Xidong Tang

Bio: Xidong Tang is an academic researcher from General Motors. The author has contributed to research in topics: Adaptive control & Actuator. The author has an hindex of 22, co-authored 78 publications receiving 2003 citations. Previous affiliations of Xidong Tang include University of Virginia.

Li-ion battery parameter estimation for state of charge

Abstract: Battery state of charge (SOC) is a critical parameter for the control of propulsion systems in plug-in hybrid electric vehicles (PHEV) and electric vehicles (EV). As SOC is not measureable during vehicle operation, an onboard adaptive algorithm is developed in this paper. The algorithm estimates six electrical parameters for Li-ion batteries and provides a reliable SOC based on one of the estimated battery parameters, i.e. open circuit voltage (OCV). Simulation and vehicle validation results show good robustness and adaptation of the algorithm with high computational efficiency and low implementation cost.

Adaptive output feedback actuator failure compensation for a class of non-linear systems

Abstract: An adaptive compensation control scheme using output feedback is designed and analysed for a class of non-linear systems with state-dependent non-linearities in the presence of unknown actuator failures. For a linearly parameterized model of actuator failures with unknown failure values, time instants and pattern, a robust backstepping-based adaptive non-linear controller is employed to handle the system failure, parameter and dynamics uncertainties. Robust adaptive parameter update laws are derived to ensure closed-loop signal boundedness and small tracking errors, in general, and asymptotic regulation, in particular. An application to controlling the angle of attack of a non-linear hypersonic aircraft dynamic model in the presence of elevator segment failures is studied and simulation results show that the developed adaptive control scheme has desired actuator failure compensation performance. Copyright © 2004 John Wiley & Sons, Ltd.

A Survey of Fault Detection, Isolation, and Reconfiguration Methods

Abstract: Fault detection, isolation, and reconfiguration (FDIR) is an important and challenging problem in many engineering applications and continues to be an active area of research in the control community. This paper presents a survey of the various model-based FDIR methods developed in the last decade. In the paper, the FDIR problem is divided into the fault detection and isolation (FDI) step, and the controller reconfiguration step. For FDI, we discuss various model-based techniques to generate residuals that are robust to noise, unknown disturbance, and model uncertainties, as well as various statistical techniques of testing the residuals for abrupt changes (or faults). We then discuss various techniques of implementing reconfigurable control strategy in response to faults.