Attitude control systems for ground vehicles have been an important topic in automotive research for decades, and have been extensively studied by resorting to classical continuous-time nonlinear design. Although this approach can incorporate saturation constraints and actuator dynamics in the design, the computed control laws are often approximated and applied within digital environments in absence of formal performance guarantees. In this letter, we present a quantized sampled-data approach to the vehicle attitude control problem. Starting from classical nonlinear design achieving tracking of prescribed trajectories in continuous time (emulation approach), we derive conditions for preserving the practical stability of the error dynamics by means of quantized sampled-data event-based controllers. Simulations performed in an non-ideal setting confirm the potential of the approach.

We present a novel solution to the attitude control problem of ground vehicles by means of the Active Front Steering (AFS) system. The classical feedback linearization method is often used to track a reference yaw dynamics while guaranteeing vehicle stability and handling performance, but it is difficult to apply because it relies on the exact knowledge of the nonlinearities of the vehicle, in particular the tire model. In this work, the unknown nonlinearities are real-time learnt on the basis of the universal approximation property, widely used in the area of neural networks. With this approximation method, the Uniform Ultimate Boundedness (UUB) property with respect to tracking and estimation errors can be formally proven. Preliminary simulation results show good tracking capabilities when model and parameters are affected by uncertainties, also in presence of actuator saturation.

Modeling and control of ground vehicles have become a major research field in the last few years, due to the increasing diffusion of driver assistance technologies and the persistent need for guaranteeing safety and comfort of driver and passengers. Although vehicle control systems require a large set of measurements to perform accurate computations, due to the possible unavailability and the non-negligible cost of sensors, it is not always possible to assume the direct knowledge of some important dynamic quantities involved, among which a major role is taken by its lateral velocity. In this letter, we address the design and implementation of an asymptotic sampled-data state observer for the vehicle single-track model. The observer only exploits the knowledge of sparse yaw-rate samples, which are obtained by relatively cheap digital gyroscopic devices, to reconstruct the continuous behavior of lateral velocity and yaw rate. However, due to the inherent properties of the vehicle single-track model, the observer is theoretically guaranteed to work only in the presence of sufficiently bounded driver maneuvers. Consequently and in view of a practical exploitation in a realistic setting, we preliminarily validate the approach by means of simulations of the observer applied to a more general vehicle model, including parameter variations, unmodeled dynamics, and quantization. The results seem to confirm the potential of the approach.

Electro-mechanical valve actuators (EMVA) formed by two opposed electromagnets and two balanced springs are appealing solutions to implement advanced combustion concepts for camless engines. A crucial control problem for this valve actuator regards the first valve lift manoeuvre (termed 'first catching') to be rapidly performed after each insertion of the engine ignition key, when the EMVA rests at middle position where electromagnets offer low control authority. The control problem is challenging due to system nonlinear behavior. Mathematically, the EMVA system can be assumed to be a spring-mass impacting system affected by a non-smooth friction force and a dynamic saturated magnetic force. In this work an effective valve position-based first catching control strategy is proposed to control the strongly nonlinear system. Bifurcation analysis and parameter space simulations are used to study the closed-loop system behavior and to tune the controller gains as well. The effectiveness of the control approach is validated through numerical simulations of a highly predictive dynamic model of the valve actuator developed by authors in a previous work.

Hyperstability-based model reference adaptive controllers that use a Minimal Controller Synthesis (MCS) algorithm are known in the literature as MCS controllers. The adaptation gains that arise in the continuous-time MCS algorithm are nonlinear and, as a consequence, practical implementations have to be carried out in a digital fashion. However, the discrete-time versions of the MCS that have been used so far for implementation purposes are not proved to be asymptotically stable for plants from second order up. Recently, some of the authors have developed a hyperstable, discrete-time MCS algorithm with a formal proof of asymptotic stability for generic n-dimensional plants that can be used to control discretized continuous-time plants. This paper deals with the implementation and performance analysis of this algorithm to the problem of controlling an electronic throttle body in automotive engineering.

Gasoline Direct Injection (GDI) spark ignition engines equipped with the Common Rail (CR) system strongly improve engine performance in terms of fuel consumption and pollutant emission reduction. As a drawback the fuel pressure in the rail has to be kept as constant as possible to the demanded pressure working set-points in order to achieve the advantages promised by this technology. In this work a Model Reference Adaptive Control (MRAC) algorithm based on the Minimal Control Synthesis (MCS) strategy is proposed to reduce the residual pressure in the rail. Numerical results based on a CR mean value model, previously proposed in the literature and experimentally validated, show that a very satisfactory attenuation of the pressure ripple as well as pressure tracking are attained in different working conditions. A quantitative comparison with a classical gain scheduling model-based control approach confirms furthermore the effectiveness of the proposed adaptive control strategy.

This paper is concerned with the design of a novel adaptive controller, namely the linear quadratic new extended minimal control synthesis with integral action (LQ-NEMCSI). We present for the first time the analytical proof of asymptotic stability of the controller and experimental evidence of the algorithm effectiveness for controlling an electronic throttle body: an element of any drive-by-wire system in automotive engineering, affected by many nonlinear perturbations.

This paper is concerned with the design and comparison of a new optimal-adaptive control of an electronic throttle body. Numerical results are complemented by experiments.