Recently proposed adaptive platooning strategies for connected automated vehicles are able to cope with uncertain vehicle parameters (uncertain driveline time constants), but can handle only acyclic graphs like look-ahead graphs. This prevents from enhancing platooning protocols with synchronized merging maneuvers, where cyclic communication is needed and creates algebraic loops that require well posedness of the inputs. We propose an adaptive platooning strategy for synchronized merging in the cyclic communication scenario. The protocol adopts a set of adaptive control laws, designed via Lyapunov stability theory to cope with uncertain driveline time constants. Well-posedness of the inputs is proven in a distributed way (using information from neighboring vehicles) in spite of uncertainty and cyclic communication. The proposed strategy is shown in a benchmark merging scenario.

In recent years, field experiments have been performed on ring roadways with human-driven vehicles or with a mix of human-driven and autonomous vehicles. While these experiments demonstrate the potential for controlling traffic flows by a small number of autonomous vehicles, the theoretical framework about such a possibility is to a large extent incomplete. Indeed, most work on mixed traffic focused on classical asymptotical stability notions, neglecting that human drivers are prone to the interconnected instability known in the literature as string instability. This work aims to enhance the existing theories to meet the questions raised by the field experiments. It starts from the observation that the standard notion of string stability on a ring roadway is too demanding for a mixed traffic scenario: therefore, a new interconnected stability definition, named weak ring stability, is proposed. This new interconnected stability notion, in combination with classical stability, is able to explain phenomena observed in field experiments and to highlight possibilities and limitations of traffic control via sparse autonomous vehicle. Furthermore, it allows designing AV controllers with improved string stability specifications, at the price of reducing the sparsity of the autonomous vehicles.

In this paper we consider the problem of controlling a limited number of target nodes of a network. Equivalently, we can see this problem as controlling the target variables of a structured system, where the state variables of the system are associated to the nodes of the network. We deal with this problem from a different point of view as compared to most recent literature. Indeed, instead of considering controllability in the Kalman sense, that is, as the ability to drive the target states to a desired value, we consider the stronger requirement of driving the target variables as time functions. The latter notion is called functional target controllability. We think that restricting the controllability requirement to a limited set of important variables justifies using a more accurate notion of controllability for these variables. Remarkably, the notion of functional controllability allows formulating very simple graphical conditions for target controllability in the spirit of the structural approach to controllability. The functional approach enables us, moreover, to determine the smallest set of steering nodes that need to be actuated to ensure target controllability, where these steering nodes are constrained to belong to a given set. We show that such a smallest set can be found in polynomial time. We are also able to classify the possible actuated variables in terms of their importance with respect to the functional target controllability problem.

This work deals with state synchronization of heterogeneous linear agents with unknown dynamics. The problem is solved by formulating the synchronization problem as a special model reference adaptive control where each agent tries to converge to the model defined by its neighbors. For those agents that do not know the reference signal that drives the flock, a fictitious reference is estimated in place of the actual one: the estimation of such reference is distributed and requires measurements from neighbors. By using a matching condition assumption, which is imposed so that the agents can converge to the same behavior, the fictitious reference estimation leads to adaptive laws for the feedback and the coupling gains arising from distributed matching conditions. In addition, the coupling connection is not scalar as in most literature, but possibly vector-valued. The proposed approach is applicable to heterogeneous agents with arbitrarily large matched uncertainties. A Lyapunov-based approach is derived to show analytically asymptotic convergence of the synchronization error: robustification in the presence of bounded errors or unknown (constant) leader input is also discussed. Finally, a motivational example is presented in the context of Cooperative Adaptive Cruise Control and numerical examples are provided to demonstrate the effectiveness of the proposed method.

This article considers criteria to determine when stop-and-go waves form in platoons of human-driven vehicles, and when they can be dissipated by the presence of an autonomous vehicle. Our analysis takes the start from the observation that the standard notion of string/ring stability definition, which requires uniformity with respect to the number of vehicles in the platoon, is too demanding for a mixed traffic scenario. The setting under consideration is the following: the vehicles run along a ring road and the human-driven vehicles obey a combined follow-the-leader and optimal velocity model, while the autonomous vehicle obeys an appropriately designed model. The criteria are tested on a linearized version of the resulting platoon dynamics and simulation tests using nonlinear model are carried out.

Using a setting in which the input is communicated among neighbors (without exchanging any distributed observer variables), the problem of synchronizing an acyclic network of linear uncertain agents has been formulated recently as a distributed model reference adaptive control (MRAC) where each agent tries to converge to the model defined by its neighbors. In this work we show how to parametrize the distributed MRAC in cyclic and undirected graphs.

This work presents a distributed model reference adaptive methodology for output synchronization of heterogeneous linear agents with unknown dynamics. We consider a setting in which the control input is communicated among neighbors, instead of observer variables. For those agents that can access the signals of the reference model, classical model reference adaptation laws lead to leader synchronization; for those agents that cannot access such signals, synchronization must be achieved by taking the neighboring agents as an alternative reference model. We show that these two groups of agents give rise to two types of matching conditions: the standard conditions to match the reference model, and new distributed matching conditions among neighboring agents. Since all matching gains are unknown, the gains are adapted online via Lyapunov-based estimation. Asymptotic synchronization is proven analytically, and numerical examples show the effectiveness of the approach.