The general realization algorithm (GRA) is developed to identify modal parameters of linear multi-degree-of-freedom dynamic systems subjected to measured (known) arbitrary dynamic loading from known initial conditions. The GRA extends the well known eigensystem realization algorithm (ERA) based on Hankel matrix decomposition by allowing an arbitrary input signal in the realization algorithm. This generalization is obtained by performing a weighted Hankel matrix decomposition, where the weighting is determined by the loading. The state-space matrices are identified in a two-step procedure that includes a state reconstruction followed by a least-squares optimization to get the minimum prediction error for the response. The statistical properties (i.e., bias, variance, and robustness to added output noise introduced to model measurement noise and modeling errors) of the modal parameter estimators provided by the GRA are investigated through numerical simulation based on a benchmark problem with nonclassical ...

Large-scale PV microgrids constitute a growing portion of distributed renewable generation in many grids worldwide and are expected to continue their growth in the foreseeable future. Managing the operation of such microgrids and in particular their interaction with the main electricity grid is a challenging task as it involves controlling large intermittent PV resources. The addition of auxiliary DERs and energy storage systems to such microgrids is a promising solution that provides some flexibility to the operation of these fluctuating PV resources. In this work, a hierarchical predictive controller is designed to perform daily scheduling and real-time control of a PV microgrid with energy storage and auxiliary diesel generation. The controller aims to increase utilization of available PV resources, reduce the daily variability of power flow to the main grid, while at the same time controlling power fluctuations at the point of connection. The controller is designed to utilize updating load and PV predictions on a receding time horizon and uses the flexibility of energy storage and diesel resources to compensate for fluctuations and prediction errors. A study of multiple operational scenarios demonstrates successful performance of the proposed controller.

We present a data-based approach for modeling and controller design of a dual-stage tape servo actuator. Our method uses step response measurements and a generalized realization algorithm to identify a multivariable discrete-time model of the actuator. The data acquisition and modeling can be implemented in the servo firmware of a tape drive. We have designed a dual-stage controller, based on the model, using loop shaping techniques adopted for multivariable control problems. We applied the procedure to the prototype of a dual-stage actuator tape head to reduce the effect of lateral tape motion. The prototype consists of a conventional voice coil motor for coarse positioning and a micro-actuator for fine positioning. The micro-actuator, which is mounted on the voice coil motor, uses a piezo crystal to follow high-frequency lateral tape motion (up to the kilohertz regime), while the voice coil motor follows only low-frequency lateral tape motion. Compared to a single-stage design, the dual-stage servo design provides a 25% bandwidth improvement and a voice coil motor control signal that is much smaller in magnitude.