9 research outputs found
Stochastic Stability of Event-triggered Anytime Control
We investigate control of a non-linear process when communication and
processing capabilities are limited. The sensor communicates with a controller
node through an erasure channel which introduces i.i.d. packet dropouts.
Processor availability for control is random and, at times, insufficient to
calculate plant inputs. To make efficient use of communication and processing
resources, the sensor only transmits when the plant state lies outside a
bounded target set. Control calculations are triggered by the received data. If
a plant state measurement is successfully received and while the processor is
available for control, the algorithm recursively calculates a sequence of
tentative plant inputs, which are stored in a buffer for potential future use.
This safeguards for time-steps when the processor is unavailable for control.
We derive sufficient conditions on system parameters for stochastic stability
of the closed loop and illustrate performance gains through numerical studies.Comment: IEEE Transactions on Automatic Control, under revie
Stability analysis of event-triggered anytime control with multiple control laws
To deal with time-varying processor availability and lossy communication
channels in embedded and networked control systems, one can employ an
event-triggered sequence-based anytime control (E-SAC) algorithm. The main idea
of E-SAC is, when computing resources and measurements are available, to
compute a sequence of tentative control inputs and store them in a buffer for
potential future use. State-dependent Random-time Drift (SRD) approach is often
used to analyse and establish stability properties of such E-SAC algorithms.
However, using SRD, the analysis quickly becomes combinatoric and hence
difficult to extend to more sophisticated E-SAC. In this technical note, we
develop a general model and a new stability analysis for E-SAC based on Markov
jump systems. Using the new stability analysis, stochastic stability conditions
of existing E-SAC are also recovered. In addition, the proposed technique
systematically extends to a more sophisticated E-SAC scheme for which, until
now, no analytical expression had been obtained.Comment: Accepted for publication in IEEE Transactions on Automatic Contro
Sparse and Constrained Stochastic Predictive Control for Networked Systems
This article presents a novel class of control policies for networked control
of Lyapunov-stable linear systems with bounded inputs. The control channel is
assumed to have i.i.d. Bernoulli packet dropouts and the system is assumed to
be affected by additive stochastic noise. Our proposed class of policies is
affine in the past dropouts and saturated values of the past disturbances. We
further consider a regularization term in a quadratic performance index to
promote sparsity in control. We demonstrate how to augment the underlying
optimization problem with a constant negative drift constraint to ensure
mean-square boundedness of the closed-loop states, yielding a convex quadratic
program to be solved periodically online. The states of the closed-loop plant
under the receding horizon implementation of the proposed class of policies are
mean square bounded for any positive bound on the control and any non-zero
probability of successful transmission
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
Event-triggered anytime control with limited resources
In networked and multi-tasking environment, measurement data and processing resources may not be available at times when control calculations need to be executed. Based on the anytime algorithm been proposed in Stochastic Stability of Event-triggered Anytime Control[1]for control of event-triggered systems(nonlinear)which the processing resources available are time-varying, the algorithm recursively calculates a sequence of tentative plant inputs when a plant state measurement is successfully received and while the processor is available for control, which are stored in a buffer for potential future use. This safeguards for the time-steps when processor is unavailable for control. To make more efficient use of communication and processing resources, we extend this algorithm with two controllers in this dissertation. We present an anytime algorithm which features two control policies: a coarse policy and a fine policy. The fine control policy requires more processing resources than the coarse policy. With this scheme, the network and processing resources can be used more efficiently, and performance can be improved. Specifically, for a given packet dropout rate and process availability, the proposed two-controller scheme achieves better closed-loop performance with a lower channel utilization than alternative control formulations. The stability region is also enlarged with the two-controller scheme.Master of Science (Computer Control and Automation