CN114590090A - Direct-drive semi-active suspension control system construction method based on self-adaptive LQR (Low-speed response) wheel hub - Google Patents
Direct-drive semi-active suspension control system construction method based on self-adaptive LQR (Low-speed response) wheel hub Download PDFInfo
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Abstract
The invention discloses a construction method of a direct-drive semi-active suspension control system based on a self-adaptive LQR (Low-speed response) wheel hub, which is used for setting 12 typical working conditions of vehicle body vertical acceleration, eccentricity between a stator and a rotor, vehicle body pitch angle acceleration, front wheel suspension moving stroke, rear wheel suspension moving stroke, front wheel tire moving load, rear wheel tire moving load, front wheel optimal suspension force and rear wheel optimal suspension force to obtain 12 corresponding target weight matrixes and control weight matrixes, respectively optimizing the target weight matrixes and the control weight matrixes by utilizing a genetic algorithm to obtain 12 groups of optimal target weight matrixes and optimal visual control weight matrixes, inputting the optimal target weight matrixes and the optimal visual control weight matrixes into an LQR controller for storage, forming a self-adaptive LQR controller, calculating a cost function by the self-adaptive LQR controller and solving the optimal suspension force of the front wheel and the optimal suspension force of the rear wheel; when the vehicle runs to a certain typical working condition, the optimal weight matrix coefficient is selected and the optimal suspension force is solved, so that the problem that the optimal suspension force cannot be obtained under continuous different running working conditions by the traditional suspension control is solved.
Description
Technical Field
The invention belongs to the field of automobiles, and relates to a semi-active suspension system, in particular to a construction method of a hub direct-drive semi-active suspension control system adopting an LQR (Linear quadratic response) controller.
Background
On one hand, the hub motor increases unsprung mass due to introduction, so that the grounding performance and the driving smoothness of a vehicle tire are deteriorated; on the other hand, the introduction of the hub motor enables a plurality of parts to be coupled to form a complex electromechanical system, and when the hub motor works, an electric wheel is excited by a road surface, so that the stator and the rotor of the motor are eccentric, unbalanced electromagnetic force is generated, and the vertical vibration problem and the noise problem of the electric automobile are caused.
The automobile suspension system has great influence on the performance of the whole automobile such as the running smoothness, the operation stability and the like. Currently, suspension systems can be classified into passive suspension, active suspension, and semi-active suspension. The spring stiffness and the damper damping coefficient in the passive suspension can not be changed once being selected during design, and the passive suspension can not adapt to different requirements of automobiles on smoothness and operating stability under different working conditions. The active suspension can meet the requirements of both the running smoothness and the operation stability of the vehicle through a series of complex mechanical and electrified structures, but the active suspension is high in cost, complex in structure and low in vehicle economy. The semi-active suspension uses a passive element (generally a damping element) with a passive but adjustable parameter to replace an active element of an actuating mechanism, can improve riding comfort and operating stability, has performance superior to that of the passive suspension, is close to that of the active suspension in control quality, has a simple structure, low cost and low energy consumption, and is a commonly applied technology at present.
In the field of optimal control, a Linear Quadratic Regulator (LQR) is one of the most representative optimal control strategies because of its simple structure, simple control method, and easy formation of closed-loop optimization. As a full-state feedback controller, the LQR controller evaluates the effectiveness of a solution through a cost function and a gain matrix, thereby ensuring the good performance of the system. The cost function is established according to the expected value or requirement of the system, and the gain matrix is obtained by solving the Algebraic Riccati equation. However, when the existing LQR controller is applied to a vehicle with a semi-active suspension directly driven by a hub motor, adaptive adjustment and control of suspension force cannot be performed according to different driving conditions, so that the control performance is poor.
Genetic algorithms are computational models that model the theory of evolution of darwinian and the theory of genetics of mendelian. It obtains the optimal solution of the solved problem by continuously copying, crossing and varying a group of chromosomes according to the survival principle of the fittest. Different from other optimization algorithms, the genetic algorithm needs to perform decimal-to-binary coding on the initial population, namely forming a chromosome population. And then, according to the designed fitness function and the cross mutation probability, transmitting the individuals in the chromosome population to the next generation in a copying, cross and mutation mode. In the repeated process, the generated new generation chromosome population can better meet the requirement of the fitness function than the previous generation, and the optimal solution in the final generation population is decoded to be the approximate optimal solution of the solved problem.
Disclosure of Invention
The invention aims to provide a construction method of a direct-drive semi-active suspension control system based on a self-adaptive LQR (Low-speed response) wheel hub, which starts from top-level design, always attributes the influence of a damping coefficient on performance to suspension force provided by an adjustable damping shock absorber, takes the optimal suspension performance, the reduction of the pitching angular acceleration of a vehicle body, the reduction of the eccentricity of a motor and the prolongation of the service life of the motor as control targets, adopts a self-adaptive LQR controller to realize high-performance control on the direct-drive semi-active suspension of the wheel hub, and improves the comprehensive performance of the direct-drive suspension system of the wheel hub.
The invention relates to a construction method of a direct-drive semi-active suspension control system based on a self-adaptive LQR (Low-speed response) hub, which adopts the following technical scheme: the method comprises the following steps of establishing a vertical vibration control model of a half-vehicle seven-degree-of-freedom hub direct-drive half-active suspension system to obtain a suspension dynamics differential equation, and further comprising the following steps of:
step (1): simulating road surface excitation on 12 typical working conditions, and setting corresponding 12 vehicle body vertical accelerations
Weight coefficient of m1The eccentricity e between the stator and the rotor has a weight coefficient of m2Vehicle body pitch angle acceleration
Weight coefficient of m3Front wheel suspension dynamic stroke fd1Weight coefficient of m4Rear wheel suspension dynamic stroke fd2Weight coefficient of m5Front wheel tire dynamic load Fd1Weight coefficient of m6Rear wheel tire dynamic load Fd2Weight coefficient of m7Front wheel optimum suspension force Fu1Weight coefficient of p1And rear wheel optimum suspension force Fu2Weight coefficient of p2To obtain the corresponding 12 target weight matrixes Q ═ diag [ m ═ m-1,m2,m3,m4,m5,m6,m7]And 12 control weight matrices R ═ diag [ p ═ p1,p2]Diag is a diagonal matrix, corresponding to a set of target weight matrix Q and control weight matrix R under a typical condition;
step (2): respectively optimizing the 12 groups of target weight matrixes Q and the control weight matrix R by utilizing a genetic algorithm to obtain 12 groups of optimal target weight matrixes QbAnd an optimal eye control weight matrix RbAnd inputting the signal into an LQR controller for storage to form a self-adaptive LQR controller;
and (3): the self-adaptive LQR controller is used for controlling the optimal target weight matrix Q according to the suspension dynamics differential equationbCalculating a cost function J by using each weight coefficient;
and (4): the objective is to obtain the minimum cost function min (J) and the optimal suspension force F of the front wheelu1And rear wheel optimum suspension force Fu2And input into the damping coefficient controller;
and (5): the sensor module, the extended observer module, the self-adaptive LQR controller, the damping coefficient controller and the damping coefficient executing mechanism are sequentially connected in series, and a self-adaptive LQR-hub-based direct-drive semi-active suspension control system is jointly constructed.
The invention adopts the technical scheme and has the remarkable effects that:
1. according to the method, 12 typical working conditions of low speed, medium speed and high speed are operated on A, B, C, D four road surfaces, and the weighting matrix coefficients of the LQR controller under different typical working conditions are subjected to offline optimization and storage through a genetic algorithm. When the vehicle runs to a certain typical working condition, the adaptive LQR controller selects the optimal weight matrix coefficient and solves the optimal suspension force of the system, the optimal suspension force is obtained by solving the optimal damping coefficient, and the problem that the traditional suspension control can not obtain the optimal suspension force under continuous different running working conditions is solved.
2. Aiming at the optimal control problem of the hub direct-drive semi-active suspension, the constructed control system calculates the optimal suspension force of the front wheel and the optimal suspension force of the rear wheel by adopting a self-adaptive LQR controller based on a genetic algorithm, provides the required suspension force through a damping coefficient executing mechanism, namely the optimal damping force of the front wheel provides the required optimal suspension force of the front wheel, and the optimal damping force of the rear wheel provides the required optimal suspension force of the rear wheel, so that the vertical acceleration of a vehicle body, the dynamic stroke of the suspension, the dynamic load of a tire, the pitch angle acceleration of the vehicle body and the eccentricity between a stator and a rotor are reduced, the comprehensive performance of the hub direct-drive suspension system is improved, the riding comfort is improved, and the road friendliness is improved.
Drawings
FIG. 1 is a schematic diagram of a control model of a half-vehicle seven-degree-of-freedom hub direct-drive semi-active suspension system,
FIG. 2 is a block diagram of a schematic configuration of an adaptive LQR controller;
FIG. 3 is a block diagram showing the structure of a control system constructed by the method of the present invention.
Detailed Description
Referring to fig. 1, taking a half wheel hub direct drive suspension model as an example, the vehicle advancing direction is shown by an arrow of the vehicle advancing speed v in fig. 1, the half wheel hub direct drive suspension model adopts a rear wheel drive mode, a wheel hub motor is mounted on a rim of a rear wheel and drives the wheel to rotate, and the electric vehicle has the problem of coupling vertical vibration due to the introduction of the wheel hub motor. The electronic round of during operation receives the road surface excitation, causes motor stator and rotor eccentric, and motor stator and rotor eccentric can produce unbalanced electromagnetic force, and unbalanced electromagnetic force is for aggravating the off-centre on the motor structure, arouses vibration problem and noise problem. The rear wheel is divided into a motor inner stator and an outer rotor by taking a hub motor air gap as an interface, and is divided into a stator iron core mass block, a rim, a rotor iron core mass block and a tire mass block; the front wheel is taken as a driven wheel, the wheel is split into a front wheel tire mass block and a rim mass block, a vertical vibration control model of a half-vehicle seven-degree-of-freedom wheel hub direct-drive half-active suspension system is established, and a suspension dynamic differential equation is obtained as follows:
in the formula, Fu1Optimum suspension force for front wheel，Fu2Optimum suspension force for the rear wheels, Fspr1For front wheel air spring force, Fspr2Is the rear wheel air spring force, c13For front suspension damping, c23For rear suspension damping, zs1For the vertical displacement of the front wheel suspension,
for front wheel suspension vertical velocity, zs2For the vertical displacement of the rear wheel suspension,
for rear wheel suspension vertical speed, z12Is used for the vertical displacement of the front wheel rim,
is the vertical velocity of the front wheel rim, z23In order to displace the stator core vertically,
is the stator core vertical velocity, msIn order to provide a sprung mass,
for vertical acceleration of the vehicle body, IpAs the moment of inertia of the vehicle body,
for acceleration of pitch angle of semi-vehicle, /)1Distance of the front wheel to the centre of mass of the vehicle, l2Distance of rear wheel to vehicle mass center, m11For front wheel tire mass, m12M is the mass of the rim of the front wheel21For rear wheel tire mass, m22For rear wheel hub motor rim and rotor mass block, m23A stator core mass block of the hub motor of the rear wheel,
is the vertical speed of the front wheel tyre,
for front wheel tyre pendantsIn the direction of the acceleration,
is the vertical acceleration of the rim of the front wheel, z21For the vertical displacement of the rear wheel tire,
is the vertical speed of the rear wheel tire,
is the vertical acceleration, z, of the rear wheel tyre22For vertical displacement of the rim and the rotor core,
for the rim and rotor core vertical speeds,
for the vertical acceleration of the rim and rotor core,
is the vertical acceleration, k, of the stator core11To the residual stiffness of the front tire, k12To front tire radial stiffness, k21For the residual stiffness of the rear tire, k22For rear tyre radial stiffness, k23Is the rigidity of the vertical bearing of the in-wheel motor, c12For front tyre radial damping, c22For rear tyre radial damping, z11For front wheel tire vertical displacement, q1 front wheel road excitation, q2 rear wheel road excitation, FezThe electromagnetic force is the vertical unbalance of the hub motor, and e is the eccentricity between the stator and the rotor.
Based on the differential equation of suspension dynamics of equation (1), the following state space equation is established.
Wherein:
representing the first derivative of the control model state quantity x (t), the control model state quantity x (t) is composed of 14 state variables, which are respectively the vertical displacement z of the vehicle bodysFront wheel rim vertical displacement z12Vertical displacement z of front wheel tyre11Stator core vertical displacement z23Rim and rotor core vertical displacement z22Vertical displacement z of rear wheel tire21Half car pitch angle theta and car body vertical speed
Vertical speed of front wheel rim
Vertical velocity of front wheel tire
Stator core vertical velocity
Rim and rotor core vertical velocity
Vertical velocity of rear wheel tire
And half car pitch angle velocity
These 14 state variables, namely:
y (t) is a control output quantity, u (t) is a control variable, ω (t) is a disturbance input, A is a state space system matrix, BuAnd BωIs an input matrix, C is an output matrix, DuAnd DωFor the feedforward matrix, the state space system matrix A, B can be obtained by solving the Jacobian matrix,Input matrix BuAnd BωOutput matrix C, feedforward matrix DuAnd DωThe following were used:
wherein the content of the first and second substances,
represents the first derivative of y (t).
Control variable u (t) is the optimum suspension force F for the front wheelsu1Rear wheel optimum suspension force Fu2。
u(t)＝[Fu1 Fu2] (5)
Other nonlinear inputs of the system are used as disturbance inputs omega (t) of the system, specifically front wheel road excitation q1, rear wheel road excitation q2 and vertical unbalanced electromagnetic force F of the hub motorez：
ω(t)＝[q1 q2 Fez]T (6)
Where T is the matrix transpose.
The filtered white noise is used to simulate random road surface excitations q1, q2, the mathematical model of which can be expressed as:
in the formula (I), the compound is shown in the specification,
represents the vertical speed (m/s) of the road surface, q (t) represents the vertical displacement (m) of the road surface, and q (t) represents the road surface excitations of the front and rear wheels q1, q2 and f0At a lower cut-off frequency (HZ), Gd (n0) is a road surface irregularity coefficient, and G is set from A to H according to road surface gradesd(n0) The corresponding values are also different, the road surface unevenness classification standard is shown in the following table 1, l (t) represents Gaussian white noise, v is the vehicle speed, km/h:
TABLE 1
As can be seen from the formula (7), the road surface excitation is related to the road surface grade and the vehicle speed, therefore, the simulated road surface excitation is divided into 12 typical working conditions of 30km/h of A-grade road surface vehicle speed, 60km/h of A-grade road surface vehicle speed, 90km/h of A-grade road surface vehicle speed, 30km/h of B-grade road surface vehicle speed, 60km/h of B-grade road surface vehicle speed, 30km/h of C-grade road surface vehicle speed, 60km/h of C-grade road surface vehicle speed, 30km/h of D-grade road surface vehicle speed, 60km/h of D-grade road surface vehicle speed and 90km/h of D-grade road surface vehicle speed, and covers low speed, medium speed and high speed under A-grade road surface, B-grade road surface and C-grade road surface, wherein the vehicle speed of 30km/h represents the low-speed working condition, the vehicle speed of 60km/h represents the medium-speed working condition, the vehicle speed of 90km/h represents high speed conditions, which in some degree represent the general operating conditions of the vehicle. Through the simulation of the road surface excitation, 12 different road surface excitations are obtained.
The control output y (t) of the hub direct-drive semi-active suspension system has seven evaluation indexes which are respectively the vertical acceleration of the vehicle body
Eccentricity e between stator and rotor, dynamic load F of front wheel tyred1Rear wheel tire dynamic load Fd2Front wheel suspension dynamic stroke fd1Rear wheel suspension dynamic stroke fd2And vehicle body pitch angle acceleration
Namely:
therefore, the control target of the hub direct-drive semi-active suspension system is descendingLow vertical acceleration of vehicle body
Eccentricity e between stator and rotor, dynamic load F of front wheel tyred1Rear wheel tire dynamic load Fd2Front wheel suspension dynamic stroke fd1Rear wheel suspension dynamic stroke fd2Vehicle body pitch angle acceleration
Aiming at 12 typical working conditions, namely 12 different road surface excitations, respectively setting 12 corresponding vehicle body vertical accelerations
Weight coefficient of m1The eccentricity e between the stator and the rotor has a weight coefficient of m2Vehicle body pitch angle acceleration
Weight coefficient of m3Front wheel suspension dynamic stroke fd1Weight coefficient of m4Rear wheel suspension dynamic stroke fd2Weight coefficient of m5Front wheel tire dynamic load Fd1Weight coefficient of m6Rear wheel tire dynamic load Fd2Weight coefficient of m7Front wheel optimum suspension force Fu1Weight coefficient of p1And rear wheel optimum suspension force Fu2Weight coefficient of p2(ii) a There are 12 different sets of seven weight coefficients m1,m2,m3,m4,m5,m6,m7Respectively obtaining corresponding 12 target weight matrixes Q ═ diag [ m1,m2,m3,m4,m5,m6,m7]Composed of 12 different sets of two weight coefficients p1,p2Respectively obtaining corresponding 12 control weight matrixes R ═ diag [ p ═ p1,p2]Wherein diag refers to a diagonal matrix of mathematical meaning, a square matrix with all 0 elements not on the diagonal; therefore, under a typical working condition, a group of target weight matrixes Q and a control weight matrix R are corresponding to 12 groups of targetsA weight matrix Q and a control weight matrix R.
Respectively optimizing the 12 groups of target weight matrixes Q and the control weight matrix R by using a genetic algorithm to obtain 12 groups of optimal target weight matrixes QbAnd an optimal eye control weight matrix RbThe specific optimization method comprises the following steps:
first, an initial population is generated and encoded to form a chromosome population. Assigning the individuals generated by the population to m in the target weight matrix Q one by one1,m2,m3,m4,m5,m6,m7And controlling p in the weight matrix R1，p2Establishing an initial target weight matrix Q0＝[m1’,m2’,m3’,m4’,m5’,m6’,m7’]Initial control weight matrix R0＝[p1’,p2’]. In the continuous and repeated process, the generated new generation chromosome population needs to meet the requirement of the fitness function better than the previous generation, and the global optimal solution of the fitness function is solved.
Then, the vertical acceleration of the vehicle body is selected
Eccentricity e between stator and rotor, acceleration of pitch angle of vehicle body
Front wheel suspension dynamic travel fd1Rear wheel suspension dynamic stroke fd2Front wheel tire dynamic load Fd1Rear wheel tire dynamic load Fd2The optimization target of the genetic algorithm is formed, because the unit and the magnitude of each performance index are different, normalization processing needs to be carried out on each component of the target function when the fitness function is constructed, and the minimum value of the sum of the ratios of the root mean square values of each evaluation index under the LQR control and the uncontrolled evaluation index is selected as the fitness function:
where RMS is the root mean square value of the mathematical calculation, JwIs a fitness function value; subscript "p" represents no control under the same operating conditions, and no subscript p represents the same operating conditions LQR control. As the suspension performance under the LQR control is selected to be compared with the passive suspension under the uncontrolled condition, the fitness function is selected as the sum of the normalization processing of the suspension performance indexes under the uncontrolled condition and the LQR control under the same working condition.
And finally, the following constraint conditions of the suspension system are required to be met when the global optimal solution of the optimization solving problem is searched, the new generation of chromosome population in the genetic algorithm searching process needs to meet the requirement of the fitness function more than the previous generation, and the constraint conditions are as follows:
in the above formula, the vertical acceleration of the car body
Weight coefficient m1Eccentricity e between stator and rotor, weight coefficient m2Vehicle body pitch angle acceleration
Weight coefficient m3Front wheel suspension dynamic stroke fd1Weight coefficient m4Rear wheel suspension dynamic stroke fd2Weight coefficient m5Front wheel tire dynamic load Fd1Weight coefficient m6Rear wheel tire dynamic load Fd2Weight coefficient m7Constraint range of [1,1e ]10](ii) a Front wheel optimum suspension force Fu1Is given by a weight coefficient p1And rear wheel optimum suspension force Fu2Is given by a weight coefficient p2With a constraint of [1e-5,1]。
If each output parameter meets the constraint, a fitness function value (J) is output through an equation (9)w) Judging whether the termination condition min (J) of the genetic algorithm is reachedw) If not, by genetic manipulation such as selection, crossover, mutation, etc., to produceAnd (4) new population to ensure that the population evolves towards the direction of satisfying the constraint. After continuously iterative optimization through the genetic algorithm, the optimal fitness function min (J) in the global range is obtainedw) So as to obtain the optimal target weight matrix Q under the working conditionbAnd the optimal control weight matrix Rb。
As shown in fig. 2, 12 sets of optimal target weight matrices Q are obtainedbAnd an optimal control weight matrix RbAnd inputting the data into an LQR controller for storage to form the self-adaptive LQR controller.
Adaptive LQR controller suspension dynamics differential equation according to equation (1) and using optimal target weight matrix QbConverting a control target of the hub direct-drive air suspension system into a cost function J with each weight coefficient as follows:
after writing over to the standard quadratic form, the following cost function J is obtained:
the optimization problem of the system is described as follows, and the goal of the adaptive LQR controller is to find the minimum cost function, namely:
min(J) (13)
in the formula, Qc，RcAnd NcWeighting matrix for cross product terms:
Qc＝CTQbC,Nc＝CTQbDu,Rc＝Du TQbDu+Rb。
the adaptive LQR controller solves an algebraic Riccati equation ATP+PA+Qc-PBuRc -1Bu TP is 0, a solution of the semi-positive definite matrix P can be obtained, resulting in the state feedback matrix K Rb -1Bu TP, A is state space system matrix A, BuIs an input matrix. Therefore, u (t) ═ F according to the state feedback matrix K and equation (5)u1 Fu2]Obtaining the optimal control variable u (t) as:
u(t)＝[Fu1 Fu2]＝ -Kx(t) (14)
the minimum LQR cost function J in the formula (13) depends on the selection of a state feedback matrix K, and the optimal target weight matrix Q is used as KbAnd an optimal eye control weight matrix RbDetermining, therefore, a control variable u (t) of the adaptive LQR controller for the half-wheel hub direct drive suspension system [ F [, ]u1 Fu2]Dependent on the optimal target weight matrix QbAnd an optimal eye control weight matrix RbFinally, the adaptive LQR controller outputs the optimal control variable u (t) under the corresponding working condition.
As shown in fig. 2 and 3, a sensor module, an extended observer module, a self-adaptive LQR controller, a damping coefficient controller and a damping coefficient actuator are sequentially connected in series to form a direct-drive semi-active suspension control system based on a self-adaptive LQR hub, an output end of the sensor module is respectively connected with input ends of the extended observer module and the self-adaptive LQR controller, an output end of the extended observer module is respectively connected with input ends of the self-adaptive LQR controller and the damping coefficient controller, an output end of the self-adaptive LQR controller is connected with an input end of the damping coefficient controller, and the self-adaptive LQR controller outputs a front wheel optimal suspension force Fu1And rear wheel optimum suspension force Fu2To the damping coefficient controller.
When the constructed direct-drive semi-active suspension control system based on the self-adaptive LQR wheel hub works, the sensor module consists of seven acceleration sensors which are respectively arranged at the sprung mass of the mass center of the semi-vehicle, the rim of the front wheel, the tire of the front wheel, the stator of the motor of the rear wheel, the rim and the rotor of the motor of the rear wheel and the tire of the rear wheel and are used for measuring the vertical acceleration of the vehicle body
Acceleration of pitch angle of vehicle body
Vertical acceleration of front wheel rim mass block
Vertical acceleration of front wheel tyre mass block
Vertical acceleration of stator core mass block of rear wheel motor
Vertical acceleration of rear wheel rim and rotor core mass block
And vertical acceleration of mass block of rear wheel tire
The sensor module measures the vertical acceleration of the vehicle body
Acceleration of pitch angle of vehicle body
Vertical acceleration of front wheel rim mass block
Vertical acceleration of front wheel tyre mass block
Vertical acceleration of stator core mass block of rear wheel motor
Vertical acceleration of rear wheel rim and rotor core mass block
And vertical acceleration of mass of tire
Transmitting to an extended observer module to vertically accelerate the vehicle body
And vehicle body pitch angle acceleration
And transmitting the data to the adaptive LQR controller. Extend the vertical acceleration of the car body that the input of the observer module is the output of the sensor module
Acceleration of pitch angle of vehicle body
Vertical acceleration of rim mass block of front wheel
Vertical acceleration of front wheel tyre mass block
Vertical acceleration of stator core mass block of rear wheel motor
Vertical acceleration of rear wheel rim and rotor core mass block
And vertical acceleration of mass of tyre
Used for estimating the information required by the system but can not be measured by a common sensor, and the estimated information is the vertical displacement z of the vehicle bodysAnd velocity thereof
Vertical displacement z of front wheel suspensions1And its speedDegree of rotation
Vertical displacement z of front wheel rim12And velocity thereof
Vertical displacement z of front wheel tyre11And velocity thereof
Vehicle body pitch angular velocity
Vertical displacement z of rear wheel suspensions2And speed thereof
Stator core vertical displacement z23And velocity thereof
Rim and rotor core vertical displacement z22And velocity thereof
Vertical displacement z of rear wheel tyre21And velocity thereof
Transmitting the estimated vertical displacement of the vehicle body to a self-adaptive LQR controller
Vertical velocity of front wheel suspension
And vertical velocity of rear wheel suspension
And transmitting the signal to the damping coefficient controller. The system has certain nonlinearity, and the extended Kalman filtering algorithm can better solve the nonlinearity problem, so that the method selectsA system state observer is constructed by an extended Kalman filtering algorithm, and a specific algorithm of an observer module is expanded (from Wenhao research on cooperative control method of vehicle interconnected air suspension system [ D)]Jiangsu university, 2020).
The self-adaptive LQR controller directly calls a group of corresponding optimal target weight matrixes Q stored in the self-adaptive LQR controller according to different typical working conditions of vehicle runningbAnd the optimal control weight matrix RbCalculating the current optimal suspension force F of the front wheel through the formulas (11) to (14)u1And rear wheel optimum suspension force Fu2And output to the damping coefficient controller.
The input of the damping coefficient controller is the optimal suspension force F of the front wheel output by the adaptive LQR controlleru1Rear wheel optimum suspension force Fu2And expanding the vertical speed of the front wheel suspension output by the observer module
Vertical velocity of rear wheel suspension
Vertical speed of front wheel rim
Vertical speed of stator core
Providing the required optimal suspension force using the damping control force, i.e. by the front wheel suspension damping force FD1Providing the required optimal suspension force F for the front wheelsu1By rear wheel suspension damping force FD2Providing the required rear wheel optimum suspension force Fu2The required damping force is provided by a damping coefficient controller. Based on the adjustable damping shock absorber model, in order to ensure that the optimal damping control force is always within the actual output force range of the shock absorber, the solving range needs to be limited when the damping control force is solved. Front wheel suspension damping force F during suspension extension or compressionD1And rear wheel suspension damping force FD2The constraints of (c) are as follows:
in the formula, cminIs the minimum damping coefficient of the damping coefficient controller, cmaxIs the maximum damping coefficient of the damping coefficient controller,
is the rate of change of the dynamic stroke of the front wheel suspension,
is the front wheel suspension dynamic stroke change rate. Rate of change of current wheel suspension stroke
Damping force F of front wheel suspensionD1<0, the damping force direction of the front wheel is downward, and the damping shock absorber is in a stretching state to provide damping force; rate of change of front wheel suspension travel
Damping force F of front wheel suspensionD1>0, indicating the direction of the front wheel damping force is upward, the damped shock absorber is in a compressed state to provide the damping force. Similarly, the rate of change of the dynamic stroke of the rear wheel suspension
Damping force F of rear wheel suspensionD2The relationship (2) of (c).
The damping coefficient controller is internally integrated with a damping coefficient control method, and a front wheel suspension damping force F is adopted in semi-active suspension controlD1Providing the required optimal suspension force F for the front wheelsu1Rear wheel suspension damping force FD2Providing the required rear wheel optimum suspension force Fu2Solving to obtain the optimal damping coefficient c of the front wheel13 *And optimal damping coefficient c of rear wheel23 *：
The damping coefficient controller optimizes the damping coefficient c of the front wheel13 *And optimal damping coefficient c of rear wheel23 *The damping coefficient is input into a damping coefficient executing mechanism, the damping coefficient executing mechanism is composed of an adjustable damping shock absorber, the adjustable damping shock absorber has obvious influence on the operation stability and the running smoothness of the vehicle, the damping coefficient can be changed by the adjustable damping shock absorber according to the performance requirement of the vehicle so as to improve the damping performance of a suspension system under different running working conditions, and the damping coefficient of front and rear wheels is adjusted to the corresponding optimal damping coefficient c of the front wheels by adjusting the opening degree of the damping valve13 *And optimal damping coefficient c of rear wheel23 *。
Claims (10)
1. A construction method of a self-adaptive LQR (Low-resolution quick response) hub direct-drive semi-active suspension control system is used for establishing a vertical vibration control model of a semi-vehicle seven-degree-of-freedom hub direct-drive semi-active suspension system to obtain a suspension dynamics differential equation, and is characterized by comprising the following steps of:
step (1): simulating road surface excitation on 12 typical working conditions, and setting corresponding vehicle body vertical acceleration of 12 typical working conditions
Weight coefficient of m1The eccentricity e between the stator and the rotor has a weight coefficient of m2Vehicle body pitch angle acceleration
Weight coefficient of m3Front wheel suspension dynamic stroke fd1Weight coefficient of m4Rear wheel suspension dynamic stroke fd2Weight coefficient of m5Front wheel tire dynamic load Fd1Weight coefficient of m6Rear wheel tire dynamic load Fd2Weight coefficient of m7Front wheel optimum suspension force Fu1Weight coefficient of p1And rear wheel optimum suspension force Fu2Weight coefficient of p2To obtain the corresponding 12 target weight matrixes Q ═ diag [ m ═ m-1,m2,m3,m4,m5,m6,m7]And 12 control weight matrices R ═ diag [ p ═ p1,p2]Diag is a diagonal matrix, corresponding to a set of target weight matrix Q and control weight matrix R under a typical condition;
step (2): respectively optimizing the 12 groups of target weight matrixes Q and the control weight matrix R by utilizing a genetic algorithm to obtain 12 groups of optimal target weight matrixes QbAnd an optimal eye control weight matrix RbInputting the data into an LQR controller for storage to form a self-adaptive LQR controller;
and (3): the self-adaptive LQR controller is used for controlling the optimal target weight matrix Q according to the suspension dynamics differential equationbCalculating a cost function J by each weight coefficient;
and (4): the objective is to obtain the minimum cost function min (J) and the optimal suspension force F of the front wheelu1And rear wheel optimum suspension force Fu2And input into the damping coefficient controller;
and (5): the sensor module, the extended observer module, the self-adaptive LQR controller, the damping coefficient controller and the damping coefficient executing mechanism are sequentially connected in series, and a self-adaptive LQR-hub-based direct-drive semi-active suspension control system is jointly constructed.
2. The construction method of the direct-drive semi-active suspension control system based on the adaptive LQR wheel hub as claimed in claim 1, characterized by comprising the following steps: in the step (2), when the 12 groups of target weight matrixes Q and the control weight matrix R are optimized, the individuals generated by the population are assigned to m one by one1，m2，m3，m4，m5，m6，m7And p1，p2Selecting the minimum value of the sum of the ratios of the root mean square values of the evaluation indexes under the LQR control and the uncontrolled evaluation index as a fitness function Jw：
RMS is a mathematical calculation of the root mean square value, subscript "p' represents no control under the same working condition, no subscript p represents that LQR control is carried out under the same working condition, and the global optimal solution of the fitness function is solved to obtain the optimal target weight matrix QbAnd an optimal eye control weight matrix Rb。
3. The construction method of the direct-drive semi-active suspension control system based on the adaptive LQR wheel hub as claimed in claim 2, characterized by comprising the following steps: and when solving the global optimal solution of the fitness function, the constraint conditions of the suspension system are required to be met:
4. the construction method of the direct-drive semi-active suspension control system based on the adaptive LQR wheel hub as claimed in claim 1, characterized by comprising the following steps: in step (3), the cost function
Is the vertical acceleration of the vehicle body, e is the eccentricity between the stator and the rotor,
for vehicle body pitch angular acceleration, fd1For the front wheel suspension stroke, fd2For rear wheel suspension stroke, Fd1For the dynamic load of the front wheel tyre, Fd2For rear wheel tyre dynamic loads, Fu1Optimum suspension force F for the front wheelu1，Fu2The suspension forces are optimized for the rear wheels.
5. The construction method of the direct-drive semi-active suspension control system based on the adaptive LQR wheel hub as claimed in claim 1, characterized by comprising the following steps: in step (4), the cost function J is rewritten into a standard quadratic form as:
wherein the model state quantity is controlled
Respectively the vertical displacement z of the car bodysFront wheel rim vertical displacement z12Vertical displacement z of front wheel tyre11Stator core vertical displacement z23Rim and rotor core vertical displacement z22Vertical displacement z of rear wheel tire21Half car pitch angle theta and car body vertical speed
Vertical speed of front wheel rim
Vertical velocity of front wheel tire
Stator core vertical velocity
Rim and rotor core vertical velocity
Vertical velocity of rear wheel tire
And half car pitch angle velocity
Control variable u (t) ═ Fu1 Fu2]；Qc，RcAnd NcWeighting matrices, Q, for cross-product termsc＝CTQbC,Nc＝CTQbDu,Rc＝Du TQbDu+RbSolving equation ATP+PA+Qc-PBuRc -1Bu TObtaining the solution of the semi-positive definite matrix P when P is 0, and obtaining the state feedback matrix K when Rb -1Bu TP, A is a state space system matrix, BuFor the input matrix, a control variable u (t) ═ F is obtained from the state feedback matrix Ku1 Fu2]Solving for the front wheel optimum suspension force F ═ kx (t)u1And rear wheel optimum suspension force Fu2。
6. The construction method of the direct-drive semi-active suspension control system based on the adaptive LQR wheel hub as claimed in claim 1, characterized by comprising the following steps: in the step (5), the output end of the sensor module is respectively connected with the input ends of the extended observer module and the adaptive LQR controller, the output end of the extended observer module is respectively connected with the input ends of the adaptive LQR controller and the damping coefficient controller, the output end of the adaptive LQR controller is connected with the input end of the damping coefficient controller, and the adaptive LQR controller outputs the optimal suspension force F of the front wheelu1And rear wheel optimum suspension force Fu2To the damping coefficient controller.
7. The construction method of the adaptive LQR-based hub direct-drive semi-active suspension control system according to claim 6 is characterized in that: damping coefficient controller equation
Calculating the optimal damping coefficient c of the front wheel13 *And optimal damping coefficient c of rear wheel23 *And input damping coefficient actuators, FD1And FD2Respectively a front wheel suspension damping force and a rear wheel suspension damping force,
for the vertical speed of the front wheel suspension,
is the vertical speed of the front wheel rim,
for the vertical speed of the rear wheel suspension,
is the stator core vertical velocity.
8. The construction method of the direct-drive semi-active suspension control system based on the adaptive LQR wheel hub as claimed in claim 7, characterized in that: the front wheel suspension damping force F is generated during the suspension stretching or compressing processD1And rear wheel suspension damping force FD2Comprises the following steps:
cminis the minimum damping coefficient of the damping coefficient controller, cmaxIs the maximum damping coefficient of the damping coefficient controller,
is the rate of change of the dynamic stroke of the front wheel suspension,
is the front wheel suspension dynamic stroke change rate.
9. The construction method of the direct-drive semi-active suspension control system based on the adaptive LQR wheel hub as claimed in claim 1, characterized by comprising the following steps: the suspension dynamics differential equation is as follows:
Fu1optimum suspension force for the front wheel, Fu2Optimum suspension force for the rear wheels, Fspr1For front wheel air spring force, Fspr2Is the rear wheel air spring force, c13For front suspension damping, c23For rear suspension damping, zs1For the vertical displacement of the front wheel suspension,
for front wheel suspension vertical velocity, zs2For the vertical displacement of the rear wheel suspension,
for rear wheel suspension vertical speed, z12Is used for the vertical displacement of the front wheel rim,
is the vertical speed of the front wheel rim, z23In order for the stator core to be displaced vertically,
is the stator core vertical velocity, msIn order to obtain a sprung mass,
for vertical acceleration of the vehicle body, IpAs the moment of inertia of the vehicle body,
for acceleration of pitch angle of semi-vehicle, /)1Distance of the front wheel to the centre of mass of the vehicle, l2Distance of rear wheel to vehicle mass center, m11For front wheel tire mass, m12M is the mass of the rim of the front wheel21For rear wheel tire mass, m22For rear wheel hub motor rim and rotor mass block, m23A stator core mass block of the hub motor of the rear wheel,
is the vertical speed of the front wheel tyre,
is the vertical acceleration of the front wheel tyre,
vertical acceleration of the front wheel rim, z21For vertical displacement of the rear wheel tire,
is the vertical speed of the rear wheel tire,
is the vertical acceleration, z, of the rear wheel tyre22For vertical displacement of the rim and the rotor core,
for the rim and rotor core vertical speeds,
for the vertical acceleration of the rim and rotor core,
is the vertical acceleration, k, of the stator core11To the residual stiffness of the front tire, k12Radial stiffness of the front tire, k21For the residual stiffness of the rear tire, k22For rear tyre radial stiffness, k23Is the rigidity of the vertical bearing of the in-wheel motor, c12For front tyre radial damping, c22For rear tyre radial damping, z11For front wheel tire vertical displacement, q1 front wheel road excitation, q2 rear wheel road excitation, FezThe electromagnetic force is vertical unbalance of the hub motor, and e is the eccentricity between the stator and the rotor.
10. The construction method of the direct-drive semi-active suspension control system based on the adaptive LQR wheel hub as claimed in claim 1, characterized by comprising the following steps: the 12 typical working conditions are as follows: the speed of the A-level road is 30km/h, the speed of the A-level road is 60km/h, the speed of the A-level road is 90km/h, the speed of the B-level road is 30km/h, the speed of the B-level road is 60km/h, the speed of the B-level road is 90km/h, the speed of the C-level road is 30km/h, the speed of the C-level road is 60km/h, the speed of the D-level road is 30km/h, the speed of the D-level road is 60km/h and the speed of the D-level road is 90 km/h.
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