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The paper presents a novel control strategy for autonomous vehicles, which can effectively handle the nonlinear effects and uncertainties of the controlled system. This solution is based on a new formulation of the ultra-local model-based control approach. This method can deal with the unknown dynamics and other uncertainties of the vehicle without any further measurement or complex modelling process. The control methods require a reduced-complexity nominal linear model of the system, and the unknown part is continuously estimated by the proposed ultra-local model. The proposed control-oriented vehicle modelling method leads to an increased performance level of vehicle motion. The effectiveness of the proposed method through a high-fidelity vehicle simulator (CarMaker) is illustrated. Moreover, the operation of the control through its implementation on a test vehicle of ThyssenKrupp is also demonstrated. [ABSTRACT FROM AUTHOR]

The paper deals with a robust autonomous path-following functionality, in which the safe velocity and the motion profile of the vehicle with varying tyre-road contact must be guaranteed. The integration of Linear Parameter-Varying (LPV) control and the results of the machine learning-based analysis on the big data of autonomous vehicles is proposed. The integration is achieved in two steps. First, an estimation method on the adhesion coefficient through decision trees is presented. The result of the estimation is incorporated in the robust LPV control through a scheduling variable. During the control design, the error of the machine-learning algorithm is incorporated. Second, an optimisation method of the longitudinal velocity on a predicted horizon is proposed, which is aided with the machine learning-based reachability set approximation of the steering intervention. The effectiveness of the control strategy is illustrated through CarSim simulations. [ABSTRACT FROM AUTHOR]

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

This paper proposes enhanced prediction and control design methods for improving traffic flow with human-driven and automated vehicles. To achieve accurate prediction for the entire time horizon, data-driven and model-based prediction methods were integrated. The goal of the integration was to accurately predict the outflow of the traffic network, which was selected as the highway section in this paper. The proposed novel prediction method was used in the optimal design for calculating controlled inflows on highway ramps. The goal of the design was to reach the maximum outflow of the traffic network, even against disturbances on uncontrolled inflows of the network. The control design leads to an optimization problem based on the min–max principle, i.e., the traffic outflow is considered to be maximized by controlled inflows and to be minimized by uncontrolled inflows. The effectiveness of the prediction and the control methods through simulation examples are illustrated, i.e., traffic outflow can be maximized by the control system under various uncontrolled inflow values. [ABSTRACT FROM AUTHOR]