CN107102551B - Damping force constraint-based damping adjustable semi-active suspension hybrid model prediction control method - Google Patents
Damping force constraint-based damping adjustable semi-active suspension hybrid model prediction control method Download PDFInfo
- Publication number
- CN107102551B CN107102551B CN201710437023.2A CN201710437023A CN107102551B CN 107102551 B CN107102551 B CN 107102551B CN 201710437023 A CN201710437023 A CN 201710437023A CN 107102551 B CN107102551 B CN 107102551B
- Authority
- CN
- China
- Prior art keywords
- semi
- active suspension
- control
- damping
- time domain
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
- G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
Landscapes
- Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Artificial Intelligence (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Computation (AREA)
- Medical Informatics (AREA)
- Software Systems (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Vehicle Body Suspensions (AREA)
Abstract
The invention discloses a damping force constraint-based damping adjustable semi-active suspension hybrid model predictive control method, which comprises the steps of establishing a semi-active suspension hybrid system model, carrying out finite time domain optimization control on a semi-active suspension hybrid system based on a model predictive control theory, converting the finite time domain optimization control into a mixed integer quadratic programming problem containing a real-valued variable and a binary variable, and realizing hybrid model predictive control on a damping continuously adjustable semi-active suspension. The invention has the advantages that the contradiction between comfort and operation stability is solved; the nonlinear constraint condition existing in the semi-active suspension optimization method is solved; converting the semi-active suspension control problem into an optimization control problem with limited time domain constraint; the problem of rolling time domain optimization with nonlinear constraints is effectively solved.
Description
Technical Field
The invention relates to the technical field of automobile suspension control, in particular to a damping force constraint-based damping adjustable semi-active suspension hybrid model prediction control method.
Background
Suspension systems are important to improve the ride and handling stability of a vehicle. The semi-active suspension system overcomes the technical defects that the rigidity and the damping of a passive suspension are not adjustable, and the cost of the semi-active suspension system is far lower than that of an active suspension, and represents the main direction of the development of an automobile suspension. The damping-adjustable semi-active suspension can independently track a damping force demand signal, the damping force can be continuously adjusted to any point of a damping working area, and the vehicle performance requirements under different road conditions and working conditions can be met. At present, damping-adjustable semi-active suspensions are mainly divided into magnetorheological (or electrorheological) suspensions and electromagnetic valve type suspensions, wherein the electromagnetic valve type damping-adjustable semi-active suspensions have the advantages of simple structure, quick response, reliable performance, easiness in engineering realization and the like.
At present, the logical relationship between the working range of the damping force and the control force demand signal and the relative speed of the suspension is not directly considered in the semi-active suspension control process, and the optimal control damping force is solved and then limited or restrained according to the output force range of the actual shock absorber and the relative speed of the suspension, so that the suspension cannot obtain the optimal control effect. Giorgetti N et al, based on model predictive Control theory, convert a constrained semi-active suspension Control system into a piecewise affine system and solve it by mixed integer quadratic programming, but do not consider the nonlinear boundary constraints of the damper damping forces (Giorgetti N, Bemporad A, Tsengz H E, et al. Hybrid model predictive Control application damping [ J ]. International Journal of Control, 2006, 79(5):391 398).
The patent document (CN 105974821A) proposes a hybrid control method for a semi-active suspension of a vehicle based on a damping multi-mode switching shock absorber, which determines the recovery damping coefficient and the compression damping coefficient of the shock absorber in different damping modes through simulation analysis, and designs a hybrid model predictive controller for the semi-active suspension based on a hybrid logic dynamic modeling method.
The main difficulty in semi-active suspension control is how to deal with the various nonlinear constraints that the damper damping force must satisfy. The invention provides a hybrid model predictive control method for comprehensively balancing comfortableness and operation stability and considering damping force constraint of a semi-active suspension aiming at a solenoid valve type damping continuously adjustable semi-active suspension system, a semi-active suspension hybrid system model is established, limited time domain optimization control of the semi-active suspension hybrid system is carried out based on a model predictive control theory, the limited time domain optimization control is converted into mixed integer quadratic programming comprising a real value variable and a binary value variable, and hybrid model predictive control of the damping continuously adjustable semi-active suspension is realized.
Disclosure of Invention
The invention aims to provide a damping adjustable semi-active suspension hybrid model predictive control method which comprehensively balances comfort and operation stability and is based on damping force constraint.
In order to solve the technical problems, the invention provides a damping force constraint-based damping adjustable semi-active suspension hybrid model predictive control method, which comprises the following steps of establishing a semi-active suspension hybrid system model, carrying out finite time domain optimization control on a semi-active suspension hybrid system based on a model predictive control theory, converting the finite time domain optimization control into a mixed integer quadratic programming containing a real-valued variable and a binary variable, and realizing hybrid model predictive control on a damping continuously adjustable semi-active suspension, wherein the finite time domain optimization control comprises the following steps:
step 1: dynamic modeling of a damping-adjustable semi-active suspension hybrid system;
step 2: optimizing nonlinear constraint of the damping adjustable semi-active suspension;
and step 3: semi-active suspension hybrid model predictive control;
and 4, step 4: and (3) solving the rolling time domain optimization of the semi-active suspension hybrid system.
The dynamic modeling of the damping-adjustable semi-active suspension hybrid system in the step 1 comprises the following steps: a) introducing auxiliary variables, and converting logical and nonlinear constraint conditions into a group of threshold values and logical conditions; b) and establishing a hybrid system model containing system equations and constraint conditions by using a hybrid system description language, and converting the hybrid system model into an MLD model by using a self-contained HYSDEL compiler.
The logical constraint condition refers to a passive constraint condition of the semi-active suspension, namely when the sign of the damping force demand is the same as the relative speed of the suspension, the required control force is equal to the optimal control force.
The nonlinear constraint condition means that the damping force of the shock absorber must meet a certain boundary condition, namely the optimized damping force is located in the force action range of the actual shock absorber.
The semi-active suspension hybrid model predictive control of the step 3 comprises the following steps: a) in the prediction time domain
Keeping the function type and weight function of the objective function unchanged to the firstkThe predicted input and output of the point are independent variables, the first one is establishedkThe discrete target function of the points, and then the linear weighting of the target function of each point is carried out to obtain the target function in the prediction time domain; b) expanding the constraint condition to the whole prediction time domain, namely constraining each prediction point to obtain a state space equation and the constraint condition in the prediction time domain; c) and constructing model predictive control of the semi-active suspension hybrid system based on an objective function and a constraint condition in a prediction time domain, solving the optimization through a rolling time domain at each sampling moment, taking a first element of an optimal control sequence as control input of the current moment, and repeating the optimization process at the next sampling moment.
The rolling time domain optimization solution of the semi-active suspension hybrid system in the step 4 comprises the following steps: a) loosening the integral constraint limit of part or all of the binary variables in the optimized variables to generate a series of quadratic plans corresponding to the original MIQP; b) and solving the series of quadratic programming to obtain a suboptimal solution or a global optimal solution of the MIQP which meets the integer constraint condition.
The invention has the following advantages:
1) the invention solves the contradiction between comfort and operation stability by constructing a multi-objective optimization method;
2) the invention solves the nonlinear constraint condition existing in the optimization problem of the semi-active suspension by a hybrid logic dynamic modeling method;
3) the invention adopts a hybrid model predictive control method to convert the semi-active suspension control problem into an optimization control method with constraint in a limited time domain;
4) the invention converts the optimization problem with nonlinear constraint into mixed integer quadratic programming containing real-valued variables and binary variables, and solves the problem by means of a branch-and-bound algorithm, thereby effectively solving the rolling time domain optimization problem with nonlinear constraint.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the invention and, together with the description, serve to explain the invention and not to limit the invention. In the drawings:
FIG. 1a is a passive suspension of a model single-wheeled vehicle according to an embodiment of the present invention;
FIG. 1b is a semi-active suspension with adjustable damping shock absorbers for a model single wheel vehicle according to an embodiment of the present invention;
FIG. 2 is a damping characteristic curve obtained from an actual shock absorber characteristic test according to an embodiment of the present invention;
FIG. 3 is a table of main parameters for simulation verification in Matlab \ Simulink environment according to the present embodiment of the present invention;
FIGS. 4-6 show the RMS curves of vibration responses at different speeds under random road conditions;
FIGS. 7-10 illustrate sinusoidal wave raised road surface shapes and corresponding vibration response time history curves;
FIG. 11 is a root mean square table of the system for three different road surface grades in accordance with an embodiment A, B, C of the present invention.
Detailed Description
The invention is described in detail below with reference to the accompanying drawings, taking a model of a semi-active suspension single-wheel vehicle as an example, but it can be implemented in many different ways, as defined and covered by the claims.
The suspension optimization control objective function comprehensively balancing comfort and operation stability at least comprises two parts of contents: a) the vertical vibration acceleration of the vehicle body and the two-norm of the dynamic stroke of the suspension are used as comfort performance indexes; b) the two-norm of the tire dynamic displacement is used as an operation stability performance index.
The invention provides a damping force constraint-based damping adjustable semi-active suspension hybrid model predictive control method, which comprises the following steps of establishing a semi-active suspension hybrid system model, carrying out finite time domain optimization control on a semi-active suspension hybrid system based on a model predictive control theory, converting the finite time domain optimization control into mixed integer quadratic programming containing real-valued variables and binary variables, and realizing hybrid model predictive control on a damping continuously adjustable semi-active suspension, wherein the mixed integer quadratic programming comprises the following real-valued variables and binary variables:
step 1: dynamic modeling of a damping-adjustable semi-active suspension hybrid system;
step 2: optimizing nonlinear constraint of the damping adjustable semi-active suspension;
and step 3: semi-active suspension hybrid model predictive control;
and 4, step 4: and (3) solving the rolling time domain optimization of the semi-active suspension hybrid system.
The dynamic modeling of the damping-adjustable semi-active suspension hybrid system in the step 1 comprises the following steps: a) introducing auxiliary variables, and converting logical and nonlinear constraint conditions into a group of threshold values and logical conditions; b) and establishing a hybrid system model containing system equations and constraint conditions by using a hybrid system description language, and converting the hybrid system model into an MLD model by using a self-contained HYSDEL compiler.
The logical constraint condition refers to a passive constraint condition of the semi-active suspension, namely when the sign of the damping force demand is the same as the relative speed of the suspension, the required control force is equal to the optimal control force.
The nonlinear constraint condition means that the damping force of the shock absorber must meet a certain boundary condition, namely the optimized damping force is located in the force action range of the actual shock absorber.
Step 1: dynamic modeling of a damping-adjustable semi-active suspension single-wheel vehicle:
fig. 1a shows a passive suspension of a model of a single wheel vehicle, and fig. 1b shows a semi-active suspension of a model of a single wheel vehicle equipped with a damper adjustable shock absorber, wherein:
in order to obtain a sprung mass,
is the unsprung mass of the spring,
in order to be the spring rate, the spring,
in order to provide a passive suspension damping coefficient,
in order to be the rigidity of the tire,
、
and
respectively the sprung mass, the unsprung mass and the vertical displacement of the road surface. Suspension stiffness in FIG. 1a
And damping
Is a constant value; the damping force of fig. 1b can be continuously adjustable according to the actual driving road conditions.
For the damping continuously adjustable semi-active suspension, the dynamic equation is
In the formula:
、
the vertical acceleration of the sprung and unsprung masses respectively,
a control damping force is applied to damp the shock absorber.
Selection state variable
Output variable
Control quantity of
Obtaining a system state equation of
Wherein the content of the first and second substances,
step 2: optimizing nonlinear constraint of the damping adjustable semi-active suspension:
comprehensively considering the requirements of the semi-active suspension on the smoothness and the operation stability of the vehicle, defining performance indexes as follows:
wherein the content of the first and second substances,
，
and
weighting coefficients of the vertical vibration acceleration of the vehicle body, the dynamic stroke of the suspension and the dynamic displacement of the tire are selected according to the relative importance of the smoothness and the operation stability;
weighting coefficients are input for control.
According to the formulas (3) and (4), the objective function (5) can be expressed as
Wherein the content of the first and second substances,
，
the required control force of the semi-active suspension is subject to certain constraints, i.e.
(7) The equation gives the passive constraint condition of the semi-active suspension that the required control force is equal to the optimal control force when the damping force demand sign is the same as the relative velocity of the suspension. (8) The formula gives the boundary condition that the damping force of the shock absorber must satisfy, namely the optimized damping force is located in the force action range of the actual shock absorber. Fig. 2 is a damping characteristic curve obtained by a characteristic test of an actual shock absorber, and the characteristic curve of the present embodiment is drawn only under partial current, and the actual current action range of the shock absorber is 0.29-1.6A. The damping force at the maximum current (1.6A) and the minimum current (0.29A) are taken as control quantity boundaries, which are related to the relative speed of the suspension and exhibit strong nonlinearity.
For the purpose of description of optimization, the boundary of the expression (8) is expressed by a set of linear inequalities of the control quantity and the state quantity, that is
Wherein the content of the first and second substances,
，
and
in order to be a coefficient of fit,
。
combining equations (6) - (9), the optimal control of the semi-active suspension can be regarded as hybrid system optimal control under the nonlinear constraint condition.
And step 3: semi-active suspension hybrid model predictive control:
1) dynamic modeling of a semi-active suspension hybrid system:
for the nonlinear constraint problem of the semi-active suspension, in this embodiment, a Mixed Logical Dynamic (MLD) modeling method proposed by Bemporad a is adopted, and a system equation and constraint conditions are described under a unified model framework in consideration of logic, dynamics, constraints and the like of the system.
Introducing binary auxiliary variables
And
the constraint (7) is converted into a set of thresholds and logic conditions, i.e.
In the formula: "
"(equivalent) and"
"(implication) is a logical operator.
Introducing binary auxiliary variables
And
continuous auxiliary variable
、
、
、
、
、
、
、
The constraints (8) (9) are converted into a set of thresholds and logic conditions, i.e.
Establishing a Hybrid System model containing System equations (3) (4) and constraints (10) (11) (12) (13) (14) by using Hybrid System Description Language (HYSDEL), and converting the Hybrid System model into an MLD model by using an own HYSDEL compiler, namely converting the Hybrid System model into the MLD model
Wherein the content of the first and second substances,
，
，
，
，
，
，
。
2) semi-active suspension hybrid system model predictive control
The present embodiment is based on Hybrid Model Predictive Control (HMPC) theory, and researches the finite time domain optimization Control of the semi-active suspension Hybrid system. In the prediction time domain
Keeping the function type and weight function of the objective function (6) unchanged, andkthe predicted input and output of the point are independent variables, the first one is establishedkA discrete objective function of points; then linearly weighting the objective function of each point to obtain the objective function in the prediction time domain, namely
Similarly, the two types of (15) and (16) are maintained, and are extended to the whole prediction time domain
I.e. constraining each predicted point, obtaining the state space equation and constraint condition in the prediction time domain
Wherein the content of the first and second substances,
，
，
，
，
。
in thattAt the moment of time, the time of day,
for the state values of the MLD system, model predictive control of the semi-active suspension hybrid system is regarded as solving the following optimization problem:
wherein the variables are optimized
；
At each sampling instanttSolving the above optimization by rolling time domain, from the optimal control sequence
First element of (1)
As control input for the current time, i.e.
At the next sampling instantt+1, repeating the optimization process. It can be concluded that the optimized variables of the quadratic program (20) include real-valued variables and binary variables, which can be further converted into a Mixed Integer Quadratic Program (MIQP) solution.
The rolling time domain optimization solution of the semi-active suspension hybrid system in the step 4 comprises the following steps: a) loosening the integral constraint limit of part or all of the binary variables in the optimized variables to generate a series of quadratic plans corresponding to the original MIQP; b) and solving the series of quadratic programming to obtain a suboptimal solution or a global optimal solution of the MIQP which meets the integer constraint condition.
And 4, step 4: and (3) solving the rolling time domain optimization of the semi-active suspension hybrid system:
the optimization solution is convenient, and the output vector in the prediction time domain is defined
Controlling the input vector
Weighting matrix
And
namely:
the objective function (17) is written in the form:
meanwhile, equation of state space (18) is written in the form:
wherein the content of the first and second substances,
the formula (22) is substituted into the formula of the objective function (21) to obtain
Order to
The objective function (23) is expressed as
The constraint (19) is written as follows:
wherein
Is further written as
Wherein
，
，
；
To this end, a mixed integer quadratic programming MIQP of the semi-active suspension system is obtained according to an objective function (24) formula and a constraint condition (26) formula, namely
In the formula:
in order to optimize the vector, the vector is optimized,
i.e. by
Both real and binary variables.
For the problem posed by equation (27), Branch-and-bound (Branch) is based on&Bound) algorithm, first relaxing the optimization variables
Medium two-value variable
Generating a series of quadratic plans corresponding to the original MIQP by limiting part or all of the integer constraints; and then solving the series of quadratic programming to obtain a suboptimal solution or a global optimal solution of the MIQP which meets the integer constraint condition, wherein the solving of the quadratic programming is completed by means of a Matlab optimization tool box.
In this embodiment, simulation verification is performed in a Matlab \ Simulink environment, and main parameters are shown in fig. 3.
Two simulation working conditions are set: 1) inputting working conditions on a random road surface; 2) and inputting the working condition of the road surface with the sine wave protrusions. The control effect is compared with a passive suspension and an LQR control semi-active suspension, wherein the LQR control parameter is selected to be the same as the hybrid model predictive control algorithm of the invention, and the optimal control law is
In the formula:
the gain is feedback controlled for LQR.
Derived from LQR control algorithms due to nonlinear constraints of semi-active suspensions
Cannot be completely realized, and additional control laws are required
For the convenience of later simulation comparison, a Passive suspension simulation result is represented by 'Pasive', an LQR algorithm control result is represented by 'LQR', and a hybrid model predictive control algorithm for research is represented by 'HMPC'. Fig. 4 to 10 show simulation effect diagrams of the present embodiment.
Random road input conditions are widely used to test vehicle comfort and handling stability. A, B, C three-level road surfaces are respectively adopted, the vehicle speeds are 40km/h, 60km/h, 80km/h and 100km/h in sequence, and the simulation time is 20 s. Fig. 11 shows the root mean square values of A, B, C systems at three different road surface levels, and it can be seen that the root mean square values of the evaluation indexes (vehicle body vertical acceleration, suspension dynamic stroke and tire dynamic load) of the Passive suspension, the LQR suspension and the HMPC suspension all show an ascending trend from level a to level C, while on the road surface at different levels, the HMPC suspension can effectively reduce the numerical values of the indexes relative to the Passive suspension and the LQR suspension, and improve the comprehensive performance of the suspension. In order to test the vibration response at different vehicle speeds, the root mean square values of the vibration response quantity at different vehicle speeds are shown in fig. 4 to 6, and it can be seen that the root mean square values of various evaluation indexes of the Passive suspension, the LQR suspension and the HMPC suspension, such as the vehicle body vertical acceleration, the suspension dynamic stroke and the tire dynamic load, all show an increasing trend along with the increase of the vehicle speed. Under different vehicle speeds, the LQR suspension and the HMPC suspension can effectively reduce each evaluation index value, and the control effect of the HMPC suspension is better than that of the LQR suspension.
The sine wave protrusion road surface input is used for testing the suspension bounce performance, in order to simulate the suspension performance more clearly, the amplitude and the width of the sine wave protrusion are respectively set to be 0.05m and 6m, the amplitude and the width exceed the actual vehicle wheelbase, the vehicle speed is 30km/h, 60km/h and 90km/h in sequence, and the simulation time is 10 s.
FIGS. 7-10 show sinusoidal wave raised road surface shapes and corresponding vibration response time history curves, wherein the vehicle speed is
And only intercepting the simulation result when 2-5 s is given. As seen from fig. 8, the HMPC suspension can effectively control the vertical acceleration of the vehicle body, and can rapidly attenuate the vibration acceleration of the vehicle body after the wheel passes the sinusoidal bump, as compared to the Passive suspension and the LQR suspension. As can be seen from fig. 9 and 10, the Passive suspension and the LQR semi-active suspension both fluctuate to different degrees at the later stage of the sine wave bulge, while the HMPC suspension can rapidly attenuate the dynamic stroke of the suspension and the dynamic load of the tire at the later stage of the sine wave bulge, and has a better vibration attenuation effect.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (1)
1. A damping force constraint-based damping adjustable semi-active suspension hybrid model prediction control method is characterized by comprising the following steps: establishing a semi-active suspension hybrid system model, performing finite time domain optimization control on the semi-active suspension hybrid system based on a model prediction control theory, converting the finite time domain optimization control into a mixed integer quadratic programming containing a real-valued variable and a binary variable, and realizing hybrid model prediction control on the damping continuously adjustable semi-active suspension, wherein the method comprises the following steps:
step 1: dynamic modeling of a damping-adjustable semi-active suspension single-wheel vehicle:
for the damping continuously adjustable semi-active suspension, the dynamic equation is
In the formula:
、
the vertical acceleration of the sprung and unsprung masses respectively,
a control damping force applied to the damping vibration absorber;
in order to obtain a sprung mass,
is the unsprung mass of the spring,
in order to be the spring rate, the spring,
in order to provide a passive suspension damping coefficient,
in order to be the rigidity of the tire,
and
the vertical displacement of the sprung mass, the unsprung mass and the road surface respectively;
selection state variable
Output variable
Control quantity of
Obtaining a system state equation of
Wherein the content of the first and second substances,
step 2: optimizing nonlinear constraint of the damping adjustable semi-active suspension;
comprehensively considering the requirements of the semi-active suspension on the smoothness and the operation stability of the vehicle, defining performance indexes as follows:
wherein the content of the first and second substances,
，
and
weighting coefficients of the vertical vibration acceleration of the vehicle body, the dynamic stroke of the suspension and the dynamic displacement of the tire are selected according to the relative importance of the smoothness and the operation stability;
inputting a weighting factor for the control;
according to the formulas (3) and (4), the objective function (5) is expressed as
Wherein the content of the first and second substances,
，
the required control force of the semi-active suspension is subject to certain constraints, i.e.
(7) The formula gives a passive constraint condition of the semi-active suspension, and the formula (8) gives a boundary condition which must be met by the damping force of the shock absorber;
the boundary of the expression (8) is expressed by a set of linear inequalities of the control quantity and the state quantity, i.e.
Wherein the content of the first and second substances,
，
and
in order to be a coefficient of fit,
；
combining the formulas (6) to (9), wherein the optimization control of the semi-active suspension is regarded as hybrid system optimization control under the nonlinear constraint condition;
and step 3: semi-active suspension hybrid model predictive control;
dynamic modeling of a semi-active suspension hybrid system:
introducing binary auxiliary variables
And
the constraint (7) is converted into a set of thresholds and logic conditions, i.e.
In the formula: "
"and"
"is a logical operator;
introducing binary auxiliary variables
And
continuous auxiliary variable
The constraints (8) (9) are converted into a set of thresholds and logic conditions, i.e.
Establishing a hybrid system model containing system equations (3) (4) and constraints (10) (11) (12) (13) (14), and converting the hybrid system model into an MLD model by using an own HYSDEL compiler thereof, namely
Wherein the content of the first and second substances,
；
2) semi-active suspension hybrid system model predictive control:
in the prediction time domain
Keeping the function type and weight function of the objective function (6) unchanged, andkthe predicted input and output of the point are independent variables, the first one is establishedkA discrete objective function of points; then linearly weighting the objective function of each point to obtain the objective function in the prediction time domain, namely
The two types of (15) and (16) are maintained and are extended to the whole prediction time domain
I.e. constraining each predicted point, obtaining the state space equation and constraint condition in the prediction time domain
Wherein the content of the first and second substances,
；
in thattAt the moment of time, the time of day,
for the state values of the MLD system, model predictive control of the semi-active suspension hybrid system is regarded as solving the following optimization problem:
wherein the variables are optimized
；
At each sampling time t, the optimization is solved through a rolling time domain, and an optimal control sequence is used
First element of (1)
The optimized variables of the quadratic programming (20) comprise real-valued variables and binary variables and are further converted into mixed integer quadratic programming MIQP solution;
and 4, step 4: and (3) solving the rolling time domain optimization of the semi-active suspension hybrid system:
defining an output vector in the prediction time domain
Controlling the input vector
Weighting matrix
And
namely:
the objective function (17) is written in the form:
meanwhile, equation of state space (18) is written in the form:
wherein the content of the first and second substances,
the formula (22) is substituted into the formula of the objective function (21) to obtain
Order to
The objective function (23) is expressed as
The constraint (19) is written as follows:
wherein:
is further written as
Wherein:
；
to this end, a mixed integer quadratic programming MIQP of the semi-active suspension system is obtained according to an objective function (24) formula and a constraint condition (26) formula, namely
In the formula:
in order to optimize the vector, the vector is optimized,
i.e. by
Both real and binary variables.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710437023.2A CN107102551B (en) | 2017-06-12 | 2017-06-12 | Damping force constraint-based damping adjustable semi-active suspension hybrid model prediction control method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710437023.2A CN107102551B (en) | 2017-06-12 | 2017-06-12 | Damping force constraint-based damping adjustable semi-active suspension hybrid model prediction control method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN107102551A CN107102551A (en) | 2017-08-29 |
| CN107102551B true CN107102551B (en) | 2020-12-11 |
Family
ID=59659238
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710437023.2A Active CN107102551B (en) | 2017-06-12 | 2017-06-12 | Damping force constraint-based damping adjustable semi-active suspension hybrid model prediction control method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107102551B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111186275A (en) * | 2020-02-05 | 2020-05-22 | 哈尔滨工业大学 | Automobile magneto-rheological suspension static output feedback control method for ensuring damping force constraint |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101847270A (en) * | 2010-05-14 | 2010-09-29 | 东南大学 | Virtual prototype-based suspension system control simulation method of four-axle heavy truck |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7251638B2 (en) * | 2004-03-03 | 2007-07-31 | Yamaha Hatsudoki Kabushiki Kaisha | Intelligent robust control system for motorcycle using soft computing optimizer |
| US7739014B2 (en) * | 2006-08-30 | 2010-06-15 | Ford Global Technolgies | Integrated control system for stability control of yaw, roll and lateral motion of a driving vehicle using an integrated sensing system to determine a final linear lateral velocity |
| CN105974821B (en) * | 2016-05-16 | 2019-01-18 | 萨克斯汽车零部件系统（上海）有限公司 | Vehicle Semi-active Suspension mixed control method based on damping multimode formula switching damper |
-
2017
- 2017-06-12 CN CN201710437023.2A patent/CN107102551B/en active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101847270A (en) * | 2010-05-14 | 2010-09-29 | 东南大学 | Virtual prototype-based suspension system control simulation method of four-axle heavy truck |
Non-Patent Citations (2)
| Title |
|---|
| Effectiveness evaluation of hydro-pneumatic and semi-active cab suspension for the improvement of ride comfort of agricultural tractors;Kyuhyun Sim等;《Journal of Terramechanics》;20170228;第69卷;第23-32页 * |
| Survey and performance evaluation on some auto-motive semi-active suspension control methods：a comparative study on a single-corner;Poussot-vassal C;《Annual Reviews in Control》;20121231;第36卷(第1期);第148-160页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN107102551A (en) | 2017-08-29 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Guo et al. | Robust H∞ control of active vehicle suspension under non-stationary running | |
| Zheng et al. | Active full-vehicle suspension control via cloud-aided adaptive backstepping approach | |
| Pan et al. | Adaptive tracking control for active suspension systems with non-ideal actuators | |
| Liao et al. | Semiactive vibration control of train suspension systems via magnetorheological dampers | |
| Ahmed et al. | PID controller of active suspension system for a quarter car model | |
| Nguyen et al. | A Model Predictive Control approach for semi-active suspension control problem of a full car | |
| Nie et al. | Velocity & displacement-dependent damper: A novel passive shock absorber inspired by the semi-active control | |
| Wu et al. | A load-dependent PWA-H∞ controller for semi-active suspensions to exploit the performance of MR dampers | |
| Maurya et al. | Optimal control of vehicle active suspension system | |
| Bouvin et al. | CRONE control of a pneumatic self-leveling suspension system | |
| Muhammed et al. | Linear-control vs. ADRC for automatic management of the handling-comfort contradiction of a quarter-car system | |
| CN107102551B (en) | Damping force constraint-based damping adjustable semi-active suspension hybrid model prediction control method | |
| Lauwerys et al. | Design and experimental validation of a linear robust controller for an active suspension of a quarter car | |
| Chen et al. | Stiffness-damping matching method of an ECAS system based on LQG control | |
| Metered et al. | Vibration control of vehicle active suspension using sliding mode under parameters uncertainty | |
| Lu et al. | A coordinated control system for truck cabin suspension based on model predictive control | |
| Walavalkar et al. | Performance comparison of various controllers on semi-active vehicle suspension system | |
| CN110765554A (en) | Intelligent control method of automobile semi-active suspension system based on TS model | |
| Long | Study on fuzzy control of MR into semi-Active suspension | |
| Allaboyena et al. | Vehicle suspension system uncertainty modeling, control and performance evaluations | |
| Eligar et al. | A survey on passive, active and semiactive automotive suspension systems and analyzing tradeoffs in design of suspension systems | |
| Colina et al. | Modelling and control of a semi-active suspension system | |
| Divekar et al. | Response optimization and LQR based control for Quarter Car suspension with Seat-Driver model | |
| Novikov et al. | VIBRATION-PROTECTIVE PROPERTIES OF THE SUSPENSION WITH SMOOTH REGULATIONOF INELASTIC RESISTANCE IN THE VIBRATION CYCLE | |
| Skoglund | Strategies for Road Profile in Adaptive Suspension Control |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| CB03 | Change of inventor or designer information | ||
| CB03 | Change of inventor or designer information |
Inventor after: Wu Guangqiang Inventor after: Sun Anning Inventor after: Wei Hong Inventor after: Xiong Yunliang Inventor after: Zhang Liangxiu Inventor after: Chen Jie Inventor after: Wang Yu Inventor after: Guo Jiongmin Inventor after: Jin Jie Inventor after: Ju Lijuan Inventor after: Ma Chang Inventor after: Dou Chuanwei Inventor before: Wu Guangqiang Inventor before: Zhang Liangxiu Inventor before: Chen Jie Inventor before: Wang Yu Inventor before: Guo Jiongmin Inventor before: Jin Jie Inventor before: Ju Lijuan |
|
| TA01 | Transfer of patent application right | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20171122 Address after: 200092 Shanghai City, Yangpu District Siping Road No. 1239 Applicant after: Tongji University Applicant after: SAIC VOLKSWAGEN AUTOMOTIVE COMPANY LIMITED Address before: 200092 Shanghai City, Yangpu District Siping Road No. 1239 Applicant before: Tongji University |
|
| GR01 | Patent grant | ||
| GR01 | Patent grant |