3 research outputs found
Statistical Learning for Analysis of Networked Control Systems over Unknown Channels
Recent control trends are increasingly relying on communication networks and
wireless channels to close the loop for Internet-of-Things applications.
Traditionally these approaches are model-based, i.e., assuming a network or
channel model they are focused on stability analysis and appropriate controller
designs. However the availability of such wireless channel modeling is
fundamentally challenging in practice as channels are typically unknown a
priori and only available through data samples. In this work we aim to develop
algorithms that rely on channel sample data to determine the stability and
performance of networked control tasks. In this regard our work is the first to
characterize the amount of channel modeling that is required to answer such a
question. Specifically we examine how many channel data samples are required in
order to answer with high confidence whether a given networked control system
is stable or not. This analysis is based on the notion of sample complexity
from the learning literature and is facilitated by concentration inequalities.
Moreover we establish a direct relation between the sample complexity and the
networked system stability margin, i.e., the underlying packet success rate of
the channel and the spectral radius of the dynamics of the control system. This
illustrates that it becomes impractical to verify stability under a large range
of plant and channel configurations. We validate our theoretical results in
numerical simulations
Model-Free Design of Control Systems over Wireless Fading Channels
Wireless control systems replace traditional wired communication with
wireless networks to exchange information between actuators, plants and sensors
in a control system. The noise in wireless channels renders ideal control
policies suboptimal, and their performance is moreover directly dependent on
the way in which wireless resources are allocated between control loops. Proper
design of the control policy and the resource allocation policy based on both
plant states and wireless fading states is then critical to achieve good
performance. The resulting problem of co-designing control-aware resource
allocation policies and communication-aware controllers, however, is
challenging due to its infinite dimensionality, existence of system constraints
and need for explicit knowledge of the plants and wireless network models. To
overcome those challenges, we rely on constrained reinforcement learning
algorithms to propose a model-free approach to the design of wireless control
systems. We demonstrate the near optimality of control system performance and
stability using near-universal policy parametrizations and present a practical
model-free algorithm to learn the co-design policy. Numerical experiments show
the strong performance of learned policies over baseline solutions.Comment: Submitted to IEEE TS