Sparse Sensing, Communication, and Actuation via Self-Triggered Control Algorithms
This work addresses the need for resource-efficient control in networked systems, offering a theoretically grounded method to reduce communication and computation overhead.
The paper proposes a self-triggered control algorithm that reduces processor usage, communication bandwidth, and energy consumption in linear time-invariant networked control systems by penalizing l0-measures and maximizing dwell time, achieving a sparse schedule for sensing, communication, and actuation with guaranteed stability and performance bounds.
We propose a self-triggered control algorithm to reduce onboard processor usage, communication bandwidth, and energy consumption across a linear time-invariant networked control system. We formulate an optimal control problem by penalizing the l0-measures of the feedback gain and the vector of control inputs and maximizing the dwell time between the consecutive triggering times. It is shown that the corresponding l1-relaxation of the optimal control problem is feasible and results in a stabilizing feedback control law with guaranteed performance bounds, while providing a sparse schedule for collecting samples from sensors, communication with other subsystems, and activating the input actuators.