Adaptive Fuzzy Scheme Research Articles

Adaptive fuzzy scheme for efficient, fast valving control

Fast valving has long been regarded as an effective and economical method of performing transient control in a power generation plant. Due to the inherent nonlinearities that exist in this operation, fast valving controllers designed in a conventional way cannot deliver satisfactory control. This paper introduces a new approach to construct a fast valving controller by applying an adaptive fuzzy technique. A rule-generation and adaptation scheme is formulated in a fuzzy-system framework. Then the implementation issue is discussed in detail. From the outcome of on-line testing, it is shown that the controller constructed in this way not only maintains global stability, but also appears to be robust in different fault situations.

Development of a feedback controller tuner using virtual fuzzy sets

This paper presents the development of a fuzzy tuner for tuning PI and PID feedback controllers. The implementation is based on the concept of virtual fuzzy sets and an adaptive fuzzy inference scheme. The tuner uses the standard Ziegler-Nichols tuning rules for initial gain setting according to the open-loop step response of the process. Then the fuzzy system fine tunes the controller iteratively based on the performance of the closed-loop controlled system response. The tuning knowledge is extracted automatically from the tuning of a representative process. Expert rules of thumb can be incorporated into the rule base, and explanatory capability is also available through the use of the IF-THEN rule base. Finally, examples covering a wide range of process dynamics are tested to demonstrate the excellent performance of the tuner.

A Fuzzy Engineering Approach for Time Series Forecasting of Electric Load

A hybrid neural network-fully expert system is developed to forecast short-term electric load accurately. The fuzzy membership values of load and other weather variables are the inputs to the neural network and the output comprises the membership values of the predicted load. An adaptive fuzzy correction scheme is used to forecast the final load by using a fuzzy rule base and fuzzy inference mechanism. Extensive studies have been performed for all seasons, and some examples are presented in this paper, which include average, peak, and hourly load forecasts.