Adaptive filter theory (2nd ed.)
Adaptive filter theory (2nd ed.)
Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Applied system identification
Adaptive filter theory (3rd ed.)
Adaptive filter theory (3rd ed.)
System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
Multiagent reinforcement learning in a distributed sensor network with indirect feedback
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
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Migration of the identified system poles for a dynamical system indicates changes in its global properties. In civil engineering structures, these changes are most often due to changes in global stiffness or damping parameters associated with both environmental effects as well as deterioration of the structure. In structures that employ automated feedback control systems to mitigate unwanted vibrations, feedback control laws and state estimators (if used) are reliant upon a theoretical or identified model of the plant. Any loss in fidelity between the plant model and its actual condition will result in degradation of the controller performance. Low-cost, wireless control networks that by nature are more likely to utilize state-estimation, are therefore more vulnerable to problems associated with property changes in the system. In this paper, recursive identification of system poles is proposed for use in a wireless sensing network engaged in feedback control. Because it is based on system poles, the algorithm is ideally suited for adaptive control methods that update control and estimation gains as system properties change. The algorithm proposed is based on the fast transversal filter and is designed to minimize computation as well as data transmission requirements to optimally utilize the distributed data that is stored within a low-power wireless sensor network.