In this paper, an adaptive filter, based on a third-order generalized integrator, is proposed to estimate all the parameters of a biased sinusoid. The averaging theory is used to prove that the filter identifies the unknown frequency of the signal, in the case of a pure biased sinusoid in input. Moreover, in the case of a generic periodic signal, the method provides an estimate of the fundamental frequency by converging to a limit cycle in its vicinity. The robustness of the proposed approach with respect to noise in the input signal is analyzed. A filter bank is also presented to deal with the reconstruction problem of a generic multi-sinusoidal signal. Simulation results are also provided to compare the performances of the method with existing ones.

The number of pieces of equipment based on static converters such as uninterrupted power systems, series or shunt compensators, and distributed generation systems is increasing in the actual power distribution systems. For correct operation in grid-connected condition, these equipment need the information about amplitude, phase angle, and frequency of the grid fundamental voltages and currents. Since noise, harmonic pollution, and frequency variations are common problems in the utility grid, then it is necessary to have systems able to extract information about the fundamental values from highly distorted signals. For these reasons, robust and accurate estimation and synchronization methods are necessary to obtain the aforementioned information also in noise environmental. This paper presents a power electrical signal tracking strategy consisting in the combined use of a simple and robust frequency estimation method based on modulating functions and an orthogonal system generator including the second-order generalized integrator. The proposed strategy has the advantages of a fast and accurate signal tracking capability and a good rejection to noise due to the low-pass filter properties of the modulating functions. The effectiveness of the proposed method is validated through comparisons with existing methods performing simulated and laboratory experiments.

This note presents a new algorithm that is designed to identify the frequency, magnitude, phase and offset of a biased sinusoidal signal. The structure of the algorithm includes an orthogonal system generator based on a second-order generalized integrator. The proposed strategy has the advantages of a fast and accurate signal reconstruction capability and a good rejection to noise.

In this paper, platoons of autonomous vehicles operating in urban road networks are considered. From a methodological point of view, the problem of interest consists of formally characterizing vehicle state trajectory tubes by means of routing decisions complying with traffic congestion criteria. To this end, a novel distributed control architecture is conceived by taking advantage of two methodologies: deep reinforcement learning and model predictive control. On one hand, the routing decisions are obtained by using a distributed reinforcement learning algorithm that exploits available traffic data at each road junction. On the other hand, a bank of model predictive controllers is in charge of computing the more adequate control action for each involved vehicle. Such tasks are here combined into a single framework: the deep reinforcement learning output (action) is translated into a set-point to be tracked by the model predictive controller; conversely, the current vehicle position, resulting from the application of the control move, is exploited by the deep reinforcement learning unit for improving its reliability. The main novelty of the proposed solution lies in its hybrid nature: on one hand it fully exploits deep reinforcement learning capabilities for decision-making purposes; on the other hand, time-varying hard constraints are always satisfied during the dynamical platoon evolution imposed by the computed routing decisions. To efficiently evaluate the performance of the proposed control architecture, a co-design procedure, involving the SUMO and MATLAB platforms, is implemented so that complex operating environments can be used, and the information coming from road maps (links, junctions, obstacles, semaphores, etc.) and vehicle state trajectories can be shared and exchanged. Finally by considering as operating scenario a real entire city block and a platoon of eleven vehicles described by double-integrator models, several simulations have been performed with the aim to put in light the main features of the proposed approach. Moreover, it is important to underline that in different operating scenarios the proposed reinforcement learning scheme is capable of significantly reducing traffic congestion phenomena when compared with well-reputed competitors.

In this article, a novel distributed model predictive control architecture is proposed for the coordination and control of multivehicle formations moving within uncertain environments. As one of its main merits, multiagent swarm modeling and leader-follower configurations are jointly exploited within an ad hoc model predictive control framework to reduce as much as possible the use of onboard sensors that is essential in long-range missions. Moreover, the feasibility and asymptotic closed-loop stability of the resulting scheme are formally proved. Finally, a laboratory experiment is used to show the effectiveness of the proposed algorithm with particular attention to the follower capability of operating in a so-called blind fashion.