CN101691124B - Method for controlling electronic power steering system of vehicle without sensor - Google Patents

Abstract

The invention provides an electronic power steering EPS system of a vehicle without a sensor and a control method thereof. The method comprises the following steps: 1, saving a torque sensor of a steering column, an angle sensor of the steering column and a rotor angle sensor of a booster motor in the prior electronic power steering EPS system for the vehicle; and 2, realizing control of the electronic power steering system by adopting a new control method. The torque and the turn angle of the steering column and a rotor turn angle of the booster motor, which are required for the control of the EPS system is calculated by establishing a state-space mathematical model of the electronic power steering system of the vehicle without the sensor, using current of the booster motor (state component of a system) as measurement input and observing the state components of other systems by an improved Kalman observer algorithm, and thus, the boost torque of the booster motor controlled by a closed loop control system of the EPS without the sensor is established. The method simplifies the structure and manufacturing difficulty of a steering gear box, improves the reliability of the system, reduces complexity of the EPS system and greatly reduces the cost of the EPS system.

Description

A kind of control method of electronic power steering system of vehicle without sensor

Technical field

The present invention relates to automobile electric power-assisted steering controlling method, be specifically related to a kind of control method of electronic power steering system of vehicle without sensor.

Background technology

Automobile electric power-assisted steering EPS system has than traditional hydraulic power-assist steering system remarkable advantages in each side such as fuel efficiency, modularization, road feel adjustability and environment friendly.Present automobile electric power-assisted steering EPS system mainly contains four types, is respectively Steering gear booster type, miniature gears booster type, tooth bar booster type and double pinion booster type.All these four kinds of EPS systems all have three basic elements of character: control unit (ECU), assist motor and be installed in torque sensor and the rotary angle transmitter on the Steering gear.Used assist motor is not having under the situation of brush, also needs motor rotor position sensor to commutate with helper motor.So also existing EPS control policy all depends on these sensors.Because torque sensor and rotary angle transmitter are the special-purpose parts of EPS, not only price is higher, and has the problem of the inconvenience of installing.In addition, existing torque sensor technology all realizes the measurement to moment of torsion through the angle deformation of its torsion bar.In order to increase sensitieness, its torsion bar rigidity is less than Steering gear rigidity, and this has increased its output noise; Also there is the rigidity softening problem to Steering gear in it in addition, has influenced the stability of chaufeur road feel and control.

Summary of the invention

Above-mentioned deficiency to the existence of existing automobile electric power-assisted steering EPS system; The purpose of this invention is to provide a kind of Steering gear torque sensor, Steering gear angular transducer and assist motor rotor angle sensor of not needing; Reduce the complexity of EPS system; Increase system reliability, reduce the control method of the vehicle without sensor EPS system of EPS system cost.

The objective of the invention is to realize like this: a kind of control method of electronic power steering system of vehicle without sensor, this steering swivel system comprises control unit ECU and assist motor; Said control unit ECU is according to the space mathematical model of the electronic power steering system of vehicle without sensor of setting up; Execute card Germania observer algorithm; It is characterized in that; This steering swivel system also comprises the assist motor current sensor, and this assist motor current sensor is imported the state of the system component of assist motor electric current as the measurement of control unit ECU, observe the Steering gear rotational angle theta _c, assist motor rotor angle θ _mWith rack displacement p state of the system component, by formula $T_c=K_c({\mathrm{\θ}}_c-\frac{p}{r_p})$ K _cBe Steering gear stiffness coefficient, r _pBe the miniature gears radius; Obtain Steering gear torque capacity T _c, control the booster torquemoment of this no sensor EPS closed loop control system assist motor thus.

Compare prior art, the present invention has following beneficial effect:

1, passes through the space mathematical model of the no sensor EPS of foundation system; State of the system component with the assist motor electric current serves as to measure input; Adopt Kalman's observer algorithm to observe other unknown state of the system component of system; Draw steering column for vehicles torque capacity in the EPS system, Steering gear corner and rotor corner; Set up the booster torquemoment of no sensor EPS closed loop control system control motor thus, thereby cancelled torque sensor essential in the existing EPS system architecture and Steering gear rotary angle transmitter and assist motor rotor-position sensor, simplified the EPS system architecture; Saved the loaded down with trivial details operation of the installation and the debugging of each sensor in the EPS system; Saved processing to the output noise of each sensor; And each sensor among the former automobile electric booster steering system EPS is a special-purpose member, and price is higher, and the present invention eliminates each sensor the cost that can reduce the EPS system greatly.

2, adopted advanced state space designs model rather than traditional input-output design mock-up, having obtained with the assist motor electric current is output, and has combined the no sensor EPS system state space math modeling of assist motor kinetics equation; Improved the Kalman and observed algorithm can not effectively handle the shortcoming of Non-zero Mean signal, made it be suitable for not having the observation of each state of the system in the sensor EPS system; Used improvement Kalman observes algorithm, can effectively eliminate the influence of vibration random noise, and the estimation of moment of torsion is had accuracy height, characteristics that cumulative errors are little.

3, eliminated because of the influence of sensor fault, strengthened the reliability of EPS system greatly the EPS system; Increase the mechanical rigid of Steering gear in the EPS system, improved the stability of chaufeur road feel and control thus.

4, reduce the complexity of EPS system, be easy to the installation and the debugging of EPS system.

Description of drawings

Fig. 1 is vehicle without sensor electric power steering of the present invention (EPS) system architecture scheme drawing.

Fig. 2 is vehicle without sensor electric power steering of the present invention (EPS) system closed loop control system scheme drawing.

Fig. 3 is applied to improved Kalman's observer recursive algorithm flow process of the present invention.

The specific embodiment

As shown in Figure 1, a kind of control method of electronic power steering system of vehicle without sensor, this steering swivel system comprises control unit (ECU) 1 and assist motor 2; Said control unit (ECU) 1 is according to the space mathematical model of the electronic power steering system of vehicle without sensor of setting up, execute card Germania observer algorithm; This steering swivel system also comprises assist motor current sensor 3, and this assist motor current sensor 3 is imported the state of the system component of assist motor electric current as the measurement of control unit (ECU) 1, observe the Steering gear rotational angle theta _c, the rotor rotational angle theta _mWith other state of the system components such as rack displacement p, by formula $T_c=K_c({\mathrm{\θ}}_c-\frac{p}{r_p})$ (K _cBe Steering gear stiffness coefficient, r _pBe the miniature gears radius) obtain Steering gear torque capacity T _c, set up the booster torquemoment that no sensor EPS closed loop control system is controlled motor thus.

Among the figure, the 4th, Steering gear, the 5th, power-transfer clutch and speed reduction gearing, the 6th, tooth bar, the 7th, miniature gears, the 8th, wheel, the 9th, steering handwheel.

Innovation of the present invention is:

1, Steering gear torque sensor, Steering gear angular transducer and the assist motor rotor angle sensor in the cancellation EPS system.Because each sensor is a special-purpose member, price is higher, and the present invention eliminates each sensor the cost that can reduce the EPS system greatly.

Existing four kinds of EPS systems all have three basic elements of character: control unit (ECU), and assist motor, and be installed in torque sensor and the rotary angle transmitter on the Steering gear.Used assist motor is not having under the situation of brush, also needs motor rotor position sensor to commutate with helper motor.The present invention cancels Steering gear torque sensor and the rotary angle transmitter torque sensor in the EPS system, and motor rotor position sensor, and its no sensor EPS system architecture scheme drawing is as shown in Figure 1.

2, the space mathematical model of the no sensor EPS of foundation system

The kinetic model of EPS is by the quality in the system and rotor inertia and each spring and damping element mutual action and constitute.System is mainly low frequency motion, can ignore the influence of high stiffness elements.Among Fig. 1, no sensor EPS system is made up of three essential parts: steering rack 6 is coupled to the Steering gear 4 of tooth bar through miniature gears 7; Be connected with the assist motor 2 of independent rotation axle, it is connected with Steering gear 4 with speed reduction gearing 5 through power-transfer clutch; Intermediate rod connects tooth bar 6 and tire 8.The inertia of having ignored tire pipe link and drive disk assemblies such as tire quality, tire motion, friction and gear in the model.The elastic constant that has added the tire pipe link in the model.

Respectively to Steering gear, motor shaft and tooth bar, can set up the kinetics equation of following no sensor EPS system by the Lagrange's dynamical equations and the moment of momentum theorem:

$J_c{\stackrel{\·\·}{\mathrm{\θ}}}_c+B_c{\stackrel{\·}{\mathrm{\θ}}}_c+K_c({\mathrm{\θ}}_c-\frac{p}{r_p})=T_d---\left(1\right)$

$J_m{\stackrel{\·\·}{\mathrm{\θ}}}_m+B_m{\stackrel{\·}{\mathrm{\θ}}}_m+K_m({\mathrm{\θ}}_m-\frac{\mathrm{pG}}{r_p})=\mathrm{ki}---\left(2\right)$

$M_r\stackrel{\·\·}{p}+B_r\stackrel{\·}{p}+K_{t}p=\frac{K_c}{r_p}({\mathrm{\θ}}_c-\frac{p}{r_p})+\frac{K_{m}G}{r_p}({\mathrm{\θ}}_m-\frac{\mathrm{pG}}{r_p})---\left(3\right)$

In the formula, J _cBe Steering gear rotor inertia, B _cBe Steering gear damping coefficient, K _cBe Steering gear stiffness coefficient, θ _cBe Steering gear corner, T _dBe steering-wheel torque; J _mBe motor shaft rotor inertia, B _mBe motor shaft damping coefficient, K _mBe motor shaft stiffness coefficient, θ _mBe the rotor corner, G is the speed reduction gearing reduction ratio, and k is the assist motor torque factor, and i is a current of electric; M _rBe tooth bar quality, B _rBe tooth bar damping coefficient, K _tBe the elasticity modulus of tooth bar, p is a rack displacement, r _pBe the miniature gears radius.

The kinetics equation of assist motor is:

$L\stackrel{\·}{i}+\mathrm{Ri}+k{\stackrel{\·}{\mathrm{\θ}}}_m=v---\left(4\right)$

In the formula, L is an assist motor stator winding inductance value, and R is the assist motor stator winding resistance, and v is a motor terminal voltage.

Can set up the state space description of non-torque sensor EPS system linearity by set of equations ((1)-(4)):

$\left\{\begin{array}{c}\stackrel{\·}{x}=\mathrm{Ax}+\mathrm{Bu}\\ y=\mathrm{cx}+n\left(t\right)\end{array}\right.---\left(5\right)$

N in the formula (t) is the random measurement noise, $x={\left[\begin{array}{ccccccc}{\mathrm{\θ}}_c& {\stackrel{\·}{\mathrm{\θ}}}_c& {\mathrm{\θ}}_m& {\stackrel{\·}{\mathrm{\θ}}}_m& p& \stackrel{\·}{p}& i\end{array}\right]}^T$ State for system; Steering handwheel input torque and motor terminal voltage are imported as system, u=[T _dV] ^TThe assist motor electric current is measured by current sensor 3, and as the output of this two single-input single-output system (SISO system).System matrix A, input matrix B, output matrix C is provided by following (6) formula.

$A=\left[\begin{array}{ccccccc}0& 1& 0& 0& 0& 0& 0\\ -\frac{K_c}{J_c}& -\frac{B_c}{J_c}& 0& 0& -\frac{K_c}{J_c{r}_p}& 0& 0\\ 0& 0& 0& 1& 0& 0& 0\\ 0& 0& -\frac{K_m}{J_m}& -\frac{B_m}{J_m}& -\frac{k_{m}G}{J_m{r}_p}& 0& \frac{k}{J_m}\\ 0& 0& 0& 0& 0& 1& 0\\ \frac{K_c}{M_r{r}_p}& 0& \frac{K_{m}G}{M_r{r}_p}& 0& -(\frac{K_t}{M_r}+\frac{K_c}{M_{\mathrm{<figure-callout\; id="2"\; label="r\; r\; p"\; filenames="H2009101911434E00011.png"\; state="\{\{state\}\}">r</figure-callout>}}{r}_p^{}{}_2^{}}+\frac{K_m{G}^2}{M_r{r}_p^2}& -\frac{B_r}{M_r}& 0\\ 0& 0& 0& -\frac{k}{L}& 0& 0& -\frac{B}{L}\end{array}\right],$

$B=\left[\begin{array}{cc}0& 0\\ 0& 0\\ \frac{1}{J_c}& 0\\ 0& 0\\ 0& 0\\ 0& 0\\ 0& \frac{1}{J_c}\end{array}\right],$ C＝[0?0?0?0?0?0?1] (6)

3, be input with the assist motor electric current, adopt improved Kalman's observer algorithm to observe system's unknown state,, set up the closed loop control system of no sensor EPS through calculating the EPS moment of torsion by system's known state;

System state space shown in the formula (6) is described, and (A, B) controlled to fully, (A is C) to observing fully.Can see system as single v input, i is the system of measurement output, and regards the Td input interference of system as.With system outlet i and motor terminal voltage v is input, adopts Kalman's observer to obtain system state estimation amount

by formula

$T_c=K_c({\mathrm{\&theta;}}_c-\frac{p}{r_p})---\left(7\right)$

Can obtain Steering gear torque capacity T _cIt is attached to speed V _SThe power-assisted curve is tabled look-up, and assist motor current deviation value is adopted controller (PID or other controller) control assist motor terminal voltage, can set up as shown in Figure 2 no sensor EPS closed loop control system.

The Kalman's observer that adopts in the system is a kind of online minimum variance recursive algorithm, and it is image data on one side, calculate on one side, realize real-time monitored to state of the system.Its stepping type calculates can be by micro controller system (MCU) or the online completion of digital signal processor (DSP).Also accurate observation system state under zero-mean randol noise and noise.The equation of state of Kalman's observer does

$\stackrel{\&CenterDot;}{\hat{x}}=A\hat{x}+\mathrm{Bu}+k_o(y-C\hat{x})---\left(8\right)$

In the formula, k _oBe the estimator gain matrix.Can be made as at random to Gaussian white noise disturbs disturbing in the EPS system, and adopt its noise covariance to be used for the design of Kalman's observer from the measurement noise of sensor and road surface.To steering handwheel input torque T _d, more than mention its interference as system handled, but when design Kalman observer, can not it be regarded as the zero-mean signal.For this reason, need do further to improve to Kalman's observer.As shown in Figure 2, with the estimated valve T of Kalman's observer _cT as its recursion calculating next time _dEstimation.

Kalman's observer is applied to micro controller system (MCU) or digital signal processor (DSP) before, needs discretization to handle.To coefficient matrices A, B, C adopt following approximate formula

A′＝e ^AT≈I+AT

$B^{\&prime;}={\&Integral;}_0^T{e}^{\mathrm{At}}\mathrm{Bdt}\&ap;\mathrm{BT}$

C′＝C (9)

T is the employing time in the formula, T=t _K+1-t _kIn order to obtain satisfied observation effect, the sampling time is littler than the electric time of EPS system.Kalman's state estimation is divided into two stages, is respectively forecast period and calibration phase.At forecast period, at first calculate that by the k time estimated result

predictor

of estimation is next time provided by following formula

${\tilde{x}}_{k+1}=A^{\&prime;}{\hat{x}}_k+B^{\&prime;}{u}_k---\left(10\right)$

The cooresponding output of this premeasuring

does

${\tilde{y}}_{k+1}=C^{\&prime;}{\tilde{x}}_{k+1}---\left(11\right)$

The equation of state of Kalman's observer of corresponding discretization does

${\hat{x}}_{k+1}=A\&prime;{\hat{x}}_k+B\&prime;u_k+K_{\mathrm{ok}+1}(y_{k+1}-{\tilde{y}}_{k+1})---\left(12\right)$

With (11) formula substitution (12) formula, can get

${\hat{x}}_{k+1}={\tilde{x}}_{k+1}+K_{\mathrm{ok}+1}(y_{k+1}-C\&prime;{\tilde{\text{x}}}_{k+1})---\left(13\right)$

Y in the formula _K+1Be measured value, represent the assist motor current i.The correction of subordinate phase is mainly reflected in (13) formula, promptly utilizes the deviation of actual measurement output and prediction output that predicted state is carried out feedback compensation, to obtain satisfied state estimation.The result of feedback compensation also depends on gain matrix K _Ok+1Effect.K _Ok+1Selection principle be to make

Mean square error matrix minimalization.Usually, utilize covariance matrix P _K+1K derives _Ok+1

The mean square error matrix get the minimum P of being equal to _K+1Get minimumly, make P _K+1To K _Ok+1Derivative be zero, can derive K _Ok+1

Gradient matrix G _K+1And H _K+1, can obtain by following two formulas respectively:

$G_{k+1}=\frac{\mathrm{\&theta;}}{\mathrm{\&theta;x}}(A\&prime;x+B\&prime;u){|}_{x={\tilde{x}}_{k+1}}---\left(14\right)$

$H_{k+1}=\frac{\mathrm{\&theta;}}{\mathrm{\&theta;x}}(C\&prime;x){|}_{x={\tilde{x}}_{k+1}}---\left(15\right)$

Finally, can get Kalman's recursion formula as follows,

${\tilde{x}}_{k+1}=A\&prime;{\hat{x}}_k+B\&prime;u_k---\left(16\right)$

${\hat{P}}_{k+1}=G_{k+1}{\hat{P}}_k{G}_{k+1}^T+Q---\left(17\right)$

$K_{\mathrm{ok}+1}=\frac{{\tilde{P}}_{k+1}{H}_{k+1}^T}{H_{k+1}{\tilde{P}}_{k+1}{H}_{k+1}^T+R}---\left(18\right)$

${\hat{x}}_{k+1}={\tilde{x}}_{k+1}+k_{\mathrm{ok}+1}(y_{k+1}-{\tilde{y}}_{k+1})---\left(19\right)$

${\hat{P}}_{k+1}={\tilde{P}}_{k+1}-K_{\mathrm{ok}+1}{H}_{k+1}{\tilde{P}}_{k+1}---\left(20\right)$

Q and R are covariance matrix.The key of Kalman's state estimation is to confirm gain matrix K _Ok+1, and the designing gain matrix K _Ok+1Key be the selection of initial value of Q, R and P.Usually, Q and R are unknown, can only be according to the qualitative selection of noise random character.

Fig. 3 is the diagram of circuit that utilizes Kalman's observer algorithm recursion estimation Steering gear torque capacity, comprises the steps:

At first initialized card Germania observer algorithm is promptly given initial condition and initial variance assignment;

Measure k+1 assist motor current value y constantly earlier by testing circuit _K+1(promptly surveying value);

Utilized the estimated valve in a last moment

Substitution (16) formula calculates this predictor constantly

Again by Calculate gradient matrix G _K+1And H _K+1, and obtain the two transposed matrix

With

Use the covariance matrix in a moment

Calculate the covariance matrix of current time

Predictor ((17) formula) calculates gain matrix K afterwards _Ok+1((18) formula);

Estimate the state estimation value of current time with (19) formula

Calculate current Steering gear torque capacity T by (7) formula _Ck+1

Calculate out covariance matrix

estimated valve of current time with (20) formula.

This takes turns end, can repeat state of the system and the estimation of Steering gear torque capacity that said process carries out a new round.

Explanation is at last; Above embodiment is only unrestricted in order to technical scheme of the present invention to be described; Although the present invention is specified with reference to preferred embodiment; Those of ordinary skill in the art should be appreciated that the improvement of in aim that does not break away from technical scheme of the present invention and scope, being done, and it all should be encompassed among the claim scope of the present invention.

Claims (1)

1. the control method of an electronic power steering system of vehicle without sensor, said electronic power steering system of vehicle without sensor is not for comprising the automobile electric booster steering system of Steering gear torque sensor, Steering gear angular transducer and assist motor rotor angle sensor; This electronic power steering system of vehicle without sensor comprises control unit and assist motor; Said control unit is according to the space mathematical model of the electronic power steering system of vehicle without sensor of setting up, execute card Germania observer algorithm; It is characterized in that,

Set up the linear state-space of electronic power steering system of vehicle without sensor:

$\left\{\begin{array}{c}\stackrel{\&CenterDot;}{x}=\mathrm{Ax}+\mathrm{Bu}\\ y=\mathrm{cx}+n\left(t\right)\end{array}\right.---\left(5\right)$

N in the formula (t) is the random measurement noise, $x={\left[\begin{array}{ccccccc}{\mathrm{\&theta;}}_c& {\stackrel{\&CenterDot;}{\mathrm{\&theta;}}}_c& {\mathrm{\&theta;}}_m& {\stackrel{\&CenterDot;}{\mathrm{\&theta;}}}_m& p& \stackrel{\&CenterDot;}{p}& i\end{array}\right]}^T$ State for system; Steering handwheel input torque and motor terminal voltage are imported as system, $u={\left[\begin{array}{cc}{T}_d& v\end{array}\right]}^T;$ The assist motor electric current is measured by current sensor, and as the output of this two single-input single-output system (SISO system); System matrix A, input matrix B, output matrix C is provided by following (6) formula:

$A=\left[\begin{array}{ccccccc}0& 1& 0& 0& 0& 0& 0\\ -\frac{K_C}{J_c}& -\frac{B_c}{J_c}& 0& 0& -\frac{K_c}{J_c{r}_p}& 0& 0\\ 0& 0& 0& 1& 0& 0& 0\\ 0& 0& -\frac{K_m}{J_m}& -\frac{B_m}{J_m}& -\frac{k_{m}G}{J_m{r}_p}& 0& \frac{k}{J_m}\\ 0& 0& 0& 0& 0& 1& 0\\ \frac{K_c}{M_r{r}_p}& 0& \frac{K_{m}G}{M_r{r}_p}& 0& -(\frac{K_i}{M_r}+\frac{K_c}{M_r{r}_p^2}+\frac{K_m{G}^2}{M_r{r}_p^2}& -\frac{B_r}{M_r}& 0\\ 0& 0& 0& -\frac{k}{L}& 0& 0& -\frac{B}{L}\end{array}\right],$

$B=\left[\begin{array}{cc}0& 0\\ 0& 0\\ \frac{1}{J_c}& 0\\ 0& 0\\ 0& 0\\ 0& 0\\ 0& \frac{1}{J_c}\end{array}\right],C=\left[\begin{array}{ccccccc}0& 0& 0& 0& 0& 0& 1\end{array}\right]---\left(6\right)$

With the assist motor electric current is input, adopts improved Kalman's observer algorithm to observe system's unknown state by system's known state, sets up the closed loop control system of electronic power steering system of vehicle without sensor;

Electronic power steering system of vehicle without sensor state space description shown in the formula (6), (A, B) controlled to fully, (A is C) to observing fully; Can see system as single v input, i is the system of measurement output, and with T _dThe interference of system is regarded in input as; With system outlet i and motor terminal voltage v is input, adopts Kalman's observer to obtain the system state estimation amount

Observe the Steering gear rotational angle theta by Kalman's observer _c, assist motor rotor angle θ _mWith rack displacement p state of the system component, obtain Steering gear torque capacity T by formula (7) _c

$T_c=K_c({\mathrm{\&theta;}}_c-\frac{p}{r_p})---\left(7\right)$

In the formula (7), K _cBe Steering gear stiffness coefficient, r _pBe the miniature gears radius;

Control the booster torquemoment of the assist motor of this electronic power steering system of vehicle without sensor thus;

Wherein, said Kalman's observer algorithm is a kind of online minimum variance recursive algorithm, on one side its one side image data is calculated, realizes the real-time monitored to state of the system, and execution in step comprises:

At first initialized card Germania observer algorithm is promptly given initial condition and initial variance assignment;

Measure k+1 assist motor measured current value y constantly earlier by the assist motor current sensor _K+1

Utilized the estimated valve in a last moment

Substitution

In calculate the predictor of carving this moment A '=e wherein ^AT≈ I+AT, $B^{\&prime;}={\&Integral;}_0^T{e}^{\mathrm{At}}\mathrm{Bdt}\&ap;\mathrm{BT},$ T=t _K+1-t _k

Again by

Calculate gradient matrix G _K+1And H _K+1, wherein

C '=C, and obtain the two transposed matrix G _K+1 ^TAnd H _K+1 ^T

Use the covariance matrix in a moment Calculate the covariance matrix of current time Predictor calculates gain matrix K afterwards _Ok+1Wherein ${\tilde{P}}_{k+1}=G_{k+1}{\hat{P}}_k{G}_{k+1}^T+Q,$ $K_{\mathrm{Ok}+1}=\frac{{\tilde{P}}_{k+1}{H}_{k+1}^T}{H_{k+1}{\tilde{P}}_{k+1}{H}_{k+1}^T+R};$

Use ${\hat{x}}_{k+1}={\tilde{x}}_{k+1}+K_{\mathrm{Ok}+1}(y_{k+1}-{\tilde{y}}_{k+1})$ Estimate the state estimation value of current time

Wherein

By

Calculate current Steering gear torque capacity T _Ck+1

With

calculate the covariance matrix of the current moment estimate values.

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