Resource-aware time-optimal control with multiple sparsity measures
From MaRDI portal
Publication:2059349
DOI10.1016/j.automatica.2021.109957zbMath1479.49011OpenAlexW3206396801MaRDI QIDQ2059349
Masaaki Nagahara, Takuya Ikeda
Publication date: 14 December 2021
Published in: Automatica (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.automatica.2021.109957
Linear programming (90C05) Methods involving semicontinuity and convergence; relaxation (49J45) Existence of optimal solutions belonging to restricted classes (Lipschitz controls, bang-bang controls, etc.) (49J30)
Related Items (3)
Time-optimal control strategies for tungiasis diseases with limited resources ⋮ Sparse feedback stabilization in high‐order linear systems ⋮ Time‐optimal L1/L2 norms optimal control for linear time‐invariant systems
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Characterization of maximum hands-off control
- Resource-aware MPC for constrained nonlinear systems: a self-triggered control approach
- Value function in maximum hands-off control for linear systems
- Stochastic \(L^1\)-optimal control via forward and backward sampling
- Time-optimal hands-off control for linear time-invariant systems
- An introduction to compressed sensing
- CLOT norm minimization for continuous hands-off control
- Error-dependent data scheduling in resource-aware multi-loop networked control systems
- Functional analysis and time optimal control
- Maximum Hands-Off Control: A Paradigm of Control Effort Minimization
- A Framework for Structural Input/Output and Control Configuration Selection in Large-Scale Systems
- Functional Analysis, Calculus of Variations and Optimal Control
- Sparse and Redundant Representations
- Minimal Reachability is Hard To Approximate
- Controllability Metrics, Limitations and Algorithms for Complex Networks
- Minimal Controllability Problems
- Minimal Actuator Placement With Bounds on Control Effort
This page was built for publication: Resource-aware time-optimal control with multiple sparsity measures