11,332 research outputs found
On Reverse Engineering in the Cognitive and Brain Sciences
Various research initiatives try to utilize the operational principles of
organisms and brains to develop alternative, biologically inspired computing
paradigms and artificial cognitive systems. This paper reviews key features of
the standard method applied to complexity in the cognitive and brain sciences,
i.e. decompositional analysis or reverse engineering. The indisputable
complexity of brain and mind raise the issue of whether they can be understood
by applying the standard method. Actually, recent findings in the experimental
and theoretical fields, question central assumptions and hypotheses made for
reverse engineering. Using the modeling relation as analyzed by Robert Rosen,
the scientific analysis method itself is made a subject of discussion. It is
concluded that the fundamental assumption of cognitive science, i.e. complex
cognitive systems can be analyzed, understood and duplicated by reverse
engineering, must be abandoned. Implications for investigations of organisms
and behavior as well as for engineering artificial cognitive systems are
discussed.Comment: 19 pages, 5 figure
Synchronization of Nonlinear Circuits in Dynamic Electrical Networks with General Topologies
Sufficient conditions are derived for global asymptotic synchronization in a
system of identical nonlinear electrical circuits coupled through linear
time-invariant (LTI) electrical networks. In particular, the conditions we
derive apply to settings where: i) the nonlinear circuits are composed of a
parallel combination of passive LTI circuit elements and a nonlinear
voltage-dependent current source with finite gain; and ii) a collection of
these circuits are coupled through either uniform or homogeneous LTI electrical
networks. Uniform electrical networks have identical per-unit-length
impedances. Homogeneous electrical networks are characterized by having the
same effective impedance between any two terminals with the others open
circuited. Synchronization in these networks is guaranteed by ensuring the
stability of an equivalent coordinate-transformed differential system that
emphasizes signal differences. The applicability of the synchronization
conditions to this broad class of networks follows from leveraging recent
results on structural and spectral properties of Kron reduction---a
model-reduction procedure that isolates the interactions of the nonlinear
circuits in the network. The validity of the analytical results is demonstrated
with simulations in networks of coupled Chua's circuits
A distributed accelerated gradient algorithm for distributed model predictive control of a hydro power valley
A distributed model predictive control (DMPC) approach based on distributed
optimization is applied to the power reference tracking problem of a hydro
power valley (HPV) system. The applied optimization algorithm is based on
accelerated gradient methods and achieves a convergence rate of O(1/k^2), where
k is the iteration number. Major challenges in the control of the HPV include a
nonlinear and large-scale model, nonsmoothness in the power-production
functions, and a globally coupled cost function that prevents distributed
schemes to be applied directly. We propose a linearization and approximation
approach that accommodates the proposed the DMPC framework and provides very
similar performance compared to a centralized solution in simulations. The
provided numerical studies also suggest that for the sparsely interconnected
system at hand, the distributed algorithm we propose is faster than a
centralized state-of-the-art solver such as CPLEX
Plug-and-Play Model Predictive Control based on robust control invariant sets
In this paper we consider a linear system represented by a coupling graph
between subsystems and propose a distributed control scheme capable to
guarantee asymptotic stability and satisfaction of constraints on system inputs
and states. Most importantly, as in Riverso et al., 2012 our design procedure
enables plug-and-play (PnP) operations, meaning that (i) the addition or
removal of subsystems triggers the design of local controllers associated to
successors to the subsystem only and (ii) the synthesis of a local controller
for a subsystem requires information only from predecessors of the subsystem
and it can be performed using only local computational resources. Our method
hinges on local tube MPC controllers based on robust control invariant sets and
it advances the PnP design procedure proposed in Riverso et al., 2012 in
several directions. Quite notably, using recent results in the computation of
robust control invariant sets, we show how critical steps in the design of a
local controller can be solved through linear programming. Finally, an
application of the proposed control design procedure to frequency control in
power networks is presented
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