|Institution:||University of Pretoria|
|Keywords:||Vehicle Engineering; Autonomous Vehicle; Optimal Control; Off-road vehicle dynamics; UCTD|
|Full text PDF:||http://hdl.handle.net/2263/43784|
A ground vehicle is a dynamic system containing many non-linear components, ranging from the non-linear engine response to the tyre-road interface. In pursuit of developing driver-assist systems for accident avoidance, as well as fully autonomous vehicles, the application of modern mechatronics systems to vehicles are widely investigated. Extensive work has been done in an attempt to model and control the lateral response of the vehicle system utilising a wide variety of conventional control and intelligent systems theory. The majority of driver models are however intended for low speed applications where the vehicle dynamics are fairly linear. This study proposes the use of adaptive control strategies as robust driver models capable of steering the vehicle without explicit knowledge of vehicle parameters. A Model Predictive Controller (MPC), self-tuning regulator and Linear Quadratic Self-Tuning Regulator (LQSTR) updated through the use of an Auto Regression with eXogenous input (ARX) model that describes the relation between the vehicle steering angle and yaw rate are considered as solutions. The strategies are evaluated by performing a double lane change in simulation using a validated full vehicle model in MSC ADAMS and comparing the maximum stable speed and lateral offset from the required path. It is found that all the adaptive controllers are able to successfully steer the vehicle through the manoeuvre with no prior knowledge of the vehicle parameters. An LQSTR proves to be the best adaptive strategy for driver model applications, delivering a stable response well into the non-linear tyre force regime. This controller is implemented on a fully instrumented Land Rover 110 of the Vehicle Dynamics Group at the University of Pretoria fitted with a semi-active spring-damper suspension that can be switched between two discrete setting representing opposite extremes of the desired response namely: ride mode (soft spring and low damping) and handling mode (stiff spring and high damping). The controller yields a stable response through a severe double lane change (DLC) up to the handling limit of the vehicle, safely completing the DLC at a maximum speed of 90 km/h all suspension configurations. The LQSTR also proves to be robust by following the same path for all suspension configurations through the manoeuvre for vehicle speeds up to 75 km/h. Validation is continued by successfully navigating the Gerotek dynamic handling track, as well as by performing a DLC manoeuvre on an off-road terrain. The study successfully developed and validated a driver model that is robust against variations in vehicle parameters and friction coefficients.