An original fuzzy team control model is presented in this article. The model is based on a non-traditional combination of classical and contemporary achievements of management and mathematical theories of fuzzy logic and fuzzy sets. In methodological terms, the article also offers a set of tools for measuring and evaluating both team performance and the effectiveness of the team control system in the organization. Fuzzy tools and techniques for decision-making, studying of hidden effects and joint influences, and quantification of evaluations are employed in this set of tools. The suggested fuzzy model contributes to overcoming theoretical deficits on the issues of team control, and the methodology of team control fills a gap in the toolkit of team management. The results from verification of the fuzzy team control model at a small-sized Bulgarian enterprise are also discussed in this article. They indicate that it is possible to develop a fuzzy model for team control, increasing the effectiveness of the team control system in the enterprise.
For many adaptive noise control systems the Filtered-Reference LMS, known as the FXLMS algorithm is used to update parameters of the control filter. Appropriate adjustment of the step size is then important to guarantee convergence of the algorithm, obtain small excess mean square error, and react with required rate to variation of plant properties or noise nonstationarity. There are several recipes presented in the literature, theoretically derived or of heuristic origin. This paper focuses on a modification of the FXLMS algorithm, were convergence is guaranteed by changing sign of the algorithm steps size, instead of using a model of the secondary path. A TakagiSugeno-Kang fuzzy inference system is proposed to evaluate both the sign and the magnitude of the step size. Simulation experiments are presented to validate the algorithm and compare it to the classical FXLMS algorithm in terms of convergence and noise reduction.
Foundry resistance furnaces are thermal devices with a relatively large time delay in their response to a change in power parameters. Commonly used in automation classical PID controllers do not meet the requirements of high-quality control. Developed in recent years, fuzzy control theory is increasingly being used in various branches of economy and industry. Fuzzy controllers allow to introduce new developments in control systems of foundry furnaces as well. Correctly selected fuzzy controller can significantly reduce energy consumption in a controlled thermal process of heating equipment. The article presents a comparison of energy consumption by control system of foundry resistance furnace, equipped with either a PID controller or fuzzy controller optimally chosen.