Modeling and comparison of IP and fuzzy-pi regulators of speed control of DFIM for supply of power to the electrical network

This paper deals with a comparison between a fuzzy logic controller and a  conventional IP controller utilized for speed control with a direct stator flux orientation control of a doubly fed induction. The effectiveness of the proposed control strategy is  measured under diverse operating conditions such as of reference speed and for load torque step changes at nominal parameters and in the presence of parameter variation. Results obtained from simulation indicte that the fuzzy logic controller is more robust than a conventional IP controller against parameter variation and uncertainty, and is less sensitive to external load torque disturbance with a fast dynamic response; the stator side power factor is controlled at unity level. Then, an intelligent artificial fuzzy control of a wind energy system based on DFAM for supply of power to the electrical network. Its simulated performances are then compared to those of a classical PI controller. Specifically fuzzy systems are created to overcome the disadvantages of fuzzy systems. Results obtained in Matlab/Simulink environment show that the fuzzy control is more robust, have superior  dynamic performance and hence found to be a suitable replacement of the conventional PI controller for the high performance drive applications.Key words: Doubly fed asynchronous machine (DFAM); Field oriented control; Fuzzy control, Fuzzy PI controller, conventional IP controller

Designing a Battlefield Fire Support System Using Adaptive Neuro-Fuzzy Inference System Based Model

Fire support of the maneuver operation is a continuous process. It begins with the receiving the task by the maneuver commander and continues until the mission is completed. Yet it is a key issue in combat in the way gain success. Therefore, a real-time mannered solution to fire support problem is a vital component of tactical warfare to the sequence that auxiliary forces or logistic support arrives at the theatre. A new method for deciding on combat fire support is proposed using adaptive neuro-fuzzy inference system (ANFIS) in this paper. This study addresses the design of an ANFIS as an efficient tool for real-time decision-making in order to produce the best fire support plan in battlefield. Initially, criteria that are determined for the problem are formed by applying ANFIS method. Then, the ANFIS structure is built up by using the data related to selected criteria. The proposed method is illustrated by a sample fire support planning in combat. Results showed us that ANFIS is valid especially for small unit fire support planning and is useful to decrease the decision time in battlefield.Defence Science Journal, 2013, 63(5), pp.497-501, DOI:http://dx.doi.org/10.14429/dsj.63.371

Self organizing fuzzy sliding mode controller for the position control of a permanent magnet synchronous motor drive

AbstractIn this paper, a self organizing fuzzy sliding mode controller (SOFSMC) which emulates the fuzzy controller with gain auto-tuning is proposed for a permanent magnet synchronous motor (PMSM) drive. The proposed controller is used for the position control of the PMSM drive. The performance and robustness of the control system is tested for nonlinear motor load torque disturbance and parameter variations. It has a novel gain self organizing strategy in response to the transient or tracking responses requirement. To illustrate the performance of the proposed controller, the simulation studies are presented separately for the SOFSMC and the fuzzy controller with gain auto-tuning. The results are compared with each other and discussed in detail. Simulation results showing the effectiveness of the proposed control system are confirmed under the different position changes