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The periodic process of cell replication by division, known as cell-cycle, is a natural phenomenon occurring asynchronously in any cell population. Here, we consider the problem of synchronising cell-cycles across a population of yeast cells grown in a microfluidics device. Cells were engineered to reset their cell-cycle in response to low methionine levels. Automated syringes enable changing methionine levels (control input) in the microfluidics device. However, the control input resets only those cells that are in a specific phase of the cell-cycle (G1 phase), while the others continue to cycle unperturbed. We devised a simplified dynamical model of the cell-cycle, inferred its parameters from experimental data and then designed two control strategies: (i) an open-loop controller based on the application of periodic stimuli; (ii) a closed-loop model predictive controller (MPC) that selects the sequence of control stimuli which maximises a synchronisation index. Both the proposed control strategies were validated in-silico, together with experimental validation of the open-loop strategy.

Cells are defined by their unique ability to self-replicate through cell division. This periodic process is known as the cell-cycle and it happens with a defined period in each cell. The budding yeast divides asymmetrically with a mother cell generating multiple daughter cells. Within the cell population each cell divides with the same period but asynchronously. Here, we investigate the problem of synchronising the cell-cycle across a population of yeast cells through a microfluidics-based feedback control platform. We propose a theoretical and experimental approach for cell-cycle control by considering a yeast strain that can be forced to start the cell-cycle by changing growth medium. The duration of the cell-cycle is strictly linked to the cell volume growth, hence a hard constraint in the controller design is to prevent excessive volume growth. We experimentally characterised the yeast strain and derived a simplified phase-oscillator model of the cell-cycle. We then designed and implemented three impulsive control strategies to achieve maximal synchronisation across the population and assessed their control performance by numerical simulations. The first two controllers are based on event-triggered strategies, while the third uses a model predictive control (MPC) algorithm to select the sequence of control impulses while satisfying built-in constraints on volume growth. We compared the three strategies by computing two cost functions: one quantifying the level of synchronisation across the cell population and the other volume growth during the process. We demonstrated that the proposed control approaches can effectively achieve an acceptable trade-off between two conflicting control objectives: (i) obtaining maximal synchronisation of the cell cycle across the population while (ii) minimizing volume growth. The results can be used to implement effective strategies to unfold the biological mechanisms controlling cell cycle and volume growth in yeast cells. &nbsp

In this paper, we propose a cloud-supported control framework for multi-agent circumnavigation missions. We consider a network of planar autonomous agents. Our objective is for the agents to circumnavigate a target with a desired angular speed, while forming a regular polygon around the target. We propose self-triggered rules to schedule the bearing measurements and the cloud accesses for each agent.

We investigate the problem of controlling a network of heterogeneous Kuramoto oscillators affected by phase lags in the communication with their neighbours. We find analytical conditions that allow to determine the control effort required to guarantee convergence of all the oscillators towards a common collective evolution despite the presence of heterogeneities and phase lags. After presenting some numerical simulations that confirm the theoretical results, we discuss the application of the theory to the problem of inducing motor coordination in a mixed group of human players and artificial agents performing a joint task.

The interaction of robots or physical/virtual avatars with humans will be increasingly common in a number of different scenarios intended to improve the quality of human life. For example, in the domain of healthcare, they can support therapists aiding patients in need of motor rehabilitation and so on. In this context, a fundamental control problem is to synthesize strategies to make the artificial agents interact with humans in a “natural” and human-like fashion. This is particularly relevant when artificial agents are required to coordinate their motion with humans performing joint tasks. It has been shown that for rehabilitation purposes, virtual agents in motor coordination tasks must exhibit certain kinematic properties (or Individual Motor Signature) that are characteristic of human motion. In this paper we discuss a method based on the use of Markov chain to generate artificial individual motor signatures that can be used to provide online reference signals for the control of virtual agents. The methodology is also used to classify and identify individual motor signatures belonging to individuals affected by social disorders.

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.

Much research attention has focussed on improving strategies for traffic light control to address the problem of increasing congestion in road networks. Here we present a modular decentralized traffic light controller, and propose several algorithms which harness the potential of V2I communication in order to increase the flow of vehicles in the network and reduce travel time. We compare the performance of these algorithms with those already found in the literature, as well as exploring the impact of network topology on the performance of the controller, finding how different controllers operate when used on different types of road networks.

A distributed proportional-integral multilayer strategy is proposed, to achieve consensus in networks of heterogeneous first-order linear systems. The closed-loop network can be seen as an instance of so-called multiplex networks currently studied in network science. The strategy is able to guarantee consensus, even in the presence of constant disturbances and heterogeneous node dynamics. Contrary to previous approaches in the literature, the proportional and integral actions are deployed here on two different layers across the network, each with its own topology. Explicit expressions for the consensus values are obtained together with sufficient conditions guaranteeing convergence. The effectiveness of the theoretical results are illustrated via numerical simulations using a power network example.

In this paper, we present switched adaptive strategies to incrementally stabilise a class of Carathéodory nonlinear systems. Using the concept of switched matrix measures based on the use of multiple norms, and the transaction coefficients between these norms, sufficient conditions are presented to prove incremental stability of a switched system. Furthermore, based on these sufficient conditions, two strategies are proposed to incrementally stabilise a switched system of interest: one based on monitoring the switching signal in a continuous way, the other on monitoring it only at selected discrete-time instants. We prove that in both situations, the switched system can be effectively stabilised. As a representative application, we illustrate the efficiency of these strategies by synchronizing a network system with blinking (switching) graph structures.

We present an approach to study contraction of sliding mode solutions in n-dimensional Filippov systems and characterize global convergence of their trajectories towards each other. The results extend to n-dimensional systems with discontinuous right-hand sides the approach proposed in [1] for the planar case. Sufficient conditions for the incremental stability of the sliding vector field are derived using contraction theory. These are then complemented with conditions for global attractivity of the sliding region to prove global convergence of trajectories of the Filippov system of interest towards each other within a region of interest. The theoretical derivations are illustrated by means of a representative numerical example.

Multi-terminal VSC-HVDC transmission is a promising solution to transport energy from the generating plants to the distribution stations, and the number and size of the corresponding power grids is expected to increase in the next years. Such a system presents challenging control problems that, so far, have been approached with classical control techniques. However, large scale systems of interconnected nodes fall within the framework of the emerging field of complex networks. This paper analyzes the stability of multi-terminal VSC-HVDC systems from a complex dynamical network perspective, and provides sufficient conditions to ensure bounded synchronization of its trajectories. The obtained results are validated via numerical simulations.

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 study the problem of proving incremental stability of a planar Filippov system. In particular, referring to systems that present an attractive sliding region on their discontinuity boundary, we will give a differential condition on such region able to guarantee incremental exponential stability of sliding mode trajectories. We will then derive conditions for the incremental stability of the whole system. The approach is based on using tools from contraction theory, extending their applicability to include discontinuous dynamical systems.

In this paper, we solve the consensus problem in networks of integrators by evolving the network structure. Specifically, by extending the edge snapping mechanism presented in the literature, we propose evolving approaches leading to the emergence of directed networks at steady-state. We show both theoretically and numerically that these novel strategies can be employed to promote the emergence of consensus on a specified value, which is not necessarily the average of the initial conditions, as in the original work on edge snapping.

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 is concerned with the synchronization of networks of switching dynamical systems. In particular, conditions are derived for all nodes in a network of such systems to converge towards a common synchronous evolution. The key assumption is for the vector field to be in a suitable form that we call QUAD affine. Under this assumption, we show that the network of interest synchronizes even if the vector field is discontinuous and sliding motion is possible. The theoretical results are complemented by numerical simulations on a testbed example.

This paper is concerned with the design and implementation of a simple but effective switching control strategy to regulate the dynamics of synthetic gene networks. The testbed circuit is IRMA a recently developed network in S. Cerevisiae which is proposed as a good benchmark problem for control design. The proof-of-concept of an implementation of the control strategy in vivo is presented which is based on the use of microfluidics devices and fluorescence microscopy. Preliminary experimental results are given showing the good matching between the model predictive ability and the experimental observations.

This paper is concerned with the stability of networked control systems. Using contraction theory, a result is established on the network structure and the properties of the individual component subsystems and their couplings to ensure the overall contractivity of the entire network. Specifically, it is shown that a contraction property on a reduced-order matrix that quantifies the interconnection structure, coupled with contractivity/expansion estimates on the individual component subsystems, suffices to ensure that nearby trajectories of the overall system converge towards each other. The paper also describes a relative contraction result of interest in synchronization problems.

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 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 introduces a new algorithm to prove contraction of nonlinear dynamical systems. The algorithm is then used to devise novel strategies to achieve synchronization and consensus of networked control systems. Numerical simulations illustrate the results on a set of representative examples.

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.