The article presents the use of the Mamdani fuzzy reasoning model to develop a proposal of a system controlling partnering relations in construction projects. The system input variables include: current assessments of particular partnering relation parameters, the weights of these parameters’ impact on time, cost, quality and safety of implementation of construction projects, as well as the importance of these project assessment criteria for its manager. For each of the partnering relation parameters, the project’s manager will receive controlrecommendations. Moreover, the parameter to be improved first will be indicated. The article contains a calculation example of the system’s operations.
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.
Currently commercialization of electric vehicle (EV) is based to minimize the time of starting and acceleration. To undergo this problem multi-input multi-output fuzzy logic controller (MIMO-FLC) affect on propelled traction system forming MMS process was proposed. This paper introduces a MIMO-FLC applied on speeds of electric vehicle, the electric drive consists of two directing wheels and two rear propulsion wheels equipped with two light weight induction motors. The EV is powered by two motors of 37 kilowatts each one, delivering a 476 Nm total torque. Its high torque (476Nm) is instantly available to ensure responsive acceleration performance in built-up areas. Acceleration and steering are ensured by an electronic differential system which maintains robust control for all cases of vehicle behavior on the road. It also allows controlling independently every driving wheel to turn at different speeds in any curve. Direct torque control based on space vector modulation (DTC-SVM) is proposed to achieve the tow rear driving wheel control. The MIMO-FLC control technique is simulated in MATLAB SIMULINK environment. The simulation results have proved that the MIMO-FLC method decreases the transient oscillations and assure efficiency comportment in all type of road constraints, straight, slope, descent and curved road compared to the single input single output fuzzy controller (SISO-FLC).
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.
Brushless DC motors are often used as the power sources for modern ship electric propulsion systems. Due to the electromagnetic torque ripple of the motor, the traditional control method reduces the drive performance of the motor under load changes. Aiming at the problem of the torque ripple of the DC brushless motor during a non- commutation period, this paper analysis the reasons for the torque ripple caused by pulse- width modulation (PWM), and proposes a PWM_ON_PWM method to suppress the torque ripple of the DC brushless motor. Based on the mathematical model of a DC brushless motor, this method adopts a double closed-loop control method based on fuzzy control to suppress the torque ripple of the DC brushless motor. The fuzzy control technology is integrated into the parameter tuning process of the proportional–integral–derivative (PID) controller to effectively improve the stability of the motor control system. Under the Matlab/Simulink platform, the response performance of different PID control methods and the torque characteristics of different PWM modulation methods are simulated and compared. The results show that the fuzzy adaptive PID control method has good dynamic response performance. It is verified that the PWM_ON_PWM modulation method can effectively suppress the torque ripple of the motor during non-commutation period, improve the stability of the double closed-loop control system and meet the driving performance of the motor under different load conditions.