6 research outputs found
Anytime Control using Input Sequences with Markovian Processor Availability
We study an anytime control algorithm for situations where the processing
resources available for control are time-varying in an a priori unknown
fashion. Thus, at times, processing resources are insufficient to calculate
control inputs. To address this issue, the algorithm calculates sequences of
tentative future control inputs whenever possible, which are then buffered for
possible future use. We assume that the processor availability is correlated so
that the number of control inputs calculated at any time step is described by a
Markov chain. Using a Lyapunov function based approach we derive sufficient
conditions for stochastic stability of the closed loop.Comment: IEEE Transactions on Automatic Control, to be publishe
Stabilizing Stochastic Predictive Control under Bernoulli Dropouts
This article presents tractable and recursively feasible optimization-based
controllers for stochastic linear systems with bounded controls. The stochastic
noise in the plant is assumed to be additive, zero mean and fourth moment
bounded, and the control values transmitted over an erasure channel. Three
different transmission protocols are proposed having different requirements on
the storage and computational facilities available at the actuator. We optimize
a suitable stochastic cost function accounting for the effects of both the
stochastic noise and the packet dropouts over affine saturated disturbance
feedback policies. The proposed controllers ensure mean square boundedness of
the states in closed-loop for all positive values of control bounds and any
non-zero probability of successful transmission over a noisy control channel
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
Optimal Sequence-Based Control of Networked Linear Systems
In Networked Control Systems (NCS), components of a control loop are connected by data networks that may introduce time-varying delays and packet losses into the system, which can severly degrade control performance. Hence, this book presents the newly developed S-LQG (Sequence-Based Linear Quadratic Gaussian) controller that combines the sequence-based control method with the well-known LQG approach to stochastic optimal control in order to compensate for the network-induced effects
Stability of sequence-based control with random delays and dropouts
We study networked control of nonlinear systems where system states and tentative plant input sequences are transmitted over unreliable communication channels. The sequences are calculated recursively by using a pre-designed nominally stabilizing state-feedback control mapping to plant state predictions. The controller does not require receipt acknowledgments or knowledge of delay or dropout distributions. For the i.i.d. case, in which case the numbers of consecutive dropouts are geometrically distributed, we show how the resulting closed loop system can be modeled as a Markov nonlinear jump system and establish sufficient conditions for stochastic stability. © 1963-2012 IEEE