Various automotive vehicles have recently begun including vehicle system controls. Vehicle system controls use the sensors to sense a vehicle's dynamics, the driver's intention or even the environmental information around the vehicle; use the electronic control units (ECUs) to process the sensed information; and use the available actuators to conduct suitable control actions requested from the ECUs so as to achieve controlled performance for a driving vehicle. The ongoing goal of the vehicle system controls is to achieve an improved system performance or new functions. Ultimately, the various vehicle control systems need to be coordinately controlled so as to achieve enhanced system level control performances for ride, handling, safety and fuel economy of the vehicle.
One of the vehicle system controls are the various types of vehicle dynamics controls, which use electronically controlled chassis, powertrain or drivetrain subsystems to augment the driver's controllability of the vehicle for ride, handling and safety purposes.
Several existing stability control systems are available. For example, the yaw stability control system (YSC) or a roll stability control system (RSC) are currently equipped on millions of vehicles. The separated stability control functions are the focuses of the existing systems.
It may be desirable to integrate the functions of the various dynamic control systems. One reason that such an integration is desirable is that there is overlap among the operation ranges of the individual control systems. For example, a rollover control system tries to force the vehicle under-steer more during an un-tripped rollover event, while an ESC system might take the vehicle's response under RSC control as an indication of the vehicle's true under-steer to conduct the vehicle's under-steer correction. If there is no integration between RSC and YSC, the control action of those may cancel each out. Hence a successful implementation of RSC and YSC in the same vehicle will need control coordination. One of the enablers of the coordination relies in the accurate discrimination of the features of different unstable vehicle dynamics, which require different control functions.
With current advances in mechatronics, the aforementioned technology enabler is possible. For example, the advanced sensors might be used. Such advanced sensors together with other mechatronics not only help function integration, they also helps improve the individual vehicle stability control performances and provides opportunities for achieving control performances which were previously reserved for spacecraft and aircraft. For example, gyro sensors, previously only used in aircraft and spacecraft, have now been incorporated in various vehicle dynamics controls and the anti-lock brake systems once invented for airplanes are now standard automotive commodities.
With the application of the advanced sensors and mechatronics, superior information about the vehicle's dynamics states can be obtained which can be used to calculate effective feedback and feedforward controls for various functions and their integrations. It is especially effective to use the superior information from the advanced sensors and their innovative sensing algorithms to identify the scenarios, which are otherwise undetectable during a driving involved with complicated road conditions and aggressive driving inputs from the driver.
A typical vehicle stability control and an integrated stability control system sense and control a vehicle's dynamics conditions described in the 3-dimensional space. Those control systems might require the measurements of all or part of the three-dimensional motions which include the rotational motions along the vehicle's roll, pitch, and yaw directions and the translation motion along the vehicle's longitudinal, lateral and vertical directions.
More specifically, in a typical vehicle stability control system such as a yaw stability control system or a roll stability control system, the primary control task is to stabilize the vehicle in yaw or roll directions, which will likely involve motions along its roll and yaw directions, and longitudinal and lateral directions. The coupling between different motion directions may not be as strong as in an aircraft or a spacecraft; however, they cannot be neglected in most of the control regions where unstable vehicle dynamics is seen. Unstable vehicle dynamics might involve rolling over or yawing out of the traveling course during aggressive driving when the driver's inputs are well beyond the values allowed by the adhesion limit of the road surface. For example, the excessive steering of a vehicle will likely lead to a unstable yaw and lateral motions, which further cause large rolling motion towards the outside of the turn. If the driver brakes the vehicle during such an excessive steering, the vehicle will also have roll and pitch motions, large load transfer and large lateral discursion. It is desirable for a high performance stability control system to integrate YSC, RSC and lateral stability control (LSC) such that the coupled unstable vehicle dynamics can be stabilized.
Theoretically, if there are sensors which can directly measure the vehicle's roll states, yaw states and lateral states during complicated unstable dynamics, the successful integration would involve: (i) identifying the dominated direction where a primary unstable dynamics can occur; (ii) prioritizing control functions when multiple safety-critical unstable vehicle dynamics can occur; (iii) maximizing control functions when multiple unstable vehicle dynamics are equally critical; (iv) determining transition control actions from one stability control function to another or to a normal operation function. The decision rule for any of the above actions needs close discrimination. Unfortunately, there are no direct measurements of the aforementioned vehicle states. Even the advanced sensors can only provide indirect measurements of the involved vehicle states. Therefore, intelligent sensing algorithms are required.
Therefore, it would be desirable to integrate the YSC, RSC and LSC functions to provide accurate determination of the involved roll, yaw and lateral motions of a vehicle. The controllable variables associated with those motions might be the vehicle's global and relative angular motion variables such as attitudes and the directional motion variables such as the longitudinal and lateral velocities. For instance, in RSC, the relative roll angle between the vehicle body and the road surface is controlled. In LSC, the relative yaw angle between the vehicle's travel direction and the path is controlled. The coupled dynamics among roll, yaw and pitch motions and the elevated (banked or inclined) road caused vehicle motions all need to be differentiated. The driver's intention-based vehicle dynamic behavior will also need to be determined. Due to the fact that the sensors measure the total motion of the vehicle, the sensor measurements contain the total information that includes the values of the driver-induced dynamics, the road geometry-induced dynamics and the dynamics from the gravity together with sensor uncertainties. It is not hard to find that separating the driver-induced dynamics and the road-induced dynamics from the total sensor measurements need intelligent algorithms. Only if such information is determined from the total sensor measurements, the control system can provide adequate control actions in needed situations (driver-induced excessively dynamic or unstable) and generate less occurrences of false control action in unneeded situations such as in certain road geometry-induced dynamics.
In order to achieve the aforementioned separation of the driver maneuver-induced vehicle dynamics information and the road geometry-induced dynamics information from the sensor uncertainties, gravity-induced dynamics terms, a new vehicle sensing technology which contains an inertial measurement unit (IMU) has been pursued at Ford Motor Company. This sensing system is called an Integrated Sensing System (short to ISS) in this invention.
The IMU has been used in inertial navigation system (INS) for aircraft, spacecraft and satellite for decades. Typically an INS system determines the attitude and directional velocity of a flight vehicle through the sensor signals from the IMU sensors and GPS signals. The IMU sensor set includes 3 gyros and 3 linear accelerometers. The INS contains an IMU and a processor unit to compute the navigation solutions necessary for navigation, attitude reference and various other data communication sources. As the same token, the ISS will also be used (but not limited) to determine the vehicle's attitude and directional velocities with the exception that GPS signals are not necessarily used but, instead, the other sensor signals such as ABS wheel speed sensor signals are used.
With the availability of the IMU sensor cluster equipped with a vehicle stability control system, several effects which are impossible to differentiate in the traditional stability control may now be included. For example, the effect of the road bank was not accurately included in the existing control algorithm. Many known systems either rely upon basic assumptions regarding conditions such as driving on a flat surface (no pitch or bank angle) or on an estimated road bank information that is usually accurate in non-event situation (e.g., steady state driving) but inaccurate in vehicle stability control events. Due to the use of IMU sensor cluster in the ISS system, the road bank and grade information may now be fairly accurately determined.
Notice that the vehicle's attitudes are required to separate the gravity contamination in the acceleration sensor signals. This can be seen from the following example. The vehicle's lateral sliding used in LSC can be characterized by its lateral velocity defined along the lateral direction of the vehicle body. Such a velocity cannot be directly measured and it is determined from the lateral accelerometer measurement. The total output of the lateral accelerometer contains information that is related to the variables other than the lateral velocity. This information includes gravity contamination, the centripetal accelerations, the derivative of the lateral velocity, the sensor uncertainties. On a banked road, the gravity contributes to the lateral accelerometer measurement as significantly as the combination of the vehicle's true lateral velocity derivative and the centripetal acceleration. Due to the fact that the gravity is fixed in both its magnitude and its direction with respect to the sea level, the vehicle global attitudes can be used to find the relative position between the gravity vector and the vehicle's body directions. For this reason, the vehicle global attitudes can be used to compensate the gravity influence in the measured lateral acceleration such that the vehicle lateral velocity might be isolated and determined from the lateral acceleration sensor measurement. The same argument would hold for using pitch attitude to compensate the longitudinal accelerometer sensor signals for computing the vehicle's longitudinal velocity.
While driving on a level ground, the vehicle lateral acceleration tends to be larger than the value sustained by the limits of adhesion on a dry surface with high adhesion due to the large load transfer. On a slippery surface with low adhesion, the vehicle's lateral acceleration might be close to the limit of adhesion of the road surface. The acceleration sensors used in the existing YSC system could not provide information to differentiate this especially when a vehicle experiences large roll and pitch accelerations in excessive maneuvers. Due to the relative attitude determination from the RAD unit in the ISS system, such load transfer effects may be easily characterized. Therefore a stability control system using ISS system output would be able to achieve the same physics in the low adhesion road surface with in the high adhesion road surface, and less deviation of control performance in low adhesion road from the control performance in the high adhesion road will be experienced.
It is the objective of the current disclosure to provide an integrated sensing system and use such information for an integrated stability control system to coordinate a yaw stability control function, roll stability control function and a lateral stability control function to achieve superior control performance in comparison with the existing vehicle stability control systems.