In this paper, we propose a novel approach to minimize the effects of small duty cycle perturbations, due to extremum seeking control (ESC), on the output current of a photovoltaic (PV) cell array connected to the electrical grid. Specifically, we formulate a bilevel optimization problem that incorporates the power maximization objective together with a current quality objective. Next, by means of monotone operator theory, we show how to solve the problem via optimized ESC. Finally, we test the effectiveness of the proposed approach on a numerical simulation example.

We present a singular perturbation theory applicable to systems with hybrid boundary layer systems and hybrid reduced systems {with} jumps from the boundary layer manifold. First, we prove practical attractivity of an adequate attractor set for small enough tuning parameters and sufficiently long time between almost all jumps. Second, under mild conditions on the jump mapping, we prove semi-global practical asymptotic stability of a restricted attractor set. Finally, for certain classes of dynamics, we prove semi-global practical asymptotic stability of the restricted attractor set for small enough tuning parameters and sufficiently long period between almost all jumps of the slow states only.

In this paper we present an averaging technique applicable to the design of zeroth-order Nash equilibrium seeking algorithms. First, we propose a multi-timescale discrete-time averaging theorem that requires only that the equilibrium is semi-globally practically stabilized by the averaged system, while also allowing the averaged system to depend on ``fast"states. Furthermore, sequential application of the theorem is possible, which enables its use for multi-layer algorithm design. Second, we apply the aforementioned averaging theorem to prove semi-global practical convergence of the zeroth-order information variant of the discrete-time projected pseudogradient descent algorithm, in the context of strongly monotone, constrained Nash equilibrium problems. Third, we use the averaging theory to prove the semi-global practical convergence of the asynchronous pseudogradient descent algorithm to solve strongly monotone unconstrained Nash equilibrium problems. Lastly, we apply the proposed asynchronous algorithm to the connectivity control problem in multi-agent systems.

Vehicle automation and connectivity bring new opportunities for safe and sustainable mobility in urban and highway networks. Such opportunities are however not directly associated with traffic flow improvements. Research on exploitation of connected and automated vehicles (CAVs) toward a more efficient traffic currently remains at a theoretical level, and/or based on simulation models with limited reliability. Furthermore, testing CAVs in the real world is still costly and very challenging from an implementation perspective. A possible alternative is to use automated robots. By designing and testing both the low- and the high-level controllers of CAVs, it is indeed possible to reach a better understanding of the challenges that future vehicles will need to face. Robotic applications can effectively test these challenges within a wide variety of research communities—for example, via robotic competitions. Along this direction, the Joint Research Centre has organized the first European robotic traffic competition for automated miniature vehicles. Each team participated with four robots and was judged based on a set of indicators that assess the collective behaviors of the vehicles. Results show the suitability of the methodology with different teams proposing completely different approaches to deal with the challenge and thus achieving different results. Future competitions may further raise awareness about the possibility of using CAVs to improve traffic and to engage with a broader community to design systems that are really capable of achieving this goal.

In this paper we consider the problem of finding a Nash equilibrium (NE) via zeroth-order feedback information in games with merely monotone pseudogradient mapping. Based on hybrid system theory, we propose a novel extremum seeking algorithm which converges to the set of Nash equilibria in a semi-global practical sense. Finally, we present two simulation examples. The first shows that the standard extremum seeking algorithm fails, while ours succeeds in reaching NE. In the second, we simulate an allocation problem with fixed demand.

In this paper, we present a hybrid adaptive feedback (HAF) obstacle-avoidance algorithm for source seeking applications that overcomes the obstacle-avoidance problem, as defined in [1], when using artificial potential functions (APF). Differently from [2], our algorithm does not require any knowledge on the location and orientation of the obstacle with respect to the source. Finally, we show via numerical simulations the effectiveness of our algorithm compared with the APF approach.