9,119 research outputs found
Scalable Approach to Uncertainty Quantification and Robust Design of Interconnected Dynamical Systems
Development of robust dynamical systems and networks such as autonomous
aircraft systems capable of accomplishing complex missions faces challenges due
to the dynamically evolving uncertainties coming from model uncertainties,
necessity to operate in a hostile cluttered urban environment, and the
distributed and dynamic nature of the communication and computation resources.
Model-based robust design is difficult because of the complexity of the hybrid
dynamic models including continuous vehicle dynamics, the discrete models of
computations and communications, and the size of the problem. We will overview
recent advances in methodology and tools to model, analyze, and design robust
autonomous aerospace systems operating in uncertain environment, with stress on
efficient uncertainty quantification and robust design using the case studies
of the mission including model-based target tracking and search, and trajectory
planning in uncertain urban environment. To show that the methodology is
generally applicable to uncertain dynamical systems, we will also show examples
of application of the new methods to efficient uncertainty quantification of
energy usage in buildings, and stability assessment of interconnected power
networks
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
Rate analysis of inexact dual first order methods: Application to distributed MPC for network systems
In this paper we propose and analyze two dual methods based on inexact
gradient information and averaging that generate approximate primal solutions
for smooth convex optimization problems. The complicating constraints are moved
into the cost using the Lagrange multipliers. The dual problem is solved by
inexact first order methods based on approximate gradients and we prove
sublinear rate of convergence for these methods. In particular, we provide, for
the first time, estimates on the primal feasibility violation and primal and
dual suboptimality of the generated approximate primal and dual solutions.
Moreover, we solve approximately the inner problems with a parallel coordinate
descent algorithm and we show that it has linear convergence rate. In our
analysis we rely on the Lipschitz property of the dual function and inexact
dual gradients. Further, we apply these methods to distributed model predictive
control for network systems. By tightening the complicating constraints we are
also able to ensure the primal feasibility of the approximate solutions
generated by the proposed algorithms. We obtain a distributed control strategy
that has the following features: state and input constraints are satisfied,
stability of the plant is guaranteed, whilst the number of iterations for the
suboptimal solution can be precisely determined.Comment: 26 pages, 2 figure
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