System identification

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

Exact and Approximate Modeling of Linear Systems: A Behavioral Approach

Abstract: Exact and Approximate Modeling of Linear Systems: A Behavioral Approach elegantly introduces the behavioral approach to mathematical modeling, an approach that requires models to be viewed as sets of possible outcomes rather than to be a priori bound to particular representations The authors discuss exact and approximate fitting of data by linear, bilinear, and quadratic static models and linear dynamic models, a formulation that enables readers to select the most suitable representation for a particular purpose This book presents exact subspace-type and approximate optimization-based identification methods, as well as representation-free problem formulations, an overview of solution approaches, and software implementation Readers will find an exposition of a wide variety of modeling problems starting from observed data The presented theory leads to algorithms that are implemented in C language and in MATLAB

An adaptive power system stabilizer based on the self-optimizing pole shifting control strategy

Abstract: An adaptive power system stabilizer (APSS) employing a new self-optimizing pole shifting control strategy and its application to a power system are described in this paper. Based on an identified model of the system, the control is computed by an algorithm which shifts the closed-loop poles of the system to some optimal locations inside the unit circle in the z-domain to minimize a given performance criterion. With the self-optimization property, outside intervention in the controller design procedure is minimized, thus simplifying the tuning procedure during commissioning. Also, a new method of calculating the variable forgetting factor in real-time parameter identification is discussed. Studies show that the proposed APSS can provide good damping of the power system over a wide operating range and significantly improve the dynamic performance of the system. >

Implicit Linear Systems

Abstract: System models.- The Kronecker form.- Analysis of singularities.- Systems of minimal dimension.- Canonical representations.- Algebraic design applications.- Optimization with quadratic cost.- System identification.- Large-scale systems.- Extensions.

A hierarchical least squares identification algorithm for Hammerstein nonlinear systems using the key term separation

Abstract: Mathematical models are basic for designing controller and system identification is the theory and methods for establishing the mathematical models of practical systems. This paper considers the parameter identification for Hammerstein controlled autoregressive systems. Using the key term separation technique to express the system output as a linear combination of the system parameters, the system is decomposed into several subsystems with fewer variables, and then a hierarchical least squares (HLS) algorithm is developed for estimating all parameters involving in the subsystems. The HLS algorithm requires less computation than the recursive least squares algorithm. The computational efficiency comparison and simulation results both confirm the effectiveness of the proposed algorithms.