169 research outputs found
Quantised control of nonlinear systems: analysis of robustness to parameter uncertainty, measurement errors, and exogenous disturbances
International audienceWe propose a variant of the recently introduced strategy for stabilisation with limited information by D. Liberzon and J.P. Hespanha and analyse its robustness properties. We show that, if the nominal plant can be made input-to-state stable with respect to measurement errors, parameter uncertainty and exogenous disturbances, then this robustness is preserved with this quantised feedback. More precisely, if a sufficient bandwidth is available on the communication network, then the resulting closed loop is shown to be semiglobally input-to-state practically stable
Multi-agent decision-making dynamics inspired by honeybees
When choosing between candidate nest sites, a honeybee swarm reliably chooses
the most valuable site and even when faced with the choice between near-equal
value sites, it makes highly efficient decisions. Value-sensitive
decision-making is enabled by a distributed social effort among the honeybees,
and it leads to decision-making dynamics of the swarm that are remarkably
robust to perturbation and adaptive to change. To explore and generalize these
features to other networks, we design distributed multi-agent network dynamics
that exhibit a pitchfork bifurcation, ubiquitous in biological models of
decision-making. Using tools of nonlinear dynamics we show how the designed
agent-based dynamics recover the high performing value-sensitive
decision-making of the honeybees and rigorously connect investigation of
mechanisms of animal group decision-making to systematic, bio-inspired control
of multi-agent network systems. We further present a distributed adaptive
bifurcation control law and prove how it enhances the network decision-making
performance beyond that observed in swarms
Feedback design of spatially-distributed filters with tunable resolution
peer reviewedWe derive gain-tuning rules for the positive and negative spatial-feedback loops of a spatially-distributed filter to change the resolution of its spatial band-pass characteristic accordingly to a wavelet zoom, while preserving temporal stability. The filter design is inspired by the canonical spatial feedback structure of the primary visual cortex and is motivated by understanding attentional control of visual resolution. Besides biology, our control-theoretical design strategy is relevant for the development of neuromorphic multiresolution distributed sensors through the feedback interconnection of elementary spatial transfer functions and gain tuning
Applications of Input-to-State Stability theory to chaos and non-linear adaptive control.
The aim of this work is to study the stability of a particular class of nonlinear control systems, that is Adaptive Control with Communications Constraints. As a subclass of such a system, we study stability properties of chaos control.
The technical instrument used is the Input-to-State Stability theory.
As a font of inspiration to find original solutions to the problem of adaptive control and face new interesting problems we collect known results about vertebrate movement learning and propose an inspired control scheme
Robust and tunable bursting requires slow positive feedback.
We highlight that the robustness and tunability of a bursting model critically rely on currents that provide slow positive feedback to the membrane potential. Such currents have the ability to make the total conductance of the circuit negative in a timescale that is termed "slow" because it is intermediate between the fast timescale of the spike upstroke and the ultraslow timescale of even slower adaptation currents. We discuss how such currents can be assessed either in voltage-clamp experiments or in computational models. We show that, while frequent in the literature, mathematical and computational models of bursting that lack the slow negative conductance are fragile and rigid. Our results suggest that modeling the slow negative conductance of cellular models is important when studying the neuromodulation of rhythmic circuits at any broader scale. NEW & NOTEWORTHY Nervous system functions rely on the modulation of neuronal activity between different rhythmic patterns. The mechanisms of this modulation are still poorly understood. Using computational modeling, we show the critical role of currents that provide slow negative conductance, distinct from the fast negative conductance necessary for spike generation. The significance of the slow negative conductance for neuromodulation is often overlooked, leading to computational models that are rigid and fragile.ER
Cellular switches orchestrate rhythmic circuits.
Small inhibitory neuronal circuits have long been identified as key neuronal motifs to generate and modulate the coexisting rhythms of various motor functions. Our paper highlights the role of a cellular switching mechanism to orchestrate such circuits. The cellular switch makes the circuits reconfigurable, robust, adaptable, and externally controllable. Without this cellular mechanism, the circuit rhythms entirely rely on specific tunings of the synaptic connectivity, which makes them rigid, fragile, and difficult to control externally. We illustrate those properties on the much studied architecture of a small network controlling both the pyloric and gastric rhythms of crabs. The cellular switch is provided by a slow negative conductance often neglected in mathematical modeling of central pattern generators. We propose that this conductance is simple to model and key to computational studies of rhythmic circuit neuromodulation
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