Input Design in Worst-Case System Identification Using Binary Sensors

DOI10.1109/TAC.2011.2107091zbMath1368.93318MaRDI QIDQ5347721

Marco Casini, Andrea Garulli, Antonio Vicino

Publication date: 25 August 2017

Published in: IEEE Transactions on Automatic Control (Search for Journal in Brave)

Mathematics Subject Classification ID

System identification (93B30) Control/observation systems with incomplete information (93C41) Identification in stochastic control theory (93E12)

Related Items (11)

A new kernel-based approach to system identification with quantized output data ⋮ Input design in worst-case system identification with quantized measurements ⋮ Asymptotically efficient identification of FIR systems with quantized observations and general quantized inputs ⋮ Identification of ARMA models with binary-valued observations ⋮ Recursive identification of FIR systems with binary-valued outputs and communication channels ⋮ FIR systems identification under quantized output observations and a large class of persistently exciting quantized inputs ⋮ Fixed-order FIR approximation of linear systems from quantized input and output data ⋮ Event-triggered identification of FIR systems with binary-valued output observations ⋮ A unified identification algorithm of FIR systems based on binary observations with time-varying thresholds ⋮ Robust and nonlinear control literature survey (No. 25) ⋮ On the optimal worst-case experiment design for constrained linear systems

This page was built for publication: Input Design in Worst-Case System Identification Using Binary Sensors