Huazhen Fang

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This work is devoted to solving simultaneous input and state estimation (SISE) problem for discrete-time linear systems. Our aim is to develop stable SISE algorithms. By applying the minimum variance unbiased estimation technique, we derive two SISE algorithms in the presence or absence of direct feedthrough, respectively. Riccati-like equations are(More)
Monitoring the state-of-charge (SoC) for batteries is challenging, especially when a battery has time-varying parameters. We propose to improve SoC estimation using an adaptive strategy and multiple models in this study, developing a unique algorithm called MM-AdaSoC. Specifically, two submodels in state-space form are generated from a modified Nernst(More)
State of charge (SoC) estimation is a fundamental challenge in designing battery management systems. An adaptive SoC estimator, named as the AdaptSoC, is developed in this paper. It is able to estimate the SoC when the model parameters are unknown, through joint SoC and parameter estimation. Design of the AdaptSoC builds up on (1) a reduced complexity(More)
This paper presents theoretical and experimental results of a newly developed automatic controller tuning algorithm called Robust Estimation for Automatic Controller Tuning (REACT) to tune a linear feedback controller to the unknown spectrum of disturbances present in a feedback loop. With model uncertainty and controller perturbations described in (dual)(More)
State of charge (SoC) estimation is of key importance in the design of battery management systems. An adaptive SoC estimator, which is named AdaptSoC, is developed in this paper. It is able to estimate the SoC in real time when the model parameters are unknown, via joint state (SoC) and parameter estimation. The AdaptSoC algorithm is designed on the basis(More)
This paper considers the state of charge (SoC) and parameter estimation of lithium-ion batteries. Different from various prior arts, where estimation is based on local linearization of a nonlinear battery model, nonlinear geometric observer approach is followed to design adaptive observers for the SoC and parameter estimation based on nonlinear battery(More)
This paper studies the problem of simultaneous input and state estimation (SISE) for nonlinear dynamical systems with and without direct input-output feedthrough. We take a Bayesian perspective to develop a sequential joint input and state estimation approach. Our scheme gives rise to a nonlinear Maximum a Posteriori optimization problem, which we solve(More)
This paper considers State of Charge (SoC) estimation of Lithium-ion battery. Different from various prior art, where estimation is performed based on local linearization of a nonlinear battery model, a nonlinear adaptive observer is proposed to estimate the SoC and the parameters of a simplified but nonlinear battery model. A major advantage of the(More)
This paper studies control-theory-inspired optimal sensor and actuator deployment to improve the temperature monitoring and control performance of HVAC (heating, ventilation and air conditioning) systems in buildings. The deployment strategies are based on maximizing observability- and controllability-based metrics such as the respective Gramians. Our(More)