The variation of earthquake input energy with characteristics of various structural systems, particularly in hysteretic states, has not been studied to such extent that creates enough con-fidence for proposing energy-based design criteria. In this paper, at first, based on a somehow new insight into the concept of earthquake input energy, two concepts of ‘Received Energy’ (ERec) and ‘Returned E...

Algorithms for observation, identification and fault detection of linear time-invariant strongly observable systems with unknown inputs are developed, based on high order sliding modes. The possibility of their extension is discussed to strongly detectable and nonlinear systems. Some applications of the proposed algorithms are presented.

an adaptive version of growing and pruning rbf neural network has been used to predict the system output and implement linear model-based predictive controller (lmpc) and non-linear model-based predictive controller (nmpc) strategies. a radial-basis neural network with growing and pruning capabilities is introduced to carry out on-line model identification.an unscented kalman filter (ukf) algor...

Abstract: By using the concept of parameter varying (C,A)-invariant subspace and parameter varying unobservability subspace, this paper investigates the problem of fault detection and isolation in linear parameter varying (LPV) systems. The so called detection filters approach, formulated as the fundamental problem of residual generation (FPRG) for linear time invariant (LTI) systems is extende...

1 This work (Sycadi project) was supported by Region Rhones-Alpes, France Abstract: In this paper, a linear matrix equality (LMI) based H∞ filtering formulation is presented for fault detection and isolation (FDI) problems of linear time-invariant descriptor systems. The fixed-order H∞ FDI filter design is characterized in terms of definite LMIs with no equality constraint, which are done recen...