Vehicle dynamics control and vehicle dynamics control system for a vehicle

Abstract

The present invention relates to a vehicle dynamics control for a vehicle (102), wherein: a feedforward algorithm (108) of the vehicle dynamics control is parameterized using a model behavior of a virtual analogous model (112) of the vehicle (102); and the model behavior (110) is compared to a real-life behavior (116) of the vehicle (102) and the analogous model (112) is adapted if the model behavior (110) deviates from the real-life behavior (116).

Description

[0001] Description

[0002] Driving dynamics control and driving dynamics control system for a vehicle

[0003] Field of the invention

[0004] The invention relates to a driving dynamics control system for a vehicle, a corresponding driving dynamics control system, and a corresponding computer program product.

[0005] State of the art

[0006] A vehicle may have a vehicle dynamics control system. The vehicle dynamics control system can control the vehicle's actuators to influence the vehicle's driving dynamics. For this purpose, the vehicle dynamics control system can read sensor signals from, for example, the vehicle's inertial sensors and, in response to a vehicle skidding reflected in the sensor signals, control the actuators to counteract the skidding.

[0007] The inertia of the vehicle leads to a certain time delay in the occurrence of events, since the breakout can only be reflected in the sensor signals when a corresponding movement of the vehicle actually takes place.

[0008] To counteract this, the vehicle dynamics control system can incorporate model-based feedforward control. Inputs from a driver are processed by a virtual surrogate model of the vehicle, and the actuators are controlled based on the model's calculated response. The model can predict that the vehicle will likely reach a limiting range given predetermined inputs. The vehicle dynamics control system can thus pre-control actuators before the skidding is detectable in the sensor signals.

[0009] EP 2 832 599 A1 describes a method and a device for operating a vehicle.

[0010] Disclosure of the invention

[0011] Against this background, the approach presented here provides a driving dynamics control system for a vehicle, a corresponding driving dynamics control system, and a corresponding computer program product according to the independent claims. Advantageous further developments and improvements of the approach presented here emerge from the description and are described in the dependent claims.

[0012] Advantages of the invention

[0013] A conventional virtual surrogate model cannot fully replicate the behavior of a real vehicle because it is preconfigured for all vehicles of a vehicle type or configuration. Individual differences between essentially identical vehicles, such as different tires and / or different loads, cannot be taken into account.

[0014] In the approach presented here, the actual behavior of an individual vehicle during a drive is evaluated and compared with a model behavior of the surrogate model. The individual characteristics of the vehicle are reflected in the individual actual behavior. If there are deviations between the actual behavior and the model behavior, the surrogate model is modified to minimize these deviations.

[0015] Using the approach presented here, the surrogate model can map the individual characteristics of the vehicle and thus predict the vehicle's behavior with a high degree of accuracy. This expected behavior will closely reflect the actual behavior, even at the dynamic driving limits. This allows the expected behavior to be advantageously used to parameterize a feedforward control algorithm for a vehicle dynamics control system. A vehicle dynamics control system is proposed, wherein a feedforward control algorithm for the vehicle dynamics control system is parameterized using a model behavior of a virtual surrogate model of the vehicle. The model behavior of the surrogate model is compared with the actual behavior of the vehicle, and the surrogate model is adapted if the model behavior deviates from the actual behavior.

[0016] Ideas for embodiments of the present invention can be considered, among other things, to be based on the thoughts and findings described below.

[0017] A vehicle's driving dynamics control system can be implemented by the vehicle's driving dynamics control system. The driving dynamics control system can, in particular, influence the vehicle's lateral dynamics. The lateral dynamics determine, for example, cornering behavior or yaw behavior of the vehicle. The driving dynamics control system can comprise a feedforward control algorithm and a controller. The driving dynamics control system can act on at least one actuator of the vehicle that influences the lateral dynamics. The actuator can, for example, be a braking system of the vehicle. The actuator can also be a rear-axle steering system. The actuator can also be a controllable differential. These actuators can influence the vehicle's yaw rate.

[0018] The feedforward algorithm and the controller can each send control commands to at least one actuator. The feedforward algorithm acts proactively, while the controller acts reactively. The feedforward algorithm intervenes before something happens, preventing it from happening. The controller intervenes once it has happened.

[0019] In order to proactively intervene, the feedforward control algorithm requires prior knowledge of the expected driving behavior of the vehicle in response to an input, such as a steering input, acceleration input and / or deceleration input. This prior knowledge is represented in a virtual surrogate model of the vehicle. The surrogate model processes the same inputs as the vehicle and represents the driving behavior expected based on these inputs in a model behavior as output. Parameters of the model behavior can be input variables of the feedforward control algorithm. Here, the model behavior is compared with the actual behavior of the vehicle in response to these inputs. The actual behavior is referred to as real behavior. If the real behavior deviates from the model behavior, the surrogate model is changed until the real behavior and the model behavior essentially agree.

[0020] The real behavior can be observed using at least one current measured variable recorded on the vehicle. The real behavior can be observed by an observer of the vehicle dynamics control system. A measured variable can, for example, be represented by a sensor signal from a sensor on the vehicle. The measured variable can, for example, be a yaw rate, a steering angle, or a lateral acceleration. The real behavior can, in particular, be detected using multiple measured variables. The at least one measured variable can be evaluated by the observer. The observer can detect the real behavior based on the at least one measured variable. The real behavior is influenced by actual vehicle conditions. The observer can observe the at least one measured variable and the input to detect the actual vehicle conditions.A vehicle state can be, for example, the vehicle's center of gravity, its payload, the condition of the vehicle's tires, or the condition of the road surface beneath the vehicle. The actual vehicle states can be compared with model states of the substitute model, and deviations between the vehicle states and the model states can be output as state corrections.

[0021] The vehicle states can be detected by comparing at least one measured variable with an ideal value representing ideal behavior. With ideal behavior, an ideal vehicle literally drives like it's on rails in response to the input. Ideal behavior is unattainable in reality.

[0022] At least one model state of the surrogate model can be adapted using a state correction for the model state. Adaptable model states can be predefined. Adaptable model states can be a selection of the model states of the surrogate model. The direction of change of the state correction can be predetermined by an observed deviation of the real behavior from the model behavior.

[0023] A maximum of two model states can be adapted simultaneously. By limiting the number of simultaneous changes to two, a clear relationship between cause and effect can be observed. If more than two model states are changed, it is no longer possible to clearly determine which change led to which effect. If more than two adaptable model states are available, the model states to be adapted can be cycled through iteratively.

[0024] The state correction can be limited to a predefined value range around the current model state. The state correction can be limited to a limited state correction. The limitation can be implemented in a limiter of the vehicle dynamics control system. This limitation can prevent excessive changes in the model states. Larger changes can be implemented gradually. After each incremental change to the model states, the new model behavior can be compared with the real-world behavior to verify the success of the change.

[0025] The value range can be adjusted depending on the vehicle's driving situation. The value range can be restricted, especially when the vehicle approaches a limiting range. If the vehicle dynamics control system intervenes to stabilize the vehicle, the state corrections can be omitted. This allows larger state corrections to only be made far from the limiting range. This ensures that a state correction does not change the model behavior into a critical or unstable range.

[0026] The state correction can be limited to physically reasonable values. These reasonable values ​​can be based on prior knowledge. These reasonable values ​​can be limited by reasonable limits. These limits can ensure that the model behavior remains outside the critical or unstable region.

[0027] The method is preferably computer-implemented and can be implemented, for example, in software or hardware, or in a hybrid form of software and hardware, for example, in a driver assistance system. The approach presented here further creates a driving dynamics control system for a vehicle, wherein the driving dynamics control system is configured to perform, control, or implement the steps of a variant of the method presented here in corresponding devices.

[0028] The vehicle dynamics control system can be an electrical device with at least one computing unit for processing signals or data, at least one memory unit for storing signals or data, and at least one interface and / or a communication interface for reading in or outputting data embedded in a communication protocol. The computing unit can be, for example, a signal processor, a so-called system ASIC, or a microcontroller for processing sensor signals and outputting data signals depending on the sensor signals. The memory unit can be, for example, a flash memory, an EPROM, or a magnetic storage unit. The interface can be designed as a sensor interface for reading in the sensor signals from a sensor and / or as an actuator interface for outputting the data signals and / or control signals to an actuator.The communication interface can be configured to read or output data wirelessly and / or via a wired connection. The interfaces can also be software modules, which are present, for example, on a microcontroller alongside other software modules.

[0029] Also advantageous is a computer program product or computer program with program code that can be stored on a machine-readable carrier or storage medium such as a semiconductor memory, a hard disk memory or an optical memory and is used to carry out, implement and / or control the steps of the method according to one of the embodiments described above, in particular when the program product or program is executed on a computer or a device.

[0030] It should be noted that some of the possible features and advantages of the invention are described herein with reference to different embodiments. A person skilled in the art will recognize that the features of the control device and the method can be combined, adapted, or exchanged as appropriate to achieve further embodiments of the invention.

[0031] Short description of the drawings

[0032] Embodiments of the invention are described below with reference to the accompanying drawings, wherein neither the drawings nor the description are to be construed as limiting the invention.

[0033] Fig. 1 shows a block diagram of a vehicle dynamics control system according to an embodiment; and

[0034] Fig. 2 shows a detail of a vehicle dynamics control system according to an embodiment.

[0035] The figures are merely schematic and not to scale. Like reference numerals denote like or equivalent features.

[0036] Embodiments of the invention

[0037] Fig. 1 shows a block diagram of a vehicle dynamics control system 100 of a vehicle 102. The vehicle dynamics control system 100 acts on at least one actuator of the vehicle 102 to influence a yaw rate of the vehicle 102. The actuator can be, for example, a braking system of the vehicle 102, a steerable rear axle of the vehicle or a controllable torque vectoring axle of the vehicle.

[0038] The vehicle dynamics control system 100 has a feedforward control 104 and a controller 106. Both the feedforward control 104 and the controller 106 generate control commands for the actuator to influence a yaw rate. A feedforward control algorithm 108 of the feedforward control 104 acts predictively and proactively outputs control commands to prevent undesirable driving behavior of the vehicle 102. Should the vehicle nevertheless exhibit undesirable driving behavior, the controller 106 outputs control commands to counteract the undesirable driving behavior. In order to adapt the control commands as closely as possible to the vehicle 102, the feedforward control algorithm 108 and the controller 106 are parameterized by an online adaptation algorithm 114 using a model behavior 110 of a virtual surrogate model 112 of the vehicle 102.

[0039] In the approach presented here, the model behavior 110 of the substitute model 112 is compared with a real behavior 116 of the vehicle 102. Based on the result of the comparison, the substitute model 112 is adapted to approximate the model behavior 110 as closely as possible to the real behavior 116.

[0040] In one embodiment, the online adaptation algorithm 114 reads in at least one model state 118 of the replacement model 112 and, based on the result of the comparison, outputs a state correction 120 for this model state 118 in order to adapt the replacement model 112.

[0041] In one embodiment, at least one measured variable 124 detected on the vehicle 102 by a measuring device 122 is evaluated by the online adaptation algorithm 114 to detect the real behavior 116. The model behavior 110 can be derived from output variables of the substitute model 112 and / or the model states 118.

[0042] Fig. 2 shows a detail of a vehicle dynamics control system 100 according to an exemplary embodiment. The detail shows the online adaptation algorithm 114 from Fig. 1. Here, the online adaptation algorithm 114 has a model-based observer 200 for observing the measured variables 124 and model states 118. The observer 200 determines the state correction 120 from the observed measured variables 124 and observed model states 118.

[0043] In one embodiment, the online adaptation algorithm 114 includes a limitation 202. The limitation 202 reads the unlimited state correction 120 and limits the state correction 120 to prevent excessive changes to the replacement model.

[0044] In one embodiment, the unlimited state correction 120 is limited to realistic values ​​in order to obtain the limited state correction 120.

[0045] In one embodiment, the limitation 202 limits the state correction

[0046] 120 depending on the current stability state of the vehicle. Thus, state corrections 120 are limited at least when the vehicle is in an unstable driving state. Limitation 202 therefore limits state correction 120 when vehicle dynamics control system 100 is active.

[0047] In the following, possible embodiments of the invention are summarized again or presented with slightly different wording.

[0048] An online adaptation of model-based vehicle dynamics control systems to increase performance and robustness is presented.

[0049] Today's vehicle dynamics control systems for ensuring vehicle stability typically employ a model-based approach with fixed parameters for controlling lateral dynamics. Typically, the parameters are determined once and then stored in the control unit code. This results in a firmly defined or applied model behavior. Typical vehicle dynamics models are linear and nonlinear single-track models with the states yaw rate and sideslip angle.

[0050] The vehicle dynamics models feature a two-degree-of-freedom structure with feedforward (FF) and feedback (FB), as described, for example, in EP 2 832 599 A1. The control loop is typically closed via the yaw rate. Deviations between reality and the model always occur. Typical reasons for this include tire variations (wear, model, summer / winter tires, etc.), load variations (empty, full, high center of gravity, etc.), road disturbances, model inaccuracies (unconsidered dynamics, unknown friction coefficient), and lateral road inclinations. These uncertainties lead to deviations between the desired and actual yaw rate.

[0051] This can lead to incorrect control via the controller path. This is compensated for by increased control thresholds. In the application, this can lead to delayed control and thus reduced performance. Furthermore, the feedforward control path can no longer fully guarantee the control behavior because the model and thus the states do not match reality.

[0052] The approach presented here extends the vehicle dynamics control described in EP 2 832 599 A1 by allowing for real-time adaptation to current conditions. Inaccuracies in the system being controlled (e.g., tires, aging, load) are taken into account. This results in increased model validity compared to EP 2 832 599 A1 and thus improved control behavior via the pre-control path.

[0053] The approach presented here enables targeted, early intervention of the feedforward control to achieve a desired driving behavior. This makes the feedforward control significantly more effective. Harsh, uncomfortable controller interventions are prevented. Furthermore, it results in increased robustness against incorrect control during normal driving.

[0054] The feedforward control interventions eliminate the need for feedback, as the actual and target yaw rates match. This reduces hardware load. Furthermore, control thresholds can be reduced, which leads to increased performance in certain applications.

[0055] In the approach presented here, the real-time adaptation of the vehicle model to reality is carried out using a model-based observer approach. The observer continuously compares the measurable states of the real vehicle with the states of the vehicle model. Based on this and on physical models, it estimates the magnitude of the disturbances to the states (beta, dPsi) and corrects them to better match the actual vehicle behavior. This allows the model behavior to be adapted to the actual behavior in real time. The effect of the sum of all uncertainties in the model is corrected.

[0056] The observer is only allowed to move within predefined uncertainties. These are based on physical estimates of the influence of various disturbances on the dynamics, such as the influence of tire variances. These estimates apply to the linear range, i.e. at large slip angles these automatically become very small. This means that each project can individually define which disturbances occur and to what extent. During normal driving, the observer therefore always stays within the defined uncertainty band and can correct the conditions completely. This means that no control deviation can arise, incorrect control is avoided, and modeled states remain valid for longer, i.e. the feedforward control can impose the desired control behavior for longer. In application cases, the disturbances due to dynamic effects can become so large that they are significantly larger than the uncertainty band. The disturbances can then be limited.The limitation can be so extensive that no disturbances are permitted in the application. This allows the application of the target behavior (target yaw rate) to continue, as it is not influenced by the observer approach. Application via feedback is still possible.

[0057] With the approach presented here, the adaptation is much faster than with known parameter adaptations (e.g. vch). The influence of road disturbances, such as potholes, bumps and friction coefficient changes on the

[0058] The vehicle can thus be directly recognized and modeled.

[0059] The approach presented here describes an online adaptation algorithm that adapts the vehicle model to the real vehicle in real time using a model-based observer approach. The algorithm uses measured variables such as yaw rate, steering angle, and lateral acceleration, as well as the states of the feedforward vehicle model. Based on this information, the algorithm calculates the appropriate state corrections for the vehicle model so that it better matches the current environment.

[0060] The algorithm consists of two blocks. The first block is the model-based observer. The second block is the constraint calculation.

[0061] In the model-based observer, a calculation rule is derived based on the physical equations underlying the vehicle, which compares the model states with the measured variables and calculates the necessary corrections to adapt the model states to the actual states.

[0062] In the limitation calculation, a range is calculated in real time using predefined uncertainties and physical estimates within which the disturbance variables and thus the state corrections can maximally move. This range also depends on the extent to which the vehicle is in the application case. This ensures that the algorithm only corrects in non-application cases, guaranteeing robustness, while still allowing the application of the target behavior and feedback in the application case.

[0063] Blocks 104 to 106 represent the standard control loop structure, as also described in EP 2 832 599 A1. Block 114 refers to the extension described here for online adaptation of the vehicle model. In detail, block 114 consists of a model-based observer 200 and a limitation calculation 202.

[0064] The approach presented here allows the target yaw rate to be adjusted directly (immediately after the uncertainty is imposed) when physical vehicle parameters vary.

[0065] Finally, it should be noted that terms such as "comprising," "having," etc., do not exclude other elements or steps, and terms such as "a" or "an" do not exclude a plurality. Reference signs in the claims are not to be considered limiting.

Claims

Claims 1 . Driving dynamics control for a vehicle (102), wherein a pre-control algorithm (108) of the driving dynamics control is parameterized using a model behavior (110) of a virtual substitute model (112) of the vehicle (102), wherein the model behavior (110) of the substitute model (112) is compared with a real behavior (116) of the vehicle (102) and the substitute model (112) is adapted if the model behavior (110) deviates from the real behavior (116).

2. Vehicle dynamics control according to claim 1, wherein the real behavior (116) is observed using at least one current measured variable (124) recorded on the vehicle (102).

3. Vehicle dynamics control according to one of the preceding claims, in which at least one model state (118) of the substitute model (112) is adapted using a state correction (120) for the model state (118).

4. Vehicle dynamics control according to claim 3, in which a maximum of two model states (118) are adapted simultaneously.

5. Vehicle dynamics control according to one of claims 3 to 4, wherein the state correction (120) is limited to a predefined value range around the model state (118).

6. Driving dynamics control according to one of claims 3 to 5, wherein the value range is set depending on a driving situation of the vehicle (102).

7. Driving dynamics control according to one of claims 3 to 6, wherein the state correction (120) is limited to physically reasonable values.

8. Driving dynamics control system (100), wherein the driving dynamics control system (100) is configured to execute, implement and / or control the driving dynamics control according to one of the preceding claims in corresponding devices.

9. A computer program product configured to instruct a processor, upon execution of the computer program product, to execute, implement and / or control the vehicle dynamics control according to one of claims 1 to 7.

10. A machine-readable storage medium on which the computer program product according to claim 9 is stored.

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