Wheel Forces Estimation via Adaptive Sub-Optimal Second Order Sliding Mode Observers

In this work a system for the estimation of the forces (both longitudinal and lateral) exerted between the tires and the road is presented. Starting from two of the most commonly used descriptions of the vehicle dynamics, the single-corner and the single-track models, a system composed of Sub-Optimal Second Order Sliding Mode observers in a cascade structure plus an adaptive element is developed and verified to be effective in conditions in which the effect of the weight transfer can be neglected. One notable property of this approach is that only standard sensors, which are present in most of the stock cars, are exploited. The practical implementation is done using a switched/time-based adaptation law for the gains of the observers, in order to be able to track the quantities in a wide range of conditions while keeping the chattering low. Simulation results are presented in IPG Car-Maker

Sliding mode control algorithms for wheel slip control of road vehicles

Sliding mode control approaches are presented in this paper for the wheel slip control of road vehicles. The major design requirement for the controllers is to make the wheel slip ratio follow a desired value, while guaranteeing that the sliding mode control is stabilizing. Its robustness in front of matched and unmatched uncertainties and data transmission delays is assessed in simulation. In the present paper different algorithms of first and second order type and integral or non integral nature are discussed. Simulation results are reported and analyzed, putting into evidence the superior performance, in the considered automotive context, of the integral sliding mode control

Adversarial Learning of Robust and Safe Controllers for Cyber-Physical Systems

We introduce a novel learning-based approach to synthesize safe and robust controllers for autonomous Cyber-Physical Systems and, at the same time, to generate challenging tests. This procedure combines formal methods for model verification with Generative Adversarial Networks. The method learns two Neural Networks: the first one aims at generating troubling scenarios for the controller, while the second one aims at enforcing the safety constraints. We test the proposed method on a variety of case studies

Search-Based Task and Motion Planning for Hybrid Systems: Agile Autonomous Vehicles

To achieve optimal robot behavior in dynamic scenarios we need to consider complex dynamics in a predictive manner. In the vehicle dynamics community, it is well know that to achieve time-optimal driving on low surface, the vehicle should utilize drifting. Hence many authors have devised rules to split circuits and employ drifting on some segments. These rules are suboptimal and do not generalize to arbitrary circuit shapes (e.g., S-like curves). So, the question "When to go into which mode and how to drive in it?" remains unanswered. To choose the suitable mode (discrete decision), the algorithm needs information about the feasibility of the continuous motion in that mode. This makes it a class of Task and Motion Planning (TAMP) problems, which are known to be hard to solve optimally in real-time. In the AI planning community, search methods are commonly used. However, they cannot be directly applied to TAMP problems due to the continuous component. Here, we present a search-based method that effectively solves this problem and efficiently searches in a highly dimensional state space with nonlinear and unstable dynamics. The space of the possible trajectories is explored by sampling different combinations of motion primitives guided by the search. Our approach allows to use multiple locally approximated models to generate motion primitives (e.g., learned models of drifting) and effectively simplify the problem without losing accuracy. The algorithm performance is evaluated in simulated driving on a mixed-track with segments of different curvatures (right and left). Our code is available at https://git.io/JenvBComment: Accepted to the journal Engineering Applications of Artificial Intelligence; 19 pages, 18 figures, code: https://git.io/JenvB. arXiv admin note: text overlap with arXiv:1907.0782

Trends in vehicle motion control for automated driving on public roads

In this paper, we describe how vehicle systems and the vehicle motion control are affected by automated driving on public roads. We describe the redundancy needed for a road vehicle to meet certain safety goals. The concept of system safety as well as system solutions to fault tolerant actuation of steering and braking and the associated fault tolerant power supply is described. Notably restriction of the operational domain in case of reduced capability of the driving automation system is discussed. Further we consider path tracking, state estimation of vehicle motion control required for automated driving as well as an example of a minimum risk manoeuver and redundant steering by means of differential braking. The steering by differential braking could offer heterogeneous or dissimilar redundancy that complements the redundancy of described fault tolerant steering systems for driving automation equipped vehicles. Finally, the important topic of verification of driving automation systems is addressed

Modelling of a Torque Converter and Control of the Lock-up Clutch

Modellizzazione di un convertitore di coppia per un cambio automatico con modello nonlineare attraverso stima delle coppie. Vengono proposti tre diverse tipologie di controllo: High Gain, Input-to-state Stability e Feedback Linearization. L'ultimo viene implementato su un simulatore per un intero veicol

Vehicle Dynamics Control in MAGV: Forces Estimation and Motion Planning Methods

In recent years car manufacturers and tech companies have been investing huge resources to develop autonomous driving vehicles, with the aim of enhancing the efficiency of transports and the safety of passengers on the roads. Nowadays, several vehicles on the market are equipped with driving assistant devices, ranging from self parking and automatic braking systems up to an effective and real autonomous driving. A Multi-Actuated Ground Vehicles (MAGV) can be considered as a complex engineering system having a set of parallel subsystems requiring both individual and integrated control to secure simultaneously criteria of efficient dynamics, safety, and user (and environment) friendly operation. Within this context, the topics of passenger safety, driving quality and energetic efficiency have become of interest in particular for computer and control engineers, with this trend increasing its momentum exponentially. To shed light on the scientific aspects of these devices, a survey of some of the most successful vehicle stability control system is provided in the first part of this dissertation, with particular focus on those which utilize the well established Sliding Mode Control technique (SMC). Advanced Driver Assistance Systems (ADAS), such as the already mentioned ESC and ABS, as well as Automated Driving (AD) technologies, can be enhanced by the knowledge of vehicle planar motion states (longitudinal and lateral velocities of the center-of-gravity and side-slip angles). In particular, the estimation of tire-ground contact forces has become an important subject of investigation. In fact, the knowledge of the forces exerted can help to prevent over-steering or under-steering phenomena, which often generate accidents. This can be caused by a tire undergoing excessive slip/skid, so that the driver does not reach the intended trajectory. Currently, sensors exist able to measure tire-ground forces. Nevertheless, their cost amounts to several tens thousand euros per piece, which makes them incompatible with commercial automobiles mass production. The introduction of observers to estimate these forces is an effective solution for this problem. However, to provide estimates of the forces with sufficient accuracy is still considered an arduous task, since the variation of vehicle mass, Center of Gravity (COG) position, road slope or bank angle, along with road irregularities and load transfer effects, increase the problem complexity considerably. In the second part of this work this problem is faced, with the proposal of a novel method for model-based tire forces estimation, which relies on a development of SMC, namely the Second Order Sliding Mode (SOSM). One of the most challenging tasks for engineers in the implementation of vehicle stability control systems, which concerns both fields of AD and sport racing, is the handling of the vehicle on different kinds of road surface. In fact, the vehicle should autonomously interact with the environment, without endangering the safety of people on board and their surroundings. However, safety aspects are not the only ones to be considered, in fact by means of proper control techniques, it is also possible to improve the car performances. In the last part of this dissertation, the problem of minimizing the travel time of an AV running on a low friction terrain (such as gravel or dirt road) is considered. Such setting can be seen as an emulation of the behavior of a rally driver which, on slippery surfaces, might require to exploit a drifting maneuver while cornering. Two solutions to this problem are proposed, which exploit the information regarding the physical limits of the vehicle and the tire-road interaction forces, in order to obtain high speed solutions which guarantee the stability of the vehicle

Longitudinal vehicle dynamics control via sliding modes generation

In this chapter the application of Sliding Mode Control techniques is illustrated to solve the longitudinal vehicle dynamics control problem. Sliding Mode Control is a nonlinear control method capable of offering a number of benefits, the majority of which is its robustness versus a significant class of uncertainties. It can also be profitably used to efficiently solve automotive control and observation problems, as widely testified in the literature. The aim of this chapter is to provide an overview on the longitudinal dynamics control of vehicles, in particular electric vehicles with individual motors for each wheel, focusing on recent developments based on Sliding Mode Control theory

Optimization-based adaptive sliding mode control with application to vehicle dynamics control

This paper presents the design of a new adaptive optimization‐based second‐order sliding mode control algorithm for uncertain nonlinear systems. It is designed on the basis of a second‐order sliding mode control with optimal reaching, with the aim of reducing the control effort while maintaining all the positive aspects in terms of finite‐time convergence and robustness in front of matched uncertainties. These features are beneficial to guarantee good performance in case of vehicle dynamics control, a crucial topic in the light of the increasing demand of semi autonomous and autonomous driving capabilities in commercial vehicles. The new proposal is theoretically analyzed, as well as verified relying on an extensive comparative study, carried out on a realistic simulator of a 4‐wheeled vehicle, in the case of a lateral stability control system