This paper presents an integral sliding mode (ISM) formulation for the torque-vectoring (TV) control of a fully electric vehicle. The performance of the controller is evaluated in steadystate and transient conditions, including the analysis of the controller performance degradation due to its real-world implementation. This potential issue, which is typical of sliding mode formulations, relates to the actuation delays caused by the drivetrain hardware configuration, signal discretization, and vehicle communication buses, which can provoke chattering and irregular control action. The controller is experimentally assessed on a prototype electric vehicle demonstrator under the worst-case conditions in terms of drivetrain layout and communication delays. The results show a significant enhancement of the controlled vehicle performance during all maneuvers. Index Terms-Actuation delays, experimental tests, integral sliding mode (ISM), torque-vectoring (TV), yaw rate control. NOMENCLATURE a, b Front and rear semi-wheel bases. a x , a y Longitudinal and lateral vehicle accelerations. c F , c R Front and rear track widths. c hs , k hsHalf-shaft torsion damping coefficient and torsional stiffness. f , h, n, k Known functions of the states (x), the contribution due to uncertainties and disturbances, the term multiplied by the control input, and the yaw acceleration contribution due to lateral tire forces and selfaligning torques (SAT), respectively.

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The ongoing connection and automation of vehicles leads to a closer interaction of the individual vehicle components, which demands for consideration throughout the entire development process. In the design phase, this is achieved through co-simulation of component models. However, complex co-simulation environments are rarely (re-)used in the verification and validation phases, in which mixed real-virtual prototypes (e.g. Hardwarein-the-Loop) are already available. One reason for this are coupling errors such as time-delays, which inevitably occur in co-simulation of virtual and real-time systems, and which influence system behavior in an unknown and generally detrimental way. This contribution introduces a novel, adaptive method to compensate for constant time-delays in potentially highly nonlinear, spatially distributed mixed real-virtual prototypes, using small feedforward neural networks. Their optimal initialization with respect to defined frequency domain features results from a-priori frequency domain analysis of the entire coupled system, including coupling faults and compensation methods. A linear and a nonlinear example demonstrate the method and emphasize its suitability for nonlinear systems due to online training and adaptation. As the compensation method requires knowledge only of the bandwidths, the proposed method is applicable to distributed mixed real-virtual prototypes in general.

Non-iterative co-simulation is a prerequisite for the time correct coupling of distributed solved numerical problems. For this coupling approach typically signal-based extrapolation schemes are used to resolve existing bidirectional dependencies between the interacting subsystems. Nevertheless, the introduced coupling errors influence the entire system behavior. In the case of coupled real-time systems inherent timedelays and noisy measurements lead to significant additional distortions. Thus, to avoid a deteriorating dynamic behavior of coupled systems -which can even lead to instability -new coupling approaches are mandatory. A model-based extrapolation scheme is motivated to realize a compensation of the occurring round-trip-times and noise-handling. Besides the description of the fundamentals a representative example demonstrates the effectiveness of the proposed coupling approach.