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Fault diagnostic monitoring in nonlinear hybrid processes requires dedicated techniques being capable of dealing with both nonlinearity and hybrid dynamic characteristics. This paper introduces a modified particle filtering estimation-based methodology for online diagnostic purposes by individual tracking of the most likely faulty modes and the process(More)
Traditional centralized state estimation algorithms pose stringent scaling restrictions for modern distributed hybrid plants due to their enormous communication overhead requirements. This paper presents a novel distributed estimation approach for hybrid systems composed of a proposed distributed particle filter based on a learning vector quantization(More)
This paper presents a new distributed monitoring approach for nonlinear, non-Gaussian hybrid systems incorporating multiple sensors in an embedded network configuration. The estimation engine of the proposed approach is particle filter (PF) which estimates locally the mode and continuous state of hybrid system at each sensor location or node. Decision on(More)
The trend towards electrification of vehicles demands advanced battery management systems. The core of a BMS is an accurate model upon which the control and monitoring can be established. Accuracy and low-computational load are the two important factors that the applied model for online tasks have to possess. This paper presents a Takagi-Sugeo (T-S)(More)
The paper presents a novel adaptive neural-network based nonlinear model predictive control (NMPC) methodology for hybrid systems with mixed inputs. For this purpose an online self-organizing growing and pruning redial basis function (GAP-RBF) neural network is employed to identify the hybrid system using the unscented kalman filter (UKF) learning(More)