609 research outputs found
Coordination of passive systems under quantized measurements
In this paper we investigate a passivity approach to collective coordination
and synchronization problems in the presence of quantized measurements and show
that coordination tasks can be achieved in a practical sense for a large class
of passive systems.Comment: 40 pages, 1 figure, submitted to journal, second round of revie
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
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Experimental Implementation of Distributed Average Tracking for Heterogeneous Physical Agents Using Neighbors’ Positions
The focus of this thesis is on average tracking algorithm implementation for a group of heterogeneous physical agents consisting of single-integrator, double-integrator and Euler-Lagrange dynamics. In the algorithms, each agent is able to track the average of the time-varying reference inputs, where each agent has access to only its own position, its own input signals and the relative positions between itself and its neighbors. The algorithms are experimentally implemented on a multi-robot platform under an undirected communication topology. Simulation results and the experimental results based on the multi-robot platform are shown to validate the proposed algorithms. Finally, the error in simulation and experiment is analyzed based on experimental environment and robot characteristics
Finite-Time Convergent Algorithms for Time-Varying Distributed Optimization
This paper focuses on finite-time (FT) convergent distributed algorithms for
solving time-varying distributed optimization (TVDO). The objective is to
minimize the sum of local time-varying cost functions subject to the possible
time-varying constraints by the coordination of multiple agents in finite time.
We first provide a unified approach for designing finite/fixed-time convergent
algorithms to solve centralized time-varying optimization, where an auxiliary
dynamics is introduced to achieve prescribed performance. Then, two classes of
TVDO are investigated included unconstrained distributed consensus optimization
and distributed optimal resource allocation problems (DORAP) with both
time-varying cost functions and coupled equation constraints. For the previous
one, based on nonsmooth analysis, a continuous-time distributed discontinuous
dynamics with FT convergence is proposed based on an extended zero-gradient-sum
method with a local auxiliary subsystem. Different from the existing methods,
the proposed algorithm does not require the initial state of each agent to be
the optimizer of the local cost function. Moreover, the provided algorithm has
a simpler structure without estimating the global information and can be used
for TVDO with nonidentical Hessians. Then, an FT convergent distributed
dynamics is further obtained for time-varying DORAP by dual transformation.
Particularly, the inverse of Hessians is not required from a dual perspective,
which reduces the computation complexity significantly. Finally, two numerical
examples are conducted to verify the proposed algorithms
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