Online multiple-model-based fault diagnosis and accommodation

While most research attention has been focused on fault detection and diagnosis, much less research effort has been devoted to failure accommodation. Due to the inherent complexity of nonlinear systems, most model-based analytical redundancy fault diagnosis and accommodation (FDA) studies deal with the linear systems, which are subjected to simple additive or multiplicative faults. This assumption has limited the effectiveness and usefulness in practical applications. In this paper, the online fault accommodation (FA) control problems under multiple catastrophic or incipient failures are investigated. The main interest is focused on dealing with the unanticipated component failures in the most general formulation. Through discrete-time Lyapunov stability theory, the sufficient conditions to guarantee the system online stability and to meet performance criteria under failures are derived. A systematic procedure for proper FA under the unanticipated failures is developed. The approach is to combine the control technique derived from discrete-time Lyapunov theory with the modern intelligent technique that is capable of self-optimization and online adaptation for real-time failure estimation. In addition, a complete architecture of FDA is proposed by incorporating the intelligent fault tolerant control strategy with a cost-effective fault detection scheme and a multiple-model based failure diagnosis process to efficiently handle the false alarms and the accommodation of both the anticipated and unanticipated failures in online situations. The simulation results, including a three-tank benchmark problem, substantiate the feasibility study of the proposed FDA framework and provide a promising potential to spin-off applications in industrial and aerospace engineering.