A PID controller is the most widely used controller in industry for control applications due to its simple structure and easy parameter adjusting. When the process becomes too complex to be described, a classical PID control methodology does not provide good performance. Therefore, it is incapable of capturing all design objectives and specifications for a wide range of operating conditions and disturbances [1, 2]. For these reasons, under different operating conditions of the controlled systems, various types of online fuzzy self-tuning for PID controller parameters have been presented in several studies to achieve minimum steady-state error and improve the dynamic behavior [3, 4]. Most of these researches focus on the type-1 fuzzy self-tuning (T1FST) of PID controller [4, 5]. It has been noted that the T1FST PID controllers might not be able to

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this paper presents simple implementation for two different control techniques applied on brushless DC motor. The first technique is the conventional PID control while the second technique is the self-tuning fuzzy PID control. In the second technique the main role of fuzzy logic control adjusts the PID control output according to error and change of error. The Arduino microcontroller is used to send the control signal to real system and receive the speed feedback signal. The experimental results show that the superiority for self-tuning fuzzy PID control compared to conventional PID control.

This paper presents a procedure to coordinated design of Power System Stabilizers (PSSs) and Static VAR Compensators (SVCs) in a multimachine power system. The aims of the proposed method are to find the best location and the optimal parameters of these compensators in order to improve the steady state and transient performances and also to increase the system damping over a wide range of operating conditions. The objective function of the Genetic Algorithm (GA) allows the selection of the PSSs and SVCs to shift critical closed loop eigenvalues to the left side in the complex s-plane. The multimachine power system considered in this study consists of nine buses, three generating units (steam, hydro and nuclear) and three static loads. Digital simulation studies show that the proposed design procedure provides good damping for the power system at different operating conditions, and moreover improves steady-state and transient performance of the system.

This paper implements a practical interval type-2 fuzzy self-tuning (IT2FST) of optimal PID (OPID) controller to servo permanent magnet synchronous motor (SPMSM). The proposed method IT2FST updates the OPID controller gains in an online manner to drive the SPMSM with better speed response during variable load and parameter uncertainty occurrence. In this work, the industrial SPMSM system comprises threephase PMSM with internal break, drive and mechanical parts. Due to the incomplete real information of the SPMSM, nonlinear least square algorithm has been utilized for its model identification. A comparative analysis in a real time of the SPMSM with an OPID, type-1 fuzzy self-tuning and IT2FST for OPID controllers under the influence of parameter uncertainties and external load disturbances has been carried out. The real-time practical implementation results illustrated that the proposed IT2FST of OPID controller gives a simple opportunity to enhance the speed performance of the SPMSM than the other controllers.