Design of Dynamic System Fault-Tolerant Control using IMM Estimation and RBF Neural Network

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.

Evaluation of a Fault-Tolerant Scheme in a Six-Degree-of-Freedom Motion Flight Simulator

“…Recent advances in control theory and mathematical modeling have lead to the development of FDII implementations based on analytical redundancy methods. Analytical redundancy approaches using model-based implementations [10,11], knowledge-based implementations [12,13], or a combination of the two [14] have been successful.…”