Fuzzy Logic Predictor Research Articles

Fuzzy logic-model predictive control energy management strategy for a dual-mode locomotive

Fuel cell hybrid electric vehicles (FC–HEV) combine the high energy density of hydrogen with a high-power density energy storage system. This favors the response to sudden changes in load and the vehicle’s autonomy. A challenge on FC–HEVs is to develop an adequate energy management strategy (EMS) to determine the power distribution among the available sources. This paper proposes a modular EMS for a dual-mode locomotive FC–HEV. The EMS seeks to minimize a cost function, including the energy cost and embedded sources degradation. This EMS uses i) fuzzy logic control to guarantee the state of charge of the energy storage system (ESS) at the desired range, ii) model predictive control (MPC) to define the operation of the ESS and iii) rule-based control to manage additional sources and ensure power balance. The MPC strategy uses a fuzzy logic predictor algorithm for load prediction based on previous power and speed values. The proposed EMS is validated in a hybrid locomotive equipped with a fuel cell, batteries, and supercapacitors. Results show 95% global electrical efficiency and 0.20 and 1.38 power variation coefficient for fuel cell and battery, respectively.

Prototype of an adaptive disruption predictor for JET based on fuzzy logic and regression trees

Disruptions remain one of the most hazardous events in the operation of a tokamak device, since they can cause damage to the vacuum vessel and surrounding structures. Their potential danger increases with the plasma volume and energy content and therefore they will constitute an even more serious issue for the next generation of machines. For these reasons, in the recent years a lot of attention has been devoted to devise predictors, capable of foreseeing the imminence of a disruption sufficiently in advance, to allow time for undertaking remedial actions. In this paper, the results of applying fuzzy logic and classification and regression trees (CART) to the problem of predicting disruptions at JET are reported. The conceptual tools of fuzzy logic, in addition to being well suited to accommodate the opinion of experts even if not formulated in mathematical but linguistic terms, are also fully transparent, since their governing rules are human defined. They can therefore help not only in forecasting disruptions but also in studying their behaviour. The analysis leading to the rules of the fuzzy predictor has been complemented with a systematic investigation of the correlation between the various experimental signals and the imminence of a disruption. This has been performed with an exhaustive, non-linear and unbiased method based on decision trees. This investigation has confirmed that the relative importance of various signals can change significantly depending on the plasma conditions. On the basis of the results provided by CART on the information content of the various quantities, the prototype of an adaptive fuzzy logic predictor was trained and tested on JET database. Its performance is significantly better than the previous static one, proving that more flexible prediction strategies, not uniform over the whole discharge but tuned to the operational region of the plasma at any given time, can be very competitive and should be investigated systematically.

Fuzzy Logic Bandwidth Prediction and Policing in a DiffServ-Aware Network

Differentiated Services (DiffServ)-aware network potentially can provide the next generation platform for multimedia support in the Internet. In this work we look at improving bandwidth allocation in such a network. We study how to implement bandwidth predicting and policing in a DiffServ aware network using fuzzy logic. A token bucket fuzzy logic bandwidth predictor for real time variable bit rate traffic class is proposed. Here, the AF traffic class is associated with real time variable bit rates traffic. The fuzzy logic bandwidth predictor facilitates bandwidth predicting and dynamic policing based on the class based packet aggregates. This improves the admission control of connections to the network. A simulation study was performed for the fuzzy logic predictor using Network Simulator-2. The simulation results show that the fuzzy logic predictor gave commendable bandwidth prediction value compared to a deterministic bandwidth allocation for the traffic class.