Title :
Design of Dynamic System Fault-Tolerant Control using IMM Estimation and RBF Neural Network
Author :
Wang, Xudong ; Syrmos, Vassilis L.
Author_Institution :
Res. Corp., Hawaii Univ., Honolulu, HI
Abstract :
In this paper, a strategy of failure detection, identification and reconfigurable scheme for a dynamic system is proposed. The proposed scheme provides detection and identification of sensor, actuator and/or system component failures, dynamic system state estimation and system performance recovery. Fault detection and identification is carried out using radial basis function (RBF) neural network and interacting multiple model (IMM) estimation. The RBF-NN is used to form a statistical model of nominal or faulty data and estimate the mode-conditional probability densities as the choice of likelihood function. The IMM mechanism carries out the interaction among mode-based filters, update the mode probability and provide the overall state estimate as the control input. Eigenstructure assignment (EA) technique is used for the reconfigurable controller design. The proposed approach is evaluated using an aircraft example, and the results obtained show that it can reliably and accurately detect, identify the faults and recover the impaired dynamic performance to the desired one
Keywords :
aircraft control; control system synthesis; eigenstructure assignment; fault tolerance; linear quadratic control; maximum likelihood estimation; neurocontrollers; nonlinear dynamical systems; probability; radial basis function networks; state estimation; RBF neural network; aircraft control; dynamic system design; dynamic system state estimation; eigenstructure assignment technique; failure detection strategy; fault-tolerant control; identification strategy; interacting multiple model estimation; maximum likelihood function; mode-based filter; mode-conditional probability density estimation; radial basis function neural network; reconfigurable controller design; statistical model; system performance recovery; Actuators; Control systems; Fault detection; Fault diagnosis; Fault tolerant systems; Neural networks; Probability; Sensor systems; State estimation; System performance;
Conference_Titel :
Control and Automation, 2006. MED '06. 14th Mediterranean Conference on
Conference_Location :
Ancona
Print_ISBN :
0-9786720-1-1
Electronic_ISBN :
0-9786720-0-3
DOI :
10.1109/MED.2006.328822
