Over the past few decades, vehicle control systems have been developed to enhance vehicle handling and passenger safety. These systems seek to prevent unintended vehicle behavior through active vehicle control, assisting drivers in maintaining control of their vehicles. Unfortunately, these systems are limited by the lack of knowledge of the vehicle’s state and operating conditions. Knowledge of the vehicle’s sideslip angle, which relates its lateral velocity to its longitudinal velocity, is important information that is largely unavailable for current safety systems. The tire’s lateral handling limit, which is the maximum grip a tire has on the road during a turn, is also generally unknown. As a result, current systems are reactive; they must detect a problem before corrective action can be taken. If onboard systems had accurate knowledge of sideslip angle (or equivalently, tire slip angle) and could predict the peak friction limit, control systems could anticipate rather than react to loss of control situations, further enhancing vehicle handling and increasing passenger safety.
This thesis presents several model-based estimation methods which utilize the early lateral limit information contained in steering torque measurements and the added sensing capability of GPS. During periods of GPS signal loss, a nonlinear observer is developed that utilizes pneumatic trail information in steering torque to identify both vehicle sideslip angle and the lateral force limits. Mathematically guaranteed to converge, the nonlinear observer uses readily available measurements on production vehicles. Most importantly, it takes advantage of the friction information encoded in the tire pneumatic trail, enabling early detection of the limits before they are reached. Finally, this work develops an envelope controller to keep the vehicle in a safe operating region using the estimated handling limit information from the nonlinear observer. Theoretical results are confirmed by implementation on an experimental steer-by-wire vehicle. Testing conditions include maneuvers performed on dry, flat paved road, as well as on lower-friction, dry gravel.