Document from the year 2020 in the subject Engineering - Automotive Engineering, grade: 2, , language: English, abstract: Global chassis controller (GCC) design for autonomous vehicles relies on the information of the environmental factors, weather conditions, vehicle dynamics, actuation bandwidth, among others. Typically, various sensors and actuators are employed to provide such information. Challenges such as cost of sensors, actuator complexity and constraints, fail-safe operations, control authority allocation, and adaptability to a wide range of driving scenarios such as acceleration/ deceleration at set speed, double lane change, and driving on a circular path among others persist for design of such GCC architectures.Specifically for longitudinal-vertical vehicle controllers tuned to achieve safety and comfort objectives, the performance is significantly affected by the precise knowledge of road conditions i.e., tire friction and road elevation in the presence of nonlinearities such as aerodynamic drag, rolling resistance, spring and damper nonlinearities. For the longitudinal vehicle motion, tire-road friction conditions, aerodynamic forces, engine friction, and rolling nonlinearities critically affect the design of safety controllers such as traction control or active cruise control. Similarly, for vertical vehicle motion control using active suspension, the random road roughness and road defects, spring and damper nonlinearities, hydraulic actuator nonlinearities, and multi-objective design criteria, make design of controller a challenging task.With that motivation, the use cost effective virtual sensors to detect such external inputs and subsequent output feedback control solutions for the longitudinal-vertical autonomous vehicle motion is proposed in this book. The focus lies on adaptability of designed controllers and estimators to road friction conditions such as road conditions such as asphalt, snow, ice and the road elevation based on various rough
