1,947 research outputs found
Minimum-Information LQG Control - Part I: Memoryless Controllers
With the increased demand for power efficiency in feedback-control systems,
communication is becoming a limiting factor, raising the need to trade off the
external cost that they incur with the capacity of the controller's
communication channels. With a proper design of the channels, this translates
into a sequential rate-distortion problem, where we minimize the rate of
information required for the controller's operation under a constraint on its
external cost. Memoryless controllers are of particular interest both for the
simplicity and frugality of their implementation and as a basis for studying
more complex controllers. In this paper we present the optimality principle for
memoryless linear controllers that utilize minimal information rates to achieve
a guaranteed external-cost level. We also study the interesting and useful
phenomenology of the optimal controller, such as the principled reduction of
its order
Memory Resilient Gain-scheduled State-Feedback Control of Uncertain LTI/LPV Systems with Time-Varying Delays
The stabilization of uncertain LTI/LPV time delay systems with time varying
delays by state-feedback controllers is addressed. At the difference of other
works in the literature, the proposed approach allows for the synthesis of
resilient controllers with respect to uncertainties on the implemented delay.
It is emphasized that such controllers unify memoryless and exact-memory
controllers usually considered in the literature. The solutions to the
stability and stabilization problems are expressed in terms of LMIs which allow
to check the stability of the closed-loop system for a given bound on the
knowledge error and even optimize the uncertainty radius under some performance
constraints; in this paper, the performance measure is
considered. The interest of the approach is finally illustrated through several
examples
Longitudinal Dynamic versus Kinematic Models for Car-Following Control Using Deep Reinforcement Learning
The majority of current studies on autonomous vehicle control via deep
reinforcement learning (DRL) utilize point-mass kinematic models, neglecting
vehicle dynamics which includes acceleration delay and acceleration command
dynamics. The acceleration delay, which results from sensing and actuation
delays, results in delayed execution of the control inputs. The acceleration
command dynamics dictates that the actual vehicle acceleration does not rise up
to the desired command acceleration instantaneously due to dynamics. In this
work, we investigate the feasibility of applying DRL controllers trained using
vehicle kinematic models to more realistic driving control with vehicle
dynamics. We consider a particular longitudinal car-following control, i.e.,
Adaptive Cruise Control (ACC), problem solved via DRL using a point-mass
kinematic model. When such a controller is applied to car following with
vehicle dynamics, we observe significantly degraded car-following performance.
Therefore, we redesign the DRL framework to accommodate the acceleration delay
and acceleration command dynamics by adding the delayed control inputs and the
actual vehicle acceleration to the reinforcement learning environment state,
respectively. The training results show that the redesigned DRL controller
results in near-optimal control performance of car following with vehicle
dynamics considered when compared with dynamic programming solutions.Comment: Accepted to 2019 IEEE Intelligent Transportation Systems Conferenc
Upper Bounds on the Capacities of Noncontrollable Finite-State Channels with/without Feedback
Noncontrollable finite-state channels (FSCs) are FSCs in which the channel
inputs have no influence on the channel states, i.e., the channel states evolve
freely. Since single-letter formulae for the channel capacities are rarely
available for general noncontrollable FSCs, computable bounds are usually
utilized to numerically bound the capacities. In this paper, we take the
delayed channel state as part of the channel input and then define the {\em
directed information rate} from the new channel input (including the source and
the delayed channel state) sequence to the channel output sequence. With this
technique, we derive a series of upper bounds on the capacities of
noncontrollable FSCs with/without feedback. These upper bounds can be achieved
by conditional Markov sources and computed by solving an average reward per
stage stochastic control problem (ARSCP) with a compact state space and a
compact action space. By showing that the ARSCP has a uniformly continuous
reward function, we transform the original ARSCP into a finite-state and
finite-action ARSCP that can be solved by a value iteration method. Under a
mild assumption, the value iteration algorithm is convergent and delivers a
near-optimal stationary policy and a numerical upper bound.Comment: 15 pages, Two columns, 6 figures; appears in IEEE Transaction on
Information Theor
Energy Optimal Transmission Scheduling in Wireless Sensor Networks
One of the main issues in the design of sensor networks is energy efficient
communication of time-critical data. Energy wastage can be caused by failed
packet transmission attempts at each node due to channel dynamics and
interference. Therefore transmission control techniques that are unaware of the
channel dynamics can lead to suboptimal channel use patterns. In this paper we
propose a transmission controller that utilizes different "grades" of channel
side information to schedule packet transmissions in an optimal way, while
meeting a deadline constraint for all packets waiting in the transmission
queue. The wireless channel is modeled as a finite-state Markov channel. We are
specifically interested in the case where the transmitter has low-grade channel
side information that can be obtained based solely on the ACK/NAK sequence for
the previous transmissions. Our scheduler is readily implementable and it is
based on the dynamic programming solution to the finite-horizon transmission
control problem. We also calculate the information theoretic capacity of the
finite state Markov channel with feedback containing different grades of
channel side information including that, obtained through the ACK/NAK sequence.
We illustrate that our scheduler achieves a given throughput at a power level
that is fairly close to the fundamental limit achievable over the channel.Comment: Accepted for publication in the IEEE Transactions on Wireless
Communication
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