Simulation of Distributed-Parameter and Large-Scale Systems
Simulation of Distributed-Parameter and Large-Scale Systems
Distributed Parameter Control Systems: Theory and Application
Distributed Parameter Control Systems: Theory and Application
Distributed Parameter Systems: Identification, Estimation and Control
Distributed Parameter Systems: Identification, Estimation and Control
Paper: Optimal sensor location in the presence of nonstationary noise
Automatica (Journal of IFAC)
Brief paper: Sensor and controller location problems for distributed parameter systems
Automatica (Journal of IFAC)
Some recent applications of distributed parameter systems theory-A survey
Automatica (Journal of IFAC)
A survey of optimal control of distributed-parameter systems
Automatica (Journal of IFAC)
On parameter identification for distributed systems using Galerkin's criterion
Automatica (Journal of IFAC)
Optimal sensor locations for nonparametric identification of viscoelastic materials
Automatica (Journal of IFAC)
Survey paper: Optimal experimental design and some related control problems
Automatica (Journal of IFAC)
Optimal control of switched distributed parameter systems with spatially scheduled actuators
Automatica (Journal of IFAC)
Sensor location and classification for disturbance rejection by measurement feedback
Automatica (Journal of IFAC)
Hi-index | 22.15 |
A survey of the field of optimal sensors and/or controllers location for dynamical distributed parameter systems modelled by partial differential equations is presented. The recent contributions in this field are grouped according to the main goal for which the location problem is developed, namely: system identification, state estimation, and optimal control. In order to pose the sensors and controllers location problem, the semigroup approach for modelling distributed linear systems is briefly reviewed together with its equivalent (infinite dimensional) and approximate (finite dimensional) Fourier expansion representations. After presenting a concise general review of the several methods considered in the current literature, a classification of methods is also proposed. The main classifying factor concerns the use of N-modal approximation schemes, and the different stages of the optimization procedure in which they are required.