according to the damping force.
In the specific implementation process, the load variation range is considered to be [0,2kN ] under the actual condition, and three test conditions of no-load, load 1kN and load 2kN are set in the simulation to obtain a time domain curve of the vehicle body acceleration, the suspension dynamic travel and the tire dynamic load of the semi-active suspension system under model reference self-adaptive control. At the same time, in contrast to passive suspensions. The simulation parameters are shown in the following table.
Under no-load conditions, the time domain response curves of the magneto-rheological semi-active suspension are shown in fig. 4-6. The comparison of the performance index root mean square values of the magnetorheological semi-active suspension control system and the passive suspension system adopting the model reference adaptation is shown in the following table.
As can be seen from fig. 4, the maximum absolute values of the vehicle body accelerations of the passive suspension and the semi-active suspension under the control strategy are 1.732 and 2.577, respectively, and the improvement rate is 32.79%; as can be seen from fig. 5, the maximum absolute values of the suspension travel of the passive suspension and the semi-active suspension under the control strategy are 0.01825 and 0.03145, respectively, and the improvement rate is 41.97%; as can be seen from fig. 6, the maximum tire dynamic load absolute values of the passive suspension and the semi-active suspension under the control strategy are 1330 and 1979, respectively, and the improvement rate is 32.79%; as is clear from the table, the rms optimization rate of the vehicle body acceleration under this control strategy was 19.24%, the rms optimization rate of the suspension run was 25.93%, and the rms optimization rate of the tire running load was 33.18% with respect to the passive suspension.
The time domain response curves of the magnetorheological semi-active suspension are shown in figures 7-9 under the condition of 1kN load. The comparison of the performance index root mean square values of the magnetorheological semi-active suspension control system and the passive suspension system adopting model reference adaptive control is shown in the following table.
As can be seen from fig. 7, the maximum absolute values of the vehicle body accelerations of the passive suspension and the semi-active suspension under the control strategy are 1.135 and 1.580, respectively, and the improvement rate is 28.16% under the condition of a load of 1kN, namely, a condition of adding 100kg on the basis of the sprung mass; as can be seen from fig. 8, the maximum absolute values of the suspension travel of the passive suspension and the semi-active suspension under the control strategy are respectively 0.0254 and 0.03484, and the improvement rate is 27.10%; as can be seen from fig. 9, the maximum tire dynamic load absolute values of the passive suspension and the semi-active suspension under the control strategy are 1339 and 1807, respectively, and the improvement rate is 25.90%; the table shows that the rms optimization rate of the vehicle body acceleration is 26.43%, the rms optimization rate of the suspension travel is 33.07%, and the rms optimization rate of the tire dynamic load is 33.59% with respect to the passive suspension.
The time domain response curves of the magneto-rheological semi-active suspension are shown in fig. 10-12 under the condition of 2kN load. The comparison of the performance index root mean square values of the magnetorheological semi-active suspension control system and the passive suspension system adopting the model reference adaptation is shown in the following table.
As can be seen from fig. 10, the maximum absolute values of the vehicle body accelerations of the passive suspension and the semi-active suspension under the control strategy are 0.8135 and 1.190, respectively, and the improvement rate is 31.64% under the condition of a load of 2kN, namely, a condition of adding 200kg on the basis of the sprung mass; as can be seen from fig. 11, the maximum absolute values of the suspension travel of the passive suspension and the semi-active suspension under the control strategy are 0.02173 and 0.02849, respectively, and the improvement rate is 23.73%; as can be seen from fig. 12, the maximum tire dynamic load absolute values of the passive suspension and the semi-active suspension under the control strategy are 1337 and 1797, respectively, and the improvement rate is 25.60%; the table shows that the rms optimization rate of the vehicle body acceleration is 19.71%, the rms optimization rate of the suspension travel is 26.05%, and the rms optimization rate of the tire dynamic load is 33.45% with respect to the passive suspension.
By combining the graphs of fig. 4-12, the performance index of the model reference adaptive control under the variable load control method can be obviously improved in both peak value and root mean square value, and a good vibration damping effect can be achieved. The control strategy is very effective for the problem of load change of the vehicle, can ensure that the vehicle keeps good riding comfort and operation stability under complex road conditions, improves the road trafficability of the vehicle and obtains better driving performance.
It should be noted that the foregoing implementation procedure is only for illustrating the feasibility of the present application, but this does not represent that the model-based adaptive control magnetorheological semi-active suspension control method of the present application has only one implementation procedure, and instead, the model-based adaptive control magnetorheological semi-active suspension control method of the present application can be implemented and incorporated into the feasible implementation of the present application.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing examples illustrate only a few embodiments of the invention and are described in detail herein without thereby limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of the invention should be assessed as that of the appended claims.
Claims (5)
1. A magnetorheological semi-active suspension control method based on model reference adaptive control, which is characterized by comprising the following steps:
Establishing a dynamic model with a semi-active suspension variable load;
Solving required parameters based on target constraint conditions, and establishing a reference model based on the required parameters and through a random pavement excitation model;
determining a parameter updating law and an adaptive control law by a Lyapunov method based on the difference value of the state variables of the dynamic model and the reference model;
Determining optimal control force of the magneto-rheological damper based on the parameter updating law and the self-adaptive control law, and calculating to obtain optimal input current through an inverse model of the magneto-rheological damper based on the optimal control force so as to control the magneto-rheological damper;
the expression of the reference model is as follows:
Wherein, ，，AndA state feedback gain matrix and a feedforward gain matrix for the reference model,In order for the road surface to interfere,In order for the output to be controlled,Is the first derivative of the state variable of the reference model,For the system matrix of the reference model,As a state variable of the reference model,As an input matrix for road disturbances,For the input matrix of the reference model,In order to have a non-turbulent input,In order to output the matrix of the matrix,Is a direct transmission matrix of road surface disturbances,For the direct transmission matrix of the reference model,For the input matrix of the kinetic model,A system matrix for the kinetic model;
the step of determining the parameter updating law and the self-adaptive control law by the Lyapunov method comprises the following steps:
determining an interval value of the variable load of the semi-active suspension, and acquiring a matrix form of the variable load of the semi-active suspension;
combining the matrix form of the semi-active suspension variable load with the difference value of the state variables of the dynamic model and the reference model to obtain a corresponding error equation;
Calculating a parameter updating law and a self-adaptive control law of the error equation by using a Lyapunov method;
the expression of the error equation is as follows:
Wherein, As the first derivative of the difference of the state variables of the reference model and the kinetic model,In order to suppress the input of the disturbance,For the system matrix of the reference model,For the difference in state variables of the reference model and the kinetic model,A correlation function matrix when external load changes;
the expression of the inverse model of the magnetorheological damper is as follows:
Wherein, For the input current of the magnetorheological damper,X h is the stroke displacement of the magnetorheological damper for the output damping force of the magnetorheological damper,The stroke displacement change rate of the magnetorheological damper is the piston speed of the magnetorheological damper.
2. The magnetorheological semi-active suspension control method based on model reference adaptive control according to claim 1, wherein the expression of the dynamics model is as follows:
Wherein, In the form of a sprung mass,For the mass of the wheel of the vehicle,In order to provide a spring rate,For the rigidity of the wheel of the vehicle,For the axial displacement excitation of the road surface,For the axial displacement of the tyre,For the sprung mass to be displaced axially,For the dynamic travel of the suspension,For the dynamic load of the tire, the tire is provided with a plurality of grooves,In order to be able to carry the force,In order to control the damping force of the damper,Is the vertical acceleration of the vehicle body,For the vertical acceleration of the wheel,Is the opposite number of suspension strokes.
3. The model reference adaptive control-based magnetorheological semi-active suspension control method according to claim 2, further comprising:
defining a plurality of specific parameters, and acquiring state space equations based on the specific parameters and the dynamics model, wherein the state space equations and the expressions of the specific parameters are respectively as follows:
Wherein, Is the first derivative of the state variable of the state space equation,For the system matrix of the kinetic model,As an input matrix for road disturbances,For the input matrix of the kinetic model,As an input matrix for an external load,In order to output the matrix of the matrix,Is a direct transmission matrix of road surface disturbances,Direct transmission matrix for the reference modelIs a direct transmission matrix of an external load,In order for the road surface to interfere,For the external input to be made,For a defined amount of the external load,Is the vertical speed of the vehicle body,Is the vertical speed of the wheel.
4. The method for controlling a magnetorheological semi-active suspension based on model reference adaptive control according to claim 1, wherein the required parameters include a state feedback gain matrix and a feedforward gain matrix, and the step of solving the required parameters based on target constraint conditions includes:
Taking three performance indexes of vehicle body acceleration, suspension dynamic travel and tire dynamic load as weights, establishing a half positive definite matrix and a positive definite matrix, and constructing a cost function of the reference model based on the positive definite matrix and the half positive definite matrix;
And solving to obtain a state feedback gain matrix of the reference model based on the minimum value of the cost function, and obtaining a feedforward gain matrix based on debugging when the state of the suspension system is stable.
5. The method for controlling a magnetorheological semi-active suspension based on model reference adaptive control according to claim 1, wherein prior to the step of establishing an actual model and a reference model based on the desired parameters and by a random road surface excitation model, the method further comprises:
random road surface is used as road surface excitation to build a random road surface excitation model.
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