The paper deals with a robust autonomous path-following functionality, in which the safe velocity and motion profile of vehicle varying tyre-road contact must be guaranteed. integration Linear Parameter-Varying (LPV) control results machine learning-based analysis on big data vehicles is proposed. achieved two steps. First, an estimation method adhesion coefficient through decision trees presented. result incorporated LPV scheduling variable. During design, error machine-learning algorithm...

In this paper, the coordination and control design of a variable-geometry suspension is proposed, in which independent steering torque vectoring are integrated. The motivation paper research on novel smart actuators for autonomous urban vehicles, by various functionalities can be achieved. goal to propose trajectory tracking with increased reachability domain. Therefore, hierarchical system using robust linear-parameter varying (LPV) methods presented. stability hierarchy guaranteed through...

This paper presents a novel modeling method for the control design of autonomous vehicle systems. The goal is to provide control-oriented model in predefined Linear Parameter Varying (LPV) structure. scheduling variables LPV through machine-learning-based methods using big dataset are selected. Moreover, parameters an optimization algorithm computed, with which accurate fitting on achieved. proposed illustrated nonlinear lateral dynamics. resulting LPV-based used path following functionality...

Model Free Control (MFC) is a novel strategy to handle nonlinear and unknown dynamices in the control of vehicle systems. The method based on an ultra-local model, which approximates dynamical behavior system for short time period. Although this algorithm has already been successfully applied several problems, there are some open questions, related its implementation tuning issues. This paper proposes new parameter model achieving improved trajectory tracking performances with MFC. A...

Big data analysis has an increasing importance in the field of autonomous vehicles. It is related to vehicular networks and individual control. The paper proposes improvement a lateral vehicle control design through big on measured signals. Based decision tree generated by using C4.5 MetaCost algorithms. results regions dynamic states guarantees tracking vehicle. problem formed MPC (Model Predictive Control) structure, which are built as constraints. efficiency proposed method illustrated...

The paper presents the control design of an independent wheel steering system for in-wheel electric vehicles. wheels is based on variable-geometry suspension control, which influences camber angle and scrub radius wheel. A nonlinear formulation derived, validated through a high-fidelity model. Furthermore, impact in lateral model vehicle incorporated. In robust hierarchical structure presented. trajectory tracking guaranteed by H∞ while intervention designed Linear Quadratic theorem.

The paper proposes the implementation of a control system for variable-geometry suspension to achieve steering functionality. is performed in Hardware-in-the-Loop (HiL) simulation environment with unique test bed. purpose path-following functionality vehicle automatic based on proposed system. design and hierarchical high-level responsible computation requested angle, low-level aims realise angle through intervention linear actuator effectiveness demonstrated HiL examples.

In this paper, a novel neural network-based robust control method is presented for vehicle-oriented problem, in which the main goal to ensure stable motion of vehicle under critical circumstances. The proposed can be divided into two steps. first step, model matching algorithm proposed, adjust nonlinear dynamics controlled system nominal, linear model. aim eliminate effects nonlinearities and uncertainties increase performances closed-loop system. process results an additional input,...

This paper presents an analysis of autonomous vehicle dynamics which focuses on lateral stability. The is based the collection big data from signals vehicle. core method C4.5 machine learning algorithm. purpose examinations to determine relationship between various (e.g., yaw rate, side slip angle, longitudinal speed, adhesion coefficient) and results approach are incorporated in stability analysis. regions calculated used as constraints predictive control design vehicles.

The paper presents a new variable-geometry suspension system which is applied in-wheel electric vehicles. It able to realize the steering of vehicle by independent wheel camber angles and create differential yaw moment harmonizing longitudinal forces. In order perform trajectory tracking control signals are virtual signals, such as angles. two physical active torques realized on either side front axle forces motors. design based hierarchical structure. purpose high-level controller calculate...

Tire pressure has a high impact on the tire-road contact because it influences characteristics of tire forces. During maneuvering vehicle pressures tires may decrease over time, which results in performance degradation or loss controllability. This paper proposes novel integration estimation and path-following control design based machine learning Linear Parameter-Varying (LPV) methods. In process dynamic signals, are available from conventional on-board sensors, fused. The values estimated...

The paper proposes a side-slip angle estimation method for autonomous road vehicles using big data approach. core of the solution is that on-board signals numerous are available, which can be used to generate estimator. based on ordinary linear regression with OLS subset selection large amount collected from car sensors. advantage proposed numerically complex mining operations performed off-line, while vehicle only its own requires low computation effort. efficiency presented through several...

The concept of vehicle automation is a promising approach to achieve sustainable transport systems, especially in an urban context. Automation requires the integration learning-based approaches and methods control theory. Through integration, high amount information can be incorporated. Thus, operation, i.e., energy-efficient safe motion with automated vehicles, achieved. Despite advantages approaches, enhanced poses crucial safety challenges. In this paper, novel closed-loop matching method...

Model Free Control (MFC) with ultra-local model is novel design method to provide high performance control for systems nonlinear dynamics. The basic concept of this approach compute an model, which valid a short period time. Although the has already been successfully applied various vehicle problems, there are some open issues regarding robustness and stability closed-loop controlled system. This paper presents designing robust based on <tex xmlns:mml="http://www.w3.org/1998/Math/MathML"...

This paper proposes the modeling and control design of an electro-hydraulic actuator for variable-geometry suspension system. The purpose this novel type is to guarantee trajectory tracking through variation camber angle. motion wheel realized by actuator, whose elements are cylinder electric valve. Thus, vehicle requires positioning cylinder. First, system presented using hydraulic mechanical equations. Since dynamics contains several nonlinearities, a control-oriented linear model formed....

The paper proposes the analysis and control of a steering system which is linked to requirements autonomous vehicles. an vehicle must guarantee reliable highly efficient intervention even in extreme conditions. Therefore, determination reachability domain important. Both simulations frequency methods are applied calculation. shows that significantly depends on both angle speed, utilized design. Two results incorporated robust design steering. First, built into ℋ <sub...

AbstractThe paper presents a novel control strategy for autonomous vehicles, which can effectively handle the nonlinear effects and uncertainties of controlled system. This solution is based on new formulation ultra-local model-based approach. method deal with unknown dynamics other vehicle without any further measurement or complex modelling process. The methods require reduced-complexity nominal linear model system, part continuously estimated by proposed model. control-oriented leads to...

This paper presents a control design framework for the integration of robust and reinforcement learning-based (RL) agent. The proposed method is applied motion autonomous road vehicles, providing safe motion. In integrated control, longitudinal lateral dynamics are incorporated. high-performance vehicle, e.g., high-velocity motion, path following, reduction acceleration, through RL-based agent achieved. training Proximal Policy Optimization during episodes performed. Safe with guaranteed...

In the last decades vehicles have been equipped with more and sensors. These sensors provide a lot of data about vehicle its environment. This might contain hidden information vehicle, which can be revealed using big approaches. The paper presents design MPC (Model Predictive Control) based lateral controller for autonomous vehicles, in results analysis are exploited. is provided high-fidelity car simulation software, CarSim. Moreover, analyses, C4.5 decision algorithm used, augmented...

The paper proposes a new road surface estimation algorithm for autonomous vehicles using machine-learning based method, which is in cooperation with the lateral control of vehicle. uses large datasets, can be collected e.g. from on-board sensors vehicle, or it also provided by vehicle dynamic simulation softwares. result built-in system as scheduling parameter. Furthermore, design on Linear Parameter Varying (LPV) guarantees safe motion against varying parameters system. Finally,...

The paper presents a new big data based control design for autonomous vehicles. main contribution of this work is the longitudinal velocity optimization process, which on approximation reachability sets passenger vehicle by using machine-learning approach. data, used approximation, provided high-fidelity car simulation software, CarSim. performed applying well-known decision tree algorithm, C4.5. are computed different velocities. Moreover, LPV technique lateral proposed, to guarantee...

The paper proposes a robust control design for the integration of steering and torque vectoring using variable-geometry suspension system. Torque is based on independent steering/driving wheel systems, which an actual challenge in field electric vehicles. Simultaneously, angle generated by system changing camber position wheel-road contact independently at each front. tilting focus paper. modeling lateral dynamics vehicle leads to interconnection models. A hierarchical structure both formed....

The paper presents a reconfigurable control strategy for the lateral stability of autonomous vehicles. is based on analysis big data, which are provided by sensor networks core method machine learning algorithm, with impacts various vehicle signals dynamics have been examined. In several scenarios faults in steering and in-wheel systems considered using high-fidelity simulation software. results examination built into fault-tolerant reconfiguration strategy.

The paper presents a data-driven control design for steering system, which is based on variable-geometry suspension. proposed system into hierarchical structure with different roles each layer ordered, i.e., low-level and high-level controls are designed. controller responsible the realization of angle, while guarantees trajectory tracking vehicle. polytopic model designed, through identification approach carried out. effectiveness operation Hardware-in-the-Loop (HiL) simulation...