Conceitos Básicos
Optimizing control inputs for sparsity constraints using a greedy algorithm with performance guarantees.
Resumo
The content discusses the optimization of control inputs under sparsity constraints in linear quadratic regulation problems. It introduces a greedy algorithm to find suboptimal solutions and provides performance guarantees based on submodularity ratios and curvature metrics. The paper presents an explicit form of optimal control input, establishes bounds on submodularity ratio and curvature, and demonstrates the effectiveness through numerical simulations. It also compares the greedy algorithm's performance with other methods and analyzes its conservativeness in providing guarantees.
- Introduction to sparse control inputs for energy efficiency.
- Previous studies focusing on maximizing sparsity without considering control performance.
- Introducing the greedy algorithm as a practical approach for actuation timings.
- Establishing bounds on submodularity ratio and curvature for performance guarantees.
- Numerical simulations demonstrating the effectiveness of the proposed method.
- Comparison with existing results and analysis of conservativeness in guarantees.
Estatísticas
"The design of sparse control signals has attracted much research attention due to its energy-saving potential."
"In [6] and [7], the authors have examined a strategy that applies control inputs at the beginning of the control horizon."
"The spectral norm of A is denoted by ∥A∥."
Citações
"Maximum hands-off control: A paradigm of control effort minimization." - M. Nagahara et al.
"Guarantees for greedy maximization of non-submodular functions with applications." - A.A. Bian et al.