Evolutionary robotics and neuroscience

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Distributed Optimal State Consensus for Multiple Circuit Systems with Disturbance Rejection

This paper investigates the distributed optimal state consensus problem for an electronic system with a group of circuit units, where the dynamics of each unit is modeled by a Chua's circuit in the presence of disturbance generated by an external system. By means of the internal model approach and feedback control, a compensator-based continuous-time algorithm is proposed to minimize the sum of all cost functions associated with each individual unit in a cooperative manner. Supported by convex analysis, graph theory and Lyapunov theory, it is proved that the proposed algorithm is exponentially convergent. Compared with the centralized algorithms, the proposed protocol possesses remarkable superiority in improving scalability and reliability of multiple circuit systems. Moreover, we also study the distributed uncertain optimal state consensus problem and a linear regret bound is obtained in this case. Finally, a state synchronization example is provided to validate the effectiveness of the proposed algorithms

Chaotic exploration and learning of locomotor behaviours

Recent developments in the embodied approach to understanding the generation of adaptive behaviour, suggests that the design of adaptive neural circuits for rhythmic motor patterns should not be done in isolation from an appreciation, and indeed exploitation, of neural-body-environment interactions. Utilising spontaneous mutual entrainment between neural systems and physical bodies provides a useful passage to the regions of phase space which are naturally structured by the neuralbody- environmental interactions. A growing body of work has provided evidence that chaotic dynamics can be useful in allowing embodied systems to spontaneously explore potentially useful motor patterns. However, up until now there has been no general integrated neural system that allows goal-directed, online, realtime exploration and capture of motor patterns without recourse to external monitoring, evaluation or training methods. For the first time, we introduce such a system in the form of a fully dynamic neural system, exploiting intrinsic chaotic dynamics, for the exploration and learning of the possible locomotion patterns of an articulated robot of an arbitrary morphology in an unknown environment. The controller is modelled as a network of neural oscillators which are coupled only through physical embodiment, and goal directed exploration of coordinated motor patterns is achieved by a chaotic search using adaptive bifurcation. The phase space of the indirectly coupled neural-body-environment system contains multiple transient or permanent self-organised dynamics each of which is a candidate for a locomotion behaviour. The adaptive bifurcation enables the system orbit to wander through various phase-coordinated states using its intrinsic chaotic dynamics as a driving force and stabilises the system on to one of the states matching the given goal criteria. In order to improve the sustainability of useful transient patterns, sensory homeostasis has been introduced which results in an increased diversity of motor outputs, thus achieving multi-scale exploration. A rhythmic pattern discovered by this process is memorised and sustained by changing the wiring between initially disconnected oscillators using an adaptive synchronisation method. The dynamical nature of the weak coupling through physical embodiment allows this adaptive weight learning to be easily integrated, thus forming a continuous exploration-learning system. Our result shows that the novel neuro-robotic system is able to create and learn a number of emergent locomotion behaviours for a wide range of body configurations and physical environment, and can re-adapt after sustaining damage. The implications and analyses of these results for investigating the generality and limitations of the proposed system are discussed

Analysis and control of biomolecular networks by microfluidics

The process by which the cells respond and adapt to internal and external stimuli, is almost always controlled by a complex network of genes, proteins, small molecules, and their mutual interactions, called signalling network. Over the last years, it has become apparent that quantitative and methodological tools from Biomedical and Control Engineering can be used to understand how these networks work, but also to engineer "synthetic" networks to robustly steer cellular behavior in a prescribed fashion. This possibility will be transformative, enabling myriad applications in biotechnology, chemical industry, health and biomedicine, food, and the environment. Cybergenetics is a new discipline merging the tools of Synthetic Biology with those of Biomedical and Control Engineering, with the aim of building robust synthetic gene networks to engineer biological processes. This Thesis is within this research topic, and comprises two different applications, one in yeast cells and one in human cells: (1) closed-loop feedback control to synchronise the cell cycle across a population of yeast cells (Saccharomyces cerevisiae); (2) quantitative analysis and model of TFEB nuclear translocation dynamics following mTOR inhibition in human cells (HeLa)

Decentralised static output feedback stabilisation and synchronisation of networks

Dynamic multi-agent networks are important in a wide range of areas in science and engineering, including mobile sensor networks, distributed robotics such as underwater vehicles and cooperative unmanned air vehicles, biological synchronisation, networked economics and social networks. The paper makes a fundamental theoretical contribution to the field by blending methods from graph theory and control theory. [Impact Factor: 2.631, second highest of all Control Engineering journals]This is the author's pre-print. The definitive published version is available via doi:10.1016/j.automatica.2009.09.029In this paper global stabilisation of a complex network is attained by applying local decentralised output feedback control to a minimum number of nodes of the network. The stabilisation of the network is treated as a rank constrained problem. Strict positive realness conditions on the node level dynamics allow nonlinearities/uncertainties which satisfy the sector conditions to be considered. A network of Chua oscillators with 75 nodes is considered to demonstrate the efficacy of the approach