Expansion and estimation of the range of nonlinear functions
Mathematics of Computation
Robust model-based fault diagnosis for dynamic systems
Robust model-based fault diagnosis for dynamic systems
Revising hull and box consistency
Proceedings of the 1999 international conference on Logic programming
Computing rigorous bounds on the solution of an initial value problem for an ordinary differential equation
Algorithm 852: RealPaver: an interval solver using constraint satisfaction techniques
ACM Transactions on Mathematical Software (TOMS)
Brief paper: Near optimal interval observers bundle for uncertain bioreactors
Automatica (Journal of IFAC)
Model-based Fault Diagnosis Techniques: Design Schemes, Algorithms, and Tools
Model-based Fault Diagnosis Techniques: Design Schemes, Algorithms, and Tools
Automatica (Journal of IFAC)
Ellipsoidal parameter or state estimation under model uncertainty
Automatica (Journal of IFAC)
Brief Causal fault detection and isolation based on a set-membership approach
Automatica (Journal of IFAC)
Brief Guaranteed state estimation by zonotopes
Automatica (Journal of IFAC)
Detection filter design for LPV systems-a geometric approach
Automatica (Journal of IFAC)
Interval estimation for LPV systems applying high order sliding mode techniques
Automatica (Journal of IFAC)
Interval state observer for nonlinear time varying systems
Automatica (Journal of IFAC)
Passive robust fault detection using RBF neural modeling based on set membership identification
Engineering Applications of Artificial Intelligence
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This paper deals with fault detection for nonlinear continuous-time systems. A procedure based on interval analysis is proposed to build a guaranteed qLPV (quasi-Linear Parameter-Varying) approximation of the nonlinear model. The interval qLPV approximation makes it possible to derive two point observers which estimate respectively the lower and the upper bound of the state vector using cooperativity theory. A set guaranteed to contain the actual value of the residual is then designed. The modelling uncertainties and measurement errors are taken into account at the design stage. The proposed methodology is illustrated through numerical simulations.