6,866 research outputs found
Stabilization of Networked Control Systems with Sparse Observer-Controller Networks
In this paper we provide a set of stability conditions for linear
time-invariant networked control systems with arbitrary topology, using a
Lyapunov direct approach. We then use these stability conditions to provide a
novel low-complexity algorithm for the design of a sparse observer-based
control network. We employ distributed observers by employing the output of
other nodes to improve the stability of each observer dynamics. To avoid
unbounded growth of controller and observer gains, we impose bounds on their
norms. The effects of relaxation of these bounds is discussed when trying to
find the complete decentralization conditions
Event-Triggered Estimation of Linear Systems: An Iterative Algorithm and Optimality Properties
This report investigates the optimal design of event-triggered estimation for
first-order linear stochastic systems. The problem is posed as a two-player
team problem with a partially nested information pattern. The two players are
given by an estimator and an event-trigger. The event-trigger has full state
information and decides, whether the estimator shall obtain the current state
information by transmitting it through a resource constrained channel. The
objective is to find an optimal trade-off between the mean squared estimation
error and the expected transmission rate. The proposed iterative algorithm
alternates between optimizing one player while fixing the other player. It is
shown that the solution of the algorithm converges to a linear predictor and a
symmetric threshold policy, if the densities of the initial state and the noise
variables are even and radially decreasing functions. The effectiveness of the
approach is illustrated on a numerical example. In case of a multimodal
distribution of the noise variables a significant performance improvement can
be achieved compared to a separate design that assumes a linear prediction and
a symmetric threshold policy
Robust Control
The need to be tolerant to changes in the control systems or in the operational environment of systems subject to unknown disturbances has generated new control methods that are able to deal with the non-parametrized disturbances of systems, without adapting itself to the system uncertainty but rather providing stability in the presence of errors bound in a model. With this approach in mind and with the intention to exemplify robust control applications, this book includes selected chapters that describe models of H-infinity loop, robust stability and uncertainty, among others. Each robust control method and model discussed in this book is illustrated by a relevant example that serves as an overview of the theoretical and practical method in robust control
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