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Complex engineering systems are usually described by the interaction of several agents and characterized by highly nonlinear dynamics. Control of multivariable nonlinear systems is a widely explored topic, and many different studies have been presented in the scientific literature. However, most of the existing methods strongly rely upon an accurate model of the system, which is generally costly and/or hard to undertake in practice. In this work, we propose a multivariable extension of the data-driven inversion-based control (D2-IBC) method, where a model of the system is derived from data and considered relevant or not, based only on its weight on the final control performance. This method will prove its effectiveness on a challenging application: the stability control of a four-wheel steering autonomous vehicle.

Abstract-Knock is an undesired phenomenon occurring in spark ignited engines and is controlled acting on the spark timing. This paper presents a closed-loop architecture that makes possible to address the knock control problem with a standard model-based design approach. An engine knock margin estimate is feedback controlled through a PI regulator and its target value is computed starting from the desired knock probability. A black-box modelling approach is used to identify the dynamics between the spark timing and the knock margin and a traditional model-based controller synthesis is performed. Experimental results at the test bench show that, compared to a conventional strategy, the proposed approach allows for a better compromise between the controller speed and the variability of the spark timing. Moreover, another advantage w.r.t. the conventional strategies is that closed-loop performance prove to be constant for different reference probabilities, leading to a more regular engine behaviour.