1,721,017 research outputs found
Distributed Automation System Lab at University of Naples Federico II [Its Research Lab]
No full textDDPG Based End-To-End Driving Enhanced With Safe Anomaly Detection Functionality for Autonomous Vehicles
No full textH∞-PID distributed control for output leader-tracking and containment of heterogeneous MASs with external disturbances
No full textThis paper addresses both the output leader-tracking and output containment control problems for heterogeneous linear high-order Multi-Agent Systems (MASs) sharing information over a directed communication topology and subject to external unknown disturbances. To solve these control problems, we propose a robust fully distributed Proportional-Integral-Derivative (PID) control strategy, equipped with a filter on the derivative action that allows obtaining both a simpler closed-loop formulation, without the need of the descriptor transformation, and good tracking performances in the case of fast reference signal behaviours. The stability analysis is analytically proven via the Lyapunov theory and the H∞ approach. The derived robust stability conditions are expressed as a set Linear Matrix Inequalities (LMIs) whose solution provides the proper tuning of the robust PID control gains. Numerical simulations confirm the effectiveness and robustness of the proposed approach in solving both the output leader-tracking and output containment control problems
Editorial Special Section on Coordination, Cooperation, and Control of Autonomous Vehicles in Smart Connected Road Environments
No full textMobility is facing a transformation in terms of connectivity, allowing vehicles to communicate with each other, to the road infrastructure, and to other road users. This enables coordination and cooperation, hence managing traffic and mobility at an entirely new level. Indeed, Cooperative, Connected and Automated Mobility enables and provides ITS services with better Quality of Service (QoS), compared to the same ITS services by only one of the ITS sub-systems (personal, vehicle, roadside, and central, infrastructures), thus improving the road management, reducing congestion, and contributing to sustainable and eco-mobility. By leveraging a network of Smart Infrastructures, it is possible to be continuously and promptly aware about the circulation and environment conditions, as well as the status of connected devices, along with the related technological services. Such knowledge, gained via the adoption of advanced sensing/communication technologies, has the potential to fundamentally shift the mobility paradigm towards mobility as a service. This contributes to more safe, efficient, and comfortable transportation systems. Along this line, information is continuously communicated/shared to vehicles and travellers thanks to dedicated communication services, thus enabling mobility automation and control. Different services - such as providing information about traffic light signal phases and their predicted changes or barriers on the route in realtime- support smooth and comfortable traveling by avoiding strong accelerations/decelerations, by reducing fuel/energy consumption of vehicles with favoured effects on lowering noise and emissions. In this perspective, the special section aims at exploring how to face Coordination and Cooperation challenges for autonomous vehicles in this new connected environment, also in the transition phase where connected human-driven vehicles are present
Design of Resilient Supervisory Control for Autonomous Connected Vehicles Approaching Unsignalized Intersection in presence of Communication Delays
No full textOn the Virtual Testing of ADAS in CCAM environment via Vehicle-in-the-Loop framework
No full textThis paper proposes a novel Vehicle-in-the-Loop (ViL) framework for automated/autonomous vehicles in compliance with the brand-new Connected Cooperative and Automated Mobility (CCAM) paradigm. To this end, the proposed solution creates the virtual world by leveraging the MOSAIC C-ITS platform for the emulation of a realistic connected traffic environment and realistic Vehicle-to-Everything (V2X) communication. Instead, the real world is represented by the DAiSY car, i.e. a scaled 1:5 electric vehicle, whose autonomous driving system is developed in the ROS2 framework. To disclose the effectiveness of the proposed virtual testing procedure, the Adaptive Cruise Control (ACC) in the CCAM environment is considered as case of study. Experimental results prove the engineering value of the proposed solution and highlight its potentiality in virtually testing different CCAM services
Signed Average Consensus in Cooperative-Antagonistic Multi-Agent Systems with multiple communication time-varying delays
No full textThis article addresses the distributed average consensus problem for cooperative-antagonistic Multi-Agent Systems (MASs) in the presence of multiple and unknown communication time-varying input delays, whose actual values depend on the effective conditions of the wireless channels. A distributed delayed control strategy, able to counteract the unavoidable communication impairments, is proposed for the achievement of the exponential stability of the networked control system. The convergence analysis, which leverages the Lyapunov stability and Halanay's lemma, also handles the fast time-varying delay case and analytically revels the relationship among the estimation of the maximum delay upper-bound, the smallest nonzero eigenvalue of Laplacian matrix, i.e. the Fiedler eigenvalue, and the control gain. Finally, numerical simulations confrm the theoretical derivation
Design of Resilient Supervisory Control for Autonomous Connected Vehicles Approaching Unsignalized Intersection in presence of Cyber-Attacks
No full textDistributed robust output consensus for linear multi-agent systems with input time-varying delays and parameter uncertainties
No full textThis study addresses the leader-tracking problem for linear multi-agent systems in the presence of both parameter model uncertainties and time-varying communication delays. To solve the robust output consensus problem, a delayed distributed proportional–integral–derivative control is proposed and the overall closed-loop stability is proven by exploiting the Lyapunov–Krasovskii theory. Delay-dependent robust stability conditions are given via linear matrix inequalities which allow the proper tuning of robust control gains. The effectiveness of the theoretical derivation is confirmed through a numerical analysis in the practical application domain of cooperative driving for connected vehicles
Sustainable DDPG-Based Path Tracking for Connected Autonomous Electric Vehicles in Extra-Urban Scenarios
No full textThis paper addresses the path-tracking control problem for Connected Autonomous Electric Vehicles (CAEVs) moving
in a smart Cooperative Connected Automated Mobility (CCAM)
environment, where a smart infrastructure suggests the reference
behaviour to achieve. To solve this problem, a novel energy-oriented
Deep Deterministic Policy Gradient (DDPG) control strategy, able
to guarantee the optimal tracking of the suggested path while minimizing the CAEVs energy consumption, is proposed. To this aim,
the power autonomy, the battery state of charge (SOC), the overall
power train model -comprehensive of the electric motor equations,
inverter dynamics and the battery pack model- is embedded within
the training process of the DDPG agent, hence letting the CAEV to
travel according to the best sustainable driving policy. The training
procedure and the validation phase of the proposed control method
is performed via an own-made advanced simulation platform
which, combining Matlab & Simulink with Python environment,
allows the virtualization of real driving scenarios. Specifically, the
training process confirms the capability of DDPG agent in learning
the safe eco-driving policy, while, the numerical validation, tailored
for the realistic extra-urban scenario located in Naples, Italy, discloses the capability of the DDPG-based eco-driving controller in
solving the appraised CCAM control problem despite presence of
external disturbances. Finally, a robustness analysis of the proposed
strategy in ensuring the ecological path tracking control problem
for different CAEV models and driving path scenarios, along with a
comparison analysis with respect model-based controls, is provided
to better highlights the benefits/advantages of the proposed Deep
Reinforcement Learning (DRL) control
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