Control System Architecture for a Cement Mill Based on Fuzzy Logic

Intelligent Design Environment for Second-Order Positioning Systems

At the time being knowledge-based systems are used in almost all life aspects. The main reason for trying to use knowledge-based systems in real-time control is to reduce cognitive load on users (overload), their application proving to be important when conventional techniques have failed or are not sufficiently effective [1]. The development of automated diagnosis techniques and systems can help also to minimize downtime and maintain efficient output. This paper presents some issues of applying knowledge-based systems to real-time control systems. It describes and analyzes the main issues concerning the real-time domain and provides possible solutions, such as a set of requirements that a real-time knowledge-based system must satisfy. The paper proposes a possible architecture for applying knowledge-based techniques in real-time control systems. Finally, a way of employing knowledge-based techniques for extending the existing automatic control and monitoring system for the geothermal plant from the University of Oradea is presented.

In this paper, a MATLAB-SIMULINK-based model for the Well Station from the Geothermal Plant at the University of Oradea is developed. The Well Station component is in charge for producing (extracting) geothermal water that should be used for heating purposes in the university campus. This paper presents the development of the dynamic model of the Well Station that includes also a control strategy based on a classical PI controller. The main purpose of the implementation is to create a model that could be used further to analyze and verify how the Well Station system is adapting at different consumption variations in order to support decisions regarding later possible modifications in the Geothermal Plant system. Simulation results are presented in the end, validating the model and the control solutions adopted in the paper.

An electric vehicle (EV) uses an electric motor for traction and chemical batteries, superconductors, combustion cells and / or inertial masses as energy sources. To avoid problems regarding the accuracy of the mathematical model of the system, the use of artificial intelligence in electric drives is a viable alternative. Among other advantages of using artificial intelligence in the electric drive system, it can be emphasized that its application reduces the design time and leads to avoiding problems with the introduction of the mathematical model in the system control algorithm. This paper presents several case studies of electrical vehicles and some considerations about intelligent control of EVs. Finally some experimental results that compare classical control system with fuzzy logic control system for EV are presented.