Human administration of storage systems is a large and growing issue in modern IT infrastructures. We are exploring new storage architectures that integrate automated management functions and simplify the human administrative task. Self-* storage systems (pronounced “self-star”— a play on the unix shell wild-card character) are self-configuring, self-organizing, self-tuning, self-healing, self-...

In this paper, we developed the parametric estimation and the self-tuning control problem of the nonlinear systems which are described by discrete-time nonlinear mathematical models, with unknown, time-varying parameters, and operative in a stochastic environment. The parametric estimation is realized by using the prediction error method and the recursive least squares techniques. The self-tuni...

Abstract: This paper proposes a new robust two-degree-of-freedom (DoF) design method for controlling the nonlinear longitudinal speed problem of hybrid electric vehicles (HEVs). First, the uncertain parameters of the HEV model are described by fuzzy α-cut representation, in which the interval uncertainty and the possibility can be simultaneously indicated by the fuzzy membership function. For t...

This research work proposes a multi-input multi-output (MIMO) online adaptive feedback linearization NeuroFuzzy control (AFLNFC) scheme to improve the damping of low frequency oscillations (LFOs) in an AC/DC power system. Optimized NeuroFuzzy identification architecture online captures the oscillatory dynamics of the power system through wide area measurement system (WAMS)-based measured speed ...

In direct adaptive control, the adaptation mechanism attempts to adjust a parameterized nonlinear controller to approximate an ideal controller. In the indirect case, however, we approximate parts of the plant dynamics that are used by a feedback controller to cancel the system nonlinearities. In both cases, ‘‘approximators’’ such as linear mappings, polynomials, fuzzy systems, or neural networ...

Currently, a considerable amount of human effort and time is spent for initialization or calibration of operational traffic control systems. Typically, this optimization (fine-tuning) procedure is conducted manually, via trial-and-error, relying on expertise and human judgment and does not always lead to a desirable outcome. This paper presents a new learning/adaptive algorithm that enables aut...

This project is based on the self-tuning fuzzy logic controller, for the design of a temperature control process , the performance of the system should be reliable. The proposed control system combines the advantages of Self Tuning and Fuzzy Logic Control schemes. In order to evaluate the performance of the proposed control system methods, results from simulation of the process are presented. T...

Problem statement: Conventional Proportional-Integral-Derivative (PID) controllers exhibit moderately good performance once the PID gains are properly tuned. However, when the dynamic characteristics of the system are time dependent or the operating conditions of the system vary, it is necessary to retune the gains to obtain desired performance. This situation has renewed the interest of resear...

A model-based design and tuning procedure is proposed for feedforward controllers, which accounts for model uncertainties in SISO systems. Proper relationships for the analysis of feedforward–feedback control systems show that tuning the feedforward controller is not completely independent from the feedback-loop spectral characteristics. Similarly, fine tune of the associated feedback controlle...