Contemporary Supervised Machine Learning (SML) and explainable AI (artificial intelligence) methods can be employed to both model and understand the decision making behavior of human actors within a multi-agent task setting. Here, we apply such modeling approach to capture the decision-making behavior of human actors playing a 3-player online herding game called “Desert Herding”. Of particular interest is whether the modeling approach can be employed to predict and understand the target switching strategies of human herders at variable prediction horizons and whether the explainable AI tool SHAP can be leveraged to identify the key informational variables (features) underlying the players’ target selection decisions.

Designing artificial avatars able to interact with humans in a safe, smart, and natural way is a current open problem in control. Solving such an issue will allow the design of cyber-agents capable of cooperatively interacting with people in order to fulfil common joint tasks in a multitude of different applications. This is particularly relevant in the context of healthcare applications. Indeed, the use for rehabilitation has been proposed of artificial agents able to interact and coordinate their movements with those of patients suffering from social or motor disorders. Moreover, it has also been shown that the level of motor coordination between the avatar and the human patient is enhanced if the kinematic properties of the avatar’s motion are similar to those of the individual it is interacting with. In this article, we discuss, first, a new method based on Markov chains to confer “human motor characteristics” on the motion of a virtual agent so that it can coordinate its motion with that of a target individual while exhibiting specific kinematic properties. Then, we embed such synthetic model in a novel control architecture based on reinforcement learning to synthesize a cyber-agent able to mimic the behavior of a specific human performing a joint motor task with one or more individuals.

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.

In many joint-action scenarios, humans and robots have to coordinate their movements to accomplish a given shared task. Examples include lifting an object together, sawing a wood log, transferring objects from a point to another. While dyadic coordination between a human and a robot has been studied in previous investigations, the multi-agent scenario in which a robot has to be integrated into a human group still remains a less explored field of research. In this paper we discuss how to synthesise an artificial agent, driven by a control architecture based on deep reinforcement learning, able to coordinate its motion in human ensembles. As a paradigmatic coordination task we take a group version of the so called mirror game from the human movement literature.

Leadership is a complex yet fascinating phenomenon crucial in many application where coordination of multi-agent systems is involved. This is particularly true in the case of animal groups and other natural systems including human ensembles where, for example in orchestras, a leader is often selected to steer the group towards some desired coordinated behaviour. In this paper we study the emergence of leadership in networks of heterogeneous Kuramoto oscillators, which offer a good model for capturing and describing coordination in human groups performing a joint oscillatory task. We present a mathematical framework to establish phase leadership in the network and apply the results to inform some preliminary experimental results in human groups.

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.

The mirror game has been recently proposed as a simple, yet powerful paradigm for studying interpersonal interactions. It has been suggested that a virtual partner able to play the game with human subjects can be an effective tool to affect the underlying neural processes needed to establish the necessary connections between the players, and also to provide new clinical interventions for rehabilitation of patients suffering from social disorders. Inspired by the motor processes of the central nervous system (CNS) and the musculoskeletal system in the human body, in this paper we develop a novel interactive cognitive architecture based on nonlinear control theory to drive a virtual player (VP) to play the mirror game with a human player (HP) in different configurations. Specifically, we consider two cases: 1) the VP acts as leader and 2) the VP acts as follower. The crucial problem is to design a feedback control architecture capable of imitating and following or leading an HP in a joint action task. The movement of the end-effector of the VP is modeled by means of a feedback controlled Haken-Kelso-Bunz (HKB) oscillator, which is coupled with the observed motion of the HP measured in real time. To this aim, two types of control algorithms (adaptive control and optimal control) are used and implemented on the HKB model so that the VP can generate a human-like motion while satisfying certain kinematic constraints. A proof of convergence of the control algorithms is presented together with an extensive numerical and experimental validation of their effectiveness. A comparison with other existing designs is also discussed, showing the flexibility and the advantages of our control-based approach.

We analyze and solve the platooning problem by treating it as the problem of achieving consensus in a network of dynamical systems affected by time-varying heterogeneous delays due to wireless communication among vehicles. Specifically, a platoon is modeled as a dynamical network where: 1) each vehicle, with its own dynamics, is a node; 2) the presence of communication links between neighboring vehicles is represented by edges; and 3) the structure of the intervehicle communication is encoded in the network topology. A distributed control protocol, which acts on every vehicle in the platoon, is derived. It is composed of two terms: a local action depending on the state variables of the vehicle itself (measured onboard) and an action depending on the information received from neighboring vehicles through the communication network. The stability of the platoon is proven by using Lyapunov-Razumikhin theorem. Numerical results are included to confirm and illustrate the theoretical derivation.

In this paper we introduce the hypercube topology and show effects over the performance of communication networks in term of throughput and average lifetime. Hypercube network structures have been proposed as effective topologies to increase the network performance. We test this hypothesis by considering appropriate nonlinear traffic generation models and comparing the performance of hypercube networks with that of other network topologies which were already studied in the literature. We show that the hypercube network structure presents better features combining some of the advantages of regular lattices with those of other more complex network structures.

In this paper we investigate the use of distributed PI actions to achieve consensus and synchronization in complex networks. We show that by extending the classical linear diffusive coupling with an integral action it is possible to achieve better performance and steady-state behavior than with more traditional strategies. After briefly summarizing the theoretical results, we investigate the viability of the proposed strategy via numerical simulations including a representative example inspired from power system models recently presented in the literature.

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 the Cooperative Adaptive Cruise Control strategy for vehicles platooning is extended to the case when each vehicle can communicate with a subset of vehicles in the fleet. The control objective is to guarantee that the fleet moves forward with a given spacing policy at the leader velocity. To this aim each vehicle decides its control action using information from all neighboring vehicles through wireless communication. In so doing, a network of dynamical systems is formed, and it is shown that achieving platooning is equivalent to find a control algorithm so that the resulting network is asymptotically stable. A network protocol able to deal with heterogeneous time-varying communication delays is then proposed to solve the problem. A consistent proof of stability of the closed-loop system is provided and numerical results confirm the effectiveness of the approach and its robustness with respect to variations of the leader velocity, as well as to generic topologies of the underlying network emerging from the communication features.

We present the analysis of data collected in the mirror game setting. In our set-up two players are asked to mirror each other movements (with or without a designated leader). First, we study kinematic characteristics of motion of individual players, and next we investigate how they are affected by interactions between the players. Results of the presented analysis will be used to inform the design of interactive virtual players with kinematics based on the similarity principle.

The so called mirror game, which in its simplest formulation involves two people mirroring each other's hand's movement, provides a paradigm to study social interaction. However, a customized virtual player can replace either of the two human participants and hopefully help with the rehabilitation of patients suffering from social disorders by regulating its kinematics. In this paper we investigate the coordination movement between an avatar (virtual player) and a human player in the mentioned game. A novel cognitive architecture is proposed to drive the motion of the virtual player so that it generates a human-like trajectory in two different experimental models. In order to achieve this objective, the Haken-Kelso-Bunz (HKB) equation is adopted to describe the social motor coordination between the virtual and the human player. In addition, both an adaptive algorithm for the coupling parameters in the HKB equation and a feedback controller are developed in order to guarantee human features for the virtual player in its kinematics. Finally, extensive experiments are conducted to validate the approach described above.

We describe an innovative experimental and computational approach to control the expression of a protein in a population of yeast cells. We designed a simple control algorithm to automatically regulate the administration of inducer molecules to the cells by comparing the actual protein expression level in the cell population with the desired expression level. We then built an automated platform based on a microfluidic device, a time-lapse microscopy apparatus, and a set of motorized syringes, all controlled by a computer. We tested the platform to force yeast cells to express a desired fixed, or time-varying, amount of a reporter protein over thousands of minutes. The computer automatically switched the type of sugar administered to the cells, its concentration and its duration, according to the control algorithm. Our approach can be used to control expression of any protein, fused to a fluorescent reporter, provided that an external molecule known to (indirectly) affect its promoter activity is available.

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.

Networks are widely used to describe many natural and technological systems. Understanding how these evolve over time poses a challenge for existing visualization techniques originally developed for fixed network structures. We describe a method of incorporating the concept of aging into evolving networks, where nodes and edges store information related to the amount of local evolutionary change they have experienced. This property is used to generate visualizations that ensure stable substructures maintain relatively fixed spatial positions, allowing them to act as visual markers and providing context for evolutionary change elsewhere. By further supplementing these visualizations with color cues, the resultant animations enable a clearer portrayal of the underlying evolutionary process.

This paper reviews some recent results on the analysis of a little known phenomenon in PWS systems: two-fold singularities. Such singularities were shown to be present in Filippov systems when the boundaries of the sliding region intersect transversally. After briefly describing a two-fold singularity and its associated dynamics into a generic Filippov vector field, we present its occurrence in two representative nonsmooth electrical systems.

This paper addresses the problem of synchronization of piecewise smooth systems. We introduce conditions to guarantee bounded synchronization of two coupled PWS systems as a preliminary work for a future study of a huger network of discontinuous agents. Numerical examples are used to confirm theoretical results.

This paper is concerned with the experimental analysis of synchronization in networks of non identical Chua's circuits, with emphasis on robustness. We validate the theoretical expectations of the so-called Extended Master Stability Function approach by numerical simulations and experiments. We show that the EMSF is an effective tool to understand the onset of synchronization in real-world networks of non-identical nodes.

This paper presents the first digital implementation of a novel model reference adaptive scheme for the control of piecewise affine circuits and systems. The control law is implemented by using a low-cost commercial microcontroller. The aim is to control a piecewise-linear electrical circuit. The experimental validation process is made challenging by the presence of measure uncertainties, noise, quantization errors, unmodelled nonlinear dynamics, computational delays. Moreover, a digital microcontroller is used to implement an analogue continuous-time control law. Nevertheless, experiments confirm the effectiveness of the controller to cope with switching in the circuit dynamics, establishing the strategy as a viable control 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.

The aim of this paper is to introduce the special session on Recent Advances in Complex Networks: Theories and Applications at ISCAS 2010 by giving a brief outline of the subject and presenting some recent advances in a field of our own interest, i.e., Control and Synchronization of Complex Networks, with special attention to adaptive synchronization and control strategies.

This paper addresses the problem of providing mathematical conditions that allow one to ensure that biological networks, such as transcriptional systems, can be globally entrained to external periodic inputs. Despite appearing obvious at first, this is by no means a generic property of nonlinear dynamical systems. Through the use of contraction theory, a powerful tool from dynamical systems theory, it is shown that certain systems driven by external periodic signals have the property that all their solutions converge to a fixed limit cycle. General results are proved, and the properties are verified in the specific cases of models of transcriptional systems as well as constructs of interest in synthetic biology. A self-contained exposition of all needed results is given in the paper.

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 a novel algorithm to study networks of biological clocks. A new set of sufficient conditions is established that can be used to give guidelines to construct synthetic biological oscillators that synchronize.

This paper is concerned with the design and comparison of a new optimal-adaptive control of an electronic throttle body. Numerical results are complemented by experiments.

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.

This paper is concerned with the investigation of a novel hybrid LQ-adaptive strategy based on the so called minimal control synthesis algorithm (MCS). A model reference MCS adaptive control scheme is implemented where the reference model is controlled via an LQ regulator. An additional supervisory control action updates on-line the reference model and the gains of the LQ control, switching between a control and an identification phase. The resulting hybrid LQ-MCS algorithm is validated on a representative case study.

The paper is concerned with the interplay between network structure and traffic dynamics in a communications network, from the viewpoint of end-to-end performance of packet transfer. We use a model of network generation that allows the transition from random to scale-free networks. Specifically, we are able to consider three different topological types of networks: (a) random; (b) scale-free with gamma = 3; (c) scale-free with gamma = 2. We also use an LRD traffic generator in order to reproduce the fractal behavior that is observed in real world data communication. The issue is addressed of how the traffic behavior on the network is influenced by the variable factors of the transmission rates and queue length restrictions at the network vertices. We show that these factors can induce drastic changes in the throughput and delivery time of network performance and are able to counter-balance some undesirable effects due to the topology

In this paper, we describe a simple second-order discrete-time model for the transmission control protocol (TCP) with random early detection (RED) algorithm. The TCP/RED mechanism is viewed as a feedback control system where TCP adjusts its sending rate depending on packet loss. We investigate bifurcations and chaos in a TCP/RED system with a single TCP connection. We conjecture that the complex behavior observed in the system is attributed to a class of discontinuity-induced bifurcations observed in piecewise smooth systems.

Power electronics circuits are rich in nonlinear dynamics. Their operation is characterized by cyclic switching of circuit topologies, which gives rise to a variety of nonlinear behavior. This paper provides an overview of the chaotic dynamics and bifurcation scenarios observed in power converter circuits, emphasizing the salient features of the circuit operation and the modeling strategies. In particular this paper surveys the key publications in this field, reviews the main modeling approaches, and discusses the salient bifurcation behaviors of power converters with particular emphasis on the disruption of standard bifurcation patterns by border collisions.

This paper is concerned with the control of bifurcations and chaos in piecewise-smooth dynamical systems. An appropriate feedback strategy is presented, which exploits the occurrence in the system of so-called C-bifurcations (or border-collisions). An application to a simple power converter is also presented.