Abstract
Integrated Chassis Control (ICC) is one of the most appealing subjects for vehicle dynamics specialists and researchers. ICC ensures that the potential of the increasing number of vehicle chassis actuators is completely exploited while conflicts and redundancies are prevented, resulting in enhanced vehicle performance, ride comfort and safety. The doctorate study portrayed in this thesis is organised in three major activities. The first part investigates the last three decades of ICC literature and provides guidelines to classify the architectures and coordination strategies, depicting their pros and cons. Hence, the investigation recommends the most suitable testing procedures and driving requirements to objectively assess the ICC performance. The second part of the study deals with the first layer of a generalised ICC structure, where the environmental data from sensing systems and state estimators are gathered. The thesis focuses on the implementation of a vehicle state estimator (VSE) for unmeasurable data, which combines the information of the vertical and longitudinal tyre contact forces determined by a smart tyre system with the signals obtained from conventional vehicle sensors. The sensing systems aid the mathematical model inside the VSE to improve the estimation accuracy. The study assesses the UKF design via experimental data post-processed from a high-performance passenger car, with a comprehensive set of driving scenarios. In the third and final part, the activity implements a nonlinear model predictive control (NMPC) in the control layer of the proposed ICC architecture. The target is to optimally allocate the control actions of electronic limited slip differential (ELSD), independent friction brakes and throttle valve actuation, aiming to enhance vehicle cornering response while minimizing longitudinal dynamics degradation due to brake actuation. The ICC is assessed through simulations via the comparison to the standalone NMPC formulations and a benchmarking control strategy.