Showing papers by "Riccardo M.G. Ferrari published in 2019"
01 Dec 2019
TL;DR: Stability of the observer and robustness of the detection threshold in the case of event-triggered communication, following a realistic Vehicle-to-Vehicle network protocol are proved.
Abstract: Platoons of autonomous vehicles are being investigated as a way to increase road capacity and fuel efficiency. Cooperative Adaptive Cruise Control (CACC) is one approach to controlling platoons longitudinal dynamics, which requires wireless communication between vehicles. In the present paper we use a sliding mode observer to detect and estimate cyber-attacks threatening such wireless communication. In particular we prove stability of the observer and robustness of the detection threshold in the case of event-triggered communication, following a realistic Vehicle-to-Vehicle network protocol.
17 citations
TL;DR: This work formulate active inference from a control perspective, deriving a model-free control law which is less sensitive to unmodeled dynamics, and shows that the AIC outperformed the MRAC in terms of adaptability, providing a more general control law.
Abstract: More adaptive controllers for robot manipulators are needed, which can deal with large model uncertainties. This paper presents a novel active inference controller (AIC) as an adaptive control scheme for industrial robots. This scheme is easily scalable to high degrees-of-freedom, and it maintains high performance even in the presence of large unmodeled dynamics. The proposed method is based on active inference, a promising neuroscientific theory of the brain, which describes a biologically plausible algorithm for perception and action. In this work, we formulate active inference from a control perspective, deriving a model-free control law which is less sensitive to unmodeled dynamics. The performance and the adaptive properties of the algorithm are compared to a state-of-the-art model reference adaptive controller (MRAC) in an experimental setup with a real 7-DOF robot arm. The results showed that the AIC outperformed the MRAC in terms of adaptability, providing a more general control law. This confirmed the relevance of active inference for robot control.
12 citations
Posted Content•
01 Nov 2019TL;DR: In this article, a sliding mode observer is used to detect and estimate cyber-attacks threatening wireless communication between vehicles in a platoon of autonomous vehicles, in the case of event-triggered communication, following a realistic V2V network protocol.
Abstract: Platoons of autonomous vehicles are being investigated as a way to increase road capacity and fuel efficiency. Cooperative Adaptive Cruise Control (CACC) is one approach to controlling platoons longitudinal dynamics, which requires wireless communication between vehicles. In the present paper we use a sliding mode observer to detect and estimate cyber-attacks threatening such wireless communication. In particular we prove stability of the observer and robustness of the detection threshold in the case of event-triggered communication, following a realistic Vehicle-to-Vehicle network protocol.
3 citations
TL;DR: This work proposes a mechanism based on differential privacy to render such model identification techniques ineffective while preserving the utility of the state samples for data aggregation purposes and describes the privacy-utility trade-off that arises when deploying differential privacy.
1 citations