JP4291863B2 - Method and apparatus for predicting intake pipe pressure of internal combustion engine - Google Patents

Description

  The present invention relates to an intake pipe pressure prediction method and apparatus for an internal combustion engine. In particular, the present invention relates to an intake pipe pressure prediction method using a fuzzy algorithm and a fuzzy estimator.

  Fuel injection control is performed so as to realize efficient combustion of the internal combustion engine. FIG. 1 shows a configuration of an intake portion of an internal combustion engine related to the present invention. The intake air is supplied to the cylinder via the throttle 1. The throttle valve opening is controlled to a desired value. The intake pipe pressure is measured by the sensor 2. Here, in order to appropriately perform the fuel injection control, it is necessary to estimate the intake air amount in the cylinder, and therefore it is necessary to predict the intake pipe pressure.

  Conventionally, the current intake pipe pressure is predicted based on the operating state of the internal combustion engine, and the intake pipe pressure at a prediction point ahead of a predetermined period is predicted based on the predicted intake pipe pressure and the detected value of the intake pipe pressure. An intake pipe pressure prediction device has been proposed (Japanese Patent Laid-Open No. 2-42160).

  However, in the above-described conventional prediction device, the predicted intake pipe pressure when the intake valve is closed during the transition is uniformly corrected based on the deviation ΔP of the intake pipe pressure regardless of the operating state of the internal combustion engine. A sufficient prediction could not be made over a wide range of engine speeds.

  In Japanese Patent No. 2886771, in order to solve the above-mentioned problems, an intake pipe that performs prediction in consideration of the operating state of the internal combustion engine and can perform high-precision control even in a low rotation range or when the intake pipe pressure is high. A pressure prediction device is disclosed.

  In the above conventional method, based on the difference between the intake pipe pressure values (hereinafter referred to as PB) (hereinafter referred to as ΔPB) and the difference between the throttle valve opening values (hereinafter referred to as ΔTH). A predicted value of PB (hereinafter referred to as HATPB) is calculated, and fuel injection control and retrieval of a fuel adhesion correction parameter are performed based on the HATPB. Here, ΔTH and ΔPB are expressed by the following equations, where k is a control time synchronized with the suction stroke (TDC).

ΔTH (k) = TH (k) + TH (k−1) (1)
ΔPB (k) = PB (k) + PB (k−1) (2)

  On the other hand, recently, in order to prevent water from entering the PB sensor and preventing damage due to freezing of the water that has entered the water, there is water on the intake pipe side or in the PB sensor between the detection part of the PB sensor and the intake pipe. A labyrinth mechanism is provided to prevent entry. For this reason, the delay and dead time of the PB sensor output with respect to the actual pressure value have increased.

Therefore, an attempt was made to compensate for this dead time by a conventional prediction algorithm. However, since delay and dead time to be compensated increased, HATPB overshoots actual PB as shown in FIG. It started to appear or showed discontinuous behavior. The cause of the overshoot is that the conventional prediction algorithm compensates for the insufficient accuracy of the PB prediction value by feeding back the deviation between the PB prediction value a predetermined time ago and the current PB. Moreover, the cause which shows discontinuous behavior is because the conventional prediction algorithm switched and used the predicted value calculated based on each of (DELTA) PB and (DELTA) TH according to predetermined conditions. The conventional prediction algorithm exhibiting such behavior has a problem in that fuel control is affected, fluctuation during transient operation becomes large, and emission of harmful components of exhaust gas increases.
JP-A-2-42160 Japanese Patent No. 2886771

  Therefore, there has been a demand for a new PB prediction algorithm that can compensate for the increased delay and dead time without causing overshoot or discontinuous behavior of HATPB.

  An intake pipe pressure prediction method according to an aspect of the present invention includes a step of obtaining a difference between intake pipe pressure values and a difference between throttle valve opening values, a step of obtaining a predicted difference in intake pipe pressure using a fuzzy estimation algorithm, and an intake pipe pressure Adding a value and a predicted difference value of the intake pipe pressure to obtain a predicted value of the intake pipe pressure. The fuzzy estimation algorithm includes a fuzzy rule determined based on the magnitude of the difference between the intake pipe pressure values and the magnitude of the difference between the throttle valve opening values. In this method, a throttle valve opening value and a throttle valve opening target value are modeled using a dead time element and a delay system, and a value estimated by the model and the target value is used as a throttle valve opening value.

  An intake pipe pressure predicting apparatus according to another aspect of the present invention includes a differencer for obtaining a difference between intake pipe pressure values, a differencer for obtaining a difference between throttle valve opening values, a fuzzy estimator, a throttle valve opening value, The model which modeled the throttle valve opening target value using the dead time element and the delay system is included. The fuzzy estimator receives the difference between the intake pipe pressure values and the difference between the throttle valve opening values, obtains a predicted difference in the intake pipe pressure by a fuzzy estimation algorithm, and outputs the predicted difference as an output. The fuzzy estimation algorithm includes a fuzzy rule determined based on the magnitude of the difference between the intake pipe pressure values and the magnitude of the difference between the throttle valve opening values. In this device, the value estimated by the model and the target value is used as the throttle valve opening value.

  Thus, in the above two aspects of the present invention, the predicted value of the PB sensor output is calculated by the fuzzy estimation algorithm based on the PB sensor output and the TH opening, and the fuel injection of the internal combustion engine is performed based on the predicted value. Determine the fee. In particular, by using a fuzzy rule determined based on the magnitude of ΔPB and the magnitude of ΔTH, it is possible to perform control in which information on the change in TH preceding the change in PB is taken in effectively. As a result, even when the PB sensor output has a large dead time or delay with respect to the actual intake pipe pressure (negative pressure), the predicted value does not become discontinuous as in the conventional method. A continuous prediction value can be calculated with high accuracy. Therefore, the air-fuel ratio of the internal combustion engine does not show discontinuous behavior. Further, the overshoot of the predicted value with respect to the actual intake pipe pressure can be remarkably reduced as compared with the conventional method.

In the two aspects of the present invention, the throttle valve opening value and the throttle valve opening target value are modeled using a dead time element and a delay system, and the value estimated by the model and the target value is throttled. Used as a valve opening value. That is, when the throttle valve opening estimated value and the throttle valve opening target value at time k are THHAT (k) and THCMD (k), the dead time equivalent value is ddly, and the constant is Kdly,
THHAT (k) = Kdly x THHAT (k) + (1-Kdly) x THCMD (k-ddly)
The throttle valve opening estimated value THHAT (k) obtained by the above is used.

  Therefore, a highly accurate predicted value can be calculated even when using an electronically controlled throttle that has an invalid time before TH is read.

  According to one embodiment of the present invention, the magnitude of the difference in the intake pipe pressure value is divided into positive, zero, and negative, and the magnitude of the difference in the throttle valve opening value is divided into positive, zero, and negative, A fuzzy rule is defined for each area determined by two types of classification.

  Therefore, the fuzzy estimation calculation can be performed easily and efficiently without making it complicated.

  According to another embodiment of the present invention, the two-time difference between the intake pipe pressure values is further obtained, and the difference between the intake pipe pressure values, the difference between the throttle valve opening values, and the intake pipe pressure value of 2 are calculated. A fuzzy rule is determined based on the magnitude of the time difference.

  Therefore, it becomes possible to perform control that effectively incorporates information about the future behavior of ΔPB included in the difference of ΔPB (hereinafter referred to as ΔΔPB).

  According to another embodiment of the present invention, the magnitude of the two-time difference in the intake pipe pressure value is divided into positive, zero, and negative, the magnitude of the difference in the intake pipe pressure value, and the difference in the throttle valve opening value. A fuzzy rule is defined for each region determined by a total of three types of size and the size of the difference between the two times of the intake pipe pressure value.

  Therefore, the fuzzy estimation calculation can be performed easily and efficiently without making it complicated.

  According to another embodiment of the invention, the consequent membership function of the fuzzy estimation algorithm is a singleton bar function.

  Therefore, the fuzzy estimation calculation can be performed easily and efficiently without making it complicated. In particular, it is difficult to execute the minimax centroid method of the fuzzy estimation algorithm with the computing power of the processor that can withstand the vehicle usage conditions such as cryogenic temperature, high temperature, high humidity, and vibration, but the consequent membership By making the function a singleton bar-like function, the calculation can be performed, and a prediction having sufficient accuracy can be calculated.

  According to another embodiment of the invention, input filtering is performed.

  Therefore, by setting the input data to the fuzzy estimation algorithm as filtered data, when noise is mixed in the predicted value, it is possible to prevent the predicted value from oscillating due to the influence of the noise.

  According to another embodiment of the invention, the filtering is performed by an adaptive filter.

  Therefore, it is possible to sufficiently remove noise while keeping the phase delay to a minimum so that it can be used as data for prediction calculation.

  An intake pipe pressure prediction method according to another aspect of the present invention includes a step of obtaining a difference between a current intake pipe pressure value, a two-time difference between current intake pipe pressure values, and a difference between current throttle valve opening values, and fuzzy estimation. A step of obtaining a prediction difference of the intake pipe pressure by an algorithm, and a step of obtaining a predicted value of the intake pipe pressure by adding a current intake pipe pressure value and a value of the prediction difference of the intake pipe pressure. The fuzzy estimation algorithm is determined based on the magnitude of the difference between the current intake pipe pressure values, the magnitude of the difference between the current throttle valve opening values, and the magnitude of the two-time difference between the current intake pipe pressure values. Includes fuzzy rules.

  An intake pipe pressure predicting apparatus according to another aspect of the present invention includes a differencer for obtaining a difference between current intake pipe pressure values, a differencer for obtaining a difference between current intake pipe pressure values twice, and a current throttle valve opening degree. A differencer for obtaining a difference between the values, a difference between the current intake pipe pressure value, a difference between the current throttle valve opening value and a second difference between the current intake pipe pressure values and an intake air by a fuzzy estimation algorithm A fuzzy estimator that calculates a prediction difference of the tube pressure and outputs the prediction difference. The fuzzy estimation algorithm is determined based on the magnitude of the difference between the current intake pipe pressure values, the magnitude of the difference between the current throttle valve opening values, and the magnitude of the two-time difference between the current intake pipe pressure values. Includes fuzzy rules.

  According to the above two aspects, the fuzzy rule is determined based on the magnitude of the difference between the intake pipe pressure values, the magnitude of the difference between the throttle valve opening values, and the magnitude of the twice difference between the intake pipe pressure values. It is possible to effectively take in the information on the change (ΔTH) of the TH preceding the change (ΔPB) in the control, and effectively take in the information on the future behavior of ΔPB included in the ΔΔPB.

  An intake pipe pressure prediction method according to another aspect of the present invention includes a step of obtaining a difference between a current intake pipe pressure value and a current throttle valve opening value, and a difference between the intake pipe pressure value and the throttle valve opening degree. A step of filtering the difference between the values, a step of obtaining a prediction difference of the intake pipe pressure by a fuzzy estimation algorithm, and a predicted value of the intake pipe pressure by adding a value of the current intake pipe pressure value and the prediction difference of the intake pipe pressure Determining. The fuzzy estimation algorithm includes a fuzzy rule determined based on the magnitude of the difference between the filtered current intake pipe pressure values and the magnitude of the filtered difference between the current throttle valve opening values.

  An intake pipe pressure predicting device according to another aspect of the present invention includes a first differentiator that obtains a difference between current intake pipe pressure values, a second differentiator that obtains a difference between current throttle valve opening values, A fuzzy estimator that calculates a difference in the intake pipe pressure by a fuzzy estimation algorithm using the difference in the current intake pipe pressure value and the difference in the current throttle valve opening value as an input, and outputs the prediction difference . The fuzzy estimation algorithm includes a fuzzy rule determined based on the magnitude of the difference in the current intake pipe pressure value and the magnitude of the difference in the current throttle valve opening value. Furthermore, the apparatus includes a first filter disposed between the first differencer and the fuzzy estimator, and a second filter disposed between the second differencer and the fuzzy estimator. And a filter.

  According to the above two aspects, it is possible to perform control that effectively incorporates information on the change in TH preceding the change in PB. Further, by making the input data to the fuzzy estimation algorithm into filtered data, when noise is mixed in the predicted value, it is possible to prevent the predicted value from becoming oscillating due to the influence of the noise.

  The present invention obtains a predicted change amount (hereinafter referred to as ΔFZPB) by fuzzy estimation of PB using a fuzzy estimation algorithm including a fuzzy rule determined based on the magnitude of ΔPB and the magnitude of ΔTH. Next, a predicted value by fuzzy estimation (hereinafter referred to as FZPB) is calculated by the following equation.

FZPB (k) = PB (k) + ΔFZPB (k) (3)
That is, FZPB (k) is obtained by adding ΔFZPB (k) to the current PB sample value PB (k). Here, k represents a control time synchronized with the suction stroke (TDC). FIG. 3 shows the relationship between PB, TH, ΔPB, ΔTH and ΔFZPB.

  The fuzzy rules used in this embodiment are shown in the table of FIG. The magnitude of ΔPB is divided into positive, zero, and negative, and the magnitude of ΔTH is divided into positive, zero, and negative, and fuzzy rules are defined for each of nine regions determined by two types of classification. It should be noted here that the change in TH precedes the change in PB and includes information on the future behavior of PB. Therefore, by using a fuzzy rule determined based on the magnitude of ΔPB and the magnitude of ΔTH, it is possible to perform control that effectively incorporates information on the change in TH preceding the change in PB.

  The antecedent part membership function and the consequent part membership function used in the fuzzy estimation algorithm of the present invention are shown in FIGS. 4 and 5, respectively. The antecedent part membership functions for ΔPB and ΔTH were trapezoidal for positive (P) and negative (N) and triangular for zero (Z). As the consequent part membership function, a singleton bar function was used to simplify the fuzzy estimation calculation.

  Next, rules in each area (rules 1 to 9 in FIG. 6) will be described with reference to the drawings.

  FIG. 7 shows a situation in which ΔPB and ΔTH are both negative, where rule 1 is applied. Since ΔPB and ΔTH are both negative, ΔFZPB of the consequent part membership function is also negative.

  FIG. 8 illustrates a situation where rule 2 is applied and ΔPB is negative and ΔTH is zero. Since the preceding ΔTH is zero but ΔPB is negative, ΔFZPB of the consequent part membership function is negative.

  FIG. 9 shows a situation where rule 3 is applied and ΔPB is negative and ΔTH is positive. This situation corresponds to a case where the engine speed rises faster than the increase in the amount of air passing through the throttle due to the increase in TH during engine braking. The consequent part membership function ΔFZPB is assumed to be zero.

  FIG. 10 shows a situation where rule 4 is applied and ΔPB is zero and ΔTH is negative. Since the preceding ΔTH is negative, ΔFZPB of the consequent part membership function is negative.

  FIG. 11 shows a situation in which ΔPB and ΔTH are both zero, where rule 5 is applied. Since both ΔPB and ΔTH are zero, ΔFZPB of the consequent part membership function is also zero.

FIG. 12 shows a situation where rule 6 is applied and ΔPB is zero and ΔTH is positive. Since the preceding ΔTH is positive, ΔFZPB of the consequent part membership function is positive (P ₆ ).

  FIG. 13 shows a situation where rule 7 is applied and ΔPB is positive and ΔTH is negative. This situation corresponds to a case where the engine speed is greatly reduced by an external force when the engine speed is reduced, rather than by a decrease in TH. The consequent part membership function ΔFZPB is assumed to be zero.

FIG. 14 shows a situation where rule 8 is applied and ΔPB is positive and ΔTH is zero. The consequent part membership function ΔFZPB is positive (P ₈ ), but since the preceding ΔTH is zero, the value is smaller than P _{6 of} rule 6.

FIG. 15 shows a situation in which ΔPB and ΔTH are both positive, to which rule 9 is applied. Although ΔFZPB of the consequent part membership function is positive (P ₁₀ ), since ΔPB is already positive, it is set to a value smaller than P _{6 of} rule 6 in which ΔPB is zero.

  The final ΔFZPB is calculated by the minimax centroid method using the above-described membership function and fuzzy rules. The rule 6 will be specifically described below as an example.

  First, minimax selection will be described. Let the current sample values of ΔPB and ΔTH be ΔPB (k) and ΔTH (k). The degree of conformity of these values to rule 6 is obtained. As shown in FIGS. 6 and 12, rule 6 is applied to a region where ΔPB is zero and ΔTH is positive. Therefore, the degree of fitness of ΔPB with respect to the antecedent part membership function (zero) is mΔPB (6) as shown in FIG. Further, the degree of fitness of ΔTH with respect to the antecedent part membership function (positive) is mΔTH (6) as shown in FIG. Here, in the minimax selection, since the degree of conformity of the antecedent part is the smallest degree of conformance in the same rule as the conformity m (i) of the rule, mΔPB (6).

mΔPB (6) <mΔTH (6) (4)
Therefore, m (6) = mΔPB (6) (5)
It becomes.

Further, as shown in FIG. 17, the membership function of the consequent part of rule 6 is a position pP6 and a reference weight (the length of a bar function) wP6. Therefore, the weight of rule 6 in estimating ΔFZPB, The weight w (6) at pP is
w (6) = m (6) × wP6 (6)
It becomes.

  Next, the weight w (i) is similarly obtained for each rule. Since all the weights of the respective rules thus obtained are used for estimation, this is a maximum selection.

Next, ΔFZPB is estimated based on the calculated weights for each rule, that is, fuzzy rules are defuzzified based on the centroid method shown in the following equation.

Therefore, Formula 3 is specifically expressed by the following formula, and ΔFZPB (k) is calculated.

  FIG. 18 shows FZPB estimation results using the above-described fuzzy estimation algorithm. As can be seen from the figure, FZPB reproduces the estimated value that should be estimated roughly, with some overshooting compared to PB, and is greatly estimated compared to the estimation by the conventional control algorithm shown in FIG. The accuracy is high.

  Next, another embodiment of the present invention will be described. In the fuzzy prediction according to the above-described embodiment, some overshoot was observed as shown in FIG. Such an overshoot is particularly likely to occur during sudden acceleration or snapping. Therefore, in order to eliminate such overshoot, the amount of change of ΔPB, that is, the two-time difference of PB (hereinafter referred to as ΔΔPB) is set as the control time in which k is synchronized with the suction stroke (TDC). Defined in

  ΔΔPB (k) = ΔPB (k) −ΔPB (k−1) (9)

  A fuzzy rule is determined based on the magnitude of ΔΔPB in addition to the magnitude of ΔPB and the magnitude of ΔTH. In other words, fuzzy rules were respectively defined for areas determined by three types of divisions, that is, the size of ΔPB, the size of ΔTH, and the size of ΔΔPB. A fuzzy rule according to this embodiment is shown in FIG. While the fuzzy rule in FIG. 6 is two-dimensional, the fuzzy rule in FIG. 20 is three-dimensional. Here, it should be noted that ΔΔPB includes information about the future behavior of ΔPB. Therefore, by using a fuzzy rule based on ΔΔPB, it is possible to perform control that effectively incorporates this information. FIG. 19 shows an antecedent part membership function according to this embodiment.

  In the fuzzy rule of FIG. 20, only the rules 8 and 9 in the fuzzy rule of FIG. 6 are changed when ΔΔPB is positive and negative. The other rules are unchanged regardless of ΔΔPB.

  Next, each of the new rules (rules 10 and 11 in FIG. 12) will be described with reference to the drawings.

FIG. 22 shows a situation where ΔPB, ΔTH, and ΔΔPB are all positive where rule 10 is applied. Since ΔPB, ΔTH, and ΔΔPB are all positive, ΔFZPB of the consequent part membership function is set to the largest positive value (P ₁₀ ).

  FIG. 23 shows a situation where rule 11 is applied, ΔPB is positive, ΔTH is zero, and ΔΔPB is negative. The consequent part membership function ΔFZPB is assumed to be zero.

  FIG. 24 shows a situation where rule 6 is applied in the present embodiment and ΔPB is zero and ΔTH is positive. As described above, when ΔPB, ΔTH, and ΔΔPB are all positive, rule 10 is applied. Therefore, the range to which rule 6 is applied is reduced as compared with the case where ΔΔPB is not used.

  FIG. 25 shows a situation in which ΔPB and ΔTH are both positive, to which rule 9 is applied in the present embodiment. As described above, when ΔPB, ΔTH, and ΔΔPB are all positive, rule 10 is applied. Therefore, the range to which rule 9 is applied is reduced as compared with the case where ΔΔPB is not used.

FIG. 21 shows the consequent part membership function of this embodiment. As described above, P ₁₀ is the largest value. Compared with the consequent membership functions of FIG. 5, the rule 10 is newly provided, the range of the rule 6 and rule 9 is reduced, PP6 (position of P ₆₎ and PP9 (position of P ₉₎ is zero Is approaching.

  FIG. 26 shows the FZPB estimation result using the fuzzy estimation algorithm of this embodiment. As can be seen, the overshoot seen in FIG. 18 is eliminated.

In the present embodiment, when the dead time to be compensated is small, P ₆ and P _{9 that} are close to zero can be set to zero in order to simplify data setting. Further, rule 8 in the case where ΔΔPB is positive in FIG. 20 is a situation that is unlikely to appear in reality, so this rule is deleted or the corresponding consequent part is changed to zero to invalidate the contribution to the prediction. May also be performed.

  Next, an embodiment for performing input filtering will be described. The present invention uses a fuzzy estimation algorithm with ΔPB, ΔTH and ΔΔPB as inputs. Therefore, when noise is mixed into TH or PB, the difference value or the two-time difference value becomes vibrational or spike-like, and the FZPB estimated by fuzzy estimation sometimes becomes vibrational or spike-like.

  Therefore, in this embodiment, as shown in FIG. 27, the input to the fuzzy estimator is not set to ΔPB, ΔTH, and ΔΔPB, and values after filtering these values as shown in FIG. 28 are input. did. In FIG. 27, reference numerals 2711 to 2713 denote differentiators, and 2701 denotes a fuzzy estimator. In FIG. 28, 2811 to 2813 indicate differentiators, 2821 to 2823 indicate filters, and 2801 indicates a fuzzy estimator.

  As the filters for ΔPB, ΔTH, and ΔΔPB, adaptive filters represented by the following equations were used. As a result, as shown in FIG. 29, FZPB is prevented from becoming a vibrational spike due to the influence of noise.

X_f (k) = X_f (k-1) + KP (k) ・ ide (k) (10)
KP (k) = P (k-1) / (1 + P (k-1)) (11)
ide (k) = X_f (k-1)-X (k) (12)
P (k + 1) = (1 / λ ₁ ) [1-λ ₂ · P (k) / (λ ₁ + λ ₂ · P (k))] (13)
Here, X_f represents adaptive filter values of ΔPB, ΔTH, and ΔΔPB, and X represents sample values of ΔPB, ΔTH, and ΔΔPB. λ _{1 and} λ ₂ represent weight parameters.

  Next, an embodiment in which a value obtained in consideration of the dead time from the throttle valve opening target value is used as TH will be described. In recent years, electronic throttles have been increasingly used to improve fuel efficiency because of demands for cooperative control with the transmission and steering stability control. The electronic control throttle is often driven by a driver different from the electronic control unit, and is further connected to the electronic control unit by an in-vehicle network (CAN or the like).

  Therefore, the time delay caused by the communication cycle (10 msec) between the electronic control unit and the driver is between the throttle valve opening command value THCMD calculated by the electronic control unit and the actual valve opening THACT of the electronic control throttle. Occurs. That is, an invalid time occurs before THCMD is reflected in TH. In addition, an invalid time occurs until THACT is reflected in TH observed by the electronic control unit via CAN or the like. FIG. 30 shows the situation when the electronic control throttle is used. In FIG. 30, as described above, THCMD indicates the throttle valve opening command value, THACT indicates the actual valve opening, and TH indicates the valve opening value (observed value).

  For this reason, since the change in TH observed by the electronic control unit is delayed from the change in PB, it is impossible to predict PB using ΔTH as in the above-described fuzzy estimation algorithm.

  Therefore, in the present embodiment, THHAT is estimated from THCMD in consideration of the dead time due to communication and the response delay of the electronic throttle as shown in the following equation, and this THHAT difference ΔTHHAT is used instead of ΔTH. .

THHAT (k) = Kdly x THHAT (k) + (1-Kdly) x THCMD (k-ddly) (14)
ΔTHHAT (k) ＝ THHAT (k) − THHAT (k-1) (15)
Here, ddly is a dead time equivalent value, and Kdly is a constant.

  FIG. 31 shows the configuration of the apparatus according to the present embodiment, and FIG. 32 shows the prediction result. In FIG. 31, 3111 to 3113 denote differentiators, 3121 to 3123 denote adaptive filters, 3131 denotes a module for performing the calculation of Expression 14, and 3101 denotes a fuzzy estimator.

  Next, an embodiment in which a dead time is inserted into TH will be described. Due to recent demands for increasing low-speed torque, the intake manifold volume of the internal combustion engine is often excessive. At this time, a dead time dth occurs in the actual change in the intake pipe pressure with respect to the change in TH. In this case, if the actual intake pipe pressure is estimated using the current TH, the TH change time is too fast with respect to the actual intake pipe pressure change, and therefore the TH information cannot be used for the prediction calculation. . Therefore, as shown in FIG. 33, this problem is solved by inserting the dead time dth into TH used for the prediction calculation. 33, reference numerals 3311 to 3313 denote differentiators, 3341 denotes a dead time module, and 3301 denotes a fuzzy estimator.

  The procedure of the embodiment using the adaptive filter of the present invention will be described with reference to the flowchart of FIG. In step S10, it is checked whether the PB sensor is active. If the PB sensor is not active, the process proceeds to step S80, and the substitute value of PB is substituted for the sample value of PB. Further, in step S90, the sample value of PB, which is an alternative value of PB, is ended as FZPB. If the PB sensor is active, the process proceeds to step S20 to check whether the PB sensor is normal. If not normal, the process proceeds to step S80. If normal, the process proceeds to step S30 to calculate an estimated value THHAT of TH (Formula 14). Note that THHAT is used instead of TH in the case of an electronically controlled throttle. Next, the process proceeds to step S40, and ΔPB, ΔΔPB and ΔTHHAT are calculated (equations 2, 7, and 14). In step S50, the adaptive filter is calculated (Equations 10 to 13). Next, in step S60, ΔFZPB is estimated. In step S70, ΔFZPB is added to the sample value of PB to obtain FZPB, and the process ends.

1 shows a configuration of an intake portion of an internal combustion engine. The behavior of a conventional intake pipe pressure prediction algorithm is shown. The relationship between PB, TH, ΔPB, ΔTH and ΔFZPB is shown. Fig. 5 shows an antecedent part membership function according to an embodiment of the present invention. 6 illustrates a consequent part membership function according to one embodiment of the present invention. 2 illustrates a fuzzy rule according to an embodiment of the present invention. The situation where rule 1 is applied is shown. The situation where rule 2 is applied is shown. The situation where rule 3 is applied is shown. The situation where rule 4 is applied is shown. The situation where rule 5 is applied is shown. The situation where rule 6 is applied is shown. The situation where rule 7 is applied is shown. The situation where rule 8 is applied is shown. The situation where rule 9 is applied is shown. A method for obtaining the conformity of rule 6 from the antecedent part membership function will be described. A method of obtaining the weight of rule 6 from the consequent part membership function is shown. FIG. 6 shows an estimation result of FZPB using a fuzzy estimation algorithm according to an embodiment of the present invention. FIG. Fig. 5 illustrates an antecedent membership function according to another embodiment of the present invention. 6 illustrates a fuzzy rule according to another embodiment of the present invention. 6 illustrates a consequent membership function according to another embodiment of the present invention. The situation where rule 10 is applied is shown. The situation where rule 11 is applied is shown. FIG. 6 shows a situation where rule 6 is applied in another embodiment of the present invention. FIG. The situation where rule 9 is applied in another embodiment of the present invention is shown. Fig. 6 shows the estimation results of FZPB using a fuzzy estimation algorithm according to another embodiment of the present invention. 2 shows a configuration of a fuzzy estimator according to an embodiment of the present invention. 2 shows a configuration of a fuzzy estimator with an adaptive filter according to an embodiment of the present invention. The behavior of FZPB when the adaptive filter is not used and when it is used is shown. Shows the situation when using electronically controlled throttle. A configuration of a fuzzy estimator corresponding to an electronically controlled throttle is shown. The prediction result of the fuzzy estimator corresponding to the electronic control throttle is shown. The prediction result of the fuzzy estimator corresponding to the excessive intake manifold volume is shown. Fig. 4 shows a procedure of an embodiment using the adaptive filter of the present invention.

Explanation of symbols

1 Throttle Valve 2 Intake Pipe Pressure Sensor 2701, 2801, 3101, 3301 Fuzzy Estimator

Claims (18)

Obtaining a difference between a current intake pipe pressure value and a current throttle valve opening value;
Obtaining a predicted difference of the intake pipe pressure by a fuzzy estimation algorithm including a fuzzy rule determined based on the magnitude of the difference in the current intake pipe pressure value and the difference in the current throttle valve opening value; and
Adding a current intake pipe pressure value and a predicted difference value of the intake pipe pressure to obtain a predicted value of the intake pipe pressure,
An intake pipe pressure prediction method in which a throttle valve opening value and a throttle valve opening target value are modeled using a dead time element and a delay system, and a value estimated from the model and the target value is used as a throttle valve opening value.   The magnitude of the difference in the current intake pipe pressure value is divided into positive, zero, and negative, and the magnitude of the difference in the current throttle valve opening value is divided into positive, zero, and negative, each being positive, The intake pipe pressure prediction method according to claim 1, wherein fuzzy rules are respectively defined for nine regions determined depending on whether they are zero or negative.   In the step of obtaining the difference, a difference between the current intake pipe pressure values is further obtained, and in the step of obtaining a predicted difference in intake pipe pressure, the magnitude of the difference in the current intake pipe pressure value, the current throttle valve opening is determined. The intake pipe pressure prediction method according to claim 1, wherein a fuzzy rule is determined based on a magnitude of a difference in degree value and a magnitude of a two-time difference in the current intake pipe pressure value.   In the step of obtaining the difference, a difference between the current intake pipe pressure values is further obtained twice, and in the step of obtaining a predicted difference in the intake pipe pressure, the magnitude of the second difference in the current intake pipe pressure value is positive, zero, The difference between the current intake pipe pressure value, the current throttle valve opening value difference, and the current double difference of the intake pipe pressure value is positive. The intake pipe pressure prediction method according to claim 2, wherein fuzzy rules are respectively defined for 27 regions determined depending on whether they are zero, zero, or negative.   The intake pipe pressure prediction method according to any one of claims 1 to 4, wherein the consequent part membership function of the fuzzy estimation algorithm is a singleton bar function.   The intake pipe pressure prediction method according to any one of claims 1 to 5, further comprising a filtering step between the step of obtaining the difference and the step of obtaining the prediction difference.   The intake pipe pressure prediction method according to claim 6, wherein the filtering is performed by an adaptive filter.   A differencer for obtaining a difference in the current intake pipe pressure value, a differencer for obtaining a difference in the current throttle valve opening value, a difference in the current intake pipe pressure value, and a difference in the current throttle valve opening value As an input, a predicted difference in the intake pipe pressure is obtained by a fuzzy estimation algorithm including a fuzzy rule determined based on the difference in the current intake pipe pressure value and the difference in the current throttle valve opening value. A fuzzy estimator that outputs the prediction difference, and a model in which the throttle valve opening value and the throttle valve opening target value are modeled using a dead time element and a delay system, depending on the model and the target value. An intake pipe pressure predicting device that uses an estimated value as a throttle valve opening value.   In the fuzzy estimator, the difference in intake pipe pressure value is divided into positive, zero, and negative, and the difference in throttle valve opening value is divided into positive, zero, and negative. 9. The intake pipe pressure prediction apparatus according to claim 8, wherein fuzzy rules are respectively defined for nine regions determined depending on whether they are negative or negative.   In the fuzzy estimator, the difference between the current intake pipe pressure values and the difference between the intake pipe pressure values, the throttle valve opening value difference, and the intake pipe pressure value are calculated. The intake pipe pressure prediction apparatus according to claim 8, wherein a fuzzy rule is determined based on the size.   In the fuzzy estimator, the difference between the current intake pipe pressure values is further inputted, the magnitude of the difference between the current intake pipe pressure values is divided into positive, zero, and negative, and the current intake pipe pressure is divided. Depending on whether the magnitude of the difference in the value, the magnitude of the difference in the current throttle valve opening value, and the magnitude of the two-time difference in the current intake pipe pressure value are positive, zero, or negative The intake pipe pressure prediction apparatus according to claim 9, wherein fuzzy rules are respectively defined for 27 fixed regions.   The intake pipe pressure prediction apparatus according to any one of claims 8 to 11, wherein the consequent part membership function of the fuzzy estimation algorithm is a singleton bar function.   The intake pipe pressure prediction apparatus according to any one of claims 8 to 12, further comprising a filter for filtering an input.   The intake pipe pressure prediction apparatus according to claim 13, wherein the filter is an adaptive filter. Obtaining a difference in the current intake pipe pressure value, a difference in the current intake pipe pressure value twice and a difference in the current throttle valve opening value;
A fuzzy rule determined based on the magnitude of the difference between the current intake pipe pressure values, the magnitude of the difference between the current throttle valve opening values, and the magnitude of the second difference between the current intake pipe pressure values Obtaining a prediction difference of the intake pipe pressure by a fuzzy estimation algorithm;
An intake pipe pressure prediction method comprising: adding a current intake pipe pressure value and a predicted difference value of the intake pipe pressure to obtain a predicted value of the intake pipe pressure. Obtaining a difference between a current intake pipe pressure value and a current throttle valve opening value;
Filtering the difference between the intake pipe pressure values and the difference between the throttle valve opening values;
Prediction of intake pipe pressure by a fuzzy estimation algorithm including a fuzzy rule determined based on the magnitude of the difference between the filtered current intake pipe pressure values and the filtered difference between the current throttle valve opening values Obtaining a difference;
An intake pipe pressure prediction method comprising: adding a current intake pipe pressure value and a predicted difference value of the intake pipe pressure to obtain a predicted value of the intake pipe pressure.   A differencer for obtaining a difference between the current intake pipe pressure values, a differencer for obtaining a difference between the current intake pipe pressure values twice, a differencer for obtaining a difference between current throttle valve opening values, and the current intake pipe The difference in the pressure value, the difference in the current throttle valve opening value, and the two-time difference in the current intake pipe pressure value are input, and the magnitude of the difference in the current intake pipe pressure value is determined. A fuzzy estimation algorithm including a fuzzy rule determined based on the magnitude of the difference in the degree value and the magnitude of the two-time difference in the current intake pipe pressure value is used to obtain a predicted difference in the intake pipe pressure, and the predicted difference is output as And a fuzzy estimator.   A first subtractor for obtaining a difference between the current intake pipe pressure values; a second subtractor for obtaining a difference between the current throttle valve opening values; the difference between the present intake pipe pressure values and the current throttle valve; An intake pipe is obtained by a fuzzy estimation algorithm including a fuzzy rule determined based on the difference in the current intake pipe pressure value and the difference in the current throttle valve opening value. A fuzzy estimator that obtains a prediction difference of pressure, and outputs the prediction difference; a first filter disposed between the first differencer and the fuzzy estimator; and the second differencer; And a second filter disposed between the fuzzy estimator and an intake pipe pressure prediction device.

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