This study concerns the development and testing of three types of Anti-lock Brake Systems (ABS): a standard on-off wheel’s acceleration control; a wheel’s longitudinal slip controller based on a discrete Proportional-Integral-Derivative (PID) control; and a novel type of ABS that involves controlling the wheel’s speed through a discrete PID. This work was developed inside a wider project that will lead to the implementation of stability control systems in a prototype car. For this reason, the typologies of ABS must not require extra sensors compared to those in standard vehicles: Inertial Measurement Unit (IMU) and 4-wheel speed sensors. Furthermore, they must be easily integrated with other controls and electronic components in terms of sampling time and values. The standard ABS seems more appropriate than the others two because it uses only parameters defined by sensors and it has a simple architecture that does not have the problem of computational time. However, in recent years, cars have been equipped with Electro-Hydraulic-Braking (EHB) units that improve the performance of the system controls. In fact, it is possible to use a control that allows actuators to follow a continuous target and smooth out pressure actions. Even if the longitudinal Slip Controller has a simple architecture and uses a PID control, it is limited to using quantities estimated instead of measured: the tires’ friction coefficient, the tires’ longitudinal stiffness, and the car’s speed. Therefore, the use of a Wheel Speed Controller is the right compromise to link the advantages of both controllers by following the braking pressure continuously and not needing to know the condition and properties of the tires. The results of tests carried out in a Hardware-In-the-Loop (HiL) system are showed and involved a complex vehicle model implemented in real-time.

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The aim of this study was to develop trajectory planning that would allow an autonomous racing car to be driven as close as possible to what a driver would do, defining the most appropriate inputs for the current scenario. The search for the optimal trajectory in terms of lap time reduction involves the modeling of all the non-linearities of the vehicle dynamics with the disadvantage of being a time-consuming problem and not being able to be implemented in real-time. However, to improve the vehicle performances, the trajectory needs to be optimized online with the knowledge of the actual vehicle dynamics and path conditions. Therefore, this study involved the development of an architecture that allows an autonomous racing car to have an optimal online trajectory planning and path tracking ensuring professional driver performances. The real-time trajectory optimization can also ensure a possible future implementation in the urban area where obstacles and dynamic scenarios could be faced. It was chosen to implement a local trajectory planning based on the Model Predictive Control(MPC) logic and solved as Linear Programming (LP) by Sequential Convex Programming (SCP). The idea was to achieve a computational cost, 0.1 s, using a point mass vehicle model constrained by experimental definition and approximation of the car’s GG-V, and developing an optimum model-based path tracking to define the driver model that allows A car to follow the trajectory defined by the planner ensuring a signal input every 0.001 s. To validate the algorithm, two types of tests were carried out: a Matlab-Simulink, Vi-Grade co-simulation test, comparing the proposed algorithm with the performance of an offline motion planning, and a real-time simulator test, comparing the proposed algorithm with the performance of a professional driver. The results obtained showed that the computational cost of the optimization algorithm developed is below the limit of 0.1 s, and the architecture showed a reduction of the lap time of about 1 s compared to the offline optimizer and reproducibility of the performance obtained by the driver.

The developing of autonomous drive is needed to make people life more comfortable and safer, and one of the important skills to make possible the reliability of the all control system is a good localization of the vehicle. In this study, a no-linear state observer was developed using the Unscented Kalman Filter (UKF) algorithm, to estimate the global position, global orientation, and local speeds of a car inside a known path. A characterization of the sensors input measures was made and the measures of longitudinal and lateral vehicle speed were added using an Artificial Neural Network (ANN) trained in simulated manoeuvres. In this way, it was possible to reduce the error that the observer make on the estimation of the lateral vehicle speed, and so of the side slip angle, making possible an improvement of the control activity. To assess this increase in performance, a Montecarlo analysis was made comparing the architecture proposed, ANN+UKF, with state observed, UKF, with no input measure of lateral speed. The tests were done in co-simulation environment of Vi-Grade’s CarRealTime software and Matlab-Simulink.

This study concerns the comparative investigation of two advanced lateral stability automotive controllers with respect to a commercial solution. The research aims to improve the stability performances achieved by a combined tracking of yaw rate and side-slip angle through the application of optimal efforts. The proposed solutions are based on Linear Quadratic Regulation and Sliding Mode Control, respectively. Both rely on the same approach for the control objective definition but differ from the action perspective. This solution involves the adoption of a differential braking actuation technique to deliver a desired yaw moment to the car body to track controlled states. Indeed, a sliding controller can also traction torques of hub-motor configurations as well as steering corrections, achieving vehicle stability and a driving response in accordance with the pilot’s intentions. Calibration and validation of the controllers are performed through a Hardware-in-the-Loop simulation rig, along with a real-time static simulator, performing different close-loop maneuvers to assess achievements in terms of lateral stability. Results show that both solutions ensure higher handling performances if compared to Non-controlled or Commercial-controlled vehicle scenarios.