In this survey article, we give a comprehensive review of sparse control for continuous‐time
systems, called maximum hands‐off control. The maximum hands‐off control is the optimal …

The method of sparsity has been attracting a lot of attention in the fields related not only to
signal processing, machine learning, and statistics, but also systems and control. The …

In this paper, we consider a security-aware and learning-based actuator selection
formulation for cyber–physical systems. A set of actuators is chosen to be used by the …

This article treats an optimal scheduling problem of control nodes in networked systems. We
newly introduce both the and constraints on control inputs to extract a time-varying small …

In this paper, we investigate an L 0 optimization problem with constraints in a form of
Volterra integral equation and the L∞ norm. In particular, the equivalence theorem among …

In this paper, we investigate a sparse optimal control of continuous-time stochastic systems.
We adopt the dynamic programming approach and analyze the optimal control via the value …

This article investigates a novel sparsity-constrained controllability maximization problem for
continuous-time linear systems. For controllability metrics, we employ the minimum …

In this survey paper, we review recent advances of compressed sensing applied to systems
and control. Compressed sensing has been actively researched in the field of signal …

We consider the time-varying actuator placement in continuous time, where the goal is to
maximize the trace of the controllability Grammian. A natural relaxation of the problem is to …

Emerging applications of control, estimation, and machine learning, from target tracking to
decentralized model fitting, pose resource constraints that limit which of the available …