35 research outputs found
Performance and design of consensus on matrix-weighted and time scaled graphs
In this paper, we consider the -norm of networked systems with
multi-time scale consensus dynamics and vector-valued agent states. This allows
us to explore how measurement and process noise affect consensus on
matrix-weighted graphs by examining edge-state consensus. In particular, we
highlight an interesting case where the influences of the weighting and scaling
on the norm can be separated in the design problem. We then
consider optimization algorithms for updating the time scale parameters and
matrix weights in order to minimize network response to injected noise.
Finally, we present an application to formation control for multi-vehicle
systems.Comment: 10 pages, 5 figures, accepted to the IEEE Transactions on Control of
Network Systems. arXiv admin note: text overlap with arXiv:1909.0786
Self-Tuning Network Control Architectures with Joint Sensor and Actuator Selection
We formulate a mathematical framework for designing a self-tuning network
control architecture, and propose a computationally-feasible greedy algorithm
for online architecture optimization. In this setting, the locations of active
sensors and actuators in the network, as well as the feedback control policy
are jointly adapted using all available information about the network states
and dynamics to optimize a performance criterion. We show that the case with
full-state feedback can be solved with dynamic programming, and in the
linear-quadratic setting, the optimal cost functions and policies are piecewise
quadratic and piecewise linear, respectively. Our framework is extended for
joint sensor and actuator selection for dynamic output feedback control with
both control performance and architecture costs. For large networks where
exhaustive architecture search is prohibitive, we describe a greedy heuristic
for actuator selection and propose a greedy swapping algorithm for joint sensor
and actuator selection. Via numerical experiments, we demonstrate a dramatic
performance improvement of greedy self-tuning architectures over fixed
architectures. Our general formulation provides an extremely rich and
challenging problem space with opportunities to apply a wide variety of
approximation methods from stochastic control, system identification,
reinforcement learning, and static architecture design for practical
model-based control.Comment: 12 pages, submitted to IEEE-TCNS. arXiv admin note: text overlap with
arXiv:2301.0669