System identification

About: System identification is a research topic. Over the lifetime, 21291 publications have been published within this topic receiving 439142 citations.

Robust adaptive regulation without persistent excitation

Abstract: A globally convergent adaptive regulator for minimum or nonminimum phase systems subject to bounded disturbances is presented. The control strategy is designed for a particular input-output representation obtained from the state-space representation of the system. The leading coefficient of the novel representation is the product of the observability and controllability matrices of the system. The controller scheme uses a least-squares identification algorithm with a dead zone. The dead zone is chosen to obtain convergence properties on the estimates and on the covariance matrix as well. This allows the definition of modified estimates which secure well-conditioned matrices in the adaptive control law. Explicit bounds on the plant output are given. >

Hybrid Systems Diagnosis

Abstract: This paper reports on an on-going project to investigate techniques to diagnose complex dynamical systems that are modeled as hybrid systems. In particular, we examine continuous systems with embedded supervisory controllers that experience abrupt, partial or full failure of component devices. We cast the diagnosis problem as a model selection problem. To reduce the space of potential models under consideration, we exploit techniques from qualitative reasoning to conjecture an initial set of qualitative candidate diagnoses, which induce a smaller set of models. We refine these diagnoses using parameter estimation and model fitting techniques. As a motivating case study, we have examined the problem of diagnosing NASA's Sprint AERCam, a small spherical robotic camera unit with 12 thrusters that enable both linear and rotational motion.

Fault Detection Using Canonical Variate Analysis

Abstract: The system identification method canonical variate analysis (CVA) has attracted much attention from researchers for its ability to identify multivariable state-space models using experimental data. A model identified using CVA can use several methods for fault detection. Two standard methods are investigated in this paper: the first is based on Kalman filter residuals for the CVA model, the second on canonical variable residuals. In addition, a third method is proposed that uses the local approach for detecting changes in the canonical variable coefficients. The detection methods are evaluated using three simulation examples; the examples consider the effects of feedback control; process nonlinearities; and multivariable, serially correlated data. The simulations consider several types of common process faults, including sensor faults, load disturbances, and process changes. The simulation results indicate that the local approach provides a very sensitive method for detecting process changes that are dif...