Modeling and identification of uncertain-input systems
ACCESS Linnaeus Center, KTH Royal Institute of Technology（ACCESS林奈中心，皇家理工学院）；Department of Electrical Engineering, Eindhoven University of Technology（埃因霍温理工大学电气工程系）
Riccardo Sven Risuleo, Giulio Bottegal, Håkan Hjalmarsson
In this work, we present a new class of models, called uncertain-input models, that allows us to treat system-identification problems in which a linear system is subject to a partially unknown input signal. To encode prior information about the input or the linear system, we use Gaussian-process models. We estimate the model from data using the empirical Bayes approach: the input and the impulse responses of the linear system are estimated using the posterior means of the Gaussian-process models given the data, and the hyperparameters that characterize the Gaussian-process models are estimated from the marginal likelihood of the data. We propose an iterative algorithm to find the hyperparameters that relies on the EM method and results in simple update steps. In the most general formulation, neither the marginal likelihood nor the posterior distribution of the unknowns is tractable. Therefore, we propose two approximation approaches, one based on Markov-chain Monte Carlo techniques and one based on variational Bayes approximation. We also show special model structures for which the distributions are treatable exactly. Through numerical simulations, we study the application of the uncertain-input model to the identification of Hammerstein systems and cascaded linear systems. As part of the contribution of the paper, we show that this model structure encompasses many classical problems in system identification such as classical PEM, Hammerstein models, errors-in-variables problems, blind system identification, and cascaded linear systems. This allows us to build a systematic procedure to apply the algorithms proposed in this work to a wide class of classical problems.
Zonotope-based Set-membership Parameter Identification of Linear Systems with Additive and Multiplicative Uncertainties and Its Application to Engine Condition Monitoring
Equation Discovery for Nonlinear System Identification
Jožef Stefan Institute（约瑟夫·斯特凡研究所）；University of Cambridge（剑桥大学）；University of Ljubljana（卢布尔雅那大学）；University of Nova Gorica（新戈里察大学）；Jožef Stefan International Postgraduate School（约瑟夫·斯特凡国际研究生院）
Production Scheduling Identification: An Inverse Optimization Approach for Industrial Load Modeling Using Smart Meter Data
State Key Laboratory of Power System Operation and Control, Department of Electrical Engineering, Tsinghua University（清华大学电机工程与应用电子技术系电力系统运行与控制国家重点实验室）
