JP2008197848A - Fuzzy controller, lane travel support device and steering auxiliary device - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To reasonably select a fuzzy rule in order to solve the problem that a lot of labor and time are spent to select the fuzzy rule through experience and trial-and-error in a conventional fuzzy controller. <P>SOLUTION: This fuzzy controller perform fuzzy inference to a plurality input variables to generate output variable, and outputs the generated output variable to a control target for control. The fuzzy rule used for the fuzzy inference is selected based on a relational expression showing correspondence relation between the output variable and at least one temporal differentiation of the plurality of input variables, and a conditional expression dV/dt<0 showing a stability condition of a Lyapunov function related to the plurality of variables. Thereby, the number of membership functions corresponding to each the input variable is reduced to two to reduce the number of the fuzzy rules to 2<SP>n</SP>(n is the number of the input variables). <P>COPYRIGHT: (C)2008,JPO&INPIT

Description

  The present invention relates to a fuzzy control device that generates an output variable by performing fuzzy inference on a plurality of input variables, and outputs and controls the generated output variable to a control target.

As a conventional example of the fuzzy control device, there is a fuzzy controller described in Patent Document 1. The fuzzy controller normalizes the input signal to form a membership function, applies a fuzzy rule to the formed membership function, obtains a membership function for the output signal, and adds the membership function to the obtained membership function. The normalized output signal is generated by performing the average value formation based on this. By using the fuzzy controller, it is possible to perform the closed-loop control as optimally as possible to the amount that affects the running dynamics and running state of the vehicle.
JP-A-6-206559 (published July 26, 1994) JP 2006-236238 A (published September 7, 2006) Japanese Patent Laid-Open No. 5-201345 (published on August 10, 1993) JP 2006-315634 A (published on November 24, 2006)

  The fuzzy control device has an advantage of a simple configuration and an advantage of excellent robustness as compared with a control device using another control method. However, in the conventional fuzzy control device, selection of the fuzzy rule is performed based on experience and trial and error, resulting in a great amount of labor and time.

  The present invention has been made in view of the above-described problems, and an object thereof is to provide a fuzzy control device using a rational fuzzy rule.

  As a result of intensive studies to solve the above-mentioned problems, the present inventors have found that a fuzzy rule can be rationally selected by using the Lyapunov stability condition.

  That is, a fuzzy control device according to the present invention is a fuzzy control device that performs fuzzy inference on a plurality of input variables to generate output variables, outputs the generated output variables to a control target, and controls them. The fuzzy rule used for inference is based on a conditional expression indicating the stability condition of the Lyapunov function V related to the plurality of input variables, and a relational expression indicating a correspondence relationship between at least one of the plurality of input variables and the output variable. It is characterized by being selected.

According to the above configuration, the output variable can be incorporated into the conditional expression of the Lyapunov function V related to a plurality of input variables using the above relational expression. The conditional expression of the Lyapunov function V is V = 0 when all the input variables are 0, V> 0 otherwise, and dV / dt = V ′ regardless of the value of the input variables. <0. Therefore, by referring to the conditional expression in which the output variable is incorporated, the positive and negative values of the output variable and the magnitude thereof can be defined depending on whether each input variable is positive or negative. Can be selected. At this time, the number of selected fuzzy rules can be suppressed to 2 ⁿ or less, ^where n is the number of input variables.

  Therefore, according to the present invention, from the stability condition of the Lyapunov function V, a conditional expression to be satisfied by the plurality of input variables and the output variable is derived, and the fuzzy rule is selected with reference to the conditional expression. Reasonable fuzzy rules can be selected. As a result, the labor and time for selecting a fuzzy rule can be greatly reduced.

  In addition, since the selected fuzzy rule is based on the case where each input variable is positive and the case where it is negative, the membership function corresponding to each input variable corresponds to the case where it is positive, Only two are needed, corresponding to the negative case. For example, Patent Document 3 introduces seven membership functions for each of two input variables. Therefore, the membership function can be set more easily than before.

  In the fuzzy control device according to the present invention, it is preferable that the plurality of input variables include a certain input variable and time integration and time differentiation of the input variable. In this case, it is possible to control following the variation of the controlled object well.

  By the way, generally, when the input variable is increased, it is necessary to increase the number of fuzzy rules exponentially. However, in the present invention, even if the input variable is increased by one, the number of fuzzy rules is doubled. Therefore, it is not necessary to spend more labor and time for selecting the fuzzy rules than before.

  In the fuzzy control device according to the present invention, the membership function corresponding to the input variable and the output variable is preferably one or both of a Gaussian function and a sigmoid function. In this case, the control target can be controlled in accordance with the actual situation.

  Conventionally, a triangular shape has been used as a membership function in order to simplify selection of fuzzy rules. In contrast, in the present invention, as described above, the labor and time for selecting a fuzzy rule can be greatly reduced. Therefore, even if a Gaussian function and / or a sigmoid function is used as a membership function, the labor and time are reduced. There is no need to spend more than before.

  Note that the fuzzy control device of the present invention can be applied to control on an arbitrary control target, but is particularly preferably applied to a device that supports traveling of a vehicle.

  For example, the vehicle includes a steering control unit that controls steering of the vehicle and a traveling lane detection unit that detects a traveling lane on the traveling road surface of the vehicle, and the vehicle travels along the traveling lane detected by the traveling lane detecting unit. By controlling the steering control means so as to, a vehicle lane travel assistance device for assisting the travel of the vehicle along the travel lane, the vehicle position detection means for detecting the position of the vehicle, A distance calculation unit that calculates a distance between the travel lane detected by the travel lane detection unit and the vehicle position detected by the vehicle position detection unit, and a steering adjustment amount is generated based on the distance calculated by the distance calculation unit. The steering adjustment in the lane travel support apparatus, comprising: a steering adjustment amount generation unit; and a steering adjustment unit that adjusts steering by the steering control unit based on the steering adjustment amount. Relative to the amount producing means include applying the fuzzy control apparatus of the above configuration.

  In addition, the vehicle steering assist device assists the driver in steering the vehicle, the steering control means for controlling the steering of the vehicle, and information on the target trajectory that is the target trajectory of the vehicle. A target trajectory calculating means for calculating using a scientific model; a vehicle position detecting means for detecting the position of the vehicle; a target trajectory calculated by the target trajectory calculating means; and a vehicle position detected by the vehicle position detecting means; A distance calculation means for calculating the distance, a steering adjustment amount generation means for generating a steering adjustment amount based on the distance calculated by the distance calculation means, and a steering by the steering control means based on the steering adjustment amount. For example, the fuzzy control device having the above configuration may be applied to the steering adjustment amount generation unit in the steering assist device including the steering adjustment unit.

  As described above, in the fuzzy control device according to the present invention, the fuzzy rules used for fuzzy inference include the conditional expression dV / dt <0 indicating the stability condition of the Lyapunov function V related to a plurality of input variables, and the plurality of inputs. Since the selection is made based on the relational expression indicating the correspondence between at least one time derivative of the variable and the output variable, the fuzzy rule can be rationally selected.

[Embodiment 1]
An embodiment of the present invention will be described with reference to FIGS. FIG. 2 shows a schematic configuration of the control system of the present embodiment. As illustrated, the control system 10 is a feedback control system including a setting device 11, a coupling point 12, a fuzzy control device 13, a control object 14, and a detection device 15. Note that the present invention can also be applied to a feedforward control system.

  The setting device 11 outputs the target value w to the connection point 12. The coupling point 12 obtains a deviation e between the target value w from the setting device 11 and the actual value y detected by the detection device 15 and outputs it to the fuzzy control device 13. The fuzzy control device 13 performs various determinations and calculations based on the deviation e, obtains the operation amount u, outputs the obtained operation amount u to the control object 14, and controls the control object 14. The detection device 15 detects a control amount indicating the result of the control, obtains an actual value y based on the detected control amount, and outputs the actual value y to the coupling point 12.

  In the control system 10 of FIG. 2, the components other than the fuzzy control device 13 are the same as the components included in the conventional feedback control system.

  FIG. 3 shows a schematic configuration of the fuzzy control device 13. As illustrated, the fuzzy control device 13 includes an integrator 20, a differentiator 21, a fuzzy unit 22, a fuzzy inference unit 23, and a non-fuzzy unit 24.

  The integrator 20 performs time integration of the input deviation e and outputs it to the fuzzification unit 22. The differentiator 21 performs time differentiation of the input deviation e and outputs the result to the fuzzification unit 22.

  The fuzzification unit 22 obtains a membership function corresponding to the input deviation e, the time integral ∫e of the deviation e, and the time derivative e ′ of the deviation e. Hereinafter, the time integral ∫e of the deviation e and the time derivative e ′ of the deviation e will be simply referred to as a time integral ∫e and a time derivative e ′, respectively. The fuzzy inference unit 23 applies a fuzzy rule to the membership function to obtain a membership function for the manipulated variable u. The defuzzification unit 24 generates the operation amount u by a known method such as a centroid method based on the membership function for the operation amount u.

  FIG. 1 shows the fuzzy rules used in the fuzzy inference unit 23 in a tick type. In the present embodiment, the fuzzy rule is derived from the Lyapunov stability condition. Hereinafter, a method for deriving a fuzzy rule will be described.

  As described above, since the variables input to the fuzzification unit 22 are the deviation e, the time integration ∫e, and the time differentiation e ′, the Lyapunov function V (e) is selected as follows: In the following, the one with one dot on the deviation e is the same as the time differential e ′ of the first floor, and the one with two dots on the deviation e is the second floor. It is the same as the time derivative e ″. The same applies to other variables.

The Lyapunov stability condition is a condition for the stability of the Lyapunov function V (e), and includes the following conditions.
[First condition]: V (0) = 0
[Second condition]: V (e)> 0 (where e ≠ 0)
[Third condition]: V ′ (e) <0
In the present embodiment, the third condition is used among these conditions. From the above equation (1), the third condition is expressed by the following equation.

  Next, based on the relationship between the deviation e and the manipulated variable u, the manipulated variable u is substituted into the above equation (2). For example, when the second-order time differential e ″ and the manipulated variable u are in a proportional relationship as in the embodiments described later, the second-order time differential e ″ and the manipulated variable u can be handled as being substantially equal in fuzzy inference. Therefore, as shown in the following equation, the second-order time differential e ″ can be replaced with the manipulated variable u.

  Referring to the above equation (3), it can be understood how much the manipulated variable u should be based on the sign of the deviation e, the time integral ∫e, and the time derivative e ′. For example, when the deviation e, the time integral ∫e, and the time derivative e ′ are positive, the first term and the second term on the left side of the inequality sign in the above equation (3) are positive. To always satisfy, the third term needs to be negative and large. At this time, since the time differential e ′ is positive, the manipulated variable u needs to be negative and large.

  Further, when the deviation e and the time integral ∫e are positive and the time derivative e ′ is negative, the first term on the left side of the inequality sign in the above equation (3) is positive and the second term is negative. In order to always satisfy the above equation (3), the third term needs to be negative. At this time, since the time differential e ′ is negative, the manipulated variable u needs to be positive. Further, when the deviation e is positive and the time integral ∫e and the time derivative e ′ are negative, the first term and the second term on the left side of the inequality sign in the above equation (3) are negative. In order to always satisfy 3), the third term should be almost zero. Therefore, the operation amount u may be almost zero.

  Therefore, the fuzzy set of the deviation e, the time integral ∫e, the time differential e ′, and the manipulated variable u in the above equation (3) is expressed by the following equation. Here, μ is a membership function, and the subscripts u, p, i, d of μ indicate that they correspond to the manipulated variable u, the deviation e, the time integration 、 e, and the time derivative e ′, respectively. The subscripts + and − of μ indicate positive and negative membership functions, respectively.

If the fuzzy set of the above equation (4) is used, the fuzzy rule shown in FIG. 1 can be created from the above equation (3). As illustrated, when fuzzy rules are created based on Lyapunov stability conditions, the number of fuzzy rules can be set to the number of positive and negative combinations of input variables (2 ³ = 8 in this embodiment).

  As described above, from the conditional expression (2) of the stability condition of the Lyapunov function V regarding the deviation e, the time integral ∫e, and the time derivative e ′, the deviation e, the time integral ∫e, the time derivative e ′, and the manipulated variable u Since the conditional expression (3) to be satisfied is derived and the fuzzy rule is selected with reference to the conditional expression (3), a reasonable fuzzy rule can be selected. As a result, the labor and time for selecting a fuzzy rule can be greatly reduced.

  In addition, since the number of membership functions corresponding to each of the deviation e, the time integral ∫e, and the time derivative e ′ can be reduced to two and the number of fuzzy rules can be reduced to eight, Fuzzy rules can be selected more easily than before.

  Further, since not only the deviation e and its time derivative e ′ but also the time integral ∫e of the deviation e is input to the fuzzification unit 22, it is possible to follow the lamp input to the controlled object. Instead of the time differential e ′ of the deviation e and the time integration ∫e, state quantities from other sensors may be input to the fuzzification unit 22.

  In this embodiment, a Gaussian function is used as the membership function. In this case, compared with the triangular function that has been frequently used as the membership function, fuzzy control that is more realistic can be performed. The triangular function may be used as a membership function, or a sigmoid function may be used as a membership function.

  Specifically, the membership function of the above equation (4) is as follows.

Here, c _{1 to} c ₆ and w _{1 to} w ₃ are parameters adjusted by design. Among these, the approximate values of the parameters c _{1 to} c ₃ can be quickly determined according to the performance and specifications of the controlled object 14. Therefore, the designer only has to adjust the balance of the parameters c _{4 to} c ₆ and adjust the parameters w _{1 to} w ₃ , which is the same level as the design of the PID control device.

  Further, defuzzification by the centroid method in the defuzzification unit 24 is performed by the following equation.

[Embodiment 2]
Next, another embodiment of the present invention will be described with reference to FIGS. In the present embodiment, the control system 10 shown in FIGS. 1 to 3 is applied to a vehicle lane travel support system.

  The lane travel support system assists the vehicle traveling along the travel lane. FIG. 4 schematically shows that the vehicle travels so that the center of gravity of the vehicle follows the travel lane. In the figure, the traveling lane is indicated by a one-dot chain line, and the locus of the center of gravity CG of the vehicle 100 is indicated by a broken line as the vehicle locus. In the illustrated example, since the center of gravity CG of the vehicle 100 is located on the right side of the travel lane, the travel direction of the vehicle 100 (direction of the speed vector v) may be steered to the left.

  FIG. 5 shows a schematic configuration of the lane travel support system of the present embodiment. A lane travel support system (lane travel support device) 110 is mounted on the vehicle 100, and as shown in the figure, an in-vehicle camera 111, a lane recognition calculation device (travel lane detection means, vehicle position detection means, distance calculation means). ) 112, GPS (Global Positioning System) device (travel lane detection means, vehicle position detection means, distance calculation means) 113, fuzzy controller (fuzzy control device, steering adjustment amount generation means) 114, steering angle sensor 115, coupling point ( The feedback control system includes a steering adjustment unit 116 and a steering actuator (steering control unit) 117.

  The in-vehicle camera 111 captures the situation outside the vehicle 100 and outputs information of the captured image (camera image) to the lane recognition calculation device 112. The lane recognition calculation device 112 recognizes a travel lane based on the captured image, and the GPS device 113 detects a position representing the vehicle 100 such as the center of gravity of the vehicle 100. Using one or both of the lane recognition calculation device 112 and the GPS device 113, a position error e indicating an error in the position of the vehicle 100 with respect to the position of the traveling lane is obtained and output to the fuzzy controller 114.

  The fuzzy controller 114 performs fuzzy inference based on the position error e, obtains the wheel steering angle δ as an output variable, and outputs the obtained steering angle δ to the coupling point 116. In the present embodiment, the fuzzy controller 114 has the same configuration as the fuzzy control device 13 shown in FIGS.

  The steering angle sensor 115 detects the steering angle of the steering wheel and outputs the detected steering angle to the coupling point 116. The coupling point 116 adds the steering angle δ from the fuzzy controller 114 to the steering angle of the steering wheel from the steering angle sensor 115, and outputs the added steering angle to the steering actuator 117. The steering actuator 117 controls the steering of the vehicle 100 so that the steering angle of the wheel becomes the steering angle from the coupling point 116.

  In the present embodiment, the position error e is acquired by using the image captured by the camera and the position of the vehicle by GPS. However, various known methods such as using a lane marker can be used. However, recently, car navigation systems using GPS have become widespread in commercial vehicles, and recently, driving support systems using captured images from cameras have begun to be installed in commercial vehicles. It is most desirable at this stage to acquire the position error e using GPS.

Further, in the fuzzy controller 114 of the present embodiment, if the parameters w _{1 to} w ₃ of the above equation (5) are intentionally increased, the steering assistance is hardly performed in the vicinity of the traveling lane, and powerful steering is performed as the traveling lane is deviated. Support can be provided. That is, it is possible to prevent an adverse effect on the operation that makes the driver feel better.

  Further, the steering angle δ from the fuzzy controller 114 may be output to the steering actuator 117 as it is. However, as in the above-described embodiment, when the steering angle δ from the fuzzy controller 114 is added to the steering angle of the steering wheel at the coupling point 116 and output to the steering actuator 117 for any reason, Even if it is shut off, it is desirable in that it is possible to avoid being unable to steer.

[Example 1]
Next, simulation results of the tracking performance with respect to the traveling lane using the lane traveling support system 110 of the present embodiment will be described with reference to FIGS. In addition, the following performance with respect to the traveling lane of the lane traveling support device described in Patent Document 2 is cited as a reference example.

  First, the following table shows the parameters used below and their meanings. FIG. 6 shows a vehicle model used in the simulation of this embodiment. This vehicle model is a 2DOF (degree of freedom) vehicle model.

  In addition, as a mathematical model of a tire, there are a Magic formula type, a Fiala type, and the like. However, in the simulation of this embodiment, since a road surface condition is assumed to be changed, a Finala type ring derived from a tire configuration is used. A beam model was used. Further, in the simulation of the present embodiment, a cross wind model in which wind was applied to the vehicle from the side was used as a disturbance model for the vehicle.

  From the above, the vehicle dynamics model is as follows.

  Next, parameters set in the simulation of this embodiment will be described. The following table shows the membership function parameters and their set values in the fuzzy controller 114.

  The following table shows the parameters of the vehicle model and their set values. This vehicle model is assumed to be a general automobile, and is a four-wheel drive automobile whose center of gravity is made up of the front wheels.

  FIGS. 7 to 9 show the simulation results of the tracking performance for the traveling lane of the vehicle using the above model and set values. FIG. 7 shows the movement locus of the vehicle relative to the target locus (travel lane).

Considering that the curve of a general driving lane is a clothoid curve, the radius of curvature of the driving lane should be considered to be constantly changing, and it is necessary to cope with such a change. Therefore, in the simulation of this example, the target trajectory R _r is set to R _r = 15 + 10 × cos (φ / 2) (−4π <φ ≦ 4π). Therefore, the target locus has a maximum turning radius of 25 m and a minimum turning radius of 5 m. The vehicle 100 turned counterclockwise as viewed in the drawing, and the target speed of the vehicle was 7.5 m / s.

  In FIG. 7, the solid line indicates the movement locus of the present embodiment, and the broken line indicates the movement locus of the reference example. Referring to the figure, it can be understood that the vehicle 100 follows the target locus satisfactorily by the lane travel support system 110 of the present embodiment.

  FIG. 8 shows temporal changes in the tracking error (position error) e and the steering angle δ of the vehicle 100. In the figure, the solid line shows the time change of the present embodiment, and the broken line shows the time change of the reference example. When the time t is 0 to 30 seconds, the road surface condition is fine, and when the time t is 30 to 60 seconds, the road surface condition is rainy. Further, the tracking error e is positive on the left side (inside the target locus) and negative on the right side (outside the target locus) in the traveling direction of the vehicle.

  Referring to FIG. 8, in the present embodiment, the vehicle 100 follows the target locus with a slight follow-up error (within 10 cm outside) even though only the position error e is used as the state quantity. I understand that. In addition, it can be understood that even if the road surface condition changes, it follows well.

  On the other hand, it can be understood that the tracking error is larger in the reference example than in the embodiment. Further, in the reference example, it can be understood that the tracking error greatly fluctuates when the vehicle 100 passes through the position where the time t is around 20 seconds, that is, the target locus has the minimum turning radius. In the reference example, when the set value of the parameter of the vehicle model is changed, the vehicle 100 often spins when passing through the position.

  FIG. 9 shows temporal changes in the tracking error e and the steering angle δ of the vehicle 100 in the present embodiment. In the figure, when the time t is 15 to 25 seconds and 35 to 45 seconds, a cross wind of 15 m / s is given to the vehicle 100. 8 and 9, it can be understood that the tracking error e and the steering angle δ hardly change even when the vehicle 100 receives a crosswind. Therefore, it can be understood that the lane travel support system 110 of this embodiment is excellent in robustness. In the reference example, the vehicle 100 is spun when the time t is around 20 seconds and cannot be measured.

  Although not shown in the figure, a similar simulation was performed with the fuzzy controller 114 in which the integrator 20 shown in FIG. 3 was omitted. In this case, the vehicle 100 was able to follow the target locus satisfactorily, but the tracking error e was shifted to the negative side. That is, the vehicle 100 travels substantially outside the target locus.

  Next, the present embodiment and the conventional example will be considered. Patent Document 2 discloses a vehicle lane travel support device that appropriately detects the tilt state of the travel road surface of the vehicle and reliably supports the lane travel of the vehicle without being influenced by the tilt state. The lane travel support device detects the inclination state of the vehicle traveling road surface, sets a compensation steering amount (steering torque or steering angle) according to the inclination state, and corrects the steering control based on the compensation steering amount.

  In the lane travel support device of Patent Document 2, the position of the travel lane, which is a state quantity of the vehicle in the tilted state, the fluctuation speed of the position, the yaw angle, using the camera image, the vehicle speed, the steering angle, the yaw rate, and the lateral acceleration. , And yaw rate are detected. In order to acquire the camera image, the vehicle speed, the steering angle, the yaw rate, and the lateral acceleration, it is necessary to provide the camera, the vehicle speed sensor, the steering angle sensor, the yaw rate sensor, and the lateral acceleration sensor on the vehicle body, respectively.

  As described above, in the conventional lane travel support device, it is necessary to grasp a large number of state quantities of the vehicle. For this reason, it is necessary to provide a large number of sensors on the vehicle. In addition, the behavior of the vehicle also changes in a complicated manner depending on state quantities such as a side slip of the vehicle, load fluctuations, aerodynamics, a lane bank, and road surface conditions. For this reason, in order to reliably support the lane travel of the vehicle, it is necessary to grasp the state quantity as well, but it is necessary to provide more sensors in the vehicle. In addition, it is difficult to measure state quantities such as a side slip of a vehicle and a road surface condition.

  Therefore, in order to realize vehicle lane travel support, it is conceivable to use fuzzy control that is excellent in robustness and effective in realizing nonlinear control. Although fuzzy control is a practical control method used in many vehicle control systems, an example applied to a lane travel support device is not known. This is considered to be due to the fact that the selection of the conventional fuzzy rule is performed by experience and trial and error, and therefore requires a great deal of labor and time.

For example, Patent Document 3 discloses a rear wheel steering angle control device as a driving assistance device using fuzzy control. In the rear wheel rudder angle control device, a simple and easily adjustable triangular shape is used as a membership function. Seven membership functions (negative large, negative negative, negative negative, zero, positive small, positive medium, and positive large) are available for the vehicle speed and front wheel rudder angle. On the other hand, three membership functions are prepared, and two membership functions are prepared for the steering speed. For this reason, in this document, 7 ² × 3 × 2 = 294 fuzzy rules are selected.

Therefore, when the fuzzy control described in Patent Document 3 is applied to the lane travel support device described in Patent Document 2, since four state quantities are used, seven membership functions are assigned to each state quantity. When prepared, it is necessary to select 7 ⁴ = 2041 fuzzy rules. It is virtually impossible for an engineer to select such a huge number of fuzzy rules.

On the other hand, in the fuzzy controller 114 of the present embodiment, there are three input variables used for fuzzy inference: the position error e and its time integration and time differentiation. However, in the present embodiment, since the fuzzy rule is selected using the Lyapunov stability condition as described above, it is only necessary to prepare two membership functions for each input variable, and 2 ³ = 8 fuzzy rules. It only has to be selected. Therefore, an engineer can easily prepare a membership function and easily select a fuzzy rule. Further, since the membership function can be easily prepared, the parameters of the membership function can be easily adjusted even if a Gaussian function and / or a sigmoid function is used as the membership function.

[Embodiment 3]
Next, another embodiment of the present invention will be described with reference to FIGS. 10 and 11. In the present embodiment, the control system 10 shown in FIGS. 1 to 3 is applied to a vehicle steering assist system.

  In general, the behavior of a vehicle changes depending on vehicle characteristics such as a driving method and an environment such as a road surface state, such as turning the vehicle oversteering or understeering. Therefore, in the conventional steering assistance, power steering is performed to stabilize the behavior of the vehicle by changing the steering torque by controlling the auxiliary motor and / or the variable gear mechanism on the basis of the state quantities such as the vehicle speed and the steering speed. It was. Recently, a vehicle steering apparatus has been proposed in which vehicle steering characteristics are determined from vehicle speed, yaw rate, and the like, and a corrected rotation angle of a steered wheel is derived according to the determined characteristics. Yes.

  However, as described above, since it is difficult to completely grasp the state of the vehicle, the vehicle steering device described in Patent Document 4 determines whether oversteering or understeering. Only. Therefore, a method for evaluating the degree of oversteer and understeer and a specific method for assisting steering based on the evaluation are not yet known.

  Accordingly, the inventors of the present application can calculate an ideal vehicle trajectory from the dynamic model of the vehicle 100, and use this as a target trajectory to evaluate the degree of oversteer and understeer from the error between the target trajectory and the actual vehicle trajectory. I found. Then, based on the error between the target locus and the vehicle locus, a steering support system has been devised to make the vehicle locus closer to the target locus by correcting the steering angle of the steering wheel by the driver's steering operation.

  FIG. 10 schematically shows that the vehicle travels so that the vehicle locus that is the locus of the center of gravity CG of the vehicle 100 follows the target locus. In the figure, the target locus is indicated by a broken line, and the vehicle locus is indicated by a solid line. In the example shown in the figure, the vehicle trajectory is located on the right side of the target trajectory, and therefore the traveling direction of the vehicle 100 (direction of the speed vector v) may be steered to the left.

  FIG. 11 shows a schematic configuration of the steering assist system of the present embodiment. The steering assist system (steering assist device) 120 according to the present embodiment includes a target position calculator (target trajectory calculator) 121 and a coupling point (distance calculator) 122 as compared to the lane travel assist system 110 shown in FIG. The difference is that either one or both of the lane recognition calculation device 112 and the GPS device 113 outputs the position information of the vehicle 100 to the coupling point 122, and the other configurations are the same. In addition, the same code | symbol is attached | subjected to the structure which has the function similar to the structure demonstrated in the said embodiment, and the description is abbreviate | omitted.

  The target position calculation device 121 uses the dynamic model of the vehicle 100 as shown in the above equation (7) to determine the target position that is the position on the target locus from the steering angle of the steering wheel from the steering angle sensor 115. The calculated target position information is output to the connection point 122. The coupling point 122 obtains an error e between the target position from the target position calculation device 121 and the position of the vehicle 100 from the lane recognition calculation device 112 and / or the GPS device 113 and outputs it to the fuzzy controller 114.

  The steering assist system 120 of the present embodiment has the following operational effects as well as the operational effects described in the above embodiment. That is, the steering assist system 120 of the present embodiment corrects the steering angle of the steering wheel and does not affect the steering torque, and therefore can be used in combination with other power steering devices.

  By the way, when the cornering forces of the front and rear wheels are equal, neutral steer is obtained, and an ideal traveling locus (target locus) is obtained. Therefore, the steering support system 120 of the present embodiment provides steering support so that the vehicle 100 is always neutral steer. However, the driver can enjoy driving by changing the balance according to the traveling state or the judgment of the driver and changing the balance to understeer or oversteer.

Although not practically used at this stage, if a sensor for detecting the vehicle body side slip angle β can be provided, the error e is e = β _m −β (where β _m is the target vehicle body side slip angle. ).

[Example 2]
Next, a simulation result of steering assistance using the steering assistance system 120 of this embodiment will be described with reference to FIGS. 12 and 13. The parameters set in the simulation of this embodiment are the same as those in the embodiment shown in FIGS. FIG. 12 shows the movement locus of the vehicle 100 with respect to the target locus.

Considering that the general turning of the vehicle 100 is a clothoid curve, it is considered that the radius of curvature of the turning of the vehicle 100 is constantly changing, and it is necessary to cope with such a change. Therefore, in the simulation of this embodiment, the target steering angle δ _r is set to δ _r = 0.115 × (1 + cos (1.115 × t)). The vehicle 100 turned counterclockwise as viewed in the drawing, and the target speed of the vehicle was 7.5 m / s.

  Referring to FIG. 12, it can be understood that the vehicle 100 follows the target locus satisfactorily by the steering assist system 120 of the present embodiment. FIG. 13 shows temporal changes in the tracking error e and the steering angle δ of the vehicle 100 in this embodiment. In the figure, when the time t is 15 to 25 seconds and 35 to 45 seconds, a cross wind of 15 m / s is given to the vehicle 100.

  Referring to FIG. 13, in this embodiment, the vehicle 100 follows the target locus with a very small error (in millimeters) even though only the tracking error e is used as the state quantity. Understandable. Further, since the steering angle δ from the fuzzy controller 114 is appropriately changed according to the turning state of the vehicle 100, it can be understood that the steering support is appropriately performed. It can also be understood that the tracking error e and the steering angle δ hardly change even when the vehicle 100 receives a crosswind. Therefore, it can be understood that the steering assist system 120 of this embodiment is excellent in robustness.

  The present invention is not limited to the above-described embodiments, and various modifications are possible within the scope shown in the claims, and embodiments obtained by appropriately combining technical means disclosed in different embodiments. Is also included in the technical scope of the present invention.

For example, in the above-described embodiment, fuzzy inference is used to obtain the steering angle δ from the position error e to control the steering, thereby performing tracking to the driving lane or steering assistance. by controlling the driving and braking mechanism seeking error e _v from the driving and braking force F _x, it may be performed following or steering assistance to the traveling lane.

Figure 14 is a fuzzy speed controller 130 for controlling to lane running support system 110, the driving and braking mechanism of the vehicle 100 seeking driving and braking force F _x from the velocity error e _v a (not shown) shown in FIG. 5 The structure which added is shown. 15 shows a configuration in which the fuzzy speed controller 130 having the above configuration is added to the steering assist system 120 shown in FIG.

As shown in FIGS. 14 and 15, the fuzzy speed controller 130 can be provided independently of the fuzzy controller 114, so that each controller 114, 130 can be designed separately. As a result, the design efficiency of the controllers 114 and 130 can be improved. In the case of FIG. 15, it is desirable that the target position calculation device 121 calculates the target position based not only on the target steering angle δ _r but also on the target speed v _r .

  Finally, each block of the control system 10, the lane driving support system 110, and the steering support system 120, particularly the fuzzy control device 13 and the fuzzy controller 114, may be configured by hardware logic, or the CPU may be configured as follows. And may be realized by software.

  That is, the control system 10, the lane driving support system 110, and the steering support system 120 include a CPU (central processing unit) that executes a command of a control program that realizes each function, a ROM (read only memory) that stores the program, A RAM (random access memory) for expanding the program, a storage device (recording medium) such as a memory for storing the program and various data, and the like are provided. The object of the present invention is the program code (execution format program, intermediate code program, source program) of the control program for the control system 10, the lane travel support system 110, and the steering support system 120, which is software that implements the functions described above. Is supplied to the control system 10, the lane driving support system 110, and the steering support system 120, and the computer (or CPU or MPU) is recorded on the recording medium. This can also be achieved by reading out and executing.

  Examples of the recording medium include tapes such as magnetic tapes and cassette tapes, magnetic disks such as floppy (registered trademark) disks / hard disks, and disks including optical disks such as CD-ROM / MO / MD / DVD / CD-R. Card system such as IC card, IC card (including memory card) / optical card, or semiconductor memory system such as mask ROM / EPROM / EEPROM / flash ROM.

  Further, the control system 10, the lane traveling support system 110, and the steering support system 120 may be configured to be connectable to a communication network, and the program code may be supplied via the communication network. The communication network is not particularly limited. For example, the Internet, intranet, extranet, LAN, ISDN, VAN, CATV communication network, virtual private network, telephone line network, mobile communication network, satellite communication. A net or the like is available. Also, the transmission medium constituting the communication network is not particularly limited. For example, even in the case of wired such as IEEE 1394, USB, power line carrier, cable TV line, telephone line, ADSL line, etc., infrared rays such as IrDA and remote control, Bluetooth ( (Registered trademark), 802.11 wireless, HDR, mobile phone network, satellite line, terrestrial digital network, and the like can also be used. The present invention can also be realized in the form of a computer data signal embedded in a carrier wave in which the program code is embodied by electronic transmission.

  The fuzzy control device according to the present invention can be applied to any control device that obtains and controls an output variable based on a plurality of input variables, in addition to the vehicle travel support.

It is a figure which shows the fuzzy rule utilized in the control system which is one Embodiment of this invention. It is a block diagram which shows schematic structure of the said control system. It is a block diagram which shows schematic structure of the fuzzy control apparatus in the said control system. It is a figure which shows driving | running | working a vehicle so that the gravity center of a vehicle may follow a driving | running lane. It is a block diagram which shows schematic structure of the lane driving assistance system which is another embodiment of this invention. It is a figure which shows a vehicle model. It is a graph which shows the movement locus | trajectory of the vehicle with respect to the driving | running lane by the said lane driving assistance system. It is a graph which shows the time change of the position error of a vehicle and the steering angle by the said lane driving assistance system. It is a graph which shows the time change of the position error of a vehicle and the steering angle by the said lane driving assistance system. It is a figure which shows driving | running | working a vehicle so that a vehicle locus may follow a target locus | trajectory. It is a block diagram which shows schematic structure of the steering assistance system which is other embodiment of this invention. It is a graph which shows the movement locus | trajectory of the vehicle with respect to the target locus | trajectory by the said steering assistance system. It is a graph which shows the time change of the following error of a vehicle by the above-mentioned steering support system, and a steering angle. It is a block diagram which shows the structure which added the fuzzy speed controller to the said lane driving assistance system. It is a block diagram which shows the structure which added the fuzzy speed controller to the said steering assistance system.

Explanation of symbols

DESCRIPTION OF SYMBOLS 10 Control system 11 Setting apparatus 12 Connection point 13 Fuzzy control apparatus 14 Control object 15 Detection apparatus 20 Integrator 21 Differentiator 22 Fuzzification unit 23 Fuzzy inference unit 24 Non-fuzzification unit 100 Vehicle 110 Lane driving support system (lane driving support apparatus )
111 vehicle-mounted camera 112 lane recognition calculation device (running lane detection means, vehicle position detection means, distance calculation means)
113 GPS device (vehicle position detection means, vehicle position detection means, distance calculation means)
114 Fuzzy controller (fuzzy control device, steering adjustment amount generating means)
115 Steering angle sensor 116 Connection point (steering adjusting means)
117 Steering actuator (steering control means)
120 Steering support system (steering assist device)
121 Target position calculation device (target locus calculation means)
122 Connection point (distance calculation means)
130 Fuzzy Speed Controller V Lyapunov Function

Claims (6)

A fuzzy control device that generates an output variable by performing fuzzy inference on a plurality of input variables, outputs the generated output variable to a control target, and controls the output variable,
The fuzzy rules used for the fuzzy inference include a conditional expression indicating a stability condition of the Lyapunov function V related to the plurality of input variables, a relational expression indicating a correspondence relationship between at least one of the plurality of input variables and the output variable. The fuzzy control device is selected based on the above. A fuzzy control device that generates an output variable by performing fuzzy inference on a plurality of input variables, outputs the generated output variable to a control target, and controls the output variable,
There are two membership functions corresponding to each input variable, and the number of fuzzy rules is 2 ⁿ (where n is the number of input variables).   3. The fuzzy control device according to claim 1, wherein the plurality of input variables include a certain input variable and time integration and time differentiation of the input variable.   3. The fuzzy control device according to claim 1, wherein the membership function corresponding to the input variable and the output variable is one or both of a Gaussian function and a sigmoid function. Steering control means for controlling the steering of the vehicle and traveling lane detection means for detecting a traveling lane on the traveling road surface of the vehicle are provided, so that the vehicle travels along the traveling lane detected by the traveling lane detecting means. A vehicle lane travel support device for supporting travel along the travel lane of the vehicle by controlling the steering control means.
Vehicle position detecting means for detecting the position of the vehicle;
Distance calculation means for calculating the distance between the travel lane detected by the travel lane detection means and the vehicle position detected by the vehicle position detection means;
Steering adjustment amount generating means for generating a steering adjustment amount based on the distance calculated by the distance calculating means;
Steering adjustment means for adjusting the steering by the steering control means based on the steering adjustment amount,
The lane driving support device according to any one of claims 1 to 4, wherein the steering adjustment amount generating means is the fuzzy control device according to any one of claims 1 to 4. A vehicle steering assist device that assists the driver in steering the vehicle,
Steering control means for controlling the steering of the vehicle;
Target trajectory calculation means for calculating information on a target trajectory, which is a trajectory to be a target of the vehicle, using a dynamic model of the vehicle;
Vehicle position detecting means for detecting the position of the vehicle;
Distance calculating means for calculating a distance between the target locus calculated by the target locus calculating means and the position of the vehicle detected by the vehicle position detecting means;
Steering adjustment amount generating means for generating a steering adjustment amount based on the distance calculated by the distance calculating means;
Steering adjustment means for adjusting the steering by the steering control means based on the steering adjustment amount,
The steering assist device according to any one of claims 1 to 4, wherein the steering adjustment amount generating means is the fuzzy control device according to any one of claims 1 to 4.

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