Connectivity is a key component of modern intelligent vehicles, which increasingly rely on vehicle-to-everything communication and on-board exteroceptive sensors. The integration of data from these diverse sources, combined with enhanced actuation capabilities from emerging powertrain electrification and active chassis control systems, plays a crucial role in improving vehicle safety and performance.

Among the available control technologies, model predictive control (MPC) is particularly suitable for incorporating preview-based information because: i) it relies on predictions of the system response, which can be computed based on external inputs over a finite horizon; ii) it is capable of concurrently and effectively managing multiple variables and control inputs, making it ideal for over-actuated vehicles; and iii) it can easily and systematically account for constraints on states, actuation commands, and their rates, as well as on functions of states and control inputs.\\

The scope of this thesis is to explore how the external information available through vehicle connectivity -- without delving into the details of its implementation -- can be utilized within the nonlinear model predictive control (NMPC) algorithm to significantly enhance the control systems of modern intelligent vehicles.

Firstly, path-tracking (PT) controllers with collision avoidance capabilities for automated vehicles are introduced. These controllers integrate multiple actuation systems, such as front-to-total longitudinal tire force distribution, direct yaw moment actuation, and rear-wheel steering, and are designed to operate beyond conventional handling limits, including the ability to induce drift when beneficial for PT. Simulation results from emergency scenarios, such as avoiding a crash at an intersection, demonstrate the PT benefits provided by each individual actuation system and highlight significant safety improvements compared to the concurrent operation of PT algorithms and current-generation vehicle stability controllers.

Next, innovative control algorithms are presented that adjust the powertrain torque requested by the driver based on the expected road profile ahead, with the aim to: (i) enhance wheel slip control under traction conditions, and (ii) mitigate longitudinal acceleration oscillations caused by uneven surfaces. Simulation and experimental results across various road scenarios, including comparisons with state-of-the-art benchmark controllers, demonstrate significant advancements in both wheel slip control and ride comfort. The proposed preview-based NMPC also exhibits robustness against model uncertainties and inaccuracies in the preview information.

In conclusion, this research demonstrates how leveraging connectivity-based external information within NMPC algorithms can significantly enhance overall vehicle control, improving both safety and performance, while showcasing the significant impact of preview-based control solutions on the next generation of intelligent vehicles.