The possibility of regulating the behavior of live animals using biologically-inspired robots has attracted the interest of biologists and engineers for over twenty-five years. From early work on insects to recent endeavors on mammals, we have witnessed fascinating applications that have pushed forward our understanding of animal behavior along new directions. Despite significant progress, most of the research has focused on open-loop control systems, in which robots execute predetermined actions, independent of the animal behavior. We integrate mathematical modeling of social behavior toward the design of realistic feedback laws for robots to interact with a live animal. In particular, we leverage recent advancements in data-driven modeling of zebrafish behavior. Ultimately, we establish a novel robotic platform that allows real-time actuation of a biologically-inspired 3D-printed zebrafish replica to implement model-based control of animal behavior. We demonstrate our approach through a series of experiments, designed to elucidate the appraisal of the replica by live subjects with respect to conspecifics and to quantify the biological value of closed-loop control.

A new algorithm to enhance the pinning controllability of complex networks of chaotic maps is presented. Leveraging the numerical observation that the existence of directed cycles in the graph hinders controllability, we develop an iterative procedure to obtain a forest structure in the graph. The effectiveness of the algorithm is illustrated with reference to the test-bed example of a network of coupled logistic maps.

In this paper, we present a numerical study on a modified Kuramoto model, in which the oscillators are coupled only if in mutual proximity. In particular, we show that the onset of phase locking is associated to the phenomenon of topological bifurcation. When the coupling strength belongs to a finite interval of values, all the oscillators asymptotically share the same frequency, but the topology of the interconnection may converge towards different steady-state values. This emerging phenomenon is thoroughly analyzed for the case of 5 nodes, while preliminary results are illustrated for larger networks.

This paper reports sufficient conditions to achieve bounded pinning synchronisation in complex networks of non-identical systems. The conditions are used to derive an easy-to-use algorithm for control design where the selection of the control gain is performed by solving a linear optimization problem. Experimental validation is provided of the theoretical results.

In this paper, we discuss a generalized model for studying bounded synchronization in complex networks. Namely, a novel adaptive strategy is proposed to control the network nodes. The aim of this strategy is to asymptotically make the mismatch between the nodes' trajectories lower than a prescribed threshold. The proposed strategy is then illustrated with reference to the Power Network model recently derived in [7]. After showing simulations that confirm the effectiveness of the approach, its limitations in view of possible implementations are discussed.

This paper is concerned with the experimental validation of pinning controllability in networks of Chua's circuits. Firstly, the implementation of an appropriate setup to carry out experiments on networks of Chua's with settable topology is presented. Then, for the first time, the stability thresholds on coupling and control gains estimated through the Pinning Controllability approach are shown to be in excellent agreement with the experiments performed in a real scenario, affected by unavoidable uncertainties and noise. The results illustrated in the paper confirm the effectiveness of the considered approach as an effective analysis and design tool.

In this paper we propose a novel adaptive pinning control strategy for synchronization of complex networks. The novelty of this strategy is the fully decentralized adaptive selection of the coupling and control gains. After giving a sketch of the stability proof, we validate the analysis with numerical simulations on a testbed example.

This paper discusses the robustness properties of a novel decentralized strategy for synchronization of complex networks based on adaptive local gains. Namely, the strategy assumes that each vertex adaptively selects the coupling strength with all of its local neighbours. The strategy was proved to guarantee synchronization and it is shown here to be surprisingly robust to topological variations of the underlying network. The case of time-delays on the coupling between oscillators in the network is also considered showing that, under certain conditions, the strategy still guarantees that the network will synchronize. The observations are supported by numerical simulations on the representative case of networks of Chua's circuits.

This paper is concerned with the analysis of the synchronization of networks of nonlinear oscillators through an innovative adaptive strategy. In particular, time-varying feedback coupling gains are considered, whose derivative is made directly dependent on the local synchronization error at each vertex in the network. It is shown that, under appropriate conditions, the strategy is indeed successful in guaranteeing the achievement of a common synchronous evolution for all oscillators in the network. The theoretical derivation is complemented by its validation on a set of representative examples.