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Human expert drivers have the unique ability to combine correlated sensory inputs with repetitive learning to build complex perceptive models of the vehicle dynamics as well as certain key aspects of the tire-ground interface. This ability offers significant advantages for navigating a vehicle through the spatial and temporal uncertainties in a given environment. Conventional traction control algorithms utilize measurements of wheel slip to help insure that the wheels do not enter into an excessive slip condition such as burnout. This approach sacrifices peak performance to ensure that the slip limits are generic enough suck that burnout is avoided on a variety of surfaces: dry pavement, wet pavement, snow, gravel, etc. In this paper, a novel approach to traction control is developed using an anthropomimetic control synthesis strategy.

This work pertains to laboratory testing of truck cab suspensions for the purpose of improving in-cab ride quality. It describes the testing procedure of a complete truck cab suspension while still being mounted on the vehicle. It allows for testing with minimal amount of resources, limited to two mobile actuators and minimal modifications to the stock vehicle. The actuators can be attached to any axle through a set of modified brake drums and excite the drive axle in a vertical plane. The excitation signal sent to the actuators can be in phase for a heave type motion or out of phase for a roll motion. The chassis shock absorbers are replaced with rigid links to prevent the actuator input from becoming filtered by the primary suspension. This allows the input to reach the cab suspension more directly and the cab to be excited across a broader range of frequencies.

This study pertains to motion control algorithms using statistical calculations based on relative displacement measurements, in particular where the rattle space is strictly limited by fixed end-stops and a load leveling system that allows for roll to go undetected by the sensors. One such application is the cab suspension of semi trucks that use widely-spaced springs and dampers and a load leveling system that is placed between the suspensions, near the center line of the cab. In such systems it is possible for the suspension on the two sides of the vehicle to settle at different ride heights due to uneven loading or the crown of the road. This paper will compare the use of two moving average signals (one positive and one negative) to the use of one root mean square (RMS) signal, all calculated based on the relative displacement measurement.

One popular complement to track testing that successful race teams use to better understand their vehicle’s behavior is dynamic shaker rig testing, such as 7-post and 8-post testing. Compared to track testing, rig testing is more repeatable, costs less, and can be conducted around the clock. While rig testing certainly is an attractive option, an extensive number of tests may be required to find the best setup. To make better use of rig test time, more efficient testing methods are needed. One method to expedite rig testing is to use rig test data to perform system identification and generate a model of the experiment, which may then be applied to identify potential gains for further rig study. This study develops a system identification method for use in rig testing, using data generated from a known physical model. The results show that this method can be used to accurately predict sensor response during an 8-post test for different shock selections.

Restrictions on track testing, combined with advances in technology, have contributed to an increased dependence on sensors and data acquisition for diagnosing problems and improving performance in motorsports vehicles. This dependence has created a new set of challenges for race engineers to collect quality data from a vehicle at the track. Successful 7- or 8-post shaker rig testing is highly dependent on the quality of the data acquired at the track. An improperly configured data acquisition system can actually be worse than a faulty sensor. This paper highlights a few of the most common problems in motorsports data acquisition: aliasing and sample rate selection. The effects of these problems are described for typical suspension sensors such as accelerometers, shock potentiometers, load cells, and laser ride height sensors. An experimental case study is presented to explain the implications of these problems.

This paper documents a simple theoretical analysis and an experimental performance comparison of the advantages of an 8-post shaker rig relative to a conventional 7-post shaker rig. A simple static model describing the chassis roll and warp characteristics is first presented to illustrate the differences between 7-post and 8-post configurations, and the conditions where an additional aeroloader provides an advantage. Using a late model NASCAR Sprint Cup car, a series of experimental tests were conducted with the 8-post shaker rig at the Virginia Institute for Performance Engineering and Research (VIPER) facility in both 7-post and 8-post configurations. Experimental results confirm the hypothesis that an 8-post configuration is able to more accurately reproduce target motions of the chassis and suspension when those motions include a chassis warp condition.

One popular complement to track testing that successful race teams use to better understand their vehicle's behavior is dynamic shaker rig testing. Compared to track testing, rig testing is more repeatable, costs less, and can be conducted around the clock. While rig testing certainly is an attractive option, an extensive number of tests may be required to find the best setup. To make better use of rig test time, more efficient testing methods are needed. One method to expedite rig testing is to use rig test data to generate a model of the experiment and then applying the model to identify potential gains for further rig study. This study develops the method at the quarter-car scale, using data from a quarter-car rig with a Penske 7300 shock absorber. The method is first validated using data generated from a known quarter-car model to assure the method can reproduce the original model behavior.

This paper addresses modeling and system identification of a McPherson strut quarter-car suspension. Constrained equations of motion in the Lagrange multiplier form are used to characterize the dynamic behavior of both a linear and a non-linear McPherson strut quarter-car suspension model. A Hilber-Hughes-Taylor integrator is used to simulate the suspension's response to a displacement input. The simulated response of both models is compared to the experimental response recorded from a quarter-car test rig equipped with a McPherson strut suspension, for system identification purposes. The performance of both models is discussed in the context of the basic linearity of the experimental suspension.